By Josh Poduska, chief data scientist at Domino Data Lab

Consolidation of power and platforms will accelerate in 2020

The explosion of AI efforts will be accompanied by a growing trend toward consolidation of data science organizational power. The idea of setting up an internal data science practice is not new, and most companies have already invested here. Executives realize that the competitive advantage of the next five years will belong to those who can build the best data science flywheel. Integrating model insights into decision flows and significantly increasing the number of quality machine learning (ML) and AI projects are the high-level keys to success in building this flywheel, but the former is harder than the latter. 

In order to get lucrative models integrated into the fabric of the business, leaders are seeking a better process - best practices and workflows for collaborative data science that start with the end in mind. Efforts to increase the funnel of AI projects lean on recent technology advancements, specifically a new class of enterprise software called Data Science Platforms, which remove dev ops barriers to model research and deployment. They also facilitate collaboration and reproducibility, two key elements of running effective modern data science teams. With access to better centralized platforms, data scientists will be significantly more productive, but business leaders will be slower to define and enforce the processes needed to ensure that work gets successfully into production to improve decision making. While the particular implementation details will vary, this trend of consolidation of power and platforms will also accelerate in 2020.

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About the Author

Josh Poduska is the chief data scientist at Domino Data Lab. He has 18 years of experience in analytics. His work experience includes leading the statistical practice at one of Intel's largest manufacturing sites, working on smarter cities data science projects with IBM, and leading data science teams and strategy with several big data software companies. Josh holds a master's degree in applied statistics from Cornell University.