“It doesn’t matter how well data is presented if the dataset isn’t complete or is incorrect in the first place.” I’ve seen this statement scattered across numerous articles and blogs – all warning of the dangers of “dirty data.” Heck, we’ve even written about the risks of bad data.
Of course it’s crucial to present complete, correct datasets to your end users. But it also absolutely matters how well the data is presented. In fact, when data is shown in a poor interface or presented in outdated designs, users are likely to reject your analytics solution altogether.
According to our 2017 State of Analytics Adoption report, 67% of business users say they have to switch to separate analytics tools to get the data or analysis that they need – something that analysts have said can waste up to one to two hours a week per worker.
All this toggling between applications has contributed to a two-year decline in adoption of self-service analytics tools. Even though access to these tools has increased over the past few years, actual usage of self-service analytics is down 20% since 2014. It goes to show that presenting data in standalone applications that force users to leave their current workflows for analysis is not only inefficient, but also outdated. It doesn’t matter how simple a tool is: If a user has to go outside their usual workflow to use it, there will be friction and an unwillingness to adopt.
There’s a better way to present data (after you’ve made sure it’s complete and correct, of course!) – and that’s by embedding analytics into the applications people use every day. This kind of integrated access to analytics is something that 84% of users say is important to them.
The goal of embedding is to help users work smarter and more efficiently by incorporating relevant data and analytics in context of their workflows. This is in contrast to traditional BI, which shows insights from data in the silo of a separate analysis tool.
Business users want streamlined tools that help them work efficiently. Standalone analytics solutions fail to meet this demand, while embedded analytics injects insights right where people are already working.
Take, for example, Service King Collision Repair Centers, a US-based collision repair company with over 300 locations. To ensure high user adoption, they embedded analytics into their native proprietary management solution. Upon logging in, over 3,500 teammates can immediately see the analytics dashboard embedded in an already familiar environment.
Within the tool, each dashboard is tailored to individual roles within the company, providing access to nearly 70 key metrics related to the business and individual location. By giving frontline managers a complete view across their entire repair center operations, embedded analytics has helped Service King increase productivity, real-time feedback, and efficiency.
Embedded analytics have come a long way from simple static report modules in a business application. Today, embedded analytics can be interactive, offer drill-down and drill-through capabilities, and even provide self-service to end users that need to answer questions they haven’t yet thought of. The future of embedded analytics goes beyond these capabilities – ultimately allowing for the analytics and the application to talk to each other and kick off workflows, further reducing the need for users to ever leave the app.
So whether you’re a product manager at a software company or an IT leader in an enterprises, 2017 is the time to focus on improving or updating the way your analytics are presented in your products and applications. Otherwise you risk experiencing the continued decline in analytics adoption we’ve been seeing.