7 Key Ingredients for Knock-out Data Visualizations

by   |   April 1, 2015 5:30 am   |   1 Comments

Bernard Marr

Bernard Marr

Big data analytics will all amount to nothing if you don’t report the results properly to the right people in the right way. After all, what’s the point of investing in big (or small) data analytics if the resulting insights don’t get to the people who need those insights to make better decisions? Make sure you report the results effectively by following these 7 steps:

1. Identify your target audience. Whether you are creating a traditional report or a modern infographic, ask yourself who is going to see it and what do they already know about the issues being discussed? What do they need to know and want to know? And what will they do with the information?

2. Customize the data visualization. Based on the answers to these questions, be prepared to customize your data visualization to meet the specific requirements of each decision maker. Too often in business, reports are disseminated to everyone – “just in case” it’s useful. Or parts of the report are sliced off and sent separately to different people. This just adds to the confusion and overload, and it also increases the chance of key distinctions and insights that are relevant to one group being lost or missed among data that is useful for another group. Data visualizations always should be customized to the recipients and include only what they need to know, putting the information into a context that is relevant or meaningful to them.

3. Give the data visualization a clear label or title. Don’t be cryptic or clever. Just explain what the graphic does. This helps to put the visualization into context immediately.

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4. Link the data visualization to your strategy. If the data visualization is seeking to present data that answers key strategic questions, then include the question in the opening narrative. Linking the data back to the strategy helps to position the data so readers can immediately see the relevance and value of the visualization. As a result, they are much more likely to engage and use the information wisely.

5. Choose your graphics carefully. Use whatever type of graphic that best conveys the story as simply and succinctly as possible. That means:

    • Use only relevant visuals that deliver important information that your target audience wants. Looking good is not a good enough reason to add a graphic, regardless of how clever or funky it is.


    • Don’t feel the need to fill every space on the page. Too much clutter makes the important information harder to find, harder to remember, and easier to dismiss.


    • Use color appropriately to add depth to the information. And be mindful that some colors have unconscious meanings. For example, red is considered a warning or danger color.


    • Don’t use too many different types of graphs, charts, or graphics. If it’s going to be useful to compare various graphs with each other, then make sure you use the same type of graph to illustrate the data so that comparison is as easy as possible.


  • Make sure everything on the visualization serves at least one purpose.


eBook: Big Data Analytics: Advice from Big Data Guru Bernard Marr

6. Use headings to make the important points stand out. This allows the reader to scan the document and get to the crux of the story very quickly.

7. Add a short narrative where appropriate. A narrative helps to explain the data in words and adds depth to the story while contextualizing the graphics. Numbers and charts might give only a snapshot. A narrative allows you to expand on key points, make observations, and highlight implications.

Data visualization pioneer Edward Tufte, referred to as “The da Vinci of Data” by The New York Times, suggests that graphical displays should do the following:

    • Show the data


    • Induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else


    • Avoid distorting what the data have to say


    • Present many numbers in a small space


    • Make large data sets coherent


    • Encourage the eye to compare different pieces of data


    • Reveal the data at several levels of detail, from a broad overview to the fine structure


  • Serve a reasonably clear purpose: description, exploration, tabulation, or decoration


  • Be closely integrated with the statistical and verbal descriptions of a data set.


According to Tufte, “Graphics reveal data. Indeed, graphics can be more precise and revealing than conventional statistical computations.” Although written in 1983, before the Internet, Tufte’s advice still holds true in this modern era of big data – especially in the field of data visualization and infographics.

Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher.

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One Comment

  1. Gail Olin
    Posted April 14, 2015 at 7:36 pm | Permalink

    Very nice tip sheet, concisely and well-written with excellent advice about effective data presentation. Looks like Mr. Marr is a student of Tufte, whom we studied perfunctorily at the SBMI. (Great link to the article about Tufte, by the way!) SBMI mostly teaches Few and Shneiderman, but they’re all bascially on the same philosophical visual graphics page. This is definitely advice worth following!

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