In the past few years, much has been written about problems like discoverability, observability, data quality, and the need for data teams to become more “engineering oriented” in their mindset. Movements like analytics engineering and open source tooling like dbt, Dagster, and Great Expectations have done an amazing job arming data practitioners with the tools that they need to start adopting the best practices of software engineering like modularity, testing, and release management. This shift in mindset has resulted in
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Not enough effort has been made to understand the people using data products, what they want to get done, and the broader context in which they operate. I believe there
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Proactively sharing data insights broadly with the people in our organizations encourages engagement and collaboration, brings additional visibility to the data team, and provides a way to work in partnership
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Your team invested a lot of effort into building self-service data tools, but your stakeholders aren’t using it as much as you hoped, or in the ways that you hoped.
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Data teams aim to help the people in their organization make better decisions. Many data teams aren’t doing this as well as they could and are missing out on a
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In part 2 of this series, we learned that every new initiative a team undertakes should be accompanied by an expectation on how the initiative will perform. For example,
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The MBR meeting is the focal point of the MBR program. Here you will publicly interrogate KPIs, align on action items and hold people accountable. There are typically
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Getting regular feedback from your organization can help the data team to prioritize the work that will have the biggest impact on your stakeholders. However, it’s hard to turn ad-hoc
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KPIs are central to the business review program since they provide a way to reason about the success or failure of an initiative. Core to the success of a
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