When you google “analytics user adoption”, you get 70,900,000 results. That’s almost 71 million pages! Clearly, this topic is pretty big. And it’s big for good reason. Ever since “Business Intelligence” and “Analytics” became widely-used terms in enterprises, user adoption has continued to be a challenge. But as we head into a new decade, is it time to think differently about analytics user adoption?
In the 1990s and early 2000s, we experienced the first generation of analytics, or what we call BI/Analytics 1.0 companies. The users in this generation were mainly a select few in IT. The tools were pretty complex and these technical users would have to use their coding and SQL skills to create reports for the business. It took considerable time to create and email reports to groups of users. Reports were sometimes made available on the company’s network or even its intranet. Revisions and new reports would would take days or weeks to create and that was only when they didn’t compete with other priorities. As a result, analytics user adoption really, really sucked!
Spurred by the acquisition of several 1.0 companies, and the advent of SaaS, the 2010s saw the rise of Analytics 2.0 companies. These companies focused on self service and embedded capabilities. Users could now build reports on their own using drag and drop functionality. They could experience analytics from inside the applications they were using on a day to day basis. Analytics user adoption grew to include specially trained business analysts. These analysts understood both what questions to ask of the data and how to build reports and analysis. However, they still needed some technical skills to create reports that make sense. Reports and analyses were typically available on internal websites based on specific user permissions. As a result of the focus on simplification, analytics user adoption rose considerably. But most companies still license by user, which didn’t fix the analytics user adoption problem.
As we begin to enter the 2020s, we see a similar shake-up in the Analytics space. Several Analytics 2.0 companies have been acquired in recent years. The modernization of software moved from just being cloud-enabled to being cloud-native. Machine Learning and large data volume processing are essential. Self service and embedded capabilities are table stakes. Enter Analytics 3.0! The companies that fit this profile can analyze all of your data, are easy to use, are cloud-native and serverless. They fix the analytics user adoption problem by bringing analytics to every user through automation.
With Analytics 3.0 companies, analytics come to the user instead of users needing to go to their analytics. Analytics is always on, always running and constantly scanning your data for anomalies and the crossing of established KPI thresholds. However, with analytics now running in the “automation layer” and not in the “visualization layer” as it has been with Analytics 2.0 companies, the question of analytics user adoption shouldn’t exist anymore. Every app will become an analytic app and every user will be an analytics user. We have reached a state where measuring analytics user adoption separately is unnecessary. The promise of analytics is finally here.