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. (View Highlight)
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. (View Highlight)
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. (View Highlight)
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. (View Highlight)
One metric that might help in such situations is the time-to-decision framework proposed by Benn Stancil. The framework is simple: the performance of a data team is measured by how quickly decisions are made. The quicker, the better you are performing (View Highlight)
Another business-oriented measurement is to tie data objectives to the company’s OKRs if it’s possible to align those objectives. This approach makes it clearer how the data team’s work impacts functional outcomes. (View Highlight)
the senior leadership should value your team’s work without having to prove it, but if you need to prove it, be ready for it. Show, don’t tell. Show what your team delivered and how it solved business problems in the past. Show the impact of incremental process changes that wouldn’t have happened without this team’s involvement. Show what you changed for the better — how money and time were saved and how operational efficiency improved. And whatever metric you choose, iterate on it — don’t let perfect be the enemy of good. (View Highlight)
