Command and Control of Your Data Landscape with erwin Data Intelligence

Welcome, everybody to erwin by Quest presentation on Command and Control, Taking Charge of Your Data Landscape. My name is Sam Benedict, erwin VP of Solution Strategy. I'm going to take you through a very brief overview and invite you to explore further all the ways that erwin can help you manage and take control of your data landscape. Now when we think about data, we've really got a lot of things to think about these days, lots of data, almost information overload. And things are becoming more and more time sensitive as we get into data privacy legislation and all sorts of other ramifications that have to do with timely data, like data science, producing actionable output. There's so much to consider when we think about the amount of data we're dealing with and the timeliness that we have to react to that data and what it's telling us. Companies today still don't know very basic things. What data do I have and where is it? What people and systems are available and how are we leveraging those people and systems? What processes should I use to govern this data? And how is this data relevant or accessible to the business? Do I have a way to feed that to them with context and meaning so that it becomes useful to them and not just more stuff that they don't know what to do with? They need data intelligence. Ultimately, everyone in the business and on the IT side of the equation have to have a good level of intelligence about the data, meaning where did it come from, how are we leveraging it, are we curating it in all the right ways? These are things that are becoming more and more important as data volumes grow, as regulations grow, and as we enter sort of a new millennium of technology that allows us to do things faster, easier, and smarter with AI, machine learning, and algorithms. Things that make managing data in a traditional sense really obsolete and they sound almost foolish. Now the erwin DI Suite has a series of modules. It is designed to allow any company, regardless of size, to crawl, walk, run. And really get a handle on their data, whether they have data chaos meaning data everywhere, they just don't know which end is up. Or maybe you're managing your data in a way that's very particular and you're very good at it and you're very well organized, but you're still struggling with things like data lineage and producing business semantic graphs that allow the business to see how things fit together. And maybe you don't have the technology to take all of that great information from DevOps and leverage that in DataOps outputs. Things that can be consumed by the rest of your community inside your business to help them understand how you're leveraging your data, where it lives, what it means, how it's calculated and derived. All those things become increasingly important. So along the way we're going to harvest everything into a common repository. We're going to do some analysis on it, provide some structure. Things like lineage views, impact analysis views, mind maps, which are that business semantic graph component. And we're going to apply data governance and diligence to it so that we can better visualize and fully leverage all the data assets that we have as they grow. And perhaps find new ways to leverage our data assets over the course of time. Ultimately the goal is better, more auditable, and more accessible information. Automation is critical in this process. When we think about things that keep us from advancing our cause, generally speaking, it's time and money. We don't have the people to do these things. We don't have the money to do these things. And it creates issues around getting to data lineage, understanding our level of data quality, the visibility of everything that we're doing across the enterprise. Forget about growth. Forget about acquisitions. Forget about all the things that we could be doing. Even the most basic things become very difficult if we don't have a way to get there quickly. And that shortening that path from zero to real business value automation is critical. And we're going to talk a little bit about how we do that. That's part of that command and control, it's getting things done quickly and at the lowest cost possible for the fastest path to business value. What we're going to explore today are the erwin Enterprise data modeling and Data Intelligence Software. Now we're not going to talk about the traditional modeling suites. These are all important in that journey to understand how everything is designed and why it's designed that way. Once it's designed though, we've been using database structures, whether it's an operational data store, a data warehouse, a cloud, repository, or data link. All of these things have come about through data modeling in many cases, maybe not. But ultimately, we're feeding data in and we're getting data out. But we don't have a real good transactional record of how it got there, where it came from, and how it's been derived or calculated before landing in reports and landing in historical repositories. So we're going to talk about Data Intelligence Suite today. That's the center of the screen here. Our Data Catalog Suite particularly, to talk about how we do data mapping. Data mapping is one of the missing pieces in the evolution of data management and it's become a real sticking point for things like data literacy, as well as auditability and reporting and compliance measures. Everything you're seeing today with GDPR, CCPA, and other regulatory and compliance initiatives, data mapping is that number one thing that's missing. And we have ways to get you there called smart data connectors, that will give you the power to get there faster. Recognized as an industry leader, erwin Data Intelligence Suite took a