The Rise of Data Science Platforms: Key Features for Automating Analytics and Driving Value
Stephen J. Smith
December 14, 2018
Data science is on the rise as companies pursue data-driven digital transformation. But as these companies evolve, their data science tools must also change. Stand-alone model-building products, or even workbenches, are insufficient. Complete data science platforms must support the end-to-end process of creating, deploying, and managing analytic models in an open, collaborative environment.
Such data science platforms are still maturing, but one thing they have in common is support for the complete model production pipeline. Other shared characteristics include agility, reproducibility, scalability, model management, model sharing, model deployment, and business optimization. These components are critical for companies seeking to operationalize data science activities.
This report will introduce a data science maturity model, describe warning signs that an organization has outgrown its current data science solutions, and provide a checklist of key features to look for when selecting a data science platform.
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