Alan Jacobson: How to Deliver ROI from Data Science
With the growing popularity of machine learning and artificial intelligence, creating a data science program is a key initiative at most companies today. However, it’s not always clear to executives how they can deliver a return on investments in data science. To explain this, we invited an expert who has spent most of his career in the data science trenches and has a clear-minded perspective on how to deliver ROI with data science.
Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.
Alan was recognized as a top leader in the global automotive industry as an Automotive Hall of Fame Leadership & Excellence award winner and an Outstanding Engineer of the Year by the Engineering Society of Detroit, and works with the National Academy of Engineering and other organizations as an advisor on data science topics.
Key Takeaways:
- Companies should consider hiring data science teams permanently to see long term value.
- Tracking metrics which are directly affected by lift can help assign value to the data science initiatives.
- The business partner helps define the impact of the data science projects to their organization.
- Finance should act as an unbiased auditor to sign-off on ROI calculations.
- Calculating ROI in advance can prioritize projects.
- A data science team should contain a hybrid of specialists who can do prescriptive, predictive, and descriptive.
- KPIs, dashboards, and reports serve as a mechanism to clean data which can further help in modeling.
- It is easier to clean the data when you have a problem to solve.
- Machines augment humans and help automate decision making.