A recent study conducted by MarketsandMarkets predicts the market for data science platforms will climb to $101.4 billion by the year 2021. But since the space is so new, there's still some confusion about what a data science platform actually is. What components make up a data science platform, and how do you know if one is worth the investment for your company?
A data science platform is a software hub around which all data science work takes place. That work usually includes integrating and exploring data from various sources, coding and building models that leverage that data, deploying those models into production, and serving up results, whether that’s through model-powered applications or reports.
Having a centralized location for data science work is important. Data science projects typically involve many disparate tools designed for each step of the process, so it’s no surprise that a recent study conducted by Forrester Consulting on behalf of DataScience.com revealed that tool sprawl is a common challenge for data science teams. A data science platform puts the entire data modeling process in the hands of data science teams so they can focus on deriving insights from data and communicating them to key stakeholders in your business. Features like project-based organization and streamlined model deployment help make this work intuitive.
The best data science platforms offer the flexibility of open-source tools and the scalability of elastic compute resources. That’s because the most popular tools for data science work are always evolving, so it’s important that the platform your data scientists use can keep up with these changes.
A quality data science platform will also leverage best practices that have been refined through decades of software engineering, such as version control. That way, your team can collaborate on projects without losing valuable work along the way. On top of that, a good data science platform will orchestrate resources with containers and easily align with any type of data architecture. The combination of these features will allow your business to centralize data science work and compete in a data-driven economy.