Best Practices for Operationalizing Complex Analytic Models at Scale
The adoption of predictive and prescriptive analytics across multiple industries is accelerating rapidly, fueled by the competitive need to drive business agility.
The adoption of predictive and prescriptive analytics across multiple industries is accelerating rapidly, fueled by the competitive need to drive business agility. The rationale for adoption is clear enough: Companies are sitting on mountains of relevant data that could help inform their business processes and decisions. To unlock the value of that data, they’ve hired data scientists and invested in data modeling and analytics tools. They have given the green light to advanced analytics projects, and the financial and operations stakeholders are looking for evidence that they are getting value from their investments. Many are still asking themselves whether they ultimately will derive the hoped-for business outcomes. Practical ROI metrics include business growth, increased automation and agility, and success in optimizing complex decisions.
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