Data governance is explained using the analogy of cleaning a house before renovation. The process includes discovery, classification, creating rules, enforcing policies, and using metadata for effective data organization. This helps organizations understand and leverage their data, leading to value creation. Automation is emphasized for streamlining the data governance process.
### Methodology
- Discovery: Understanding all data assets across repositories.
- Classification: Assigning data to different categories.
- Creating rules: Enforcing data policies.
- Metadata: Describing data assets for easier use and retrieval.
- Value creation: Monetizing data assets.
- Automation: Streamlining the data governance process.
Notable Quotes
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00:59
— « Discovery is a process of understanding all of the different data assets you have across your repositories which may be in the cloud on-prem or even from some sas applications.
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01:34
— « In the data world the process of classification is assigning data to different categories whether it's customer data product data financial data and providing that label or that classification to it.
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02:22
— « Let's take toys for example my mom loved to keep a lot of toys from when i was growing up. In certain toys there were missing parts. The policy was if they were missing parts we would donate them hoping that somebody may be able to use it if it was broken we would throw it away.
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02:44
— « Rules are ways to help you enforce your data policies and again policies are about setting guidelines and standards about what to do with your data. In the data world one policy that is very common yet critical is around personally identifiable data.
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04:55
— « That's the importance of data governance, that's the value that it brings to your organization. »