A data science team’s process is a key driver to their projects’ success. However, as will be discussed below, there is not an existing AI / data science maturity model that is focused on how to evaluate (and improve) a team’s process. Hence, after reviewing several Analytics / Data Science […]
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By their nature, data science products are risky. Building a Data Science MVP can reduce that risk by focusing the early development life cycle on discovery and learning. The onset of a data science project has a lot of unknowns – on the data, algorithm, systems, and business side. You […]
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The machine learning process defines the flow of work that a data science team executes to create and deliver a machine learning model. In addition, the ML process also defines how the team works and collaborates together, to create the most useful predictive model. A High Level Machine Learning Process […]
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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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Data Driven Scrum (DDS) enables lean data science project agility and addresses the key challenges that have been identified when using Scrum in a data science context. This post describes five common questions teams might encounter when trying to implement DDS, as well as how to address these challenges. 1 […]
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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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The following is an interview by Jeff Saltz with Hector Rangel. Hector, a certified DSPA Team Lead, is a Consultant and Data Science lead at Arena Analytics, a small consulting company located in Mexico. Jeff: Can you tell me a bit about yourself? Hector: I’m an industrial engineer and a business […]
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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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So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.
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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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