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Metadata
- Author: Anna Geller
- Full Title:: Should You Measure the Value of a Data Team?
- Category:: 🗞️Articles, Data Culture, Data Team Vision And Mission, 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 (View Highlight)
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)
You can’t directly quantify (especially in advance) the impact of a new table, dashboard, or pipeline (View Highlight)
In the same way that engineering teams don’t need to prove their ROI, data teams shouldn’t either (View Highlight)
You need to first identify who is your customer 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 progresswith 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) 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 and easier access to data or insightswithout back-and-forth communication.
- (Semi good) Satisfaction rate with access to data—this measurement can be a useful heuristic 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 rather than actual data team’s performance and therefore not representative of the work being delivered.