Data is useless if it isn’t being used, and you can’t use it if you don’t know where it is. Data catalogs were the first solution to this problem, but they are only helpful if you know what you are looking for. In this episode Shinji Kim discusses the challenges of data discovery and how to collect and preserve additional context about each piece of information so that you can find what you need when you don’t even know what you’re looking for yet.
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- Your host is Tobias Macey and today I’m interviewing Shinji Kim about data discovery and what is required to build and maintain useful context for your information assets
- How did you get involved in the area of data management?
- Can you share your definition of "data discovery" and the technical/social/process components that are required to make it viable?
- What are the differences between "data discovery" and the capabilities of a "data catalog" and how do they overlap?
- discovery of assets outside the bounds of the warehouse
- capturing and codifying tribal knowledge
- creating a useful structure/framework for capturing data context and operationalizing it
- What are the most interesting, innovative, or unexpected ways that you have seen data discovery implemented?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data discovery at SelectStar?
- When might a data discovery effort be more work than is required?
- What do you have planned for the future of SelectStar?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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