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1. Decisions First

  1. Decisions, especially operational decisions, link an organization’s metrics and objectives to its operational systems.
  2. Decisions are first class objects, just like business processes or data, and should be identified, described, modeled, reviewed, and managed in business terms as part of a business architecture.
  3. Decisions should be modeled first before considering how business rules, predictive analytics, and/or machine learning will be used.
  4. Decisions support business processes and help organizations respond to events, but they are not subsumed by either processes or events, simplifying their expression and management.
  5. Business, IT, and analytic professionals all have a role in identifying, describing, modeling, reviewing, and managing decisions.

2. Explicitly Design Decisions

  1. The best way to define a decision is with a question and a set of known, possible answers.
  2. Making a decision requires defined information—input data—such as transaction information, reference data, and other verifiable, definitive information.
  3. Making a decision often requires information—answers—generated by making other decisions.
  4. A decision has authorities such as policies, regulations, best practices, and expertise that define how it should be made.
  5. A decision can have analytic insight that shows how it can be improved or made more accurately.
  6. Not all decisions are automated; a manual decision can still be modeled and managed.

3. Use Decision Management Technologies

  1. The details of how a decision will be made can be represented with business rules, decision tables, predictive analytic models, machine learning or optimization algorithms and other decision metaphors.
  2. When implementing a decision-making solution, a mix of technologies (business rules, machine learning, predictive analytics, and optimization) may be appropriate.
  3. If technology is applied to a decision, it may be to support a human decision-maker or to explicitly automate and manage the decision.
  4. Technology may be applied to a decision and any decisions on which it depends, or only to some decisions in a model or process.

4. Deploy Digital Decisioning Systems

  1. Digital Decisioning requires both decision-making services and supporting infrastructure for managing the definition of decision-making–not simply business rules or machine learning embedded in business processes or user interfaces.
  2. A Decision Service is decoupled from and provides decision-making to existing systems, business processes, or event processing environments.

5. A Decision Service Requires:

  1. Design transparency—to see exactly how the decision will be made in the future.
  2. Execution transparency—to reconstruct how a specific instance of a decision was made in the past.
  3. Impact analysis—to assess the business impact of a change before it is made.
  4. A closed loop—for continuous improvement, and to test and learn, experiment and adapt.

v. 10/7/2013


We’ve published the Decision Management Manifesto to help organizations like yours design, build, and implement decision management systems with business rules and predictive analytics. The manifesto was developed in collaboration with many experienced practitioners, software vendors, and consulting firms. Because the field of Decision Management is evolving, so will the manifesto. Because it is focused on core principles, not on technology features, the manifesto will remain largely stable but it is not static. We welcome your feedback.

To learn more about the reasoning and value proposition of the Manifesto, download The Decision Management Manifesto White Paper (updated on October 6 2019).

Translations of the Manifesto white paper are available:

Thank you to Sako-san, Shimizu-san, Tsushima-san and Fukushima-san, Emmanuel Bonnet, Plugtree, BCS, Gestão Inovadora, Lux Magi and Tobias Vigmostad of Decisive AS for providing these translations.

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