presentation interpretation and analysis of data

Home Blog Design Understanding Data Presentations (Guide + Examples)

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.

If you need a quick method to create a data presentation, check out our  AI presentation maker . A tool in which you add the topic, curate the outline, select a design, and let AI do the work for you.

[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

Like this article? Please share

Data Analysis, Data Science, Data Visualization Filed under Design

Related Articles

Filed under Business • October 8th, 2024

Data-Driven Decision Making: Presenting the Process Behind Informed Choices

Discover how to harness data for informed decision-making and create impactful presentations. A detailed guide + templates on DDDM presentation slides.

Filed under Google Slides Tutorials • June 3rd, 2024

How To Make a Graph on Google Slides

Creating quality graphics is an essential aspect of designing data presentations. Learn how to make a graph in Google Slides with this guide.

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

Detailed step-by-step instructions to master the art of how to make a presentation graph in PowerPoint and Google Slides. Check it out!

Leave a Reply

Student Login

• Areas of Study
• Courses and Curriculum
• Open Courses
• Register for a Program
• Associate in Addiction Counseling
• Associate in Agriculture Food And Resources
• Associate in Anti Terrorism Security
• Associate in Behavior Analysis In Special Education
• Associate in Bioethics
• Associate in Climatology
• Associate in Cultural Theological Communication
• Associate in Culinary Arts
• Associate in Ecotechnology
• View all Associates Programs
• Bachelors in Community Development
• Bachelors in Environmental Science
• Bachelor in Education (B.Ed, BS)
• Bachelors in Economics
• Bachelors in Entrepreneurship
• Bachelors in Financial Administration
• Bachelors in Human Resource Management
• Bachelors in Linguistics
• Bachelors in Nutritional Science
• Bachelors in Occupational Health and Safety
• Bachelors in Psychology
• View all Bachelor Programs
• Doctor | of Biology (PhD)
• Doctorate in Business Administration (DBA, PhD)
• Doctor of Economics (PhD)
• Doctor of Electrical Engineering (D.Sc, PhD)
• Doctor of Finance (PhD)
• Doctorate in International Relations
• Doctorate in Information Technology (D.Sc)
• Doctor of Legal Studies (PhD)
• Doctor of Project Management (PhD)
• Doctor of Sociology (PhD, D.Sc)
• Doctorate in Sustainable Natural Resources Management
• View all Doctorate Programs
• Master in Autonomous Vehicle Technology
• Masters in Business Administration
• Masters in Civil Engineering
• Master of Construction Management
• Master of Hydrology (MS)
• Masters in Mechanical Engineering
• Masters in Nutrition
• Masters of science in Educational Administration
• View all Masters Programs
• Postdoctoral in Animal Science
• Postdoctoral in Anti Terrorism Security
• Postdoctoral in Behavior Analysis In Special Education
• Postdoctoral in Bioethics
• Postdoctoral in Blockchain Technology and Digital Currency
• Postdoctoral in Business Management
• Postdoctoral in Cloud Computing
• Postdoctoral in Computer Engineering
• View all Postdoctoral Programs

Distance Learning at AIU is enhanced by vast academic resources and innovative technologies build into the Virtual Campus: Hundreds of self-paced courses with video lectures and step by step lessons, thousands of optional assignments, 140,000 e-books, the Social Media & Networking platform allowing collaboration/chat/communications between students, and MYAIU develop students holistically in 11 areas beyond just academics.

The world is YOUR campus!”, that is the message of AIU’s month magazine Campus Mundi. Hear the voices and see the faces that make up AIU. Campus Mundi brings the world of AIU to you every months with inspirational stories, news and achievements by AIU members from around the world (students and staff are located in over 200 countries).

Please enter your credentials

Data Analysis: Interpretation and Presentation

This document explores the essential aspects of data analysis, including definitions, tools, phases, and software. It highlights the significance of data warehousing and mining, detailing their architectures, advantages, and applications. The content aims to provide a comprehensive understanding of data analysis for effective interpretation and presentation.

Click here: Unlock the Power of Data

• Doctorate in Data Communication & Networking
• Database and Web Development
• Examining R’s Significance
• Bioinformatics and Computational Biology
• Data Systems and Knowledge Management

Publication:

The PDF titled “Data Analysis: Interpretation and Presentation” by Anthony Babajide Balogun serves as a vital resource for students and professionals alike. It delves into the intricacies of data, information, and databases, while also examining various data analysis tools and techniques. The document outlines the phases of data analysis, from requirement gathering to visualization, and discusses the importance of data warehousing and mining. Additionally, it addresses challenges faced in data analysis and offers insights into overcoming these barriers. This comprehensive guide is designed to enhance understanding and application of data analysis principles, making it an essential read for anyone looking to excel in this field.

Atlantic International University

Get to know the aiu experience, contact us today.

We understand how busy adults do not have time to go back to school. Now, it’s possible to earn your degree in the comfort of your own home and still have time for yourself and your family. The Admissions office is here to help you, for additional information or to see if you qualify for admissions please contact us. If you are ready to apply please submit your Online Application and paste your resume and any additional comments/questions in the area provided.

Pioneer Plaza 900 Fort Street Mall 905 Honolulu, HI 96813

800-993-0066 (Toll Free in US) 808-924-9567 (Internationally) 808-947-2488 (Fax)

AIU Success Stories

Begin Your Journey! AIU’s Summer of Innovation and Growth gives you the ability to earn up to $5000 in tuition credit by completing free lessons and courses. Whether you’re looking to acquire new skills, advance your career, or simply explore new interests, AIU is your gateway to a world of opportunities. With free access to 3400 lessons and hundreds of courses the ability to earn credits and earn certificates there’s no better time to start learning. Join us today as a Guest Student and take the first step towards a brighter, more empowered future. Explore. Learn. Achieve.

Contact Us Atlantic International University

Quick links.

Home | Online Courses |  Available Courses |  Virtual Campus |  Career Center |  Available Positions |  Ask Career Coach |  The Job Interview |  Resume Writing |  Accreditation |  Areas of Study |  Bachelor Degree Programs |  Masters Degree Programs |  Doctoral Degree Programs |  Course & Curriculum |  Human Rights |  Online Library |  Representations |  Student Publication | Sponsors |  General Information |  Mission & Vision |  School of Business and Economics | School of Science and Engineering |  School of Social and Human Studies |  Media Center |  Admission Requirements |  Apply Online |  Tuition |  Faculty & Staff |  Distance Learning Overview |  Student Testimonials |  AIU Blogs | Register for Program | Privacy Policy  | FAQ

Data Collection, Presentation and Analysis

• First Online: 25 May 2023

Cite this chapter

• Uche M. Mbanaso 4 ,
• Lucienne Abrahams 5 &
• Kennedy Chinedu Okafor 6  

953 Accesses

This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

Abdullah, M. F., & Ahmad, K. (2013). The mapping process of unstructured data to structured data. Proceedings of the 2013 International Conference on Research and Innovation in Information Systems (ICRIIS) , Malaysia , 151–155. https://doi.org/10.1109/ICRIIS.2013.6716700

Adnan, K., & Akbar, R. (2019). An analytical study of information extraction from unstructured and multidimensional big data. Journal of Big Data, 6 , 91. https://doi.org/10.1186/s40537-019-0254-8

Article   Google Scholar  

Alsheref, F. K., & Fattoh, I. E. (2020). Medical text annotation tool based on IBM Watson Platform. Proceedings of the 2020 6th international conference on advanced computing and communication systems (ICACCS) , India , 1312–1316. https://doi.org/10.1109/ICACCS48705.2020.9074309

Cinque, M., Cotroneo, D., Della Corte, R., & Pecchia, A. (2014). What logs should you look at when an application fails? Insights from an industrial case study. Proceedings of the 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks , USA , 690–695. https://doi.org/10.1109/DSN.2014.69

Gideon, L. (Ed.). (2012). Handbook of survey methodology for the social sciences . Springer.

Google Scholar  

Leedy, P., & Ormrod, J. (2015). Practical research planning and design (12th ed.). Pearson Education.

Madaan, A., Wang, X., Hall, W., & Tiropanis, T. (2018). Observing data in IoT worlds: What and how to observe? In Living in the Internet of Things: Cybersecurity of the IoT – 2018 (pp. 1–7). https://doi.org/10.1049/cp.2018.0032

Chapter   Google Scholar  

Mahajan, P., & Naik, C. (2019). Development of integrated IoT and machine learning based data collection and analysis system for the effective prediction of agricultural residue/biomass availability to regenerate clean energy. Proceedings of the 2019 9th International Conference on Emerging Trends in Engineering and Technology – Signal and Information Processing (ICETET-SIP-19) , India , 1–5. https://doi.org/10.1109/ICETET-SIP-1946815.2019.9092156 .

Mahmud, M. S., Huang, J. Z., Salloum, S., Emara, T. Z., & Sadatdiynov, K. (2020). A survey of data partitioning and sampling methods to support big data analysis. Big Data Mining and Analytics, 3 (2), 85–101. https://doi.org/10.26599/BDMA.2019.9020015

Miswar, S., & Kurniawan, N. B. (2018). A systematic literature review on survey data collection system. Proceedings of the 2018 International Conference on Information Technology Systems and Innovation (ICITSI) , Indonesia , 177–181. https://doi.org/10.1109/ICITSI.2018.8696036

Mosina, C. (2020). Understanding the diffusion of the internet: Redesigning the global diffusion of the internet framework (Research report, Master of Arts in ICT Policy and Regulation). LINK Centre, University of the Witwatersrand. https://hdl.handle.net/10539/30723

Nkamisa, S. (2021). Investigating the integration of drone management systems to create an enabling remote piloted aircraft regulatory environment in South Africa (Research report, Master of Arts in ICT Policy and Regulation). LINK Centre, University of the Witwatersrand. https://hdl.handle.net/10539/33883

QuestionPro. (2020). Survey research: Definition, examples and methods . https://www.questionpro.com/article/survey-research.html

Rajanikanth, J. & Kanth, T. V. R. (2017). An explorative data analysis on Bangalore City Weather with hybrid data mining techniques using R. Proceedings of the 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) , India , 1121-1125. https://doi/10.1109/CTCEEC.2017.8455008

Rao, R. (2003). From unstructured data to actionable intelligence. IT Professional, 5 , 29–35. https://www.researchgate.net/publication/3426648_From_Unstructured_Data_to_Actionable_Intelligence

Schulze, P. (2009). Design of the research instrument. In P. Schulze (Ed.), Balancing exploitation and exploration: Organizational antecedents and performance effects of innovation strategies (pp. 116–141). Gabler. https://doi.org/10.1007/978-3-8349-8397-8_6

Usanov, A. (2015). Assessing cybersecurity: A meta-analysis of threats, trends and responses to cyber attacks . The Hague Centre for Strategic Studies. https://www.researchgate.net/publication/319677972_Assessing_Cyber_Security_A_Meta-analysis_of_Threats_Trends_and_Responses_to_Cyber_Attacks

Van de Kaa, G., De Vries, H. J., van Heck, E., & van den Ende, J. (2007). The emergence of standards: A meta-analysis. Proceedings of the 2007 40th Annual Hawaii International Conference on Systems Science (HICSS’07) , USA , 173a–173a. https://doi.org/10.1109/HICSS.2007.529

Download references

Author information

Authors and affiliations.

Centre for Cybersecurity Studies, Nasarawa State University, Keffi, Nigeria

Uche M. Mbanaso

LINK Centre, University of the Witwatersrand, Johannesburg, South Africa

Lucienne Abrahams

Department of Mechatronics Engineering, Federal University of Technology, Owerri, Nigeria

Kennedy Chinedu Okafor

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

Download citation

DOI : https://doi.org/10.1007/978-3-031-30031-8_7

Published : 25 May 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-30030-1

Online ISBN : 978-3-031-30031-8

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

lesson 7 - PRESENTATION, ANALYSIS, AND INTERPRETATION OF DATA  

Competency: Presents and interprets data in tabular and graphical forms with implications and citations. 

Presentation, Analysis, and Interpretation of Data

         Presentation is the process of organizing data into logical, sequential, and meaningful categories and classifications to make them amenable to study and interpretation.

Three Ways of Presenting Data

1.    Textual – statements with numerals or numbers that serve as supplements to tabular presentation

2.    Tabular – a systematic arrangement of the related idea in which classes of numerical facts or data are given each row, and their subclasses are given each a column in order to present the relationships of the sets of numerical facts or data in a definite, compact and understandable form.

Two general rules regarding the independence of tables and text

a)    The table should be so constructed that it enables the reader to comprehend the data presented without referring to the text;

b)    The text should be so written that it allows the reader to understand the argument presented without referring to the table (Campbell, Ballou, and Slade, 1990)

3.    Graphical – a chart representing the quantitative variations or changes of variables in pictorial or diagrammatic form.

Types of Graphs and Charts

1.    Bar graphs

2.    Linear graphs

3.    Pie charts

4.    Pictograms

5.    Statistical maps

6.    Ratio charts

Analysis of Data

         Separation of a whole into its constituent parts (Merriam-Webster, 2012). The process of breaking up the whole study into its constituent parts of categories according to the specific questions under the statement of the problem (Calderon, 1993).

         Two Ways of Data Analysis

1.    Qualitative Analysis – is not based on precise measurement and quantitative claims.

Examples of Qualitative Analysis:

a)    Social analysis;

b)    From the biggest to the smallest class;

c)    Most important to the least important;

d)    Ranking of students according to brightness;

2.    Quantitative Analysis – is employed on data that have been assigned some numeral value.

It can range from the examination of simple frequencies to the description of events or phenomenon using descriptive statistics, and to the investigation of correlation and causal hypothesis using various statistical tests.

Interpretation of Data

         It is often the most difficult to write because it is the least structured. This section demands perceptiveness and creativity from the researcher.

How do we Interpret the Result(s) of our Study?

1.    Tie up the results of the study in both theory and application by pulling together the:

a.    Conceptual/ theoretical framework;

b.    The review of literature; and

c.     The study’s potential significance for application

2.    Examine, summarize, interpret and justify the results; then, draw inferences. Consider the following:

a.     Conclude or summarize – this technique enables the reader to get the total picture of the findings in summarized form, and helps orient the reader to the discussion that follows.

b.    Interpret – questions on the meaning of the finding, the methodology, the unexpected results and the limitations and shortcomings of the study should be answered and interpreted.

c.     Integrate – This is an attempt to put the pieces together. Often, the results of the study are disparate and do not seem to “hang together.” In the discussion, attempt to bring the findings together to extract meaning and principles.

d.    Theorize – when the study includes a number of related findings, it occasionally becomes possible to theorize.

* Integrate your findings into a principle:

* Integrate a theory into your findings; and

* Use these findings to formulate an original theory

e.    Recommend or apply alternatives

Level of Significance

The significance level denoted as alpha or α is a measure of the strength of the evidence that must be present in your sample before you reject the null hypothesis and conclude that the effect is statistically significant. The researcher determines the significance level before conducting the experiment.

The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis.

Use significance levels during hypothesis testing to help you determine which hypothesis the data support. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to reject the null hypothesis at the population level.

In Deducting Interpretation from Statistical Analysis, the Following Key Words or Phrases may be Useful:

1.     Table ___ presents the…

2.     Table ___ indicates the …

3.     As reflected in the table, there was…

4.     As observed, there was indeed…

5.     Delving deeper into the figures…

6.     The illustrative graph above/below shows that…

7.     In explaining this result, it can be stated that…

8.     Is significantly related to…

9.     Is found to be determinant of…

10.  Registered positive correlation with…

11.  Is revealed to influence…

12.  Has significant relationship with…

13.  Is discovered to be a factor of…

14.  In relation with the result of ____, it may be constructed that…

15.  And in viewing in this sense, it can be stated that…

16.  The result establishes the fact that…

17.  This finding suggests that…

18.  With this result, the researcher developed an impression that…

19.  This finding also validates the findings of…

20.  This improvement in ____ could be understood in the context of…

21.  These findings also accept the framework of the study…

22.  The interpretation marked as ____ reveals that…

23.  Nevertheless, this finding could be attributed to the fact that…

24.  Probably, this was also influenced…

25.  In the rational sense, the juxtaposition of…

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Download Free PDF

CHAPTER-4 PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

"Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Data analysis is a search for answers about relationships among categories of data."-Marshall and Rossman, 1990:111 Hitchcock and Hughes take this one step further: "…the ways in which the researcher moves from a description of what is the case to an explanation of why what is the case is the case."-Hitchcock and Hughes 1995:295 IV.1 INTRODUCTION In Chapter three, researcher had discussed the research design and methodology, origin of the research, design of the research, variable of the research, population and sample of the research, tools for data collection, development stage of the CAI package, procedure for data collection, statistical analysis done in research work. Data analysis is considered to be important step and heart of the research in research work. In the beginning the data is raw in nature but after it is arranged in a certain format or a meaningful order this raw data takes the form of the information. The most critical and essential supporting pillars of the research are the analysis and the interpretation of the data. With the help of the interpretation step one is able to achieve a conclusion from the set of the gathered data. Interpretation has two major aspects namely establishing continuity in the research through linking the results of a given study with those of another and the establishment of some relationship with the collected data. Interpretation can be defined as the device through which the factors, which seem to explain what has been observed by the researcher in the course of the

Related papers

Research is a scientific field which helps to generate new knowledge and solve the existing problem. So, data analysis is the crucial part of research which makes the result of the study more effective. It is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. In a research it supports the researcher to reach to a conclusion. Therefore, simply stating that data analysis is important for a research will be an understatement rather no research can survive without data analysis. It can be applied in two ways which is qualitatively and quantitative. Both are beneficial because it helps in structuring the findings from different sources of data collection like survey research, again very helpful in breaking a macro problem into micro parts, and acts like a filter when it comes to acquiring meaningful insights out of huge data-set. Furthermore, every researcher has sort out huge pile of data that he/she has collected, before reaching to a conclusion of the research question. Mere data collection is of no use to the researcher. Data analysis proves to be crucial in this process, provides a meaningful base to critical decisions, and helps to create a complete dissertation proposal. So, after analyzing the data the result will provide by qualitative and quantitative method of data results. Quantitative data analysis is mainly use numbers, graphs, charts, equations, statistics (inferential and descriptive). Data that is represented either in a verbal or narrative format is qualitative data which is collected through focus groups, interviews, opened ended questionnaire items, and other less structured situations.

This chapter deals with the presentation, analysis and interpretation of data. The researchers gathered information and discussed the finding in a sequential manner in line with the statement of the problem indicated in the study.

The Information Audit, 2001

Springer eBooks, 2017

Evidence Based Nursing, 2000

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Fuentes: Revista de la …, 2008

Apeiron, 2024

BSAA arte, 2018

The Biblical Annals

Produtos Educacionais do Mestrado Profissional em Astronomia: Paradidáticos I, 2024

Revista Direito Público, 2024

Quaderni di Aristonothos 7, 2021

ACS sensors, 2017

Pathology & Oncology Research, 2006

Majallah-i Dānishgāh-i 'Ulūm-i Pizishkī-i Shahīd Ṣadūqī Yazd, 2018

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024