💡The decision model is the critical part of successful adoption of intelligence and knowledge in your organization.
Organizations rely on various types of intelligence and knowledge to make and execute business decisions. Ranging from basic task completion to domain expertise and problem-solving within specific industries such as insurance banking financialservices government etc.
All of these have something in common: the model that becomes the core foundation of how reason about “things” and create the outcome — a decision model.
A decision model is at the core of all decisions organization makes and executes.
But… a “decision model” is:
❌ Not a ruleset or businessrules
❌ Not a process, workflow, rpa
❌ Not a machinelearning or algorithms
❌ Not a dasboard or data bi
❌ Not an optimization model
Structurally, a decision model is a graph that connects other subcomponents of the decision together. These subcomponents are called decision units. There are several types of subcomponents, such as Input Data, Business Knowledge, Decision, Knowledge Source, and so on, each with its own specific behavior.
✨Once the graph representing the decision model is created, every decision unit can be connected to different data sources and decision logic. They can receive requests, ingest data, apply rules, execute machine learning models, run predictive analytics algorithms, and so on.
The core part of the Decision Graph is the ability to integrate multiple different techniques of decision-making, from rules optimization machinelearning, into a unified, hierarchical definition.
🚀You can take this graph and integrate it into any process, system and workflow. Orchestrate everything, such as data, humans, ai agents, and so on, around it. Share the decision graphs between multiple processes and systems to ensure the outcomes they create are accurate, consistent and transparent.
Read more about it at https://lnkd.in/g66y27kb
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Published December 12th, 2024 at 07:30 am
