Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining this DataVercity webinar, the Five Pillars of Data Governance 2.0 Success, sponsored today by Irwin. Just a couple of points to get us started. Due to a large number of people that attend these sessions, you will be muted during the webinar. For questions, we will 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 our questions via Twitter using hashtag DataVercity. 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 Mary Ann 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. Mary Ann is a market creator and builder with 30 years of strategic business leadership experience in enterprise software and technology. Career highlights include serving as Chief Marketing Officer for In Contact, SVP of Corporate Marketing and Investor Relations for Extralis, and a Senior VP of Corporate Marketing for Variant Systems. And with that, I will give the floor to Mary Ann and Jamie for today's webinar. Hello and welcome. Hey, thanks, Shannon. Really great to start off with the tunes and set a lovely groove for this afternoon's webinar. With me is Jamie Knowles. He's going to sit around and be part of the background for a little bit of time while I take you through some recent research that we just completed. So here's what we're going to cover in today's session. First, we'll be reviewing our recent state of data governance research that I talked about and some of the key findings, which I think you guys will find really interesting. We'll then discuss how organizations are getting on the right track with their data governance programs. We're going to focus a lot around preparation and readiness as the foundation of data governance success and its most important pillars. And then finally, we'll talk a little bit about how technology that's designed for true enterprise data governance can help address both compliance and growth. So let's just jump in and get started. This, I would say, summarizes Irwin's stand. We believe that data governance 1.0 has failed to live up to its promise. It needs to be an enterprise initiative that drives more value than simple delivery against regulatory compliance. And its value needs to be felt across the entire enterprise. And we believe that readiness is key to a successful data governance initiative. As we work with some of the largest customers on the planet, we see this as really one of the recurring themes out there. And when you hear us talk a little bit about enterprise data governance and the enterprise data governance experience, something we call EDGE, let me just break that down a little bit for you in terms of what we really mean. We believe that a different persona-based approach is required so that everyone from executives on down are invested in and accountable for data use. This way, you create an enterprise data governance experience that's characterized by, first and foremost, support for any data anywhere, whether it's relational and structured on premise, cloud assets coupled with well-documented business rules to ensure that standards are followed. The second is really about collaboration and organizational empowerment. Both business and IT stakeholders have consistent and role-based views to data that are relevant to their roles, and this helps to build trust and ensure alignment and enhanced decision-making across the enterprise. The third thing that we believe is absolutely required is an integrated ecosystem. So the systems that are managing and protecting data are unified by a common metadata repository for consistent exchange and understanding and processing. Fourth is visibility across domains so that we break down the silos between business and IT with a common data vocabulary, so the changes to systems and processes and people can be measured quickly to produce desired organizational outcomes. And last but not least, and probably the focus of many of the folks that are on the webinar today, is regulatory peace of mind, mitigating a wide range of risks from GDPR to cybersecurity to protect customer trust and prevent reputational damage. And we're going to talk a little bit about some of these key drivers as we begin to get into the findings of our recent research. And click through. There we go. Because what we're talking about is using data and data governance to create more value for your organization, because obviously compliance and privacy and security are often the center of people's initiative as we see here on the slide, but data governance can certainly help you accomplish much more than that. And we'll see this as we look at the research report. So let's jump right into that right now. A few weeks back, Erwin commissioned UBM to conduct an online survey exploring the state of data governance. The data from the survey measured North American IT professionals at large companies with a thousand or more employees, and nearly two-thirds of the respondents had IT management titles such as CIO, director, manager of IT, data steward, data governance, or risk compliance. So these were people that had a really significant role in the organization and in the strategy of data usage and data governance. Now, you might ask, why would we want to commission a strategy? Well, a survey. The goal of the research was for us to get a baseline understanding of where the DG market is today and what are the perceptions and experiences of major enterprises. We would like to understand who owns data governance, the key drivers behind its adoption, how DG decision-making is done, and how the organization views its assets. And the reason why we did this is because we really believe and we're seeing that data governance can be a complex and mystifying topic. And we also believe that it's a market in its early stages. It doesn't mean that there haven't been many companies that have deployed initiatives, but we find that many, many, many large enterprises are really in the business of learning about data governance and they're in the information gathering and fact-finding piece of their strategy. So let's jump into some of the major findings of the State of the DG report. So one of the things that was really interesting was we started by asking folks how they define data governance. And one of the things that was really clear was there was a lot of different definitions that depended on the perspective and the role of the person in the organization. And while there's some similarity between these definitions, there obviously is a difference between data governance as understanding data quality then building a set of policies that governs the organization around data. Those things may have commonality between them. But again, in terms of the overall definition of a strategy, they really are very different concepts. And while they can all be fit into the overall rainbow umbrella of the data governance initiatives, I think what's really interesting about this is that organizations are coming at data governance from many different angles. And that's something that we have to keep in mind, that it's all about the perspective of the organization in terms of how they've defined it for their company and what they're trying to get out of it based on the questions that they ask and the information that they're looking for. So that lens to data governance is an important thing to keep in mind. Next, we looked at really the overall sort of assessment of how important data governance is to your organization. We see it's clearly important, only 2% of the people that we surveyed said it wasn't very important at all. The majority say it's critically important and they have a formal strategy in place. But nearly half, 46% say it's important to them but they've yet to implement a formal strategy. So I find these responses very much a contradiction in terms. So while that 98%, which is a massive number, view DG is important or very important, the fact that 46% of them don't have a formal strategy in place is consistent in our view of the market being early. Organizations are aware that they have a problem, they need a governance solution to address regulatory compliance and digital information, but they aren't exactly sure what the strategy will take. It's also very consistent with the behavior we see around Google searches, for example. Instead of Googling specific technology applications or brands, the leading data governance searches are for things like the definition of data governance and the benefits of data governance and what is data governance. So we find this to be a really interesting place to start. As we move into more of the details, we start to look at budget. And in this question on budget, over a third of the respondents are okay. 37% say they have a data governance program that's funded. But 39% say there's no separate budget. Nearly a quarter of them say they just don't know. So it's a very interesting finding that 63% of those survey don't have a budget or don't know what it is or where it resides. Clearly this lack of certainty can kill the data governance program before it even starts. And we're going to talk later in Jamie's portion of the program about sponsorship and its importance to the success of a DG initiative. The other thing that's pretty interesting here is that when asked exactly where they are with their data governance program, more than one in five people or 21% reported that they're just getting started. This means that they're in the initial phase of understanding enterprise data, where it lives, who uses it, and how it should be defined. And one would hope that the budget question gets sorted out in that process. So when we look at the second question, which is where does that budget come from? 40% of the people responded that IT continues to be the budget holder for data governance. 20% say the data governance budget comes from audit and compliance, which is good because there are really sort of relatively new players in the data space for us. And 8% say that it comes from the business unit, and a third say it's shared across various groups. So this is not a surprising finding, but it's one that's still mildly problematic. Because we believe that DG is such a strategic initiative that the budget and associated skin in the game should be shared across the business as we see with the 38% of responses. And 20% of the responses that said the DG budget comes from audit and compliance is great. But with an audit-only view of data governance, this can often limit its effectiveness in achieving outcomes where data can be used for more, like business growth and not just risk avoidance. So there's really a number of things that we can read into this question around where the budget comes from. One of the things that was really interesting with the upcoming and looming dates around GDPR, and you see my sort of commentary objects in the mirror closer than they appear, hopefully you appreciate the sense of humor in that, is that only 6% of those surveyed were completely prepared for GDPR. That is an incredible number and relatively scary if you think about it, because when it comes to regulatory requirements, GDPR is a completely new animal. It goes well beyond the requirement to keep personal information private and secure. It specifies the rights of EU citizens as it pertains to their personally identifiable information and requires organizations to support and enable those rights and the options available to citizens. They must provide citizens with access to review and remediate personal data, enable portability of personal data between different entities, and facilitate the right to be forgotten or deleted from any and all systems in an organization. Even more importantly, it requires that all new systems include this privacy by design whereby GDPR capabilities must be built into these systems upon deployment. Organizations have to create and maintain an ongoing compliance framework complete with enterprise-wide visibility and replete with new collaboration processes by which they can maintain the level of interaction with their EU citizens and facilitate these new rights and respond appropriately. Of course, this also comes with a requirement to be able to prove to interested parties, i.e. auditors, that this framework is in place and working and will not be broken by the next new data thing developed. This is a significant shift in the compliance mindset and will require serious governance and architectural investments to meet this new age of democratized data. One of the things that I think is really interesting in this slide, too, that I'll just comment on before I move on, is that 17% of the people that were surveyed say GDPR does not affect us. Well, I believe that many of that 17% are wrong and that they clearly are not necessarily educated enough about what the impact is if they're a US or a multinational company based here in the US in terms of their movement and their protection of EU citizen data. So let's take a quick look at who's in charge and who's driving the boat. So in terms of ownership, more than half of the respondents said that both IT and the business are responsible for data, but the CIO still drives the process in 70% of the cases. You know, we'll talk a little bit more about this later on, but we believe that data governance is really everybody's business and successful initiatives require the support and buy-in and ongoing engagement from data stewards across multiple departments. You know, you'll see when we talk a little bit about resources and the engagement of sponsors, how important that is. Take, for example, my function. As a CMO, I'm one of the company's biggest data stakeholders. So my organization generates and consumes and analyzes a tremendous amount of prospect and customer data every day as we're constantly tuning and tweaking our approach and enhancing our market strategy. So folks like me should absolutely be engaged in data governance and could potentially contribute to the budget too, if you think about that question of the folks that didn't know if they had a budget and didn't know where it was located. So now we move into what I think is some of the most interesting findings of the report. When asked about the Drivers for the DG Initiative, 60% of people said, and this is something that I think we would all go, okay, yeah, it makes sense, regulatory compliance is leading the way. But what I find the most fascinating about this slide is that 49% of them, not far away from 60, said that trust and satisfaction is a key driver as well. I believe that that is huge because data transparency and trustability are rapidly becoming a very important aspect of a consumer's view, selection and loyalty to a brand. If you're like me and you're one of the 143 million people that were impacted by the Equifax breach and still don't know whether they received your street address when you were nine or your past four tax returns, really thinking about how an organization handles your data is becoming an absolute key criteria for the kinds of companies that I want to do business with. After having had that bad experience. So organizations that are actually thinking of their data and how they handle it and govern it and its impact on customers really have a great head start. Because trustability and then the fourth one, reputation management at 30%, are really board level concerns. These are the kinds of things that your board is talking about and that are really interested in. It's more than just regulatory cost avoidance. It's really business growth based on trustability. And I think that that's a very interesting change and something new that we're seeing in terms of the overall data gain.  So what are the key obstacles? Well, it shouldn't really surprise us that the first big obstacle is the cost of data governance projects at 58%. And the other thing that I think is really interesting is that the rest of this, I believe, goes to approach and strategy. And so we're going to spend a lot more of the rest of this session digging into these issues of strategy and readiness, things like the approach, the executive support and sponsorship, the kinds of tools. How do we build a business case within the business and all that sort of good stuff. So I'm going to turn it over right here. And maybe I should go back one to Shannon who's going to do a quick poll for us. Shannon, shall I stop touching things? Sure. Yeah, I've got the poll open. So go ahead and move it to the next slide. So how ready are you for GDPR? Click in. There we are. There we go. Okay. So here's the question is not so much GDPR, but overall in your business for data governance, how ready do you believe that you are? Do you think that your organization is a laggard that you haven't even really begun to start on a DG project in earnest? Are you guys a novice? And you have a strategy and you're just beginning to assemble a team? Or do you feel like you guys are in a leadership position where you've got a comprehensive strategy that's enterprise-wide and resource-and-funded? All right. Well, I opened the poll a little early there. But it's closing, and so we've got the results here in just a few seconds. Three, two, one. All right. Here's the poll of the countdown. Okay. So really, really a mix of responses. We've got some laggards. We've got a few leaders, and most everybody else is sitting in that sort of novice place where you've got a strategy. You're beginning to assemble a team. And that's the sort of thing that we're seeing. So, you know, great timing because hopefully some of the things that are coming in the rest of the presentation will really help you on the score. The other thing is I'll give you a reason to stay on until the end of the next section is that we're going to rerun this poll a few slides down after Jamie has had the point, the opportunity to talk about readiness. Because we're going to see whether you think you're in exactly the same spot as you were before you heard all this stuff about getting ready for data governance. So great stuff. Thanks, Shannon. All right. So now I am going to turn it over to my colleague Jamie and Jamie, yes, I will be moving your slides. You just say next slide and I will do that. And Jamie is actually going to take you through as soon as I can click and get it moving. So five pillars of data governance readiness and their initiative, initiative sponsorship, organizational support, team resources, enterprise data management methodology and delivery capabilities. So those are the five areas that Jamie is going to talk to you. And at the end of that, we'll do another poll. So go right ahead, Jamie. I'm going to move to the first one for you. So we've been talking to a number of our customers and quite a few of them are really struggling in their first attempts at data governance. So we've been having a good think about why we took a look at a number of projects that have been running and we've built a framework to help understand what is needed for success. So as Marianne says, we've broken down into these five pillars. So the first one here, data governance requires a sea change. So who in the organization should be driving the program? And how should they be driving? So the first area is around sponsorship. Executive sponsors must be actively engaged and motivated and to really help achieve data governance success. And without that sponsorship, your program is going to have difficulty obtaining the funding, the resources, the support, and the line necessary for success through implementation. The next thing is availability of those sponsors. So you might have the right people associated with your initiative, but that doesn't guarantee their active involvement. They're pulled in other directions and don't have time to devote to your data governance program. It's likely you don't have a sponsor at all. If you don't have an active sponsor involved, your sponsors are passive or any figureheads. You should really look at ways to increase their involvement or solicit additional sponsors. And the last one on this slide is expectations. So that's sort of a reality check. Mission Impossible was a great movie. We all looked at it, but it's a dangerous way to run a data governance initiative. Realistic expectations are totally needed. Or the data governance team won't be able to deliver against expectations. And the initiative is likely to fail and be abandoned. Okay, next slide. Thanks, Brian. So we've seen projects fail because the wider organization is not on board. So what are the key requirements here? It's a culture shift. And for any kind of culture shift, a significant change program is needed. Education and awareness is needed over time to build that new culture. People need to understand their part in data governance. And they need to understand the significance of their actions. And we know the value that to an organization that data governance brings, but this value won't be realized without representation and active involvement in all areas of the business. So the data stewardship teams should be supported by the organization and should plan to include all enterprise business areas. But the most effective framework for organizing data stewardship teams is your organization's subject area model. So you should have a data stewardship team with a lead steward taking responsibility for each of those subjects in that subject area model. The organization then needs to provide resources such as subject matter experts and then individuals who have the authority to approve content. This is what we call the data owners or business owners. And the last thing here is sustainable data governance requires continuous funding like that of other departments. So marketing, human resources, finance, et cetera. And funds should come from the enterprise and not be project driven. So if you don't have clear funding, then you should engage with your executive sponsors on this one. Okay, next slide. So team resources. This is about the resources that are going to deliver against those data policies of the organization that was set. Most successful organizations have established a formal data management group at the enterprise level and outside of IT. Although this unit might be called in other names such as data management, information management, enterprise data management, et cetera. The successful organization recognizes the need for managing data as an enterprise asset. Data governance is a foundational component of enterprise data management residing in such a group. Successful data governance initiatives then consider the relationship between enterprise data management and data governance. So successful data management departments need to align metadata management and data governance as foundational to enterprise data management. Then they should support all of the components of the organization's data management department. And although data management should be outside of IT, IT has to be an active partner in data governance. But unfortunately, as we're finding many IT departments, one are either the only unit involved in data governance. Two may help with data governance if required but are not actively involved. Or three view data governance as a threat working against it. So successful data governance initiatives need to include IT departments as active partners to support the business's efforts and the enterprise mission for data governance. But we're also finding that many organizations lack the enterprise level experience required to advance data governance initiative. And if that's the case, they shouldn't hesitate to turn to experience consultants. Organizations that rely on current but inexperienced staff to perform data governance functions without appropriate guidance. Industry standards and best practices generally don't succeed. Okay, enterprise data management methodology. So this brings us to the approach. We said earlier, so we moved to the next slide. Enterprise data management methodology. Thanks, Mary-Anne. So we said earlier that data governance... Sorry, Damien, they're sticking a little bit on the movement. That's like teamwork. We believe in teamwork at the moment. Yeah, okay. So early on we were saying data governance is a foundational component of enterprise data management without the other essential components of enterprise data management. That's metadata management, enterprise data architecture, data quality management, et cetera. Data governance really will struggle. So metadata management is the key enabler of successful data governance providing every organization with the contextual information concerning its data assets. Without metadata, data stewards can't perform their duties properly. Without metadata, data governance is often hampered by lack of understanding of the validity of data sources. The quality of the data in those sources and the host of additional challenges to the organization's ability to achieve its business goals. Accurate BI is a valuable. So we've seen many organizations now derive more and more value from business intelligence. And BI really depends on quality data in context. So for many organizations, BI and analytics are the main beneficiaries of enterprise data governance. If your organization doesn't understand the value of BI and analytics to be effective, nimble and responsive in a 21st century business, the need for data governance also will be lost on the organization's leaders. The value of data governance to BI and analytics is the ability to govern data from its sources to its destinations in warehouses and masts and to define the standards for data across those stages and to promote common algorithms and calculations where appropriate. So these benefits really allow the organizations to achieve its business goals with BI and analytics. Next slide, please, Marion. Delivery capability. Next slide again, Marion. There we go. Delivery capability. So we've got sponsorship. We've got the organization on board. We've got the people running with an approach. What's next? So most organizations want high-quality data and enterprise data governance is foundational for the success of data quality management. In fact, one of the main drivers of organizations' site for implementing data governance is to improve organizational data quality. If your organization doesn't understand the value of high-quality data, the support for data governance will not exist. So data governance supports data quality efforts through the development of standard policies, practices, data standards, common definitions, et cetera. Data stewards implement those data standards and policies and support the data quality professionals. These standards, policies, and practices lead to effective and sustainable data governance. So the development and implementation of a formal data governance team or unit is essential for successful data governance. Having the capabilities to manage all data governance and data stewardship activities has a positive effect on DG. The lack of a formal data governance team or unit has been cited as a leading cause of data governance failure. Enterprise DG relies heavily on the structures and artifacts of enterprise data architecture and enterprise data modeling. So if your organization doesn't understand the value of enterprise data architecture and enterprise data modeling, it's going to be hard for it to support enterprise data governance. Enterprise data architecture supports data governance through concepts such as data movement, data transformation, and data integration, since data governance develops policies and standards for these activities. Data modeling, a vital component of data architecture, is also critical to data governance. Data stewards serve as subject matter experts in the development and refinement of data models and assist in the creation of data standards that are represented by data models. So these artifacts allow your organization to achieve its business goals using enterprise data architecture. And with regards to tools, data stewards work with metadata rather than data 80% of the time. And as a result, successful and sustainable data governance initiatives are supported by a full-scale enterprise-grade metadata management tool. And finally here, many organizations don't think about enterprise data governance technologies when they begin a data governance initiative. They believe that using some general-purpose tool suite like those from Microsoft or such like can support a data governance initiative. But this really is faulty thinking because data governance is a specific function that requires a specialist tool suite. So next thing, the proper data governance solution should really be part of developing the data governance initiatives and technical requirements. Okay, Marianne, that was you for the poll. Thanks, Jamie. So we're going to ask Shannon now to push the second poll. And remember now we've covered five areas, initiative sponsorship, organizational support, team resources, enterprise data management methodology, and delivery capability. So our question for you on this second poll is reflect on your initial answer in terms of your readiness and let us know. Did any of the things that we talked about change your perspective? So you can go ahead and drop that poll in for us, Shannon. There we go. Alrighty, there it is. I don't see the slides yet. Yeah, the slides, sorry to the folks that have been chatting for some reason.  It must be the northeaster that's headed my way. I'll blame another rain in the satellite dish, but that's a really old answer. So Jamie, what do you think? You think we're going to get change? No change? I think one of the things that's really interesting is people don't consider the cultural aspects and the cultural implications of data governance. And so that's one for me that was a little bit of an eye opener when we started doing this work. Yeah, my gut feeling is there's going to be some change there. I'm on site with a customer today and they're really quite surprised that some of these things, they're now realizing they're all behind the curve. And somebody just popped in to say that neither of these is mutually exclusive. I appreciate that. But really, we want to know is there any change? Did you learn something and is there something you're going to go back and rethink? I think that's really the point. So do we have the poll is closed now, Shannon? We do. So here we go. Poll results coming your way. Okay. So we've got a little bit of both. We've got some folks that have said that they're staunch in their initial assessment, about 60-40, and 47 of the folks who answered said that they'll need to consider some of them. Well, that's great. When it comes back to my piece at the end, I'm actually going to introduce you to an application that we'll be debuting in the next 10 days or so that might actually help you work through some of that assessment. But in the meantime, I'm going to turn it now back to Jamie. Just going to talk a little bit more about the technology and what to look for, given all the things that we've discussed so far. So take it back away there, Jamie. Great. Thanks, Mary-Anne. Okay. So you might expect us to say this as a tool vendor, but a good tool is really critical to success. We've been looking around and we've identified a number of capabilities required to support a successful, an abundance program. So the sort of things we've seen are some capabilities that should enable the people, processes and perspectives needed to deliver the desired outcomes based on the pillars of success that we've just outlined. Ready to give us a platform that will allow us to govern a single understanding of the information assets of the organization. So you need to create an enterprise data governance experience to support more than just the IT and traditional data stewards. And again, I mean, this was reflected with the customer today. So they really started their data governance campaign from the IT organization and haven't yet got the business on board. So that really is a key one there. So a tool set that really allows the business and IT to collaborate in that data governance experience. So the tool should orchestrate the key mechanisms required to discover, actively given, fully understand and effectively socialize your data assets and their alignment to the business. And the last thing is it should allow you to tailor an interface to the solution. Every company that we've spoken to has got a very different understanding of what is data governance and how it should work. So a tool set should provide a good out-of-the-box experience but be totally configurable to support the needs of your particular organization. So, yeah, on the screen that you can see four capabilities areas, I think there were a number of sub-capabilities that we believe are critical for data governance. Okay, next slide. So, okay, the Erwin Edge, the enterprise data governance experience. So we built a platform and the platform is going to give you data-driven insights that support agile innovation to transform your organization while ensuring regulatory compliance. So the center of this wheel here, we see data governance. So data assets are depicted in context by integrating three bodies of knowledge. So the first one is a business glossary which allows us to capture and document business terms. So that's the language, the vocabulary of the business and in the larger business, different parts of the business are going to use very different vocabularies. So you need to understand that language of the business. And against those business terms, we want to better categorize them, connect them, we want to provide rules around the data. And we need to set high-level policies for how the organization is going to govern their business data. So that's the business glossary. The second part is the data dictionaries. So this is a dictionary of atomic data elements. So really like a periodic table of data. So we can manage that catalog and bring all our understanding of data together across the enterprise. And three, so understanding the data itself is not enough and Marianne was talking about this earlier on. We need to understand how the data is used. So we talk about a daily usage catalog that identifies where data resides, its quality, what it's used for, by whom and how it navigates the enterprise. Again, that's the slide we saw earlier on, the data movement across the enterprise. And then the last one is who has access to which data. So yeah, understanding data alone is not enough. We need to know which people and systems are using it, what they're using it for, where it's stored and where it's moving. The open-edge platform really provides tool sets that helps us answer some of these questions. So bringing together enterprise architecture, business process analysis and data modeling and connect them all together through a common metamode.  So asking those really tough questions. And the example we use is if you change customer reference number from 15 characters to 20 characters, what's the impact on the organization? There's a lot of things that can be affected. You've got your physical data. You've then got the systems that uses that data. You've got business processes that are going to be using that data. A lot of things that are going to be affected. So that impact analysis question is a biggie. Lineage is a great one that comes up again and again in data governance. And this might be looking at BI reports, understanding the movement of data across the organization. So movement of data through ETL operations, stored procedures and views, very complex movement of data to feed your BI reports. We need to understand that. Good tools include things like AI and machine learning. We're doing a lot of being able to understand this huge quantity of information. So a large organization may have hundreds of thousands, if not millions of business terms and understanding where is that data. So being able to process large quantities of data is vast and having an AI capability to help with that is vital. Metrics, being able to measure success is really important. Being able to report and slice and dice the information from different perspectives. Being able to harvest information from different places. Putting a workflow around the change of data. So having a governance platform is, the key is the word governance. But there's a lot of writing on having accurate definitions and standards for our data. So if we're going to change any of that, we need to be able to put it through a workflow and approval process. The last part is socialization. So allowing the wider organization to access that information in a consumable way. So the years and years has, Erwin has been really sort of an IT-centric tool through our acquisitions of case-wise and course. We've been doing a lot more work with business communities and getting to understand business communities. And we've learned a lot. So when you're engaging the business communities on things like data governance, we've got to be able to provide data in a user-friendly way, in a non-threatening, non-technical way. And to pull it all out in a SaaS-based platform. So reducing the total cost of ownership has been key for a lot of our customers. The attitude towards SaaS has become more and more positive. So allowing the software vendor that already knows about their software to manage that, that system for you is absolutely key. So lower total cost of ownership and fast time to value. Next slide, please. Okay, so, well, from our perspective, and we've been working with data for a long time, data governance is the organizing or driving principle for mitigating risk, improving operational performance, and accelerating growth. And that's because it enables you to discover, understand, govern, and socialize your data assets to see all of your mission-critical information in context. And with such data visibility, such control and collaboration, you can really unlock more business value from ensuring regulatory compliance and security generating more top-line revenue. And David Williams drives all of this. There we are, over to you. Hey, thanks, Jamie, and sorry to you guys in terms of the delay and the movement of the slides I've been clicking and they've just been, again, a little pokey. So I wanted to invite you, first of all, to visit erwin.com, especially those of you who are hungry for information. We've got a tremendous number of data governance resources there. You see at the top right, you can download a copy of the full state of data governance report. I've presented you only really a limited number of the findings. It's about a 27-page report, stock full of interesting information. And what we find is that, you know, people are sharing it within their organization as both an educational tool and a little bit of a lever. We've got an excellent e-book called Data Governance is Everyone's Business that really explores some of what we talked about before around the idea of really expanding and extending data governance beyond IT and getting it into the lines of business and how to actually connect the lines of business back to IT. You can download a trial of our Erwin data governance software or any of the other Erwin products. But I'd like you, before we turn it over to Q&A, to invite you to come visit us and we'll send you all an email out on the 15th when it's live to come experience the Erwin DG Ready Check application. So as you heard, lots of the focus that we've had in the past several months around data governance and really exploring it with our customers has been around this readiness idea, right? And that there are these five areas where organizations sometimes suffer from really a lack of visibility to them, a lack of support. So the Erwin DG Ready Check is actually a custom application that we built. It's 27 questions that step you through the key things that you need to know in these five areas. In filling out those questions, you've got a series of insights that we've culled from a lot of our customers and industry experts around why things like executive sponsorship are so important and some of the key tips to get the right executive sponsors on board. At the tail end of that, when you've completed the questionnaire, you'll receive a score back and the score will give you a sense of kind of where you sit on the continuum of readiness for data governance. So we think that this is a tool that's really going to help people understand where they are, get some additional insights, and we invite you to come visit us on the 15th when we debut this tool. I am going to just bring this to a conclusion and turn it back to the world's greatest moderator, Shannon, to take us through some Q&A. I love that title, sure. Just a reminder, I answered the most commonly asked question. We will be sending out a follow-up email by end of day Thursday to all registrants with links to the slides and links to the recording of this fabulous presentation. Thanks, you guys, as always. All right, so diving right in. So how do we play catch-up with data governance when so much transformation is going on? Are we always just behind? That's such a great question. Jenny, you want to take a hack at it first and then I'll follow up. Wow, what a question. Are we always behind? Yeah, I mean, in data governance, you can drive value in a whole range of areas. It might be something as simple as just providing a core business glossary to an organization. So we do a lot of work in process modeling. So a lot of companies will produce a set of procedures for the organization and publish them out. So that the language in those procedures has to be precise. So having a good glossary of approved definitions of the terms in those procedures and the policies and the standards of the organization has enormous value. So being able to pull out different parts of the data governance experience and there's value to be harvested from those individually. There I am. Yeah, you know, it's an interesting question because on the one hand it sort of depresses you and makes you want to put your face down on the desk. But, you know, I think it's the same as any other ongoing initiative. And, you know, in the readiness process and in some of the commentary, we talked about the fact that it needed to be funded, not like a project, but like something that had a continuous life cycle. And I think that that's how we have to think about it. Are we always going to be behind? Yes, I mean maybe on some level we are. But I think that the most important thing is that we can, you know, we can begin and institute the policies and programs and then begin to sort of put things into the data governance framework as they emerge, as we bring new systems on board, as we, you know, as we change the way that we do business, as we give birth to new business applications, especially those that are consuming customer data. So, you know, while I don't think that we're doomed by any means, there's certainly work to do. And I think it's just a question of beginning where it's most important or, you know, identifying the low-hanging fruit in your organization and a place to start.  And you have no idea that it's the whole elephant. So I think it's important to jump in and to begin and to think about it as more of an ongoing way that we do business and something that needs to be baked into our culture and our methodology than, you know, a project that we're never going to be successful with. Does that make sense, Amy? Yeah, I'd agree with that. I think that the culture part is a great one to start off with, just getting the organization to realize that data is an asset and a valuable asset at that. Many organizations without their data, the value is negligible. So people should be treating their data assets in the same way that you treat any valuable assets. So culture change is a really great place to start. So where does data quality fall into the EDGE platform? That's a great question. So data quality is a key part of data governance. So our mission is really to understand what are the data assets of the organization to make sure that they're accessible to the right people in the right place at the right time and to make sure that those data assets are of good quality. So the EDGE platform has to understand the quality of this data so that the platform, first of all, needs to be able to take stock of what are the data stores in the estate and then to be able to understand the quality of those data stores. And everything centers around that dictionary of data, that periodic table of data that we were talking earlier on, the catalog of metadata. So against that amount of data, you need to be starting to look at what is the most critical data elements there and then start to put in place data quality rules that can be executed on your data stores. So you're going to be seeing a lot of work from us over the next 12 months or so in ongoing, where we're working with data quality tools to be able to bring in information and then slice and dice it across the wider model. So data quality is absolutely key. Jamie, is DG doomed if we don't have a lot of discipline around data models? No, I don't think it's doomed without data models. I think data models are a really valuable way for you to start understanding your data and to help you boil out that catalog of data elements and understand and prioritize your data. So it's a really powerful tool. And as you're designing data structures, data modeling has enormous value. But I think in the core of a data governance exercise where we're trying to understand what data we've got, where is it? What are the rules that applies to that data? Are we following those rules, et cetera? It's not totally vital, but there's huge value in it. We can have a whole session just on the value of data modeling. So what is the value to prove that data governance provides to sell data owners and stewards to own and take responsibility to do the work that like data mapping, definition, et cetera, what is the value we can sell? So the value to better owners of doing data governance. So, I mean, business leaders have to be accountable for their data. So in their areas then... Hey, Jamie, can I throw in and tell a little CMO perspective story here? Yeah, you'd be a great example. Yeah, so I think that one of the challenges that we have is that often, and this is no knock against IT, we love IT. IT has given her with life for many, many years. But when it comes to selling the value of data and data governance to business owners, we have to be speaking a common language. I just actually did a piece of contributed editorial on this subject in terms of coming to me and asking me to support, be a sponsor of, give budget to an initiative like this. And one of the challenges that I have is that often, my partners in IT don't know my perspective of the business, don't understand my persona enough, don't see the world from my point of view enough to be able to articulate the things that are meaningful to me. And I think that that's an opportunity, right? As we begin to bring people together in the business to actually define data from the perspective of the business users. For example, it's been a challenge for marketing leaders since time immemorial to have consistent definitions of things that we track vis-a-vis sales. I've often been in board meetings where I'm sitting next to a sales leader and I have, you know, I have a dollar figure that says that my organization has generated, you know, $200,000 worth of opportunities. Sales leader has a completely different number because his data is based on, you know, DEF and mine is on ABC. And that's the kind of thing that is, you know, if we can get to the core of what that problem is and somebody says to me, I can help you, you know, solve this issue that you have where you stand up in front of the board and you're presenting inconsistent numbers and you and sales aren't tracking against the same things even though you've got the same goals through engaging you in this data governance initiative. Are you interested? That changes the conversation completely because you're speaking directly to my pain as a business owner with data inconsistencies that make me look bad, make my organization ineffective and give me a challenge with good decision-making. Jamie, you want to just rip on that a little? Well, I was going to say about what you were just saying. Yeah, absolutely. And if you wanted to, as a business leader, change some of those definitions, then who's going to be impacted? What systems are going to be impacted? It's just enormous value to the business leader. You know, we have a couple of really funny comments. We've just got a few minutes left, but I think we've got enough time for at least one more question. You know, we do have an inquiry here. If you guys can translate this presentation to Greek, that would be awesome. Just want to throw that out there. And you do, you know, maybe think about this so we can get this in the follow-up email and off bunny as well. Do you have specific training for organization regarding cloud data and what it means for an organization and how to govern it there? If you want to add anything immediately or like I said, I can get some links from you later for the follow-up. Jamie, I just want to talk a little bit to sort of the cloud piece and maybe some of the things that people might be concerned about with cloud, with security and functionality and all the rest of that. Just a quick riff on that before we close. Yeah, that's a great one. And cloud is a great driver for data governance. I mean, understanding where is your data is really key. So quite often you'll be looking at working with a software and providing software as a service. Understanding where is your data going to be physically residing is critical. So on top of the information that you're going to put on the cloud, what are the rules that apply? It might be that some of that data, you're just not allowed to put in an offshore server. I mean, we have this a lot with German customers that German data has to reside on German soil. So yeah, understanding which systems you want to put into the cloud is a great question. I think this really brings together our sort of enterprise architecture and data governance disciplines. So understanding which systems looks after and requires what data. So if I do want to start moving a system to the cloud, understanding the impacts of that movement is key. Anything to add, man? No, I think that's awesome. All righty. So we've got two minutes left. Let me see if I can throw in one more question at you. Just super. In the big data or IoT world, Internet of Things, how well data governance can involve and provide consistency in glossary? What are the problems you've encountered? Yeah, so we've been having a lot of conversations around big data and the Internet of Things. I mean, it's combined sort of machine data with business data. So we've been looking at some partnerships with external companies who are really good at understanding machine data. So where the data comes from a device or it's part of a core business system, it's still just data. And that data may have rules associated with it and specific value. We need to treat it all in exactly the same way and bring it into the wider data governance fold. Yeah, and that's exactly what I was going to say, that really machine data or data from devices that's coming in. It needs to be governed in the same way as other data. And the value of informed decision making is really being able to bring that back in a way where we can combine it with other data for patterns and trends and better understanding and then better outcomes. Because if you think about really what IoT was all about, it was really bringing information and bringing intelligence from the edge back to the center so that we could understand what was happening and then take better action. So I would say that it needs to be addressed and governed and dictionaryed and glossaryed to all words I'm making up as we go along in the same way as everything else so that it can be part of the overall data fold, if you will. I love it. All right. Well, thank you so much, you guys, for another great presentation. I just love it as always. It's fun to do a webinar with. And just again, to shout out to our attendees, thanks for being so engaged in everything we do and all the great questions coming in. I'm afraid that is all the time we have, however. And just a reminder, again, once again, I will be sending a follow-up email to all registrants by end of day Thursday with links to the slides and links to the recording of this presentation and additional information. Thank you, guys. I hope everyone has a great day and enjoy. Thanks, all. Thanks, everybody. Thank you.


The T2 Tile project is building an indefinitely scalable computational stack. Follow our progress here on T Tuesday updates. So the big realization last week was that maybe it's not about the tiles individually or the entire grid indefinitely scalably, but about the connection between the tiles and because that's the thing that has to actually deal with the tiles being plugged in or disconnecting or things changing the physics that they're running and so forth and that was kind of a new idea for me and in fact after the last update and after last Tuesday I continued to think about it you know so over the last several weeks now we've gone from this very high-level scripting language this is Perl code you know wherever you see dollar signs in front of everything you know that's Perl code just to figure out what the issues were and then took us to you know C++ code that I was just writing from the Perl script to try to understand how it would work you know just running a program at the command line to do this and that's what we saw last week and that's just can't really see me but what back you know the important point was seeing the tiles and you know so they were running this very simple version and the point was was that you know they were the simulated tiles kept changing what physics they were running and so then we had to negotiate our do we have compatible physics across this intertile connection so that we can allow events to start going in between them and again in the normal scheme of things we expect to mostly have the same physics running on large patches of grid but in general for the indefinitely scalable case we have to be able to deal with the fact that maybe we do maybe we don't maybe we did before and don't now and that's what this code was doing but it also started to confuse me because you know all of this intertile connection code was all part of the MFM T2 engine the thing that's actually running the simulation or is it really a simulation when it's on the tile when it's running the physics on the tile and if that program is going down and coming up and going down and coming up in order to change the physics which is really what you need to do the way the thing is designed you know the ITC's the intertile connector code is going up and down with them and so that didn't seem right I kept trying to figure out how does it make sense and trying to figure out you know okay as we step through the sequence of getting to know you across the tile intertile connection we have things that say well just to the very basis we need packet sync so that we can send packets to each other and that's the base level if we have if we have packet sync we have level zero and at level one we want to know that we've actually gotten a packet talking about the levels that we're at and so forth I thought about it for a couple of days trying to make sense of it trying to simplify it trying to organize it into similar categories you know okay so for the tile for the ITC's and for the MFN the atoms atomic simulation itself we need to coordinate things and we need to compute things means so computation and communication both sides we have to consider at all these different levels and so forth and finally trying to figure it all out the pin dropped in my head totally obvious but of course it's only not until you actually think of it what if we move the compatibility negotiation into the kernel into the Linux kernel instead of having it in MFM T2 the program that's just running at the command line or the program that we're booting into and moving things into the kernel is it's bad for a lot of reasons one because the kernel programming is more tricky and it's more of a pain to debug as we have gone through several times before but the good thing about it is that the kernel runs as long as the tile is functional at all so trying to understand how we could have intertile communications and negotiations saying oh by the way my tile just went crazy and is now doing a completely different laws of physics how could that happen that could happen if the kernel is talking to the kernel on the neighboring tile and there's you know there's reasons for security reasons and so forth where they say you know you shouldn't put too much stuff in the kernel and you try to keep the kernel lean and mean but this is the purpose of these tiles and so that's what I spent this week doing and it was a whole bunch of stuff so now we have the idea of KITC kitsy I don't know kind of makes me think of that that Knight Rider what was the name of the car and that I don't remember anyway this is kernel intertile connection code and so there's a whole new chunk of the intertile connector packet stuff that was already there that was handling getting the packets back and forth between the tiles that's now going to be doing level negotiations saying do you have MFM T2 running what kind of what physics is it can we compare them can we decide if it's compatible and so forth and through the last week I just kept making up to do's and knocking them off starting with you know look at the Linux kernel code because it's unbelievable how fast I forget everything that I've done I mean kind of that's the point of programming is that you don't have to remember it once you program it but if you have to go back and change it then you have to remember it draft the so right yet design the levels and I was trying as hard as I could to make the sort of contact compatible communicate you know something like that that the the intertile negotiation would go on as systematic as I could to avoid complexity and avoid that graph spaghetti of states pointing all over the place trying as much as possible to have that idea of always falling toward the level that you're at and if you have support on this level you stay here otherwise you fall back and so forth and you know it's still happening I'm still making up to do and solving it but we've made a bunch of progress and you know a lot of design try you know so again trying to make it as systematic as possible eventually I actually got it down to you know this xxxxxx these are a summary of what the different levels are supposed to do it's a name does it need a custom code for you know the things that it requires nor to preserve the level does it need custom code for time out blah blah blah all of this stuff that this stuff now gets ground up by the C compiler as part of the kernel module compilation as part of making the kernel module and but and creates a bunch of tables that we can now use if we're in this level we automatically go off to this for this and that for that and so forth and and that's seem you know a bunch of the stuff still needs to be written so for example you know now in the handle KITC packet which is the entry point for getting into the whole level negotiation code we have you know all it does is print out a message handle KITC traffic here now but yeah the point is that code is actually being executed you know there it is here's our tile that we're all the way back to running on a loop back again you know two steps forward two steps back but progress underneath the hood and there it is handle KITC traffic you know okay so that's progress and that's mostly what I've been doing this last week there's two weeks left more or less in November I haven't talked at all about the wall of science stuff this scientific submission I'm working on for the LF 2020 conference what I have been thinking is this I've been doing these t Tuesday updates now for well over a year for 71 weeks in a row sometimes a few a few hours late and this the last several of them have been several hours late just because I ended up you know continuing to develop code because I wanted to show as much as I could so the whole thing got pushed back and it wasn't close to noon mountain time when the time came out really hoping this one will this week but here's the thing I think I'm gonna take a month off of making these updates that's gonna take us until the second week or so in March when it's almost surely going to be the case that the scientific paper will be done and submitted for the a live conference a bunch of other stuff is going to be inbound as well I mean this is really hard for me you know cuz cuz getting into a regular rhythm of doing these things was important for keeping everything moving and for just sort of orienting myself around the week but at the same time the actual you know even though these t Tuesday updates are so simple and so not very produced right they're just me talking live very little editing and so forth except to put the screen grabs in the camera together and all that afterwards by comparison to typical you know maker videos and stuff out there and yet it takes pretty much all of Monday and a much of Monday night to Tuesday morning in the first half of Tuesday and so forth to get each of these things out and more over it keeps me focused on something that I can show you know getting short term results and I have had several cases where I kind of went for you know crappy code in order to get something working so that I could show it and you know it's always a trade-off and you know my normal thing is to be overly concerned with perfection and never ship anything so forcing myself to have something to show week by week has been really important on the other hand I don't really feel like the last several updates have been really very exciting I mean you know from the point of view according to the pilot episode the purpose of the updates was for y'all to help me rather than me to entertain you now you know ha sure but you know it has worked to a degree that way but on the other hand there is this implicit contract that it's supposed to be interesting enough to keep you coming back and I don't know that I've really achieved that in the last few things I mean it's just screens of text and typing out a thing it's the exact same problem every movie about computers has ever had about making it exciting when you're seeing the development process from the outside so I'm gonna take four weeks off I am going to focus on finishing the paper and you know getting as far as I can I mean at this point I don't I feel like you know if I had a month maybe I could make something that would actually look pretty good I could actually get pretty far down the road of getting the kernel module set up and you know maybe make some real significant progress progress on intertile events as well as the paper who knows we'll see this is an experiment it may turn out that my initial feeling that no you got to do it every week is really important or otherwise I will start letting it slide I don't know we'll find out but that's it you know Martin from winter good time the marble machine acts that he's been building this crazy marble machine that makes music for years three years I guess now he's taking a break for the for the whole month of February as well and you know and Luke Wilson the Sam Boyce I guy he's taken February off I started to think yeah maybe I should have taken February off as well it's a strange month so I'm gonna take the rest of it off in the beginning of March we'll see what happens and hopefully taking a break will not be a bad thing it'll be a good thing so I've made my little my little logo oops there it is we can make it yeah okay with you know living in Albuquerque in New Mexico got you know special connection to the TV show so hoping it's gonna be breaking good well we shall see so that's it the next update will be in a month March 10th do you think this is a good idea who knows we'll find out but thanks to everybody who's come to watch them so far and I hope to see you in a month