A data science team’s process is a key driver to their projects’ success. However, as will be discussed below, there is not an existing AI / data science maturity model that is focused on how to evaluate (and improve) a team’s process. Hence, after reviewing several Analytics / Data Science […]
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By their nature, data science products are risky. Building a Data Science MVP can reduce that risk by focusing the early development life cycle on discovery and learning. The onset of a data science project has a lot of unknowns – on the data, algorithm, systems, and business side. You […]
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There’s no single data science project documentation recipe. Rather, your documentation needs will vary by project, team, organization, and industry. And it’s not just about producing data science model documentation. Instead, think broader and ask – What do I need to document and why? Once you’ve thought this through and […]
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A data science strategic plan isn’t just about data. Rather, it is a comprehensive plan that defines how you build and maintain an ecosystem that delivers sustainable value from your data science investments.
Developing this plan is not easy. So why bother?
Well, without one, you could be steering down a path without knowing where it is going…
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The demands for data science insights and systems far outstrip the capacity for data science teams to deliver. How do you best balance this? Enter the Data Science Product Manager. Unlike more tactical roles like the project manager who oversees the project life cycle or a process master who drives […]
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Based on my personal experience, below are the 8 key data science team roles to think about when building and leading a data science team. These are not in any specific order, as their importance might vary from one project to another, or from one organization to another. Furthermore, not […]
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Data science projects are challenging. To increase your odds of success, start them by asking several key data science project questions. As Ben Franklin said, “an ounce of preparation saves a pound of headaches in your data science projects”.* To help you prepare for a project and evaluate whether to […]
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What can you learn if you observe data science teams across 20 large companies? I asked Mac Steele, Director of Product at Domino Data Lab, to find out. Mac combined the lessons he learned from observing data science teams with concepts from CRISP-DM and agile to create the Domino Data Science Lifecycle Domino […]
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