The Art and Science of Measuring Data Teams Value

Data holds immense value for businesses of all sizes, as it is used to make decisions, identify new opportunities, and improve performance. However, measuring the success of data projects can be challenging due to their indirect nature and difficulty in quantifying outcomes. The main problem with evaluating a data team's worth lies in its indirect impact on other company functions, such as marketing or sales, rather than generating revenue directly. To measure the value of data teams, it is essential to break down their components into smaller parts. One helpful way to understand a data team's layers and the value they deliver is by examining the "data science hierarchy of needs." This concept draws inspiration from Maslow's hierarchy of needs and asserts that reaching full potential requires first meeting basic needs. The data hierarchy of needs can be divided into four levels: Collect and model, Describe, Predict, and Prescribe and influence. Each level represents the outcomes that data teams deliver, with information moving from raw data to knowledge to wisdom. To measure value at each level, various metrics and methods can be used, such as intrinsic value of information (IVI), cost value of information (CVI), business value of information (BVI), market value of information (MVI), economic value of information (EVI), performance value of information (PVI), and engagement and usage metrics. Ultimately, measuring the exact value of data projects can be subjective, as it depends on the users and their actions with the data assets.