Turning tabular data into decision models is not as simple as it sounds

💥Turning tabular data into decision models is not as simple as it sounds

* Data tables are familiar faces
* Decision Tables are simple to understand
* Computational logic for calculation a reminder of Excel

But here is the truth…

Complex decision logic is very powerful but hard to build:
⭕Operations Research: Techniques such as Constraint Programming (CP), linear programming (LP), and others are not easy to understand for everyone. It is definitely harder to build them from scratch.
⭕Machine Learning: Wide ranges of algorithms exist to accomplish different tasks such as prediction, classification, etc. This requires a data pipeline, model training, and optimization that not everyone has.
⭕Reinforcement Learning: Enabling the best outcome in real time using an adaptive model. It definitely is one area that brings significant benefits to optimizing the outcome, yet it is very hard to make it work at scale.

Any alternatives?

💡The “Decision Knowledge”.

Decision Knowledge is a new way to operationalize decisions:
🔹 It transforms raw grids and charts into dynamic, executable knowledge.
🔹By assigning roles, applying logic, filtering, and linking knowledge, users build decisions just by interacting with their data.
🔹 Modular knowledge that can construct and share context for decision execution
🔹It composes a Decision Model through Rules, Machine Learning, Optimization, or Reinforcement Learning

✨Decision Knowledge closes the gap between tabular data and operationalized decisions, which is the aim of Decision Intelligence, which is to improve outcomes.

🚀Data → Information → Knowledge → Decision Logic (Rules, Optimization, Machine Learning, Reinforcement Learning) → Decision Model

💣 And the best part? You do it just by interacting with data. There is no need for scripts, code, or flows! Only data, semantics, and roles.

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Published April 30th, 2025 at 07:30 am