数据资产、数据产品和数据服务

Summary of Perspectives on Treating Data as an Asset, Product, or Service

The article explores how treating data as an asset, product, or service impacts the roles and perspectives of organizational owners, data owners, data teams, and consumers. Each approach has its advantages and drawbacks, and the article uses the analogy of company cars to illustrate these differences.

Data as an Asset

When data is seen as an asset, it is valued and managed similar to how a company manages its vehicles. The executive team focuses on maximizing its value, and data owners act as guardians, regulating access and usage. Data teams deliver data assets like a project, often treating it as a one-off event, and consumers face difficulties in accessing the data, which is often locked up or assigned to specific individuals, much like company cars.

Data as a Product

Treating data as a product equates it with items like car tires, where the focus is on unit economics. Executives are concerned with cost and performance for internal use or profit margins if data products are sold externally. Data owners act as product managers, aiming to maximize returns and bring new products to market efficiently. Data teams work on design and delivery improvements but may lose the advantage of repeatability, and consumers often find the products unsatisfactory or ill-fitting, akin to purchasing unsuitable tires for their vehicle.

Data as a Service

Viewing data as a service is compared to treating it like a mechanical team, with executives optimizing resources to achieve organizational goals. Data owners manage the balance between consumer needs and available resources, which often results in idle teams and resources due to fluctuating demands. Data teams are overwhelmed with urgent requests, and consumers find data work to be custom, costly, and full of surprises, similar to last-minute car maintenance before a big trip.

Data as a Service - A Future Perspective

The article questions whether the future of data should follow the evolution of software from physical disks to a service model, akin to how Uber transformed the transportation service into a product. It invites readers to share their views on this paradigm shift.

Recommended Reading

• Issues with managing data as an asset
• Basics of data management from structured to data lakes
• Applying data quality measurement theories in practice
• Building high-impact data governance teams
• In-depth comparison between leading data warehouses and databases
• The future of master data: Dynamic, AI-driven, and data lake-powered