A data science process can make or break a team. Indeed, we see time and time again that many of the reasons behind data science project failures are not technical in nature but rather stem from process-related issues. Simply throwing compute power and PhDs at the problem doesn’t work. Rather, […]
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
The rapid increase in the number of data science projects and teams is driving an increasing need for data science management. To address this gap, it is tempting to think that any project manager (from a different field) could do the job. Or that a good senior data scientist would […]
Read more
The following is an interview by Jeff Saltz with Dr Thibaut Jombart (who is a certified DSPA Team Lead). Thibaut is an Associate Professor in Outbreak Analytics at the London School of Hygiene and Tropical / Imperial College London (LSHTM / ICL) He is also a Senior Data Scientist for […]
Read more
Data Driven Scrum™ (DDS) is an agile framework specifically designed for data science teams. In short, DDS aims to improve a data science team’s collaboration and communication. The Data Science Process Alliance created Data Driven Scrum to address the fact that other well-known agile approaches (such as often Scrum and […]
Read more
A data science workflow defines the phases (or steps) in a data science project. Using a well-defined data science workflow is useful in that it provides a simple way to remind all data science team members of the work to be done to do a data science project. One way […]
Read more
Many data science teams now have at least some team members working remote. However, many managers are not used to having team members work remote and so, are not aware of the challenges that need to be considered. With a well defined process, and a focus on 5 keys actions described […]
Read more
As explained in the previous post, much of the debate on agile’s potential fit for data science focuses on the use of a specific framework (such as Scrum), and the associated processes and artifacts such as story pointing, burn down charts, or sprint lengths. Unfortunately, this drowns the argument into […]
Read more
Kanban, which literally means billboard in Japanese, started as a supply chain and inventory control system for Toyota manufacturing in the 1940s to minimize work in progress and to match the supply of automotive parts with demand. Kanban is simple and at its core focuses on two principles: 1) visualize […]
Read more
Given Scrum’s popularity with software teams, it’s no surprise that many organizations are turning to Scrum for data science product development. But, Does Scrum work for Data Science? Well…results vary. So we’ll start by defining Scrum, then identify Scrum’s use in data science, evaluate its pros and cons, and finally […]
Read more
Data science projects are unique. It’s time to start managing them as such.
Get the jumpstart guide to better manage your next project.