presentation on data communication

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Data Communication Tutorial

Data communication plays an important role in today’s interconnected world and enables the exchange of information between devices and networks. Whether you’re sending an email, making a video call, or browsing the web, data communication ensures that information flows smoothly.

This Data Communication tutorial is designed for both beginners and experienced professionals, covering basic and advanced concepts of data communication. It includes topics such as the basics of data communication, OSI Model, TCP/IP Model, addressing, and more.

What is Data Communication?

Data communication refers to the process of transmitting and receiving data between two or more devices over a communication channel. It involves the conversion of data into signals that can be transmitted and then decoding those signals at the receiving end. Effective data communication requires the use of appropriate protocols, encoding techniques, and hardware devices.

Components of Data Communication

Data communication systems consist of several components that work together to enable the transfer of data. These components include:

• Sender : The device or system that initiates the data transmission.
• Receiver : The device or system that receives the transmitted data.
• Medium/Channel : The physical pathway through which data is transmitted, such as cables or wireless connections.
• Protocol : A set of rules and conventions that govern the transmission and reception of data.
• Modem : Short for modulator-demodulator, a device that converts digital signals into analog signals for transmission and vice versa.

Types of Data Transmission

Data transmission can occur in two primary ways:

• Serial Transmission: In serial transmission, data is transmitted bit by bit over a single communication channel. It is commonly used for long-distance communication and is more reliable but slower compared to parallel transmission.
• Parallel Transmission : In parallel transmission, multiple bits are transmitted simultaneously over separate communication channels. It allows for faster data transfer but is more susceptible to errors in long-distance transmissions.

Basics of Data Communication

• Data Communication – Definition, Components, Types, Channels
• Network Criteria
• Physical Structures
• Network Models
• Transmission Modes
• Difference between Serial and Parallel Transmission
• How Data Encapsulation & De-encapsulation Works?
• Physical Layer
• Data Link Layer
• Network Layer
• Transport Layer
• Session Layer
• Presentation Layer
• TCP/IP Model
• Application Layer in TCP/IP
• Transport Layer in TCP/IP
• Internet Layer in the TCP/IP Model
• Physical Layer in TCP/IP
• Difference between OSI and TCP/IP Model
• Physical Addresses
• Logical Addresses
• Port Addresses
• Specific Addresses

Data and Signals

• Analog and Digital Signals
• Composite Signals
• Periodic and Nonperiodic Signals
• Time and Frequency Domains
• Difference between Bit Rate and Baud Rate

Transmission of Signals

• What is Data Transmission.
• TRANSMISSION IMPAIRMENT
• Noiseless Channel: Nyquist Bit Rate
• Noisy Channel: Shannon Capacity
• What is Bandwidth? Definition, Working, Importance, Uses
• DIGITAL-TO-DIGITAL CONVERSION
• Line Coding
• ANALOG-TO-DIGITAL CONVERSION
• Pulse Code Modulation (PCM)
• Delta Modulation (DM)
• DIGITAL-TO-ANALOG CONVERSION
• Aspects of Digital-to-Analog Conversion
• Amplitude Shift Keying
• Frequency Shift Keying
• Phase Shift Keying
• Quadrature Amplitude Modulation
• ANALOG-TO-ANALOG CONVERSION
• Modulation and its type
• Multiplexing
• Types of Multiplexing in Data Communications
• Frequency-Division Multiplexing
• Wavelength-Division Multiplexing
• Synchronous Time-Division Multiplexing
• Statistical Time-Division Multiplexing
• Difference between FDM,TDM and WDM

Transmission Media

• Types of Transmission Media
• GUIDED MEDIA
• Twisted-Pair Cable
• Coaxial Cable
• Fiber-Optic Cable
• Difference between Twisted pair cable, Co-axial cable and Optical fiber cable
• UNGUIDED MEDIA
• Radio Waves
• Microwaves Transmission System
• Infrared Signals
• Difference between Guided and Unguided Media

Error Detection and Correction

• Types of Errors
• Error Detection
• Forward Error Correction Versus Retransmission
• Parity Check
• Vertical Redundancy Check (VRC)
• Longitudinal Redundancy Check (LRC)/2-D Parity Check
• CRC( Cyclic Redundancy Check)
• Error Correction
• Hamming Code
• Hamming Distance
• Minimum Hamming Distance

Channelization

• Frequency-Division Multiple Access (FDMA)
• Time-Division Multiple Access (TDMA)
• Code-Division Multiple Access (CDMA)
• Bridged Ethernet
• Switched Ethernet
• Full-Duplex Ethernet
• FAST ETHERNET
• GIGABIT ETHERNET

Logical Addressing

• Classful Addressing
• Classless Addressing
• Network Address Translation (NAT)

Network Security

• Introduction to Cryptography
• Substitution Ciphers
• Transposition Ciphers
• One-Time Pads
• SYMMETRIC-KEY ALGORITHMS
• DES—The Data Encryption Standard
• AES—The Advanced Encryption Standard
• Cipher Modes
• PUBLIC-KEY ALGORITHMS
• DIGITAL SIGNATURES
• Public-Key Signatures
• Message Digests
• The Birthday Attack
• Virtual Private Networks
• Wireless Security
• The Diffie-Hellman Key Exchange
• EMAIL SECURITY
• PGP—Pretty Good Privacy

Applications of Data Communication

Computer systems and other devices are connected to form a network. They provide numerous advantages:

• Video conferences
• Parallel computing
• Resource sharing such as printers and storage devices
• Exchange of information by means of e-Mails and FTP
• Information sharing by using the Web or the Internet
• Interaction with other users using dynamic web pages
• Instant messaging

FAQs on Data Communication

Q.1 what is the importance of data communication.

Data communication is important for connecting devices and for the transfer of information in the digital world and various technologies and services we dependent in daily life.

Q.2 How does data transmission occur?

Data transmission occurs through various mediums such as cables, wireless connections, or optical fibers. The data is encoded, transmitted, received, and decoded using protocols and devices.

Q.3 What are the benefits of wireless data communication?

Wireless data communication provides mobility, flexibility, and convenience. It allows devices to connect and communicate without the limitations of physical cables.

Q.4 How does data communication ensure security?

Data communication provide encryption, authentication, and other security measures to protect data from unauthorized access, interception, and tampering.

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

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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

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Introduction to Data Communication

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Data Science

What Is Data Communication? Components & Benefits

• Data Communication
• Storytelling
• Future Implications

Our modern, connected world hinges on data communication. It’s the fundamental basis that allows our devices to interact, providing us with instant and convenient access to information like never before.

However, the innovations we’ve seen in the last 50 years since the development of the first wireless networks and the creation of the internet are just the tip of the iceberg. The possible technologies we could witness within the next few years are fertile ground for those looking to create new business models, which is a major driving factor of why the University of San Diego developed the Master of Science in Applied Data Science program .

Before diving into detail about what may be on the horizon, it’s important to take a step back to understand the basic models of data communication and its general benefits. These insights will provide some helpful context on where we might be headed and might inspire you to develop the next great innovation in data communication.

What Is Data Communication?

Data communication is functionally similar to what we think of as “regular” communication, which is simply a sender transmitting a message to a destination. Data communication specifically refers to the process of using computing and communication technologies to transfer data (the message) from a sender to a receiver — or even back and forth between participating parties. The concept encompasses technologies like telecommunications, computer networking and radio/satellite communication.

Modern data networks all provide the same basic functions of transferring data from sender to receiver, but each network can use different network hardware and software to achieve these ends. Communication between devices adheres to industrial communications protocols, which is the set of rules that define how data is exchanged. Today’s data communications protocols are defined and are managed by interconnected bodies, including private businesses, standards-making organizations, regulatory agencies and common carriers.

4 Benefits of Data Communication

Before we can get into the benefits of data communication, it’s important to separate the concept of connectivity from communications. Connectivity is the capability of connecting one party to another. The benefits that arise from those connections depend on who’s connecting to whom — or to what.

We can think of it along three types of connections:

• Person to Person — such as when you call someone on a cell phone or have a chat session.
• Person to Machine — whenever you access information from a computer or automated system.
• Machine to Machine — when devices transfer information directly between each other.

Rather than being confined to these simple interactions, you can think of connectivity as a continuum. For example, when you text with someone else, the following steps occur:

• A person connects to a machine to send a message.
• The machine connects to another machine to deliver the message.
• The second machine translates and displays the message to another person.
• That person responds, and the process repeats, enabling a person-to-person connection.

The one thing that remains true in all of these connections is that some kind of information is transmitted, whether it’s retrieving a report from an archive, uploading data to a cloud server or holding a meeting in a Zoom call. Here’s a video that talks about the basics of connectivity in a little more detail.

Now, when we talk about data communications and networking, what we’re usually talking about are specific platforms. A data communications platform is essentially any technology that — whether it’s a cellphone, a laptop or the internet itself — enables connectivity. Today, data communication has become as ubiquitous as electricity itself, which has brought some incredible advances:

Instant communications. All of our modern digital communications, from email and instant messaging to video calls and TikTok, are all built on data communication networks. You can instantly connect with anyone in the world — or broadcast a message to thousands of people.

Greater business efficiency. Data communications has revolutionized how businesses interact with data. More effective ways of collecting and processing data leads to greater insights, which allow businesses to streamline productions, reduce expenses and improve operational efficiencies.

Innovations in automation. The Internet of Things (IoT) enables even more connection between different devices, allowing for new types of automation. For example, when we think of self-driving cars, providing them the ability to directly connect with other cars on the road over a 5G network makes the concept much more workable than trying to rely entirely on cameras and other sensors to determine positioning.

Smart monitoring systems. Sensors in wearable devices allow for advanced human health monitoring, which can transmit real-time data on someone’s condition or send alerts in an emergency. Wider applications include the development of smart cities that can offer improved traffic conditions, waste management, energy consumption and more.

Components of Data Communication

There are several components of data communication, but to keep things relatively brief, we should look at three of the most important elements: communication functionality, network models and standards of communication.

Communication Functionality

We’ve talked about the nature of communication being between a sender and a receiver. As data platforms have advanced, there has been increased functionality in how senders and receivers communicate:

• Simplex communications , which were the first and simplest means of communication where the transmission of data goes only in one direction. Simplex is still used in one-way data communication mediums such as radio stations and TV broadcasts.
• Half-duplex communication , where information can go both ways, but not at the same time. An example would be a CB radio, where a receiver has to wait for the system to be clear before responding.
• Full-duplex data communications models accommodate simultaneous two-way communication of data. The landline telephone is the most widely known means of full-duplex communication.
• Serial data communications is what we think of when we talk about networking. Data is packaged into units and then sent serially to the receiver by the sender. Once it is received, the units are reassembled to recreate the original data.

Network Models

Serial data communications rely upon networks to transmit data. The two most important network models are the Open Systems Interconnection Reference (OSI) model and the Internet model:

• The Open Systems Interconnection Reference model was developed by the Open System Interconnection Subcommittee in 1984. The OSI model consists of seven layers: the physical layer, data link layer, network layer, transport layer, session layer, presentation layer and application layer. Though it isn’t widely used today, it still has value as a foundational understanding of networking.
• The Internet model , though actually older than OSI, is the network model that has arisen to be the dominant model for all current hardware and software. Also referred to as the Transfer Control Protocol/Internet Protocol (TCP/IP) model , it combines the top three OSI layers into a single layer, making it a five-layer model consisting of a physical layer, data link layer, network layer, transport layer and application layer. The Internet model allows different independent networks to connect to one another and together, creating what we know as the global internet.

Standards of Communication

Standards define a set of rules known as protocols, which ensure that the software used in the different layers of the network models are compatible. Without standards, it would be virtually impossible for computers to communicate with each other. With standards, all hardware and software can communicate if they conform to the same specifications.

Previously, standards of communications were set by telecommunications standards bodies specific to different countries. Today, the Third Generation Partner Project (3GPP) initiative unites seven telecommunications standard development organizations — Japan’s Association of Radio Industries and Businesses (ARIB) and Telecommunication Technology Committee (TTC), Alliance for Telecommunications Industry Solutions (ATIS), China Communications Standards Association (CCSA), European Telecommunications Standards Institute (ETSI), Telecommunications Standards Development Society, India (TSDSI) and Korea’s Telecommunications Technology Association (TTA) — to establish a converging set of standards to maintain the global communications network.

All of these fundamentals essentially underpin what is covered in USD’s Master of Applied Data Science program’s foundational coursework , providing a comprehensive introduction to data science principles, including network models and communication standards critical to global data systems. The program covers predictive modeling, machine learning, data engineering and the use of cloud computing, equipping students with the necessary skills to develop advanced data-driven solutions.

Data Science Data Communication & Storytelling

While the term “data communication” often pertains to the transmission of data across technical networks utilizing various models and standards, within the field of data science it assumes a distinctly different role. Here, data communication — or more aptly, data science communication and storytelling — is central to the effectiveness and impact of data science.

Data Science Communication Defined

Data science communication involves the articulate and strategic relay of complex data insights to varied audiences, ensuring that these insights are both understood and actionable. This form of communication is crucial as it transcends the mere presentation of data, elevating it to strategic storytelling that engages stakeholders and drives business decisions.

Karen Church , Vice President of Research, Analytics and Data Science at Intercom, underscores the importance of this skill set : “I believe that communication is one of the most critical skills in data science…the ability to communicate effectively is just as important, if not more so, to driving real impact in data science.” Through effective data science communication, methodologies are impactfully applied across different platforms and environments.

Key Aspects of Effective Data Science Communication

Understanding and translating business requirements.

Data scientists must adeptly convert high-level business objectives into specific, data-driven tasks. Church notes, “Communication helps you understand and translate business requirements into specific data problems and research questions.” This alignment is vital for ensuring that data science projects fulfill organizational goals.

Framing or Reframing Problems

Accurately defining the data science problem is crucial as it ensures that the analysis addresses the right questions. Church explains the importance of problem framing: “Framing a problem in a way that a whole group or team can get a shared understanding of it, rally around it and take action on it [is essential].” Effective communication aids in clarifying the scope and nature of problems, thereby optimizing the focus and resources of data science initiatives.

Collaborating and Influencing

Data science frequently necessitates collaboration across various technical and non-technical stakeholders. Church asserts, “Communication plays a vital role in facilitating effective collaboration and helps influence and persuade others to take action or drive to a decision.” This collaborative process is imperative for seamlessly integrating data science into business strategies.

Presenting Results and Insights Effectively

The ability to communicate findings clearly and compellingly is critical. Data storytelling, which integrates data, visuals and narratives, is a pivotal technique highlighted by Church for making data insights accessible and impactful to stakeholders.

By distinguishing data science communication from traditional data communication, professionals are equipped to analyze, interpret and integrate these insights into the operational fabric of organizations. This specialized form of communication is integral to the creation of robust, data-driven solutions that significantly influence business outcomes and strategic decisions.

Future of Data Communication

Today’s revolutionary data communication capabilities are why the modern era is defined as “the information age.” While recent advances make it challenging to predict the exact trajectory of data communications and networking, here are some trends and developments that are likely to shape the near future:

New Applications of Data Communication Networks

The improving capabilities of wired and wireless communication networks, including 5G and IoT ( Internet of Things ), are enabling new applications such as self-driving vehicles, mixed and augmented reality and remote communication advancements that facilitate complex tasks like remote surgery. Beyond these high-end applications, the acceleration of network speeds continues to solidify cloud computing as a baseline technology for corporate computing networks. Widespread cloud adoption is expected to enhance computational efficiency across all industries.

Investment in Green Technologies

Digital platforms enable greater operational efficiency and reduced reliance on physical resources, but they must be developed sustainably. For instance, smart electricity grids are essential for the electrification of transportation, including the next generation of electric vehicles. This push towards greener and more efficient computing power will likely necessitate the creation of adaptive logical private networks, enhanced two-way data communication networks and the use of AI to optimize resource distribution.

Geopolitical Dynamics and Technological Restrictions

Although the last decade has seen enhanced international cooperation and the establishment of global standards, geopolitical tensions could lead to new restrictions or bans on certain technologies. Examples include the US and EU’s prohibitions on Huawei’s network equipment and discussions around a potential TikTok ban in the US.

Integration of Communication Networks with Cloud Computing

For businesses pursuing digital transformation, merging telecommunication networks with cloud services represents a leap forward. Software will automatically configure these combined networks to tackle business challenges with remarkable precision. Future advancements could feature ultra-wideband, low-latency networks for real-time uses like controlling drone fleets, powering augmented reality displays and enabling interactions within the metaverse. Additionally, we may see a significant expansion in Internet addresses to enhance IoT connectivity in remote areas and the creation of nanonetworks for detailed, local monitoring and data collection.

The true transformative impacts of technologies such as 5G are more substantial than just faster cell phones and enhanced service, which telecom companies roll out swiftly for quick profits. For instance, a merger between telecommunications and data networks, which would make telecom networks programmable, could enable mobile phones to access wireless networks anywhere there is a phone signal. With the full deployment of 5G — and looking toward 6G — we can expect an integration of wired, wireless and satellite communications into a comprehensive network that facilitates entirely new applications.

As we keep pushing the limits of data communication, we must understand what drives these advancements. If you are interested in advancing your career in the world of data, consider how USD’s 100% online MS in Applied Data Science program can help prepare you for success.

Choosing the right educational path is essential to succeed in data science and prepare for future challenges. Download our eBook, 7 Questions to Ask Before Selecting an Applied Data Science Master’s Degree Program , to learn how to select an option that will keep you at the forefront of data science.

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Presentation Tips to Improve Your Data Science Communication Skills

In data science, communication is critical.

Of course, all data science work requires the technical skills to acquire your data, clean it, and perform your analysis. But as you're doing this, it’s also important to keep the why in mind. When you’re given a project, it’s worth stopping to ask yourself what value it has to the company, and where it fits into the larger picture.

Knowing the answers to those why questions is the first step in a process that’s as important as your actual analysis: communicating your findings to an audience of (usually) non-data scientists.

Data science communication is a topic Kristen Sosulski knows a lot about. She’s the Clinical Associate Professor of Information, Operations, and Management Sciences at New York University Stern School of Business, and she has essentially made a career out of teaching how to effectively communicate, both in academia and in business. She’s even written a book, Data Visualization Made Simple , about communicating data science results effectively with visualizations.

“Presenting and communicating your insights across an organization can be really, really powerful,” says Kristen.

So how can you approach communicating your models in a way that’s effective?

Relating The Problem

Let’s say you’ve built a model and have the opportunity to present your findings in front of a major decision-maker in the company. It’s your job to explain what the model means and the impact it could have on the business.

Kristen advocates starting by identifying the problem or challenge you’re addressing. Relate the problem to the interests of the audience, and help them understand the larger context. To get the audience on your side, ask questions before proposing your solution. For example:

Have you ever experienced this?

Have you ever observed that in our business?

This isn’t just a rhetorical technique, it’s a way of measuring what information your audience needs to understand the rest of your pitch. “If no one thinks this is a problem, then you have to start by introducing the problem, and then building the case for the problem,” says Kristen “You don't want to lose your audience by alienating them because they think this isn't a problem at all.”

Keep in mind that what seems like an obvious problem to you isn’t necessarily going to be obvious to your audience, particularly if you’ve spent the last few weeks with your head buried deep in data sets nobody else has seen yet. The problem you found in the data and are attempting to solve with your model could be something that nobody else is really aware of yet.

Once you’ve made the case for the problem itself, you can then present common solutions and why those aren’t the best, most effective fit.

“You want to create some type of suspense, but you're rooting all of this in a narrative,” says Kristen. “Starting with a problem, showing alternative solutions, and then you're ultimately going to reveal your solution.”

Communicating with Data

Although your pitch is often going to be primarily language-based (whether it’s a written report or a standup presentation at a meeting), representing your data visually is absolutely crucial to communicating its meaning with your audience. Very few people can look at a spreadsheet or table and draw quick, clear conclusions about what the data says. Anyone can compare the size of bars on a bar chart, or follow the trend on a line graph.

Data visualization is a crucial skill at every stage of the data science process, of course. “There are a lot of angles that you can take with visualization, and ways to look at it,” says Kristen. “You can look at it purely from the technical viewpoint, you can look at it from the exploratory viewpoint, like using visualization as a tool to explore your data.”

But it’s also critical for communication.

“I think about data visualization as something that we have in the toolkit to help people better understand our insights and our data,” says Kristen.

“Just on a human level, visualizations just allow us to perceive information a lot more clearly when they're well designed.”

When designing visuals for communication outside your own team, it’s important to keep your audience in mind. Your coworkers probably don’t have the context on your problem that your team has, and they may not have the technical knowledge, either. One of the biggest challenges of data science communication is tailoring your presentation to your audience's technical level and still getting your point across without overwhelming them (or patronizing them). 

A good trick for putting yourself in the shoes of a non-technical audience is thinking about the information you want reported to you when you’ve taken a car into the auto repair shop (assuming you’re not a car mechanic yourself). Generally, the most convincing mechanics are going to be the ones who can:

• Explain your problem in clear, simple terms.
• Show you the evidence the problem exists. ​
• Explain in clear, simple terms how the problem can be fixed.
• Give you a clear timeline and price for what the fix will cost.

You don’t want a 30-minute lecture on the factors that affect engine efficiency. You just want to be confident that you know what the problem really is and that the mechanic knows the best way to fix it.

This applies to communicating in data science, too, but now you’re the mechanic. When in doubt, the best approach is to keep it simple. Leaving in all of the details can be confusing and make your charts less readable, so include only what is necessary to communicate your point.

“Know that you don't have to show every data point at once, that you can slow it down. You can show a few data points at a time to help build your story and your narrative,” says Kristen.

Remember: you can always provide more information by answering questions if your coworkers feel they haven’t seen enough. But if you throw a series of complicated, difficult-to-read charts at them, you risk completely losing them, and that's difficult to undo.

Presentation Tips

Incorporating visualizations into a presentation is a bit of an art form, especially with highly technical data. To keep things simple and effective, Kristen suggests keeping a few guidelines in mind.

First: don’t force the chart to speak for itself. Make sure that you are taking the time to clearly explain what's shown on the screen. If you’re displaying data in a graph, only show one graph at a time, and explain what it’s showing and what it means in the broader context of the problem you’re addressing. You can also show where relationships exist, where outliers are, and how effective your model is compared to other models.

Pace is important, too.

“Don't go too fast, but this whole type of presentation shouldn't be more than 10 or 15 minutes,” says Kristen. “You want to make sure that you can do this type of pitch in a short period of time without overwhelming the audience with detail, but also being able to show the data clearly, and use the data as convincing evidence.”

Don’t be afraid to talk specifics. While you don’t want to overwhelm your audience with technical details, you do need to make sure you’ve included the details that are required to understand your presentation, and the charts they’ll be looking at. Are you talking about new leads generated over a period of hours, or years? Do the math for your audience. If you’re making a prediction, quantify it for them.

It also helps to direct the audience’s attention to certain visualizations. It can be tough correlating spoken word with visual data. If you’re talking about a particular section on your graph, point to it. Build your story from there.

Ultimately, you need to remember that communication is first and foremost a human interaction. “You’re the one sitting in front of the CEO, allow yourself to provide the explanations supported by the graphics, not the other way around.”

Data Science Communication Tools of the Trade

Of course, the first step in creating any presentation like this is actually creating the data visualizations. What you use to do that depends on your programming language of choice. “For me, my tool of choice is R and R Studio, and the various packages that go along with that, which are numerous,” says Kristen.

Python programmers also have a plethora of options for data visualization.

If you don’t know yet how you like visualizing data, Dataquest has interactive online courses on exploratory data visualization and storytelling with data viz in Python as well as a free course on data visualization in R . We also have a quick guide with some design tips that’ll help you make your charts easier to read.

Whatever tools you use, remember these basic tips for data science communication and you’ll have a better chance of nailing your next presentation:

• Start with the problem. Is this a problem your audience knows about already? If not, you’ll have to begin by establishing in clear terms that there is a problem.
• Have empathy for your audience and present them with the information they want in a format and in language they can understand.
• Illustrate your conclusions with data visualizations, but let your own explanation - not the charts - drive your presentation.
• Keep it simple, and leave out unnecessary detail in both your explanations and your charts. Don’t exceed 10 to 15 minutes for the whole presentation.

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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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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.

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Presentations can be an incredibly powerful tool in communicating environmental issues. here are some key tips for designing accessible, engaging environmental presentations..

Adapted from Peter Zvirinsky’s 2021 article “4 Tips for Presenting Environmental Topics, Earth Day support”.

1. Start with thorough data research.

Be meticulous in selecting your facts. In today’s world, marked by fake news and distorted statistics, it’s crucial to build your presentations on trustworthy sources. Data from reputable international institutions, academic research, and university studies is generally more reliable than information found in tabloid articles or social media posts. In the context of presentation slides, it’s a good practice to clearly indicate the sources of your facts directly on the slide. Including the publication year helps demonstrate the timeliness and credibility of your research. Hyperlinked references on the slide or in the notes section of your presentation can help the audience readily access the references.

2. Organize Your Information Effectively

To make sure your message is understood and impactful, structure your presentation thoughtfully.For longer content, divide it into sections. A common approach is the “hamburger” structure:

• Start by grabbing attention with an enticing title, followed by a compelling quote or relevant facts.
• Then, explore each point thoroughly, analyzing reasons and illustrating their significance.
• Conclude by summarizing the main points and outlining next steps.

Below, you’ll find examples of agenda and section slides. These slides help establish a clear roadmap for your presentation, especially for longer ones.

3. Present content visually, avoid long blocks of text

To maintain your audience’s engagement, minimize the use of extensive text blocks.

While many valuable resources originate from scientific papers, they are often not presented in a visually appealing format. To effectively incorporate such facts into a presentation, distill the key points and present them visually.

If your aim is to communicate your message to a broader audience, simplify it for better comprehension. Incorporate anecdotes, examples, and illustrations that resonate with people.

Below are a few visualization techniques you can use to replace text with visuals.Choosing a proper visual depends on your slide content. Take a minute, analyze what kind of information you present and then replace or enrich it with a fitting graphics.

A. If you have text that contains a process, a set of steps, show it by flowchart diagram.

It can be simple series of blocks and arrows or you can enhance it graphically. 

B. Every set of arguments can be presented as a list diagram.

If you already have bullet points in your material, present them in a visual form instead. 

C. Present numbers as infographics 

Try to replace data tables at least with a chart. To make it more attractive you can enrich it with additional illustrations. You can illustrate categories by icons, use colors associated with nature (green, yellow, orange). Keep in mind that the clarity is important here. Every decoration support the information, not distract too much.

4. Finish with clear bottom line

Once you presented the problem, supported it with facts, it’s time to wrap it up. Tell what shall we do about it. Be clear in presenting the next steps. Whether it’s a single action or specific project steps, show it on a separate slide. Underline this is a final take-away that people should walk out from your presentation.

Make such slide stand out from the rest. You can use a full slide photograph as a background or use reverted color for a slide – if your presentation used white slides, use the color background here or vice versa.

Best platforms for presentations

Canva is a great platform to use to create visually engaging environmental presentations. The free version offers a ton of templates and icons that you can incorporate into your presentation. Collaboration is easy on Canva as you can just share the link with your team and everyone can edit.

Google Slides

Google Slides seamless integration with other Google Workspace tools such as Docs and Sheets, facilitating collaboration and content sharing. Moreover, Google Slides offers basic features for free, making it accessible to anyone with a Google account.

PowerPoint is also a great option, however you cannot collaborate in real time. This would be a better option for a presentation you are working on by yourself of if you need it to integrate with the Microsoft Office suite. SlidesGo and PresentationGo offer thousands of free presentation templates.

Select a better font.

• Use a sans-serif fonts (e.g. Arial or Helvetica) for the majority of your text. These are much easier to read than serif fonts (e.g. Times New Roman).
• Distinguish header and content fonts. Serif fonts can be used in headings if they are easy to read.
• Font size should not be smaller than 24 point.
• Align text left or right. This is much easier to read than centered or justified text.
• Be consistent in your font use throughout the entire presentation.

Prepare for technology issues.

Be prepared for the array of potential technology issues you may encounter when giving a presentation.  An unfamiliar setting can lead to challenges getting everything up and running. 

• Save your file as a .pdf or .jpeg.
• Choose common fonts (e.g. Arial, Verdana, Times New Roman).
• Embed font files, if you are using unusual fonts. ( Learn more here )
• Save any included tables and charts as images.
• Assess your presentation setting ahead of time. Practice setting up your presentation.  Is your PowerPoint clear when you stand in the back of the room? Do the colors come through the projector correctly (or acceptably)?
• Arrive early to set up your presentation.

Duke’s Office of Information Technology (OIT)  offers some excellent trainings and access to web resources.

LinkedIn Learning offers some excellent resources for improved presentation design and developing software skills, including  Designing a presentation  and  Using photos and videos effectively .

https://www.linkedin.com/pulse/3-tips-presenting-environmental-topics-earth-day-peter-zvirinsky/

An extensive list of design tips can be found on the  National Conference of Legislators’ website .

Microsoft Office  has a list of tips and resources for Powerpoint presentations.

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Express to impress: leveraging ibcs standards for powerful data presentations.

Posted on September 12, 2024 by Numbers around us in R bloggers | 0 Comments

Attention: Article looks long at word count, but remember that contains pretty long chunks of code.

Disclaimer: While my work in this series draws inspiration from the IBCS® standards, I am not a certified IBCS® analyst or consultant. The visualizations and interpretations presented here are my personal attempts to apply these principles and may not fully align with the official IBCS® standards. I greatly appreciate the insights and framework provided by IBCS® and aim to explore and learn from their approach through my own lens.

The Power of Expression in Data Reporting

In the world of business intelligence (BI) and data reporting, the ability to express data effectively can make or break the decision-making process. Amid an overwhelming flow of information, data must not only be analyzed but also communicated in a way that drives insight, action, and understanding. This is where the International Business Communication Standards (IBCS) framework comes into play, particularly its “Express” component within the SUCCESS acronym: Say, Unify, Condense, Check, Express, Simplify, Structure . The “Express” component is the critical bridge between data and comprehension, focusing on how data is visualized and presented.

At the heart of Express lies a simple question: How can we present data so that it is understood quickly and without misinterpretation? The answer is not just about using charts and tables but also about selecting the right types of visualizations that align with the information being conveyed. Leland Wilkinson’s Grammar of Graphics provides a theoretical backbone to this approach by laying out the essential building blocks of effective visual communication. Together, the principles from IBCS and the Grammar of Graphics guide us in transforming raw data into powerful visual narratives.

IBCS and the Grammar of Graphics: A Perfect Synergy

The IBCS framework emphasizes standardization and clarity in how information is visualized, calling for the replacement of ineffective chart types and encouraging the use of comparisons and explanatory visuals. This aligns well with Wilkinson’s Grammar of Graphics , which provides a systematic approach to visualizing data through a combination of geometric shapes, scales, and aesthetic properties. The Grammar of Graphics builds a foundation where every visual element — whether a point, line, or bar — serves a purpose and contributes to the clarity of the message.

These two frameworks together empower BI practitioners to not only present data but to express it in a way that makes patterns, comparisons, and insights obvious. This chapter will explore how the Express component of IBCS, complemented by the Grammar of Graphics , can turn confusing reports into clear, actionable data presentations.

Choosing the Right Object Types: Charts and Tables

One of the foundational elements of effective data presentation is selecting the correct type of visualization. According to the IBCS standards, charts and tables should be used strategically to express data in the clearest, most impactful way. Each chart type has its own strengths, and choosing the wrong one can lead to confusion, misinterpretation, or even worse, misleading conclusions. This section will focus on how to align chart types with IBCS guidelines and how the Grammar of Graphics can assist in structuring these visuals.

The Role of Appropriate Object Types

IBCS emphasizes simplicity and clarity, which translates into using visualization types that naturally align with the type of data you’re working with. The goal is to make the relationships, patterns, and insights in the data immediately apparent to the audience.

• Bar Charts : Bar charts are the workhorse of data visualization. They are ideal for showing comparisons, such as revenue across different regions or sales figures over several months. IBCS recommends horizontal bar charts to compare categories and vertical bar charts for time series data.
• Line Charts : Line charts excel at showing trends over time. In scenarios where you need to express changes, such as stock prices over a year or temperature changes, line charts are much more effective than other types like pie or radar charts.
• Tables : While charts help visualize data trends, tables are best suited for presenting precise numbers. IBCS guidelines advocate using tables when exact figures matter more than the visual trends, such as financial reports or performance metrics. A well-designed table that adheres to IBCS principles has a clear structure, avoids clutter, and presents data in a way that makes comparisons simple.

Transforming a Report with IBCS Principles

Let’s look at an example of transforming a poorly chosen chart type into a more effective IBCS-compliant visualization:

Before : Imagine a report that uses a pie chart to compare market share across different regions. While pie charts are common, they are not IBCS-compliant and make it difficult to compare exact proportions, especially when the differences are small.

After : By applying IBCS standards, we replace the pie chart with a horizontal bar chart . The bar chart not only allows for easier comparison of regions side by side but also makes it immediately clear which region has the largest or smallest market share. This simple change transforms the clarity and effectiveness of the report.

The Grammar of Graphics Approach

Leland Wilkinson’s Grammar of Graphics provides a framework to build visuals by combining geometries , scales , and aesthetics in a systematic way. In our example, the use of a bar geometry and the scale of the market share on the horizontal axis creates an immediately interpretable visual. This modular approach ensures that every element in the chart contributes to the clarity and overall goal of effective communication.

Eliminating Inappropriate Chart Types

In data visualization, some chart types are popular but not effective at conveying clear, actionable insights. The IBCS standards discourage the use of these chart types because they often distort information, waste space, or make comparisons difficult. Here’s how to eliminate these inappropriate chart types and replace them with more effective alternatives.

1. Replacing Pie and Donut Charts with Bar Charts

Before : Pie or donut charts are often used to represent proportions, such as sales by region. However, these charts make it difficult to compare slices accurately, especially when the differences are small.

After : Replace the pie or donut chart with a horizontal bar chart . Bar charts are much easier to read and allow for more precise comparisons between categories.

Replacing Gauges and Speedometers with Simple Line Charts

Before : Gauges or speedometers are often used in dashboards to show a single metric, like customer satisfaction or profit margins. However, they consume a lot of space and make it hard to track changes over time.

After : Replace gauges with a simple line chart that shows the trend of the metric over time. This not only conveys the current status but also provides context for how the metric is performing.

Replacing Radar Charts with Grouped Bar Charts

Before : Radar charts are used to compare multiple variables across categories, such as department performance metrics. However, the circular design is hard to interpret and makes comparisons less intuitive.

After : Replace radar charts with a grouped bar chart that presents the same data side by side. This allows for much clearer comparisons across categories and metrics.

Replacing Spaghetti Plots with Small Multiples or Line Charts

Before : Spaghetti plots with multiple overlapping lines make it difficult to follow individual trends, particularly when there are too many lines on the same chart.

After : Use small multiples (separate, simpler line charts for each category) or break down the plot into fewer, clearer line charts. This allows for easier interpretation of each individual trend.

Replacing Traffic Lights with Variance Analysis Charts

Before : Traffic lights (red, yellow, green) are often used to show status or performance indicators. While simple, they oversimplify complex data and lack context.

After : Replace traffic lights with a variance analysis chart that shows actual values against targets, enabling a more nuanced understanding of performance.

In each of these examples, we’ve replaced ineffective visualizations with IBCS-compliant alternatives that enhance clarity and make comparisons easier. By aligning with IBCS standards and leveraging concepts from the Grammar of Graphics , we ensure that data is expressed in a way that supports clear and informed decision-making.

Optimizing Data Representations

In data presentations, the way information is structured and represented can make all the difference. While it’s tempting to rely on lengthy textual descriptions or overly complex visuals, the IBCS standards encourage using quantitative representations wherever possible. Numbers, charts, and visualizations convey information more directly than text, and when done right, they can eliminate ambiguity and speed up understanding. This section will discuss how to optimize data representations according to IBCS principles and make use of quantitative visuals to avoid reliance on text-heavy slides.

Why Quantitative Representations Matter

Visualizing data quantitatively rather than explaining it in words provides immediate clarity and facilitates quicker decision-making. Consider a slide overloaded with paragraphs of text explaining key performance indicators (KPIs). It forces the audience to read and interpret, which slows down comprehension. In contrast, well-constructed charts, tables, or graphs can convey the same information in seconds.

IBCS emphasizes minimizing text and replacing it with visual elements that communicate the data clearly and effectively. This not only reduces cognitive load but also ensures the information is perceived accurately.

Example: Replacing Text-Heavy Slides with Charts

Before : Imagine a presentation slide with paragraphs of text explaining the company’s revenue growth over several years. The text describes the revenue trajectory and highlights which years saw increases or decreases.

After : Instead of text, replace this explanation with a simple line chart that clearly shows the revenue trend over time. A visual like this is much easier to understand at a glance, as it provides a direct view of the data without the need for lengthy descriptions.

This line chart immediately communicates the trend in revenue growth, making it clear which years saw increases and where the dips occurred — something that would have taken several paragraphs to explain in words.

The Role of the Grammar of Graphics

Leland Wilkinson’s Grammar of Graphics emphasizes the structured combination of elements such as scales , aesthetics , and geometries to create clean, informative visuals. In the example above, the line geometry and the use of scales on both the x-axis (years) and y-axis (revenue) allow for precise interpretation of the data. This approach transforms raw data into an easily digestible visual story that speaks for itself.

Avoiding Text-Only Slides

IBCS encourages replacing text-heavy slides with visuals wherever possible, but this doesn’t mean removing all text. The key is to balance text and visuals so that the text provides context while the visual delivers the core message.

For example, consider a slide that lists key performance indicators (KPIs) with lengthy descriptions of each one. Instead of using large blocks of text, create a table that lists the KPIs alongside the relevant figures, with minimal explanation.

Before : A slide with long descriptions of KPIs, such as:

• “The customer satisfaction score has increased by 10% from the previous quarter.”
• “Sales conversion rates are up by 15%, reaching the target of 75%.”

After : A simple chart that presents the KPIs clearly:

This visualized table allows the audience to immediately see the comparison between current performance and targets without the need for long explanations.

Best Practices for Optimizing Visuals

When optimizing your visuals, keep in mind these best practices, which are in line with both IBCS standards and the Grammar of Graphics :

• Simplicity : Strip away unnecessary details, labels, and decorative elements. Only include what is needed to communicate the data.
• Focus on Comparisons : Ensure that your visual enables clear comparisons, whether that’s between time periods, categories, or variables.
• Precision : Use scales and axes that accurately represent the data. Avoid distortions that can mislead the viewer.
• Balance of Text and Visuals : When text is necessary, keep it concise and complementary to the visual. Avoid long paragraphs and focus on what the audience needs to understand.

Enhancing Comparisons

One of the most powerful ways to make data meaningful is through comparisons. IBCS emphasizes the importance of showing comparisons clearly, whether between different scenarios, time periods, or variables. Comparisons help uncover trends, outliers, and relationships that would otherwise remain hidden. This section focuses on how to effectively incorporate comparisons into your reports, leveraging both IBCS standards and the Grammar of Graphics .

Why Comparisons are Critical

Without comparisons, data lacks context. For example, knowing that a company made $50 million in revenue last year is valuable, but it’s even more informative when compared to the previous year’s revenue, the industry average, or the company’s target.

Comparisons can be added in a variety of forms, such as:

• Time Comparisons : Comparing performance across different time periods (e.g., this quarter vs. last quarter).
• Scenario Comparisons : Showing different outcomes under various scenarios (e.g., best case, worst case, and expected case).
• Variance Analysis : Highlighting the difference between actual and target performance.
• Category Comparisons : Comparing different product lines, regions, or departments.

Example: Adding Variance Analysis for Target vs. Actual

Before : A report shows actual sales figures but doesn’t provide any context or comparison to targets.

After : By adding variance analysis  — comparing actual sales to target values — the report becomes much more meaningful. The audience can instantly see which regions met or missed their targets.

In this chart, we clearly see how each region performed against its target. The use of a variance analysis chart, where actual values are compared to targets using error bars, is a perfect way to communicate this comparison. The Grammar of Graphics enhances this process by using bars to represent actual performance and error bars to indicate target values. This direct comparison between actual and target performance makes it easy for the audience to spot areas of concern or success.

Enhancing Comparisons with Scenario Analysis Over Time

Before : A business report might present a single revenue forecast with no indication of uncertainty or alternative scenarios. This lacks context and doesn’t provide decision-makers with a full understanding of potential risks and opportunities.

After : By creating a scenario analysis line chart over time , we can show three scenarios — best case, expected case, and worst case — for a given metric (e.g., revenue) over several years. This allows stakeholders to see how different scenarios unfold and compare the potential outcomes in a more comprehensive way.

Interpreting the Scenario Analysis Over Time

In this scenario analysis, the best case scenario shows the most optimistic projection, where revenue grows consistently year after year. The expected case is a more conservative forecast with moderate growth, while the worst case anticipates a decline in revenue. The line chart makes it easy to compare these three scenarios over time, helping stakeholders understand the potential range of outcomes.

The Role of Grammar of Graphics in Scenario Analysis

This chart uses the line geometry to show trends over time for each scenario. The color aesthetic is used to differentiate the scenarios clearly, while the x-axis (years) and y-axis (revenue) allow the viewer to track changes over time. By using a consistent scale for all scenarios, we ensure that the audience can easily compare the growth or decline across the different scenarios.

Best Practices for Scenario Analysis Over Time

• Consistent Time Axis : Ensure that the time axis is the same for all scenarios, so that each scenario is directly comparable over the same period.
• Use Distinct Colors : Choose distinct and meaningful colors for each scenario (e.g., green for best case, red for worst case), so the viewer can easily differentiate between them.
• Highlight Key Points : Use markers (points on the line) to emphasize key moments in the forecast, such as sharp increases or decreases.

Small Multiples for Time Comparisons

Another effective technique for enhancing comparisons is the use of small multiples . Instead of cramming multiple lines into one chart (which can lead to spaghetti plots), small multiples create separate panels for each variable or time period, making comparisons across time much clearer.

Before : A single line chart shows revenue trends for multiple regions, with overlapping lines creating visual clutter.

After : Using small multiples, each region’s revenue trend is shown in a separate panel, making it easier to spot trends within each region while still allowing comparisons across regions.

Using facet grids creates a cleaner, more focused comparison of revenue trends for each region. This method keeps the charts easy to read, and the consistent scales across panels allow for straightforward comparisons between regions.

Best Practices for Effective Comparisons

When adding comparisons to your reports, here are some IBCS-aligned best practices to follow:

• Use Clear Scales : Ensure that all charts using comparisons have the same scale. Inconsistent scales can mislead the viewer and obscure important differences.
• Visualize Variances : Whenever possible, show the difference between actual and expected values, not just the raw numbers. Variance bars, error bars, and side-by-side comparisons are excellent for this.
• Avoid Overlapping Data : Use small multiples or grouped charts to break down complex datasets. This makes it easier for the audience to follow each variable or time series.
• Ensure Readability : Simplify the visual layout so that the key comparison is obvious at first glance. Avoid excessive labels or embellishments that distract from the main message.

Explaining Causes: Structure and Clarity

In data reporting, one of the most important tasks is to not only present data but to explain why certain outcomes occur. The IBCS standards recommend using tree structures to visually illustrate cause-and-effect relationships between key metrics. This helps decision-makers quickly understand the underlying factors that drive performance.

A tree structure is a hierarchical visual where a top-level metric is broken down into its contributing components. For example, profit can be broken down into its drivers, such as sales and costs . This method provides a clear visual flow, helping the audience trace back key figures to their source metrics.

In this section, we’ll explore how to use tree structures to explain causes, leveraging patchwork in R to create a multi-level visualization that breaks down a top metric into its sub-components over time.

Using Tree Structures to Explain Causes

Tree structures represent how a key metric is influenced by its underlying components, visually linking them in a cause-and-effect hierarchy. In a typical scenario, profit might be the top-level metric, which is influenced by sales and costs . These components can further be broken down into detailed metrics like units sold , price per unit , and fixed or variable costs .

This kind of breakdown not only shows what’s happening but also why it’s happening, making it easier for stakeholders to identify the drivers of success or areas of concern.

Example: Visualizing Profit Breakdown Using Patchwork

Let’s break down a company’s profit into its two key drivers: sales and costs . Each will be represented by its own chart, showing values across several quarters. Using the patchwork library, we’ll combine these charts into a tree structure, with profit at the top and sales and costs  below.

Before : In a typical report, profit, sales, and costs might be presented as individual, disconnected charts or numbers, without any clear visual indication of how they relate to each other.

After : We use a tree structure to link these metrics together, showing how profit is directly influenced by changes in sales and costs over time.

Here’s how you can create this structure in R:

Interpreting the Tree Structure

• Profit is placed at the top, showing how it evolves over four quarters (Q1–Q4).
• Sales and Costs are positioned below it, illustrating how these two components contribute to the overall profit.
• By linking these metrics visually, decision-makers can clearly see how changes in sales or costs directly affect  profit .

Expanding the Tree Structure

To provide even deeper insights, we can break down sales and costs into more specific components. For instance, sales can be split into units sold and price per unit , while costs can be divided into fixed and variable costs . This expanded tree structure helps the audience trace every dollar of profit back to its root causes.

Expanded Interpretation

• Sales is further broken down into units sold and price per unit , showing how both contribute to total sales across the quarters.
• Costs is split into fixed costs (which remain constant) and variable costs (which fluctuate), illustrating how each cost type impacts total costs.
• This expanded tree structure provides a deeper understanding of the components driving profit, allowing for a granular analysis of what’s affecting each metric.

Best Practices for Tree Structures in IBCS

• Start with the Key Metric : Place the top-level metric (e.g., profit) at the top of the structure and gradually break it down into its components.
• Show Time Trends : Using consistent time periods (e.g., quarters) across all metrics makes comparisons easier and reveals trends.
• Use Visual Links : Tree structures work best when they visually connect the metrics, clearly showing how each component contributes to the overall result.
• Keep the Layout Simple : Ensure the tree structure is easy to follow, with each chart clearly labeled and connected to its related metrics.

Beyond using tree structures to break down key metrics, other critical techniques for explaining causes in data reporting involve revealing correlations and clusters . These methods help uncover relationships between variables and group data points that share similar characteristics, allowing for deeper analysis of performance drivers.

Using Correlations to Reveal Relationships

In business reporting, it’s important to explain the relationships between different variables. For instance, you might want to know whether increasing advertising spend is correlated with an increase in sales. Correlation visualizations help demonstrate these connections, showing how one variable influences another.

Example: Visualizing Correlation Between Advertising Spend and Sales

Before : A report might present advertising spend and sales as separate figures or in separate charts, leaving it up to the reader to interpret any relationship.

After : A correlation scatter plot shows how changes in advertising spend are linked to sales, making the relationship between the two variables easy to interpret. A positive correlation, for example, could suggest that increasing advertising spend leads to higher sales.

Interpreting the Correlation Plot :

• Each point represents the relationship between advertising spend and sales for a particular period.
• The trend line shows the general direction of the relationship: a positive slope indicates that higher advertising spend correlates with higher sales.
• This visualization helps decision-makers assess whether investing more in advertising could drive additional sales, which might not be clear from viewing the figures in isolation.

Using Cluster Analysis to Group Data

Another powerful way to explain causes is through cluster analysis , which helps identify patterns or segments in your data. By grouping data points with similar characteristics, cluster analysis can reveal insights about different customer behaviors, product performance, or regional trends.

Example: Clustering Customer Purchase Behavior

Before : A report might list customer purchase behavior data by region, but it doesn’t reveal any patterns or similarities between different regions.

After : A cluster analysis plot groups customers based on similar purchase patterns, helping identify which regions or segments behave similarly, and how they differ from others. This provides actionable insights into regional strategies or product offerings.

Interpreting the Cluster Analysis :

• Each point represents a customer’s purchase amount in a given region.
• Color-coded clusters show which customers are grouped together based on similar purchasing behaviors. For example, regions like Asia might have higher purchase amounts than South America .
• Clustering allows for targeted actions, such as focusing marketing efforts on high-purchasing clusters or understanding what drives differences between segments.

Combining Tree Structures, Correlations, and Clusters

Tree structures, correlations, and clusters offer complementary ways to explain causes in data reporting:

• Tree Structures provide a hierarchical breakdown of metrics, showing how top-level results are derived from underlying factors.
• Correlations reveal relationships between different metrics, showing how changes in one variable may influence another.
• Clusters group similar data points together, highlighting patterns or segments that may not be obvious in the raw data.

Together, these techniques provide a rich, multi-faceted explanation of business performance, helping stakeholders understand both what is happening and why it’s happening.

Best Practices for Explaining Causes with Correlations and Clusters

• Highlight Relationships : When two variables are related, use correlation plots to make this relationship visually clear, especially when decision-makers need to see how one factor drives another.
• Cluster Similar Data : Use clustering when it’s important to group data points by similar behaviors or characteristics. This is especially useful for segmenting customers, regions, or product performance.
• Combine with Tree Structures : Use tree structures to provide the hierarchical context and breakdowns of key metrics, and enrich the analysis with correlation and cluster visuals to show deeper relationships or patterns.

Expressing with Purpose

Throughout this chapter, we have delved into the importance of using IBCS standards to enhance the way data is expressed in business reporting. As we’ve seen, the clarity and effectiveness of a report depend heavily on the proper selection of visualizations and their alignment with best practices. The IBCS framework’s emphasis on appropriate chart types , clear comparisons , and visual hierarchy transforms raw data into insightful, actionable information.

In the fast-paced environment of business intelligence, where decision-makers need to comprehend data quickly and accurately, the ability to express information clearly is critical. Reports that fail to meet these standards can lead to misinterpretation, confusion, or missed opportunities. By adhering to IBCS guidelines, you ensure that data reports are:

• Clear and focused : Free of unnecessary chart types that clutter or obscure insights.
• Consistent and standardized : Allowing stakeholders to easily understand, compare, and analyze the information without needing extra explanations.
• Actionable : Designed to emphasize key comparisons, causes, and insights that guide decisions.

These principles are not just about creating aesthetically pleasing charts but about communicating the right message with impact. Whether it’s ensuring your visuals provide clear comparisons, or using tree structures to explain causes, the IBCS standards provide a systematic approach to making data understandable and insightful.

Looking Ahead: The Importance of Integrating IBCS in Your Reporting

As we progress through this series on adapting IBCS standards into reporting, it’s important to recognize that the full power of IBCS lies in consistent application. By continuing to integrate these principles into every report, you’ll build a robust framework that delivers accurate and meaningful data to decision-makers.

But we’re not done yet! There are still two more chapters to go in this series, where we’ll dive deeper into other essential aspects of IBCS reporting. After completing the series, I’ll provide a comprehensive tutorial and framework that outlines how to choose the correct visualizations, validate them against IBCS standards, and adapt these guidelines to your specific reporting needs.

This final guide will serve as a step-by-step manual to ensure that every report you create is IBCS-compliant , leading to clearer, more effective communication in your organization.

Express to Impress: Leveraging IBCS Standards for Powerful Data Presentations was originally published in Numbers around us on Medium, where people are continuing the conversation by highlighting and responding to this story.

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