Speaker 1: Hi, today we're going to talk about data governance. Now, I know some of you are thinking, this is really either boring or super complex. But we're going to use an analogy today to help drive home some of the key concepts around data governance. After all, data governance is foundational and critical to helping you take advantage of your data in the AI world. Now, recently, I had the task of cleaning out a house as I prepared for a major modernization and renovation. This just wasn't any ordinary house though. It's a house I bought from my parents who lived there for over 30 years. Let's just say, my mom wasn't real good about throwing away things. The first thing I had to do was understand what I had. I started in the basement and had to go through and discover all of the different items, clothing, bins, photographs, family heirlooms, everything that was down there. That process in the data world is called discovery. 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 SaaS applications. Now, the easy part is discovering the data that you know about. The hard part is discovering the data that you don't know about. For me, that was discovering all the different things in my attic. Now, once I had the opportunity to go through each different part of my house, I had to start classifying the items. I had to understand if it was something that was a family heirloom, whether it was a picture or a set of photographs, or even toys, financial records, all kinds of different stuff. 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. Now, after that, this is where it gets really fun. In my world, I had to go through and decide what to keep. For me, I had my wife set forth some policies about what we were keeping, what we were donating, and what we were just throwing in the garbage. 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 maybe could use it. If it was broken, we would throw it away. Now, we started applying these rules. 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. Personally identifiable data, such as a social security number, must be masked. The rules help you enforce that. If it is a social security number, you must mask it. And that helps you enforce the policy. Now, for me, as I went through the process of enforcing my policies, I decided what I loved, what I was throwing away, and what I was donating to charity. In the data world, you must go through the same process. And as I did so in my world, the things that I was keeping, I repackaged in bins. And I labeled the different bins describing what was in each of them. Now, this is a little bit like metadata. Metadata in the data world helps describe what that data asset is. Simply put, it's like a card catalog at a library that describes a book. It provides the author, the subject, the copyright. In the data world, it does much of the same thing. It tells you where the data came from, what it is, so that when you want to use it, it's easier to find and easier to use when the time comes. Now, what was cool in my world was I found a few things that helped me generate some money. There were a couple of family heirlooms that I didn't necessarily need anymore, but I was able to sell. Wouldn't that be cool if you were able to monetize your data by understanding what it is and helping your organization? That's the importance of data governance. That's the value that it brings to your organization. For me, the one thing I wish I could do was automate that process. The great news for you in the data world, you can automate it. Thanks for watching. If you have any questions, please leave them in the comments below. Also, please remember to like this video and subscribe to our channel so we can continue to bring content that matters to you.