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How To Extract Business Value From Data Science: It’s All About The Teamwork Posted on Dec 05 - 2018

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It’s time for companies to move their data science efforts out of the basement lab and into production applications and strategic decision-making, according to a leading expert on the subject.

Getting in the way is a tendency for companies to silo or sideline their data science efforts; an overabundance of tools that complicates an already complex process; and the lack of an enterprise-wide methodology that would let data scientists work closely with other parts of the business.

To make an impact at the enterprise level, the data science group can’t work in isolation, said Ian Swanson, Oracle vice president of machine learning and artificial intelligence product development, during a presentation at the recent Oracle OpenWorld conference. “In order to do data science right, it has to be a team sport,” said Swanson, former CEO of DataScience.com, which Oracle acquired earlier this year.

Team Members

One of the data science group’s most valuable teammates is the IT organization, for multiple reasons, he said. The DS group relies on IT to manage and secure the data it uses; support the needed analytics tools; and deliver ready access to scalable bandwidth, compute, and storage capacity to build and train production-oriented analytic models.

Another important ally is the application development team. Developers must incorporate the models DS builds into their “ecosystem” as regular features among the many they use to build production applications, Swanson said.

That points to a significant attribute of production-oriented models: reusability. An ecommerce recommendation engine, for instance, might be reused for forecasting an item’s revenue stream, he said. A key performance indicator for one technology company Swanson worked with on a DS project was “how often that model was used by other parts of the business,” he said.

Line-of-business managers are a valuable constituency as well, because they’re tasked with performing the actions—and getting the results—from applications that use analytic models. An underestimated advantage line-of-business managers bring to the analytics model-building process, Swanson said, is their domain expertise—their experiences working with customers.

As for the top brass, they don’t need “to be involved in every step of the model, but they need to understand how it will be used, the opportunities it offers, the things it can achieve,” Swanson said. “If you’re not involving the top, if they’re not part of the team, data science is not affecting the heart of the business.” View More

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