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, a Sales team may expect that a new data source will increase the number of new partnerships by 10%. This expectation should be memorialized ahead of time to see how the team fared against their target.
Today, we go deeper on two related KPI concepts: Targets and Forecasts. These concepts
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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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As an analytics leader, my job is to ensure that my organization makes the best data-driven decisions possible. One effective mechanism that I employ — and other analytics leaders can too — is the business review program. This program is a rigorous and systematic review of the most important Key Performance Indicators (KPIs) across the company, with involvement by senior leadership in an ongoing and iterative fashion, punctuated by a regularly occurring cross-functional meeting. …
It’s becoming more and more clearly recognized that the promises of the machine-learning revolution, at least for your average non-big-tech company, are not delivering the value that many had hoped
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The nuances of data projects add an extra layer of challenge to project planning and agile project management approaches. Even the most efficient data team must manage the inherent moving-target
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There’s been a lot of discussion lately about systems for doing version control for data. Most recently, Ryan Gross wrote a blog post “The Rise of DataOps” where he
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Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue.
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