Data science platforms can help teams of data scientists collaborate on advanced analytics problems, allow them to pull in data from disparate sources, and choose from a variety of analytics and machine learning tools to produce analytical models at scale and make useful predictions from them. By bringing big data and advanced analytics techniques together, these platforms can play a critical role in helping companies get a handle on vast amounts of data and speed up the modeling process.
Data science platforms should support automation, collaboration and ongoing management of the models by analytics teams in cooperation with data engineers and application developers. Yet “most of the platforms emerging stop at model development and deployment,” said Doug Henschen, analyst at Constellation Research. “Monitoring and ongoing lifecycle management is usually a separate matter, and support for IT and app developer roles varies.”
The overall data science platform market is expected to grow to more than $101 billion by 2021 at a compound annual growth rate (CAGR) of 39%, according to MarketsandMarkets. Cognitive and AI software platforms are cited by IDC as two of the fastest growing big data and analytics technology categories at a projected 36.5% CAGR over the next few years.
