Insights that are drawn from large amounts of data are often subtle and complex. It’s naturally more challenging to communicate these insights to people, particularly crucial stakeholders, decision-makers, and executives. But if properly designed, data visualization can present data in a visually stimulating way that makes it easier to comprehend the information given to us and helps us draw answers and insights faster, enabling us to separate actionable data from noise.
It’s important to remember that data visualization is not inherently about insight but instead being a vehicle for communicating that insight. Good data visualization design is imperative to achieving this. If the wrong visual is used or is poorly designed, the end message can be skewed.
This post will focus on defining actionable data; and how good data visualization design is critical to making data actionable.
What is Actionable Data?
By definition, actionable data is data that can be acted on or data that gives substantial insight into what the future holds so that the actions decision-makers should make become apparent. Decision-makers can break down actionable data into data that communicates direct and indirect actions.
Direct actions are things decision-makers can do immediately based on the insights data visualization communicates. For example:
- A list of target customers to contact via email.
- An analysis of marketing data could kick off a targeted digital campaign.
To operationalize direct actions from your data, confidence and experience is needed to identify what’s essential in the data and how you should react. Data visualization can make it easier to identify these insights.
Indirect actions occur when data, or more specifically, data visualizations, help to point people toward making a better decision without deciding on the spot. Indirect actions often involve communication and collaboration between people. For example:
- Sharing insight with executive leadership.
- Capturing an understanding that will ultimately act as input to a strategic plan.
- Creating an action item for your team based on the results of an analysis.
- Sending a snapshot of visualization to a colleague to initiate a discussion.
These types of actions play a crucial role in making an organization smarter. For example, a sales team meeting to discuss what is and isn’t working with their strategy, is an essential step forward and contributes to the decision-making process.
The goal for data visualization should be to drive direct action based on data with scoring or optimization models for certain activities, as well as decisions that involve more human-involvement and careful consideration — indirect actions.
How Data Visualization Design Can Make Your Data Actionable
Actionable data tends to become more apparent when data visualization techniques are leveraged. This is because data visualization makes picking out the crucial details hidden within the big picture considerably more accessible. After all, they are now visible. When it comes to dealing with unstructured data, data visualization is a huge timesaver.
However, with an abundance of visualizations to choose from, sometimes people choose to represent their data with visuals that don’t serve it well, for example, trying to communicate changes over time with a pie chart, when a line graph would be a more appropriate visual. In turn, this mistake influences how people understand the data and impacts their ability to make that data actionable. The key to good data visualization design is not just the visual itself but the thinking that goes behind choosing which design would best represent that data. To make data actionable, data visualization design needs to consider the needs, goals, data sets, and what insights need to be extracted. This will help guide people to choose an appropriate visualization which in turn will ensure that necessary insights to make data actionable will be displayed.
Below we highlight three fundamental principles that good data visualization design must follow to ensure that these visualizations represent data accurately and make actionable data easily identifiable.
- Good data visualization design ensures that only one significant insight is communicated per visual.
- Everything stated in the visual points to and led to the overall story being told.
- Good data visualizations are simple but not oversimplified to ensure findings and key data are not lost.
- Good data visualization design explains the methodology used for deriving insights to help lead discussions to make the data actionable.
- All assumptions underlying the analysis should be stated. Again this helps to get everyone on the same page.
- Good data visualization design represents uncertainty and limitations accurately and honestly because it is widespread and tempting to exaggerate the data’s implications, altering or negatively impacting the decision-making process.
Let’s use this Bloomberg chart from 2012 as an example to demonstrate how essential data visualization design is to making data actionable.
By examining the above chart, we can conclude that American male workers’ median income has been in drastic decline from 1972-2012. However, is this an accurate way to represent the data? Is there something missing? Is there more information that we can pull from the data set that may impact someone’s decision-making?
Let’s look at the below visual representing the same data set.
With this new visual, we see a more accurate representation of the dataset lost in the oversimplified visualization above. In this slightly more detailed graphical representation, we can see a lot more fluctuations in the numbers and that the decline is not so drastic.
Good data visualization design is critical to making data actionable. These visual representations can be highly impressionable and shape how we interpret that data. At InterKnowlogy, we understand the vital role data visualization plays in a business’s success and its role in powering mission-critical decision-making. If your organization is ready to get the most out of its data, we would be happy to chat in more detail about how we can create your custom data visualization solution. Contact us at email@example.com.