We have been helping several organizations improve their analytic and data science projects. Like many users of analytics, these organizations find that their analytic teams often lack a clear understanding of the business problem, resulting in projects that lose their way or produce analytic models that don’t get operationalized, deployed or used. We have helped these organizations make their data science and analytic projects more effective by helping them apply decision modeling at the start of the project. A case study about one such company is now available as an International Institute for Analytics (IIA) Leading Practices Brief – Bringing Clarity to Data Science Projects with Decision Modeling: A Case Study
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.
They found that:
- 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.
“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.”
You can download the case study here.