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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

20 Free PowerPoint and Google Slides Templates for Data Presentations

Presenting the results of your data analysis need not be a hair pulling experience. These 20 free PowerPoint and Google Slides templates for data presentations will help you cut down your preparation time significantly. You’ll be able to focus on what matters most – ensuring the integrity of your data and its analysis. We’ll take care of the design end for you!

That said, I’ve divided this article into 2 sections. In the first part, I’ll share the PowerPoint templates. And in the second part, the Google Slides templates. Oh, and in case you’re wondering, yes, you can use a PowerPoint template in Google Slides and vice versa .

PowerPoint Templates For Your Data Presentations

• Playful Venn Diagram PowerPoint Template

Venn diagrams are great when it comes to showing the similarities and differences between 2 or more data sets. Just by looking at the diagram, your audience can tell if there’s anything common between data sets A and B. Or if there’s a relationship between data sets B and C.

Likewise, if you want to emphasize the differences between data sets, Venn diagrams are great for that purpose, too. Now, for this template pack, you’ve got 10 slides to choose from. You don’t need to use all of them for your presentation, simply pick one or two that does the job for you.

• Graph, Diagram & Data Sheet PowerPoint Template

There’s a reason why graphs and diagrams are so important in presentations. It’s because they make complex data look so much more understandable. Can you imagine copy and pasting all 1,000 rows of data on your slides? And then expecting your audience to understand what all those numbers mean?

Some geeks in your audience may love the challenge, but for the most part, normal people are going to hate your presentation. Fortunately, this 6-slide template pack will help simplify your job. And make it so much easier for your audience to understand the results of your data analysis!

• Cockpit Chart Presentation Template

If you’re giving a high-level presentation to decision-makers who need hard data and proper analysis, then this free template pack may be what you’re looking for. Each of the 9 slides included in this pack all include a number of charts and diagrams.

By default, text has been kept to a minimum, so there’s nothing to read off the slides. You can verbally explain what the graphs and diagrams mean. And perhaps, if the situation calls for it, you can share your recommended or suggested course of action for your stakeholders and decision-makers.

• Generic Data Driven PowerPoint Template

The best templates for data presentations will make your data come to life. This is where this 6-slide template pack comes in. It’s not only designed to make your data more understandable. But the good thing is, you can use this template for many different kinds of presentations. Whether you’re doing a presentation for a job interview, or a sales presentation, or even an academic one, this template can do the job.

If you want to make the slides look even more unique, you can quickly replace the background photo of the laptop. Then try using something that is more relevant to the type of presentation you’re doing. Slides include a pie chart slide, line chart with comments slide (this is the one in the screenshot above), and an overall statistics slide.

• Matrix Chart PowerPoint Template

The matrix chart looks simple enough. You’ve got rows and columns, pretty much like any regular table. But it’s more than just a table. A matrix chart allows you to compare and analyze different sets of data. You can use it to prove certain data sets are related. Plus, you can even show the strength of that relationship.

This template pack comes in 10 slides. In addition to the basic matrix slide shown above, this pack also include slides like the probability and impact matrix chart slide as well as the table-like matrix chart slide.

• Stair Diagram PowerPoint Template

Just like its namesake, stair diagrams are great for showing a series of steps or progression. You can use good, old-fashioned bullet points, but it’s not going to be much fun. You’ve got 10 different stair diagrams to choose from in this template; the screenshot above shows a steps stair diagram .

Now, most of the diagrams we’ve designed have room for 4 or 5 steps. So, if you need more you can always add an extra step on the same slide. Or you can copy and paste to a new slide and just update the numbers.

Stair diagrams are pretty versatile. You can use them to present how certain processes work, describe a project workflow for maximum productivity, or use it to showcase certain structures in the company.

• Tables PowerPoint Template

Tables have been around for a long time. And it doesn’t look like it’s going to go out of ‘fashion’ soon. Quite the opposite, in fact. As you may have noticed, many of the charts and diagrams included in various templates in this article are based off of tables.

That said, this template pack is also quite unique as well. In addition to the normal-looking table slide shown above, our designers have also made it a point to come up with innovative ways to display tables for your presentations.

For instance, sample slides include a subscription slide, table with symbols slide, and a matrix organization structure table slide. Check out this template right away and see which table slides will look best for your presentation!

• Flow Chart PowerPoint Template

Flowcharts are extremely useful for documenting certain company procedures. You can even use it to present the hierarchy in the company, and who’s responsible for certain tasks. Instead of verbally discussing processes, why not try using a flowchart? You don’t need to design one from scratch either. You can just download this template pack and customize it according to your needs.

The good news is you have 10 different flowchart slides to choose from. Now, if you need to change the shapes to indicate certain steps and decisions, you can quickly do so in PowerPoint.

• Financial Pie Graphs PowerPoint Templates

Whether you’re presenting in front of the higher-ups in your company or potential investors for your startup, these financial pie charts will help you get your point across. With a few clicks you can customize these pie charts and make it your own.

Your audience can quickly analyze the charts and see which departments or products are profitable. In addition to the percentages shown on the slide, you can also add a short description about your financial metrics.

This template pack has 3 slides included. These are ring pie chart slide, financial pie charts for comparison slide (shown above), and the doughnut pie chart slide.

• Research & Development Data Templates

Any startup worth their salt will have a research and development process or team in place. These things are no joke – product development can take years and cost millions of dollars! External funding is often needed to sustain the R&D process.

This is where this template pack comes in. When you present to potential investors, you want to make it as succinct as possible. So, get directly to the point and show them the slides in this template pack.

Now, design is just a small part of the overall presentation. It’s your passion in the product and your ability to persuade potential investors that will ultimately lead you to success!

• Sales Report Presentation Template

Our list of templates for data presentations won’t be complete without a sales report template. As you can see, this template is great for in-house sales reports. This pack includes a vertical bar chart slide, marketing funnel slide (pictured), and a sales associate slide.

The vertical bar chart slide is great for keeping track of your team’s sales or cash flow. The marketing funnel slide, on the other hand, can help educate the team on how a marketing funnel works and which stages they should focus on.

Lastly, the sales associate slide can be used to introduce the most successful person in the team. This will definitely help boost his or her self-esteem and encourage others to do better next time!

• Data Driven Financial Templates

This 11-slide template pack is chock-full of charts and diagrams. The slides have been designed this way because it’s targeted for high-stakes financial presentations. For presentations that talk about money, you need to support your statements with cold, hard facts. And you need to do that in a professional manner.

This template will not let you down. From the design to the types of graphs we’ve included in the slides, this will suffice for most financial presentations. So, what are you waiting for? Check out the template pack right away!

• Block Chain Data PowerPoint Template

Cryptocurrency and blockchain are all the rage nowadays. A lot of people became millionaires – literally – overnight, but many more gambled and lost their entire life savings!

Blockchain technology is practically still in infancy. Sharing what you know about it isn’t exactly a walk in the park either. To help your audience understand the complexities of blockchain technology, use this template pack. It’s got all the slides you need to inform and educate your audience about the wonderful world of blockchain technology.

Google Slides Templates For Your Data Presentations

• Google Slides Life-cycle Diagram Template

A product’s life cycle is predictable. It starts with the introduction to the market, to product growth and maturity, and eventually, its decline. And it’s important to identify these stages because each has a direct influence on the company’s marketing activities and pricing.

This template pack will not only help you identify the stages. It will also help you assure your stakeholders and potential investors that you’ve done your research. And you’ll do whatever it takes to ensure the product’s success and, of course, profitability.

• Google Slides Playful Pie Chart Template

Unlike the other pie charts I’ve featured in this article, this one is going to be easy to use. First of all, there’s no need to download the template to your computer. All you have to do is just register an account on our Template Hub, and then create a copy of the template in Google Slides. As you can imagine, editing it is going to be a breeze as well. You’ve got 10 pie chart slides to choose from. Pick the ones that will help you get your message across, edit, and present (or publish)!

• Google Slides Dashboard Template

As you can see in the screenshot above, a dashboard slide will basically tell your audience everything they need to know in just a single slide. You can stretch the content out, and use one slide for each chart. But it’s not going to be dashboard style anymore if you do this.

Dashboard template slides are great for elevator pitches. Your prospects most likely don’t have a lot of free time. And you certainly don’t want to waste their time as doing so will leave a bad taste in their mouth. A dashboard-style presentation, however, will pique their curiosity and improve the likelihood that they’ll agree to a second meeting with you!

• Google Slides Waterfall Diagram Template

Waterfall charts are great for financial presentations. You can easily show which elements or categories gained or lost over a certain period of time. It can even be used to demonstrate changes in cash flow or your company’s performance in the stock market. This template pack has a total of 10 slides. This includes the waterfall performance comparison slide (pictured), waterfall flowchart diagram, and the project timeline slide.

• Google Slides Playful Data Driven Template

You may be thinking that templates for data presentations should be serious-looking. Well, that may be the norm, but it doesn’t mean your audience won’t appreciate a change of scenery!

This 10-slide playful-looking template packs a lot of punch. You can use this for a wide variety of presentations as it includes a lot of different charts and graphs you can use to share the results of your data analysis. There’s a bar graph, radar chart, waterfall statistics chart, a treemap, and more! Login to your Template Hub account to use this free Google Slides template!

• Google Slides Circle Diagram Template

The circle diagram template pack features 10 different kinds of circle charts. From pie charts, timelines, and cyclical processes to Venn diagrams, this versatile template can be used in all types of presentations. The color theme used is playful, and at the same time, professional, so you can be sure it will appeal to a wide audience. Some of the slides include a circle tracker diagram, project management chart, and a life cycle slide.

• Google Slides Creative Data Driven Financial Chart Template

Number crunchers will love the clean design on this 9-slide template pack. Getting your audience to understand your financial presentation is going to be a breeze using this template. There’s plenty of white space, and the graphics themselves are easy on the eyes. It’s your job as presenter, however, to explain what all these charts mean. So, once you’ve replaced the placeholder content with your own, you better start practicing your presentation speech!

What are your favorites templates so far?

I hope these 20 free PowerPoint and Google Slides template for data presentations have helped you out. Presentation design is important, but it pales in comparison to the message you want to share with your audience. As visual aids, we’ve designed these templates to be attractive while still maintaining a professional and trustworthy design. So, go ahead and download your favorite templates for your next data presentation!

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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A Guide to Effective Data Presentation

Key objectives of data presentation, charts and graphs for great visuals, storytelling with data, visuals, and text, audiences and data presentation, the main idea in data presentation, storyboarding and data presentation, additional resources, data presentation.

Tools for effective data presentation

Financial analysts are required to present their findings in a neat, clear, and straightforward manner. They spend most of their time working with spreadsheets in MS Excel, building financial models , and crunching numbers. These models and calculations can be pretty extensive and complex and may only be understood by the analyst who created them. Effective data presentation skills are critical for being a world-class financial analyst .

It is the analyst’s job to effectively communicate the output to the target audience, such as the management team or a company’s external investors. This requires focusing on the main points, facts, insights, and recommendations that will prompt the necessary action from the audience.

One challenge is making intricate and elaborate work easy to comprehend through great visuals and dashboards. For example, tables, graphs, and charts are tools that an analyst can use to their advantage to give deeper meaning to a company’s financial information. These tools organize relevant numbers that are rather dull and give life and story to them.

Here are some key objectives to think about when presenting financial analysis:

• Visual communication
• Audience and context
• Charts, graphs, and images
• Focus on important points
• Design principles
• Storytelling
• Persuasiveness

For a breakdown of these objectives, check out Excel Dashboards & Data Visualization course to help you become a world-class financial analyst.

Charts and graphs make any financial analysis readable, easy to follow, and provide great data presentation. They are often included in the financial model’s output, which is essential for the key decision-makers in a company.

The decision-makers comprise executives and managers who usually won’t have enough time to synthesize and interpret data on their own to make sound business decisions. Therefore, it is the job of the analyst to enhance the decision-making process and help guide the executives and managers to create value for the company.

When an analyst uses charts, it is necessary to be aware of what good charts and bad charts look like and how to avoid the latter when telling a story with data.

Examples of Good Charts

As for great visuals, you can quickly see what’s going on with the data presentation, saving you time for deciphering their actual meaning. More importantly, great visuals facilitate business decision-making because their goal is to provide persuasive, clear, and unambiguous numeric communication.

For reference, take a look at the example below that shows a dashboard, which includes a gauge chart for growth rates, a bar chart for the number of orders, an area chart for company revenues, and a line chart for EBITDA margins.

To learn the step-by-step process of creating these essential tools in MS Excel, watch our video course titled “ Excel Dashboard & Data Visualization .”  Aside from what is given in the example below, our course will also teach how you can use other tables and charts to make your financial analysis stand out professionally.

Learn how to build the graph above in our Dashboards Course !

Example of Poorly Crafted Charts

A bad chart, as seen below, will give the reader a difficult time to find the main takeaway of a report or presentation, because it contains too many colors, labels, and legends, and thus, will often look too busy. It also doesn’t help much if a chart, such as a pie chart, is displayed in 3D, as it skews the size and perceived value of the underlying data. A bad chart will be hard to follow and understand.

Aside from understanding the meaning of the numbers, a financial analyst must learn to combine numbers and language to craft an effective story. Relying only on data for a presentation may leave your audience finding it difficult to read, interpret, and analyze your data. You must do the work for them, and a good story will be easier to follow. It will help you arrive at the main points faster, rather than just solely presenting your report or live presentation with numbers.

The data can be in the form of revenues, expenses, profits, and cash flow. Simply adding notes, comments, and opinions to each line item will add an extra layer of insight, angle, and a new perspective to the report.

Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation,  past events, and possible conclusions and recommendations that can be made for the future.

The simple diagram below shows the different categories of your audience.

  This chart is taken from our course on how to present data .

Internal Audience

An internal audience can either be the executives of the company or any employee who works in that company. For executives, the purpose of communicating a data-filled presentation is to give an update about a certain business activity such as a project or an initiative.

Another important purpose is to facilitate decision-making on managing the company’s operations, growing its core business, acquiring new markets and customers, investing in R&D, and other considerations. Knowing the relevant data and information beforehand will guide the decision-makers in making the right choices that will best position the company toward more success.

External Audience

An external audience can either be the company’s existing clients, where there are projects in progress, or new clients that the company wants to build a relationship with and win new business from. The other external audience is the general public, such as the company’s external shareholders and prospective investors of the company.

When it comes to winning new business, the analyst’s presentation will be more promotional and sales-oriented, whereas a project update will contain more specific information for the client, usually with lots of industry jargon.

Audiences for Live and Emailed Presentation

A live presentation contains more visuals and storytelling to connect more with the audience. It must be more precise and should get to the point faster and avoid long-winded speech or text because of limited time.

In contrast, an emailed presentation is expected to be read, so it will include more text. Just like a document or a book, it will include more detailed information, because its context will not be explained with a voice-over as in a live presentation.

When it comes to details, acronyms, and jargon in the presentation, these things depend on whether your audience are experts or not.

Every great presentation requires a clear “main idea”. It is the core purpose of the presentation and should be addressed clearly. Its significance should be highlighted and should cause the targeted audience to take some action on the matter.

An example of a serious and profound idea is given below.

To communicate this big idea, we have to come up with appropriate and effective visual displays to show both the good and bad things surrounding the idea. It should put emphasis and attention on the most important part, which is the critical cash balance and capital investment situation for next year. This is an important component of data presentation.

The storyboarding below is how an analyst would build the presentation based on the big idea. Once the issue or the main idea has been introduced, it will be followed by a demonstration of the positive aspects of the company’s performance, as well as the negative aspects, which are more important and will likely require more attention.

Various ideas will then be suggested to solve the negative issues. However, before choosing the best option, a comparison of the different outcomes of the suggested ideas will be performed. Finally, a recommendation will be made that centers around the optimal choice to address the imminent problem highlighted in the big idea.

This storyboard is taken from our course on how to present data .

To get to the final point (recommendation), a great deal of analysis has been performed, which includes the charts and graphs discussed earlier, to make the whole presentation easy to follow, convincing, and compelling for your audience.

CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant resources below:

• Investment Banking Pitch Books
• Excel Dashboards
• Financial Modeling Guide
• Startup Pitch Book
• See all business intelligence resources
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Data Analysis PowerPoint Template

Our Data Analysis PowerPoint Template is a slide deck for presenting the analysis report before business executives and clients. Data analysis is a practical field involving converting raw data into useful information to help companies perform better and improve different processes. Data analysts use multiple tools and procedures to prepare data reports on what should be done (prescriptive analysis), what happened (descriptive analysis), and what can happen (predictive analysis). For instance, predictive analysis is important for determining risks associated with an operation. Similarly, data analysts can prescribe strategists and planning departments based on the information gathered through data analysis. This data analysis PowerPoint template features a range of 100% editable slides for presenting various facts and details. Data analysts can grab this template and prepare professional presentations without design skills. 

This Data Analysis PowerPoint Template has a simple format and a modern hexagonal background layout. A decent color scheme is used on all slides that can suit any presentation or meeting theme. However, users can change the colors according to their requirements. Following the title slide for adding introductory details, this template carries a slide for agenda display. Professionals can mention their meeting agenda points using the list design of this slide. Next is a timeline slide featuring infographic PowerPoint icons and text boxes to showcase different events of the process. Analysts can explain their analysis protocol with this slide template. The following slide is to showcase the results of the SWOT analysis . By performing the SWOT analysis for companies, data analysts provide real information about what they have and what they should improve for success. This slide carries four hexagons arranged in a square pattern and icons and placeholder text for adding details. Our presentation data analysis template includes a chapter slide, a two-column editable slide, and a process diagram slide with icons. 

Professionals can conveniently modify and personalize these slides for their use cases. The colors, font styles, and PowerPoint diagrams can be altered accordingly. A colorful thank you slide is provided to mention contact and agency details. So, download and try this data analysis template for Google Slides to prepare impressive presentations.

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Data Analysis 101: How to Make Your Presentations Practical and Effective

• December 27, 2022
• 54 Comments

Understanding Importance of Data Analysis

The results of data analysis can give business the vital insights they need to turn in to successful and profitable ventures. It could be the difference between a successful business operation and a business operation that is in trouble.

Data analysis, though one of the most in-demand job roles globally, doesn’t require a degree in statistics or mathematics to do well, and employers from a wide variety of industries are very keen to recruit data analysts.

Businesses hire data analysts in the field of finance, marketing, administration, HR, IT and procurement, to name just a few.  Understand the big picture and provide answers. By engaging in data analysis, you can actually delve deep and discover hidden truths that most business people would never be able to do.

What skills you should master to be a data analyst?

While Data Analyst roles are on the rise, there are certain skills that are vital for anyone who wants to become a data analyst . Before the job, a candidate needs to have either a degree in statistics, business or computer science or a related subject, or work experience in these areas. 

If you’re interested in becoming a data analyst, you’ll need to know: 

• Programming and algorithms
• Data Visualization 
• Open-source and cloud technologies 
• No coding experience is required. 

How much is a data analyst worth?  Data analysts earn an average salary of £32,403 per annum, according to jobs site Glassdoor. This pays for a salary, with benefits such as medical insurance and paid leave included in the starting salary.  If you think you have the right skills, there are plenty of roles on offer.

What data analysis entails

Data analysis is an analytical process which involves recording and tabulating (recording and entering, entering and tabulating) the quantities of a product, such as numbers of units produced, costs of materials and expenses.

While data analyst can take different forms, for example in databases, in other structures such as spreadsheets, numbers are the main means of data entry. This involves entering and entering the required data in a data analysis system such as Excel.

For example, although a database doesn’t require a data analyst, it can still benefit from data analysis techniques such as binomial testing, ANOVA and Fisher’s exact tests.  Where is the data analysis courses in IT?  Given the ever-increasing reliance on technology in business, data analysis courses are vital skills.

What are the types of data analysis methods?

• Cluster analysis 

The act of grouping a specific set of data in a manner that those elements are more similar to one another than to those in other groups – hence the term ‘cluster.’ Since there is no special target variable while doing clustering, the method is often used to find hidden patterns in the data. The approach is purposely used to offer additional context to a particular trend or dataset.  

• Cohort analysis 

This type of data analysis method uses historical data to examine and compare a determined segment of users’ behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

A dependent variable is an element of a complex system that is assumed to have a single cause, but it’s affected by multiple factors, thus giving researchers an indication as to how a complex system function.  

• Regression analysis

The regression analysis is used to predict how the value of a dependent variable changes when one or more independent variables change, stay the same or the dependent variable is not moved. Regression is a sophisticated statistical method that includes mathematical functions that are typically called “segmentation,” “distribution,” and “intercept” functions.

Regression is a type of regression analysis that only contains linear and quadratic functions. You can change the types of factors (or the independent variables) that are selected in regression analysis (it’s typically called “nonlinear regression analysis”) by changing the order in which the models are constructed.To begin, let’s explain how regression analysis works.  

Examples in business world

The Oracle Corporation is one of the first multinational companies to adopt this type of analysis method, based on which the company was able to develop predictive modelling systems for marketing purposes.

In a more specific sense, a Regression analysis is a popular type of data analysis used for analyzing the likelihood that a random variable will move up or down a range of parameters in response to a change in a specific control variable.

Companies who use this type of analysis are looking for trends and patterned performance over time. For example, how a company may respond to a rising cost of labor and its effect on its business bottom line, a weather-related issue like an earthquake, a new advertising campaign, or even a surge in customer demand in some areas.

What are basic pointers to consider while presenting data

Recognize that presentation matters.

Too often, analysts make the mistake of presenting information in order to show an abstracted version of it.  For instance, say a B2B company has 4 ways to improve their sales funnel:

• More Visually Engaging 
• More Easily Transacted 
• More Cost Effective 

Then, “informative” would mean that a B2B company needs to optimize their sales funnel to each of these to be more “convenient, faster, easier, more visually engaging, or most cost effective.” Sure, it would be nice if they all improved – they would all provide a competitive advantage in some way. But that’s not what the data tells us.

Don’t scare people with numbers

When you’re presenting data, show as many as possible, in as many charts as possible. Then, try to talk through the implications of the data, rather than overwhelming people with an overwhelming amount of data.

Why? Research suggests that when a number is presented in a visual, people become more likely to process it and learn from it.  I recommend using video, text, graphs, and pictures to represent your numbers. This creates a more visually appealing data set. The number of followers on Twitter is visually appealing. The number of followers on Facebook is visually appealing. But nobody looks at their Twitter followers. If you don’t know what your numbers mean, how will your audience?  That doesn’t mean numbers aren’t important.

Maximize the data pixel ratio

The more data you show to a critical stakeholder, the more likely they are to get lost and distracted from what you’re actually trying to communicate. This is especially important in the case of people in the sales and marketing function.

Do you have a sales person out in the field who is trying to close a deal? It would be a shame if that person got lost in your Excel analytics and lost out on the sale.  This problem also occurs on the web.

Consider how web visitors respond to large, colorful charts and graphs. If we’re talking about visualizations that depict web performance, a visual might be helpful. But how often do we see this done?  Research shows that people respond better to web-based data in a simplified, less complex format.

Save 3-D for the movies

There are great stories in the universe. This is an oversimplification, but if you look at history, humans only understand stories. We are great storytellers. We develop, through trial and error, our own intuition about the “right” way to tell stories.

 One of the most powerful and effective ways to present data is to go beyond the visual to the audible, that is, to tell stories in a way that people can relate to. Everything you hear about computers being a series of numbers is wrong. We visualize numbers in a precise, quantitative way. But the numbers are not a collection of isolated events. To understand them, we need to understand the broader context.

Friends don’t let friends use pie charts

Businesses and analysts have done this since pie charts first appeared on Microsoft Excel sheets. When presenting data, break down your pie chart into its component segments.

 As opposed to an equal-sized circle for the average earnings for all the employees, share a pie chart where the percentages for each individual segment are different, with a link to the corresponding chart.

 Pair with explanatory text, show their correlation, and make your choice based on your audience, not on whether you want to scare or “educate” them. The majority of audiences will see the same image, regardless of whether it’s presented in a bar chart, bar chart, line chart, or something else.

Choose the appropriate chart

Does the data make logical sense? Check your assumptions against the data.  Are the graphs charting only part of the story? Include other variables in the graphs.  Avoid using axis labels to mislead. Never rely on axes to infer, “logical” conclusions.  Trust your eyes: you know what information your brain can process.

Think of numbers like music — they are pleasing, but not overwhelming.  Save 3D for the movies. When everyone is enjoying 4K, 8K, and beyond, it’s hard to envision your audience without the new stuff. I remember the first time I got to see HDTV. At home, I sat behind a chair and kept turning around to watch the TV. But at the theatre, I didn’t need a chair. All I had to do was look up, and see the giant screen, the contrast, and the detail.

Don’t mix chart types for no reason

Excel chart s with colored areas help people focus. Arrows give us scale. Assume your audience doesn’t understand what you’re saying, even if they do. Nobody wants to open a recipe book to learn how to cook soup. Instead, we start with a recipe.

Use a formula to communicate your analysis with as few words as possible. Keep it simple.  Resist the urge to over-complicate your presentation. A word cloud is not a word cloud. A bar chart is not a bar chart. If you use a word cloud to illustrate a chart, consider replacing a few words with a gif. A bar chart doesn’t need clouds. And a bar chart doesn’t need clouds.  If there’s one thing that’s sure to confuse your audience, it’s bar charts.

Use color with intention

Use color with intention. It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.” 

However, visualizations like maps, axes, or snapshots can help visual communication to avoid this pitfall. If you are going to show a few locations on a map, make sure each location has a voice and uses a distinct color. Avoid repeating colors from the map or bottom bar in all the visuals. Be consistent with how you present the data .  A pie chart is not very interesting if all it shows is a bunch of varying sizes of the pie.

Data analysis in the workplace, and how it will impact the future of business

Business leaders are taking note of the importance of data analysis skills in their organisation, as it can make an enormous impact on business.

 Larger organisations such as Google, Amazon and Facebook employ huge teams of analysts to create their data and statistics. We are already seeing the rise of the next generation of big data analysts – those who can write code that analyses and visualizes the data and report back information to a company to help it improve efficiency and increase revenue. 

The increasing need for high-level understanding of data analysis has already led to the role of data analyst becoming available at university level. It is no longer a mandatory business qualification but one that can enhance your CV.

By understanding the importance of each variable, you can improve your business by managing your time and creating more effective systems and processes for running your business. The focus shifts from just providing services to providing value to your customers, creating a better, more intuitive experience for them so they can work with your company for the long-term. 

Adopting these small steps will allow you to be more effective in your business and go from being an employee to an entrepreneur.

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Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Definition :

Data interpretation refers to the process of making sense of data by analyzing and drawing conclusions from it. It involves examining data in order to identify patterns, relationships, and trends that can help explain the underlying phenomena being studied. Data interpretation can be used to make informed decisions and solve problems across a wide range of fields, including business, science, and social sciences.

Data Interpretation Process

Here are the steps involved in the data interpretation process:

• Define the research question : The first step in data interpretation is to clearly define the research question. This will help you to focus your analysis and ensure that you are interpreting the data in a way that is relevant to your research objectives.
• Collect the data: The next step is to collect the data. This can be done through a variety of methods such as surveys, interviews, observation, or secondary data sources.
• Clean and organize the data : Once the data has been collected, it is important to clean and organize it. This involves checking for errors, inconsistencies, and missing data. Data cleaning can be a time-consuming process, but it is essential to ensure that the data is accurate and reliable.
• Analyze the data: The next step is to analyze the data. This can involve using statistical software or other tools to calculate summary statistics, create graphs and charts, and identify patterns in the data.
• Interpret the results: Once the data has been analyzed, it is important to interpret the results. This involves looking for patterns, trends, and relationships in the data. It also involves drawing conclusions based on the results of the analysis.
• Communicate the findings : The final step is to communicate the findings. This can involve creating reports, presentations, or visualizations that summarize the key findings of the analysis. It is important to communicate the findings in a way that is clear and concise, and that is tailored to the audience’s needs.

Types of Data Interpretation

There are various types of data interpretation techniques used for analyzing and making sense of data. Here are some of the most common types:

Descriptive Interpretation

This type of interpretation involves summarizing and describing the key features of the data. This can involve calculating measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and creating visualizations such as histograms, box plots, and scatterplots.

Inferential Interpretation

This type of interpretation involves making inferences about a larger population based on a sample of the data. This can involve hypothesis testing, where you test a hypothesis about a population parameter using sample data, or confidence interval estimation, where you estimate a range of values for a population parameter based on sample data.

Predictive Interpretation

This type of interpretation involves using data to make predictions about future outcomes. This can involve building predictive models using statistical techniques such as regression analysis, time-series analysis, or machine learning algorithms.

Exploratory Interpretation

This type of interpretation involves exploring the data to identify patterns and relationships that were not previously known. This can involve data mining techniques such as clustering analysis, principal component analysis, or association rule mining.

Causal Interpretation

This type of interpretation involves identifying causal relationships between variables in the data. This can involve experimental designs, such as randomized controlled trials, or observational studies, such as regression analysis or propensity score matching.

Data Interpretation Methods

There are various methods for data interpretation that can be used to analyze and make sense of data. Here are some of the most common methods:

Statistical Analysis

This method involves using statistical techniques to analyze the data. Statistical analysis can involve descriptive statistics (such as measures of central tendency and dispersion), inferential statistics (such as hypothesis testing and confidence interval estimation), and predictive modeling (such as regression analysis and time-series analysis).

Data Visualization

This method involves using visual representations of the data to identify patterns and trends. Data visualization can involve creating charts, graphs, and other visualizations, such as heat maps or scatterplots.

Text Analysis

This method involves analyzing text data, such as survey responses or social media posts, to identify patterns and themes. Text analysis can involve techniques such as sentiment analysis, topic modeling, and natural language processing.

Machine Learning

This method involves using algorithms to identify patterns in the data and make predictions or classifications. Machine learning can involve techniques such as decision trees, neural networks, and random forests.

Qualitative Analysis

This method involves analyzing non-numeric data, such as interviews or focus group discussions, to identify themes and patterns. Qualitative analysis can involve techniques such as content analysis, grounded theory, and narrative analysis.

Geospatial Analysis

This method involves analyzing spatial data, such as maps or GPS coordinates, to identify patterns and relationships. Geospatial analysis can involve techniques such as spatial autocorrelation, hot spot analysis, and clustering.

Applications of Data Interpretation

Data interpretation has a wide range of applications across different fields, including business, healthcare, education, social sciences, and more. Here are some examples of how data interpretation is used in different applications:

• Business : Data interpretation is widely used in business to inform decision-making, identify market trends, and optimize operations. For example, businesses may analyze sales data to identify the most popular products or customer demographics, or use predictive modeling to forecast demand and adjust pricing accordingly.
• Healthcare : Data interpretation is critical in healthcare for identifying disease patterns, evaluating treatment effectiveness, and improving patient outcomes. For example, healthcare providers may use electronic health records to analyze patient data and identify risk factors for certain diseases or conditions.
• Education : Data interpretation is used in education to assess student performance, identify areas for improvement, and evaluate the effectiveness of instructional methods. For example, schools may analyze test scores to identify students who are struggling and provide targeted interventions to improve their performance.
• Social sciences : Data interpretation is used in social sciences to understand human behavior, attitudes, and perceptions. For example, researchers may analyze survey data to identify patterns in public opinion or use qualitative analysis to understand the experiences of marginalized communities.
• Sports : Data interpretation is increasingly used in sports to inform strategy and improve performance. For example, coaches may analyze performance data to identify areas for improvement or use predictive modeling to assess the likelihood of injuries or other risks.

When to use Data Interpretation

Data interpretation is used to make sense of complex data and to draw conclusions from it. It is particularly useful when working with large datasets or when trying to identify patterns or trends in the data. Data interpretation can be used in a variety of settings, including scientific research, business analysis, and public policy.

In scientific research, data interpretation is often used to draw conclusions from experiments or studies. Researchers use statistical analysis and data visualization techniques to interpret their data and to identify patterns or relationships between variables. This can help them to understand the underlying mechanisms of their research and to develop new hypotheses.

In business analysis, data interpretation is used to analyze market trends and consumer behavior. Companies can use data interpretation to identify patterns in customer buying habits, to understand market trends, and to develop marketing strategies that target specific customer segments.

In public policy, data interpretation is used to inform decision-making and to evaluate the effectiveness of policies and programs. Governments and other organizations use data interpretation to track the impact of policies and programs over time, to identify areas where improvements are needed, and to develop evidence-based policy recommendations.

In general, data interpretation is useful whenever large amounts of data need to be analyzed and understood in order to make informed decisions.

Data Interpretation Examples

Here are some real-time examples of data interpretation:

• Social media analytics : Social media platforms generate vast amounts of data every second, and businesses can use this data to analyze customer behavior, track sentiment, and identify trends. Data interpretation in social media analytics involves analyzing data in real-time to identify patterns and trends that can help businesses make informed decisions about marketing strategies and customer engagement.
• Healthcare analytics: Healthcare organizations use data interpretation to analyze patient data, track outcomes, and identify areas where improvements are needed. Real-time data interpretation can help healthcare providers make quick decisions about patient care, such as identifying patients who are at risk of developing complications or adverse events.
• Financial analysis: Real-time data interpretation is essential for financial analysis, where traders and analysts need to make quick decisions based on changing market conditions. Financial analysts use data interpretation to track market trends, identify opportunities for investment, and develop trading strategies.
• Environmental monitoring : Real-time data interpretation is important for environmental monitoring, where data is collected from various sources such as satellites, sensors, and weather stations. Data interpretation helps to identify patterns and trends that can help predict natural disasters, track changes in the environment, and inform decision-making about environmental policies.
• Traffic management: Real-time data interpretation is used for traffic management, where traffic sensors collect data on traffic flow, congestion, and accidents. Data interpretation helps to identify areas where traffic congestion is high, and helps traffic management authorities make decisions about road maintenance, traffic signal timing, and other strategies to improve traffic flow.

Data Interpretation Questions

Data Interpretation Questions samples:

• Medical : What is the correlation between a patient’s age and their risk of developing a certain disease?
• Environmental Science: What is the trend in the concentration of a certain pollutant in a particular body of water over the past 10 years?
• Finance : What is the correlation between a company’s stock price and its quarterly revenue?
• Education : What is the trend in graduation rates for a particular high school over the past 5 years?
• Marketing : What is the correlation between a company’s advertising budget and its sales revenue?
• Sports : What is the trend in the number of home runs hit by a particular baseball player over the past 3 seasons?
• Social Science: What is the correlation between a person’s level of education and their income level?

In order to answer these questions, you would need to analyze and interpret the data using statistical methods, graphs, and other visualization tools.

Purpose of Data Interpretation

The purpose of data interpretation is to make sense of complex data by analyzing and drawing insights from it. The process of data interpretation involves identifying patterns and trends, making comparisons, and drawing conclusions based on the data. The ultimate goal of data interpretation is to use the insights gained from the analysis to inform decision-making.

Data interpretation is important because it allows individuals and organizations to:

• Understand complex data : Data interpretation helps individuals and organizations to make sense of complex data sets that would otherwise be difficult to understand.
• Identify patterns and trends : Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships.
• Make informed decisions: Data interpretation provides individuals and organizations with the information they need to make informed decisions based on the insights gained from the data analysis.
• Evaluate performance : Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made.
• Communicate findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

• Contextual : Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.
• Iterative : Data interpretation is an iterative process, meaning that it often involves multiple rounds of analysis and refinement as more data becomes available or as new insights are gained from the analysis.
• Subjective : Data interpretation is often subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. It is important to acknowledge and address these biases when interpreting data.
• Analytical : Data interpretation involves the use of analytical tools and techniques to analyze and draw insights from data. These may include statistical analysis, data visualization, and other data analysis methods.
• Evidence-based : Data interpretation is evidence-based, meaning that it is based on the data and the insights gained from the analysis. It is important to ensure that the data used in the analysis is accurate, relevant, and reliable.
• Actionable : Data interpretation is actionable, meaning that it provides insights that can be used to inform decision-making and to drive action. The ultimate goal of data interpretation is to use the insights gained from the analysis to improve performance or to achieve specific goals.

Advantages of Data Interpretation

Data interpretation has several advantages, including:

• Improved decision-making: Data interpretation provides insights that can be used to inform decision-making. By analyzing data and drawing insights from it, individuals and organizations can make informed decisions based on evidence rather than intuition.
• Identification of patterns and trends: Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships. This information can be used to improve performance or to achieve specific goals.
• Evaluation of performance: Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made. By analyzing data, organizations can identify strengths and weaknesses and make changes to improve their performance.
• Communication of findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.
• Better resource allocation: Data interpretation can help organizations allocate resources more efficiently by identifying areas where resources are needed most. By analyzing data, organizations can identify areas where resources are being underutilized or where additional resources are needed to improve performance.
• Improved competitiveness : Data interpretation can give organizations a competitive advantage by providing insights that help to improve performance, reduce costs, or identify new opportunities for growth.

Limitations of Data Interpretation

Data interpretation has some limitations, including:

• Limited by the quality of data: The quality of data used in data interpretation can greatly impact the accuracy of the insights gained from the analysis. Poor quality data can lead to incorrect conclusions and decisions.
• Subjectivity: Data interpretation can be subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. This can lead to different interpretations of the same data.
• Limited by analytical tools: The analytical tools and techniques used in data interpretation can also limit the accuracy of the insights gained from the analysis. Different analytical tools may yield different results, and some tools may not be suitable for certain types of data.
• Time-consuming: Data interpretation can be a time-consuming process, particularly for large and complex data sets. This can make it difficult to quickly make decisions based on the insights gained from the analysis.
• Incomplete data: Data interpretation can be limited by incomplete data sets, which may not provide a complete picture of the situation being analyzed. Incomplete data can lead to incorrect conclusions and decisions.
• Limited by context: Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.

Difference between Data Interpretation and Data Analysis

Data interpretation and data analysis are two different but closely related processes in data-driven decision-making.

Data analysis refers to the process of examining and examining data using statistical and computational methods to derive insights and conclusions from it. It involves cleaning, transforming, and modeling the data to uncover patterns, relationships, and trends that can help in understanding the underlying phenomena.

Data interpretation, on the other hand, refers to the process of making sense of the findings from the data analysis by contextualizing them within the larger problem domain. It involves identifying the key takeaways from the data analysis, assessing their relevance and significance to the problem at hand, and communicating the insights in a clear and actionable manner.

In short, data analysis is about uncovering insights from the data, while data interpretation is about making sense of those insights and translating them into actionable recommendations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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.css-v0jgcu{position:absolute;top:0;left:0;} .css-19sk4h4{position:relative;} Business data analytics: Definition and how to get started

By Team Multiverse

• Arrow Right Streamline Icon: https://streamlinehq.com Real-world applications of business data analytics
• Arrow Right Streamline Icon: https://streamlinehq.com The role of a Business Data Analyst
• Arrow Right Streamline Icon: https://streamlinehq.com Essential skills for Business Data Analysts
• Arrow Right Streamline Icon: https://streamlinehq.com Steps to becoming a Business Data Analyst
• Arrow Right Streamline Icon: https://streamlinehq.com Take the next step in your data analytics journey with Multiverse

Business data analytics is a cornerstone of modern decision making and innovation.

Companies use the insights they gain from business analytics to create data-driven strategies. These approaches can improve customer satisfaction, operational efficiency, and profitability. Retailers, for example, can use data analytics to predict which products will sell fastest and optimize its supply chain management.

What is business data analytics?

Business data analytics uses software and statistical techniques to interpret data and gain meaningful insights. This process allows organizations to understand their operations better and improve performance. Business analytics also assists with strategic planning and risk management.

Say, for example, a national restaurant brand wants to update its menu. Data analytics allows the company to interpret customer reviews and sales trends to determine which meals and ingredients perform best. Based on these insights, the restaurant can tailor its menu to satisfy customers and boost sales.

Understanding business data analysis

You may have already started to master some of the components of business data analytics. This process involves a few basic steps:

• Ask a question - Start with a specific question or concern you want to address with data. For instance, you could explore customer trends to discover why your business's sales have declined.
• Data collection - Identify relevant data sources and gather information. You could survey customers or use data mining techniques to harvest social media posts.
• Data cleaning and processing - Organize the raw data into a usable format. This often involves transforming data by loading it into an environment optimized for analysis. You’ll also fill in missing data, remove inconsistencies, and correct errors.
• Data analysis - Apply statistical techniques and software to reveal patterns and correlations in the data set.
• Data visualization - Use software to transform the data into easy-to-understand graphics. These visualizations may include charts, graphs, and maps.
• Data interpretation - Study the results to extract meaningful insights. For instance, you might determine your target audience’s interests have changed, leading to a sales dip.
• Communicate findings - Share your results with decision makers and recommend the next steps. In this scenario, you might advise developing new services that better align with your consumer base.

Real-world applications of business data analytics

Business data analytics has many practical applications across industries. Here are three case studies that illustrate the value and versatility of this approach.

Tracking Machine Health in Manufacturing

The company installs advanced telematics software in its construction machinery. The software collects data about different aspects of machine behavior, including fault codes, fuel consumption, and idle time. This data gets streamed through the cloud to John Deere’s Machine Health Center in Iowa.

Local dealers use this data to diagnose machine problems remotely instead of traveling to construction sites or farms. They can select the necessary parts and repair tools to bring to the service appointment, saving time and reducing trips. Additionally, John Deere uses this information to identify and fix potential manufacturing errors.

These applications allow John Deere to improve its performance over time and provide more efficient service.

Improving patient care in healthcare

Data analytics allows Stanford Medicine Children’s Health (opens new window) to understand and improve the patient experience.

The organization uses evaluation forms to collect data about patients’ experiences during their hospital stays. Analysts use AI tools to synthesize the information and reveal patterns, such as complaints about staff responsiveness and wait times.

According to Chief Analytics Officer Brendan Watkins, the organization places these insights “directly into the hands of the folks who can make a difference [and], make systemic change with this data.” These stakeholders include healthcare providers who can use the information to deliver better patient care.

Boosting productivity in retail

The grocery chain Kroger (opens new window) has developed two data-driven applications to improve employee productivity.

First, the company created a task management application for Night Crew Managers. This application displays each store’s inventory and merchandise deliveries in real time. It also uses data analytics to optimize employee to-do lists to help them restock stores efficiently.

Additionally, Kroger uses a store management application to streamline store audits. This tool also automatically recommends tasks for employees as they prepare for audits.

Both applications help Kroger associates adapt to changing store conditions and improve the customer experience.

The role of a Business Data Analyst

A Business Data Analyst uses data to solve business problems and identify growth opportunities. They also support decision makers by offering recommendations based on their findings.

The day-to-day responsibilities of these professionals vary by role but typically include these tasks:

• Collect data from a wide range of sources, such as customer feedback forms, financial records, or in-product data from a software application
• Develop databases to organize information
• Process raw data to prepare it for analysis
• Build and train machine learning (ML) models to analyze enormous data sets
• Design predictive models to forecast potential outcomes
• Create data visualizations
• Deliver presentations about their findings
• Collaborate with colleagues in marketing, sales, and other departments
• Learn about the latest advancements and trends in business data analytics

Business Data Analysts wield significant influence in their organizations. Leaders rely on their expertise for a broad range of business decisions, such as:

• Choosing marketing and sales tactics
• Deciding whether to invest in a new venture
• Managing financial resources
• Selecting prototypes to develop into new products
• Scheduling manufacturing equipment for maintenance and replacement

Because Business Data Analysts deliver considerable value, they often earn lucrative salaries . According to Glassdoor, the pay range for this career is $97,000 to $153,000, with an average salary of $121,000.

Essential skills for Business Data Analysts

You’ll need the right technical and soft skills to thrive in a business data analytics role. If you’re interested in this career path, focus on developing these foundational abilities.

Technical skills

Business Data Analysts rely heavily on technology to interpret data. After all, you wouldn’t get very far if you had to analyze a spreadsheet with thousands of data points by hand. These technical skills will help you manage and process data effectively:

• Structured Query Language (SQL) - This language allows you to organize, manipulate, and search structured databases.
• Programming languages - Use R for exploratory data analysis and data visualization. Python enables you to automate tasks, clean data, and build Machine Learning (ML)ML algorithms.
• Statistical analysis - Understand how to use statistical methods to interpret data. For example, descriptive analytics evaluates historical data to understand events and patterns. Prescriptive analytics uses past and present data to recommend future actions.
• Artificial intelligence (AI) and ML - Companies increasingly rely on AI and ML to analyze data and predict future trends. Study foundational ML concepts like clustering algorithms, decision trees, and linear regression. You should also know how to use ML frameworks and libraries like PyTorch and TensorFlow.
• Data visualization - Transform data into accessible and visually appealing graphics. Popular data visualization platforms include Microsoft Power BI, Tableau, and Zoho Analytics.
• Reporting - Use business intelligence tools like Qlikview and Sisense to create interactive dashboards and reports for stakeholders.

Soft skills

Data Analysts need strong interpersonal skills to excel in the workplace, including:

• Adaptability - Business analytics evolves quickly, so prepare to embrace new approaches and tools.
• Collaboration - Share ideas and responsibilities with team members from different backgrounds and departments.
• Communication - Express your ideas clearly in conversations, presentations, and written reports. You should also learn to translate complex technical concepts for lay audiences.
• Critical thinking - Evaluate the accuracy of data, identify potential biases in the results, and assess potential recommendations for feasibility.
• Negotiation - Collaborate with multiple stakeholders to develop solutions that meet everyone’s needs.
• Problem-solving - Learn how to approach problems from different angles and devise novel solutions.

Steps to becoming a Business Data Analyst

There’s no universal blueprint to becoming a Business Data Analyst. You can use many resources and strategies to gain the knowledge and skills required for this career. Here are a few common pathways.

Earn a college degree

A college education is a traditional — but not required — educational pathway for Business Analysts. Many colleges and universities offer degrees in business analysis, data science, mathematics, and other relevant fields. 

Enrolling in a business data analytics program offers several benefits. A structured curriculum gives you a solid foundation in data management, statistical analysis, and other necessary skills. You’ll also receive feedback and guidance from faculty.

But a college education has a few drawbacks. First, a four-year degree requires a significant investment of time and money. Undergraduate students pay an average of $36,436 per year (opens new window) for tuition, books, and other expenses. You’ll also need to dedicate extensive time to studying and attending classes. People with full-time jobs, families, and other obligations may struggle to balance their responsibilities with a college education.

Many colleges also provide limited hands-on experience. A business data analytics major may learn foundational theories but not be able to apply these concepts in the real world. As a result, they may lack the experience and portfolio needed to land a position.

Obtain relevant certifications

Certifications enable you to develop your skills and showcase your abilities to potential employers. Here are a few relevant credentials that could help you prepare for data analytics roles:

• Entry Certificate in Business Analytics (ECBA) - The International Institute of Business Analysis (opens new window) offers this certificate for aspiring and entry-level data professionals. The certification demonstrates foundational competencies in business analysis planning, elicitation and collaboration, and other areas.
• Professional in Business Analysis (PMI-PBA) - The Project Management Institute (opens new window) designed this certification for Business Analysts who use data to support projects.
• Certified Foundation Level Business Analyst - The International Qualification Board for Business Analysis (opens new window) offers this foundational certification. It demonstrates proficiency in business modeling and creating business solutions.

Certifications cost much less than the average four-year degree and typically take less than a year to earn. They can accelerate your professional development and prove your commitment to the field to potential employers.

If you’re an established professional, you may already have many skills needed to succeed in business analytics. But everyone has areas for improvement. Thankfully, upskilling can fill any gaps in your knowledge — making you more productive at work and better prepared to advance in your career.

In fact, according to Gartner, 75% of employees who participate in upskilling programs agree it contributes to career progression.

Multiverse’s Applied Analytics Accelerator is one of the most effective ways to level up your skills. This cost-free six-month program allows you to immerse yourself in the field of business analytics while working for your current employer.

The apprenticeship includes six modules that teach you how to make data driven decisions and improve business processes. You’ll also learn data analysis and visualization skills you can immediately apply in your role. This fusion of structured learning and hands-on experience will help you kickstart or grow your career in business analysis.

Gain hands-on experience

Developing practical experience strengthens your skills and gives you a competitive advantage in the job market. Look for opportunities to apply your skills with real data sets.

For example, you could volunteer to analyze customer data for your current employer and recommend ways to improve marketing initiatives. You could also help clients solve business problems as a freelancer or consultant.

As you create projects, assemble them into a digital portfolio. Include a detailed description of each project and highlight their measurable outcomes. Potential employers can review your portfolio to gauge your experience level and skills.

If you’re looking for hand-on projects, Multiverse’s Applied Analytics Accelerator equips you to upskill your data chops while staying in your current role.

Take the next step in your data analytics journey with Multiverse

As a Business Analyst, you play a critical role in business decision making and strategic planning. Your insights can help companies develop cutting-edge innovations, improve customer experiences, reduce costs, and more.

Prepare for a career in this in-demand field with a Multiverse apprenticeship. Apprentices get paid to upskill and gain hands-on experience with real business analytics projects. They also receive one-on-one coaching tailored to their personal and professional goals.

Tell us about yourself by completing our quick application (opens new window) , and the Multiverse team will get in touch with the next steps.

Team Multiverse

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R news and tutorials contributed by hundreds of R bloggers

Introduction to standardization in business reporting.

Posted on June 27, 2024 by Numbers around us in R bloggers | 0 Comments

Why Standardization Matters

Hey there! Thanks for joining me on this exciting journey into the world of International Business Communication Standards (IBCS). Before we dive into the nitty-gritty of the SUCCESS acronym, let’s take a step back and chat about why standardization in business reporting is such a game-changer. If you’ve ever felt overwhelmed by messy reports with inconsistent formatting, you’re not alone. I’ve been there too, staring at a sea of numbers that don’t quite add up.

Standardization in business reporting ensures that data is presented in a consistent manner, enhancing comprehensibility and comparability across different reports. Imagine flipping through different reports where each one tells its story in its own unique language — confusing, right? Standardization is like translating all those languages into one that everyone can understand easily.

Consistency is Key

Think of standardized reports as a well-organized bookshelf. You know exactly where to find what you’re looking for, and every book (or in this case, piece of data) is presented in a way that makes sense. This consistency is crucial for making informed business decisions quickly and accurately. No more wasting time trying to figure out what’s what!

I remember a time when I was working on a project that involved analyzing sales data across multiple brands. Each region had its own way of reporting — different formats, different terminologies, and different visualization styles. It was a nightmare to compile all this information into a coherent report. That’s when I discovered the power of standardization. By applying consistent formats and visual styles, the report not only became easier to read but also revealed insights that were previously hidden in the chaos.

Time-Saving and Efficiency

Let’s be honest, who wouldn’t want to save time? Standardization not only reduces the risk of misinterpretation but also enhances the efficiency of report generation and review processes. Once you have a standardized template, creating new reports becomes a breeze. You can focus more on analyzing the data rather than formatting the report.

Understanding IBCS Standards

Now that we’ve established why standardization is so important, let’s get to know IBCS. The International Business Communication Standards provide a comprehensive framework for the design of business communication, particularly in the context of reports, presentations, and dashboards. The goal of IBCS is to improve the clarity, efficiency, and effectiveness of business communications.

The SUCCESS Formula

The heart of IBCS is the SUCCESS formula:

• SAY : Convey a clear message.
• UNIFY : Apply consistent semantic notation.
• CONDENSE : Increase information density.
• CHECK : Ensure visual integrity.
• EXPRESS : Choose proper visualization.
• SIMPLIFY : Avoid clutter.
• STRUCTURE : Organize content logically.

Let’s break down each component briefly:

• SAY : It’s all about making your key message unmistakably clear. Your audience should be able to grasp the main point at a glance. This involves using clear titles, highlighting key figures, and ensuring that the message is front and center.
• UNIFY : Consistency is key. This principle ensures that all visual elements (like colors, shapes, and fonts) are used consistently throughout your reports. This helps in creating a familiar look and feel, making it easier for readers to navigate and understand.
• CONDENSE : More information doesn’t necessarily mean more clutter. This principle focuses on presenting data in a compact and dense format, without overwhelming the reader. Think of using small multiples, sparklines, and condensed tables that pack a lot of information in a small space.
• CHECK : Accuracy and integrity are paramount. This involves verifying the data, ensuring that scales and labels are accurate, and avoiding any visual misrepresentations. It’s about being honest and precise with your visuals.
• EXPRESS : Choosing the right type of visualization for your data is crucial. This principle guides you on selecting the most effective chart types to convey your message clearly, whether it’s bar charts, line charts, scatter plots, or more advanced visualizations.
• SIMPLIFY : Less is more. Avoiding unnecessary elements and focusing on what’s important helps in reducing cognitive load on the reader. This means removing gridlines, reducing colors, and using white space effectively.
• STRUCTURE : Organize your content logically. This involves structuring your reports in a way that guides the reader through the data naturally. Sections, subsections, and a logical flow of information are essential here.

Clarity and Comprehension

I’ve been standardizing reports in my previous roles for quite some time. But I only came across IBCS recently, and let me tell you, I’m absolutely loving it as a framework. It has transformed the way I think about presenting data. Suddenly, my reports are not just a collection of numbers but a coherent story that my audience can easily understand and act upon. Each element of the SUCCESS formula plays a critical role in achieving this clarity.

Practical Steps to Implement Standardization

Alright, let’s get practical. How can you start standardizing your reports? Here’s a step-by-step guide that I’ve found incredibly useful:

• Evaluate Current Practices : Start by evaluating your current reporting practices. Identify inconsistencies and areas for improvement. Trust me, you’ll find plenty of “aha!” moments here.
• Educate and Train : Educate your team about the importance of standardization and the principles of IBCS. Knowledge is power, after all. Conduct workshops or training sessions to get everyone on the same page.
• Develop Templates and Tools : Develop standardized templates and tools that align with IBCS guidelines. This step is crucial for ensuring consistency across all reports. Tools like Quarto can be incredibly helpful here.
• Monitor and Collect Feedback : Regularly review your reports for compliance with the standards and gather feedback from users. Continuous improvement is the name of the game. Set up a feedback loop where users can suggest improvements and share their experiences.

Personal Experience in Implementation

In my previous role, we initiated a project to standardize our sales reports. Initially, there was some resistance — change is always hard. But after a few training sessions and some hands-on practice, the team started to see the benefits. The reports were not only easier to produce but also much more impactful. We even started receiving positive feedback from our clients who appreciated the clarity of our presentations.

Here’s a personal tip: Start small. Implement standardization in one type of report first. This approach allows you to refine the process and make adjustments before rolling it out across all reports.

Challenges and Solutions

Of course, it wasn’t all smooth sailing. We faced challenges like getting everyone to adopt the new standards and ensuring consistency across all reports. But with persistent effort and open communication, we overcame these hurdles. The key was to make everyone understand the long-term benefits of standardization.

One challenge we faced was with custom reports requested by different departments. These reports often deviated from the standard format. Our solution was to create a flexible template that allowed for some customization while still adhering to the core IBCS principles. This compromise ensured that the reports remained standardized but could still meet the specific needs of each department.

Types of Data Analysis

Before we dive deeper into reporting, let’s quickly touch on the different types of data analysis. Understanding these will help you tailor your reports to your specific needs.

Descriptive Analysis: The What

Descriptive analysis is all about summarizing past data to understand what happened. Think of it as the “what” of your data. It’s like looking at your car’s speedometer to see how fast you went. This type of analysis uses statistics like mean, median, and mode to describe the data.

For instance, if we look at the nycflights13 R dataset, a descriptive analysis might involve calculating the average delay time for flights, the total number of flights, or the distribution of flight delays across different months. This helps to paint a clear picture of historical performance.

Diagnostic Analysis: The Why

Diagnostic analysis moves us to the “why.” This type of analysis examines data to understand why something happened. It’s like figuring out why your car’s speed dropped suddenly — maybe there was a traffic jam? Diagnostic analysis involves looking at correlations and potential causal relationships to uncover the reasons behind certain trends or anomalies.

In the context of nycflights13, we might investigate why certain flights are delayed more frequently. This could involve examining variables like weather conditions, carrier performance, or airport congestion. Understanding these factors can help pinpoint the causes of delays.

Predictive Analysis: The What Might Happen

Predictive analysis uses statistical models and forecasting techniques to predict future outcomes based on historical data. It’s like forecasting whether you’ll hit traffic on your next trip based on past experiences. This type of analysis helps in anticipating future trends and making proactive decisions.

Using nycflights13, a predictive analysis might involve forecasting future flight delays based on historical delay patterns and upcoming weather forecasts. This can help airlines and passengers plan better and mitigate potential issues.

Prescriptive Analysis: The What Should We Do

Finally, prescriptive analysis provides recommendations for actions based on predictive analysis. It’s like your GPS suggesting an alternate route to avoid that predicted traffic jam. This type of analysis uses algorithms to suggest various courses of action and their potential outcomes.

For nycflights13, prescriptive analysis could recommend optimal flight schedules or routes to minimize delays. It might also suggest operational changes, like adjusting staffing levels during peak hours or implementing new maintenance protocols.

Reporting Delivery Platforms

Not all reports are created equal, and neither are the platforms we use to deliver them. Let’s break down the different platforms and how they impact standardization:

Interactive Dashboards

Interactive dashboards are dynamic and allow users to explore data in real-time. Standardization here ensures consistency across various views and interactions. Think of platforms like Power BI or Tableau. These dashboards are great for providing an overview and enabling detailed drill-downs.

Using the nycflights13 dataset, an interactive dashboard might include various widgets and filters that allow users to view flight performance by date, carrier, or destination. Ensuring that these elements are standardized makes the dashboard intuitive and user-friendly.

Presentations

Presentations are typically used for communicating key findings to stakeholders. Standardized slides enhance clarity and ensure that key messages are consistently communicated. PowerPoint or Google Slides are your friends here.

Imagine preparing a quarterly review using nycflights13 data. A standardized presentation template would include consistent slide layouts, color schemes, and fonts, making it easier for the audience to follow along and understand the insights.

Static Reports

Static reports provide a fixed snapshot of data. Standardization in static reports ensures that all necessary information is included and presented clearly. PDF reports or printed documents often fall into this category.

For example, a static report using nycflights13 data could be a detailed monthly performance report. Standardized headers, footers, and table formats ensure that the report is easy to read and understand.

How Different Types and Delivery Points Affect Standardization

Alright, let’s tie it all together. Different types of analysis and delivery platforms influence how you apply standardization:

• Descriptive Analysis on Dashboards : Ensure that interactive elements are standardized so users can easily compare past performance across different metrics.
• Diagnostic Analysis in Presentations : Use consistent visuals to explain why certain trends occurred. This helps stakeholders grasp the insights quickly.
• Predictive Analysis in Static Reports : Present forecasts in a standardized format to make it easier for readers to understand and trust the predictions.
• Prescriptive Analysis Across Platforms : Whether it’s a dashboard, presentation, or report, standardized recommendations ensure that the suggested actions are clear and actionable.

Tools for Standardizing Reports in R

In this chapter, we’ll discuss the tools I’ll be using in R to ensure our reports adhere to IBCS standards. Standardizing reports involves a combination of data manipulation, visualization, and documentation tools. Here are the main tools and packages we’ll be using throughout this series:

Data Manipulation with dplyr and tidyr

To start, we need robust tools for data manipulation. The dplyr and tidyr packages from the tidyverse suite are indispensable for cleaning, transforming, and organizing our data.

• dplyr : This package is perfect for data wrangling. With functions like select(), filter(), mutate(), summarize(), and arrange(), we can easily manipulate our data frames to get them into the right shape for analysis.
• tidyr : This package helps in tidying data, ensuring that it follows the tidy data principles. Functions like pivot_longer(), pivot_wider(), unite(), and separate() make it straightforward to reshape data as needed.

Data Visualization with ggplot2

Visualization is a cornerstone of effective reporting, and ggplot2 is the go-to package for creating high-quality graphics in R. It follows the grammar of graphics, which makes it highly flexible and powerful.

• Consistent Themes : We’ll use ggplot2's theming capabilities to apply consistent colors, fonts, and layouts across all our visualizations. This aligns with the UNIFY principle of IBCS.
• Custom Visuals : We’ll create custom visuals that not only look good but also convey the right message clearly, adhering to the EXPRESS principle.

Enhancing ggplot2 with Extensions

There are several extensions to ggplot2 that can help enhance its capabilities and ensure our visualizations are both informative and aesthetically pleasing:

• ggthemes : Provides additional themes and scales that help in standardizing the look and feel of plots.
• gghighlight : Allows us to highlight specific data points in a plot, making it easier to draw attention to key information.
• ggrepel : Helps in adding labels to plots without overlapping, ensuring that our visualizations remain clear and readable.
• patchwork : Facilitates the combination of multiple ggplot2 plots into a single cohesive layout, supporting the CONDENSE principle by increasing information density.

Reporting with Quarto

For generating and maintaining our reports, we’ll use Quarto, a new, powerful tool for creating dynamic documents in R.

• Dynamic Reports : Quarto allows for the integration of R code and markdown, enabling us to create reports that are both reproducible and interactive.
• Standardized Templates : We can create standardized templates that ensure consistency across all reports.

Table Formatting with kableExtra

Tables are a crucial part of any report, and kableExtra is an excellent package for creating well-formatted tables in R.

• Enhanced Tables : kableExtra provides functionality to produce beautiful tables within Quarto documents. It supports various table styling options, including striped rows, column alignment, and more.
• Interactive Tables : This package also supports the creation of interactive tables, making it easier for readers to explore data.

Supplementary Tools

• scales : This package works with ggplot2 to ensure that our scales are appropriately formatted, enhancing readability and accuracy.
• lubridate : Useful for handling date-time data, ensuring our time series data is properly formatted and easy to manipulate.
• stringr : Helps with string manipulation, making it easier to clean and prepare text data for reporting.

So, there you have it — a comprehensive introduction to the importance of standardization in business reporting and an overview of how IBCS can help you achieve it. In the next episodes, we’ll dive deep into each component of the SUCCESS formula, starting with SAY: Convey a Message . We’ll explore how to clearly and effectively communicate the main message in your reports, using practical examples and the nycflights13 dataset to illustrate these principles in action.

Remember, the goal here is to make your reports not just informative but also engaging and easy to understand. Let’s embark on this journey together and transform your business reporting skills!

Stay tuned, and happy reporting!

Introduction to Standardization in Business Reporting was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.

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IASO Bio Presented Clinical Data and Single-cell Analysis of Equecabtagene Autoleucel for the Treatment of Central Nervous System Autoimmunity in Oral Presentation at EAN Congress 2024

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SHANGHAI , NANJING , CHINA , and SAN JOSE, Calif. , July 1, 2024 /PRNewswire/ -- IASO biotechnology ("IASO Bio"), a biopharmaceutical company engaged in discovering, developing, manufacturing and marketing innovative cell therapies and antibody products, presented  the clinical data and single-cell analysis of the fully human anti-BCMA CAR T cell therapy (Equecabtagene Autoleucel, Eque-cel) for the treatment of central nervous system autoimmunity in an oral presentation at the 2024 European Academy of Neurology (EAN) Annual Meeting .

Presentation Title:  Single-cell analysis of anti-BCMA CAR T cell therapy in patients with central nervous system autoimmunity

Presentation Type : Oral report

Session Date and Time:  1 July 2024, 8:30 - 9:45 (EEST)

Location:   Helsinki, Finland

Publication Number:  A-24-12777

Presenter:  Professor Chuan Qin, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

This report is based on an investigator-initiated study to evaluate the safety and efficacy of Eque-cel for the treatment of relapsed/refractory antibody-mediated idiopathic inflammatory disorders of the nervous system (NCT04561557).  It was conducted by Professor Wei Wang's team at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.

The study enrolled 12 subjects with aquaporin-4 (AQP4) antibody-positive relapsed/refractory neuromyelitis optica spectrum disorder (NMOSD), all of whom had been treated with at least one immunosuppressant for over a year but had poor symptom control. The study results preliminarily demonstrated the good tolerability and safety, durable pathogenic antibody clearance, and potential clinical efficacy of Eque-cel in NMOSD. And CAR-T cells with chemotactic properties can cross the blood-brain barrier and enter the central nervous system to directly kill abnormal plasma cells in the central nervous system. The action facilitates the restoration of central immunity as it reduces the secretion of autoantibodies within sheaths and the abnormal activation of immune cells. As a result, the immune disorder in the central nervous system of the NMOSD patients can be rectified and its immune system can be reset.

As one of the pioneering research teams that first use BCMA CAR-T therapy for the treatment of immune diseases in the world, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and IASO Bio have achieved breakthrough research results that validate the clinical value of innovative cell therapy for relapsed and refractory immune diseases, making its application prospects increasingly recognized and valued internationally. Currently, BCMA CAR-T therapy has been rated as one of the most promising treatment strategies   by the International Neuromyelitis Optica Study Group (NEMOS) and incorporated into the latest revised treatment recommendations (J Neurol 2024) . In addition, Professor Maximilian F. Konig from department of immunology, Johns Hopkins University , stated in Year in review, Immunotherapies in 2023: The rise of precision cellular therapies, published in Nat Rev Rheumatol , that the application of CAR-T therapy has brought groundbreaking advancements in innovative cellular therapies for immune diseases.

Professor Wei Wang from Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , the principal investigator of this study, stated: "This study was the world's first clinical trial of CAR-T therapy in the field of AQP4-mediated relapsed/refractory NMOSD. It not only demonstrated the encouraging efficacy and controllable safety of Eque-cel in NMOSD but also elucidated the cellular dynamics and immunological characteristics of CAR-T therapy for central nervous system autoimmune diseases. This provides a new therapeutic approach for antibody-mediated autoimmune diseases and also provides scientific insights into refining CAR-T cell therapies for autoimmune diseases. In addition to NMOSD, Eque-cel has shown an excellent clinical efficacy in the treatment of other antibody-mediated autoimmune diseases, including myasthenia gravis and immune-mediated necrotizing myopathy, even with early signs of reversing the disease. We have published several related research papers in leading international academic journals. There is a large patient population with autoimmune diseases, which are prone to relapses, difficult to cure, and require long-term or even lifelong medication, with a huge unmet clinical demand. We will continue to explore the application of Eque-cel in more refractory autoimmune diseases with IASO Bio team, aiming to transform the treatment landscape of autoimmune diseases."

About IASO Bio

IASO Bio is a biopharmaceutical company engaged in the discovery and development of novel cell therapies and biologics for oncology and autoimmune diseases. IASO Bio possesses comprehensive capabilities spanning the entire drug development process, from early discovery to clinical development, regulatory approval, and commercial production.

The pipeline in the company includes a diversified portfolio of over 10 novel products, including Equecabtagene Autoleucel (a fully human BCMA CAR-T injection). Equecabtagene Autoleucel received New Drug Application (NDA) approval from China's National Medical Products Administration (NMPA) and U.S. FDA IND approval for the treatment of RRMM.

Leveraging its strong management team, innovative product pipeline, GMP production, as well as integrated manufactural and clinical capabilities, IASO aims to deliver transformative, curable, and affordable therapies that fulfil unmet medical needs to patients in China as well as around the world. For more information, please visit http://www.iasobio.com or www.linkedin.com/company/iasobiotherapeutics .

SOURCE IASO Bio

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Also from this source, comprehensively covering multiple myeloma: iaso bio's gprc5d car-t product rd118 receives ind approval from nmpa.

IASO Biotechnology ("IASO Bio"), a biopharmaceutical company engaged in discovering, developing, manufacturing, and marketing innovative cell...

IASO Bio Presented New Data of FUCASO® (Equecabtagene Autoleucel) for the Treatment of High-risk Newly Diagnosed Multiple Myeloma in Oral Presentation at EHA 2024

IASO biotechnology ("IASO Bio"), a biopharmaceutical company engaged in discovering, developing, manufacturing and marketing innovative cell...

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VICR: A Novel Software for Unbiased Video and Image Analysis in Scientific Research

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In scientific research, objectivity and unbiased data analysis is crucial for the validity and reproducibility of outcomes. This is particularly important for studies involving video or image categorization. A common approach of decreasing the bias is delegating data analysis to researchers unfamiliar with the experimental settings. However, this requires additional personnel and is prone to cognitive biases. Here we describe the Video & Image Cutter & Randomizer (VICR) software (https://github.com/kkihnphd/VICR), designed for unbiased analysis by segmenting and then randomizing videos or still images. VICR allows a single researcher to conduct and analyze studies in a blinded manner, eliminating the bias in analysis and streamlining the research process. We describe the features of the VICR software and demonstrate its capabilities using zebrafish behavior studies. To our knowledge, VICR is the only software for the randomization of video and image segments capable of eliminating bias in data analysis in a variety of research fields.

Competing Interest Statement

All authors have a pending patent application on the process for randomizing video and image presentation for unbiased analysis (U.S. Patent Application No. 63/659,034).

https://github.com/kkihnphd/VICR

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Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England

• Katherine Jackson 1 ,
• Rosie Baker 2 ,
• Amy O’Donnell 1 ,
• Iain Loughran 3 ,
• William Hartrey 4 &
• Sarah Hulse 5  

BMC Health Services Research volume  24 , Article number:  772 ( 2024 ) Cite this article

Metrics details

Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context.

Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings.

Findings were grouped using the NPT domains ‘implementation contexts’ and ‘implementation mechanisms’. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring).

Conclusions

COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

Peer Review reports

Alcohol is now the leading risk factor for ill-health, early mortality, and disability amongst working age adults (aged 15 to 49) in England, and the fifth leading risk factor for ill-health across all age groups [ 1 ]. Evidence also shows significant socioeconomic inequalities in alcohol-related harm [ 2 ]. Over half of the one million hospital admissions relating to alcohol in England each year occur in the lowest three socioeconomic deciles [ 3 ] and rates of alcohol-related deaths increase with decreasing socioeconomic status [ 4 ]. In 2020 people under 75 years living in the most deprived areas in England had a 4.8 times greater likelihood of premature mortality from alcohol-related liver disease than those living in the most affluent areas [ 5 ].

Although globally, there is mixed evidence about the impact of the COVID-19 pandemic and associated social and economic restrictions on alcohol consumption [ 6 ], some studies suggest that people who were already drinking alcohol heavily increased their intake during this period [ 7 , 8 ]. Latest data for England show that the total number of deaths from conditions that were wholly attributed to alcohol rose by 20% in a single year in 2020, the largest increase on record [ 9 ]. In England, and elsewhere, it has been argued that COVID-19 should be regarded as a syndemic rather than a pandemic, as it has interacted with, and most adversely affected those in the most deprived social groups who were already experiencing the greatest inequalities [ 10 ]. In the case of alcohol use, COVID-19 may have interacted with and exacerbated the social conditions associated with alcohol use such as poverty, and loneliness and isolation [ 11 , 12 ]. Moreover, with evidence that alcohol-related harms will continue to increase, there is concern this will further widen health inequalities for those communities and regions who are likely to be most affected [ 8 , 13 ]. Thus, there is an urgent need for the implementation of evidence-based preventative strategies to reduce alcohol harm and associated inequalities, as part of a wider system level approach that includes primary, secondary and specialist care settings [ 8 ]. From here we use the term Alcohol Use Disorder (AUD), to refer to a spectrum of alcohol use from harmful to dependent alcohol use [ 14 ].

In secondary care hospitals, the UK government prioritised the implementation of Alcohol Care Teams (ACTs) in England in the National Health Service (NHS) Long Term Plan with the aim of improving care and reducing alcohol-related harms [ 15 ]. ACTs are clinician-led, multidisciplinary teams designed to support provision of integrated alcohol treatment pathways across primary, secondary and community care, and have been shown to reduce alcohol harms through reductions in avoidable bed days; readmissions; Accident and Emergency Department (AED) attendances; and ambulance call outs [ 16 ]. However, the non-specialist secondary care workforce also has an essential role in identifying and managing people at risk, using evidence-based approaches such as screening patients for excessive alcohol use and the provision brief advice [ 17 ]. Given that people may not always present primarily with alcohol-related concerns, routine screening provides an important opportunity to identify people at an earlier stage in their drinking and thereby prevent escalation of alcohol-related problems. Current NHS clinical guidance [ 18 ] requires that non-specialist healthcare staff ‘should be competent to identify harmful drinking (high-risk drinking) and alcohol dependence’ (p46). This includes having the skills to assess the need for an intervention or to provide an appropriate referral.

Despite this guidance however, evidence from prior to the pandemic suggests a range of barriers exist in the delivery and widespread implementation of alcohol prevention work by non-specialist secondary care staff. These include time pressures, limited knowledge and awareness of AUD, and a lack of training, skills, and financial support [ 19 , 20 , 21 , 22 ]. Many studies also highlight that the delivery of preventative support for AUD in secondary care is hampered by wider social cultural challenges such as the stigma of heavy alcohol use and widespread belief that problematic alcohol use is a personal responsibility and represents moral failing, leading to an emphasis on individuals to manage their own care [ 22 ]. Additionally, as AUD frequently co-occurs with other physical and mental health conditions [ 23 ], non-specialist healthcare staff can find themselves ill-equipped to provide the best standard of care for these patients who have multiple and complex needs [ 24 ]. Moreover, in England, as in other health systems, the impact of COVID-19 has created additional pressures and challenges for the whole NHS, including secondary hospitals. There are more people visiting AED than before the pandemic, with longer waiting lists for treatment and fewer hospital beds [ 25 ]. There is also record dissatisfaction amongst the workforce, with more doctors now stating they want to leave the NHS than before the pandemic [ 26 ].

Given the clear need for preventive work to reduce inequalities in alcohol-related harm and the current challenges within secondary care in a post-COVID-19 context, there is value in exploring the views of secondary care staff about supporting patients with AUD since the pandemic. Moreover, the low levels of delivery of preventative support for AUD across different sites suggest there is merit in using implementation science theory [ 27 ] to support improved explanation and understanding of this situation [ 27 , 28 ]. Normalisation Process Theory [ 29 ] has been used extensively in studies conducted in other health settings to understand and evaluate past and future implementation efforts e.g. [ 28 , 30 , 31 , 48 , 33 ], including in relation to alcohol screening and brief intervention in England and Australia [ 30 , 31 ]. NPT is a sociological implementation theory that identifies three domains as shaping the implementation of a new intervention or practice: contexts; mechanisms; and outcomes. Contexts refer to the ‘events in systems unfolding over time within and between settings in which implementation work is done.’ [ 34 ]; mechanisms are factors that ‘motivate and shape the work that people do when they participate in implementation processes’ [ 34 ]; outcomes refer to what changes occur when interventions are implemented. NPT is a conceptual tool and can be used at different stages of the research process [ 29 ]. In this study NPT has been used retrospectively during the analysis stage.

The aim of the present study is to use NPT to elucidate possible explanations for why the preventative practice of identifying, supporting, and referring patients with AUD to appropriate support is not consistently taking place in secondary care in the NENC in the post COVID-19 context. We also aim to make recommendations for areas that should be targeted by policy and practice initiatives.

Study setting

We conducted a qualitative study with health care professionals working in eight secondary care hospitals in the eight NHS Trusts in the North East and North Cumbria (NENC) region of England. The NENC experiences significant health inequalities [ 35 ], including health inequalities in alcohol-related harm. In 2021, the region had the highest reported alcohol specific and alcohol related mortality and the most alcohol related and alcohol specific admissions in England [ 36 ].

The data collection was carried out between June and October 2021. At this time, most COVID-19 restrictions had just been lifted in the NENC [ 37 ] but the impacts of COVID-19 on patients, staff and health care delivery were still ongoing.

As such, the study was planned to contribute to a baseline understanding of support for AUD in secondary care in the NENC conducted as part of a wider regional alcohol health needs assessment (2022) which would inform and direct strategic action and resource allocation in secondary care to improve alcohol-related outcomes post-COVID-19. The Principal Investigator (PI) for the study was the alcohol lead for the NENC Integrated Care System (SH), and the wider study team included representation from Primary Care, Secondary Care, Public Health, and Academia.

We used the method of qualitative semi-structured interviews to enable us to focus on issues that we wanted to explore, as well as allowing the participants flexibility to discuss the issues that were important to them [ 38 ]. We adopted a critical realist approach to the interpretation of data which purports that data can be taken as evidence for ‘real phenomena and processes’, but also recognises that the knowledge generated through qualitative research is situated and partial [ 39 ].

As part of a wider ambition to build research capacity in the study region, a novel aspect of the study design is that six junior doctors from the Gastroenterology Research and Audit through North Trainees, were trained in qualitative interview skills by a qualitative methodologist from the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) and supported by members of the study team to recruit staff and carry out the interviews with secondary care clinicians.

Participants

We used a form of stratified purposive sampling [ 40 ] as the recruitment of healthcare professionals was structured to provide insights across all the NHS Trusts in the study region, a range of clinical specialities, and a range of points across the clinical pathway, with both medical and nursing staff. As such, professionals working in AED, Medical specialties, Psychiatric Liaison (PL), Gastroenterology or Surgical specialties were eligible to participate. Junior doctor interviewers or the PI contacted potential participants either by email or face-to-face and explained the purpose of the study. People who expressed an interest were then provided with the study participant information sheet and consent form. The sampling was deemed complete when the quota of participants was met for each trust.

Data collection involved semi-structured interviews based on a topic guide. The topic guide was developed by the study team and was informed by the National Institute for Clinical Excellence – Quality Standard 11 [ 41 ], which contains guidance about identifying and supporting adults and young people who may have an AUD and caring for people with alcohol-related health problems (see Additional file 1 ).

All interviews were conducted via Microsoft Teams, lasted an average of 33 min, were audio recorded and transcribed by professional transcriptionists before being fully anonymised by KJ and IL.

Data analysis involved three stages:

Stage 1: Generating descriptive codes from each area of the data set

In the first stage of analysis, once all transcripts were available, in order to generate insights that could contribute to the baseline understanding of the current situation with regards to support for AUD in secondary care, one researcher (IL) used a method of thematic analysis [ 42 ] and drew on deductive and inductive reasoning to identify descriptive codes against each focus question area of the interview topic guide. This researcher read and re-read the full data set, allowing them to identify descriptive codes across staff accounts.

Stage 2: Generating descriptive and interpretive codes and themes from across the full data set

Following this, to generate insights which went beyond the question areas of the topic guide a second researcher (KJ) familiarised themselves with the data. In contrast to Stage 1, they were less restricted by the original topic guide and through a process of constant comparison began to identify both descriptive and interpretive broad thematic topic areas and codes, across the different areas of the interviews. After the first half of the interview transcripts were coded by the researcher in this way, the broad thematic topic areas were discussed with the wider study team in two meetings. In these meetings the broad topic areas and associated coding framework were refined. This refined framework was applied to future transcripts, with flexibility to add further codes as the analysis progressed. At the end of this process, a decision was made by the team to focus the interpretation for this paper on current practices around identifying, supporting, and signposting patients with AUD in secondary care hospitals because it was felt that this focus could make a meaningful contribution to the existing literature in a post-pandemic context.

Stage 3: Applying Normalisation Process Theory retrospectively to data to generate the final interpretation

To ensure the usefulness of the findings of the current analysis to support the design and delivery of future policy and practice to reduce inequalities in alcohol related harm, academic members of the team suggested using an appropriate implementation theory, namely NPT, to guide our interpretation and understanding of data from this point in the analysis [ 34 ]. NPT had not been used in the study to this point and has been used retrospectively as a sensitising, and partial structuring, device, as seen in previous comparable research e.g. [ 28 , 43 ].

[ 29 , 34 ]. First, when applying NPT, we returned to the codes identified at Stage 2 to identify those that related to the practice of identifying, supporting, and signposting patients with AUD to explore how they may fit alongside the domains of NPT. At this point it was evident that most of the codes related to how implementation contexts and mechanisms were felt to adversely affect provision of support for patients with AUD. In contrast, we found negligible data related to the third NPT domain of outcomes (i.e. what changes occur when interventions are implemented). It was therefore agreed that applying the context and mechanisms domains could be valuable to show how contexts and mechanisms limit the implementation of the phenomena of interest. For transparency however, data not included at this stage is indicated in Additional file 2 .

Next, we separated the codes generated in Stage 2 into overarching thematic areas, these were then labelled as either contexts or mechanisms. For example, poverty and austerity were labelled as contexts, and workforce skills and knowledge were labelled as mechanisms. Details of each stage of the analysis and where the codes generated at Stage 2 of the analysis were mapped, against the NPT context and mechanism domains are shown in Additional file 2 .

Following this we endeavoured to align the thematic topic areas in each NPT domain into its associated constructs. It should be noted that our initial researcher-generated thematic areas aligned easily with three of the four NPT mechanism constructs. Conversely, as the NPT context constructs are a new addition to NPT theory, there were few practical examples of how these should be operationalised meaning it took more interpretive work to understand how our data mapped to these constructs. Through reflective discussions as a team, however, we identified that the researcher-generated themes aligned with three of the four context constructs. Table  1 below summarises the implementation context and mechanism constructs and identifies where our data do and do not map to these constructs. COVID-19 provides an overarching context to the study however as the timing of the interviews meant it penetrated almost all the data.

In keeping with the critical realist approach which recognises the situatedness of knowledge, we see researcher positionality as important to consider in the interpretation of qualitative data. Research can never be value free but, it is necessary to be explicit about where positionality might have affected the interactions [ 45 ]. The junior doctor interviewers and the PI who collected the data had experience of clinical work on the topic of the research. Indeed, the transcripts indicated that there were times when the interviewers aligned themselves or discussed their own experiences in the interviews. Some of the junior doctor interviewers recorded reflexive notes about the interviews, these were used during Stages 1 and 2 of the analysis to support interpretation, but have not been used as data. The researcher who conducted Stage 1 of the analysis has a professional background in healthcare but no direct experience of the topic area. The researcher who led the rest of the analysis has experience of carrying out research about AUD, but no clinical experience of working with people experiencing AUD. Other members of the project team have direct experience of working in hospital settings with patients experiencing AUD. Agreement amongst this heterogeneous research team about the final interpretation gives us confidence that it is grounded in the data. Moreover, this agreement amongst the research team about the final interpretation, and the congruence of findings with the existing literature on the topic of the research prior to COVID-19, gives us confidence that the insider researchers did not compromise the quality of the original empirical data.

In total, 30 staff in the study region were interviewed across the eight NHS Trusts, including 20 nurses and 10 doctors (see Table  2 ) based in five departments: AED; PL; Medical; Surgical; and Gastroenterology ( n  = 6 each). Information related to participant gender and ethnicity are not available and we have not analysed the data with these as a focus. The absence of this data also helps to preserve the anonymity of participants because the geographical region of the study is named.

Overall, participants’ accounts suggested that they were not consistently trying to identify AUD or assessing the need for intervention in the patients they worked with. Where any identification of AUD did take place, this appeared to often be through informal questioning rather than utilising formal, validated screening questionnaires. The following response was typical:

We’ll just ask about units a week. I know that there is a screening tool, there is a chart of some sort and it’s a physical thing that I think the alcohol and drugs nurses use on medications. So we don’t use that on a regular basis. As of now, there’s still a paper–based documenting system, but we don’t use that necessarily. (Participant 14 – Doctor, Trust 4, AED)

Conversely, some staff working in PL teams suggested they more commonly tried to identify AUD. Although again, validated screening questionnaires appeared to be used inconsistently:

Substance misuse is always an integral part of the assessment that we do. . We do have specific packs that we are trained to carry out our assessments to. I think in practice, we often don’t follow those verbatim and we will just do a free form assessment and substances are always part of that… .: “Do you consider that’s an issue for you, is it something that you want help with?” We’re always having those conversations. (Participant 8 – Nurse, Trust 2, PL)

Many staff’s accounts suggested they did not consistently signpost patients with identified AUD to a service that could provide an assessment of need or provide further care. Using NPT to frame our interpretation, in the next section we aim to highlight current practice around these phenomena and identify areas that appeared to be key barriers to implementation.

Implementation contexts

The successful implementation of interventions requires supportive implementation environments both within and outside the settings in which they are delivered. Our data highlighted several key aspects of the implementation context/s that are barriers to the widespread implementation of asking about, supporting, and signposting patients with AUD in secondary care in the study region. As the data collection was conducted very soon after COVID-19 restrictions ended, COVID-19 was an overarching context of the staffs’ accounts.

Widespread poverty, austerity, and the prioritisation of acute conditions – negotiating capacity

Negotiating capacity refers to how contexts shape the extent to which interventions can fit into existing ways of working [ 34 ]. Through the participants’ accounts we identified two aspects of context which appear to limit negotiating capacity: widespread poverty and austerity within the study region; and the focus of secondary care hospitals on the acute and presenting health needs of patients.

Most staff accounts suggested they perceived AUD to be common in the communities their hospitals covered and the patients they saw. Many staff linked the prevalence of AUD in the region to the high rates of poverty. To illustrate, Participant 23 commented that the basic provision for patients with AUD in the hospital, was in stark contrast to the apparent need in the community:

The demographic for around here, people are poor, they do drink, people do smoke,. . people take drugs a lot around here and the help, there isn’t [anything for them] it’s absolutely crazy. (Participant 23 - Nurse, Trust 6, Surgical)

While the need to support patients with AUD was perceived to have been high prior to the COVID-19 pandemic, many staff noted that they had seen a rise in patients presenting with or showing signs of AUD following the pandemic, with some suggesting that they felt that the presentations of alcohol-related morbidity and mortality were likely to increase in the future:

Our numbers [of patients with AUD] have gone up by 100% in five years. . So it’s not going anywhere, and I predict that at the beginning of next year we’re going to see huge influence on alcoholic dependence. Because we’ve already seen people who are having fits, first fits, people who were drinking prior to COVID or probably drinking too much, at high risk, not necessarily dependent and then, furloughed, have begun to drink every day and developed alcohol dependence. (Participant 25 - Nurse, Trust 7, Gastroenterology)

A small number of participants mentioned that because of the observed high levels of AUD in the study region it was harder to decide how to prioritise who to ask about alcohol. They indicated that they were unlikely to ask patients about alcohol if they were drinking at what they saw as lower levels, as they perceived most people were drinking a lot. For example, Participant 7 said:

If they were a binge drinker or they drank more than was recommended, it’s kind of like, where do you take that? How do I talk to my patients about that? Thinking about where we live, our demographic of the type of patients that we see, it’s very common that patients would drink more alcohol than the recommended. So, I guess that is the challenge of how you would approach that to the patient, without coming across like you were being judgmental or self-righteous when you’re trying to give them this advice. And actually asking them; ‘do you even see it as a problem?’ A lot of patients that you would speak to you wouldn’t even say that that is a problem. (Participant 7 - Nurse, Trust 2, Surgical)

Thus, these accounts indicated that the normalisation and prevalence of heavy drinking in some communities actively constrained the extent to which staff could integrate asking about and supporting patients with alcohol use into their day to day work .

Conversely, and illustrating how contexts can be barriers to implementation in one setting but facilitate it in others [ 44 ], some staff working in PL described how they had recently begun doing more systematic screening for AUD because it was recognised as being so prevalent in the patients they saw.

[Previously] unless alcohol was kind of front and centre and was an issue that was discussed from the get-go, it wasn’t always something that was really looked into in great detail as part of our assessments. Whereas now that we do the AUDIT, there’s an AUDIT-C tool with all patients. (Participant 4 – Nurse, Trust 1, PL)

Nonetheless, staff accounts more commonly focused on the need to tackle severe alcohol harm rather than preventative work. In-keeping with other research studies and clinical knowledge, the participants’ suggested that a key reason that patients aren’t routinely being asked about AUD in secondary care is because staff need to prioritise the presenting acute condition/s. Something which is colloquially termed ‘the rule of rescue’. Thus, any identification of AUD, where it did happen, was primarily focused on managing patients whose alcohol use was already affecting, or had the potential to affect, the treatment of their acute physical or mental illness. Participants almost always linked this to the pressurised setting and the restricted time they had to work with patients, as further limiting their capacity to address a patient’s drinking. This context is illustrated in the following quotes:

‘I’m asking [about alcohol] because it effects how I care for that patient and not necessarily about educating them’ (Participant 15 – Doctor, Trust 4, Medical). . .I think asking about the preventative problems, and screening for problems, is something that we just don’t do. If someone comes in and they’re alcohol dependent, realistically the thing you think about most is, right well we need to make sure that we’ve got the right things for if they withdraw, you don’t think, oh well shall we see if there’s anything we can do and to be fair, you don’t really have the time, I don’t think. (Participant 6 - Doctor, Trust 2, AED)

Overall, time and the focus on acute conditions, were commonly cited by staff as key contextual factors, that limited their negotiating capacity to ask patients about alcohol and to provide follow-up support.

Stigma at a structural level – strategic intentions

Strategic intentions refers to how contexts shape the formulation and planning of interventions. Many staff accounts suggested that they perceived there was little visible commitment to the prevention of AUD within their NHS trust or at a national NHS level. Many staff suggested they had seen no communications about providing preventative support to patients with AUD from their trust:

There’s nothing to my knowledge, Trust–wide, of how we help this cohort of patients. There doesn’t seem to be anything written in stone, on the help that we provide. (Participant 21 – Nurse, Trust 6, AED)

Others emphasised that although they had seen some communications about alcohol from their trust, these were limited. Some participants’ accounts indicated a sense of frustration that alcohol was not being prioritised by the NHS and moreover that any care offered to patients with AUD was voluntary rather than a designated part of their core work. For example, in one trust it was noted that the role of the Alcohol Lead was not formalised:

At the moment it’s almost voluntary and there’s always something else that comes along that’s more immediate, more important or seems that way. People aren’t taking the longer view that if we don’t address this problem now then the tsunami of liver disease will just continue. (Participant 10 - Doctor, Trust 3, Gastroenterology)

Relational stigma – reframing organisational logic

Reframing organisational logic refers to the extent to which social structural and social cognitive resources shape the implementation environment [ 34 ]. The stigma which was evident at a structural level was also directly perceived to impact the care of patients with AUD at a relational level. Many staff mentioned that the identification of AUD and subsequent signposting for patients who drink heavily are obstructed because some staff perceive that heavy alcohol use is a personal failing and individual problem. Indeed, judgement or stigma was explicitly proposed by participants as one of the key reasons that AUD prevention and treatment interventions were not implemented, or attempts weren’t made to help people with AUD:

People find them incredibly frustrating and [like] they’re not real patients or people who need [help]. (Participant 4 - Nurse, Trust 4, PL)

This judgement was also seen to be compounded by austerity and the increased demands on health and social care post COVID-19, meaning those who were more challenging or difficult to help were often the easiest group to not manage.

Relational stigma appeared evident in the reluctance of some staff to speak to patients about alcohol. For example, a few participants expressed concern about how patients would respond if they were to ask them about their alcohol use because heavy alcohol consumption can sometimes be perceived by patients and wider society as a personal failing or as evidence of a lack of control:

It’s quite a personal conversation to have with somebody and you’ve got a small thin curtain between every single patient and having those conversations when everybody hears the conversation that you have in the bay, so I think that sometimes contributes to it. (Participant 24 – Nurse, Trust 7, Medicine)

Moreover, the effects of stigma seemed evident in the extent to which staff perceived people would be honest about or disclose their heavy drinking and the extent to which would subsequently make adaptions to investigate further. Some staff said that they did not have the time to build rapport with patients to generate a context where they perceived patients might be more likely to be truthful about their drinking:

It comes down to them being honest. If they say that they don’t drink a lot then we wouldn’t give any advice. (Participant 26 – Nurse, Trust 7, Surgical)

The data also suggests that the extent to which staff appeared willing to identify or support patients with AUD is related to them not seeing it as relevant to the presenting problem which relates to the prioritisation of acute conditions and the negotiating capacity.

Implementation mechanisms

Alongside contexts, we identified a number of mechanisms that appeared to be barriers to implementation across our participants’ accounts.

Workforce knowledge and skills – cognitive participation

All participants’ accounts suggested that there was no mandatory training within trusts to support staff to deliver alcohol prevention work. While participants acknowledged there was indeed very little mandatory training about most conditions, many staff suggested they had not been trained post-University in how to have conversations with patients about alcohol, to assess need, or how to refer and signpost on:

. . we’ve got team days where we go through mandatory training and do little courses and do all our training, but there’s nothing about alcohol on there whereas it might be quite useful because we do get a lot of patients with alcohol issues so that would be beneficial. . we’ve had no training or updates on what’s out there in the community. (Participant 9 – Nurse, Trust 2, Medical)

In a small number of trusts, some staff with a specific remit around alcohol stated they were in the process of developing training about identification within their teams and appeared optimistic about the spread and impact of this.

Where staff did ask about alcohol, a barrier to referring people with AUD to appropriate services was their limited awareness of relevant services within the community. Indeed, a few participants conveyed the sentiment of Participant 11 who described their perception of asking about alcohol in their hospital as a ‘ tick box exercise rather than purposeful tool .’ (Nurse, Trust 3, Medical). Only a small number of participants seemed very knowledgeable about local community services; like Participant 9 above, most staff accounts suggested a lack of awareness of relevant organisations they could refer patients to. Some staff indicated that knowledge of appropriate services was made more challenging because of the frequent change in service provision and cuts and short-term commissioning of relevant voluntary and community sector services:

It is a bit vague at the moment as to exactly what they are going to do with the provider changing over. . when the Covid stuff started, they stopped coming in and just did electronic stuff. But I think they’ve started coming in again. But I don’t quite know what hours they are planning to come in, with the new changeover of people. (Participant 1 – Doctor, Trust 1, Gastroenterology)

In a context of frequent service changeovers and decommissioning, widespread poverty and austerity, and limited awareness of appropriate local services, there appeared to be a heavy reliance on referrals to primary care by staff, even when they didn’t know what primary care would offer patients. This is illustrated by this quote from Participant 15:

Sometimes if people ask me, or if I’ve found that they’ve got like deranged liver functions, I’ll often just sort of say to them, if it fits with an alcohol picture, I would say: “It does look like your alcohol use is affecting your liver, it might be something you think about cutting down,” but at that point I’m not always sure where to refer them to, so I usually end up saying you can get support from your GP. Yes. (Participant 15 – Doctor, Trust 4, Medical)

Role legitimacy – collective action

When asked directly in the interviews about whether they felt that managing AUD was their responsibility most participants stated that it was. However, their wider accounts indicated that many participants and their colleagues relied heavily on calling on staff in other departments to manage patients with AUD who they saw as better placed to address these patients’ needs. In particular, the participants commonly suggested that alcohol nurses or other staff in gastroenterology were most able to help:

In our trust, I’m not sure if it’s the same as any others, when we do the nurse’s admission, we ask how many units they’ve had and if they score over ten then they automatically get pinged to the alcohol nurses who will come and see them. Or we refer them and call the alcohol nurses here. . (Participant 28 – Nurse, Trust 8, AED)

Staff in the site where an ACT had recently been set-up suggested that the introduction of this service had significantly improved the care that they could offer people with visible presentations of AUD and provided a clearer route for signposting. However, the reliance on this service also served to illustrate the limited support prior to this in these sites and the significant care gap at other sites who did not have this provision. Moreover, the accounts of a few participants suggested that due to the high level of need for alcohol dependent support, the ACTs appeared to have little capacity to do preventative work:

The alcohol care team nurses are building up good relationships with some of our more frequent members that are coming on ward. And then they’re able to get permission off them to do more like referrals to [community alcohol service], discussions about tapering down or alcohol reduction therapy, discussions about cognitive behavioural therapies, discussions with housing officers and things, discussions with safeguarding. . having said that, like I say they are getting an abundance of referrals daily now and I think unfortunately it’s ended up a lot bigger than they were expecting, a bit of a mammoth task. (Participant 2 – Nurse, Trust 1, Medical)

In contrast to staff in other departments, as mentioned above, staff from PL teams suggested that identifying patients’ patterns of alcohol use, usually through formalised screening, had relatively recently become part of their core work. Nonetheless, the focus was still on management of AUD rather than prevention, as most indicated that the implementation of this was in response to the prevalence of heavy drinking in the patients they saw. Here the mechanism of collective action appears to be shaped by the context of poverty and austerity.

Perceived futility and negative feedback cycle – reflexive monitoring

Participants’ accounts indicated that they had little information about the outcomes of the people that they saw with AUD. Some staff mentioned that the only time they saw patients again, whether or not they delivered an intervention, was when they re-attended. The following response was typical:

We put them on file with the GP letter, and we don’t know what happens after that. (Participant 26 – Nurse, Trust 7, Surgical)

In the context of this perceived futility, staff appeared to find it difficult to have hope for patients when they experienced only negative reinforcement. Compounding this it was also evident that the recording of information about alcohol use and any advice or signposting were limited in most departments. Although some PL services and some trusts seemed to be trying to record screening more systematically at the time of the research, it was still not mandatory and was not always prioritised as the following quote illustrates:

[We] have the AUDIT -C put on e-records, and that provided some challenges as well. . there’s a lot of things that are recorded, you get a lot of alerts, we know that. . staff just tap off them, if they’re not mandatory, So, it was about trying to sell it is an important message. (Participant 25 - Nurse, Trust 7, Gastroenterology)

Here again we see the link between contexts and mechanisms whereby the lack of systematic recording of patients’ alcohol use is likely to be influenced by the context of structural stigma and its impact on strategic intentions.

This paper reports the findings of a collaborative study between practitioners, policy makers, and academics which aimed to explore the challenges to the delivery of identification, support, and subsequent signposting for AUD in the secondary care settings in the NENC region post- COVID-19. Our findings broadly concur with what was already known about the challenges of implementing identification and support for AUD in secondary care hospitals prior to the COVID-19 pandemic. For example, the persistent contextual challenge of time pressures, and the lack of key enabling mechanisms, such as having a workforce with the skills and knowledge to confidently ask about alcohol and signpost patients appropriately [ 22 ]. However, our findings extend existing evidence by highlighting some additional barriers to alcohol prevention work in secondary care in the post-COVID-19 context. Moreover, the use of theory, specifically NPT domains, enables us to illuminate the interplay of context and mechanisms which make implementation of AUD care especially difficult in this setting.

A key contribution of this study to the extant literature is that it provides empirical evidence of how COVID-19 has served to amplify the challenges already experienced by secondary care staff trying to delivery preventative alcohol work in hospital settings. Many staff indicated that the sheer scale of people presenting with possible AUD since COVID-19, meant they did not have the time to ask people or to prioritise asking people about alcohol. Where people were identified as experiencing AUD, provision of effective signposting and support for patients was adversely affected by lack of staff awareness about relevant care providers and lack of capacity in local services due to the impact of austerity and cuts to public services. Two trusts in the study region had ACTs in place at the time of the interviews, as part of the wider NHS commitment to reduction alcohol harm in England [ 16 ]. This appeared to have increased the capacity of the non-specialist workforce at these two sites to refer patients identified as experiencing AUD onto appropriate specialist support. However, a tentative, but notable, finding of this study was that while ACTs were making a difference in these trusts for those with existing alcohol dependence, they were limited in their capacity to deliver more preventative work around AUD (initially part of their remit) due to the high level of need amongst the dependent patient population. This warrants further exploration, with further insights potentially to come via the wider programme of work around ACTs that is currently ongoing in England [ 46 ]. Overall, the study provides empirical evidence that the implementation of the preventative practices to support a reduction in AUD may be particularly difficult in areas of deprivation such as the NENC meaning that inequalities are likely to be widening with other more affluent regions.

Stigma, the process of marking certain groups as being somehow contagious or of less value than others [ 47 ], is internationally recognised as a significant constraining factor to the delivery of compassionate and appropriate healthcare for patients with AUD and other substance use in secondary care and other health and social care settings [ 47 , 48 ]. In this study we chose to approach stigma as a structural and relational concept, seeing relational stigma as developing from structural stigma [ 49 ]. The role of structural stigma for limiting the implementation of identifying, supporting, and signposting patients with AUD was striking, as our data highlighted that the prevention of heavy alcohol use does not appear to be a visible priority within individual trusts, and arguably the wider NHS. Limited resources were perceived available for this area of care, and little visible commitment to support patients with AUD despite the scale of the problem. Stigma was also evident at a relational level in our participants accounts of the interactions between staff and patients, notably staff’s reluctance to ask about alcohol use and their perception that patients did not want to disclose their AUD. However, it should be noted that many of the staff who took part in the study suggested that they did not perceive patients in this way yet continued to struggle to provide alcohol prevention care. Thus, this relational stigma is likely an important, but only partial explanation for limited care provision. Nonetheless, our findings suggest that structural stigma is one of the main barriers to the identification of alcohol use and care in secondary care settings in the NENC. This echoes the damning findings of the ‘Remeasuring the Units’ report, also published since the pandemic, that argued that stigma contributes to the missed opportunities in secondary care for patients who ultimately die from alcohol-related liver disease [ 5 ].

This study was conducted primarily as a vehicle to understand and bring about change in workforce practice around the prevention of alcohol harm in NENC secondary care services. It was an integral component of a broader Health Care Needs Assessment (2022) on alcohol undertaken in response to increasing levels of alcohol harm in this region of the UK, which led to recommendations over four overarching themes: service delivery; workforce; data; and leadership from the healthcare system. The results of the study have directly shaped the regional strategy for the reduction of alcohol harm, a key element of which is the integrated alcohol workforce strategy for the NENC which aims to better support the NHS workforce to prevent alcohol harm through: increased awareness of the Chief Medical Officer alcohol guidance; improved pathways to community-based alcohol treatment and recovery support; workforce training and development; and support for staff to address their own drinking. The evidence highlighting the importance of stigma have additionally led to a strategic drive for senior leaders to acknowledge the impact alcohol has on their organisation and the communities they serve, and to take action to work in partnership to reduce this. There is also cross-system support to tackle relational stigma, initially though a co-ordinated multi-agency media campaign.

Overall, our interpretation has signalled areas of policy and practice which can be targeted to try to increase the uptake of these preventive strategies in the secondary care settings. However, ultimately the findings illustrate that the challenge for implementation of these evidence based preventative measures is not just upskilling the workforce or increasing resources. It also indicates that we need to address the complex interplay of contextual factors and implementation mechanisms which have been compounded by the pandemic and contribute to reinforcing and increasing existing inequalities. The works contributes to calls for a multi-layered response to reducing alcohol harm and wider cultural change for how alcohol use and substance use is perceived.

Study strengths and limitations

A strength of the study is that it was undertaken in an area experiencing some of the greatest inequalities from the COVID-19 pandemic. This allowed us to see the challenges to delivering preventative work in these contexts, which might be similar in other regions. A further strength is that mapping the empirical data onto an evidence-based implementation theory, which has been widely use in different settings, enabled us to focus on the aspects of the implementation, that are likely to be important across other settings too. Framing the interpretation using the NPT domains has helped us to emphasise how contexts and mechanisms interact to make the implementation at this particular time and place difficult. A key limitation of the study is that as it was based in one region of England, we cannot know for sure if these insights are transferrable beyond this context.

Secondary care hospitals are an important setting for the delivery of preventative care for AUD, due to the frequency with which AUD co-occurs with other physical and mental health conditions. Prior to the pandemic there was evidence that non-specialist healthcare staff can find caring for patients with alcohol-related presentations difficult, meaning that identifying, supporting, and that signposting patients was happening inconsistently. In this study, we highlight the additional challenges facing secondary care staff due to post-pandemic pressures and the significant rise in alcohol-related harm in some regions such as the NENC. Thus, whilst the mechanisms for implementing alcohol prevention work in secondary care need attention, our findings suggest that the greatest barrier is contextual, including widespread structural stigma.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Normalisation Process Theory

Alcohol Care Teams

North East and North Cumbria

Alcohol Use Disorder

Accident and Emergency Department

Psychiatric Liaison Teams

Alcohol Use Disorders Identification Test

Alcohol Use Disorders Identification Test Consumption

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Acknowledgements

In addition to co-authors WH and RB we are grateful to the four junior doctors Jamie Catlow, Rebecca Dunn, Sarah Manning and Satyasheel Ramful from the Gastroenterology Research and Audit through North Trainees who collected data for the study. We are grateful to Dr Matthew Breckons the qualitative methodologist who co-trained (with AOD and KJ) the junior doctors in qualitative interview skills. We are especially grateful to the thirty staff who gave up their time to participate in the research.

The project was funded by the North East and North Cumbria Integrated Care System Prevention Programme.

AO is Deputy Theme Lead – Prevention, Early Intervention and Behaviour Change within the NIHR Applied Research Collaboration (ARC) North East and North Cumbria (NENC) (NIHR200173). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. AO and KJ are also part-funded by a NIHR Advanced Fellowship (ADEPT: Alcohol use disorder and DEpression Prevention and Treatment, Grant: NIHR300616). The NIHR have not had any role in the design, implementation, analysis, write-up and/or dissemination of this research.

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SH and RB designed the study; SH, RB and WH were involved in the data collection; IL and KJ analysed and interpreted the data with support from AOD, SH, RB and WH; KJ drafted the manuscript with support from SH, AOD, RB, IL and WH. All authors approved the submitted version.

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Jackson, K., Baker, R., O’Donnell, A. et al. Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England. BMC Health Serv Res 24 , 772 (2024). https://doi.org/10.1186/s12913-024-11232-4

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Solar-MACH and Other Open-Source Python Tools for Solar Energetic Particle Analysis Provided by the SERPENTINE Project

• Gieseler, Jan
• Palmroos, Christian
• Dresing, Nina
• Kouloumvakos, Athanasios
• Morosan, Diana
• Price, Daniel James
• Trotta, Domenico
• Vuorinen, Laura
• Yli-Laurila, Aleksi
• Vainio, Rami O.

The EU's Horizon 2020 project Solar EneRgetic ParticlE aNalysis plaTform for the INner hEliosphere (SERPENTINE) aims at answering several outstanding questions about the origin of Solar Energetic Particle (SEP) events. To help achieve this, it will provide an advanced platform for the analysis and visualization of high-level datasets to benefit the wider heliophysics community. Multiple open source Python tools have already been developed, using available Python libraries when possible. These tools are aimed at scientific users without much Python programming experience. They are mostly provided as descriptive Jupyter Notebooks that can be run locally, or online on the project's own JupyterHub server without needing any installation. Some selected tools are provided as even easier usable Streamlit apps that don't need any coding on the user's side and run completely in the cloud. One already widely-used example is the Solar MAgnetic Connection HAUS tool (Solar-MACH) that derives (using sunpy) and visualizes the spatial configuration and solar magnetic connection of different observers (i.e., spacecraft or planets) in the heliosphere at different times. Other tools aim at analyzing timeseries obeservations of the heliospheric spacecraft fleet, including obtaining, loading and visualizing data sets of recent missions like Parker Solar Probe and Solar Orbiter. In this presentation, we will show the various tools already available, illustrating the underlying structure and workflow, and present recent and upcoming additions, like extending Solar-MACH to close vicinity of the Sun using a Potential Field Source Surface (PFSS) model provided py pfsspy to connect the heliospheric magnetic field to the solar photosphere. This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004159 (SERPENTINE).