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What is Decision Management?

Modern-day business operations are centered around processes and data. However, when knowledge and expertise are forced into hard-coded, inflexible systems, bottlenecks occur. By adopting and scaling the capabilities of business rules, decision modeling, and machine learning, Digital Decisioning leverages data and expertise to create business value, improve results, and deliver a great user experience to effectively meet today’s operational requirements. For Digital Decisioning to deliver true value, it must initially focus on solving business problems that will, ultimately, contribute to positive and desirable business outcomes. Technology, automation, and methodologies are essential for Digital Decisioning, but they are always at the service of business decisions. 

The foundation for successful Digital Decisioning projects is applying a Decision Management approach. Decision Management models business decisions first. These decisions, for the most part, have to do with customers and how you engage with them, deliver services to them, or handle their transactions.Once you have a good grasp of what decisions your business needs to make and how these decisions will advance your business, Decision Management shows which technology and analytics tools you need to invest in and how to integrate them. Finally, Decision Management focuses on the processes and infrastructure needed to ensure continuous improvement and business engagement. To maximize success, adopt a proven approach such as the DecisionsFirst™ approach from Decision Management Solutions.

An International Institute for Analytics (IIA) Leading Practices Brief

International Institute for AnalyticsA global leader in information technology has a centralized data science team shared across internal operations. This team adopted decision modeling to as a key part of the business understanding phase of the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology.

The Big Ideas:

  • Decision modeling builds a shared understanding between the analytics team and the business.
  • Decision modeling can revive projects that have lost their purpose.
  • The simple diagrams built through decision modeling can bring clarity to problems long thought difficult.

Applications of Decision Modeling to Solve Business Problems:

  • Service Contract Renewal Opportunity Analysis
  • Cost Allocation
  • Lead Size Prediction for Automated Leads

“The value the team has experienced in these three examples is clear. They believe that without decision modeling’s influence to rally and focus key stakeholders, they would not have been able to successfully complete the opportunity prioritization project, the cost allocation project, or the lead size estimation project.”

The case study includes Lessons Learned and a Checklist to get started. Register below to download.

Bringing Clarity to Data Science Projects with Decision Modeling: A Case Study

  • To learn more, download the DecisionsFirst Business Decision Experimentation Datasheet.
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Harness Data-driven Decision Making