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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What do flight attendants, surgical teams, and successful data science project managers have in common? They all use a checklist. Why? Although every surgery, project, and flight is different, each has a repeatable pattern. And checklists can help remind us of each step and major consideration. Don’t reinvent the wheel. […]
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The Boston Dynamics humanoid robot tripping over a curtain and tumbling off stage? A prediction model that tips a teen’s parents off to her pregnancy? A music recommendation engine that suggests Coldplay? Yup. There are just too many big data, data science, and data analytics failure examples to cover in […]
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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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Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Life Cycle, or Data Driven Scrum describe how to execute a data science project. While useful, such models do not explicitly explain how to communicate with stakeholders on what they care most about: what deliverables will they get […]
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Should you have a centralized data science team or several decentralized teams? There are numerous options for a data science team structure in mid- to large-sized organizations. Yet, many organizations struggle to decide among having: A single centralized data science team (also known as “data science center of excellence” or […]
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Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention […]
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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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