7 Key Ingredients for Knock-out Data Visualizations

In this post I look at the key ingredients that make data-visualisations great. The post first appeared in my column for Data Informed. 

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 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 plus it increases the chance of key distinctions and insights that are relevant to one group being lost or missed amongst data that is useful for another group. Data visualizations should always be customized to the recipients and only include 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 immediately put the visualization into context.

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. Obviously linking the data back to the strategy helps to position the data so the reader 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 wisely. Use whatever type of graphic 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 it harder to find the important information, 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 graph, chart or graphic. 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.

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

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

Referred to as the “da Vinci of Data” by The New York Times, Edward Tufte suggests that graphical displays should:

• 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 and
• 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 even the internet Tufte’s advice still holds true - especially in the field of data visualization and infographics.

I hope you found this post useful. I am always keen to hear your views on the topic and invite you to comment with any thoughts you might have.

Here at LinkedIn and at Forbes I regularly write about management, technology and the mega-trend that is Big Data. If you would like to read my regular posts then please click 'Follow' and feel free to also connect via Twitter, Facebook and The Advanced Performance Institute.

Here are some other posts from my Data Informed column:

• Take a SMART Approach to Big Data Analytics
• How to Navigate the Thorny Issue of Data Privacy
• 7 Key Ingredients for Knock-out Data Visualizations 

About : Bernard Marr is a globally recognized expert in big data, analytics and enterprise performance. He helps companies improve decision-making and performance using data. His new book is Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance'.

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