Category Archives: Decision Modeling

Gartner Magic Quadrant for Cloud Platforms

Cloud adoption is the new normal for customers of all industries, sizes, and geographies. The majority of Digital Decisioning vendors today support deployment of decision services on different cloud platforms. The 2020 Gartner Magic Quadrant for Cloud Infrastructure & Platform … Continue reading

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Decision Table Simplification

In response to our Sep-2020 Challenge, Dr. Bob Moore submitted a very interesting analysis of decision table compression/simplification approaches: “Understand what a decision table is for, before thinking about compressing it! There is a trade-off between compression and maintainability. We … Continue reading

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A Farewell to In-Person Conferences? Anti-notes from DecisionCAMP-2020

This year almost all major conferences went virtual. Our 12th DecisionCAMP-2020 on June 29-July 1 in Oslo also went virtual and became a kind of success. No wonder: the registration count was 3-4 times larger than usual, people did not … Continue reading

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Challenge Sep-2020: Compressing Decision Tables

Using common sense people can replace larger decision tables with smaller ones. However, when a decision table includes more attributes (columns), the manual compression of the decision table becomes difficult or impossible even if you allow a certain level of … Continue reading

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How AI Will Automate 70% Of Software Development

On Sep 24 at noon EST Mike Gualtieri, VP/Principal Analyst at Forrester and our Keynote Speaker at DecisionCAMP-2019, will run the webinar “The Future of Software Development“. Mike asserts that “70% of business software is non-creative and doesn’t require computer-genius … Continue reading

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Self-Learning Decision Models

The next monthly DecisionCAMP session with this topic is scheduled to run on Sep 4 at 12:00 PM EST – get a Zoom URL at our Slack channel. Here is the abstract: “A new open source RuleLearner.com is oriented to business … Continue reading

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Understanding the business problem is the biggest challenge

The new TechCrunch’s post states: “The most important question in data science is not which machine learning algorithm to choose or even how to clean your data. It is the questions you need to ask before even one line of … Continue reading

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Too many AI researchers think real-world problems are not relevant

MIT Technology Review published an article with this title. “Many machine learning papers that describe new applications present both novel concepts and high-impact results. But even a hint of the word “application” seems to spoil the paper for reviewers. As … Continue reading

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Champion/Challenger and Operational Negation

In May Stacey West from FICO took a thorough look at Champion/Challenger strategies testing and how it can be successfully implemented to enhance your organisation’s decisions. In her new post she is discussing how Champion/Challenger strategies may become unreadable because … Continue reading

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“Why” of the BPMN+CMMN+DMN Triple Crown

Sandy Kemsley: “…agility often depends on the design decisions such as the split between process and decision logic – what do you model in as a decision, and what do you model as a process. Unsurprisingly, many DM practitioners don’t … Continue reading

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