The outlook for a successful business intelligence (BI) project is not good. At least that’s what Gartner says: BI projects have a 60% failure rate. But your reaction to this statistic and the reasons Gartner cited it, primarily have to do with your own experience as an implementer or end-user of BI software. Perhaps IT teams are surprised when they hear that Gartner attributed this high failure rate to the inability to understand the real needs of the business, and the failure to communicate using a common language. But, for end-users of BI solutions, who struggle to get data insights to make decisions, it's less surprising. There was no deep user adoption of the software.
IT is not entirely to blame. Rather than choose a solution that will transform the way the business makes decisions they opt for the fastest car. In this feature rich arena of BI, it is easy to lose sight of the ultimate goals of the data strategy.
User adoption is the key measure of a successful BI project
Once selection of the BI solution has been made, what follows is often times an exercise in futility. IT and the internal project team find themselves trying to anticipate every need and situation that end users will encounter. In an effort to pre-answer every question and provide every meaningful KPI, however, end-users can feel like they are looking for the proverbial needle in the haystack.
With users overwhelmed with information, and unable to make use of these complex systems to get what they need on their own, user adoption typically suffers. Which is sad because user adoption is the single most important measure of business intelligence success.
It is not uncommon for businesses to find themselves with a solution that fails to deliver user adoption, busies already stretched IT departments, and leaves users searching for meaning in a sea of charts and reports.
What if the current model was turned on its head?
What if companies decided to abandon the aforementioned approach to business intelligence? What if instead of trying to anticipate every piece of information that IT and the business thinks will be necessary for the end user, and attempt to spoon feed the answers to them. What if the model was turned on its head? What if users were empowered to go find what they are looking for, or to ask the questions and follow their train of thought to the insight or outcome that the user wants? It sounds scary to some, but this is the impetus for a different approach to data management called decision enablement.
Decision enablement starts with the premise that the ultimate desired outcome of any data solution (business intelligence included) is high user adoption. Adoption can be measured as the number of users making more data driven decisions. The more users making decisions —based on data—the better for the business. Few would disagree with that aim. Where decision enablement begins to break off from traditional BI is not in its goal (BI is well intentioned in this destination as well), but in its approach, which is a notable shift in perspective to the wants, needs and desires of the end user. Instead of empowering and enabling IT, decision enablement focuses on the end user first.
So, what does the end user want? They want —what they want —and because of the totality of that statement, it’s impossible to anticipate every possible permutation of that before it arises.
So how do you give the end user what they want when neither IT, nor the business, nor the user can anticipate what it might be? You deliver them a framework for making on the fly decisions.
And there you arrive at the emerging definition of decision enablement: A user intuitive framework for ad-hoc decision making across the broad base of a company’s user community…not requiring support or intervention from IT. This has never been more important.
In the increasingly fast paced Covid world, with economic shutdowns, supply chain disruptions, unprecedented business failures, etc., more and more companies are waking up to the need to enable and empower users to make decisions —in the moment. The traditional IT-centric business intelligence paradigm will continue to struggle to meets the needs of the growing number of decision makers on the front lines of businesses. Decision enablement will be the next chapter in companies desire to manage data and make decisions.
For more information about BI user adoption and decision enablement, get this free eBook for Executives: Building the case for data analytics.