 And here we go. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We would like to thank you for joining this DataVersity webinar, Data Governance Reality Check, sponsored today by Irwin. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A in the bottom right hand corner of your screen, or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVersity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. And if you'd like to continue the conversation after the webinar, you can continue the networking at community.dataversity.net. And as always, we will send a follow-up email within two business days, containing links to the slides, the recording of the session, and additional information requested throughout the webinar. And a special note, today's webinar is filled off a recent joint survey done by Irwin and DataVersity on Data Governance Automation. The data was also used to generate a new joint white paper, the 2020 State of Data Governance and Automation, currently available on DataVersity.net. Now let me introduce to you our speaker for today, Danny Sanwell. Danny is an IT industry veteran with more than 30 years of experience. As Director of Product Marketing for Irwin, he is responsible for communicating the technical capabilities of business value of the company's data modeling and data intelligence solutions. During Danny's 20-plus years with the company, he has also worked in pre-sales consulting, product management, business development, and business strategy roles, all giving him opportunities to engage with customers across various industries as they plan, develop, and manage their data architectures. His goal is to help enterprises unlock the value of their data assets and produce the desired results while mitigating data-related risks. And with that, I will give the floor to Danny to get today's webinar started. Hello and welcome. Hey, thank you, Shannon. That was quite a mouthful. Unfortunately, now you've told them all how old I am, but that's okay. I think we'll recover. And thank you to you all for choosing to spend the time with us today. This is one of the fun things that I get to do, which is take a look into what's going on across the board as it relates to data. And doing that in unison with data diversity is always a good thing because they are an essential source of everything data and really get us to the right people to give us the insights that we look for when we take on these types of projects. So lots to cover today. So let's take a look at what we're going to go through. So first of all, we're going to take a look at some of the key metrics and key responses that we got from that survey that we documented in the white paper. I think that some of them are eye-opening. Some of them are, you know, I think somewhat expected as we all transition along in this journey that is data governance in our various organizations. Then we'll take a look at giving you some of our take on what we think some of that means and some recommendations behind that in terms of how to build a solid foundation and how to get on the right path to being successful with data governance in the organization. And then we'll talk a little bit about how Erwin tries to help in that process and enable you guys to get the results that you want from your data governance initiatives. So this is a great slide. I also refer to it as my most unnecessary slide because if you're not aware of this already or similar metrics that are being depicted on this slide, A, I think you might be living under a rock and B, you might be on the wrong webcast. But just in case everybody wasn't really sure, there is and continues to be an enterprise data dilemma out there in terms of the sheer volume variety and the veracity of data as it exists in your organization and continues to grow. And you'll see some big players out there that are looking at the data space, data management, data governance, data consumption all understand that this continues to be a challenge and a challenge that's only going to grow as time goes on as we get more of the same types of data in terms of huge volumes, but then adding in all the new data streams that are available to us to take in and look at, analyze and leverage for benefit within our organization. But all of this data, while it represents a unique opportunity for businesses, also represents a big challenge, which is there's a lot of data out there that is that is undocumented. It's critical to the business, but not well understood in terms of how it relates to the business and how it relates to the questions that the business wants to ask and answer in order to get the most out of their data assets. And then, you know, really looking at what it takes to start to curate the information around that data so that all sorts of different stakeholders in the organization can take full advantage of it and bring that value, that true value that data represents as an asset to the organization and get the insights that they need in order to drive the business forward in this, you know, in this day and age of the speed of internet, you know, the complexity of business in terms of how we relate to our customers, how we relate to the different partners in the business. So it's an exciting time around data and having spent my entire career in data, you know, really, you know, with the goal of trying to build a better data mousetrap for organizations for that very purpose of getting the most out of it. It really is quite heartening to me to see, you know, how important in the elevation and the visibility that data is getting, you know, after all the years of looking at how infrastructure is going to make the difference or how applications are going to make the difference. It's really the data that gets delivered by that infrastructure and application that really makes the difference to businesses. So in terms of a little housekeeping, and Shannon did a great job setting this up, but I'll just give you a little bit of a deeper view into sort of, you know, what it is that we were we were doing who we were talking to, you know, so as you heard, there is a white paper that came as a result of a survey that we did in partnership with Dataversity. It's the 2020 state of data governance and automation white paper. It is a follow on to a similar survey that we did back in 2018. So you'll see some references to some of the historical numbers and trends and some comparison to that. Primarily surveyed North American organizations just to try to keep it, you know, in a manageable segment. The primary respondents, as always were the, you know, data management and data architecture roles, I think in an around 30, 30 to 40 percent, followed on by a good chunk of information and data governance professionals. And again, these are all self identifying and then followed on by business intelligence and other data analysts, data scientists and executive management. It's great to see those last three, you know, involved in this because I think that as you'll see some from some of the results of the survey, data governance is key to their success in terms of what they're trying to provide for their organizations, but it's not something that's always been fully recognized or understood the connection between data governance and, you know, some advanced analytics and algorithms that a data scientist might be applying to enterprise data or decisions that executive management makes. So great to see that they're participating and it's great to have their viewpoint. In terms of organizations, it was across the board in terms of industry verticals with some weight in technology, government, consulting, insurance, finance and health care providers. And, you know, when we look at the size of organizations, we had responses from, I think, all of the major categories in terms of size of organization with the majority being in that sort of, you know, large, I would call them large, as opposed to very large organizations, but very large being after that. But I think all organizations, regardless of size, feel the same pains. It's just the scale of those pains and then their ability to really, you know, put bodies, funding and, you know, and thought in terms of how to solve that within their organization. In terms of why did we do this? Well, you know, the key thing here is first we did the one in 2018. So we wanted to do a follow up to really see how things are changing, how things are progressing. We really want to see, you know, how data governance is being viewed, especially if you remember around 2018, was when GDPR in the EU was imminent, followed on by California and a number of other jurisdictions really taking that GDPR framework and leveraging it to to start providing the same rights for for their citizens and responsibilities for organizations that want to interact with their citizens. And then, of course, as a vendor, we really want to understand what's going on, what the current priorities are, challenges and things like that, so that we can target our solutions to best support organizations in this in this in this space. So let's get to the meat of it. So key drivers and there's a number of drivers here. We ask people to to identify their top three in terms of data governance slash data intelligence. And as you'll see, their regulatory compliance is quite high. But what's interesting is compared to 2018, where regulatory compliance was number one. Now we're starting to see other drivers overtaking that and what I would call a sort of rebalancing of of priorities behind and reasons to do data governance. So really exciting to see that that top of the line or of items like better decision making, effective analytics are at the top of the drivers because, you know, data governance over the years and people have been talking about data governance for many years has always been heavily weighted to risk avoidance, you know, regulatory compliance being one of the biggest risks for any organization. If you're out of compliance, there's there's large negatives that can come along with that in terms of fines, but also, you know, your reputation. But the problem is when an initiative is driven by by a, you know, avoiding a negative, that is a hard one to sell a lot of times to senior management. And it's a hard one to get, you know, sort of strong and consistent and long term funding behind because they figure if you can solve the problem of being in compliance and answering the audit, then why do we need to keep doing this, right? So having it above the line and having those drivers where organizations are recognizing that the governance of your data is critical to the success of the money that you're putting into advanced analytics to help decision making to help manage your reputation to drive digital transformation is really where the true value sits. And once you've built that sort of regulatory response facility, now you need to expand that out and and you know, really start to drive the top line of the business. And that's something that's going to keep you top of the line for senior management in terms of sustainable funding and making sure that they understand that data governance is not something that's done once and is finished. It's something that's ongoing will always be ongoing as long as data is critical to the organization. When we look at program maturity, I find this one of the most interesting responses because compared to 2018, these numbers are actually lower, which was at first glance quite surprising, maybe even in the category of a little disappointing. But when we look at the previous slide where people are really starting to to rebalance the drivers for data governance, I think a lot of what you're seeing here is, you know, the fact that that organizations have gone down a path, have tried to solve a problem. And now that problem statement has changed and has expanded, which means now where they may have felt that they were, you know, fully implemented and ready to go in what they defined as in terms of data governance, that they had more work to do. So I think this is a good thing. I think that what you're seeing is that there's, you know, a lot of folks and the top line number is continues to be a work in progress and it may always be a work in progress. There may not be a finish line that's easily definable, although 12 percent have said that they are fully implemented. And it'd be interesting to see what they've implemented and how they're approaching data governance with more people just getting started and a good chunk of people in that sort of planning stage. So interesting numbers. But again, once you sort of think about them, not necessarily surprising numbers, if you will. So from that perspective, let me see if I can just get this going to the next slide. Now, here we have, you know, some identification of data value chain bottlenecks, because really data governance is to enable that data value chain to make sure that it's there, that it's available, but that it's also trusted, usable and effective in delivering what it is that people expect. And top of mind for most people, and again, not necessarily surprising from our perspective, because we talk to a lot of large organizations and other organizations in terms of their priorities around operationalizing data governance, it's that document the data lineage story. And data lineage, in my mind, has always been down to at the end of the day, consumer trust in data. There's a lot of other folks that are interested in data lineage. It helps with a lot of different things in the organization, whether it's responding to audits, whether it's understanding as you build, you know, some sort of data hub, a data lake, a data warehouse, and understanding where that data came from. But at the end of the day, it's the people that are making decisions and putting their reputation and their company's reputation on the line based on things that data is telling them, data lineage is at the core of what they do in the core of what they require to trust the data enough to make those decisions. Understanding quality, the quality of source data at 58% coming in a close second. Again, not surprising, you know, because we know that garbage in equals garbage out when you do anything with data. And we're doing a lot more with data these days in terms of, as I said, analytics, you know, artificial intelligence, machine learning, those types of things. And, you know, when you think about the risk that that actually, you know, represents, it's not surprising that folks are still trying to get their arms around data quality and have, you know, a high degree of trust in terms of the quality of the data that they're using. Less, you know, less, I won't say interesting, but maybe more disappointing is it's still very tough for organizations to just find and identify and get their arms around the data that they have, which is tough because again, you know, this basically says that, you know, we're still spending far too much time looking for things as opposed to doing things, you know, with that data. And that's not a metric that's sustainable over time. I've heard different, you know, different ratios where 80 percent of the time spent looking for data versus 20 percent of the time delivering results to the business. But, you know, either way, people are still finding it very difficult just to get their arms around what they have. And then the next one, curating those data assets with business context, you know, a big part of what data stewards and other folks in the governance world are doing continues to be a challenge in terms of being able to make sure that all of the data that they have and all the data that's being used is, you know, is well understood, not just from a technical perspective, but also from a business perspective, making sure that it's the right source of data for the questions that they're trying to answer. So in terms of, you know, the biggest or the most significant challenge, again, I think a lot of these are potentially related to each other and people have expressed them in different ways. So if you look at the top number, which is length of project and delivery time, you can directly tie time to value to that because the length of that project delivery time is you come up with an idea and then you try to make that idea a reality and a solution. If the length of time to get there is too long, you may have missed the opportunity that was in front of you and the reason that you might be taking on that project. And then I think reliance on developers and other technical resources is another number that's tightly tied to that because it's manual work and labor intensive work that tends to slow down and stretch out these project delivery times, you know, from that perspective. And then again, of course, number two, at data quality and accuracy, I think there's two aspects to that. There's the idea that that is the data quality is, you know, a lot of duplicates is that the right data is there are a lot of garbage entries in that data. That's at one level. But again, data quality in terms of quality to the hypothesis that we're trying to satisfy with that data in terms of fit for use, I think can fit into that as well. So it's not surprising to see that still being a large challenge for organizations. So moving on, you know, this one was interesting because I would have thought that the numbers would have been a little bit higher. But at the same time, maybe that's a good thing because it really depends on how people define data activities. You know, it depends on your role and it depends on, you know, what it is that you're adding to the mix. But there's a lot of folks, 70% of the people are spending 10 hours plus a week and that number can go up to, you know, I'm assuming up to 4060 depending on what the average work week is these days. And most of that time is spent on source system analysis. So again, finding and assessing fit for use, you know, is still taking a lot of people's time. And then after that, it's, you know, preparing that data so that it's properly structured and ready to deliver the answers that are going to be asked of it in that analysis piece as well. When we look at data prep and technology in terms of what have people deployed. Again, very interesting, you know, data analytics, not surprisingly is the top because, you know, analytics is the pointy end of the stick. That's where organizations understand if they analyze their data, they're going to get the results that they're looking for. But it's very heartening to see things like data catalog, metadata management, which quite frankly, for at least two periods in my, in my long career, metadata has in metadata management have been somewhat of a verboten or dirty word is again, raising up in terms of how people are going about getting themselves prepared to be able to deliver data governance across their organization using that as the underpinning data quality. And again, there's a lot of great solutions out there that will look at quality from a lot of different perspectives and then cataloging, which I think is still somewhat of an emerging space where, you know, you have a lot of different things that are referring to themselves as a data catalog delivered in many ways. Some of them are, you know, more point or siloed type solutions. Some are, are enterprise viewed, but a lot of people that are at least looking at if not fully deployed on data catalogs in your organization of some sort or fashion. And then, of course, the thing that makes that data catalog really tick from a business person's perspective, the business glossary. So I think they're all good numbers. And I think that people are understanding what it is that they need to put together in terms of, of infrastructure to support this goal that they have around data in the organization. Oh, so now we start looking at automation. And when we look at automation, automation is not strictly related to data governance activities, although with that large umbrella as we define data governance in an organization, you know, they all touch governance and they all have an impact on it. But this is, you know, sort of automation around data, around data management, around data operations, around data consumption, and around data governance. You know, mildly automated and somewhat automated, again, are the top two responses. Very few people have completely automated, you know, the data function in their organization. Although, you know, last summer I did see a presentation by a chief data officer from a large pharmaceutical who had because of quite frankly, the profits and money that they were making the ability to just bring in a fully net new end to end data piece that that took everything else and basically replicated it in one space. And you know, that type of automation is interesting in itself. I would like to see the long term effectiveness of that. But, you know, not everybody can really afford to do that. So, interesting numbers, people are seeing that automation is part of the solution. They continue to struggle, I think, in terms of what to operate or automate where to automate. And then, you know, again, you always have that factor of different people defining automation in different ways. So, you know, from that perspective, but I think that it bodes well that that people are understanding that, you know, to make things sustainable and to make things, you know, you know, quicker for the business and getting them the results they need in the time frames that they need that people are understanding that we cannot just continue to go on and, you know, manually provision data from, you know, birth to, you know, to sunset and that there is a lot of opportunity to do things in a way that are going to give you speed consistency, speed and consistency, which is really at the foundation of the value of automation. What's been automated? Again, very interesting numbers here. The top is data harvesting. And what people mean by data harvesting, again, there's some different, you know, specific definitions, but it's really about getting your arms around the data in your organization and bringing it into a place where it can be identified, understood and accessed. So a lot of automation there and, but again, under 50% still. Mapping of data, which is again, I think a key and critical piece to success with data is to be able to understand and get control of how data moves from one place to another in your organization through that data lifecycle, automating around data cataloging. And again, some of these can filter into each other depending on how folks define it and how what their perspective is and what role they play vis-a-vis the catalog and vis-a-vis data operations in general. Data lineage, people are continuing to try to, you know, overcome that data lineage monster and they're starting to apply different levels of automation and there's some different, you know, approaches that are out there. Some are sort of artificial intelligence oriented where you're going around and inferring a lineage based on, you know, some machine learning or a set of rules or different ways that organizations might do that. And then there's other organizations that take more of a bottom-up approach to delay data lineage, which is to really understanding how data moves through your organization physically and then leveraging that to render meaningful lineage to the organization in different perspectives. Code generation and impact analysis kind of finish it off. The impact analysis, I'm surprised, because impact analysis is very important in all of this because it is really your forward thinking and impact analysis is much more than just what we would have considered back in the day where used, where if you, you know, want to change a column or something and you want to find out all the places it will break before you make that change so that you don't break them and have to fix them after the fact. Impact analysis is going well beyond that and the business is really starting to look at impact analysis in a much more holistic way to start understanding the value of data to the business as an asset. And the best way to do that is understand where it's being used, what it's being used for, how that fits into your organization's, you know, goals, motivations, strategies and business critical capabilities. So still low on that number. And I think, you know, people are continuing to try to find out how to get more automation there to get that at their fingertips quickly. And then when we look at desired automation, we'll see again, very similar numbers. You know, the ones that are on the top of the automation that they have, it's not surprising that they're lower down to a certain extent, because some people have been able to accomplish that level of automation. So data harvesting coming down because that's the one that's most automated. Interesting to see data lineage taking the lead, again, because of the importance across the business. And then it looks like from my perspective, data cataloging is something where people are looking for that capability in the organization to deliver more around in an automated fashion. Same with data mapping and impact analysis, code generation, it's funny, you know, the same amount of people that have been able to implement code generation, you have the same amount of people that are looking for that. And code generation sort of seems to when you at first look like it's outside of the realm of data governance. But I think that what people are understanding is that in terms of time to value and speed of preparation and the ability to have self service requires, you know, a level of capability and automation around code generation, because that's how things get built. That's how things get to where people need them to be so that they can access them. So interesting to see that around code generation, interesting to see how that number is going to change over time. So that is not all of the questions from the survey or in the white paper. But I think that these are some of the key ones that I think are the most interesting show, you know, and give you a visibility into how organizations are thinking about this and how they're approaching it. So this kind of takes us to, you know, our take on, you know, what is all this really mean in terms of the world of the state of data governance and how folks are looking at automation. So, you know, as I said earlier, I think that organizations continue to iterate on data governance from a strategy, a goal and a expected, you know, output from data governance or impact of data governance. They continue to try to get it right. And my feeling is, is because of the nature of the world we live in, that will probably always be true. And I think, you know, as in another two years, if and when we do this again, I'll be able to leave that on the slide because I think that, you know, we don't know what we don't know. We don't know what's coming next. So, so, you know, data governance will always be fluid to a certain extent. It'll always be a living, breathing organism that has needs and, you know, capabilities today, but those needs and capabilities are going to continue to change over time. And in response to that, we're seeing that rebalancing of between risk avoidance and opportunity enablement, really trying to leverage the work that's being done around, you know, understanding and getting our arms around data and curating it in an appropriate way that the business can take advantage of it in the most efficient and effective way is something that's very important for data governance in general. As I said earlier, those are the things that the sea level people in your organization, especially the ones that are outside of the, you know, data realm specifically are really looking for. How can we, you know, what are the initiatives and what are the projects that we can have that are going to help us transform our business to meet the challenges of today, tomorrow and the day after that? You know, data discovery, preparation, quality and traceability continue to be challenges. And folks are finding and looking at different ways and, you know, trying out new ways to make that better in their organization. At all levels, you know, automation coming into that. The one thing that we can all agree on is that that every organization needs an accurate high quality real time data pipeline. At the end of the day, it's all about decision making and supporting decision making, whether those decisions are to help us avoid risk or to help us, you know, take advantage of an opportunity that's in front of us. That pipeline needs to be there. It needs to be there needs to be very little latency in that pipeline. It needs to be easy, easy to access and easy to access in an effective way for the organization. And then, you know, I think we're seeing some some clear indication that, you know, automation and not just at the governance level, not just at the level of data intelligence, but right down through all of your data management data operations right out to consumption is something that's going to help all of these things along. The more that we can automate, the more that we will have, you know, what I will call a sustainable and repeatable practice that's out there that's going to, you know, reduce errors. It's going to improve all the things that we're trying to do with data. It's also going to build us a better architecture and infrastructure around data that makes all of the follow on pieces easier to work with, easier to manage over time, easier to move and adjust to the new challenges that we have not seen yet, but we know are coming down the pipeline. So in terms of recommendations, you know, you always have to be constantly, you know, reevaluating and tuning your data governance strategy. So if you are the CDO, if you are in charge of data governance, if you have that sort of, you know, panel of stakeholders that are there and really, you know, putting together and guiding and governing the governing the governance, if you will, that that job never ends, that it's not something where people can go back to their day job. That's going to be something that's there forever. So you need to be constantly doing that. You need to be aligning that with the goals, the strategies and really the motivations of the entire business to make sure that you're in line with that and that you can tie back to those things so that when you go forward for the next round of funding or for maybe some extended funding to do some new things, some different things, that you will be able to put together a meaningful and successful business case to get that done. Data quality is is everything because again, all the rest that you do around data, if the quick data that you're producing and the data that you're provisioning out to the organization is not of a high quality, then you know, you've really, you're done before you start it. But the important thing is, is to look at data quality from multiple perspectives. In my mind, it starts with metadata quality. There's a lot of things that you can put in place in a system that's reflected in the metadata that will make sure and ensure that that, you know, garbage does not go into your critical data sources. So it's not just the state of quality based on some some basic metrics as it sits today. It's how do you set things up to make sure that the on an ongoing basis that data is of a high quality by design? And then, of course, data quality goes beyond just the instance data and and metrics on that instance data. It's all about making sure that it's fit for use for the different things that we want data to do in our organization and fit for use is something that is assessed and comes from things like data literacy, curation and really harmonizing all of the different types of metadata that are out there that can help you understand technically from a business perspective and from a semantic perspective, you know, what what is it that I have here in terms of this data source? And then that will guide its fit for use and the assessment of fit for use. Data lineage, it's complex, but it's an absolute requirement. So deal with it, right? And if you think that you can carry on doing this in a sort of manual ad hoc type of way, putting together and answering data lineage questions, I will guarantee you that that's not sustainable. So if you're looking at anything from an automation perspective, data lineage and automation that leads to the ability to be more effective around rendering meaningful data lineage to all of the different stakeholders in your organization, that's a place where you should absolutely be focused. Automated code generation is a huge opportunity for efficiency and agility and consistency and data preparation, right? And quite frankly, when you start to do things like automating code generation, there's some knock on benefits around things like lineage, around things like mapping, around things like data literacy that will come from that automation because now that it's automated, that engine that's doing that automation is also has a lot of information in it that you can then pull from and leverage for those other jobs that are out there. Impact analysis, it's more than we're used. So, you know, I'm an old IT guy. I used to build databases, data warehouses, metadata repositories, you know, decision support systems to really show my age. But impact analysis is more than we're used. And that's how I used to look at what that's how I used to define impact analysis. It's really about a full picture of the value of data to your organization and data valuation is now becoming the holy grail in terms of really truly managing your data like an asset. It has to have a value beside it. If you want to list it as an asset, and you have to come up with that value. And I'm not talking about data that's for sale commercially. I'm talking about data that runs your business and runs, you know, the strategy and envisioning of your business, you know, over time and really being able to understand where and how it touches your business. And then from that be able to start coming up with a framework that assigns value based on the key business capabilities that it supports. Catalog your data assets. You can't govern what you can't catalog. So again, I think what we're seeing is a lot of folks that have gone down the governance route of trying to, you know, do stewardship and things like that, realize that you need a consistent and accurate and up to date and sustainable landscape. If you're going to do that effectively, putting together a business glossary and publishing a lot of policies is not a lot of help. If it does not tie back to the physical assets that folks are trying to and looking to access so that they can have that full visibility, which brings us to data literacy. You know, data literacy is, is, you know, an educated consumer is your best customer. And how many times have all of us gone and engaged with people in the business and trying to give them the solution that they want. And they know so little about their data that it just becomes a long teaching exercise through the process. So the more you can give them self-service data literacy and fluency in the data that's available to them, then they're going to be able to collaborate you in a much better way and in a much more efficient way so that you can both get to value a lot quicker for your organization. And then finally, you know, automation, it's always good. It's always a good option. But with some tasks, it is the only option. So you need to look at what's really holding you back in terms of your ability to do the things that you want to do around data for your organization and start picking those key places where automation is going to drive real measurable results. Put that in place. And then from that, you'll find out what the gaps are. And then you'll be able to go to that next tier down in terms of value to your organization and always look at any automation that you have in place and try to understand its relationship to other challenges that you're having. Can this automation that does something like solves, you know, time to value with code automation, can that also help me automate the rendering of something like a lineage or an impact analysis? If it can, then again, that's part of the business case and makes it a worthwhile investment and you're going to get, you know, two or three for the price of one when you really start to look at it from that perspective. So we're just going to finish off, want to leave some time for questions. You know, you know, this is something that that we, you know, promote as an organization, which is data intelligence is really at the core of solving the enterprise data dilemma. It's about giving a useful, usable, meaningful map that has the perspectives of three major constituencies in your organization, data governance, data management and data consumers, where they can all bring that together. And in order to get that map, there's basically, you know, five major steps. First, you have to harvest what you have. So identify what it is and describe it at the physical level. You need to organize it so that you understand how it all links together and how it, you know, interrelates and interacts with each other. You need to curate that with the pieces that are missing, whether it's semantic or, you know, other types of business metadata so that it's fully, fully, you know, formed so that anybody coming to can answer all the questions they might have about that to accelerate their their source system analysis, administrate that data, which is to say, make sure that that the visibility is there, not just in terms of the assets itself, but the rules behind it, the policies behind it, any things like data sharing agreements, all of the things that are out there that make the mechanics and governance of of data effective. And then you need to socialize that out to all of the different constituencies in their perspective, you know, from one place where they can have one stop shop. You know, I, I envision, you know, Amazon for data. If I have to pay Amazon for using their name in this presentation, I'm fully willing to do that. You know, it's no longer the old Christmas catalog that we had where you got a book where you could read about things, learn about them, but you still have to go find out and go, you know, physically go to a store that potentially had that and procure it. Now we want one stop shopping where I can find out everything that I need about my data and then put it in a cart and give it to me and serve it up to me so that I can actually start using it in a very, very timely fashion. When we look at how we approach, you know, enabling organizations with data intelligence, this is what we call our Erwin Edge platform. This is a number of capabilities that are available in terms of solution suites or in individual components where we bring together all of the things that you would need to fully understand your data in terms of how it's, you know, physically persisted and managed in your organization, how it relates to key elements of the business, you know, and then gives people all of the mechanisms and capabilities that they need to start doing what they need to do around data. So, you know, you know, beyond that the the, you know, business glossary and data dictionary and data catalog that's there with visibility into data quality and profiling things like that, but then you have all of the architectural capabilities around that to allow you to get your arms around the business, IT and its relationship to the business, data, data and its relationship to the business and the date and relationship to the use cases that are out there, as well as data and its relationship to each other throughout the life cycle and then bring all that into a platform that allows you to, you know, take advantage of automation that will enable you to harvest what you have, really organize it, understand the lineage and impact and and really collaborate in a self-service way so that, you know, folks that need data can do a lot of the upfront legwork themselves and then bring in the experts at the time where they have a well-formed, well-defined project and they can drive that out. When we look at a layer down into our data intelligence suite, you know, this is a combination of our data cataloging capability where we do metadata management, mapping management, reference data management, all under life cycle control, a data literacy suite where you start to do stewardship activities and associating that with the physical assets and then putting it out to the larger community through a user portal. And then our data connectors, both standard and smart, which is the foundation of our automation offerings that allows you to automate everything from harvesting your data assets to rendering data lineage, rendering impact analysis, generating code, documenting and auto-documenting what already exists out in the world. And again, all of these pieces are things that you can either procure as a solution or if you have specific pain points that you need to to solve in an iterative fashion, all of these pieces can be, you know, can be obtained and leveraged for value, individually knowing that over time, that value will grow as you bring more of those pieces together. The end of the day, it's really about making data the fuel for success across the board. And, you know, none of this, I think, should be surprising for anyone who's been listening to what I've been saying here, but these are the things that your company wants to get out of data. And these are the reasons why you need data intelligence and a clear architectural view of all aspects of the business in order to properly focus that and deliver that in a way that's meaningful. If you're wondering who Erwin is, not familiar with our modeling pet over the last years, we are nice in your ships and industries and accolades that are out there where people are realizing, you know, where we're taking Erwin since we became a separate entity from our parent company and I had the shackles taken off one of the things we're most proud of is this year we debuted on the Gartner Metadata Management Magic Quadrant and we debuted in the Leadership Quadrant, not something that a lot of organizations out there can claim. But I think they see that based on the pedigree and the capabilities that we have, not just purely in metadata management, but all of the things that support that, they really recognize that in terms of our ability to live as well as our vision for how organizations can best leverage this for success was recognized by Gartner and again, very, very proud of that. So I'm not going to give you all of the rest of it here. You can can read some of this. I think at this point we're probably about to open it up for questions. But if Erwin does interest you in terms of a call to action and next steps, please feel free to go to Erwin.com and click on try for free. You can try all of our solutions and all the components of that depending on what your pain point is and what your perspective is in terms of adding to the betterment of the value of data in your organization. We'd love to talk to you and be happy to help. So with that point, I think we will open it up for questions. Danny, thank you so much for this great presentation. If you have questions for Danny, feel free to submit them in the bottom right hand corner of your screen in the Q&A section and just answer the most commonly asked questions. Just a reminder, I will send a follow up email by end of day Thursday to all registrants with links to the slides, the recording a link to download the white paper and anything else requested throughout. So jumping in here Danny, what is the connection between data catalog and data marketplace? The connection between data catalog. Well, I think the data catalog is, you know, one of the steps that's required in order to open up a data marketplace, whether that's within, you know, your enterprise or depending on how you're, you know, interacting or what data means to you have from a business perspective. You know, I think that the long term goal for most organizations is to have an internal data marketplace where people can come, as I said, you know, using the Amazon example and really be able to, you know, in a self-service way, you know, get as far down the road to getting the right data for their use case and the value that they want to present to their organization. Again, without having to wait on and rely on, you know, technical expertise in order to really make that happen. So has catalogs mature? And again, organizations, different different catalog providers have different, you know, definitions of data catalog, if you will. But at the end of the day, the catalog is what's going to enable a marketplace, because you can't have a marketplace if you've got nowhere to find out, you know, what data is out there for me to access in that marketplace. So data catalog, I think is a step towards that marketplace. And the more effective that data catalog is in terms of fully capable of not just giving you data, but helping you understand, analyze and answer the questions that you have about data before you're going to use it, the more effective your marketplace is going to be in a long, in the long term. So I hope that answers the question. Indeed. And I'm going to jump to this other question, which is along the same line. So some of which you've answered already, but what does Erwin mean by the term data catalog and what exactly, what kind of things does it catalog? So our data catalog is primarily metadata driven. So what we do is we catalog and allow you to register data assets based on harvesting physical metadata, and then harmonizing that with the business and semantic metadata and the semantic modeling capabilities that we have up in our business glossary manager. So that data catalog is really cataloging physical assets with pure visibility and clear visibility into all of the other pieces that would curate those physical assets and make them more understood data quality metrics, the ability to actually profile and dig into data and understand the instances. So that's what we mean by the term data catalog. Now, as we move forward and as we see the requirements of organizations, you know, long term goal is to, you know, bring more things like, you know, machine learning. We've already started to implement elements of that into the solution. You know, we've already automated lineage and impact analysis, but, you know, starting to bring in, you know, more smarts, if you will, around the auto categorization and cataloging of data into, you know, different, you know, into different categories and things like that and making sure that we can curate the data that way. And then, you know, we continue to work on our sort of end user interface into that data to make it the most effective for them to really navigate that catalog. Right now, you know, we have something that's called the mind map, which in it's a type of tool that I've used throughout my career to really help me look at large complex things and break it down. That mind map gives you a view in, a visual view that is created by the system based on what you've queried and shows you all of the relationships and associations between all the things that I talked about and allows you to use that as your navigation and drill down capability into getting all those answers or all those questions answered that you want as you go about figuring out what data is available to you and what data you want to use to solve the problem that you're trying to solve for your business. So, Danny, there continues to be a disconnect and to some degree unconscious lack of cooperation and empathy between impacted divisions within organization. Can you provide any example or case studies of organizations that have been able to overcome such challenges? Absolutely, absolutely. So there's one out on our website largest power provider, I guess, one of the largest in the world. They're called EON, E-O-N, out of Europe. And they had that very specific problem, which is, you know, lots of things going on in silos, whether it's silos in the business in terms of how they want to use data or silos in the folks that were actually provisioning and managing data for the organization. A lot of disconnects, a lot of wasted time, a lot of, you know, recycling and rework that went on with very little consistency standardization. And at the end of the day, most importantly, you know, velocity behind their ability to, you know, from the time they have a question for the data to answer to the point that they can get that question answered with the data in front of them. So, you know, that they're, you know, again, one of our, you know, success stories that you'll find out on our website, I would, you know, definitely tell you to go out and look at that and read to see if you can see some, you know, affinity with the challenges that you might be having in your organization. You know, we have one of the largest, you know, financial providers in the world that are using long time users of our data modeling technology and now are using our data intelligence solution. Again, because of that exact pain point, they had hundreds of, you know, hundreds of applications, thousands of data sources, all under the, you know, control and ownership of different aspects of the business, you know, but at some point, they all needed to come together for the betterment of the entire business, but breaking down those silos really, you know, there was certain aspects, there was, you know, some turf war going on and, you know, sort of holding on to your power. But the biggest challenge was that nobody could see or envision something that was going to work for all of them and bring benefit for all of those different silos so that they could see that, yes, by giving up this power or this control that I might have, I'm actually going to get more than that back in terms of benefits from participating in the larger collective. So, you know, that's a lot of the work that we do, especially, you know, down in the data management, you know, dungeons and cellars, if you will, where we break those silos down and allow people to see that it's better to work together because you're all going to, you know, what's the saying as I get older, the sayings are never there like they used to be, but, you know, everybody's going to, you know, rise with the tide, if you will. So, you know, these concepts of data intelligence, data catalog and data literacy are designed specifically to bring more benefit than any of those silos are going to lose by giving up that control and, and, you know, really making it, you know, a no brainer for them to become part of the solution, not part of the problem. Well, Danny, thank you so much, but I'm afraid that is all the time we have a lot of great questions. I was just getting warmed up. A lot of great questions still out there, but I will send those over to you and Benny, so you can see those. But thanks, and thanks to all our attendees for being so engaged in everything we do and all the questions that have come in. Just a reminder again, I will send a follow up email by end of day Thursday for this webinar with links to the slides, the recording a link, direct link to download the white paper and more information about Irwin. Again, thanks to Irwin for sponsoring and Danny, thanks so much. Hope you all have a great day. Thanks everyone, have a great day.