Gartner recently published a piece “Top 10 Trends in Data and Analytics, 2020” that you can currently get from our friends at ThoughtSpot (registration required). It’s an interesting report you should definitely check out.
My favorite section was the one on Decision Intelligence, within which they include the kind of digital decisioning or decision management I’ve been doing for the last couple of decades (and in which the firm I founded, Decision Management Solutions, specializes).
In this section they correctly point out that, while automating decisions is a critical component of digital decisioning, it’s not necessary to automate 100% of the decision 100% of the time. Often we find that sometimes an automated decision requires some human inputs or that only a certain percentage of transactions can realistically be handled by a decision engine. We build decision models to understand the problem well enough to make these calls – to decide on the automation boundary – and it was great to see the shout out for decision modeling (and the Decision Model and Notation standard) in the report. The team at Gartner linked decision modeling to improved agility (faster changes), transparency and business user enablement – all key benefits we see in client after client.Personally I always get the biggest satisfaction from seeing how digital decisioning enables continuous improvement, generating the data you need to review, improve, simulate and compare decision-making approaches. As the report says, the key is to pass actionable insights directly to decision engines to act and then enable humans to review the effectiveness of this and close the loop. Putting business owners in the driver seat for improving their own automated decisioning systems is a powerful tool that generates a huge ROI.
There were also some good pieces of advice on how to scale your Machine Learning (ML) and Artificial Intelligence (AI) efforts. I would add to their advice that scaling ML/AI in a fast changing world requires more than just adopting the right ML/AI techniques. It needs the active engagement of business domain knowledge through decision modeling and business rules too. No matter what you do to improve your AI/ML, there’s no substitute for combining it with in-house business knowledge. I also appreciated their comment that the approaches that got you to an AI pilot won’t get you to production – you need an approach like the one Cassie Kozyrkov discussed and I comment on in Some great advice on Machine Learning from Google (and me) or the ideas in this post on Most companies are not succeeding with advanced analytics. But you can.
Anyway, it’s well worth registering for and downloading. If you want to learn more about decision modeling, check out our great white paper on Decision Modeling with DMN and if you want an overview of our approach to machine learning, check out this paper on Enabling the Predictive Enterprise.