Organizations actively adopt data product thinking as a standard practice for managing data assets. This approach treats data as a product with its own lifecycle, quality standards and focus on meeting consumer needs. We now recommend it as default advice for data management, regardless of whether organizations choose architectures like data mesh or lakehouse.
We emphasize consumer-centricity in data product thinking to drive greater adoption and value realization. This means designing data products by working backward from use cases. We also focus on capturing and managing both business-relevant metadata and technical metadata using modern data catalogs like DataHub, Collibra, Atlan and Informatica. These practices improve data discoverability and usability. Additionally, we apply data product thinking to scale AI initiatives and create AI-ready data. This approach includes comprehensive lifecycle management, ensuring data is not only well-governed and high quality but also retired in compliance with legal and regulatory requirements when no longer needed.
Data product thinking prioritizes treating data consumers as customers, ensuring they have a seamless experience across the data value chain. This encompasses ease of data discovery, understanding, trust, access and consumption. "Product thinking" is not a new concept. In the past we've embraced this in the operational world while building operational products or microservices. It also suggests a new way to build long-lived cross-functional teams to own and share data across the organization. By bringing a product mindset to data, we believe organizations can operationalize the FAIR (findable, accessible, interoperable and reusable) principles. Our teams use data catalogs such as Collibra and DataHub to enable data product discoverability. To foster trust, we publish data quality and SLI metrics like freshness, completeness, consistency for each data product, and tools such as Soda Core and Great Expectations automate the data quality checks. Data Observability, meanwhile, can be achieved with the help of platforms like Monte Carlo.
We've seen data products evolve as the reusable building blocks for multiple use cases over a period of time. This is accompanied by faster time to market for subsequent use cases as we progress on identifying and building value case-driven data products. Hence, our advice is to embrace data product thinking for FAIR data.
