Do data science teams use (or think they use) an agile data science team process framework? To help find out, my student (Sucheta Lahiri) recently presented an academic paper that reported on data science agility across 16 organizations. The key takeaway was that even if your team thinks you are […]
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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 […]
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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, […]
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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 […]
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During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. […]
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Agile has taken the software community by storm with Scrum and Kanban leading the way. What are these approaches and do they fit in the world of data science?
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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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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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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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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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