 Hello and welcome. My name is Shannon Campan. I'm the Chief Digital Manager of Data Diversity. We'd like to thank you for joining this Data Diversity webinar, Creating an Edge and Enterprise Data Governance Experience, 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'll be collecting them by 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 Data Diversity. 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. Now let me introduce to you our speakers for today, Jamie Knowles and Marianne McDonough at Irwin. Jamie is a business and enterprise architect with 20 years of experience. As Product Manager for Irwin's Data Governance Software, Jamie is unifying the company's data management platform with data modeling, enterprise architecture, and business process modeling, and fortifying it to ensure enterprise data assets are understood, governed, and protected. Marianne is a market creator and builder with 30 years of strategic business leadership and experience in enterprise software and technology. Career highlights include serving as Chief Marketing Officer for In Contact, Senior VP of Corporate Marketing and Investor Relations for Xtralis, and Senior VP in Corporate Marketing for Variant Systems. And with that, I will give the floor to Marianne and Jamie to get today's webinar started. Hello and welcome. Hey, thanks, Shannon. First of all, I just want to compliment your choice of totally grooving music that we had while we were waiting. I don't know if everybody else is out there in webinar radio land, but I'm in a very sort of smooth, standard mood right now, so let's see if we can keep it going. Jamie and I are really excited to be with you here today. I think that most of the folks that are on the webinar know, or because of our deep roots in data modeling. That's where we began. We've been doing that stuff better than anybody for in excess of 20 years, but I'm hoping that folks have also been following us as we expanded the brand to include enterprise architecture and business process modeling and to build over the past year and a half or so a strong data management platform. And then just two short weeks ago, boy, it feels like a lifetime. There's been so much activity and excitement. We expanded again and introduced a completely new data governance platform with a new approach. And we'd like to share some of the perspective that we've developed during the way working with some of the world's largest companies as we brought Erwin DG, we call it, Erwin Data Governance to Market. So from here, let's just sort of jump into it. I don't think it's any secret to anybody that we are riding the data waves. There are days when I feel like the data wave is riding me, but certainly data is proliferating exponentially. Just some fun facts that you see here on this slide. By 2020, there'll be 37 million new things connected to the internet. So those things could be everything from, you know, my father's pacemaker to my intelligent refrigerator. And, you know, those of us who are who have Alexa's or, you know, some other Google Home devices, IoT is really changing the game in terms of the number of things connected and the amount of data that flows to and from those devices. Between that and escalating personal data usage, one of my other favorites, self-driving cars. I come to you today from Rome, Italy, where we did a data governance event yesterday. And I wish they had self-driving cars here in Rome because the traffic is pretty crazy. But self-driving cars, intelligent planes, robo factories, so many things that are happening, you know, in our world that are just continuing to contribute to this immense tidal wave of data. And, you know, the volume, variety, and velocity of this data wave, the three Vs, really extends to almost every asset of our lives. And, you know, not surprisingly, totally to the way that we do business. But one of the things that I think is so interesting is it's not just the volume of data that's increasing, but also the potential of its value. You know, when you look at this slide, you can see here some of the real quantifiable value across a variety of markets that harnessing data and using data for good in the business could deliver. So we see this massive number that says big box retailers that can leverage the full power of their data can increase operating margins by 60%. Investing in better and integrating better big data, healthcare could save as much as $300 billion a year. And this is one of the ones that really blows my mind, is that for a typical Fortune 1000 company, just a 10% increase in accessibility to data will result in more than $65 million in additional net income. But here's the flip side, you know, according to recent research that we just saw from EY, only 23% of organizations have implemented an organizational wide data strategy that enables them to actually use data to look at market conditions or customer needs or preferences and then align their products and services with those data-driven insights. So there's this sort of massive dichotomy out there when you look at the potential and then we see that, you know, only, you know, 0.5% of all data that's captured across the world in major enterprises is ever used to grow exponentially. So, you know, what's that all about? And I think we have to ask ourselves that question because the majority of the time the conversation is in this place, right, is about the risk. And it's not to say that we can downplay risk. Certainly the people around the world that are struggling with how to comply with GDPR and the mandates that are coming May 18th, again, from here in Rome yesterday, a big concern across Europe, but not just Europe. I mean, American companies too that capture customer data in Europe have to be concerned. So, you know, there is a downside to data, right? That explosion of data, if you view it from a regulatory and compliance lens, really can introduce significant risk and challenge a business' ability to leverage that data safely. So the question is, you know, how can we ensure that data is not being used in a way that's harmful to the business? And there's so many cautionary tales here. You know, last year investigators found employees that misused customer data in a very well-known, if you can see me, I'm making quote marks in the air, very well-known financial services provider. They opened 2 million state credit cards and deposit accounts on behalf of customers to meet aggressive sales goals. And that fallout created 185 million dollars in fines, 2.6 million dollars in refunds and thousands of employee firings. And that's not to mention the intangibles like reputation value. So, you know, absolutely risk and mitigating risk and compliance and all of those things that we traditionally think of in terms of data and data governance are important. So the question really is this, you know, what does it all mean when you bring it together? When you look at this combination of data for growth data for power, data for market understanding, data for competitive edge and data for risk mitigation and compliance. We really believe it, and I'll sort of throw the gauntlet down here and say that data governance 1.0, as we see it, has been a failure. It's been the promise of value without the delivery of that value. And, you know, what is the root cause of that failure? And we believe this because we've been working with, again, some of the largest companies in the world over the past 18 months to understand what they've been doing in data governance and why it has or has not worked. And, you know, what we've really been told is that it's because data governance has been sitting under the dome, if you will, right? It's been under the dome of IT. And data management has historically been a technical function with IT providing important custodial service to the business. But the challenge is it's now time for the business to take a more active role. The business has the knowledge about the meaning and the purpose of the information. The business owns the regulatory challenges. The business is tasked with adapting to rapidly changing markets and needing to communicate those changes to the IT custodians. So organizations need a data strategy that's aligned with the strategic goals of the business. That makes sense in the context of the way that we operate the business. One that creates data-driven growth while mitigating the risks associated with an expanded data horizon. So an approach where both business and IT can effectively collaborate to understand and control data and to leverage it to drive more intelligent business decisions. So in order to help us better understand and define this data governance 2.0, I'm going to ask my illustrious colleague, Jamie Knowles, to talk a little bit about our perspective. What is it about this DG 2.0 approach that's different than what most people have thought about or experienced in the past, Jamie? Yeah, cool. Thanks, May. I think the key is to involve the business. And I think they're hard of it. We've got to better treat our data assets in the same way as we treat our physical assets of the organization. I was talking to an executive fairly recently, and they said, if we did treat our physical assets in the same way as we treated our data assets, we'd be busted in weeks. So how do we go about that? So generally, we start off building policies on how do we govern our data? We set out roles and resources they're going to carry out so that governance sets out some processes that they're going to perform. And the next is a series of questions. So as we've got here on the screen, we're going to start off cataloging the types of assets that we have. What do they mean to the business? How important are they? What's the value of them? And then it starts to work out, well, where in the organization are these assets? Where have we physically got them deployed? Then we can start asking our host questions and really get some good answers. So we might want to look at the rules that we all want to apply to those assets. And again, it's only the business that can really ask and answer those questions satisfactorily. So once we then start cataloging, we can then look at, are we following those rules? Look at the quality of the data in places. So really, it's a journey of solid questions. A journey of solid questions, I like that. And it's true that only the business can answer those questions. I'm going to pipe in a little bit later just about my role as a chief marketing officer. My role has changed very much over the last five years in terms of the value of data. How much data in my organization I own and am responsible for and what I do with it. And when I think about that in context of this ring, it's really significant because there are so many people in our organization that don't understand the data that resides in all of my world are what I call the marketing infrastructure. And it would be very difficult for someone to catalog and assess that without having my business owners who are sitting down and working with them to really explain, this is what that data is, this is what it connects to, this is how we use it. So that data in context again I think is really clear. So let's jump into some of the challenges that we have sort of uncovered, Jamie, as we've been on this journey ourselves. And I think one of the things that's really interesting, and we spoke to a lot of people yesterday about, and Shannon was busy shaking her head, yes, when we were discussing it in some of the prelim work, is that every organization is in a different place in terms of where they're going with data governance. And it's important to sort of be where you are and then chart the course forward from there. You can only really begin where you are today. So when I look at some of these questions, some really jump out and there's conversations that we've been having about readiness. So talk a little bit, Jamie, about readiness and its impact on data governance success and some of the sort of pre-work that has absolutely nothing to do with technology or a platform that we see customers needing to do on DG. Yeah, I mean readiness is a great one. So getting the right sponsorship. So we've been talking to so many customers and I think a lot of governance projects that have been attempted in the past and failed. So they've had the wrong sponsorship. So quite often we've seen projects that have been purely within IT again. So I think you've really got to get sponsorship at an executive level to get the adequate resources available and then get buy-in and sponsorship from the entire organization. It has to understand that they're part in data governance. It goes all the way from some sort of basic security in the office through to how we use our systems. So yeah, step one I think has to be sponsorship. Yeah, I think... There's a lot of things that... God, please go. I was going to say that there's a lot of things that jump out here that we've heard from customers. Silo's is a really good one. I mean, larger businesses struggle with this a lot more, especially where there's been some sort of mergers and acquisitions type of history. So silos of information managed by multiple systems. There might be multiple departments that claim ownership of data. So there's a lot of political challenges between different groups of people that claim that ownership. Each of these silos might have different rules and hold different copies of the same information. So there's a lot of challenges there. So how do we unify these silos and provide lenses to make sense of it all? So that was another big one that came out of late. Yeah, and it was interesting yesterday as we were doing these table conversations with many of the largest companies and organizations here in Rome, that whole concept of sponsorship or lack thereof was a recurring theme, number one. And then the second was what a challenge M&A delivered to data governance in general. So, you know, I'm absolutely feeling you there. Here's a question for you that I think is a really important one. You know, how do you create the fourth bullet there? Talked about enabling accountability. How do you create a culture of data accountability within a business? Well, that's a tough one. How do we create it? I think you've got to be able to, against the catalog, start to assign names. Simple as that. Yeah, and it's a little bit of show your work too, isn't it? Right? Absolutely. You know, as we come to a meeting with sets of numbers, you know, I think we have to be able to identify the lineage of said numbers and where we're getting our data from. And I think we need to begin to develop a language to question each other about those things that does not today exist in most businesses, right? Yeah. That's part of that data thinking. I also think that from a senior executive perspective, when I think about, you know, being a corporate officer and all of the fun stuff that comes with that, you know, I think we have to start challenging some of the assumptions that our leadership is making about the course and speed of the business and challenge the data behind it, right? And say, show me those numbers, right? Tell me where you're getting this information from. So I think that kind of behavior that is driven from the top down and becomes an expectation in terms of the language that we use with each other starts to change some of that. Yeah, and that comes into proving value as well. So having visibility of what are the assets of the organization and everybody understanding who is accountable and what does accountability mean? Absolutely. And also, I think part of the failure of Data Governance 1.0, Jamie, as we've talked about, was that the tangible value of the exercise, right? The only way to really look at it was in terms of that risk and compliance mitigation piece. But nowhere were we figuring out how do we monetize the growth pieces? How do we monetize the intelligence that the business has that it didn't have before? How do we look at it in the same way as we look at the other key four or five strategic things that we're going to do as a business this year? So again, I think that driving data in the same language in lexicon as the way the rest of the businesses run is really critical. So on that count, I'd like to just take a little bit of time to talk about our view of this edge, right? The enterprise data governance experience. When we talk about having an edge, we're referring to both an approach and an underlying technology portfolio that enables virtually every aspect of the business to benefit and grow from the investment in data governance, just like we just said. So there are five pillars, the enterprise data governance experience pillars. I'm going to run through the first two, and then I'm going to hand the three on the right over to Jamie to explain. But the first one, and many of you who have attended webinars or metta set trade shows and events, know that we've spent a lot of time over the past year and a half talking about any data anywhere, something we call the any squared. And you know, any squared becomes so critical in this day and age because according to Forbes, and it's just common sense in many businesses, only about 12% of a company's data sits in a traditional database. To create true enterprise governance, you need to leverage data from any source in any format, regardless of where it's stored. Voice recordings, web chats, documents, relational, no SQL data on-prem in the cloud, it all needs to be accessible to the data governance program. So that's number one. Number two is really around stakeholder empowerment, and we started to talk a bit about this, right? It's all about the needs of the data stewards. It's about actively engaging with the most important data holders in the business about what they need and ensuring that your approach and technology takes these things into account. I told you I was going to tell you a story about sort of my life and my role, so here it is. And I think it's a pretty good real-world example. As a CMO, I've been in many, many, many board meetings. And most of the time I'm presenting with my counterpart in senior sales, whether it's chief revenue officer, executive vice president of sales. And it's been a very common occurrence that we have been presenting completely different data for the same exact KPIs. And what always happens is the board says, whose data is correct? And if there's some, you know, question about the credibility of that, how can I rely on my data to make informed decisions about where to invest and how can the board rely on my perspective about the best places to invest? If they are looking at two data points and saying how do these match up and we don't have a really good answer to that. So I think that that's a great example of how we ensure that key stakeholders are on board and get better alignment and support for the mission critical initiative. It can even open up budgets beyond IT. So here's an interesting thing. I'm going to give you a bit of a preview. We have just fielded an amazing piece of research on the state of the Union on data governance. There'll be more coming on that shortly. But we did see this giant economy between the conversation that says, yes, enterprise-wise, stakeholders should be engaged, but we don't ask any of them for budget. And we think that this is a significant miss. We think, again, that this is a reflection of a data governance 1.0 thinking. Because I know if I'm going to get business value back, if you're going to solve the problem that I just talked about with my credibility with the board, with my making good data-driven decisions about where to spend my money, I'm going to pony up a piece of that budget, too. So now I'd like you to take over, Jamie, and talk about the third pillar in integrated ecosystem. Yeah, sure thing. So you're talking about a lot of the people of the organizations collaborating together in the data governance experience. Likewise, we need the machines to do the same thing. There's so many systems in our organization now that we need to understand the metadata. They need a real common understanding of that metadata and to start being able to share their knowledge. I think a good data governance experience needs that central body of knowledge to be shared and accessible by the systems. Visibility across domains, I think it's where we need to start applying other lenses to our data. We need to understand how the people in the systems use our data. And again, allow different roles from the business analysis analysts, IT architecture teams, also collaborate together on information. And the last one, regulatory piece of mind, is really what you were talking about earlier. There are so many regulations that apply to our data. There is so much risk associated with our data. We've got to get out of the control. We've got to catalog where is our maximum risk. Something we were talking about yesterday was the viral attack on the British National Health Service. The physical estate of the National Health Service is just so big. It's just not possible to put a great big firewall around all of the data in the systems of the organization. So you really got to look at where is our most valuable data assets and really start to understand those and focus on where you're applying your dollars. Absolutely. That's awesome. So we're going to shift gears and now talk about really data governance 2.0 and what it looks like. And I know that this is your favorite slide on Earth in today's presentation. I know you like this one. So why don't you take the folks through it and again, contrasting it versus that siloed under the dome approach? Yeah, absolutely. So we're all about modeling. So I really see the world as a model. I think that the key to this slide is that line down the middle of the screen again. So bringing the business and IT together, getting them to collaborate and communicate effectively. So the left of the line, top left-hand corner, we need to understand the language that the business uses. And different businesses communicate in very different strange ways. I spend a lot of time working in IBM where every conversation would be mainly comprised of acronyms and strange jargon. So we need to understand that language in the business and build these glossaries of terms. We need to start to understand the rules that applies those terms. And again, as we were saying earlier, we need the business to provide those rules in business language. Over on the right-hand side of the line at the top, we've got our data dictionary. So this is jelly sensors around a well-managed catalog of data elements. So we've got our periodic table of data effectively. So a single element representing each concept. And that then ties together all of our detailed descriptions of data assets there. So as you were saying earlier, Mary, data lives in all sorts of forms. So databases need to understand all forms of databases. We've got big data projects out there. We're now starting to open up to our developers the ability to create data stores with no SQL type structures. So we've got to understand all of those. Files, so it could be files that flow between the systems. We're even starting to have conversations about blockchain now. Again, we need to understand what data is in order of these things. Documents, so semi-structured and unstructured data. And it could go all the way to an Excel spreadsheet that supports a critical process of the organization. Key decisions are made on business intelligence reports. Again, what information is out there? Where is it all? And then down at the bottom of the screen. So it's not enough just to understand the data. You've got to understand what's it being used by and used for. So again, we've got the humans and the machines. We've got roles and systems. We need to understand data at rest. So data stores, which might be instances of the data designs we've got in the top right-hand corner. We need to understand how information flows between those roles and the systems and the data stores. And if we start to bring location into the equation, we can start to ask security questions. So I can start to look for where have I got business critical data flowing from a role or a system, a data store, from a high trust zone to a low trust zone. Because there are the places, again, that need to start spending my dollars to provide an adequate level of security. And I think the last piece here is process. So what is being done to the data? Where is our critical data being used in the organization and by whom it rolls? You know, you make this very complex slide seem simple when you explain it that way. So I know I appreciate that. So let's just chat a little bit more about the stakeholders. You know, some of these, I think, are sort of expected. But what's new here? And where do we really see the difference between the 1.0 to 2.0 world in terms of the stakeholders and what they get out of, you know, of being a part of the initiative, Jamie? Yeah, that's a good one. So I think the top of the list there is the data owners, the notion of data owners. So data governance, one was all about the stewards and the custodians of the data in IT land. We were talking about accountability and ownership earlier on. We were talking also about breaking down the silos. So trying to get a single approved standard for each piece of information that contains an approved definition includes the rules that the business needs to adhere to to be able to satisfy regulation. So this new group of data owners is needed to 2.0. Yeah, and the last part of the bottom there are the consumers. Everybody in the organization is a stakeholder of data. It's everybody's business. So we need everybody to have access to that information about our data. So we need a central place for people to go to find out the latest definition of the term, to be able to reduce the ambiguity in their jobs. So again, working at IBM and I don't know what an acronym means. I need somewhere to go to better look that up and then look at the rules and start to ask questions about that data. Yeah, and you know, that's a really interesting point that I think bridges back to the whole conversation about creating a culture of data accountability, right? If everybody in the organization is armed and knows where to go, what to do, how to get it, how to better understand data, then I think the data fluency that we have increases exponentially. So I think that's a big shift in terms of the DG 2.0 as well. So I'm going to shift gears a little bit to a little bit of the Irwin world. And again, for those of you who know as well, you know that we've been a modeling powerhouse for a long time in the past year again, added business process, modeling, and enterprise architecture to our award winning, really category defining data modeling. And it's very interesting because we really believe that that sweet spot of how DM and EA and BP come together is something that first of all is unique to us and something that we can really do better than anyone else and has massive impact on the success of the depth of the robustness of the data governance initiative. So would you talk a little bit about why all these things are important together, Jamie, and how they help to support, inform, expand, extend data governance in the real world? Yeah, absolutely. So I think bringing together these things gives you an exponentialization of value. So as we're saying, the data governance experience is not just about understanding the data, producing logical data models, or producing a business glossary. So you can ask far and wide questions. So bringing together enterprise architecture and business process modeling, I think, has huge amounts of value there. So when you're asking the big questions about your organization, you want to start looking at how do we improve this organization as a whole? So those enterprise architecture-tied questions, looking at where have I got issues and redundancy in my systems, where have I got underperforming capabilities, areas of risk, then that's where enterprise architecture comes in and provides us more information to then shine the data lens on it, starts asking questions around, okay, where have we got problems with our data? Where is our data at risk? So I think if you can satisfy each of these areas individually, producing value from each of these areas, that's great. But if you can then bring them together in that data-given experience, then you're going to get a much greater degree of value. Yeah, that's terrific. And can you just talk a little bit, Jamie, about something that we call data impact analysis, which is such an awesome Erwin advantage and why it matters again in this 2.0 world? Yeah, I think the organization is a bit like a Jenga puzzle where you pull one block and other blocks are going to start to collapse. So the organization is a complicated machine. So if I was to pull the plug on a server somewhere in the organization, then somewhere else a business process is going to fail and a customer's going to be unhappy. The same applies to our data. So we've got to be able to understand the impacts of changing our data. So how many customers could answer the question if you change your customer reference number from 15 characters to 20 characters, then what needs to change? That is potentially a gigantic question. There's going to be systems involved, processes, people, et cetera. There's a lot of impacts. Understanding that impact is key. Yeah, and that's something that, again, because these things work together, because the data models and the business process models and the enterprise architecture are linked at the metadata level, right? We have a view to that and a capability that we think is really important and is going to change the game in that DG 2.0 world. All right, so I'm going to give you a softball because I know you'd love to talk about this stuff. Let's switch a little bit more into the technology realm and tell us, you know, give us what you think would be a good technology prescription, if you will. You're the doctor, Dr. Jamie. Why is the prescription that will actually bring us into this DG 2.0 world? Okay, so we need a platform, a focal point for the organization to go to to be able to ask big questions about our information, to be able to raise issues about the information to make requests for information. So we've been doing a lot of work talking to customers out there about what is the ideal prescription, and we've broken it down into these four capabilities. So the discovery phase is, again, about the business being able to define what information is important to the business, so building out the business glossary, the different terms, which might span across different departments of the organization, including external partners. So if you've got to communicate with external partners, trying to understand their language is absolutely vital. Reference data is a big thing as well. So code sets, for instance, the medical industry operates around sort of a very gigantic code set which describes all the possible diseases and ailments out there in the world. So trying to understand what are those codes that we use to describe those diseases is vital. Where all our systems are using those codes, we need a central place to define them. The understanding part is now for that important data, where is it? So starting around the data dictionary, the data usage part, understanding the people, the processes, the systems. So bringing in the catalogs from the enterprise architecture and the business process world. Data quality. So where is our data and how good is that data? So we'll find that we've got duplicated pools of data and silos of data. What's the quality of the data in those places? When we start to look at governing our information, then we need to set out those high level policies. What is our policy for data governance, security, etc. And then build out an operating model of processes so everybody is clear on what are their roles and responsibilities in giving them that data. And then, again, back to the business philosophy, setting out the rules that applies to each piece of information. And the last part is socialization. We've got to make all this information as these bodies of knowledge visible to the organization. So anybody can come along and look up definitions of those standards of our information. Start to ask the impacts question. So if I change customer reference number, what's going to be impacted? Lineage is a big one. So again, a lot of regulations will require us to prove where did this data come from? What is the quality of the origins of the data? So if we're looking at business intelligence type projects, I might want to take a BI report and understand where in the data warehouse did it come from? And then those tables in the data warehouse, what are the operational data stores with the origin of those fields? Self-service data, another area. People, we want to come to a single place and request that data. So we're having a really interesting conversation yesterday based on an earlier conversation with Forrester about the data scientists. And they were saying that a good data scientist now can be commanding salaries up to half a million dollars a year, which is craziness, because that role needs to have such a broad range of knowledge coming from a deep knowledge of the business and how the business works through to sort of a PhD in mathematics and analytical science, through to understanding IT and BI engineering topics. So we've got to start breaking all those roles out and allocating them to the right people. So having a self-service data journey from the initial request through to people being able to find where is that data to then assemble the data into a format that is useful. We have to better support that. And then the last part is issue management. So it should be easy for anybody in the organization to register an issue, whether it's... I don't agree with this particular definition of a business term through to using the system and I'm seeing some data quality issues through to on a registered data breach. So I think for the perfect prescription, then you need all four of these capabilities. Yeah, and it's interesting what you said about the data scientists and the self-service access. Because if you think about what we talked about before, increasing... Creating that culture of data accountability and changing the level of fluency and data that the business has. If it sits in the hands of just a number of people who have the complete span and control, then how do we become a data-driven business? How do we become a business that is fluent? And that challenges assumptions that aren't data-driven. So I think it all comes back in a very nice loop. So thank you for that. I really appreciate when somebody puts a great end to a story that I've been working on. I'm trying to just advance to... Hang on, to the last slide. Let's see. I'm having trouble with my advance. So hold. There we go. And see, now we want one too many. So just a few notes to sort of close this piece up, and then I'll hand it back to Shannon to give us some questions. But in our experience, what we found is the DG1.0 movement has left the market wanting more. It just was not enough, right? It solved some problems and actually created some new ones. And we believe that a successful data-driven project requires something different. It requires this enterprise empowerment that we're talking about. And readiness, which is very often not a forethought, but an afterthought, is key to success. This isn't just about deployment of a tool. One of the things that we've done in bringing our solution to market is we have armed our consulting team with a host of readiness services offerings to help organizations, just like yours, do everything from discuss, as Jamie talked about before, where the ownership for the initiative should live. How does it work with the existing MDM strategy of business? I mean, there's so many things to consider before you even go out and select a technology. And then last, but certainly not least, and especially in the heart and the mind of somebody who sits in a C-level share every day, data governance has to be measured and measurable in the context of the business in order for it to be the game-changing solution that we really truly believe that it can be. So now I'm going to just hopefully click forward one more time and turn it over to the delightful Shannon to take some of your questions. Thank you. Thank you both for this great presentation. So if you have any questions, feel free to submit them in the bottom of the Q&A section in the right-hand corner there and just answer the most commonly asked questions. As a reminder, I will send a follow-up email by end of day Monday for this presentation with links to the slides and links to the recording. So, Mary Ann and Jamie, just diving right in here. So what does DGOM stand for? The Data Governance Operating Model. So this is the collection of processes that define how we're going to, given the data of the organization. Simple as that. And can you expand, please, on what you mean by expanding data governance, or, excuse me, measuring data governance? Yeah. So what we're talking about really is back to that whole idea of looking at it in the context of the business. So how can data governance help to achieve key initiatives like customer satisfaction, customer loyalty, sales growth, customer churn? What are some of the other ones that you can think of, Jamie? So the regulatory things. So being able to define what is personal data of the organization and have a clear understanding of where is it. Those are things that are very much measurable. When the regulator comes in, you need to be able to say, yep, I've ticked the box. I've done the job. So, you know, Mary Ann, you mentioned a compelling measurement of data governance in the business context. And you stated that data governance has to be measured and measurable in the context of business. So what's an example of a compelling measurement in that? So I think one of the compelling measures is really some level of growth. So whether it's revenue growth that's data driven, and I connect that back to everything from data tells us that competition is, you know, reducing our margins by X amount. We use data to actually formulate a new service offering. And then we grow the business, you know, 5% based on that new offering. I mean, really that's the kind of kind of close loop starting with an objective, you know, using data to support the understanding of the objective, making some change, and then being able to go back and deliver it again. So that's where I sort of use that, both measured and measurable, right? So if it's not tied to a business initiative that matters to the people that I work for, which is primarily the board, then it really is just a project that lives inside the business. Jamie, anything to add there? I think if some of that will. Ben, do you have any typical examples of quantifiable business impact for data governance, revenue, market share, cost avoidance, et cetera? Not yet, but we're in the early days, and that's the sort of stuff that I think you can look to us in the next six to nine months as we really begin to deploy these solutions with lots of really big customers. So doing those kinds of time and motion studies and getting that quantifiable data, we think it's going to be, you know, the most important thing that's going to change the way that people think about the initiatives in the business. Yeah, we're looking at some techniques for monetization of data as well and some metrics around that. So, yeah, lots to come in this area. Yeah, you know, we usually get that question along the lines also with, you know, how do you get execs to buy off on implementing any kind of data governance program within a company? Yeah, and that's interesting because, you know, Jamie, talk about some of the big customers that you're working with and the senior that I'm in Italian right now, so I'll do the capo di tutti. You know, what is, what's their motivation? You know, of the most senior, there's that very, very, very large financial services company that's been a customer of us for a long time. And, you know, what is, what's the motivation that they have? Why are they bought into this initiative? Yeah, I think there's, I mean, as you were saying at the start of the presentation, there's two drivers. One is risk, which could be sort of regulatory or a reputational risk. So there are often certain things to quantify. I mean GDPR is a great driver of data governance because there's a very clear penalty against it. Reputational risk, how do you quantify a reputational risk of a data breach? It's a tough one. And the value side, the value generation side of data governance and understanding your data and just general enterprise data management is another tough one to quantify. So I've been working in enterprise architecture for years and again, we've been trying for a long time to try and put sort of solid metrics against this. But the reality is sort of just understanding your data, having a framework of understanding around your data, giving that that visibility to understand the impact of change, you can see sort of the benefits of that. But it's difficult to quantify. That's our next. We'll come back and we'll talk about some of the quantifiable results, Shannon, in a couple of minutes. I love it. There's a challenge. So to what degree can the use of mobile devices and remote access added a layer of complications? So how should org policies and their implementation address this effectively? This is a good one. So it goes back to what I was saying about the National Health Service. It's impossible to put a big perimeter around your organization and no data comes in and out. In this modern world, we're dealing with more external business partners. The data supply chain is getting more complicated. As we're looking at SAS type business models, our data is moving far and wide. The blockchain again is a potential benefit, but also a possible threat as our data sort of is rapidly proliferated across geographical boundaries. So having an understanding of where is your data going is absolutely vital. Mobile devices is a part of that. Yeah, and the interesting... No, please. That's where sort of enterprise architecture and data governance come together. So having those clear models of where are the roles, the systems, and the data source, where are they located, what are the flows of data between them and how are those flows implemented in technology is vital to a complete data governance experience. Well, and you know, I think also too, Jamie, if you think about mobile in general and you look at your device, right, and you think about what you do with it, the amount of data that you create produce on this thing, and then the number of organizations that you do business with that you have a data relationship with. Think about it that way, right? There's many that I haven't, and these days I'm scared because I'm one of the 143 million people whose data was breached, you know, from Equifax. So the consumer is in many ways, you know, becoming much more of a part of your organization, whether you like it or not, right? And the moat that existed historically between your business and the people that you did business with was much more significant than it is today. And now because of the five or six different ways that I have to connect to you, and the many applications of yours that I consume and I use every day, right? The relationship that I have with you as my business provider is much different and with much greater risk. So I think that that's one of the really big things that we don't always consider about mobile, right? That we've just really sort of exponentially increased, you know, the number of data relationships that I have. Yeah, absolutely. I think a lot of companies aren't doing a great job of it. I get increasingly frustrated day to day when I try and contact my gas provider or my mobile phone provider and there's no point in where I can contact. So I have to do everything through the web or through my mobile device. And I find I can't do things that I need to do. So again, having an organization needs to have a clear understanding of the customer journeys that we expect. And then within that understanding of those customer journeys, we need to understand what data is involved. And what are the policies, the rules that we apply to that data? And are we following them? So it's a complex question. Yeah, and you know what's interesting about that is I came out of many years in the customer service space, so I was very comfortable talking about the customer journey. But I think that the customer data journey, right, and the customer data relationship is really sort of the new frontier of that. Because when you talk about reputation loss and how you quantify that, we're going to start to see, I think, trustability. And companies that have high trustability indexes become a key decision point for doing business with them, right? I'll give you a perfect example. Again, I hate to reiterate the fact that I'm in Rome and you're all not, but in connecting with the hotel that we were in yesterday, which had a sort of a funky, none of us ever really heard of it, local ISP, right, Jamie? They wanted everything from the color of my eyes to the size of my feet to, you know, my birth date and a whole bunch of other things. And we all looked at each other and said, you know what? We need to get on this network really badly and do a lot of things we need to do. But I am so not comfortable giving this company that I never heard of, you know, the color of my hair and the size of my feet. And I think that more and more we're going to see that that trustability is going to become a key competitive advantage for organizations and that we're absolutely going to be able to monetize. Do people choose to do business with us because they believe that we hold their data, you know, in great respect, that we protect their data, that we govern their data, that we know exactly what happens to it and when it happens. For me, one of the scariest things about the Equifax thing was not just that I was a part of it, was that no one could tell me what it meant, right? What data? For how long? Where did it go? Where did it come from? Was it all of my address information for the last 40 years? Was it only five years? So when you think about that sort of consternation and fear that you're creating in the hearts and minds of your customers, I truly believe that they're going to start beat feet and go someplace else when this data trustability as sort of a third party ranking becomes a big thing. So that was a long and involved and mildly passionate answer to the question, Shannon. I love it. And you both touched a little bit on BI as a self-service BI, but how does data governance fit into the evolving paradigm of just data as a service? Jamie, you want to take that? Yeah. That's a new question. It is, yeah. As our pool of information grows, as Marianne was talking about, the proliferation of data, we've got to make that data more accessible, as she was saying. So BI is going to become more and more valuable. So we've got to get the journey from the business description of information through to where is that data? Where is the best quality data? Where is the right data for the context of the request? We need to make that journey as quickly as possible. So we're looking at how do we hook our data governance platform into BI solutions and get that integration tighter? So along that line, Jamie, thoughts on the product's ability agility to Nexus issues and can the product easily be integrated with other systems such as Microsoft Office 365, other things? I've got to admit I don't know what Nexus issues are. Maybe the questioner can expand on that, but maybe a little bit more on the integration. So, again, what sort of integration are we talking about here? So as such as Microsoft Office 365, I know 365 can come with Power BI, things like that. You've talked about some... Yeah, I mean understanding unstructured data in documents is a good one. In Excel spreadsheets and Microsoft Word. So we have document management systems rammed with information. We might even have SharePoint and other such content management systems rammed with information. Again, trying to understand where is the critical company data in this world is vital. So we're starting to look at profiling technology to be able to go out and search for critical data in the organization. Search for it in structured repositories such as databases first, but then to go out into the structured world and hunt it down. Yeah, it's a big problem. So I'm going to... We've got time for one more question. I think I'm going to ask a loaded question here to both of you. We've got a few more minutes. You talked about the business being more responsible for the data rather than IT. So what if the business wants to help and move forward but they don't fully understand data? That's an interesting one. You know, I think that that goes back to this whole fluency idea, right? That there is a certain level of data that everybody has to speak its table stakes and whether that becomes part of sort of corporate training initiative or a knowledge transfer or a knowledge base initiative, you know, I think that that's sort of really the fundamental table setting to be able to have a data flow into organization. So, you know, the first thing is you have to have the will and then the second thing is you have to have the skill, right? So if the organization has the will, then I think, you know, there are some things that the traditional IT data stewards can do to help educate people in terms of what it really means to be data centric, what kind of data we collect in the business, what kind of data, you know, we think you may have in your organization. So maybe it's something as simple as having a sort of a structured way to do discovery within a business unit that doesn't have somebody that specifically speaks data and uncovers it. I think that there, go ahead. This is where organizations need to lean on external resources that they can help them on the gym to understand that the challenges of data governance and teach them. We've got a ton of great business partners that can help them with that. Yeah. All right. Well, that does bring us right to the top of the hour. Thank you both so much for the fantastic presentation. I just love it. What a great topic and how important it is. And thanks to all of our attendees for being so engaged in everything we do. We love all the great questions that have come in. Just a reminder, I will send a follow-up email by end of day Monday with links to the slides and links to the recording of this session. And again, thanks to everybody for attending today's webinar. And thanks to, again, to Marianne and Jamie, especially Marianne, as you're traveling as well as as we are. We just love it. And I hope you enjoy the rest of your time and safe travels home. So I hope everyone has a great day. Thank you. Thanks, everybody. Bye-bye. Thanks.