What Is a Data Science Platform?

Data Basics, Scaling AI Marie Merveilleux du Vignaux

Data science platforms have been a buzzword phrase since 2017, but as today’s era of advanced analytics is driving the enterprise, this popular concept is becoming a norm. In this blog post, we will review the definition of a data science platform, go over what features make up a strong data science platform, and emphasize why companies need a data science platform today.

Data Science Platforms Defined

In simple terms, a data science platform is the structure in which the entire lifecycle of a data science project takes place. This platform contains the tools and resources required to complete each phase of the data science project lifecycle represented below. It brings together people, tools, resources, and other necessary products used across the data science lifecycle, from development to deployment.

analytics project lifecycleA data science platform assists data scientists in their analysis by helping them run, track, reproduce, share, and deploy models faster and more efficiently with powerful tools that speed up the process. Through these platforms, data science evolves from a simple skill to an organizational capability.

The Values of a Good Data Science Platform

  • Centralization: Data science projects involve many diverse tools and data sources. Having all of these resources in one centralized place enables data scientists and teams to speed up the model deployment process. Not only does this increase internal collaboration between teams, but it also accelerates the training process for new hires as they directly know where to find everything they need.
  • Flexibility: Elasticity — both in the sense of on-demand compute resource management and flexibility for anyone to work with data on top of those resources — is the future of Enterprise AI. A good data science platform thus not only needs to offer the necessary flexibility to leverage existing (or any future) infrastructure investments, but also needs to assure that it benefits the whole organization and not simply specific (e.g., technical) teams.
  • Self-service capabilities: Being a data-powered organization means that everyone — no matter what their role or team — should have appropriate access to the data they need to do their jobs and make decisions based on that data. Self-service access to data and resources is necessary to incorporate collaboration and data democratization into the organization.


Why All Companies Need a Data Science Platform

According to a study performed by Analytics Insight, the data science platforms market is expected to reach $385 billion by 2025. Why is that? Here are some of the main benefits that arise from using data science platforms:

  • Increased collaboration: Data science platforms empower individuals of different roles to work together, allowing teams to take on larger problems than individuals could tackle alone. Collaboration can also enrich governance practices as well as the company as a whole by making it become more data driven and efficient through reuse and capitalization.
  • Faster and more efficient insights: Data science platforms help push more models to deployment faster, while also reducing inconsistencies and error rate. Through APIs and/or easy integration processes, model deployment is made easier and more efficient.
  • Enlarged amounts of data: These platforms provide simple, fast, and secure access to numerous types of data and enable teams to work with large volumes of data. This in turn produces more reliable data-driven insights.
  • More secure governance: Data science platforms ensure trusted and auditable results through consistent, centralized, and transparent processes.
Data science platforms continue to rise in importance as organizations realize the many benefits they can bring to their AI efforts. The question is no longer “Does my company need a data science platform?”, but rather “Which data science platform does my company need?”

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