e ## Metadata - Author: [[anna-geller|Anna Geller]] - Full Title:: Should You Measure the Value of a Data Team? - Category:: #🗞️Articles, [[Data culture|Data culture]], [[Data team vision and mission|Data Team Vision And Mission]], [[Measuring a data team impact|Measuring A Data Team Impact]] - URL:: https://medium.com/the-prefect-blog/should-you-measure-the-value-of-a-data-team-95c447f28d4a - Finished date:: [[2023-02-17]] ## Highlights > the work of data teams is inherently [unmeasurable](https://benn.substack.com/p/chasing-ghosts) ([View Highlight](https://read.readwise.io/read/01gsf9ky94wyscf65t7tvdnkqm)) >They help other teams make decisions and operate more efficiently, but their involvement in value creation is **indirect**. > 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. ([View Highlight](https://read.readwise.io/read/01gsf9m55e4yh2r0449a7xxzt0)) > You can’t directly quantify (*especially in advance*) the impact of a new table, dashboard, or [pipeline](https://docs.prefect.io/concepts/flows/) ([View Highlight](https://read.readwise.io/read/01gsf9md0wn3zdx5qcs29c1aqc)) > In the same way that [engineering teams don’t need to prove their ROI, data teams shouldn’t either](https://twitter.com/imightbemary/status/1614663891501580292) ([View Highlight](https://read.readwise.io/read/01gsf9n8d49da47szkd0ac108q)) >You need to first [identify who is your **customer**](https://cloud.google.com/blog/products/ai-machine-learning/how-to-maximize-and-measure-the-value-of-ai-teams) and related **stakeholders**, what they do and care about, what they **expect** from you, and how data can provide **value** to them. >For example, improving the reliability of data pipelines and fixing underlying data quality issues can be the ultimate goal for a data team. You can use that goal as a starting point for aligning on a **measurement of value and progress**with stakeholders affected by those issues. While those may not have a direct effect on the bottom line, they can help indirectly by improving _processes_ and _operational efficiency_, saving _time_ or infrastructure _costs_, and gaining more _trust_ in data and your work. By first writing down what each side expects, you can clarify with stakeholders how data work contributes to **incremental process changes** that couldn’t have happened without the data team’s involvement. Metrics: >- **Time saved** (...) improved time-to-insights ([[Time to reliable insight|Time To Reliable Insight]]) or ability to conduct more ML experiments within the same time frame through parallelism and better infrastructure — all of which are measurable outcomes. >- **Cut costs.** >- **Operational efficiency** — efficiency gains can be expressed through time saved thanks to [automation](https://docs.prefect.io/ui/automations/) and easier [access to data](https://medium.com/the-prefect-blog/modular-data-stack-build-a-data-platform-with-prefect-dbt-and-snowflake-part-2-cf753708a19e) or [insights](https://docs.prefect.io/ui/overview/)without back-and-forth communication. >- (Semi good) **Satisfaction rate with access to data**—[this measurement can be a useful heuristic](https://twitter.com/machsci/status/1610315606582267906) to determine how various stakeholders perceive work with data teams, but the drawback of this question is that it can be influenced by personal biases; [one could view this question as a measure of likeability](https://podcasts.apple.com/gb/podcast/discovering-the-value-of-a-data-team/id1608725419?i=1000596212815) rather than actual data team’s performance and therefore not representative of the work being delivered.