Evaluating the Value of Data Products: Scenarios, Frameworks, and Improvement Methods

The article begins by highlighting the growing business importance of big‑data technologies and the need for enterprises to productize data capabilities, prompting product managers to assess the value of data products across different stages and scenarios.

It then details why value evaluation is required, describing how product managers must justify value during planning, development, promotion, and maintenance phases, and introduces a systematic evaluation approach that includes reporting, scenario discovery, scheduling, operation promotion, and collaboration.

Next, the piece presents the DBA evaluation framework, which breaks down data product value into three dimensions—data application value (efficiency, cost optimization, quality), business value (growth, cost reduction, performance), and management value (governance, decision support, knowledge retention)—and explains how each dimension can be quantified.

The article further outlines quantitative methods such as usage‑driven, business‑driven, and data‑driven metrics, as well as valuation techniques like market‑value, income, and cost approaches, providing examples of formulas and indicators.

Finally, it offers practical suggestions for increasing data product value: improving key metrics (user coverage, activity, experience), identifying high‑value directions (generic, efficient, intelligent applications, business and personnel integration), and optimizing R&D resource allocation, followed by a brief Q&A addressing ToB product quantification, self‑built vs. SaaS solutions, and algorithm value assessment.