It’s hard to believe that it is already two years ago that Gartner first formally published our definition of modern BI and Analytics. We got a lot right in that first iteration that we recently updated in this note.
One subtle, but important change in this updated note and in the next MQ: analytics is now the first word in the title, usurping BI. We have seen this shift in our inquiries and search trends. Right or wrong, “analytics” sounds smarter, sexier, and well, more modern. I had to chuckle during a recent customer conversation with a VP for a line of business wanting better data and analytics; after I kept talking about BI and analytics platforms, she finally asked, “what’s BI?”
The first iteration of the MQ based on this modern market definition published in February 2016. For some of you, it was a painful time, with your view of your existing BI investments upended. For others, who were well on your way to modernizing, it was validation that your strategy was sound. It’s taken 18 months, but I hope most of you now understand that you need both the mode 1 and mode 2 capabilities within your portfolio. Can you get those capabilities from a single vendor and single product? Sometimes, but for the most part, it’s about augmenting traditional, enterprise BI platform capabilities, with modern analytics and BI capabilities. See this note on how to modernize and this note on positioning the right tool for the right user.
Modern BI and Analytics now reflects mainstream buying. In a webinar last March, 51% of you said you are just getting started on this journey, whereas another 41% say you plan to significantly grow your investments in modern BI. It’s a growth market, woo hoo! More innovation and more work to do, right? There is innovation, and that’s exciting. But there is also downward pricing pressure and fiercer competition. In addition to figuring out which vendors to place your bets with, power and roles within your own organizations are also shifting.
One of the key features of a modern analytics and BI platform is that it has a self-contained performance layer; this may be a columnar or in-memory engine. The analytics and BI platform engine can be useful for data mashups or for shielding users from a slow database. This functionality can challenge old work flows and thinking about the need to build an aggregated data model, or star schema, first. Data warehouse teams and analytics and BI teams need to redefine who does what – and when (see this note.)
Modern analytics and BI also does allow for self-service data preparation, authoring, and exploration. But are all users ready for the full range of these capabilities. No way. You have to walk before you can run, and for some users accustomed to static reports, exploration within a dashboard might be a giant leap forward. Other, more sophisticated users will be banging on your door for more freedom, right now!!! Do you have ongoing training and certification in place? A sandbox concept? Policies on what to govern and what’s safe to allow unfettered access to? If your users are not ready for self-service anything, the benefit of modernizing, then, may be more about giving agility to the core IT/BI team to work smarter or deliver new content faster.
These are just some of the things to consider beyond the tools and technology! If you are well on your way towards modernizing, you might also be ready for the next wave of disruption in the form of augmented analytics described in this note.
As always, there’s a growing team of us at Gartner to support you on this journey!
P.S. We are currently running the Analytics and BI MQ Customer Reference Survey! If you got the invite, I hope you’ll take the survey. Your opinion counts! As a way of thanking you for your time, we also include a free research report.
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