Whether it’s the famous Moore’s law, or our own Sentient Enterprise capability maturity model for analytics at scale – there are lots of frameworks for understanding how innovations and breakthroughs build exponentially on one another to fuel rapid digital growth. One thing that’s becoming increasingly clear is that analytic solutions must harness and scale innovation at the real-time pace it’s happening. Sometimes, this rapid success creates entirely new challenges of its own.
I’ve previously advocated fail-fast strategies for maximum insight, recovery and ultimate competitive advantage. What I’m talking about here is the “symptom of success” kind of challenges that exist only – and precisely – because some new technological capability made them possible.
Unexpected Challenges and Game-Changing Solutions
Some of these symptoms of success can be as vivid as they are technical. Consider an example by way of a senior data executive at a leading car sharing service during his discussion with colleagues at a 2016 industry forum: It turns out that – while the company’s piloting of a driver-less taxi program in Pittsburgh, Pennsylvania, has been a success – the scenario raises new and unsavory issues like, “how will the car know to clean itself if an occupant throws up during a ride?”
Fortunately, the executive told his panel audience that algorithms don’t have weak stomachs – allowing them to hypothetically leverage environmental sensors and cleaning crew logistics for new solutions to keep you, as the next passenger, from finding out the hard way that a taxi should have been taken out of service.
As another example, at that same forum, I also heard a senior data analytics executive for a Major League Baseball team talk on the legalities of using players’ health data for competitive advantage. Analytics now make it possible to align health data with real-time metrics to gauge player fitness, fatigue and performance during games. But, that has created a new conundrum: Does that valuable health data belong to the team (as a team asset), or to the player (as federally-protected personal health data under HIPAA)?
In this case, the executive said the team’s policy was to honor patient privacy while still leveraging less sensitive aggregate data or other input, like physical performance evaluations. Regardless of the exact remedy, that’s the kind of policy question that only becomes possible as new capabilities point the way.
Innovation drives capabilities; which, in turn, drive new business or policy problems. The previous two examples were fairly narrow to illustrate a point. But let’s take a broader dynamic that is affecting our entire industry – that’s the lack of digital leadership.
As with other symptoms of success, the shortage of digital leadership comes amid tremendous growth in capacity, connectivity and bandwidth that’s putting unprecedented big data capabilities within most companies’ reach. The challenge – as I’ve often quoted industry analyst Tom Davenport – is that “you can buy digital capabilities a lot more easily than you can buy digital leadership.”
“If you don’t address both the physical assets and the business processes, you don’t have sustainable change,” I was reminded recently by the chief digital officer for a major multinational networking company that’s undergoing a self-disruption. He said success involves innovating as much around your digital operating model as around technology. “You should be innovating around roles/responsibilities, innovating around insights, competencies, behaviors and operational procedures,” he said.
As you drive your organization for real answers from data, success requires a constant appetite for change. Ideally, your system’s architecture and design accommodate the prospect of increasing data, users, queries and demands well beyond those initially identified. Your model for success is grounded on advanced long-range planning and recognition of the need for consistent, scalable growth.
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