Summary

Agile methodologies have been adopted by a majority of teams for building software applications. Applying those same practices to data can prove challenging due to the number of systems that need to be included to implement a complete feature. In this episode Shane Gibson shares practical advice and insights from his years of experience as a consultant and engineer working in data about how to adopt agile principles in your data work so that you can move faster and provide more value to the business, while building systems that are maintainable and adaptable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Shane Gibson about how to bring Agile practices to your data management workflows

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what AgileData is and the story behind it?
What are the main industries and/or use cases that you are focused on supporting?
The data ecosystem has been trying on different paradigms from software development for some time now (e.g. DataOps, version control, etc.). What are the aspects of Agile that do and don’t map well to data engineering/analysis?
One of the perennial challenges of data analysis is how to approach data modeling. How do you balance the need to provide value with the long-term impacts of incomplete or underinformed modeling decisions made in haste at the beginning of a project?
- How do you design in affordances for refactoring of the data models without breaking downstream assets?
Another aspect of implementing data products/platforms is how to manage permissions and governance. What are the incremental ways that those principles can be incorporated early and evolved along with the overall analytical products?
What are some of the organizational design strategies that you find most helpful when establishing or training a team who is working on data products?
In order to have a useful target to work toward it’s necessary to understand what the data consumers are hoping to achieve. What are some of the challenges of doing requirements gathering for data products? (e.g. not knowing what information is available, consumers not understanding what’s hard vs. easy, etc.)
- How do you work with the "customers" to help them understand what a reasonable scope is and translate that to the actual project stages for the engineers?
What are some of the perennial questions or points of confusion that you have had to address with your clients on how to design and implement analytical assets?
What are the most interesting, innovative, or unexpected ways that you have seen agile principles used for data?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on AgileData?
When is agile the wrong choice for a data project?
What do you have planned for the future of AgileData?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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