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House Cleanup Mirrors Data Governance

Key Points

  • The speaker uses a house‑clean‑out analogy to illustrate data governance, emphasizing its foundational role for leveraging data in AI.
  • “Discovery” in data governance means identifying all data assets across cloud, on‑premise, and SaaS environments, including the hidden or unknown ones.
  • “Classification” involves assigning each data element to categories such as customer, product, or financial data, much like sorting household items into heirlooms, photos, or toys.
  • Policies (e.g., how to handle personally identifiable information) are enforced by rules that automatically apply actions like masking, mirroring the decisions to keep, donate, or discard items during the house clean‑up.

Full Transcript

# House Cleanup Mirrors Data Governance **Source:** [https://www.youtube.com/watch?v=uPsUjKLHLAg](https://www.youtube.com/watch?v=uPsUjKLHLAg) **Duration:** 00:05:21 ## Summary - The speaker uses a house‑clean‑out analogy to illustrate data governance, emphasizing its foundational role for leveraging data in AI. - “Discovery” in data governance means identifying all data assets across cloud, on‑premise, and SaaS environments, including the hidden or unknown ones. - “Classification” involves assigning each data element to categories such as customer, product, or financial data, much like sorting household items into heirlooms, photos, or toys. - Policies (e.g., how to handle personally identifiable information) are enforced by rules that automatically apply actions like masking, mirroring the decisions to keep, donate, or discard items during the house clean‑up. ## Sections - [00:00:00](https://www.youtube.com/watch?v=uPsUjKLHLAg&t=0s) **Data Governance Explained via House Cleanup** - The speaker likens data discovery and classification to sorting items while cleaning an inherited home, illustrating key data governance concepts. ## Full Transcript
0:00hi today we're going to talk about data 0:02governance now i know some of you are 0:04thinking this is really either boring or 0:07super complex 0:08but we're going to use an analogy today 0:11to help drive home some of the key 0:12concepts around data governance after 0:14all data governance is foundational and 0:16critical to helping you take advantage 0:18of your data in the ai world 0:20now recently i had the task of cleaning 0:23out a house as i prepared for a major 0:25modernization and renovation 0:28this just wasn't any ordinary house 0:29though it's a house i bought from my 0:31parents who lived there for over 30 0:33years and let's just say 0:35my mom wasn't real good about throwing 0:37away things 0:38so the first thing i had to do was 0:41understand what i had so i started in 0:44the basement and had to go through 0:46and discover 0:48all of the different items clothing bins 0:51photographs family heirlooms 0:54everything that was down there 0:56that process in the data world is called 0:59discovery 1:00discovery is a process of understanding 1:02all of the different data assets you 1:04have across your repositories which may 1:07be in the cloud on-prem or even from 1:09some sas applications 1:12now the easy part is discovering the 1:14data that you know about the hard part 1:16is discovering the data that you don't 1:18know about 1:20for me 1:21that was discovering all the different 1:22things 1:23in my 1:24attic 1:26now once i had the opportunity to go 1:29through each 1:30different part of my house i had to 1:32start classifying the items 1:34i had to understand if it was something 1:37that was 1:38a family heirloom whether it was a 1:40picture or a photograph set of 1:42photographs 1:43or even toys financial records all kinds 1:47of different stuff 1:48in the data world the process of 1:51classification is assigning data to 1:53different categories 1:55whether it's customer data product data 1:58financial data 2:00and providing that that label or that 2:02classification to it 2:04now after that this is where it gets 2:07really fun in my world i had to go 2:10through and decide what to keep 2:13for me i had my wife set forth some 2:15policies about what we were keeping what 2:17we were donating 2:18and what we were just throwing in the 2:20garbage 2:22let's take toys for example my mom loved 2:24to keep a lot of toys from when i was 2:26growing up 2:27in certain toys there were missing parts 2:30the policy was 2:32if they were missing parts we would 2:34donate them hoping that somebody may be 2:36able to use it if it was broken we would 2:39throw it away 2:40now 2:42we started applying these rules 2:44rules are ways to help you enforce your 2:47data policies and again policies are 2:50about setting guidelines and standards 2:52about what to do with your data in the 2:54data world one policy that is very 2:56common yet critical is around personally 2:59identifiable data 3:01personally identifiable data such as a 3:04social security number must be masked 3:08the rules help you enforce that if it is 3:10a social security number you must mask 3:13it 3:14and that helps you enforce the policy 3:17now for me 3:19as i went through the process 3:21of 3:23enforcing my policies i decided what i 3:25loved 3:28what i was throwing away 3:30and 3:31what i was donating 3:34to charity 3:36in the data world 3:38you must go through the same process 3:41and as i did so in my world the things 3:43that i was keeping 3:45i repackaged 3:48in bins and i labeled the different bins 3:51describing what was in each of them 3:55now this is a little bit like metadata 3:58metadata in the data world helps 4:00describe what that data asset is 4:03simply put it's like a card catalog at a 4:06library that describes a book it 4:08provides the author the subject the 4:10copyright 4:12in the data world it does much of the 4:13same thing it tells you where the data 4:16came from what it is 4:18so that when you want to use it it's 4:20easier to find and easier to use when 4:23the time comes 4:25now what was cool in my world was i 4:28found a few things 4:31that helped me 4:33generate some money 4:35there were a couple of family heirlooms 4:37that i didn't necessarily need anymore 4:39but i was able to sell 4:41wouldn't that be cool like if you were 4:43able to monetize your data 4:45by understanding what it is and helping 4:47your organization 4:49that's the importance of data governance 4:51that's the value that it brings to your 4:53organization 4:55for me 4:57the one thing i wish i could do 5:00was automate that process 5:02the great news for you 5:04in the data world you can automate it 5:08thanks for watching if you have any 5:10questions please leave them in the 5:11comments below also please remember to 5:13like this video and subscribe to our 5:15channel so we can continue to bring 5:16content that matters to you