Category Archives: Decision Modeling

How to Succeed with AI: James Taylor’s advice

James Taylor just published a new post about AI: “AI is a decision-making technology. As such it is a powerful complement to Decision Management – something to be considered alongside business rules and analytics, and integrated into a coherent decision model. ” To … Continue reading

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About DecisionCAMP-2018

The DecisionCAMP-2017 became a major technical decision management event of 2017. Next year DecisionCAMP will be held in Luxembourg in the mid of Sep-2018. We will join the Logic for AI Summit that will bring together several international conferences and workshops … Continue reading

Posted in Artificial Intelligence, Decision Modeling, Events, Machine Learning, Optimization | Leave a comment

Forget Processes, Think Decisions

Peter K.J. Kalmijn from Atos, Netherlands just published an article with this title. “Most organizations still run multiple rigid variations of their core processes. Even ‘dynamic processes’ are predominantly overly complex with multiple paths and complicated routing. This makes them … Continue reading

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Seven Reasons to Model Your Business Decisions

Jan Purchase just published an article “Decision Modeling: The Bottom Line“. In particular, he writes: “Decision Models are not just about requirements. The precision of the decision logic view means that decision models can be directly executed. Therefore they can be tested … Continue reading

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Five V’s of Decision Management

A new article by Koen Smit discusses 5 characteristics of operational decision management: Volume corresponds to the number of times a decision is made within a certain time period Velocity represents how fast a decision must be taken Variety corresponds to the … Continue reading

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MWD Advisors Report “Decision Management Drives Disruption”

Derek Miers from MWD Advisors just published a new report devoted to DMN. He writes: “With decision modelling, you now have way of visualising the future – using models to express strategies, goals, processes, policies, rules and constraints – and then driving … Continue reading

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Machine Learning Humor

The current machine learning hype makes some people believe that pouring data into a “big pile of linear algebra” will produce the answers you want. The more data you pour the better answers you get. Contrary, to get useful answers … Continue reading

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