6 Tips for Improving Business Decision Making through Data Visualization

Updated: Sep 3



Marketers rely heavily on understanding data to make better business decisions. But if they don't know how to use that data to tell a clear, actionable story, none of that data you are collecting will matter.


Data visualization is the art of distilling information to tell a story that conveys sharp messages and clear insights. Far from an easy task, executing effective data visualizations requires that marketers can select the right data to pull, decide how to lay it out visually, and present it in a way that provides direction and support for strategic decision-making.

Here are a few tips for how to execute data visualizations that drive strategic business decisions.

1. Be compelling

In any presentation you are competing for the audience’s time and attention. Be selective about the data you present, making sure that your visuals either affirm the current course of action or indicate that change needs to occur.

2. Know your audience

Who are you presenting to and how much do they know about the topic? This will help add structure by ensuring you are presenting only what is relevant. For example, when you’re presenting to a marketing analyst versus a campaign manager the analyst will want to see a more granular presentation of data, while the campaign manager will be interested in top level points and key takeaways. If presenting to fellow analytics colleagues, ensure you have t’s crossed and I’s dotted. The key here is to match your content to the potential interest of the audience.

3. Choose the right visual

Understanding the rationale behind using certain chart types can help you choose the best visualization for your audience. Data visualizations can generally be grouped into one of four usages: comparison, relationship, distribution or composition.

Comparison: The column chart is probably the most used chart type for comparison across two variables, e.g. geography versus product sales. Column charts present differences of amounts clearly and effectively. It is easy to consume this style of visualization for any audience with a varied level of data knowledge.

Relationship: Scatter charts are primarily used for correlation and distribution analysis, and are effective for showing the relationship between three different variables. A good example of scatter chart would be a chart showing product sold vs. revenue by family type.


Distribution: Histogram is a common variation of column charts used to present distribution of a single variable over a set of categories. A good example of a histogram would be a lead score distribution. The below example shows that the majority of leads have a score greater than 60. This indicates that the leads are interacting with the content served to them. This can be monitored over time to track progression of leads through the funnel.

Composition: Use stacked column charts to show a composition, e.g. to show totals against parts. Stacked column charts allow for the total to be apparent across categories, while showing the differences between sub-categories.

The below chart shows that Strategy 5 was the least effective overall, due to sales from Product D being much lower compared to the other strategies.

4. No room for assumptions

Visuals should not be confusing. On the contrary, they should communicate your message in the most direct, intuitive way possible.


Many assume, for example, that text should be used sparingly on data axes or labels. While too much text can distract from the effectiveness of your visualization, there is no reason to leave your audience guessing about whether they are interpreting your story correctly. Make the titles self-explanatory. When labeling or using a set of taxonomies, use a common language with an agreed meaning.

The below example clearly conveys how text can provide clarity to a visualization. Labeled axes and lines guide you to the important numbers and clearly explain what is being measured.


5. Keep it honest

Transparency is key to the effective use of data, and there are many ways that a badly designed visualization can unintentionally deceive an audience.


One of the most basic but deceptive mistakes that a marketer can make with a data visualization is to scale the axes improperly.

If you look at the visualization below, at first glance, the Product XYZ and Product ABC seem to be performing in a similar fashion but upon closer look, the data reveals that Product XYZ out-performs ABC in all the regions. Because two charts display different scales on their Y axes, the visualizations are misleading when compared.


When using two similar graphs to show performance or comparison, ensure the axes are always to scale, or clearly point out differences to the audience.



6. Less is more


Remember, the aim of data visualization is simplicity and understanding. Stay focused on the question that you are trying to answer. Creating multiple bar graphs is simpler than having one complicated heat map -- you can always create more visuals if you need to tell a multi-faceted story.


Effective visualizations are invaluable tools for driving data-driven decision-making in any B2B organization. With these tips in mind, you will be equipped to master the art of communicating your data insights in a clear, compelling way.

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