Data Mesh

Chapter 3. Principle of Data as a Product

One long-standing challenge of existing analytical data architectures is the high friction and cost of using data: discovering, understanding, trusting, exploring, and ultimately consuming quality data. There have been numerous surveys surfacing this friction. A recent report from Anaconda, a data science platform company, “The State of Data Science 2020”, finds that nearly half of a data scientist’s time is spent on data preparation—data loading and cleansing. If not addressed, this problem only exacerbates with data mesh, as the number of places and teams who provide data, i.e., domains, increases. Distribution of the organization’s data ownership into the hands of the business domains raises important concerns around accessibility, usability, and harmonization. Further data siloing and regression of data usability are potential undesirable consequences of data mesh’s first principle, domain-oriented ownership. The principle of data as a product addresses these concerns.

The second principle of data mesh, data as a product, applies product thinking to domain-oriented data to remove such usability frictions and truly delight the experience of the data users—data scientists, data analysts, data explorers, and anyone in between. Data as a product expects that the analytical data provided by the domains is treated as a product, and the consumers of that data should be treated as customers—happy and pleased. Furthermore, data as a product ...