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Operationalizing Predictive Analytics

Decision Management operationalizes predictive analytics. Traditional approaches to analytics are hard to scale and hard to use in the real-time environment required in modern enterprise architectures.

Decision Management frames a predictive analytic effort, establishing a shared understanding across business, IT, and analytics teams. CRISP-DM and other methods stress the importance of business understanding but lack a repeatable, understandable format.  Decision modeling fills this gap. Decision modeling is a successful technique that develops a richer, more complete business understanding earlier. Decision modeling using the Decision Model and Notation (DMN) standard results in a clear business target, as well as an understanding of how the results will be used and deployed, and by whom.

With decision management you will:

  • Know where to get started
  • Know where and how the results will be deployed.
  • Reuse knowledge from project to project.
  • Value analytics in terms of business impact.

Predictive Analytics

We provide a complete set of consulting and training for Decision Management to help you build your decision management capability today. Our decisions-first comprehensive approach matches business drivers to decisions. Our approach is optionally supported by our collaborative decision modeling software, DecisionsFirst Modelerd for the implementation program.

Customer Next Best Action – Retail Banking

This major European retail bank’s Next Best Action initiative deployed multiple technologies, including predictive analytics and business rules, with significant organizational and process changes.

Decision Management Solutions:

  • Conducted a rapid review of the initiative, assessing technology, organization, modeling, and other capabilities.
  • Assessed multiple vendors’ analytic and business rule technology in the context of a complex technical architecture.
  • Delivered specific go-forward recommendations about the design and the critical boundaries between products.
  • Developed a decision model that described how predictive analytics, business rules, and other insight could be arbitrated to produce the best possible results while managing the trade-offs between batch and interactive deployments.

The results were foundational for gaining approval and for the implementation program.

Harness Data-driven Decision Making