“What is the difference between a million and a billion seconds?”

You might answer that a billion seconds is 1,000 times greater than a million seconds. You might also say that a billion seconds is “a lot” more than a million seconds. But neither answer is decipherable to your audience.

A more concrete answer? At one million seconds, you are three weeks old, still in diapers. At one billion seconds, you have a student loan, a mortgage, and two kids.

This example, included in Chip Heath and Karla Starr’s book Making Your Numbers Count, concretely demonstrates the authors' imperative to translate all numbers into a context your audience can understand.

Business professionals are swimming in a sea of data. Every click, purchase, sensor reading, and social media post generates information. According to TechRepublic, the amount of data produced and cataloged grows exponentially every day. While organizations rely on that data to make sound business decisions, decision-makers need skills to sift and interpret data—basic data literacy skills—to ensure the right decisions are based on the right interpretation of the right data.

According to a study sponsored by Tableau and Forrester Consulting, 87% of employees rate basic data skills as very important for their day-to-day operations. Data literacy skills are equally important to employers.

Data-literate employees give companies a competitive edge by extracting insights, spotting trends, and driving innovation. Data literacy—the ability to understand, analyze, and communicate the meaning behind data points and statistics—enables professionals to ask the right questions, gather relevant data, and derive actionable insights.

Depending on how data is collected, analyzed, presented, and interpreted, audiences will draw different conclusions. Being data literate will help you discern faulty conclusions and effectively convey your message, giving you and your organization a competitive edge.

In this digital age, data literacy isn’t just for analysts—it’s essential for all roles. So, embrace the challenge: Translate everything.

Data Literacy

Develop your data literacy skills by learning about data types, sources, collection, analysis, interpretation, and visualization.

DATA TYPES

To develop data literacy skills, start by learning about different types of data, including structured data (e.g., databases, spreadsheets), unstructured data (e.g., text, images), and semi-structured data (e.g., XML, JSON). Some data are quantitative (e.g., numbers) and others are qualitative (e.g., text, images, audio, video).

DATA SOURCES

Next learn about various data sources like databases and repositories, surveys, administrative records (e.g., government, businesses, nonprofit organizations, etc.), web scraping, sensors, and social media.

The following data sources are available to most users:

Google Dataset Search

ICPSR

IPUMS

FRED

Bureau of Labor Statistics

Yahoo! Finance

Gallup

DATA COLLECTION

Next, learn about data collection methods, including surveys, interviews, experiments, observational studies, and data mining techniques.

DATA ANALYSIS

Once you understand the basics of types, sources, and collection, review of the basic principles of descriptive statistics (e.g., mean, median, mode, standard deviation) and inferential statistics (e.g., hypothesis testing, correlation, regression analysis) to analyze and interpret data.

If you need help understanding data, take a statistics refresher course or watch YouTube videos that explain basic data analysis principles.

DATA INTERPRETATION

Once you grasp statistical principles, develop data interpretation skills to make inferences about the data. Consider factors like context, limitations, and potential biases. The following questions help you interpret the data:

What kind of sample did the data come from—random or convenience?

Did the survey contain leading questions?

Who collected or analyzed the data?

Why did the researchers collect the data?

What units do the data represent?

Complex datasets can contain critical information that informs decision-making. Whether you’re a scientist, business analyst, or policy maker, understanding data helps you and others make informed choices. For example, a medical researcher analyzing clinical trial results needs to communicate the efficacy of a new drug to doctors and patients. Simplifying the data ensures that stakeholders comprehend its impact.

When presenting findings or insights, communicate clearly. Complex jargon or technical details can alienate your audience. By simplifying the data, you can engage a broader range of stakeholders. Imagine a climate scientist explaining global warming trends to policymakers or the public. Using accessible language and visualizations helps convey urgency and encourages action.

Try the following tips and tools for improving your overall data literacy skills:

UNDERSTAND BASIC CONCEPTS

Start by familiarizing yourself with basic data concepts such as data types data sources, and data analysis techniques discussed above.

LEARN DATA ANALYSIS TOOLS

Familiarize yourself with popular data analysis tools such as Microsoft Excel, Google Sheets, Python (with libraries like Pandas and NumPy), R, SQL; and data visualization tools like Tableau, Power BI, or Matplotlib. Many online platforms offer tutorials and courses on these tools (e.g., YouTube, LinkedIn Learning).

TAKE COURSES AND USE TUTORIALS

Universities provide a wide range of data analysis courses and even entire majors in data science. If you are not going as far as majoring in data science, consider taking a course or even using free tutorials like those offered through Khan Academy, which offers a wide range of tutorials on data literacy, data analysis, statistics, and data visualization. Look for courses that match your current skill level and learning objectives.

PRACTICE WITH REAL DATA

Hands-on experience is crucial for developing data literacy skills. Practice working with real datasets from sources like Google Dataset Search, Kaggle, UCI Machine Learning Repository, government data portals (e.g., Data.gov, Eurostat), or data provided by your organization or educational institution.

READ BOOKS AND PUBLICATIONS

Many books and publications cover data literacy, data analysis, and data visualization concepts. Find out whether your university library has access to some of the following:

Storytelling with Data by Cole Knaflic

Better Data Visualizations by Jonathan Schwabish

Fundamentals of Data Visualization by Claus Wilke

Making Numbers Count by Chip Heath and Karla Starr

Be Data Literate by Jordan Morrow

I’m Not a Numbers Person: How to Make Good Decisions in a Data-rich World by Selena Fisk

After you understand the data, you are ready to visualize it, so learn data visualization principles, review best practices, and practice using helpful tools.

Data Visualization Principles

Sometimes a graphic—not words alone—is the best way to communicate data.

Data visualization, the graphical representation of data, complements data interpretation by effectively communicating information and insights. Data visualizations present data in visual formats such as charts, graphs, maps, and dashboards to facilitate understanding, analysis, and decision-making.

Data visualization enhances communication in the following ways:

Enhances Understanding

Visual representations of data simplify complex information, making it easier to understand and interpret. Charts and graphs provide visual cues that help viewers more quickly grasp data patterns, trends, and relationships than raw numbers or text.

Facilitates Analysis

Data visualization enables users to interactively explore and analyze data. Interactive visualizations provide deep insights and meaningful comparisons for users who drill down into specific data points, filter data, and manipulate visual elements.

Identifies Patterns and Trends

Visualizations highlight data patterns, trends, and anomalies that may not be apparent from raw data or numerical summaries alone. By visualizing data, users can identify correlations, outliers, and underlying structures that inform decision-making.

Supports Decision-Making

Visualizations provide decision-makers with actionable insights to support data-driven decision-making. Clear and intuitive visual representations help stakeholders who understand the implications of data analysis to make informed choices.

Communicates Complex Information

Visualizations help concisely communicate complex information and relationships, making data more accessible. Visual representations can convey a wealth of information in a single image, simplifying complex concepts or results to diverse audiences.

Promotes Data-Driven Storytelling

Data visualization supports data-driven storytelling by enabling storytellers to weave narratives around visualizations. Visualizations can be used to illustrate key points, highlight trends, and guide the audience through a compelling data-centered narrative.

Encourages Collaboration

Visualizations facilitate collaboration and communication among team members by providing a shared understanding of the data. Collaborators can use visualizations as a common reference point for discussion, analysis, and decision-making.

Aids Memory Recall

Visual representations of data are more likely to be remembered than textual or numerical information, making visualizations an effective tool for conveying important insights and findings.

Engaging visualizations can captivate stakeholder attention, increasing their engagement with the data and improving their comprehension and retention of key information.

To maximize the audience's experience, adhere to best practices.

Data VISUALIZATION best practices

Choose the right visualization

Select a visualization type that effectively communicates the patterns, trends, and relationships in your data. Consider factors such as the data type, the relationships you want to highlight, and the story you want to tell. The choice of visualization depends on the data type, the analytical goals, and the target audience's needs for understanding the information you present. Common visualizations include basic line, bar, column, pie, bubble, and area charts. 

Depict Data Studio can help you determine which type of visualization will best fit your purpose.

If you are still having trouble figuring out which kind of graphic will best display data, try using Piktochart's "20 Essential Types of Graphs and When to Use Them". To learn more about using infographics, Piktochart has a great infographics best practices blog.

Just because a number appears in a graphic does not mean your audience will understand it. Business communicators often work with very large or very small numbers, which are too complex for the human brain to comprehend. Translate data into terms that relate to human-scale numbers.

Choosing the right graphic also refers to differentiating between data visualizations and infographics, which both communicate meaning through data but have different purposes.

A data visualization highlights a specific quantitative dataset, typically to answer one question.

For example, this well-executed graph, artfully integrated into a New York Times story, tracks automobile-related deaths and federally mandated safety policies over the past 60 years.

An infographic, in contrast, visually represents a comprehensive story. Infographics, whose purpose is to present a collection of facts that leave room for audience interpretation, often incorporate one or more data visualizations.

Here’s an example from the US Environmental Protection Agency on how households can use less water during the summer months.

After you create a graphic, ask someone else, What does this graphic say to you? The curse of knowledge can render graphics just as confusing as text.

Make an infographic about how you use your time, using one of these online tools: Canva, Easel.ly, Piktochart, Infogram, or VennGage.

Simplify the visualization 

Accurately Display Data

Use appropriate scales and axes to ensure that the data is accurately represented and that viewers can interpret the magnitudes and proportions effectively.

Avoid distorting the data by using inappropriate scales, aspect ratios, or visual encoding techniques that can misrepresent the data and lead to misinterpretation.

anchor the visualization

Storytelling with Data author Cole Knaflic said, "Don't assume that two different people looking at the same data visualization will draw the same conclusion. If there is a conclusion you want your audience to reach, state it in words."

Include contextual information and explanatory text to help viewers accurately interpret the visualization. Anchor it by providing insights and explaining data patterns and trends to guide the audience's understanding. An anchored visualization is cited in the narrative explanation.

Omit visualizations that do not contain audience-focused content, no matter how much time you spend making them. Always interpret your visualizations—unless you do not mind your audience making an incorrect inference. To reinforce the main idea, introduce, position, and interpret your visualizations.

INTRODUCE

Anchor your visualizations by clearly referencing them in the body of your document. Give readers a context for what they are about to see and a reason to care about it. Give your graphics action-oriented meaningful talking titles instead of generic titles. If the visualization uses secondary data, include the data source when you introduce the information. A good way to introduce a data visualization is as follows: “Figure 1 to the right shows data taken from (credible source) that shows (support for a main point).”

POSITION

Next, strategically position graphics so the reader’s eye can quickly identify and locate the information. In addition to stating the data source in the introduction, below the visualization a source tag (usually a small text box). Include hyperlinks to the original data if possible. Use a smaller font so the source tag does not overwhelm the graphic.

If the visualization appears on a separate page from the interpretation, include (where possible) bookmarks between the visualization and its interpretation.

INTERPRET

Finally, interpret the visualization's meaning for your readers. Help them see how your graphic enhances your document's information, recommendation, or argument. Guide the audience so they know what the visualization is, why it is important, and what they should do with the information. This part of the discussion translates the data into human terms so the audience can see why the data matters to them and what they should do about it.

Anchoring Graphics Example (click here)

 Introduce

Figure 1 illustrates results from a Kantar worldwide survey of six major global markets. The data suggests that consumers worldwide feel their cost of living prevents them from living more sustainably. Specifically, about two-thirds of global consumers reported wanting to do more to promote sustainability, but they feel their cost of living is too high to participate.

Position

Figure 1. Cost of living prevents sustainable living

Source: Kantar.com

Interpret

Although the reported sentiment was felt most in countries that ranked highest, like Brazil and India, even 59% of US respondents reported that their cost of living is too high to live sustainably. If sustainability is to become a global priority, then the cost of living must be low enough for global citizens to participate.

Test and Iterate

Test your visualizations with representative users to gather feedback and identify areas for improvement. Iterate your designs based on the feedback and refine your visualizations to enhance their effectiveness and usability.

IN CONCLUSION

Understanding data visualization principles and practices is crucial in today’s data-centric world. By understanding the principles, using and anchoring the right visualizations, adhering to best practices, and avoiding common pitfalls, you can effectively communicate insights, support decision-making, and drive impactful outcomes.

Suggestions?
Please let us know here.