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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Learning Objectives

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array} \nonumber \]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \nonumber \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array} \nonumber \]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 April, 2024 • 17 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#3 – Pie chart

#4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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1.3: Presentation of Data

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Skills to Develop

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array}\]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array}\]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

Contributor

• Template:ContribShaferZhang

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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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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When and how should you use data in a presentation?

The answer is that you should use figures and numbers whenever they give the best evidence to back up your argument, or to tell your story. But how to present that data is more difficult.

Many people are not interested in tables of numbers, and may struggle to understand graphs. How can you help walk them through the data?

This page is designed to help you to answer that question by setting out some simple rules for presenting data.

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

Human beings have been hard-wired, over millions of years of evolution, to enjoy and respond to stories. It’s best to work with it, not fight it, because if you tell your audience a story, they are likely to listen much more carefully, and also move towards a logical conclusion: the insight to which you are trying to lead them.

Once you understand this, the issue of using data falls into place: it is to provide evidence of how your story unfolds.

Use Data to Tell the Story

You are not presenting data as such, you are using data to help you to tell your story in a more meaningful way.

This means that whenever you are required to present data, you should be asking yourself:

‘ What is the story in this data? ’,

‘ How best can I tell this story to my audience? ’

A Picture Tells a Thousand Words

90% of the information sent to the brain is visual and over 90% of all human communication is visual. Processing text requires our brains to work much harder than when processing images. In fact, the brain can process pictorial information 60,000 times faster than written information.

There is considerable truth in the saying ‘a picture tells a thousand words’ . It may not be literally a thousand, but it is often much easier to use a picture than to describe numerical information in words.

The data itself may be vitally important, but without a visual presentation of that data, its impact (and therefore your message) may be lost.

There are many people in the world who do not find it easy to understand numbers.

There are also many people who will simply switch off if you show them figures in a table. But if you present data in a graph or pie chart, you make a pictorial representation of the data. It makes the numbers much easier to understand. Trends and proportions become more obvious.

Consider this set of data:

Even for the highly numerate, the immediate point is only that there are lot more sales in the first quarter. You would have to do some adding up and dividing to work out the relationships between the four numbers. It also requires much more concentration to read and absorb the information in this format.

Now consider the same data in a pie chart:

It is immediately and shiningly obvious, even for those who struggle with numbers, that more than half of all sales were in the first quarter, and that over 75% were in the first two quarters.

What’s more, nobody is going to be straining from the back of the room to read your figures. You really can see a lot more from a picture.

But, and this is important, make sure that the graph is a good one.

Check that your graph or chart is visually appealing, that all the labels are clear, and that you have used an appropriate type of graph or chart. Poor graph-making is always obvious and can lead to confusion. Your message will also have much more impact if you choose the right type of graph or chart.

For more about this, see our page on Graphs and Charts .

KISS: Keep It Simple, Stupid!

When you’re good at statistics, it’s very tempting to do some really whizzy analysis. And once you’ve done that, you really want to show everyone how clever you are, and how much work you’ve done.

But does it really help to make your point?

Then don’t present it.

In the (relatively rare) cases when you actually need some really whizzy analysis, you then need to ask yourself whether everyone will understand it. And, in these days of presentations being posted on the internet, will the casual reader of your slides understand it later?

Once again, if the answer is ‘probably not’, then don’t use it.

Leave It Out...

If you can’t summarise your analysis in one or two brief and clear sentences, then don’t include it.

It also follows that if you don’t need to include data to make your point, then it may be best not to do so. A slide that is likely to be misunderstood or produce confusion is worse than no slide at all. So cut out all unnecessary data and focus on what you really need  to tell your story .

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

We’re not suggesting that you should ‘ dumb down ’ your presentation, but there is no harm in highlighting the key features, as well as cutting out unnecessary data.

Suppose once again that you are using the sales figures from the last four quarters. You want to show the actual figures. Why not use a highlighting tool to emphasise that the first quarter is more than half?

With PowerPoint and other presentation software, you can make each circle appear separately, as you make your point and discuss the insights.

A little creative use of the technology can help you to highlight certain figures, and once again, make the story clearer.

Take-home message

Paradoxically, your presentation of any data should be designed to move the conversation away from the data and into the insight and action that should result from it.

In other words:

‘What happened there?’

‘What are we going to do about it?’

If you look at your presentation, data and all, and it’s not clear how you would get from the data to the insight and then the action, it’s probably a good idea to look at it again.

Remember, it’s the story that matters… and then what happens as a result.

Continue to: Writing Your Presentation Working with Visual Aids

See also: What is Your Story? How to Identify Your Story from Raw Data Crisis Communications Presenting to Large Groups Simple Statistical Analysis

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• Economics /

Class 11 Collection, Organisation and Presentation of Data

• Updated on  
• Jun 22, 2023

The collection of data aims to collect evidence for attaining a sound and comprehensible solution to a problem. To understand the inconsistencies in the output, we need the ‘data’ on the generation. It is a process which is conducted to measure and gather information. ‘Data’ is a device, which aids in the comprehension of problems by providing knowledge. Here is this blog, we will talk in detail about the Class 11 collection, organisation and presentation of data. 

Must Read: Business Services Class 11 Notes

This Blog Includes:

What are the sources of data, primary data, secondary data, preparation of instrument, mode of data collection, personal interviews, mailing questionnaire, telephone interviews, pilot survey, census and sample surveys, census , random sampling, non-random sampling, sampling errors, non-sampling errors, census of india and nsso.

To understand more about the chapter Class 11 collection, organisation and presentation of data, we fist need to know the sources of data. Statistical data can be obtained from two sources:

• Primary data

We further move on to the concept of primary data in class 11 collection, organisation and presentation of data. The important points of primary data are:

• The enumerator (person who assembles the data) may collect the data by administering an inquiry or research. Such data is called Primary Data , as it is formulated on first-hand information.
• Primary data are unique, do not require any modification, and are costly.

Next important form of data in class 11 collection, organisation and presentation of data is secondary data.

• If the data have been examined and analyzed by another agency, they are called Secondary Data . Usually, the issued data are secondary.
• They are already in the presence and therefore are not unique.
• It demands to be modified to satisfy the aim of the study at hand.
• Secondary data are low priced.

How do we collect Data?

Collection of data is important in class 11 collection, organisation and presentation of data. It is done by the following ways:

• The survey aims to describe characteristics like cost, worth, utility (in case of the product) and reputation, honesty, loyalty (in case of the nominee).
• The objective of the survey is to gather data and is a method of gathering information from individuals.

The most prevalent type of tool employed in surveys is a questionnaire/ interview schedule. The questionnaire is either self-directed by the interviewee or conducted by the enumerator or qualified investigator. While drawing-up the questionnaire/interview schedule, the following points should be kept in mind:

• The questionnaire should not be lengthy.
• The array of problems should move from indefinite to distinct.
• Questions should not be enigmatic.
• Questions should not use binary negatives. 
• Questions should not be leading.
• Questions should not indicate choices. 

Also Read: Emerging Modes of Business Class 11 Notes

The next important topic in class 11 collection, organisation and presentation of data is the mode of data collection. The aim of probing questions is to survey the acquisition of data. There are three ways of collecting data: 

• Mailing (questionnaire) Surveys

Personal interviews form an important part of the mode of data collection in class 11 collection, organisation and presentation of data. In this method, the researcher has the main role as he/she conducts the interviews face-to-face with the respondents. Personal interviews are preferred due to various reasons:

• Highest Response Rate 
• Allows use of all types of questions 
• Better for using open-ended questions 
• Allows clarification of ambiguous questions.

The personal interview has some demerits too:

• Most expensive 
• Possibility of influencing respondents 
• More time taking

Another important part of class 11 collection, organisation and presentation of data is the mailing questionnaire. In such a method, the data is collected through the mail. The questionnaire is mailed to each person and a  request is attached to complete and return it on time. 

The advantages of this method are:

• Least expensive 
• The only method to reach remote areas 
• No influence on respondents 
• Maintains anonymity of respondents 
• Best for sensitive questions

The disadvantages of mail surveys are:

• Cannot be used by illiterates 
• Long r esponse time  
• Does not allow an explanation of unambiguous questions  
• Reactions cannot be watched 

In telephone interviews, the investigator asks questions over the telephone. 

The advantages of telephone interviews are:

• Relatively low cost 
• Relatively less influence on respondents 
• Relatively high response rate.

The disadvantages of this method are:

• Limited use 
• Possibility of influencing respondents

Explore: Accountancy Class 11 NCERT Solutions

The pilot survey is another important tool in class 11 collection, organisation and presentation of data.

• After the questionnaire is ready, it is desirable to carry a try-out with a diminutive group, known as Pilot Survey or Pre-Testing of the questionnaire . 
• The pilot survey serves to give a preliminary impression of the survey. 
• It helps to pretest the questionnaire and know the lapses and drawbacks.
• It also aids to assess the appropriateness of questions, the accuracy of guidance, the administration of enumerators, and the expense and time required in the actual survey.

Census and sample surveys are an important tool in class 11 collection, organisation and presentation of data. 

• A survey, which encompasses every component of the population, is apprehended as Census or the Method of Complete Enumeration.
• The primary feature of this approach is that this comprises every individual unit in the whole population.

Sample Survey

• A sample refers to a section of the population from which information has to be taken. A good sample (representative sample) is usually short and competent in giving reasonably accurate information about the population at a lower cost and in less time.
• Most of the surveys are sample surveys and are preferable in statistics because of several reasons.
• A sample can give rationally secure and authentic information at a lower cost and in less time. 
• Now the question is how do you do the sampling? There are two main types of sampling:
• Non-random Sampling
• It is also known as the lottery method.
• Random sampling is where the specific units from the population (samples) are randomly selected. 
• In random sampling, each person has an equal possibility of being chosen, and the person who is selected is the same as the one who is not selected.
• Random number tables are generated to ensure an equal chance of selection of every single unit in the population.
• They are accessible either in an issued form or can be generated by employing relevant software packages.
• In this method, units of the population don’t have equal chances of being selected. 
• The convenience or interpretation of the investigator plays a crucial role in the adoption of the sample. 
• They are chiefly selected based on belief, purpose, ease, or quota and are non-random samples.

Sampling and Non-sampling Errors

While conducting surveys, in class 11 collection, organisation and presentation of data, sample and non-sampling errors find an important mention. 

• Sampling error applies to the variations between the sample estimate and the actual value.
• It is the error that transpires when you observe the sample taken from the population. 
• The point of differentiation between the actual parameter of the population and its estimate is known as sampling error. 

Non-sampling errors are more consequential than sampling errors. Sampling error can be minimized by taking a larger sample, on the other hand, it is difficult to minimize non-sampling error. Even a Census can carry non-sampling errors.

 Some of the non-sampling errors are:

• Errors in Data Acquisition: This type of error stems from recording inaccurate responses.
• Non-Response Errors: Non-response happens if an interviewer is incapable to contact a person listed in the sample or a person from the sample declined to respond. In this case, the sample research may not be representative.
• Sampling Bias: Sampling bias happens when the sampling plan is such that some portion of the target population could not possibly be incorporated into the sample.

Must Read: Class 11 Oscillations Notes

The census of India is a very important body of our country and is an important part in the chapter class 11 collection, organisation and presentation of data. 

• The Census of India and the National Sample Survey Organisation (NSSO), are two significant firms at the national level, which gather, manner, and tabulate data.
• The Census of India produces the most comprehensive and continuous demographic record of the population. 
• The NSSO was established by the Government of India to conduct nationwide surveys on socio-economic issues. 
• NSSO gives periodic measures of education, school enrolment, utilization of educational aids, employment, unemployment, manufacturing, and service sector enterprises, morbidity, maternity, child care, utilization of the public distribution system, etc.

Ans. Three methods exist for gathering data: Personal meetings. Telephonic Interviews, and mailing surveys with questions.

Ans. The term “presentation of data” refers to the display of data in a way that makes it easy for viewers to understand and examine it.

Ans. Based on the methods used to acquire them, data can be divided into four basic categories: observational, experimental, simulational, and generated. The kind of research data you gather may have an impact on how you manage that data.

Also Read: Class 11 Formation of a Company

We hope the Class 11- Collection of Data notes helped you understand the essential concepts covered in this chapter. Still unsure about which stream to choose after Class 12. Our Leverage Edu experts are here to guide you in selecting the right stream of study to make sure that you make an informed decision. Sign up for a free session with us now!

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Presentation, Analysis & Interpretation ...

University - professional development, professional development, science, business.

University -

Professional development, presentation, analysis & interpretation of data.

15 questions

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This refers to processes that convert data into information and knowledge

Data Presentation

Data Editing

Data Processing

Data Collection

These are the PROCESSING OPERATIONS

CLASSIFICATION

It is used to filter out irrelevant data from the relevant data and establishing order from chaos and giving shape to a mass of data

Data Reduction

Data Filtering

Data Layering

Data Analysis

It is the process of assigning numerals or other symbols to answers so that responses can be put into a limited number of categories

It is the process of arranging assembled data in a concise/logical manner

Classification

The process of organizing data into logical, sequential and meaningful categories and classifications to make them amenable to study and interpretation

Data Interpretation

This data presentation technique includes books, reports, research papers and articles

Type of graph with rectangular bars that usually compare different categories

Linear Graph

Type of graph that is commonly used to display change over time as a series of data points are connected by straight line segments

Statistical Map

Type of graph that gives a snapshot of how a group is broken down into smaller groups

Type of graph that represents data using images

Type of graph in which the variation in quantity of a factor in a geographical area is indicated

A category of data analysis that is used to find out if there is a relationship between 2 variables

Multivariate

This is the process that helps in reducing a large chunk of data into smaller fragments which make sense

This is a type of data that is expressed in numbers or numerical figures

Qualitative

Quantitative

Categorical

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Commerce Aspirant » Economics Class 11 MCQs » Collection Organisation and Presentation of Data MCQs

Collection Organisation and Presentation of Data MCQs

Collection Organisation and Presentation of Data MCQs Class 11 Economics are covered in this Article. Collection Organisation and Presentation of Data MCQs Test contains 50 questions. Answers to MCQs on Collection Organisation and Presentation of Data Class 11 Economics are available after clicking on the answer. These MCQs have been made for Class 11 students to help check the concept you have learnt from detailed classroom sessions and the application of your knowledge. For more MCQ’s, subscribe to our email list.

Collection Organisation and Presentation of Data MCQs Economics Class 11

1. Use of ___________ saves time and cost.

a) Secondary data b) Primary data c) Data d) Information

Answer: (a) SECONDARY DATA

2. ___________ is the method of gathering information from individuals.

a) Collection b) Survey c) Analysis d) Inspection

Answer: (b) SURVEY

3. __________ Questions are easy to use, score and codify for analysis because all respondents can choose from the given options.

a) Multiple choice b) Open-ended c) Closed-ended d) True/False

Answer: (C) CLOSE-ENDED

4. Misinterpretation and misunderstanding can be avoided by _____________

a) Personal Interaction b) Mailing c) Telephone d) Questionnaire

Answer: (a) PERSONAL INTERACTION

5. Once the question is ready, it is advisable to conduct a try-out with a small group which is known as ______________

a) Pilot Survey b) Testing c) Survey d) Observation

Answer: (a) PILOT SURVEY

6.____________ is also called lottery method.

a) Random Sampling b) Non-Random Sampling c) Population d) Sampling

Answer: (a) RANDOM SAMPLING

7. Which of the following is not a type of non-sampling error?

a) Sampling Bias b) Non-response c) Errors in data d) Difference between sampling estimate and corresponding parameter

Answer: (d) DIFFERENCE BETWEEN SAMPLING ESTIMATE AND CORRESPONDING PARAMETER

8. In spatial classification data are classified with reference to_____________

a) Geographical location b) Time series c) Chronological Classification d)Quantitative Classification

Answer: (a) GEOGRAPHICAL LOCATION

9._____________ is a comprehensive way to classify raw data of a quantitative variable.

a) Frequency distribution b) Distribution c) Information d) Analysis

Answer: (a) FREQUENCY DISTRIBUTION

10. Class Mid-point or Class Mark is equal to _____________

a) (Upper class limit + Lower class limit) / 2 b) (Upper class limit – Lower class limit) /2 c) (Upper class limit + Lower class limit) * 2 d) (Upper class limit – Lower class limit) * 2

Answer: (a) (UPPRER CLASS LIMIT + LOWER CLASS LIMIT) / 2

11. While preparing a frequency distribution, the following questions need to be addressed.

a) How many classes should we have? b) Should we have equal or unequal sized class intervals? c) What should be the size of each class? d) All of the above

Answer: (d) ALL OF THE ABOVE

12. In case of continuous variables, _____________ intervals are used very often.

a) Inclusive class intervals b) Exclusive class intervals c) Online class intervals d) Offline class intervals

Answer: (a) INCLUSIVE CLASS INTERVALS

13. Which of the following forms of presentations are true?

a) Textual / Descriptive presentation b) Tabular presentation c) Diagrammatic presentation d) All of the above

14. All of the above which of the following is not a type of Classification used in tabulation?

a) Spatial b) Temporal c) Qualitative d) None of the above

Answer: (d) NONE OF THE ABOVE

15. ___________ usually are not drawn with absolute values of a category.

a) Pie-charts b) Bar-diagram c) Histogram d) Frequency-curve

Answer: (a) PIE CHARTS

16. ____________ is drawn only for a continuous variable.

Answer: (C ) HISTOGRAM

17. The frequency-curve is obtained by drawing ___________

a) Smooth freehand curve b) Straight line c) Line with scale d) Circle

Answer: (a) SMOOTH FREE HAND CURVE

18. The use of class mark instead of actual values of the observation involves considerable ______________

a) Loss of Importance b) Gain of Importance c) Profit of Importance d) All of the above

Answer: (a) LOSS OF IMPORTANCE

19. Bar Diagram is a _______________

a) One-Dimensional diagram b) Two-Dimensional diagram c) Diagram with no Dimension d) None of the above

Answer: ( a) ONE DIMENSIONAL DIAGRAM

20. Ogives can be useful in locating graphically ____________

a) Mean b) Mode c) Median d) All of the above

Answer: ( C) MEDIAN

21. Which of the following methods give better results?

a) Census b) Sample c) Information d) Data

Answer: ( a) CENSUS

22. The purpose of the collection of data is to show _____________ for reaching sound and clear solution to a problem.

a) Design b) Figure c) Movement d) Evidence

Answer: ( D) EVIDENCE

23. ____________ is a tool which helps in understanding problems by proving information.

a) Excel b) Document c) Data d) Experiment

Answer: ( C ) DATA

24. Database on first-hand information is called ____________

a) Primary Data b) Secondary Data c) A is False d) B is False

Answer: ( A) PRIMARY DATA

25. ______________ is the method of gathering information from individuals.

a) Data b) Survey c) Analysis d) Information

Answer: ( b) SURVEY

26. The most common type of instrument used in surveys is _________________

a) Questionnaire b) Interview Schedule c) Both A and B d) None

Answer: ( C ) BOTH A AND B

27. The Questionnaire should be ____________ to understand and avoid different words.

a) Lengthy b) Easy c) Perfect d) Simple

Answer: ( B) EASY

28. Basic Way of collecting data are _______________

a) Personal Interview b) Mailing Survey c) Telephone Interview d) All of the above

Answer: ( d) ALL OF THE ABOVE

29. _______________ is used when the researcher has access to all the members.

Answer: ( A) PERSONAL INTERVIEW

30. Misinterpretation and misunderstanding can be avoided by________________

Answer: ( a) PERSONAL INTERVIEW

31. ________________ allows researchers to have access in remote areas too.

Answer: ( b) MAILING SURVEY

32. ______________ helps in providing a preliminary data about the survey.

a)  Airlines Survey b) Pilot Survey c) Mailing Survey d) All of the above

Answer: ( b) PILOT SURVEY

33. Census carried out once in every ____________ years

a) 10 b) 5 c) 15 d) 7

Answer: ( a) 10

34. Growth rate of population during 2001-2011 _______________

a) 1.97 b) 2.53 c) 1.64 d) 3.1

Answer: ( c) 1.64

35. ________________ in statistics means totality of items under study.

a) Population b) Universe c) Sample d) Both A and B 

Answer: ( d) BOTH A AND B

36. ________________refers to a group or section of the population from which information is obtained.

a) Bio-data b) Values c) Evidence d) Sample

Answer: ( d)SAMPLE

37. Good sample is capable of providing __________________ information about the population.

a) Absolute Accurate b) Reasonable Accurate c) A is false d) B is  false

Answer: ( b) REASONABLE ACCURATE

38. _________all units of population don’t have equal chance of being selected.

a) Random b) Lottery Method c) Choice d) None

Answer: ( d) NONE

39. It is possible to reduce the magnitude of sampling error by taking a __________ sample.

a) Smaller b) Larger c) Thick d) Thin

Answer: ( b) LARGER

40. Non-Sampling errors are more ____________ than sampling errors.

a) Serious b) Dangerous c) caution d) All of the above

Answer: ( a) SERIOUS

41. The raw data are summarized, and made comprehensible by ____________

a) Classification b) Division c) Information d) None

Answer: ( a) CLASSIFICATION

42. The raw data consists of observations on _____________ 

a) Constants b) Information c) Variables d) Population

Answer: ( C) VARIABLES

43. The raw data is classified in various ways depending on time is known as __________

a) Spatial classification b) Chronological classification c) Geographical classification d) Time series

Answer: ( b) CHRONOLOGICAL CLASSIFICATION

44. The population of India classified in terms of years is a ____________

Answer: ( d) TIME SERIES

45. A continuous variable can take any _____________

a) Numerical value b) Integral value c) Functional value d) All of the above

The class limits for the class 60-70 (from 46 – 49) 

46. What are its lower class limits

a) 60 b) 70 c) 75 d) None

Answer: ( a) 60

47. What are upper class limits

a) 70 b) 60 c) 65 d) 75

Answer: ( a) 70

48. What is the class mark

a) 70 b) 60 c) 75 d) 65

Answer: ( d) 65

49. What is the Class Width?

a) 70 b) 60 c) 10 d) 15

Answer: ( C) 10

50. ______________ is the difference between upper class limit and lower class limit?

a) Class Interval b) Class Width c) Class Mark d) Both A and B

Term 1 – NCERT Economics Class 11 MCQ

Part A – MCQ Questions for Class 11 Statistics Economics

• Introduction to Statistics Class 11 MCQ Questions
• Collection, Organisation and Presentation of Data
• Statistical Tools and Interpretation – Arithmetic Mean, Median and Mode

Part B – MCQ Questions for Class 11 Microeconomics

• Introduction to Microeconomics
• Consumer’s Equilibrium and Demand
• Economics Class 11 Notes
• Accountancy Class 11 Notes
• Economics Class 11 MCQs

Business Studies Class 11 MCQ

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Class 11 Economics Short Questions and Answers: Presentation of Data - 1

Q.1. What is meant by presentation of data? Ans.  The presentation of data means exhibition of the data in a clear and attractive manner such that they can be easily understood and analysed. Q.2. When is it suitable to use textual presentation of data? Ans.  It is suitable to use textual presentation of data when the quantity of data is not too large. Q.3. What is a table? Ans.  A table is the organisation of data in rows and columns. Q.4. Define tabulation? Ans.  Tabulation involves an orderly and systematic presentation of numerical data to elucidate the problem under consideration. Q.5. Name the classifying variable in temporal classification. Ans.  Time is the classifying variable in temporal classification. Q.6. Give the examples of classifying variables in spatial classification. Ans.  The examples of classifying variables in spatial classification are village, district, state and country, etc. Q.7. List the main components of a table. Ans.  The main components of a table are: (i) Table number (ii) Title (iii) Captions or column headings (iv) Stubs or row headings (v) Body of the table (vi) Unit of measurement (vii) Source note (viii) Footnote Q.8. Write a feature of the ‘title’ of a table. Ans.  The title of a table has to be very clear, brief and carefully chosen so that clear interpretations could be derived from the table. Q.9. Give another name for column and row heading in the table. Ans.  Column heading is also known as caption and row heading is also known as stub. Q.10. How is the location of a figure determined in a table? Ans.  The location of a figure in a table is determined by the row and column of the table. Q.11. State the advantages and disadvantages of textual presentation of data. Ans.  Advantages of Textual Presentation of Data (i) It provides useful and supportive evidence to the text in case of small volume of data. (ii) It enables one to emphasise the important points of the presentation. Disadvantage of Textual Presentation of Data (i) It requires going through the complete text to draw facts. Q.12. Write the important features of tabulation. Ans.  The important features of tabulation are as below: (i) It organises the raw data, making it easy and definite. (ii) It makes data comparable. (iii) It facilitates calculation of statistical indices and data analysis. (iv) It clearly indicates the features of the data. Q.13. Explain the various types of tables. Ans.  The various types of tables are explained below: (i) One-way Table: This type of table presents only single characteristic of the data. For example, a table showing the number of students in a college. (ii) Two-way Table: It presents two features of data simultaneously. For example, a table showing the male and female population of a village. (iii) Three-way Table: This table presents three features of the data. For example, presentation of population of a village on the basis of age, gender and education. Q.14. What are the features of a good table? Ans.  The following are the important features of a good table: (i) Title of the table should be according to the subject of study. (ii) Row and column headings should be clear along with the units of measurements. (iii) No short-forms should be used in the title, for example, govt. for government. (iv) Footnote is to be given, if required. (v) Combined total for every column and row should be given. (vi) Complete information regarding numbers should be given in a table such as decimal point up to which value has been taken. (vii) Source of data should always be mentioned below the table. (viii) Table should be simple and easy to understand. Q.15. What is diagrammatic presentation of data? Ans.  In diagrammatic presentation, data is presented in the form of diagrams, figures, graphs, etc. to provide quickest understanding of the real situation.

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Presentation Skills for Data Scientists

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Presentation skills for data scientists play a crucial role in extracting valuable insights from complex datasets.

However, a data scientist’s findings are only as impactful as their ability to communicate and present them effectively to decision-makers.

Understanding the importance of presentation skills for data scientists

Effective communication bridges the gap between data scientists and decision-makers.

While data holds immense potential, its true value lies in its interpretation and application.

The importance of presentation skills for data scientists cannot be overstated.

Data scientists must be able to translate their findings into meaningful insights that resonate with non-technical audiences.

By presenting their findings clearly, concisely, and compellingly, they can drive informed decision-making and influence organizational strategies.

Bridging the gap between data and decision-makers

Presenting complex data to decision-makers requires balancing technical depth and simplicity.

Data scientists must understand their audience’s needs and expectations to communicate their findings’ significance and implications effectively.

By bridging the gap between data and decision-makers, they can build trust and credibility, fostering a culture of data-driven decision-making within the organization.

The role of effective communication in data interpretation

Data interpretation is about deciphering numbers and telling a compelling story.

Effective communication involves presenting data to connect with the audience’s emotions and values.

Data scientists can leverage visualizations, storytelling techniques, and narrative structures to engage their audience and bring their insights to life.

Presentation skills for data scientists enable professionals to convey the complexities of their work effectively.

When presenting their findings, data scientists must consider the level of technical expertise of their audience.

They need to strike a balance between providing sufficient technical details to demonstrate the rigor of their analysis and simplifying the information to make it accessible to non-technical stakeholders.

Furthermore, presentation skills for data scientists allow professionals to highlight their findings’ practical applications and real-world implications.

They can demonstrate how their insights can drive tangible outcomes and inform strategic decision-making by contextualizing their analysis within the broader business context.

This enhances the value of their work and helps decision-makers understand the potential impact of data-driven solutions on their organizations.

Critical elements of a compelling data science presentation

A compelling data science presentation requires careful planning and attention to detail.

Structuring your presentation for maximum impact

The structure of a presentation can significantly influence its effectiveness.

Data scientists should develop a clear and logical flow, guiding the audience through their findings.

Presentation skills for data scientists involve using frameworks such as the problem-solution-impact model or the storytelling arc ; they can create a compelling narrative that captures attention and drives action.

It is crucial to remember that the opening of a presentation sets the tone for the entire talk.

Data scientists should start with a strong hook to grab the audience’s attention and establish the topic’s relevance.

Additionally, a well-crafted conclusion summarising key points and providing clear takeaways can leave a lasting impression on the audience.

The art of visualizing data effectively

Data visualization is a powerful tool for conveying complex information in a digestible format.

Data scientists should leverage appropriate visualizations like charts, graphs, and infographics to enhance understanding and engagement.

By applying design and aesthetics principles, they can create visually appealing and informative presentations that resonate with their audience.

Moreover, incorporating interactive elements into data visualizations can further engage the audience and allow a deeper exploration of the insights presented.

Techniques like interactive dashboards or clickable charts can provide viewers with a hands-on experience, increasing their involvement and understanding of the data.

Enhancing your presentation skills: practical tips for data scientists

Mastering the language of non-data scientists is essential for effective communication.

Data scientists should avoid jargon and technical terms that may confuse or alienate their audience.

Instead, they should use clear and concise language to convey key messages and insights. Additionally, data scientists can enhance their presentation skills by incorporating storytelling techniques.

By crafting relatable and engaging narratives, they can capture the attention and interest of their audience.

Mastery of the language of non-data scientists

Presentation skills for data scientists means communicating with stakeholders, who can come from various backgrounds and have various levels of technical expertise.

Adapting language and explanations to suit different audiences is crucial.

By avoiding jargon and using everyday language, data scientists can ensure that their findings are accessible and understandable to all, regardless of their technical knowledge.

Furthermore, data scientists must actively listen to their audience’s feedback and adjust their communication style accordingly.

This two-way interaction can help in building rapport and ensuring that the message is effectively conveyed and understood by all parties involved.

Engaging your audience: the power of storytelling in data science

Presentation skills for data scientists rely on the art of storytelling.

Storytelling is a powerful tool that data scientists can leverage to captivate their audience.

They can make their insights more relatable and memorable by framing their findings within a compelling narrative.

A well-crafted story can help with information retention, evoke emotions, and influence decision-making.

Moreover, incorporating visual elements such as graphs, charts, and infographics can further enhance the storytelling experience for the audience.

These visual aids can simplify complex data sets and trends, making it easier for the audience to grasp the key points being presented.

Overcoming common challenges in data science presentations

Data science presentations come with their fair share of challenges.

Overcoming these obstacles is key to delivering impactful presentations.

Simplifying complex data for your audience

Presentation skills for data scientists often involve communicating intricate datasets that may overwhelm non-technical audiences.

It is crucial to distill complex information into clear and concise messages.

Data scientists can make their presentations more accessible and engaging by breaking down data into digestible chunks and using visual aids.

Handling questions and feedback effectively

Data science presentations often invite questions and feedback from the audience.

Data scientists should be prepared to address queries and respond to feedback confidently and professionally.

By actively listening and providing clear and concise responses, they can foster a constructive dialogue and enhance the impact of their presentations.

The future of presentations in data science

The world of data science is constantly evolving, and so are the presentations associated with it.

As the field embraces technological advancements, the future of presentations in data science holds exciting possibilities.

The role of AI and machine learning in data presentations

Artificial intelligence (AI) and machine learning (ML) are revolutionizing data analysis and interpretation.

In the future, these technologies may also influence how data science presentations are crafted and delivered.

AI and ML algorithms can help automate data visualization, generate insights, and optimize presentation delivery.

Staying ahead: continuous improvement of presentation skills in data science

Data scientists must continually refine and improve their presentation skills as data science evolves.

This requires staying abreast of the latest trends, tools, and techniques in data visualization and effective communication.

Additionally, seeking feedback and learning from experienced presenters can help data scientists elevate their skills and create compelling presentations.

Crafting compelling presentations is an essential skill for data scientists.

Data scientists can effectively communicate their findings and drive impactful decision-making by understanding the importance of presentation skills, mastering the key elements of a compelling presentation, enhancing their communication abilities, and overcoming common challenges.

As the future of data science presentations evolves, data scientists must stay ahead, adapting to technological advancements and continuously improving their presentation skills to remain relevant in this fast-paced field.

Are you ready for a career in data science?

Whether you are new to tech or a seasoned professional looking for a change, the Institute of Data’s Data Science & AI Program equips you with the skills you’ll need to thrive in this ever-evolving field of tech. Download a Data Science Course Outline here.

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Free AI Presentation Maker for Generating Projects in Minutes

• Generate ready-to-use presentations from a text prompt.
• Select a style and Visme’s AI Presentation Maker will generate text, images, and icon.
• Customize your presentation with a library of royalty-free photos, videos, & graphics.

Generate a presentation with AI

Brought to you by Visme

A leading visual communication platform empowering 27,500,000 users and top brands.

Presentations Engineered With Visme’s AI Presentation Maker

Ai presentation maker prompt 1.

Craft a presentation outlining a leading company’s cutting-edge innovations in AI-powered hardware, emphasizing their impact on enhancing workplace productivity and efficiency.

AI Presentation Maker Prompt 2

Generate a comprehensive presentation highlighting the latest digital marketing trends, focusing on strategies for enhancing brand visibility and customer engagement across diverse platforms.

AI Presentation Maker Prompt 3

Create a detailed presentation elucidating a company’s diversified investment portfolio, emphasizing its robust performance, risk mitigation strategies, and the potential for sustainable long-term growth.

AI Presentation Maker Prompt 4

Develop a compelling presentation showcasing a company’s groundbreaking medical devices and software solutions, emphasizing their role in revolutionizing patient care, treatment efficacy, and healthcare accessibility worldwide.

How it works

How to generate AI presentations with Visme

Save time and create beautiful designs quickly with Visme AI. Available inside the Visme template library, this AI Powerpoint generator is ready to receive your prompts and generate stunning ready-to-use presentations in minutes.

• Log in to the Visme dashboard, and open the template library by clicking on Create New button -> Project -> Presentations. Inside the template library, scroll down and click on the Generate with AI option.
• In the popup that opens, type in a prompt and describe in detail what aspects your presentation should feature. If you don’t provide enough information, chatbot will ask you follow-up questions.
• Visme Chatbot will suggest template styles; choose the most relevant for your presentation, and wait for the AI to create the design. Preview, regenerate or open your project in the Visme editor.
• Customize your project in Visme: Pick a color theme or create your own, edit text, and use assets from Visme’s royalty-free library of photos, videos, and graphics, or create your own with AI tools.

Features of the AI Presentations Maker

Ready-to-use presentations in minutes.

Starting is often the hardest part of a project. Visme’s free AI presentation maker helps you overcome this block and generates results within minutes. Create AI PowerPoint online presentations quickly with a good first draft that is ready to use with minimal or no customization.

Customize every part of your presentation

Visme editor is easy to use and offers you an array of customization options. Change the color theme of your AI-generated presentation, text, fonts, add images, videos and graphics from Visme royalty-free library of assets or generate new ones with AI image generator, AI image touchup tools, or add your own. For more advanced customization, add data visualizations, connect them to live data, or create your own visuals.

Add your branding

Stay on-brand even with AI-generated presentations. Quickly and easily set up your brand kit using AI-powered Visme Brand Wizard or set it up manually. Use your brand colors and fonts in AI-generated presentations. Add your logo and upload your brand assets to make a presentation match your company’s branding.

Download, share or schedule your presentation

Share your presentations generated with Visme AI Designer in many ways. Download them in various formats, including PPTX, PDF and HTML5, present online, share on social media or schedule them to be published as posts on your social media channels. Additionally, you can share your presentations as private projects with a password entry.

More than just an AI Presentation Maker

Beautify your content

Unique Elements & Graphics

Browse through our library of customizable, one-of-a-kind graphics, widgets and design assets like icons, shapes, illustrations and more to accompany your AI-generated presentations.

Visualize your data

Charts & Graphs

Choose from different chart types and create pie charts, bar charts, donut charts, pyramid charts, Mekko charts, radar charts and much more.

Make it engaging

Interactivity

Share AI-generated presentations online with animated and interactive elements to grab your audience’s attention and promote your business.

More AI tools in Visme

Ai image generator.

The Visme AI Image generator will automatically create any image or graphic. All you need to do is write a prompt and let AI magic do the rest.

Visme AI Writer helps you write, proofread, summarize and tone switch any type of text. If you’re missing content for a project, let AI Writer help you generate it.

Save yourself hours of work with AI Resize. This feature resizes your project canvas and adjusts all content to fit the new size within seconds.

AI TouchUp Tools

The Visme AI TouchUp Tools are a set of four image editing features that will help you change the appearance of your images inside any Visme project. Erase and replace objects that you don’t want in your photos.

The Brand Wizard

The AI-based Visme Brand Wizard populates your brand fonts and styles across a beautiful set of templates.

Make the most of Visme’s features

Choose the perfect visual from our extensive photo and video library . Search and find the ideal image or video using keywords relevant to the project. Drag and drop in your project and adjust as needed.

Incorporate 3D illustrations and icons into all sorts of content types to create amazing content for your business communication strategies. You won’t see these 3D designs anywhere else as they’re made by Visme designers.

When you share your Visme projects, they’ll display with a flipbook effect . Viewers can go from page to page by flipping the page like a digital magazine. If you don’t want the flipbook effect, you can disable it and share as a standard project.

Remove the background from an image to create a cutout and layer it over something else, maybe an AI-generated background. Erase elements of the image and swap them for other objects with AI-powered Erase & Replace feature.

Create scroll-stopping video and animation posts for social media and email communication. Embed projects with video and animation into your website landing page or create digital documents with multimedia resources.

With Visme, you can make, create and design hundreds of content types . We have templates for digital documents, infographics, social media graphics, posters, banners, wireframes, whiteboards, flowcharts.

Design and brainstorm collaboratively with your team on the Visme whiteboard . Build mind maps and flowcharts easily during online planning and strategy sessions. Save whiteboards as meeting minutes and ongoing notes for projects.

Edit your images , photos, and AI image-generated graphics with our integrated editing tools. On top of the regular editing features like saturation and blur, we have 3 AI-based editing features. With these tools, you can unblur an image, expand it without losing quality and erase an object from it.

Frequently Asked Questions (FAQs)

How can i get better results with the ai presentations maker.

Like any AI generator from a text tool, the prompt is everything. To get better results with the AI Presentation maker, you need better prompts. Write the prompt to be as detailed as possible. Include all the content topics you want the presentation to cover. As for style elements, there’s no need to include it in the prompt. Focus on choosing the style that you like from the Chatbot suggestions. Try to select the style that already features the color palette and shapes that you like. AI will change icons and photos based on text it generates.

How many AI Presentations can I generate?

Visme AI Presentation Maker is available in all plans and works on a per-credit basis. Every free account gets 10 credits, Starter accounts get 200, Pro gets 500 and Enterprise is unlimited. Every design generation costs 2 credits and usage of other AI tools costs 1 credit.

Is the Visme AI Designer a third-party API?

No, Visme AI Presentation maker was developed in-house and is a unique tool. However, it does use third-party APIs: ChatGPT and Unsplash.

Are the AI-generated presentations I make copyright-free?

All designs you create with AI Presentation are copyright and royalty-free. You can use them both for personal and commercial use without any problems.

What can Visme AI Designer do?

Visme’s AI design generator can:

• Generate full designs for various content types, including presentations, documents, printables, and social media graphics. Click here to view the full list of document, printable, and social media graphics subcategories.
• Follow your instructions for the text you want in your projects, although not word for word.
• Adapt photos and icons to complement the generated text.
• Create charts, graphs, and tables.
• Handle one project at a time.
• Include animations in projects only if the selected style features animation.
• Generate designs with a maximum length of 10 pages.

What can’t Visme AI Designer do?

Visme’s AI design generator can’t:

• Generate infographics, charts/graphs, whiteboards, web graphics, videos, or GIFs.
• Reproduce your text verbatim.
• Customize based on your specifications for color, shapes, graphics, and design style. Select a style that already resembles what you want in your project, and you can manually edit all design elements in the Visme editor.
• Create or modify diagrams, data widgets or infographics.
• Crawl external websites and gather information from them.
• Handle multiple projects at once.
• Generate designs more than 10 pages in length.

What is cloud computing?

With cloud computing, organizations essentially buy a range of services offered by cloud service providers (CSPs). The CSP’s servers host all the client’s applications. Organizations can enhance their computing power more quickly and cheaply via the cloud than by purchasing, installing, and maintaining their own servers.

The cloud-computing model is helping organizations to scale new digital solutions with greater speed and agility—and to create value more quickly. Developers use cloud services to build and run custom applications and to maintain infrastructure and networks for companies of virtually all sizes—especially large global ones. CSPs offer services, such as analytics, to handle and manipulate vast amounts of data. Time to market accelerates, speeding innovation to deliver better products and services across the world.

What are examples of cloud computing’s uses?

Get to know and directly engage with senior mckinsey experts on cloud computing.

Brant Carson is a senior partner in McKinsey’s Vancouver office; Chandra Gnanasambandam and Anand Swaminathan are senior partners in the Bay Area office; William Forrest is a senior partner in the Chicago office; Leandro Santos is a senior partner in the Atlanta office; Kate Smaje is a senior partner in the London office.

Cloud computing came on the scene well before the global pandemic hit, in 2020, but the ensuing digital dash  helped demonstrate its power and utility. Here are some examples of how businesses and other organizations employ the cloud:

• A fast-casual restaurant chain’s online orders multiplied exponentially during the 2020 pandemic lockdowns, climbing to 400,000 a day, from 50,000. One pleasant surprise? The company’s online-ordering system could handle the volume—because it had already migrated to the cloud . Thanks to this success, the organization’s leadership decided to accelerate its five-year migration plan to less than one year.
• A biotech company harnessed cloud computing to deliver the first clinical batch of a COVID-19 vaccine candidate for Phase I trials in just 42 days—thanks in part to breakthrough innovations using scalable cloud data storage and computing  to facilitate processes ensuring the drug’s safety and efficacy.
• Banks use the cloud for several aspects of customer-service management. They automate transaction calls using voice recognition algorithms and cognitive agents (AI-based online self-service assistants directing customers to helpful information or to a human representative when necessary). In fraud and debt analytics, cloud solutions enhance the predictive power of traditional early-warning systems. To reduce churn, they encourage customer loyalty through holistic retention programs managed entirely in the cloud.
• Automakers are also along for the cloud ride . One company uses a common cloud platform that serves 124 plants, 500 warehouses, and 1,500 suppliers to consolidate real-time data from machines and systems and to track logistics and offer insights on shop floor processes. Use of the cloud could shave 30 percent off factory costs by 2025—and spark innovation at the same time.

That’s not to mention experiences we all take for granted: using apps on a smartphone, streaming shows and movies, participating in videoconferences. All of these things can happen in the cloud.

Learn more about our Cloud by McKinsey , Digital McKinsey , and Technology, Media, & Telecommunications  practices.

How has cloud computing evolved?

Going back a few years, legacy infrastructure dominated IT-hosting budgets. Enterprises planned to move a mere 45 percent of their IT-hosting expenditures to the cloud by 2021. Enter COVID-19, and 65 percent of the decision makers surveyed by McKinsey increased their cloud budgets . An additional 55 percent ended up moving more workloads than initially planned. Having witnessed the cloud’s benefits firsthand, 40 percent of companies expect to pick up the pace of implementation.

The cloud revolution has actually been going on for years—more than 20, if you think the takeoff point was the founding of Salesforce, widely seen as the first software as a service (SaaS) company. Today, the next generation of cloud, including capabilities such as serverless computing, makes it easier for software developers to tweak software functions independently, accelerating the pace of release, and to do so more efficiently. Businesses can therefore serve customers and launch products in a more agile fashion. And the cloud continues to evolve.

Introducing McKinsey Explainers : Direct answers to complex questions

Cost savings are commonly seen as the primary reason for moving to the cloud but managing those costs requires a different and more dynamic approach focused on OpEx rather than CapEx. Financial-operations (or FinOps) capabilities  can indeed enable the continuous management and optimization of cloud costs . But CSPs have developed their offerings so that the cloud’s greatest value opportunity is primarily through business innovation and optimization. In 2020, the top-three CSPs reached $100 billion  in combined revenues—a minor share of the global $2.4 trillion market for enterprise IT services—leaving huge value to be captured. To go beyond merely realizing cost savings, companies must activate three symbiotic rings of cloud value creation : strategy and management, business domain adoption, and foundational capabilities.

What’s the main reason to move to the cloud?

The pandemic demonstrated that the digital transformation can no longer be delayed—and can happen much more quickly than previously imagined. Nothing is more critical to a corporate digital transformation than becoming a cloud-first business. The benefits are faster time to market, simplified innovation and scalability, and reduced risk when effectively managed. The cloud lets companies provide customers with novel digital experiences—in days, not months—and delivers analytics absent on legacy platforms. But to transition to a cloud-first operating model, organizations must make a collective effort that starts at the top. Here are three actions CEOs can take to increase the value their companies get from cloud computing :

• Establish a sustainable funding model.
• Develop a new business technology operating model.
• Set up policies to attract and retain the right engineering talent.

How much value will the cloud create?

Fortune 500 companies adopting the cloud could realize more than $1 trillion in value  by 2030, and not from IT cost reductions alone, according to McKinsey’s analysis of 700 use cases.

For example, the cloud speeds up design, build, and ramp-up, shortening time to market when companies have strong DevOps (the combination of development and operations) processes in place; groups of software developers customize and deploy software for operations that support the business. The cloud’s global infrastructure lets companies scale products almost instantly to reach new customers, geographies, and channels. Finally, digital-first companies use the cloud to adopt emerging technologies and innovate aggressively, using digital capabilities as a competitive differentiator to launch and build businesses .

If companies pursue the cloud’s vast potential in the right ways, they will realize huge value. Companies across diverse industries have implemented the public cloud and seen promising results. The successful ones defined a value-oriented strategy across IT and the business, acquired hands-on experience operating in the cloud, adopted a technology-first approach, and developed a cloud-literate workforce.

Learn more about our Cloud by McKinsey and Digital McKinsey practices.

What is the cloud cost/procurement model?

Some cloud services, such as server space, are leased. Leasing requires much less capital up front than buying, offers greater flexibility to switch and expand the use of services, cuts the basic cost of buying hardware and software upfront, and reduces the difficulties of upkeep and ownership. Organizations pay only for the infrastructure and computing services that meet their evolving needs. But an outsourcing model  is more apt than other analogies: the computing business issues of cloud customers are addressed by third-party providers that deliver innovative computing services on demand to a wide variety of customers, adapt those services to fit specific needs, and work to constantly improve the offering.

What are cloud risks?

The cloud offers huge cost savings and potential for innovation. However, when companies migrate to the cloud, the simple lift-and-shift approach doesn’t reduce costs, so companies must remediate their existing applications to take advantage of cloud services.

For instance, a major financial-services organization  wanted to move more than 50 percent of its applications to the public cloud within five years. Its goals were to improve resiliency, time to market, and productivity. But not all its business units needed to transition at the same pace. The IT leadership therefore defined varying adoption archetypes to meet each unit’s technical, risk, and operating-model needs.

Legacy cybersecurity architectures and operating models can also pose problems when companies shift to the cloud. The resulting problems, however, involve misconfigurations rather than inherent cloud security vulnerabilities. One powerful solution? Securing cloud workloads for speed and agility : automated security architectures and processes enable workloads to be processed at a much faster tempo.

What kind of cloud talent is needed?

The talent demands of the cloud differ from those of legacy IT. While cloud computing can improve the productivity of your technology, it requires specialized and sometimes hard-to-find talent—including full-stack developers, data engineers, cloud-security engineers, identity- and access-management specialists, and cloud engineers. The cloud talent model  should thus be revisited as you move forward.

Six practical actions can help your organization build the cloud talent you need :

• Find engineering talent with broad experience and skills.
• Balance talent maturity levels and the composition of teams.
• Build an extensive and mandatory upskilling program focused on need.
• Build an engineering culture that optimizes the developer experience.
• Consider using partners to accelerate development and assign your best cloud leaders as owners.
• Retain top talent by focusing on what motivates them.

How do different industries use the cloud?

Different industries are expected to see dramatically different benefits from the cloud. High-tech, retail, and healthcare organizations occupy the top end of the value capture continuum. Electronics and semiconductors, consumer-packaged-goods, and media companies make up the middle. Materials, chemicals, and infrastructure organizations cluster at the lower end.

Nevertheless, myriad use cases provide opportunities to unlock value across industries , as the following examples show:

• a retailer enhancing omnichannel  fulfillment, using AI to optimize inventory across channels and to provide a seamless customer experience
• a healthcare organization implementing remote heath monitoring to conduct virtual trials and improve adherence
• a high-tech company using chatbots to provide premier-level support combining phone, email, and chat
• an oil and gas company employing automated forecasting to automate supply-and-demand modeling and reduce the need for manual analysis
• a financial-services organization implementing customer call optimization using real-time voice recognition algorithms to direct customers in distress to experienced representatives for retention offers
• a financial-services provider moving applications in customer-facing business domains to the public cloud to penetrate promising markets more quickly and at minimal cost
• a health insurance carrier accelerating the capture of billions of dollars in new revenues by moving systems to the cloud to interact with providers through easier onboarding

The cloud is evolving  to meet the industry-specific needs of companies. From 2021 to 2024, public-cloud spending on vertical applications (such as warehouse management in retailing and enterprise risk management in banking) is expected to grow by more than 40 percent annually. Spending on horizontal workloads (such as customer relationship management) is expected to grow by 25 percent. Healthcare and manufacturing organizations, for instance, plan to spend around twice as much on vertical applications as on horizontal ones.

Learn more about our Cloud by McKinsey , Digital McKinsey , Financial Services , Healthcare Systems & Services , Retail , and Technology, Media, & Telecommunications  practices.

What are the biggest cloud myths?

Views on cloud computing can be clouded by misconceptions. Here are seven common myths about the cloud —all of which can be debunked:

• The cloud’s value lies primarily in reducing costs.
• Cloud computing costs more than in-house computing.
• On-premises data centers are more secure than the cloud.
• Applications run more slowly in the cloud.
• The cloud eliminates the need for infrastructure.
• The best way to move to the cloud is to focus on applications or data centers.
• You must lift and shift applications as-is or totally refactor them.

How large must my organization be to benefit from the cloud?

Here’s one more huge misconception: the cloud is just for big multinational companies. In fact, cloud can help make small local companies become multinational. A company’s benefits from implementing the cloud are not constrained by its size. In fact, the cloud shifts barrier to entry skill rather than scale, making it possible for a company of any size to compete if it has people with the right skills. With cloud, highly skilled small companies can take on established competitors. To realize the cloud’s immense potential value fully, organizations must take a thoughtful approach, with IT and the businesses working together.

For more in-depth exploration of these topics, see McKinsey’s Cloud Insights collection. Learn more about Cloud by McKinsey —and check out cloud-related job opportunities if you’re interested in working at McKinsey.

Articles referenced include:

• “ Six practical actions for building the cloud talent you need ,” January 19, 2022, Brant Carson , Dorian Gärtner , Keerthi Iyengar, Anand Swaminathan , and Wayne Vest
• “ Cloud-migration opportunity: Business value grows, but missteps abound ,” October 12, 2021, Tara Balakrishnan, Chandra Gnanasambandam , Leandro Santos , and Bhargs Srivathsan
• “ Cloud’s trillion-dollar prize is up for grabs ,” February 26, 2021, Will Forrest , Mark Gu, James Kaplan , Michael Liebow, Raghav Sharma, Kate Smaje , and Steve Van Kuiken
• “ Unlocking value: Four lessons in cloud sourcing and consumption ,” November 2, 2020, Abhi Bhatnagar , Will Forrest , Naufal Khan , and Abdallah Salami
• “ Three actions CEOs can take to get value from cloud computing ,” July 21, 2020, Chhavi Arora , Tanguy Catlin , Will Forrest , James Kaplan , and Lars Vinter

Want to know more about cloud computing?

Related articles.

Cloud’s trillion-dollar prize is up for grabs

The cloud transformation engine

Cloud cost-optimization simulator

• Announcements

Microsoft Copilot Studio: Building copilots with agent capabilities

• By Omar Aftab

• Copilot category
• Copilot Studio

At Microsoft Build 2024 , we’re excited to announce a host of new powerful capabilities in   Microsoft Copilot Studio —t he single conversational AI tool you can use to create your very own custom copilots or extend Microsoft C opilot experiences with your own enterprise data and scenarios. The first of these are c opilots that can now act as independent agents— ones that can be triggered by events— not just conversation— and can automa te and orchestrate complex, long-running business processes with more autonomy and less human intervention.

For instance, consider the potential of a copilot that can react when an email arrives, look up the sender’s details, see their previous communications, and use generative AI to trigger the appropriate chain of actions in their response. From understanding the intent of the email, to look ing up the sender’s details and account , see ing their previous communications, checking inventory,   responding to the sender asking for their preferences, and then taking the appropriate actions to close a ticket — orchestrating and shepherding an entire process over days.  

• IT help desk .  IT support is complex, involving tickets, order numbers, approvals, and stock levels . O pening and closing a ticket can be a long-running task that spans days. A copilot can now handle this process, interfacing with IT service management applications, resolving IT tickets with context and memory, creating purchase orders for device refresh, and reaching out and getting managers approvals — all independently .
• Employee onboarding . Onboarding new employees is often expensive and slow. Now, imagine you’re a new hire. A copilot greets you, reasons over HR data, and answers your questions. It introduces you to your buddy, provides training and deadlines, assists with forms, and sets up your first week of meetings. Throughout all of this, the copilot is in touch, guiding you through the weeks -long onboarding and account set up processes.  
• Personal concierge for sales and service . Balancing exceptional customer experience while meeting ambitious revenue goals can be challenging. When a copilot serves guests, i t can use the memory of previous conversations with guests to remember their preferences, make reservations, handle complaints, and answer questions related to the products and services on offer. The copilot learns from its interactions and proposes new ways of handling customer scenarios. By doing so, copilots can increase upsell and attachment rates, driving revenue for the resort while simultaneously enhancing guest experience, satisfaction rates, and repeat business.

Let’s dig deeper into a few of the underlying capabilities that make all this possible:

• Asynchronous orchestration of complex tasks . The first is the ability to use generative AI- powered   planning and reasoning to manage complex, multi step, long-running tasks. For example, reacting to a new order means determining the need to verify inventory, trigger ing the right payment processes, pinging a supervisor for approval if the amount is above a certain threshold, and replying with a confirmation. Many of these events can take hours—or even days— to complete, but the copilot will run through them , maintaining the necessary state and context to do so.
• Memory and context . One of the frustrating things about support has traditionally been having to repeat information: who you are, what your policy number is, what your address is. There is no continuity of conversation. Copilots will now learn from previous conversations from the users and utilize this knowledge to continually personalize interactions . A copilot may not need to ask you for your laptop model or your address when you call again for the same issue. Conversations will thus become long-running, contextual, and deeply personalized.
• Monitor, learn, and improve . Copilots can now learn and adapt, offering monitoring and teaching capabilities to make their interactions better. Each copilot records a comprehensive history of its activities, providing transparency into its performance, including user interactions, actions taken, and feedback received, and you can see what decisions it made — and correct and teach them — with just a few clicks.

• Delegation with confidence and guardrails . When developing copilots with agent capabilities, establishing clear boundaries is paramount. Copilots operate strictly within the confines of the maker-defined instructions, knowledge, and actions. The data sources linked to the copilot adhere to stringent security measures and controls, managed through the unified admin center of Copilot Studio. This includes data loss prevention, robust authentication protocols, and more.

The se advanced new capabilities in Copilot Studio are currently accessible to customers participating in an Early Access Preview where organizations such as Centro de la Familia are excited to explore agent capabilities that support teachers and case workers, allowing them to spend less time on administrative tasks and more time working with children, ultimately leading to better child outcomes . Based on feedback from program participants, we will continue to iterate and refine these capabilities for broader access in a preview planned for later this year .  

Additional innovations with Copilot Studio

There’s a lot more to share at Microsoft Build with Copilot Studio, and we’ll touch on just a few of our new capabilities here. To learn more — just sign up and try it out for yourself here .

Here are a few examples of how Copilot connectors can transform copilot experiences for specific personas or functions:

• Legal and Compliance . Navigate complex legal landscapes with a Copilot extension that queries specific legal datasets, ensuring controlled and compliant responses without overwhelming users with extraneous information.
• HR Helper . Assist employees with accessing essential resources for benefits and PTO policies, and even book time off directly through Copilot.
• Incident Report Coordinator . Workers can locate the right documentation, report incidents, and track them efficiently, all within the context of the chat.

Starting in June 2024, developers can access the public preview for Copilot connectors and stay informed on updates here .

Conversational analytics (private preview) : One of the most common asks from customers has been the need for deeper insight into what their copilot is doing, how generative AI is responding, when it was unable to give the right answers and why — and recommendations on what to do to improve it.

Enhanced security and controls (public preview ) : Administrators can now configure advanced settings beyond the default security measures and controls. With Microsoft Purview , Copilot Studio administrators gain access to more detailed governance tools, including audit logs, inventory capabilities, and sensitivity labels. They will be able to review comprehensive audit logs that cover tenant-wide usage, inventory (with API support), and tenant hygiene (such as data loss prevention violations and inactive copilots), enabling them to effectively monitor business impact. Both creators and end-users will be able to view sensitivity labels when responses are generated using AI-powered answers based on SharePoint documents.

With all the amazing innovations, numerous organizations are using Copilot Studio to build transformative generative AI-powered solutions. Check out this story from Nsure on how they are using Copilot Studio:

Get started today with Copilot Studio

This is just a glimpse of all the exciting innovation around copilots and Copilot Studio — we have a host of exciting new capabilities to share in our sessions at Build. So, join us in watching the sessions below, and try out Copilot Studio yourself and build and share your very own copilot in minutes.

Watch the sessions at Microsoft Build:

• “ Microsoft Build opening keynote ”
• “ Next generation AI for developers with the Microsoft Cloud ”
• “ Shaping next-gen development: the future of Copilot in Power Platform ”

Deeper dives:

• Breakout: “ What’s new with Microsoft Copilot Studio ”
• Breakout with demos: “ Build your own copilot with Microsoft Copilot Studio ”
• Breakout with demos: “ Build Microsoft Copilot extensions with Copilot Studio ”
• Demo (live only): “ Build your own Copilot extension with Microsoft Copilot Studio ”

MCQs on Collection, Organisation and Presentation of Data

Data is basically a collection of comparative numerical facts as well as information. It consists of tools that can help conduct a proper analysis. The data that is collected can either be primary data (that is collected first hand in the process of investigation) or secondary data (which had been collected earlier by another agency or organisation for a different purpose).

We have listed below a number of multiple-choice questions on Collection, Organisation and Presentation of Data to help students get a better understanding of the topic.

• The usage of information helps to save both time and money
• The usage of primary data helps to save both time and money
• The usage of secondary data helps to save both time and money
• The usage of data helps to save both time and money
• The misrepresentation and misunderstanding of any sort can be avoided by using questionnaires
• The misrepresentation and misunderstanding of any sort can be avoided by holding personal interactions
• The misrepresentation and misunderstanding of any sort can be avoided by mailing
• The misrepresentation and misunderstanding of any sort can be avoided by using telephones
• Once a question is ready, it is advisable that a pilot survey of the questionnaire is conducted with a small group
• Once a question is ready, it is advisable that a survey of the questionnaire is conducted with a small group
• Once a question is ready, it is advisable that an observation of the questionnaire is conducted with a small group
• None of the above
• The lottery method is also known as random sampling
• The lottery method is also known as population sampling
• The lottery method is also known as non-random sampling
• The lottery method is also known as sampling
• The difference between the sampling estimate and the corresponding parameter is not a type of sampling error
• Sampling bias is not a type of sampling error
• Errors in data is not a type of sampling error
• Non-response is not a type of sampling error
• In terms of spatial classification, data is classified on the basis of geographical location
• In terms of spatial classification, data is classified on the basis of time series
• In terms of spatial classification, data is classified on the basis of quantitative classification
• In terms of spatial classification, data is classified on the basis of chronological classification
• The analysis is a comprehensive method that helps in the classification of raw data
• Frequency distribution is a comprehensive method that helps in the classification of raw data
• Distribution is a comprehensive method that helps in the classification of raw data
• Information is a comprehensive method that helps in the classification of raw data
• The exclusive class intervals are used on a frequent basis in the case of continuous variables
• The inclusive class intervals are used on a frequent basis in the case of continuous variables
• The offline class intervals are used on a frequent basis in the case of continuous variables
• The online class intervals are used on a frequent basis in the case of continuous variables
• The pie charts are drawn only for continuous variables
• The bar diagrams are drawn only for continuous variables
• The histograms are drawn only for continuous variables
• The frequency curves are drawn only for continuous variables
• The main purpose behind the collection of data is to show the evidence required to reach a clear solution for the problem
• The main purpose behind the collection of data is to show the movement required to reach a clear solution for the problem
• The main purpose behind the collection of data is to show the design required to reach a clear solution for the problem
• The main purpose behind the collection of data is to show the figures required to reach a clear solution for the problem
• Primary data
• Secondary data
• Both a and b are correct
• Both a and b are incorrect
• A pilot survey is extremely useful in providing preliminary data about the survey
• An airline survey is extremely useful in providing preliminary data about the survey
• A mailing survey is extremely useful in providing preliminary data about the survey
• A shipping survey is extremely useful in providing preliminary data about the survey
• A census is carried out once every ten years
• A census is carried out once every twenty years
• A census is carried out once every seven years
• A census is carried out once every five years
• A good sample helps to provide reasonably accurate information about the population
• A good sample helps to provide totally accurate information about the population
• A good sample does not provide reasonably accurate information about the population
• Increase, smaller
• Decrease, larger
• Decrease, smaller
• Difference Between Primary Data and Secondary Data
• Mcqs on Collection of Data
• What Are the Sources of Data
• Meaning and Objectives of Classification of Data
• Diagrammatic Presentation of Data

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