Should You Measure the Value of a Data Team?
Data teams are sometimes asked to prove their ROI to senior leadership to justify a budget for new hires, tools, projects, or process changes. But the work of data teams is inherently unmeasurable. Often the reason for this ROI question isn’t rooted in a lack of proper metrics but rather a lack of trust and relationships with stakeholders.
Should you measure the data team’s ROI? If so, which metrics are worth considering? This post summarizes key arguments from several blog posts, podcasts, discussions from data communities, and my experience.
Arguments against measuring
Most data teams work as a support function. They help other teams make decisions and operate more efficiently, but their involvement in value creation is indirect. You can’t directly quantify (especially in advance) the impact of a new table, dashboard, or pipeline.
Improving your data models or data infrastructure doesn’t immediately return any financially measurable outcomes related to the core of your business. It doesn’t mean those improvements are not valuable. But what’s valuable is not necessarily what’s valued. Data teams often don’t get credit for their work, not because they do a poor job, but because the company culture doesn’t value data work regardless of its quality or quantity.
In such situations, the underlying issue is not a lack of ROI measurement but a lack of trust. Instead of searching for the perfect metric, data teams need to slowly elbow their way in by continuously solving business problems, earning trust from stakeholders, and gradually improving culture and processes.
An alternative to slow cultural changes would be hiring a Product Manager whose responsibility is communicating this team’s value to senior leadership, managing expectations, and translating this team’s deliverables into business outcomes. In the same way that engineering teams don’t need to prove their ROI, data teams shouldn’t either — a dedicated PM could ensure that the team is focused on important work rather than invisible and hard-to-measure ad-hoc requests.