Enterprises today collect more data than ever before, yet several studies and analysts since the advent and proliferation of big data have shown that businesses aren’t using it. In fact, the large majority of data collected goes unanalyzed and, essentially, ignored.
The actual collection of all this unused data is not an issue given the plummeting cost of data storage. The real issue with unused data is potentially much more expensive: companies leave themselves open to attack as their business models become more and more surmountable by newcomers innovating with data.
Jerry Chen at GreyLock Partners talks at length about economic moats and the idea that companies not using their streams of data are actually exposing themselves to great risk (his blog post is well worth a read). But what’s a company to do if their moats are drying up and the enemy is encroaching quickly? Defend the fortress, of course. With systems of intelligence.
What Are They?
The term systems of intelligence is quickly popping up everywhere. But what exactly does it mean?
Systems of intelligence are whole new ways of packaging and consuming data —contained systems that solve a specific problem.
Basically, it’s a whole new way of packaging and consuming data — contained systems within a company that solve specific problems or automate entire workflows. Think of a system that is connected to multiple data sources, automatically cleans and prepares that data in real time (or in batch), and then provides a valuable output addressing a real business need. Like serving better ads. Or identifying customers likely to churn.
Systems of intelligence should not be confused with the idea of data democratization, though certainly if your business has systems of intelligence in place it probably also likely has data democratization figured out. So it is certainly part of the equation, but not the whole story.
Sounds Great… but Hard
Yes, building systems of intelligence isn’t going to be a short-term project, and it represents a huge shift in company culture and way of thinking. But what’s harder than building systems of intelligence? Hiring lots of data scientists, specifically hiring enough of them to scale.
Building systems of intelligence is not a short-term project, but the value they bring are worth the upfront cost.
Everyone knows that data scientists are coveted and difficult to hire. You probably already have some on staff, but the amount of data available is so large and will grow so fast in the coming years (especially if your business leverages the Internet of Things, IoT) that even a large team of data scientists (if there were enough of them to go around and if you had unlimited budget) won’t be able to scale without systems of intelligence. So the real focus of data scientists shouldn’t be solving individual problems in a one-off fashion, but putting in place systems that will continually address these problems over time, unsupervised.
Systems of Intelligence as IP/Business Assets
What’s more, systems of intelligence are not only the only answer to scaling along with available data. But there is also mounting evidence to suggest that systems of intelligence themselves are core business assets.
Anyone might be able to build the same product or provide the same service as you. Any company can collect massive amounts of data in this day and age. What every company can’t manage to do is provide value through these systems of intelligence, leveraging machine learning, artificial intelligence (AI), and IoT at scale to continually innovate to keep those customers. Not convinced? Venture investor Gil Dibner has a great post on this topic that’s also worth checking out.