PowerPoint is a great presentation tool, but it is also the final resting place for many data science initiatives. “PowerPoint,” says Kaushik Das, “is where models go to die.” If you’re a data scientist, you know what he’s talking about. Das, who heads the data science practice at Pivotal, argues operationalizing predictive models in applications and business logic is the keys to saving data science models from this grim fate.
In this episode of Pivotal Insights, host Jeff Kelly and Das talk about why operationalizing data science models is so important and why so many enterprises struggle to do so. Turns out, technology is only part of the issue. Das provides tips on how to reframe the approach to data science in order to industrialize the process of getting insights to the right people at the right time on an ongoing basis.
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About the Author
Jeff Kelly is a Principal Product Marketing Manager at Pivotal Software. He spends his time learning and writing about how leading enterprises are tapping the cloud, data and modern application development to transform how the world builds software. Prior to joining Pivotal, Jeff was the lead industry analyst covering Big Data analytics at Wikibon, an open source research and advisory firm. Before that, Jeff covered data warehousing, business analytics and other IT topics as a reporter and editor at TechTarget. He received his B.A. in American studies from Providence College and his M.A. in journalism from Northeastern University.Follow on Google Plus Follow on Twitter More Content by Jeff Kelly