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    Tips » Internet » What are the Essential Tips to Transform Your Data Visualisation?

    What are the Essential Tips to Transform Your Data Visualisation?

    Posted on February 24, 2021 | By Shinely Ainsworth


    Data visualization is incredibly important when exploring vast pools of data. By translating data into something more visual makes understanding key insights far more tangible and human.

    By brushing up on your data visualization skills, you’ll be able to gain a competitive edge over the competition with the ability to make the most of whatever data you have available. The majority of enterprises nowadays understand just how important data visualization is. When you’re making data-led decision making, the ability to quickly digest information with ease is critical, so you can see where the opportunities are from enormous pools of data, that feature multiple touchpoints.

    We all depend on data visuals to gain the insights we need to shape our strategies and viewpoints; that’s why it’s now more important than ever to extract as much as we can from every visualization that’s produced. As datasets are inherently growing, they’re also becoming increasingly more difficult to comprehend and analyze. So, having the ability and skills required to effectively convey huge chunks of data is incredibly important for the human element at the end of the decision-making process.


    There’s far more to badly designed data visualizations than just a jarring clash of colors, it could seriously weaken the data insights that could be understood and, at worst, cause the information to be potentially misleading. By causing this frustration and confusion to the reader, this could mislead them and lose the reader the competitive edge on the insights they’re looking for.

    If you want to see some of the best and worst examples of data visualization, I’d recommend this article.  However, brace yourself for some shocking examples of how NOT to present this!

    To ensure that you gain maximum insight and avoid these risks, have a look at our list of top tips that can seriously help to optimize your data visuals when using any data analytics tools. This certainly isn’t an exhaustive list as there’s plenty of other techniques to follow to make engaging data visualizations, but hopefully, these tips can help you on your journey.

    Decluttering Your Visuals

    The best data visuals articulate your insights concisely, clearly, and efficiently; displaying huge volumes of data that are easy to digest. The primary goal when building data visualizations should always be to create something that’s easy to understand and starts telling a story. This means editing out whatever’s unhelpful, and enhancing whatever lets the data speak for itself.   

    Function Over Form. Design to be Understood, Not Fancy

    Take a few paces back and consider your data visual. Ask yourself what could be further optimized to boost comprehension?’ Are all the elements there to support the data being presented? If they aren’t, consider going even further.

    If your graph or chart doesn’t present the data, or if it appears too overcrowded, less may be more. The key takeaway should be to always make what you’re presenting as digestible as you can for your audience. This could be something as simple as slightly altering the position of an element to draw attention to it.

    Be Mindful with Your Use of Color

    Do more colors mean a better-designed visualization? Absolutely not. Colour should be used purposefully, with a limit of just 6 colors in a single visual. When used mindfully, using color is an effective tool to designate, separate and categorize large sets of data to great effect.  

    However, when used badly (for example, using clashing colors) the viewer could be confused and distracted. To ensure that you use color correctly, try to be mindful of its function and role holistically within the visualization. 

    Think About the Contrast Between Colors  

    If your colors appear too similar, they may confuse your audience. There is a fine line, however, as sometimes creating too much of a contrast between colors, can become extremely distracting and detract from the information you are trying to illustrate.

    Steer Clear of Patterns and Textures 

    The inclusion of patterns and textures might add a few visual stimuli but use with caution, as they could take attention away from your data story. 

    Is Your Copy Critical?

    When data is presented in a clear and coordinated way, the requirement for excessive passages of text diminishes. By taking the time to consider what text is necessary for your data visualization – places the readers’ attention purely on your data.  

    Focus on Legible Fonts

    It may be tempting to take liberties with the font to suit something more stylistic, but in all cases, you should prioritize what’s most legible over design alongside your data visualization.  

    Placement is Also Important 

    It might not seem important at first, but where you position your text has a huge bearing on the effectiveness of your data visualization. For maximum effect, place your text in the upper left-hand corner. This is where the reader’s eyes naturally gravitate towards, so it’s worth capitalizing on. 

    Get rid of Anything that Doesn’t Optimize Your Data 

    No matter how well designed a data visualization is – if the reader becomes distracted unnecessarily from the data story you’re illustrating, let it go.  

    Remember, the best data visuals display all the relevant information concisely, at a glance.

    In Summary

    Hopefully, these tips will help you on your journey of building better data visualizations. All data tells a story and the better your visuals, the better your data will be able to tell a story. Always consider your audience and try to imagine how you’d perceive what you’ve produced. 

    Building great visuals won’t be something you’ll master overnight, so make sure you stay critical of your work and take on board feedback from others. It’s certainly a valuable skill to have.


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