But what does success look like for a data platform, and how do we measure our progress? What does data platform success look like? It’s imperative that we can measure and communicate the progress and success of the data platform, in order to justify the initial investment and any future growth & investment. Ultimately, your specific organization’s needs will determine the scope and breadth of the data platform, but I expect many of these components are shared irrespective of your goals. Data observability, privacy & governance tools (i.e.Machine Applications – algorithmic recommendations, algorithmic targeting, etc.User Applications – BI, Experimentation, CDP.In my former role as SVP of Data & Insights at the New York Times, I oversaw a data platform that encompassed these same core components: End-to-end quality controls to ensure the products are delivered on time, within budget, and to specification.Machines applications to optimize printing processes for greater efficiency or return.User applications for internal or external stakeholders of the plant.Raw materials that will be refined into finished products.Delivery mechanisms (in & out) and warehouses:.In this definition, the data platform might be thought of as similar to a printing plant – you may have: What is a data platform?īefore we dive any deeper, it probably helps to describe what we mean by data platform. How do we know it’s actually living up to this vast potential? Fortunately, three key success metrics (user, maturity, and impact metrics) can clue us in on its success – here’s how. Still, it’s one thing to build a data platform. In 2022, a rapid move to cloud technologies, an insatiable and growing demand for the application of data across the company, and the construction of “ the data platform” have made data science, analytics, and machine learning faster, cheaper, and more accessible than ever before.ĭata platforms, in particular, have turned data infrastructure into factories of scale and speed, feeding our insatiable appetite for insights, operational efficiencies, and even new revenue streams. And even now, visit a modern printing plant and you’ll find a state-of-the-art operation with advanced tech and robotics. For many data teams, the past 5 years has witnessed an evolution of technology, teams, and processes that calls to mind another significant period in time: the Industrial Revolution.įrom the late 18th century to mid-19th century, the Industrial Revolution transformed economies with new tools, cheaper power sources, and more streamlined ways of organizing work in factories.

Namely usage