 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, powering a sustainable governance program, learnings and best practices from Eon Energy, sponsored today by Irwin. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A in the bottom right hand corner of your screen, or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVercity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. And if you'd like to continue the conversation after the webinar, you can continue the networking at community.datavercity.net. As always, we will send a follow-up email within two business days, continuing 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, Romina Medici and Marianne McDonough. Romina is the program manager for data management and governance at EON. EON is a privately owned energy supplier and based in S in Germany with approximately 43,000 employees focused on energy networks, customer solutions, and renewables throughout the U.S. and MIA and APJ. Romina began her 10-year career at EON as a procurement analyst and was promoted to analytics and optimization manager. In her current role, Romina is responsible for the implementation and support of data-related and surrounding processes, technology, governance, people, and operational excellence in execution. Marianne has more than 30 years of business leadership experience and has led high performance teams through challenging business cycles to achieve sustainable growth and lasting impact. An expert in strategic growth, Marianne is currently the chief marketing officer for EON, the data governance company where she drives brand, demand, and product marketing. She was formerly CMO at SAS Leader in Contact where she helped drive stock value appreciation by 500% in five years. Marianne is an independent board member at Spark Post and holds a bachelor's degree from the University of Virginia. And with that, I will give the floor to Marianne or Romina to get today's webinar started. Hello and welcome. Hey, thanks, Shannon. It's awesome to be back on the diversity virtual stage. And I just want to give you a shout out for being the coolest pre-webinar DJ in the business. I totally love the vibe that you said every time when we're hanging out and waiting. So thanks for that. I'm completely excited to share Romina and EON's story with you today. But just before we get started, I wanted to just share a little bit of context. Nothing on this slide is a surprise to any of the data ops professionals on this call. In fact, every major enterprise customer that I work with has the same exact problem and that data proliferating from everywhere. They don't know what data they have or where it is or who's using it. And one of the things that we find so interesting is that many of these really big enterprises grew up through M&A. And for those of us that have been involved in a lot of M&A in our careers, we know that the last thing that usually is ever integrated deeply are data stores. So the question becomes, how can you leverage data to grow your business? Well, in February, we did a study with our good friends at Data Diversity here called the State of Data Governance 2.0. This is our second of these in the series. And what was really interesting is that respondents said that data governance initiatives in their business were being driven primarily by the need for better decision making. And the survey was done really just before COVID changed the landscape and business models around the world as people started to shift to a new economy. So more than ever before, your company's data needs to inform every decision you make as you bet on the future path for your business. But what stands in the way of leveraging that data? That's one of the questions that we ask. And the answer really is automation. It takes too long to find the data we need. It's hard to say where it came from and how it was transformed along the way and who knows how relevant that data actually is to the business. So our perspective at Irwin is you need to build an automated data pipeline that drives decision making through people, process, and obviously killer technology. Because at the end of the day, data readiness is everything. And it's everything because it powers everything you see below the line. So you want to improve digital experiences, have better person and virtual relationships with your customers. You want to make sure that you're spending less time in data preparation and more time in insight. You want to drive digital innovation as people are looking to reinvent themselves in so many different ways in the current world. And then last but certainly not least is none of us can do it alone, especially in the state's economy. So being able to build digital ecosystems is really key. And data readiness really drives all of that. So we're going to turn it over to Romina now. And she's going to tell you all about Eon's journey with data readiness and all the amazing things that they did in 18 months, which for those of you who are sitting in the chair like Romina has is a really short period of time. So I'm going to just click over and Romina, I'm going to ask you to tell everybody a little bit about Eon energy. Marianne, thank you very much for the hand over. I'm extremely pleased and happy to be here today and to share a bit of what we have been achieving, as you said, in 18 months. You already got a short introduction of what Eon actually is or a company or what we are about. So I would like to add up to some things which are possibly not written on the line. First of all, Eon has decided that we as a company, we want to get digital. We want to be data driven. We want to be data focused and everything that is written on the slide here to get a leading customer solutions provider to be the one who is basically implementing the smart grids. It's only possible if we really treat our data right and if we are getting digital. So there has been a decision by the board already now two years ago before I also started with my journey that we want to have data management and governance as a focus, which was extremely impressive. And I can tell one of the game changers in everything we are doing. So this is a very important thing. Now, besides me, I'm working on or responsible for data management governance. We do have several hundred, not a hundred, but we have about 100, 120 people working just on data science, data engineering, data management governance and we are growing day by day. So I can tell that they were not making a promise only 24 months ago, but they set this all to life. And I'm very happy to have the possibility to at least give you an insight on the part that I am responsible for, which is data readiness. And with this, I would basically jump into the next slide. So what you maybe can imagine if you are working in data management and governance or if you want to, this field is huge. It's huge and it's complex. And if you are then even talking about a company like Eon, which is huge and complex, it gets quite an interesting game, right? So at one point, you have to start. And what we basically started with was to understand what are our challenges. So we really wanted to go to the core instead of just trying to, for example, start with any kind of discipline and just randomly start with something. We wanted to understand what is our core challenge and then from there start to derive what we want to do. Now, what you see here on the left hand side are basically the core challenges that we have been detecting at the point in time that we started, which was basically that we do have a very limited access to the data that we have. As you can imagine, we are a huge company. We have huge volumes of data, but none of them or just very limited of them are actually available for us to create value out of them. Now, the second one is that there was no overview about all the data sources that we had. We did not really have transparency on where the data is stored and how it looks like. And although we did not have transparency on who's responsible for it. So this is a challenge if you really want to become digital. Coming from these two points, you're working with data is quite inefficient, right? You have to spend 80 percent of your time in just getting the data and trying to understand it, trying to get the quality improved and with this to really create value. So effectively, we have amazing people which are spending 80 percent of the time basically being frustrated because they cannot work with the data. And then another point that obviously for most of you will be also very relevant is the GDPR compliance. So we started shortly before GDPR has become into play and we are still working on improving ourselves and being more efficient and even better in handling the compliance of GDPR and to have GDPR as basically a key part of what we are doing in our daily business. And now last but not least, as we are coming from a very regulated business and the good business is basically regulated as you can imagine because we belong to the fundamental needs of society. We have to be very careful with what we are doing with our data because it can have an impact on you guys. If you don't have electricity anymore because we are not protecting our data properly, this will create some struggles, right? So coming only from these five examples and you can possibly imagine that the number has been growing over time of the challenges that we detected. We basically derived two work streams. The two work streams are technical data management that's basically cataloging of data and understanding the schemas and then data governance. So to set up a framework that does clarify responsibilities, put in policies, defines processes and so on and so forth to really make things happen and not just to talk about them. And the combination of the two is what we have been detecting in metadata management. So metadata management was basically the discipline that we decided to go for. And taking it from there, we got one step further that I will describe in the next slide, which is we had to find the right technology and the right partner to make all of this happen. And what we did was we started the journey of nine months. So we really took our time, I'm honest there, to talk about or talk with 11 suppliers in which we basically just got four on the shortlist and then two into a proof of concept. So the proof of concept was basically a real challenging one. It was not just testing functionalities. We basically tested the technology that we were looking for in a real-life scenario. We connected our resources and challenging one like SAP, which are not easy to handle. And we solved real-life problems. Every person which has been participating in the POC and these were coming from far countries had had a problem solved in the POC, right? And this is also what I will later talk about was giving us a huge buy-in for the later stages of the whole program. And based on this POC, we did evaluate technology, but we also evaluated partnership. We basically also looked into whom can we or should we choose who does have the same commitment and same ambition of what we have, what you would see in a minute. Now, as we are a quite economical company, our whole decision was based on a total cost of ownership. That's what PCO means. That's basically best practice for having anything that you want to judge quantified. So it's 100% price-focused. And the four components that we have in this total cost of ownership were the license cost, I think, very straightforward, hosting cost very straightforward, POC results, so basically the judgments of four countries and plus us and last but not least, which was the most challenging one, the cost of implementation. So we calculated already how much it would cost us to implement the technology. Now, as we have reviewed these four drivers of the total cost, we did the selection and we basically with this made a very good choice for our journey and we had the fundament ready to basically start off and achieve what I'm going to show to you in a minute. And obviously, as you possibly can imagine, it was urban that we have chosen. So yeah, exactly. Having said this, I want to give you some numbers because this makes the whole thing a bit easier to understand and to understand the dimensions. But before I now go into the numbers, I would like to say that when I started this job two years and nine months ago, I just checked it today, there was nothing and there was literally one person who was working with it and that was me. So quite a challenge for one person. Now, what you see here is what we achieved throughout the journey and mainly in the last 18 months. Let's start with the numbers and the effects and figures. So we did calculate only from 10 use cases which is close to nothing if you think about a big company like Eon that we can derive a value of eight million euros by implementing data management governance and specifically here was a strong focus on data management and data governance. We did onboard 10 legal entities and to make it clear, we are talking about onboarding 10 companies to the platform, legally independent companies which all have a different culture, which all have a different mindset, which all have different processes, different organizations, different maturity, but they are all in and we are actually very, very happy about that. We do have nine regional units, there's nine countries which are having data quality initiatives ongoing and we do have in 67 systems that we already have connected to our platform and where we do have full transparency on. Now, as I said, we started with one person, which was me, we are up to 169 at the moment and we are actually growing day by day. So this is really something that we are also very proud of. And obviously NASCAR, for please, we just started to investigate into data modeling and data quality and we also started to develop our first common data models, which are three at the moment. Besides these numbers, I would like to emphasize three additional points that I think have been game changers in the whole process. The first one is that we did implement a global team of experts and a chronic experts. So guys who really know exactly what they are talking about and I'm basically responsible for this team. We also developed our own framework and assessment methodology to say on what maturity level our organizations are. This has two advantages. One is you can give them a very clear guidance. They can basically read the framework and they can just move ahead and they can just be successful. And secondly, you will be able to compare and you will be able to show them if they are progressing and thus you help them simply to know where they are and to move on. And then last but not least, we developed a methodology that is to calculate the value of data management governance. I'm not sure how far you are in your journey, but I could imagine that you have been facing challenges to calculate the value of what you are doing. We have done the same or have had the same challenges and we found out a methodology which allowed us to basically overcome these challenges. Now let's go one level deeper into the next area which is metadata management. So this is basically just one level deeper. Metadata management is in operation since beginning of 2019. So beginning of last year. As already mentioned, out of the 10 legal entities, eight of them are already using the platform and they are coming from six different countries. So that was very fantastic that we had the chance to have their buy-in and I can tell you what you maybe can imagine. Everyone who participated in the POC is now using the platform because they are convinced because they were part of the decision-making process. Now, what is coming at the moment is we are onboarding new legal entities. We have another three in the pipeline that are currently onboarded to the platform. So this is not just a result, this is not the end point. This is the starting point and it has been already quite a good journey so far. Now, I already mentioned that we did have or we have 67 systems connected. To give you some examples, those are covering technologies like SAP, like Microsoft Dynamics, like Snowflake and also like Salesforce. So also the core technology is that possibly one or the other of you is using as well. We did define 44 data domains. I assume all of you know what it is, but still a data domain is basically a customer of data. It could be customer data, it could be billing data. And we defined 44, which are mainly driven by Romania. This is basically our flagship. And besides these data domains, we did train 175 users on the platform. Now, to give you a bit of a dimension, we are going to train the next 50 in the next three to four weeks. So again, this number is growing and we are onboarding and training people day by day on this topic. And then we did also start to work on the semantic layer, so a common language. And we have defined, I found it in 50 business terms that are in our platform and where everyone can have access to and which are coming from various different data domains. What I would like to emphasize here besides the numbers is that we are also using metadata management as knowledge management. Because if you cover me or if you document metadata, you document knowledge of people. And if you start to understand this, it's extremely powerful. And the left down part on the slide, you basically see one example of the calculation logic of or basically documentation of KPIs and the management of KPIs. In the middle, what you see is that we are not only, let's say providing possibilities to use the platform, but we are also providing the possibility to understand how your usage and how your development is increasing. So we have a standard reporting and monitoring for all the different units that are using the platform. Now, what does this bring as an advantage? They know where they are. They know if they achieve what they want to achieve. If not, they know where it's coming from. And they have something to go to and to show off to the board. And it's also used for basically showing off values. So it's very powerful. Then last but not least, and this also leads a bit into the next slide, we did develop an operating model that is basically defining how we are working together on the platform. And you will see more details here now. So metadata management is fascinating and cool, but it all doesn't work without the proper governance around it. So a key for implementing any discipline in data management is the governance around it. So to basically define some rules and framework for it. And this is what we did. We did the same. We started from the whole thing 18 months ago together with the implementation with metadata management. We started very easy by just saying we do have four common roles. These roles are aligned with all the legal entities we are talking with. And these roles are basically what we say is what you need if you want to get started, not making it too complex, not making it too difficult. And now I jumped one level down to the 50 where I say we have 50 people by now nominated and reallocated to these roles. So we have data owners, we have data stewards, we have local data governance leads and we have technical data stewards. So 50 people in the organization have been allocated to these roles and which are actively daily working and improving the management of data. And now I would like to give you a bit of an idea on how these 50 people are split. There we go to the left where we have the 15 business units which are basically teams where the people are split over. So 15 teams are basically having 50 people which are actively managing data every day. And besides those five countries that are actively already practicing data governance, we do have another three and now we're in the top right corner, units that are having data governance as a top priority in 2020. And that means basically also here, the number of entities and the number of countries and the number of people is growing. So, and then last but not least, to basically bring all the words together, we do have an aligned data governance and data quality playing book which is giving us a bit of more of a frame than just the roads. It's also a way of working. It's on processes, it's on policies. It basically helps us to bring all the different worlds together. Now, besides the numbers that I've just explained, we also have implemented various different processes and that we have needed. For example, as I said, we had a challenge in the beginning to get access to data. So one of the first processes we implemented was how to get access to data and how to make data available. And if you imagine this kind of process, the next step was to say, okay, someone needs to be responsible for giving access to data. And this is also, for example, how we derive the roads. We knew that the data owner is the absolute key role of everything we are doing. So the first step was to understand what processes, policies and so on we need. And the next step was then to define the roads. And the next step after this was to get people on the roads to bring access or provide access to the data just to give one example. Now, what we also did was to develop a very streamlined overview about how we measure ourselves overall. Now, I've mentioned that we started off with metadata management and data governance. As we have been reasonably developing quite well, we increased our field to having nine different work streams which are, for example, also covering data modeling and data quality. And what you basically see here in the overview is in the program overview is the three major pillars and two key KPIs for each work stream that we are working with. So again, simple but extremely effective and very easy to understand and to measure. And with this, we are basically able to steer an organization which holds at the moment around 170 people. So that's very important. And then on the right-hand side, I had to share it because I really like the design. And I don't know if my colleague Frank is in the call but he did this and he's amazing. And this is basically our draft of a real book, how to book for data quality. And this is actually going to be printed and given to the people to simply understand why they should call us now to start working on the quality of the data. Very simple, also a bit of marketing in there for sure but something that is going to be very helpful in our development and also making people aware of what we are doing. So to summarize it all in all, we are really proud as Ion of what we have achieved during the past 18 months and also actually three years that we have started to work on the topic. And I'm extremely much looking forward to the next 18 months. I cannot wait to see what we are going to deliver in those and possibly I'm going to be back at that point in time to tell you all the crazy madness that we have done through that period of time. Crazy madness. It's really interesting because this is not the first time I've seen your presentation clearly but as I sit here and I reflect on all of the things that you've accomplished and the data to back it up. The fact that you have all this reality about what there was before, what there is now. I think that you really have a tremendous level of expertise to share with the people who are listening and it's been interesting to watch the questions flying around so we'll get to those. But what I wanted to do next was just between us girls. Talk a little bit about the tough questions. So you accomplished a lot and it was a really big undertaking. So I just want to break it down because so much of the time when we talk about the technology and we don't necessarily talk as much about how the technology needs to get implemented and installed in the operating system of the business. So here's my first one. If you had to pick one thing, Romina, one thing because I know that there are many what do you think was the biggest challenge in undertaking the project? I think the biggest challenge was change management. That was the people to convince them to make them feel good, to make them get part of the journey. I think the factor of the people and the change management is something that is very, very often underestimated if you go into let's say a project or program like this. Yeah, that's something that we see. In fact, I said last week when you and I were rehearsing that we should get your t-shirt that says change agents. So follow up to that. I always wanted one myself. I have to have to tell you honestly, follow up to that. Technology or culture, which was more difficult. I pretty much think I know the answer. Yes, so definitely culture. I think technology is always something a bit logical and you can kind of understand it. I love people. Don't get me wrong. I love change. Don't get me wrong. But the culture is something which is more complex and which is more diverse. Yeah, and so when you think about the culture piece of it, let's break it down. I think one of the things that you guys did a tremendous job about was you described in the POC, right? Having the four countries and a lot of different people engaged and involved so that when you brought the system on board, it would be all good and people would start to adopt it. So tell me a little bit about that thinking. It's an investment that you take and it's a big investment. It's an investment in listening and understanding and communicating and then not stopping to do so. It's not a one-time job. You don't talk and listen and understand the people once. You do it every day. So what we did was we really did not only incorporate them into our POC, for example, but we are talking to them every week and we are also listening to them every week. And out of what we hear, we try to really, really develop something. And this is basically, I think, an extremely good fundament for a very trustful relationship. So even if you fuck things up and trust me, we did that. So we are not superheroes. We are a bit, but not enough. So, but when you talk to people like this and if you are such close to these people, then they are fine with also understanding that you are just a human and that you just do the best you can. Exactly. And I think one of the things that drove the way that you approach the culture was the fact that, and we heard about it before, that the board level, as Ian was looking at itself and looking at the transformation that it needed to have, that they drove a lot of this thinking that data has to be in the middle of everything that we do to change from a legacy power company to some new imagined version of the company that you are today. So can you just talk a little bit about that? What was the communication like with senior executives? What was the cadence? What kinds of things did you guys give to them to see? Because obviously you continue to get tremendous support in terms of the growth and the expansion of the project, which is, I think, another incredible thing about this story. It didn't just end up in one little silo. So tell us how really that engagement at the senior level helped. So I can tell that maybe, or I'm not telling, but shortly explain a bit the structure of Ian. So we do have one big board that I mentioned, which is had the buy-in and support of what we did, but we do have several hundred legal entities. So that means we have one common board, but there are also subboards in a way that have the full power about their legal entity. Now that means that it was not enough to just convince one board. It was important that we had to convince the board of each and every legal entity to really go and to really invest into the resources and into the topic. So the communication was always bottom up and top down. We had the full buy-in and backup of our boss Juan Moreno, who is responsible for data and analytics in general. He was going out and spreading the message like crazy. So this was top down convincing the board on his level. And on the other hand, we did have the people who were screaming for help, who were just in a frustrated, which were not able to save the problems and that coming from top and from the bottom, there was almost no way of saying no. And then take the 8 million euros into consideration. How can you say no to this? This is just from a rational perspective as a senior manager. You cannot say no to something which is so valuable. Absolutely. There's a lot of questions that people have around your 8 million euro number, so we'll get to that. Again, picking one and they know it's really hard. What was the biggest surprise? Like what is the thing that you learned that you never expected you would take away from this whole journey? I would literally say how complex the whole thing can get very fast. And I remember when I started that everyone told me what you are trying to do is impossible. It's too complex. It's not manageable. And I was always just saying, thank you for the motivation. I've just gotten to this new role and I'm very happy that you are motivating me that much. And now it's three years later, almost, if we remember when I started and I made it. And the complexity was there. And yes, it was shocking. And they were right. But it was manageable. And I'm sometimes seriously speaking still surprised if I listen to myself doing this presentation that we really did this. So yeah. Yeah, that's really interesting. And obviously, they didn't know you well enough to know that I don't know how you say throw the gauntlet down in German, but that was exactly what they did when they told you that it wasn't possible. So you've done big things, short period of time, lots of buy-in, lots of people engaged, things are starting to hum along. What's the next big thing? What are you guys going to tackle in the remainder of this year into next year? And first of all, how far are you looking? How long term is your plan now? Is it an 18 month plan? Is it longer than that? Let's talk about that first. So we do have a very easy way of planning in a way. So we have a 100 days plan always. That's part of our company culture in the IT as we are working agile. So we are having 100 days plan. And then we are breaking it down in sprints. And then we are basically developing topics. Now, this is for my operational part. As you can imagine, I have various different kind of challenges. I have the operational part where we really do things. I have the tactical part to really get people convinced. And I have the strategic part because I have to decide where to allocate my resources. Now, the strategic part is almost never-ending story. I have a very strong vision that I want to achieve at a point in time and this can take us 10 years. Though I'm breaking it down into priorities, which are tactical to see where the biggest pain points are and where to start with because the people are screaming the most and those who are screaming are willing to work. And then from this, we derive the 100 days basically. So what is the next big thing? I mean, how would you describe the next big hill for this project? That's data quality and data modeling, both of them, because they interact with each other. So we want to investigate into this and we want to further build up capabilities. We are already quite knowledgeable. We are currently developing the technology around it. So these two are my next challenges I want to solve. Yeah. And one of the things that's interesting about you guys for Irwin as a customer is a lot of our customers go from data modeling to data intelligence. And you guys are going from data intelligence to data modeling. So it's just an interesting journey, but we love that. So tell me how much or has COVID changed any of your priorities or did new initiatives jump onto your plate over the past 12 weeks? Anything different there? Absolutely. Yes, it's different. We have been working already quite remotely beforehand because my team is spread all over Germany and the whole organization is spread all over 12 countries. So we have been working remotely, but we have been always in person-persons. So we have been traveling a lot. We have been visiting the people. We have seen their faces. They have seen our faces. And we always had a good drink together. So that's just a joke. But we have been really close to the people. And especially because of that, we were afraid that we would rather slow down or all these new units would not really be getting part of the community because we cannot incorporate them as close and intense as we used to. It's really interesting. No, this didn't hold true. So we did make ourselves comfortable with the situation. We started to use tools like Mural to make really intense workshops. We really kept on communicating. We kept on being close to the people and we kept on being just with them. So I would say, as people had time to think, and as they possibly were a bit bored, but they had time to think and to focus, we even got a higher priority than before, especially as now we were all depending on digitalization during the last 12 weeks. Yeah, I never really thought about the impact of the face-to-face and all of that and how different it is to do a workshop like that when you're on the ground in Romania, versus being on the phone or on video. So it's good to know that it's translating for you guys for sure. So before we move into the sort of final moments where we take some questions from the live studio audience and these guys are live, I love a day diversity audience, what advice do you have for other people? So if you've got somebody who's listening right now and they are where you were 18 months ago and they're staring at a lot of chairs, a lot of initiatives, some of them competing, people that don't necessarily agree, a non-data-driven culture, what do you tell them about where they should start? I tell them what someone, I mean, I would have looked if someone would have told me and this don't hesitate. Don't be afraid, have a vision, know what you want, know what you're talking about. So go into the details, understand the topics and then go, walk, try it. Don't try to work once on a plan. Follow a bit your instincts and watch, listen and observe. So just never stop and never even let the thought come to your mind that it's not possible because it is, it's just a challenge. And always ask your vendor partners to do the impossible as well. Yes, that's absolutely true. This is the commitment we did together 18 months ago that we will make this together. And I can tell we are really demanding and we would not have been able to achieve all of this without you. And we'll look, one of the things that makes us special I think is that, you know, we believe in co-creation with our customers. And so that's what this experience was for us. So thank you so much for that. I'm just gonna finish up a little and then we're gonna turn it over to Shannon and you can start to take some of these great people's questions. So just to sort of round it all out, you heard Romina say all of this, but you know, essentially the big thing is about building a data genoculture and that's certainly not an overnight 18 month two year. I mean, it's a project, right? So, you know, one of the biggest things that you heard Romina talk about was time to value, right? She brought time to value to the organization as something for them to pay attention to, right? They could make decisions faster based on the data that she could provide them access to. So that was a really great way to get them completely built into what was going on. And then, you know, it's really also about making sure that, you know, there's company-wide data compliance. That's sort of a, you know, table stakes. But I think one of the biggest ones, and you know, Romina talked a lot about this too, is that we gotta demand insight based on data truth, right? For a long time, I would go to a board meeting and I would sit with my opposite member who is responsible for global sales and we would have two different sets of numbers, right? In a day of the driven culture, that can't happen. We all have to be making decisions based on the same, you know, the same insights, the same data and be able to point to, you know, where it derived from. And then last but not least is, you know, what we're starting to call it at everyone's social data governance, right? So we've got to be able to foster the collaboration across all of that to, you know, to keep this an ongoing project. I think one of the really exciting things about what Eon has done is they have built a business operating system installed it effectively into their business and it's something that's just gonna continue to grow and not just be a monolith over time. And that's great for the people who are, you know, working for Eon. That's great for the people who are customers of Eon. It's great for people like us who are vendor partners at Eon as well. So just a couple of things to say, hey, take a look at. I talked to you about the state of governance and data automation report that we did earlier and we'll send you out some links to that and lots of other really great content to visiteveryone.com. And we also will be participating in DataVersions Demo Day coming up on July the 29th. So I am going to now turn it over to Shannon. Thank you both so much and thank you for this great presentation, Romania. You definitely have some fans out there in what you guys have done and accomplished. And just to answer the most commonly asked questions, just a reminder, I will send a follow-up email to all registrants by end of day, it's Thursday for this webinar with links to the slides and links to the recording and anything else requested throughout here. So diving in, you know, and maybe you covered some of these questions already a little bit, but just want to ask it a little different way and maybe a little different answer. You know, what are the challenges of rationalizing the business terms from the different legal entities? So let's say before we basically took on the challenge of rationalizing them, we asked the people to think in their own world and to think in their own language, which both basically saying they were just creating the business terms however they wish to. We gave them a bit of kind of standards and frames, you know, what we want to know and what they should define for it. But in the end of the day, the creation wasn't the first step, very free and really creative. Now, at the moment in time that the first two, 300 were created, we didn't even have to like really convince them because they were realizing that they created the business term for address about 15 times. They created the term for customer about 15 times. So it was really like, you know, obvious that there is a harmonization needed. So what we basically did was we left the field and we said it's a green field, go for it. And then whenever we realized that some business terms are very similar or they are just repetitive, we started to create global business terms out of these. So that basically means that throughout the work together with the units, we had a differentiation between local and global business terms, which have always been aligned with the units directly. Now, looking further down the road, when we will have even more people working on the topics, we are going to also implement the Data Governance Council and the Data Governance Board. And the Data Governance Council is basically going to bring all these people together and make them discuss about the specific business terms. So it's going to be part of the work of them. And the participants in this council, I think there's going to be one of the questions which would come afterwards, are going to be the data owners. And the data owners are creating the business terms and then they can discuss together with other owners which have the same data domains as base and basically also align on common definitions for the business terms. So Romina, how will they all interact with each other? How do you perceive that going? Is that something that you'll do some sort of a virtual conference on a quarterly basis, plus it'll be addressed in daily workflow? What's the vision for that? So basically the single point of tools for everything is urban platform. This is where all the business terms are already checked by now and they are going to remain there. The whole interaction and collaboration can be done through the platform. So you can basically on a business term level start a chat with the data owner who is directly allocated to the business term and basically to the discussion there. If there are conflicts coming up then these are going to be brought to our team. So to the global team and this is going to raise them to the Data Governance Council. The Data Governance Council is in the beginning only going to take place every possibly other month but we do not have it implemented yet so it might change. And obviously it's going to be a conference because we have people from at that point in time possibly nine to 10 countries participating. Fabulous. So how have you measured the impact business of all this implementation? So if you look into Data Management and Governance it is hardly possible to derive a direct impact on the EBITDA and on the PNL. So this is a common challenge. What we did was to still look into impacts through cost reductions. That's basically by reducing storage costs by reducing costs for data quality corrections by people by reducing costs of external suppliers who are doing jobs for us. So this is direct cost impact PNL. Then we do have a principle in EON which is called operational excellence. And this is basically stating that you should work as a leader day by day on making your people more efficient. And there's a common way of calculating it and we are using this productivity increases also as part of the impact value so value calculation that's only value then. And another possibility is the avoidance of fees for GDPR in compliance which could be also used again value calculation. So these are just three of what we use to measure the impact. Love it. So what are some of the challenges you came across specifically for establishing data quality framework and how have you overcome them? They are still always challenges and never stops. Okay, for data quality actually wasn't the biggest challenge. If you look into data quality as data governance and data quality is a sub point of data governance then it's more challenging. But I assume you look into data quality management only. A lot of challenges that defined in the business are coming from data quality issues. Let's take following example. You want to contact your customers through email instead of letters. If you don't have the email address or the email address is incorrect you cannot contact them via letter and thus as logical consequence you cannot save the money that you would want to save by changing from letter to email. That's a very simple example which makes it very easy for us to convince people to work on the data quality. Now, if you make this a little more abstract that means you simply do data quality based on issues not because it should be done. So basically say that you take a concrete challenge in the business that is based on data quality and then you start to work on the topic. So Romina, let me just toss in there for a second because that was a really interesting way that you put that is you take a business challenge and then you address it with data quality. I mean that's very much one of the things I think that makes your approach to this project different. In general, because you guys didn't just develop some esoteric concepts about how data governance should work. As you talked about before and I wanna call your attention to the fact that you started out with 10 use cases, right? And that was just the tip of the iceberg. And those use cases were derived in the impediments that people had to being able to use data in the business every day, right? So I think that that's a different way of looking at things and probably one of the reasons why you got so far so fast. I was 100% agree on that we went mainly bottom up. We always started on the use case base. We did never try to just force people into a framework. We always solved their problems with what we were doing. And I think this is a crucial, crucial, crucial point in being successful in this. That's also where I fully agree with what you just said, Maryam. Yeah, interesting. Back to you. I love it. And I agree. So what was the size of your data government and implementation team and what were the roles of the team? So this has changed over the last 18 months. I started alone. Then I got one person who was basically my technical heart. He was amazing, an architect which was very, very strong from a technical perspective. So to summarize it, not to go through each person, we were growing constantly. And we were, after the first three months with two people, after the following 12 months, we were five. And in the implementation phase itself, we were nine. And we are still growing. And sorry, for the road, I always employed experts. So I had one expert for data governance, one expert for data management. I even stole one expert from Urban. We had an expert for architecture, as I said, and then data quality is now the latest one that we got into the team. Love it. So what was your principal reference to build your framework? So the framework is based on research, based on the Gardner Information Management Maturity Model, the Stanford Data Governance Maturity Model and the Dahmer Data Management Maturity Model. These were basically the ones that we have analyzed in more detail. And then we took about 78 books. I can give you a list of references as a base for the development of our own framework, which were from, and not only data management books. It was a lot of change management. It was about IT visions and strategy. It was a lot of different sources, but obviously, as you can imagine, major ones were from the field of data management and governance. So I have a question about that, Romina, because I know that one of the really great things that we co-create with you guys is the flexible metamodels that our early data intelligence platform runs on. But why didn't you make a decision not to just say, we'll use the Stanford framework? Why did you decide to triangulate them and then create your own? You know why? Because you always want to be in the lead. And no, actually we do it and we create our own things because we really want to make a difference. And to be able to make a difference, you need to be flexible. That's what the new metamodel is allowing us with M to N relationships instead of being a bit more static. And we really want to dig deep and we really want to understand what we are talking about. So even if we would have taken just one of the standards model of the market, we would have still invested those time to really understand all of the context around it. And for the metamodel specifically, it was allowing us to do knowledge management and not just metadata management. And when we were looking into the topics and we were having this discussions and we were incorporating all the feedback from our units, they were all screaming for a flexible metamodel. So it's not only us, it was Eon, everyone who was participating in this journey who was basically wanting us to change. Talking more about the analysts again, is the KPI in this solution for data governance only for the entire enterprise scorecard? Sorry, I'm just checking the question again. Yeah, so the guy's got it. I just had to read it again. Sorry, I have to, it's, you know, anyway. Though this KPI, and I assume that you mean the 8 million are covering all the different disciplines, but the use cases were mainly coming from metadata management data governance and a lot also on sensitive data discovery, which is basically GDPR compliance. So that was the major drivers. So yeah, it is covering more than just data governance. Yeah, but so for, are you measuring, are those analytics that you're measuring and for the data governance success, are they only enterprise-wide or are you looking at individual groups or organizations or, so they are based on use cases, the very calculation is valid per unit which has provided this calculation. So the calculation method is what we develop to feed it with content is what the business did. So every number in this 8 million is based on a concrete use case that has been formulated and calculated together with us in the business. So it's spread over the enterprise. And really they were a multiplicity of groups and countries, right? Country operations that work together on that original stuff. So it was definitely beyond just a centralized group. It had really spread into the business. Yeah, absolutely. And we did this interactively. So we calculate the value or we start off everything with value workshops where we basically get into the talking with people and we really do it together with them. We don't do it ourselves. Everything that we calculated is signed off by them because they have been part of the calculation and we have talked to the people. How do you face roles definition challenges? By allowing a bit of flexibility. So let's take the role data or not. No one wants to take responsibility for data. Everyone is scared of it, especially if you want to make them responsible for data quality because they always think it's too much work, I can't handle it, I don't have any clue how to improve my data quality, I don't want to do it, I don't care. Now this is typical and this is completely human because it's a matter of change and we all know from change management that it's normal that people are scared. So by talking to people, by I'm saying this very often but I truly mean it, listen, talk, convince them and help them define what it means. Show them that it's only 10% of the working time. Let's say we took the first data owners, we did a huge kickoff with them, for example, Irvin was all participating. We were spending two days just to show them what they shall do in the future in the daily job. So we really onboarded them very closely, we showed them the job, we showed them how to create business terms, we gave them like the guidance and we had a local data governance lead who was amazing, who was helping them throughout day by day. We have our experts in my team which are always people that you can reach out to we are every week talking to them still. So yeah, how do you define roles by making them happen and by making people feel comfortable being in this role? And then the definition almost doesn't matter any longer. Very impressive. And I think we have time to slip in one more question here. How did you go about defining agreed upon data domain and identify their quote unquote data owner? So again here, Greenfield, the guys and the units can always decide by themselves how they want to spread the data domains. Usually they found this really challenging they don't know where to start and where to end. Now, and what is also the case is that a data asset, a concrete one, technical one can actually be allocated to more than one data domain because it's used in various different processes in the organization now. So it's not easy, but usually it's very easy to basically start it off from looking into the business unit. So how is your organization structured? How are your team structured? And usually you might have a team which is responsible for customer contacts and you might going to have a team which is responsible for the billing and for the processes behind that. And then step by step, you start to make people simply responsible for what they're working with every day. So basically in the data domains, we didn't harmonize them. We just left them quite local and we basically left them in the structure that fits into the organization with the major reason that we wanted to make people only responsible for what they are actually really working with and that they do not have to spend the whole day just talking to other people to do what they want which is impossible if they are not just directly working with you or for you. So that was basically the way we did it. It was quite business-focused how the data domains have been developed. And how to find the data owner, the team lead, made it quite simple. But there might be about 500 different grades of doing it. So I wouldn't say that this is the only way. I would just say that this is the way we basically went through a fall for our flagship Romania and we basically encourage all the other organizations to go for the same approach and they are also basically all running into the same direction there. Well, Romina and Marianne, thank you so much again for this fantastic presentation and information. It's just been very valuable. Again, you got a lot of fans out there who are soaking in everything that you've been talking about. And thanks all of our attendees for being so engaged in everything that we do but I'm afraid that is all the time that we have for today. Again, just a reminder, I will send a follow-up email to all registrants by the end of the day Thursday with links to the slides and links to the recording of this presentation. Again, thank you all so much. Hope you all stay safe out there. Marianne and Romina, again, thank you. Hope you guys have a great day. Thanks Romina. Bye everybody. Thank you for your time.