Bringing Clarity to Data Science Projects with Decision Modeling: A Case Study
An International Institute for Analytics (IIA) Leading Practices Brief
A 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.