For me, the big data and analytic revolution is as much about psychology, culture and workforce dynamics, as it is about big data technology itself. I’ll be discussing a range of topics in posts to come, but many will relate to a new concept I’ve been calling “the Sentient Enterprise.”
The Sentient Enterprise is an idea that Kellogg School of Management at Northwestern University professor Dr. Mohan Sawhney and I jointly developed, after years of listening to and working with market-leading companies. It is a road map for businesses – even large multi-national corporations – to combine technology, governance and human engagement around data in ways that preserve startup-style agility. We began collaborating on this more than a year ago, literally starting as a “back of the napkin” dinner conversation, to unlock the door for businesses to collectively make faster and smarter decisions at scale. Ever since, our joint presentations on this topic to business leaders have been full of ‘aha’ moments and amazing discussions. Before I share a few snippets on the approach, let’s first tackle a basic question that often comes up: Why the word “sentient”?
Honestly speaking, most people don’t think of the word “sentient” when describing a company. “Agile”? … yes! “Dynamic”? … of course! In fact, anyone who talks or writes about business relies on these kind of active words all the time. But, sentient? Somehow that seems like a uniquely human term. It’s derived — after all — from the Latin word sentīre, which means “to feel.” How can a company have feelings?
In a way, they can. Mohan and I are trying to advance an idea where data and analytics can do more than just act as a bean counter of what’s happening within a company . The sentient enterprise actually does some of the understanding itself and takes on aspects of operational decision-making, freeing the human mind to focus on high-level strategic analysis and creativity. So we use the term “sentient” to stretch our understanding and our vision for big data technology and analytics much further than traditional concepts like “situational awareness,” “predictive modeling” or “business intelligence.”
But, to get there we will need to address the ways humans should ideally interact with data (human-data ergonomics, if you will), without getting bogged down with the purely technical challenges. Most computational neuroscientists estimate that the human brain’s storage capacity is somewhere between 10 and 100 terabytes. Compare that to a worldwide data explosion – already at more than 1.8 trillion gigabytes and doubling every two years – and you begin to understand the analytics “pain points” our industry is grappling with.
For one thing, we spend the majority of our time just sifting through data instead of making decisions. We’re constantly on our heels in reaction mode, putting out fires instead of thinking about the future. And we can’t seem to make decisions fast enough, given that our brains don’t scale the way data can.
To compound the problem, in this fast changing environment and given the range of analytic demands among business users, traditional top-down IT becomes a bureaucratic system that is quickly overwhelmed, and stigmatized as bottlenecks in the organization. That leads to work-arounds – siloed “data marts” and one-off technologies – that may seem agile, but actually fragment and duplicate information to the point where the whole organization can lurch into a wasteful “data anarchy” state filled with inconsistent, or just plain wrong answers.
This is the nightmare I and many others in the industry have lived through at one point or another, and it’s my hope that a Sentient Enterprise methodology for analytics is a way past this. It involves creating an agile, balanced and decentralized framework for analytics that is built on a unified platform that includes things like data warehouses and Hadoop. It also involves staging areas built to handle not just transactional data, but the vastly more complex behavioral data that considers interactions among people, processes and technologies. It calls for self-listening and predictive architectures that take the bulk of data sifting and decisioning off people’s shoulders, saving human intervention for critical and strategically chosen points.
Using innovations in technology, governance and human engagement strategies, the Sentient Enterprise becomes proactive with micro-trends to chart opportunities and threats around the next corner. It’s a silo-free zone for frictionless movement of data across the organization. It’s autonomous in its ability to listen and make real-time decisions without excessive human intervention. And, it can scale easily and evolve over time with intelligence that is native and emergent.
Above all, the Sentient Enterprise represents a culture shift around data, where we democratize access to information, invite experimentation and reposition IT as collaborator instead of gatekeeper. The result is a “LinkedIn for Analytics” social environment to connect data and insights and send the best solutions, ideas and analytics viral throughout the organization. Many organizations are taking the first steps toward sentience. We achieved some of this with something called DataHub when I was at eBay, and more and more organizations are putting different pieces of the Sentient Enterprise puzzle in place.
As I said at the outset, we’ll be discussing a range of issues in my future entries to come. But, I think you’ll find that many relate to the key challenges our industry is facing, and the ways that the more “sentient” analytics environment I’ve outlined here can solve these challenges. In building a common Sentient Enterprise methodology, Mohan and I are hoping it will serve as a Six-Sigma-style framework for analytic agility at any scale that can be a north star for any company with the vision and resources to try.