Category Archives: Decision Modeling

A Practitioner’s Review of DMN 1.2

Jan Purchase, a well-known DM practitioner and co-author of “Real-World Decision Modeling with DMN“, reviewed new features of the upcoming DMN Release 1.2. Which features will be genuinely useful and which are still missing? Read Jan’s article.

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Goal-Driven Approach

The Disciplined Agile goal-driven approach guides people through the process-related decisions that they need to make to tailor and scale agile strategies. It specifies 22 goals in total, each of which is described by a Process Goal Diagram below. In particular the … Continue reading

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The Dawn of Digital Decisioning – A Forrester Report

James Taylor provides a brief overview of the just published Forrester Research report  “The Dawn Of Digital Decisioning” ($499) that makes great points about the power of Decision Management and provides recommendations about how best to approach Digital Decisioning: “When and how … Continue reading

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Decision as a Service (DaaS) by Trisotech

Denis Gagné presented the latest version of Trisotech DMN Modeler at bpmNEXT-2018 as a complete modeling environment for DMN including execution, testing, searching, deployment as a decision service, collaboration, and more. It includes a Centralized Dictionary that is actually an … Continue reading

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DMN Cookbook

Bruce Silver and Edson Tirelli just published a new DMN book available from Amazon. You may download (for free) many Decision Models described in the book and import/modify/execute them using Trisotech DMN Modeler.

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Does Real-World Decision Management Need Inference?

A set of recent articles by Paul Haley again brings attention to this important question. Paul insists that “It’s time to trade rule technology dating back to the 80’s for state of the art AI” and points that “BRMS are incapable … Continue reading

Posted in Artificial Intelligence, BPM, Decision Modeling, Events, Rule Engines and BRMS, Semantic Web | 3 Comments

Open the Machine Learning Black Box with Rule-based Decision Automation

Francis Friedlander from IBM just published an article with this title. In particular, it says: “Machine learning is best in class to derive customer insight from customer data. Rules consume customer insight and are best in class to make justified … Continue reading

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