The true cost of building a data science platform
Scaling data science is the key to unlocking business value from your data. It’s also become a competitive differentiator. But it’s not easy to scale and most organizations haven’t figured out how to do it effectively.
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It’s tempting to think that building a basic platform that centralizes infrastructure and tools will be what’s needed. But it’s not that simple. To safely and universally scale data science, you need a platform that provides orchestration, security, governance, collaboration, knowledge management, and self-service capabilities across the data science lifecycle. That makes the requirements for a platform complex and ever-evolving. It also makes it expensive to support and maintain.
In this paper, you will get a comprehensive understanding of the capabilities needed to successfully scale data science to the enterprise and the level of effort it takes to build a platform with those capabilities from scratch. You will also get insight into the return on investment commonly seen by organizations who choose to focus their efforts on what truly differentiates them — their data science — rather than building a platform. Armed with this information, you will be able to make the best build vs. buy decision for your organization.
