Business Rules and Tacit Knowledge

Listening several recent presentations of Pragmatic Dave Thomas (see this and this), I cannot help getting back to the fundamental decision modeling problem:  Rules vs. Tacit Knowledge (or Intuition). Continue reading

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Automatically Learned Business Rules: Should We Understand Them?

Question: Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations?

I’ve just posted an article with the same name at the LinkedIn Pulse that addresses this question. It is especially important in the context of rules-based decision making when rules that govern our decisions have been automatically generated using predictive analytics techniques. I shared two examples from OpenRules experience that explain why the automatically generated business rules should be comprehensible. The first one talks about the use of our Rule Learner at IRS. The second example deals with our Rule Compressor.

Predictive Analytics is Becoming Mainstream

“Big Data” have brought “Predictive Analytics” (long-time available but hidden in the academic world under the names “Machine Learning” and “Data Mining”) to the spotlight of the modern Business Analytics. These days you will find many examples when analytics enables business decisions by supporting a path from data to decisions and actions. Below I will briefly talk about nowadays positioning of the Business Analytics and more about OpenRules own experience in this area including OpenRules Rule Learner. Continue reading

Don’t Program a System, Educate It!

Modern decision management techniques enable business decisions by supporting a path from data to decisions and actions. Wherever people use stand-alone Business Rules, Complex Event Processing, Predictive Analytics, Optimization systems or their combinations, they prefer to put in charge subject matter experts and not software developers.  Actually, all these systems tend to be declarative and allow customers to feed their systems with externally maintained business knowledge, e.g. historical data and/or already known business rules. Nowadays people in a way want to educate a general purpose system with their domain-specific knowledge avoiding traditional programming. Continue reading

Learning Business Rules from Data at RuleML-2014

This year RuleML-2014 will be held in Prague on Aug 18-20. For the first time it will include a special track called “Learning Business Rules from Data”.  As a member of the organizing committee, I posted the proper announcement here. It promises to become a very interesting event when the decision management practitioners meet their academic partners. Continue reading

Combining Constraint Solving with Business Rules and Machine Learning – CoCoMiLe 2013

The integration of different decision making techniques finally is finding its home under the roof of the Decision Management movement. I am glad that an integrated Constraint Programming (CP), Business Rules (BR), and Machine Learning (ML) approach is gaining in popularity as well. An interesting workshop “CoCoMiLe 2013 – Continue reading

Connecting The Dots

On January 8, 2010 after the notorious “underwear bomber” attack Tom Davenport wrote:

How easy is it to connect the dots? Granted, there were numerous indications of Abdul Mutallab’s evil intent. But it would have been difficult to put them together before the flight. Combining disparate pieces of information about people – whether they are customers or terrorists – is akin to solving a complex jigsaw puzzle.Continue reading