From 2019-2022, I was the data product manager of the business intelligence team at Unite, a sizeable European B2B procurement platform, where I guided our transformation from a “service team” to a “product team.” We went from implementing long lists of dashboards and reports for sales, customer services, and management to turning analytics inside the company upside down. When I left, we had just finished implementing a multi-year vision for the future of the company’s data platform.
We
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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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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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The sprint prioritization meeting is integral to the agile process. While many people may be more familiar with meetings such as sprint planning, stand up, back log grooming, and retro,
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Agile software engineering practices have become the standard work management tool for modern software development teams. Are these techniques applicable to analytics, or is the nature of research prohibitively distinct
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This is part 2 of my 3 part exploration of the following question: are Agile engineering practices applicable to analytics, or is the nature of research prohibitively distinct from the
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Agile software engineering practices have become the standard work management tool for modern software development teams. Are these techniques applicable to analytics, or is the nature of research prohibitively distinct
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