Gone are the days of a “lone wolf” data scientist filling all the roles in data science. Rather, today’s teams have several different data science roles, each of which executes various functions across the data science life cycle. Each team setup is different, often consisting of several different data science […]
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Do data science teams use (or think they use) an agile data science team process framework? To help find out, my student (Sucheta Lahiri) recently presented an academic paper that reported on data science agility across 16 organizations. The key takeaway was that even if your team thinks you are […]
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Different data scientists have different processes for conducting their projects. And different types of projects require different steps. However, most data science projects flow through a similar workflow. One popular representation of this workflow is called OSEMN (pronounced “awesome”). Whether you use this or another life cycle, understanding the basic […]
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To deliver useful data science projects, it is important to effectively manage the data science team. But what does it mean to manage a data science team? How is it different from managing other teams? These are the questions I’ll explore in this post. Way back in 2018, an article […]
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The rise of big data and data science is already having a significant positive impact on our lives. However, there are also dark sides to how data is collected, used, and misused. Often this misuse is not from an intentionally malevolent actor. Rather, it could be someone like you or […]
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Machine learning predictive models are often used in data science projects. These models often deliver significant value. Examples of these AI models range from identifying skin cancer to optimizing when to milk cows. Unfortunately, there are many examples of AI (machine learning algorithms) not working as desired. For example, a […]
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Data science is an exciting field that can impact nearly all industries and many facets of daily life. Yet in practice, outcomes often don’t live up to expectations. Why not? A major reason is that organizations struggle to apply effective project management practices to data science. To overcome these challenges, […]
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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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The machine learning process defines the flow of work that a data science team executes to create and deliver a machine learning model. In addition, the ML process also defines how the team works and collaborates together, to create the most useful predictive model. A High Level Machine Learning Process […]
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