12 Critical Building Blocks of an
Enterprise Data Science Platform


A Data Science Platform is the most important asset companies acquire as part of their data analytics strategy.
Growth in the data science platform segment outpaced the overall business intelligence (BI) and analytics software market growth by almost two times.
– Gartner, Machine Learning: FAQ From Clients, July 2017

Capable of tackling Small Data and Big Data business needs, a Data Science Platform can be applied to business applications pertaining to risk management, customer centricity, and business operations. This Predictive Analytics, Prescriptive Analytics, Machine Learning, and Visual Analytics workbench provides users with advanced analytic flexibility, improves productivity, and unifies infrastructure and technology.

In a data-driven marketplace, a Data Science Platform promotes collaboration and enables businesses to transition their current decisioning methods from reactive to proactive to actionable – all from within a single visual environment.

Download the Data Science Platform Buyer’s Guide and uncover valuable insights to assessing the right Analytics Strategy for your business!

Topics covered:

  • Data Science buzz terms
  • Functionality a Data Science Platforms fulfills within an organization
  • Fundamental technical elements of a Data Science Platform necessary for the data mining process
  • Typical Data Science Platform applications & ROI case studies
  • The Analytics Journey: Greater business value with higher degree of technical intelligence
  • Latest industry and market trends shaping Data Science Platforms
  • 12 Critical Building Blocks of an Enterprise Data Science Platforms
  • The Angoss Enterprise Data Science Platform architecture

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