It’s an understatement that great leadership is challenging and rare. And leading data science teams has unique challenges: Stakeholders might get disillusioned by your team’s inability to deliver magic The battle to recruit and retain data science talent is fierce Data science’s ethical dilemmas are particularly perplexing There is not an agreed-upon process for managing […]
The battle between Data Science vs Software Engineering isn’t really a battle. Rather these are two somewhat overlapping and complementary fields that are similar in many ways. However, dig deeper, and you’ll find key differences: Data science is more exploratory. Software engineers are more focused on systems building. And data science project management should be […]
So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.
Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.
Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do the fit in the world of data science?
How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.
It is ironic that data science, a field built upon rigorous scientific methodologies, has been slow to adopt much rigor from project management approaches. Rather, in data science, project management tends to be ad hoc and is often on the back-burner as a secondary consideration to technologies and algorithms. Fortunately, this is changing as organizations […]
Unlike more tactical roles like the project manager who oversees the project life cycle or a process master who drives effective processes, the product manager is more strategic. Wearing many different hats, this product person researches market needs, defines how to map the solution to the problem space, and sets the product vision. Evolving from […]
Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Lifecycle, 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 through a project lifecycle. In pre-project […]
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 as an “enterprise” or “shared […]