 From around the globe, it's theCUBE presenting Adaptive Data Governance. Brought to you by IO Tahoe. In this next segment, we're going to be talking to you about getting to know your data. And specifically, you're going to hear from two folks at IO Tahoe. We've got Enterprise Account Exec Sabita Davis here, as well as Enterprise Data Engineer Patrick Zymett. They're going to be sharing insights and tips and tricks for how you can get to know your data and quickly. And we also want to encourage you to engage with Sabita and Patrick, use the chat feature to the right, send comments, questions or feedback so that you can participate. All right, Patrick, Sabita, take it away. All right, thanks Lisa, great to be here. As Lisa mentioned, guys, I'm the Enterprise Account Executive here at IO Tahoe, new Pat. Yeah, hey everyone, so great to be here. As said, my name is Patrick Zymett. I'm the Enterprise Data Engineer here at IO Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. I couldn't agree more, Pat. So guys, well, Pat and I have actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non-technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back to Pat's point of data being everybody's business because no matter your role, we're all dependent on data. So what Pat and I wanted to do today was we wanted to walk you guys through some of those client questions slash pain points that we're hearing from different industries and different roles and demo how our platform here at IO Tahoe is used for automating those data related tasks. So with that said, are you ready for the first one, Pat? Yo, let's do it. Great. So I'm going to put my technical hat on for this one. So I'm a data practitioner. I just started my job at ABC Bank. I have like over 100 different data sources. So I have data kept in data lakes, legacy data sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data is sensitive and I don't even understand how that data is connected. So how can IO Tahoe help? Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this is tackled through numerous meetings and a combination of Excel and something similar to Visio, which are two great tools in their own part, but they're very difficult to maintain just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like IO Tahoe becomes so appealing. You can see here visualization of the data relationships created by the IO Tahoe service. Now what is fantastic about this is it's not only laid out in a very human and digestible format, in the same action of creating this view, the data catalog was constructed. So is the data catalog automatically populated? Correct. Okay, so what I'm using IO Tahoe, Pat, what I'm getting is this complete unified automated platform without the added cost of course. Exactly, and that's at the heart of IO Tahoe. A great feature with that data catalog is that IO Tahoe will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore 10s that are always such a joy to deal with. Now by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Personally, I'm a big fan of this view as it really just helps the eye be naturally drawn to these focal points that coincide with these key columns. Following that train of thought, let's examine the customer ID column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. Now you'll notice all of the connectors are in this blue color. This means that they're system-defined relationships, but IO Tahoe goes that extra mile and actually creates these orange colored connectors as well. These are ones that our machine learning algorithms have predicted to be relationships, and you can leverage to try and make new and powerful relationships within your data. So I hope that answers the first part of your question. So this is really cool and I can see how this can be leveraged quickly. Now, what if I added new data sources or multiple data sources and needed to identify what data is sensitive? Can IO Tahoe detect that? Yeah, definitely. Within the IO Tahoe platform, there are already over 300 predefined policies such as HIPAA, FERPA, CCPA, and the like. One can choose which of these policies to run against their data, allowing for flexibility and efficiency in running the policies that affect your organization. Okay, so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? Yeah, that's no issue and is something that clients leverage fairly often. To utilize this function, one simply has to write a regex that our team has helped many deploy. After that, the custom policy is stored for future use. To profile sensitive data, one then selects the data sources they're interested in and selects the policies that meet your particular needs. The interface will automatically tag your data according to the policies it detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface, so one can work these into the action items within your project management systems. And I think this lends to the collaboration as a team can work through the discovery simultaneously and as each item is confirmed or rejected, they can see it nigh instantaneously. All this translates to confidence that with IO Tahoe, you can be sure you're in compliance. So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying, when I use the IO Tahoe automated platform, we'd be 90% more compliant than if we were, other than if we were going to be using a human. Yeah, definitely. The collaboration and documentation that the IO Tahoe interface lends itself to can really help you build that confidence that your compliance is sound. Does that answer your question about sensitive data? Definitely. So Pat, I have a next question for you. So we're planning a migration and I have a set of reports I need to migrate. But what I need to know is, well, what data sources those reports are dependent on and what's feeding those tables? Yeah, it's a fantastic question, Savita. Identifying critical data elements and the interdependencies within the various databases can be a time consuming but vital process in the migration initiative. Luckily, IO Tahoe does have an answer. And again, it's presented in a very visual format. So what I'm looking at here is my entire data landscape. Yes, exactly. So let's add another data source. I can still see that unified 360 view. Yeah, one feature that is particularly helpful is the ability to add data sources after the data lineage discovery has finished, allowing for the flexibility and scope necessary for any data migration project. If you only need to select a few databases or your entirety, this service will provide the answers you're looking for. This visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections are driven by both system-defined flows as well as those predicted by our algorithms. The confidence of which can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. Now, this also provides a tabular output in case you need it for your own internal documentation or for your action items, which we can see right here. In this interface, you can actually also confirm or deny the pair rejection, the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? Definitely. So, Pat, my next big question here is, so now I know a little bit about my data. How do I know I can trust it? So what I'm interested in knowing really is, is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that Io Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules and your data's overall quality. Okay, so Pat, so on the accuracy scores there, so if my marketing team needs to run a campaign, can we depend on those accuracy scores to know what cables have quality data to use for our marketing campaign? Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view. So you no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. I hope that helps answer your questions about data quality. Oh, definitely. So I have another one for you here, Pat. So I get now the value that AYETAHO brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules we were just looking at can then be tied into these definitions, allowing insight into the strength of these business rules. It is this service that empowers stakeholders across the business to be involved with the data lifecycle and take ownership over the rules that fall within their domain. Okay, so those custom rules, can I apply that across data sources? Yeah, you can bring in as many data sources as you need, so long as you can tie them to that unified definition. Okay, great. Thank you so much, Pat. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us via our website, the chat below or LinkedIn, and let's get a conversation started on how AYETAHO can help you guys automate all those manual tasks to help save you time and money. Thank you. Thank you, Erin. Hey, Pat, if I could ask you one quick question, how do you advise customers? You just walk in this great example, this banking example that you and Simi could talk through. How do you advise customers get started? Yeah, I think the number one thing that customers can do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as these quality rules, as well as identifying those kind of tricky columns that might exist in your data, those custom variable underscore tens I mentioned before. And last question Simi, anything to add to what Pat just described as a starting place? No, I think actually Pat summed it up pretty well. I mean, just by automating all those manual tasks, I mean, it definitely can save your company a lot of time and money. So we encourage you just reach out to us, let's get that conversation started. Excellent, Savita and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data, make sure that it's quality. So that you can maximize the value of it. Thanks for watching.