Gartner backs Decision Modeling

Lisa Kart and Roy Schulte recently published a new research report Develop Good Decision Models to Succeed at Decision Management (subscription required). This is the first piece of formal research published by Gartner on decision modeling. Their introduction text says

The industry trends toward algorithmic business and decision automation are driving wider adoption of the decision management discipline. To succeed at decision management, data and analytics leaders need to understand which decisions need to be modeled and how to model them.

I really like this phrase “algorithmic business.” I was just co-hosting the TDWI Solution Summit on Big Data and Advanced Analytics with Philip Russom and we discussed what “advanced analytics” meant. We concluded that it was the focus on an algorithm, not just human interpretation, that was key. This phrase of Gartner’s builds on this and I think it is clear that advanced analytics – data mining, predictive analytics, data science – is central to an algorithmic business. But it’s not enough as they also make clear – you need decision management wrapped around those algorithms to deliver the business value. After all as an old friend once said “predictive analytics just make predictions, they don’t DO anything.” It is this focus on action, on doing, that’s drives the need to manage (and model) decisions.

Lisa and Roy make three core recommendations:

• Use Analytic Decision Models to Ensure the “Best” Solution in Light of Constraints
• Use Business-Logic Decision Models to Implement Repeatable Decision-Making Processes
• Build Explicit Analytic and Business-Logic Decision Models at Conceptual, Logical and Physical Level

All good advice. The first bullet point relates to the kind of decision models that are prevalent in operations research. These are a powerful tool for analytical work and should definitely be on the radar of anyone doing serious analytic work.

The second point discusses Business-Logic Decision Models, the kind of model defined in the Decision Model and Notation standard. These decision models are focused on defining what decision-making approach (both explicit logic and analytic results) should be used to make a decision. While using these to structure business rules is the more known use case, these kinds of models are equally useful for predictive analytics as Roy and Lisa note in their paper. Business logic models can embed analytics functions such as scoring to show exactly where in the decision-making the analytic will be applied. More importantly we know from our clients using this kind of decision modeling in their advanced analytics groups that the model provides a clear statement of the business problem, focusing the analytic team on business value and providing requirements that mesh seamlessly with the predictive model development process.

As for the third point, we see clients gaining tremendous value from conceptual models that cover decision requirements as well as more detailed models linked to actual business logic or analytic models to fully define a decision. Any repeatable decision, but especially high volume operational decisions, really repays an investment in decision modeling.

Roy and Lisa also address one of the key challenges with decision modeling when they say that “many data and analytics leaders are unfamiliar with decision models.” This is indeed a key challenge. Hopefully the growing number of vendors supporting it, the case studies being presented at conferences, books and the general uptick in awareness that comes from consultants and others suggesting it to projects will start to address this.

They list some great additional Gartner research but my additional reading list looks like this:

• Available now! Real-World Decision Modeling with DMN
• Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics
• Decision Modeling with DMN (White Paper)