An application can live or die by its embedded analytics. It doesn’t matter if the rest of your product is perfectly designed. If your dashboards and reports have a disappointing user experience (UX), user adoption and customer satisfaction can plummet.
“User experience matters,” writes Gartner in their recent report, 5 Best Practices for Choosing an Embedded Analytics Platform Provider. “Embedded analytics [should] not only support embedding of charts and visualizations, but also go deeper and integrate the data and analytics into the fabric of the application. This ‘seamless’ approach means that users don’t even know they are using a multiproduct application.”
How solid is your analytics UX? Ask yourself these three questions to gauge where and how you can improve your analytics experience:
#1. Do you have a deep understanding of your users?
A lack of understanding about what users need from their dashboards and reports is a challenge that plagues product teams. Many companies fill their analytics with data they think their users want, and never do the due diligence to find out what users actually need.Don’t assume what your business intelligence users want. Take the time to research how users will interact with your application, so you can build it with them in mind. It’s a seemingly obvious but often-missed point: Different end users want to use your application’s embedded analytics in different ways.
#2. Does your embedding stop at the visualizations?
Embedded analytics involves more than white-labeling some charts and graphs. Application teams need to look at the complete experience—not just the visuals—to ensure end users can’t tell where your application ends and the embedded analytics begins.A truly seamless experience “allows users to take immediate action from within the application, without shifting context,” notes Gartner in their report. Ideally, you want to integrate the analytics into your users’ workflows by letting them take action from the analytics, write-back to the database, and share insights in context.
#3. Do the visualizations match your data?
Another common problem is choosing the wrong data visualizations to illustrate your datasets. Most visualizations are good for some types of data, but not every type. For example, a scatter chart works well to display two variables from a dataset, but it’s only useful when there is a number value on each axis; without that, it will appear to be a line chart without the line. Or consider the common pie chart, which is great for four or five values—but completely breaks down when sliced into dozens of sections. These are just two examples of how poor UI/UX can make information difficult for a user to understand.
If you’ve answered “yes” to any of the questions above, it’s time to update your analytics before your customers start abandoning your product for the competition. Learn how to take the next steps in our Blueprint to Modern Analytics guide.