I had the honor of presenting an Americas SAP User Group (ASUG) session on the latest big trends in BI & Analytics at the SAPPHIRENOW SAP User Conference this week in Orlando, Florida.
It was a packed audience, the feedback was very positive, and the slides are now available for review and download on Slideshare.
As usual, it was a real challenge to try to fit everything I’d like to have covered in under an hour — to give you a flavor of the talk, here’s some notes on the first section concerning the top trends.
Analytics is as hot as ever! When you look at Gartner’s list of the top strategic trends for 2017 you can see that “intelligence” is at the top of the list, including machine learning, intelligent apps, and intelligent things. And according to Gartner’s long-running annual CIO survey, BI & Analytics is yet again the top priority, as it has been for ten out of the last twelve years!
Digital transformation = analytics. One of the reasons that Analytics remains a hot topic is that the amount and variety of data available has skyrocketed, constantly creating new analytic challenges. But even more importantly, analytics has become an essential part of digital transformation.
For the last few decades, we’ve typically thought of business intelligence as a byproduct of our operational processes. In other words, we manufacture products, and ship them around the world, and sell them to customers. Each of these processes generates a lot of data, and we use that data both to keep track of operations and to create more optimized processes in the future.
This remains as true and important today as it’s ever been in the past. But now there’s another dimension coming into play. Organizations are increasingly realizing that digital transformation doesn’t just require new processes – it requires a new approach to implementing processes. They have to be more agile, more intelligent, and more responsive to change.
These new processes flip the traditional equation on its head. New processes are created on the fly, powered by analytics. The typical customer journey is a great example. Think about how you purchase a product today. In the old days, it was a fairly linear process, that companies characterized as a “sales funnel”. But now it’s more like a “write your own adventure” book – where there are many different possible interaction paths, and at each point in the process, you as a customer get to choose the next chapter. Analytics is being used to help the customer make the right choice at each point – “what other products might you be interested in?” or “Would a discount get you to purchase right now?” – in other words, every “customer process” is unique, we can let analytics do all the work, creating thousands or millions of personalized “processes” based on the needs of each individual.
Because these new processes are analytics-powered, they can be much more agile and responsive to change – indeed, new machine learning approaches mean that they can update themselves.
Effectively creating and managing these kinds of flexible, on-the-fly processes is THE big new opportunity in digital business. But it also means that analytics has to have a more process-oriented approach, not just treated as a series of one-off decisions. SAP has always been a leader in traditional processes, and we’re now hard at work extending our leadership into these new digital transformation processes.
Gartner believes that information and analytics will be used to reinvent, digitalize, or eliminate 80% of today’s business processes. Analytics is no longer just an afterthought to the ”real business” – it’s a the heart of the new business models of the future.
The problem with analytics. While analytics is a hot topic, it doesn’t mean that everything is roses. Various reports indicate that the reported success rate of BI deployments has stalled. It’s not completely clear what BI “success” really means – very few organizations actually define what success would look like before they start on a projects. But I believe that numbers remain low because even though BI technology continues to improve, business expectations have risen even faster.
The penetration of BI remains low, with 55% of organizations reporting less than 20% of their staff using BI — but the good news is that user adoption grew noticeably in all sizes of organization over the last year.
Users of enterprise analytics complain that it’s too slow, with almost a third having to wait days or weeks for a BI request. And in theory, people should be able to access information themselves without needing IT, but a third said that they find their enterprise BI too complex, too complicated, and too cumbersome to use. Finally, and most importantly, over half the data that business people want to access is now from outside the organization, and is therefore unlikely to be in the corporate system in the first place.
The number one thing that organizations give themselves low marks on when implementing BI projects is “people” and “process” — changing culture is much harder than implementing technology. At the recent Gartner Data & Analytics event in London, fully 85% of the audience agreed with the statement “The most significant internal roadblock that I face in my role is culture and change management” — and the SAPPHIRENOW audience agreed!
Taxis vs Uber. From the point of view of business users, traditional analytics organizations look like the taxi companies that have been displaced by more flexible car-sharing applications like Uber and Lyft. People always found taxis too expensive, and annoyingly hard to find when you wanted one – but there was no alternative, so they put up with it. Uber came along using all the new technologies to provide a much easier, more seamless experience. But the taxi companies complained that they were breaking all the rules.
We’re seeing something similar inside organizations, as traditional business intelligence teams try to adapt to the rise of self-service “modern BI” tools. There are now lots of powerful, lightweight analytics products available, and business departments increasingly have their own IT budget to spend. The result it that the older ways of doing things are being disrupted, and just like the taxi companies, outright resistance is both futile and bad for customers. Traditional analytics organizations have to adapt to the new tools and new ways of working.
Other sections of the talk included an introduction to “Modern BI“, the need for decision processes, the transformation of analytics through predictive and artificial intelligence, the latest trends in effectively staffing and organizing analytics initiatives in organizations, and a bonus section on the trends in big data architectures.