Today I had the honor to keynote the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016).
While consumer applications have become increasingly intelligent, I see three main challenges facing the enterprise. First, most enterprise data still lives in silos, whereas the intelligence comes from joining across data sets. Second, the enterprise suffers from weak signals — there’s little in the way of the labels or behavioral data that consumer application developers take for granted. Third, there’s an incentive problem: everyone promotes data reuse and knowledge sharing, but most organizations don’t reward it.
Nonetheless, I see many reasons for hope. Open source and cloud computing have reduces the cost of developing intelligent applications. Consumerization of the enterprise has not only raised UI expectations, but also makes people expect greater interoperability.
It’s hard to make broad prescriptions, but I suggested some general approaches in my talk. First, take advantage of opportunities to combine public and enterprise data. Second, invest in data standardization. Third, create better incentives to reward the development of reusable data assets.
In short, we need to do a better job of putting the pieces together to enable enterprise intelligence.
Enjoy the slides below!