Working document: https://docs.google.com/document/d/1bodpI0Gz-vxd2nbxUOEgHlEJASCBqqT_urK3kieQ1_0/edit?usp=sharing

Data lakes, data warehouses, etc.

Patterns on making them discoverable in the most popular tooling.

How do we populate the data storage systems with our ISPO guidance to help people with all of this:

https://innersourcecommons.github.io/managing-innersource-projects/innersource-and-ai/shaping-for-ai.html

https://opendataproducts.org/

Are these patterns, or a page in the Managing InnerSource Projects book?

• Customer Solutions
• GitHub repositories
  • What fields are necessary?
  • How can we find the repo automatically using AI?
• User data
• Infrastructure Footprint (GCP, AWS, Azure, Etc.)
• Software Development teams (Jira, GitHub Issues, AHA, Etc.)
• Architectural/system diagrams
• Design artifacts (Figma, Google's Stitch)
• Artifact Repositories (Artifactory, GitHub Artifacts)

1. Expose all these data products to Agents
2. Automate InnerSource activities
3. A working model between a data science organization in your company and the ISPO.