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 […]Read more
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 […]Read more
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, […]Read more
Agile Business Intelligence Project Management Best Practices Data by itself is often useless. However, by transforming the data into human understandable insights, organizations can harness the power of data to make meaningful decisions that drive outcomes. But making this happen is easier said than done. And traditional BI approaches often […]Read more
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 […]Read more
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…Read more
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. […]Read more
Machine Learning Model Operations is a multi-disciplinary field that is gaining traction as organizations are realizing that there’s a lot more work even after model deployment. Rather, the model maintenance work often requires more effort than the development and deployment of a model. Hence, the world of Machine Learning Operations […]Read more
The importance of AI project management, or more specifically, managing AI projects, is highlighted by a survey showing that 85% of AI projects do not deliver on their promises to business. In other words, as noted by Forbes, most AI projects fail. An AI project is different than a typical […]Read more
This post will explore managing analytics projects by focusing on three questions: What is the difference between a data scientist and a data analyst? What is the difference between analytics projects and data science projects? What project management / project coordination framework might be helpful to manage analytics projects? Project […]Read more