As organizations increasingly strive to become model-driven, they recognize the necessity of a data science platform. According to a recent survey report, “Key Factors on the Journey to Become Model-Driven”, 86% of model-driven companies differentiate themselves by using a data science platform. And yet the question of whether to build or buy still remains.
For most organizations, purchasing a data science platform is the right choice from both a business strategy and project cost efficiency perspective. However, many organizations confuse the criticality of models to their long-term success with the need to build the underlying platform themselves. In a few select situations, the platform itself is the differentiator. These organizations have highly specialized workflows (e.g., Uber), a stellar track record of internal software development (e.g., Airbnb), and deep data science expertise that recognizes the unique traits of models (e.g., Google). For the vast majority of organizations, the competitive differentiator is not the platform, but the entire organizational capability — what we call Model Management — encompassing many different technologies, stakeholders, and business processes. Buying the platform is the logical choice for most.
You’re probably thinking, “Of course Domino, the data science platform vendor, believes everyone should buy a data science platform.” We at Domino do admittedly have our opinion on the topic, but this opinion stems from thousands of interactions with organizations of all shapes and sizes around the world that have faced common struggles and obstacles on their journey to become model-driven. Most that have opted to build one on their own have stalled or failed. Those who have purchased a platform are operationalizing data science at scale. These interactions and experiences working with organizations trying to decide whether they should build or buy led us to develop a rigorous and objective framework to facilitate the decision process. In this framework, we examine three major factors:
Figure 1. Four-year projection of TCO of build vs. buy
If you are inclined to dive into more details of this framework, you can peruse the Domino whitepaper: “Should You Build Your Own Data Science Platform?”. Ultimately, organizations need to decide where their differentiation lies with data science: in the models they build and overall organizational capability, or in the underlying infrastructure? For most, it is the former, so a “buy” approach likely offers the lowest TCO and most aligned strategic choice.