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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The following is an interview between Nick Hotz and Dr. Matthew Edwards. Dr. Matthew Edwards Organization: National Innovation Centre for Data Role: Data Scientist Location: Newcastle, England Certification: Data Science Team Lead NICD & Matthew’s Background Nick: Can you describe your work at the National Innovation Centre for Data? Matthew: […]
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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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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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Conceptually Agile and data science are a great match. Most notably, both Agile and data science emphasize the same underlying concept that you build something, you learn from what you delivered, and then you improve it based on what you learn. And yet the results of implementing an Agile data […]
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