Businesses are clamoring to use data to get a competitive edge, but all the data in the world won’t help if you can’t understand it! 

Whether you use Chameleon BI, Power BI, Data Studio, Domo, Tableau, or another data visualization tool, the principles are the same. But, don’t assume your vendors are in lock-step with data visualization best practices! 

Vendor defaults frequently violate key principles of data visualization, so it’s up to the analyst to put these principles into practice.

Here are our 10 tips for presenting data:

  1. Recognize that presentation matters: The first step to presenting data is to understand that how you present data matters. It’s common for analysts to feel they’re not being heard by stakeholders, or that their analysis or recommendations never generate action. The problem is, if you’re not communicating data clearly for business users, it’s really easy for them to tune out.
  2. Don’t scare people with numbers: Analysts like numbers. Not everybody does! Many of your stakeholders may feel overwhelmed by numbers, data, charts. But when presenting data, there are little things you can do to make numbers immediately more “friendly.”
  3. Maximize the data pixel ratio: A simpler way of thinking of it: Your pixels (or ink) should be used for data display, and not for fluff or decoration. 
  4. Save 3D for the movies: With a 3D chart, you’re adding an extra cognitive step, where someone has to think about what they’re looking at.
  5. Don’t use pie charts: We aren’t as good at judging the relative differences in area or circles, versus lines. For example, if we look at a line, we’re more easily able to say “that line is about a third bigger.” We are not adept at doing this same thing with area or circles, so often a bar or column chart is simply easier for us to process. They’re used incorrectly. Pie charts are intended to show “parts of a whole”, so a pie chart that adds up to more than 100% is a misuse of the visualization. They have too many pieces. Perhaps they do add up to 100%, but there’s little a pie chart like this will do to help you understand the data. If you do want to use a pie chart, it shouldn’t represent more than three items. The data has to represent parts of a whole – the pieces must add to 100%. You should only use one pie chart. As soon as you need to compare data (for example, three series across multiple years) then pie charts are a no-go. Instead, go for a stacked bar chart.
  6. Choose the appropriate chart: A chart should be carefully chosen to convey the message you want someone to take from your data presentation. Use line charts to demonstrate trends. If there are important things that happened, you can also highlight specific points.
  7. Don’t mix chart types for no reason: Presenting data sets together should tell a story or reveal insights together, that isn’t possible if left apart. Unfortunately, far too many charts involving cramming multiple data series on them is purely to conserve the space of adding another chart. The problem is, as soon as you put those two series of data together, your end users are going to assume there’s a connection between them (and thus, waste valuable brainpower trying to figure out what it is).
  8. Don’t use axes to mislead: One easy way to mislead readers is to change the axes of your data. Doing so quickly magnifies what might be small differences, and can distort the story your data is telling you. The most truthful option is to always start your axes at zero. 
  9. Never rely solely on the color: Color is commonly used as a way to differentiate “good” vs. “bad” results, or “above” or “below” target. The problem is, about ten percent of the population is colorblind! And it’s not just red/green colorblind. There are many other kinds of colorblindness. As a result, ten percent of your stakeholders may actually not be comprehending your color scheme. 
  10. Use color with intention: Being careful with color also means using it consistently. If you are using multiple charts with the same values, you have to keep the colors consistent. Consider the tax on someone’s interpretation of your visualization if they constantly have to think “Okay, Instagram is blue on this chart, but it’s green on this other one.” Not only are you making them think really hard to do those comparisons, but more likely, they’re going to draw an incorrect conclusion.

If you ever need assistance with presenting your data, feel free to reach out to the Chameleon BI team to help with your visualization!

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