Data Visualization: Understanding User Adoption

Getting a high user adoption rate for your data visualization (data viz for short) is your most important goal. Although this may seem obvious, user adoption (UA) is an afterthought in many organizations. It’s the thing that everyone focuses on after the solution is rolled out to users.

User adoption (UA) is defined as the measure of how much of the intended audience uses the provided solution (in this case, the data visualization). This concept gets a bit murky, however, when you delve into what should actually be measured. Should you measure how many times the data viz is being viewed or the average length of time for which the data is viewed? Perhaps you should measure how many times the data is used to conduct exploratory activities.

The secret of measuring UA is that UA is a combination of many elements. In the business data world, UA isn't just a measure of use, but also a measure of the value added to a user.

As you begin to analyze UA rates, you need to understand the following five metrics:

  • Frequency of use: Frequency of use measures the number of times an individual user uses your data viz. To gain an accurate number, you want to make this metric an average based on overall frequency of use.

  • Interval of frequency of use: This measures when your data viz is actually being used, as in time of day, month, quarter, year, and so on. For example you could look at data that has been used between January 2013 through December 2013. Interval of frequency of use involves how often the data being displayed is updated, but it should measure when users access the data viz and perhaps when they find the most value.

  • Area of frequency of use: This metric is one of the most important to consider. It tells you which sections of the data viz users visit most. It also tells you what areas need to be enhanced or removed from future updates. Finally, it provides a clear focus on what is most valuable to the user. When you see what is used and what is ignored, you get a clear idea about what is truly useful to viewers.

  • Type of use: Measuring how a data viz is actually being used may be a bit tricky, but it's critical to the long-term adoption and success of the tool. If you built a data viz that has drill-down capability, and no one ever clicks to go to more details, that particular feature (or type of use) isn't providing much value to the user.

    Unfortunately, many of the data viz tools or systems in the market lack the ability to track UA metrics. You may want to conduct monthly or quarterly polls or surveys of your users to gain insight into how, when, and for what purpose the data viz is being used. Doing your own investigation is the only sure-fire way to ensure that you can provide continuous improvements to your data viz so that it will be continually used by your audience.

    If you find that users are frequently exporting the raw data in a visualization, that's a clear indication that users don't trust the data viz itself and are using the data viz as nothing more than an export tool. This measure in conjunction with the frequency of use metric tells you a lot about the actual value, or lack thereof, that the users are getting from the data viz.

  • Number of total users compared with targeted audience size: This metric is perhaps the most popular measure of user adoption and is best measured as a percentage. You derive it by taking the total count of the intended audience and the number of users who are actually using the data viz and expressing that figure as a percentage. Suppose that you build a data viz for a sales organization of 500 people. If 50 of those people access the data viz on a regular basis, you have a 10 percent UA rate.

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