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 […]
While there is no standard process for a team to use when working on a data science project, CRISP-DM (CRoss-Industry Standard Process for Data Mining) is one framework that is often considered for data science projects. Perhaps because of this, there are lots of web sites describing the 6 phases of a CRISP-DM project, and […]
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 […]
Due to efforts to lower the spread of COVID-19, the vast majority of data science teams are now working remote. Unfortunately, many teams are not used to working remote and so, are not aware of the challenges that need to be considered when working in a remote data science team. Most importantly – teams need […]
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 […]
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 all roles are required to […]
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 […]
Integrating Ethics in a Data Science Project: 10 Questions a Data Science Project Team Should Ask While the potential ethical issues that might arise when using data science and artificial intelligence have certainly been in the popular press recently, there is not been as much discussion with respect to how a data science team should […]
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. It is defined in a 25-page whitepaper, The […]