What is Agile Data Science?
Exploring how to “do” agile data science
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Data Driven Agile With Data Driven Scrum
When teams try to have a data driven agile approach, they often try to use an existing framework, such as Scrum or Kanban. Yet, there are key challenges teams have in leveraging these frameworks. Therefore, the Data Science Process Alliance created an alternative framework called Data Driven Scrum which is […]
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Vertical vs Horizontal Slicing Data Science Deliverables
Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention […]
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Is Agile a Fit for Data Science?
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 […]
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5 Agile Data Science Myths
One of the most common questions of data science project management is some variation of: “Is agile a fit for data science?” Unfortunately – like a lot of questions in data science – this question itself is often misunderstood. Many (if not most) blog posts, on-line forums, and conversations debate […]
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This blog ain’t much but Isaac Newton would like it
At the time of posting this, this blog has a generic template, broken links, no logo, only one post, and is all around…well, bad. So why would we voluntarily share with the world something so embarrassing? Because Jeff and I have been thinking about starting a blog for several months but up […]
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What is TDSP?
Team Data Science Process If you combine Scrum and CRISP-DM, you would get something that looks like Microsoft’s Team Data Science Process. Launched in 2016, TDSP is “an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently.” (Microsoft, 2020 ). This is a modern data science […]
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Kanban for Data Science
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 […]
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Scrum for Data Science
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 […]
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Agile Data Science
Do you think data science should be agile? When framing agility in the context of delivering usable insights frequently, iterating on these insights, and validating the outcomes, I think all of us would say “yes”. Yet, how do we achieve this? Even more basic, what does agile data science even […]
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