 Hello, everyone, and welcome to our next EW-SAMFAM called Developing a Data Governance Roadmap that Generates Results. This presentation will be presented by David Woods and the Executive Vice President Strategic Services at InfraJocs, and Colleen Henderson, the Senior Director of Enterprise Data Governance at Paragot. All audience members are muted during these sessions, so please submit your comments and questions in the Q&A window on the right of the screen throughout the presentation. And Dave and Colleen will respond to as many questions as possible at the end of the talk. Please note that there is a link from the bottom, linked form at the bottom of the page titled EDW Conference Section Survey. This is where you can submit session feedback and we encourage you to do so. So let's begin our presentation now. Thank you and welcome Colleen and David. Great, thanks so much for the introduction, Laura. Welcome, everybody. Just to provide a little bit of context about what Colleen and I will be discussing here today and provide a little bit of introduction to ourselves personally. Colleen and I are going to take you guys over the next 45 minutes or so through a somewhat academic exercise, working through how proven practice, successful programs have initiated, operationalized, and then got efficacy of their data governance programs, but we're going to do it in the context of a real use case. So kind of that balance between the core constructs and core components and the actual use case examples that Colleen will take you through that she's really successful and delivered a lot of value to power go through today. So as part of that, we're going to do a little bit of a deep dive into the core components, provide some examples, and we look forward to the conversation and some Q and A afterwards. As Laura indicated, my role at InfoJix is the Executive Vice President of Strategic Services. For those of you who don't know InfoJix, we're really an organization at its core that helps companies control, manage, and then really optimize and get value out of their data assets. We do that through a series of applications within a suite of technology platforms called Data360, and then really add on to that with some strategic implementation services that really help organizations take advantage of the technology and really drive meaningful results. So really, really happy to be joined by Colleen Henderson, who I've known for a long time. One of the unique things about Colleen and I are, we're both our preseason data practitioners. My past experience, I initiated and ran the enterprise data program for Johnson and Johnson. As part of that, I did meet Colleen through some peer relationships. Colleen also has, as she'll explain here in a second, some deep practitioner knowledge, and we've learned some lessons over the years, Colleen. I'm sure we'll agree, and we hope to share those for you guys so you don't get bloody going through the wall like we did a decade or so ago. So with that, Colleen, I'll turn it over to you. All right, thanks, Dave. So welcome, everybody. You were able to join our event today. I just want to give you a little bit of background around the Parago, and many of you probably know Parago, but let me give you a brief overview of the company, and then I'll tell you a little bit about myself and my role within the organization. So Parago is a leading private label provider of quality, affordable, self-care products. We not only provide customers and consumers with much needed for-the-counter health wellness solutions, but also serve as a leader in our industry. Many of the products that you see on the stage you may have purchased at your local retail stores, if not, please do so, or look for these products and purchase them, but some of the products include acetaminophen, ibuprofen, cough and cold, osteoporosis, et cetera. I am Colleen Henderson, and I'm the leader of the Enterprise Data Governance Parago. I joined Parago in October of 2019, and as Dave stated, my career has been built around data management and data governance, and I have a strong passion for data and the importance of data to drive a successful organization. I worked in other CPG companies and healthcare industries, so my name may sound familiar, maybe not tried Parago, but from other companies, and I've implemented a data governance program in two of those other industries. What I've learned is not all implementations for a data governance program are approached the same, but the foundations and the methodology are in approach to all that. I was hired at Parago to start a data governance and implement a global data governance program, which was one of the top 10 transformation in touch at Parago, which is phenomenal to have this type of level of awareness around data. So the first hurdle of a successful program was already underway and that is that leadership adoption. As Dave said, Dave and I have worked together many years and he and the other company have successfully helped me with the data governance journeys that I've been on. He's also helped me in putting in place the fundamentals of great programs, helped me with challenges along the way and provided knowledgeable consultants when I've needed them. The input company has a great methodology and they have some really cool tools. So if you're a data person and you need a way of doing your documentation tools and that has helped us and allowed us to be successful implementation. So Dave and his team are currently helping me with the implementation data governance program here at Parago, as well as the implementation of a global energy tool around customers and materials, vendor master and the finance master team. So today, Dave and I are gonna share with you that journey and the challenges and the wins and hopefully you'll be able to take it to the board. Dave, we'll turn it back. Yeah, terrific Colleen and for those of you who don't know Colleen, you'll learn over the next hour. So Colleen is definitely one of the most sought after voices in the customer community as it relates to data and data programs. So Colleen, why don't we just jump right in and why don't you kind of speak a little bit about some of the challenges that the Parago team and your team faces as it relates to the data and then, you know, how you've kind of transitioned that into some of the outcomes you expect and you guys have been able to realize as part of the initiatives. Well as you can imagine, some of the challenges are varied and pretty overarching. They also span a broad set of organizational things and functions. So I'm sure many of these challenges that you see on this page, you folks on the webinar, you're dealing with them and if you're not, you may come across them as you set up your programs. I've highlighted a few here that are most heavily influenced in our approach and the evolution of our organization. So the increasing the process and the system complexity, for us, this means being able to adapt to the ever evolving set of improved capabilities, including the deploying of leading edge processes and tools that rely on more accurate and timely data that we've never historically captured. It also means being able to seamlessly deal with more complex set of technology platforms. The not only involves our internal operations, but also our external network of partners that often vary from their data requirements. On the digital strategy side, our industry, but particularly in CPG organizations, digitalization is a primary focus and is driving new sources of competitive advantage growth and value creation specifically here at Paragro. What this means from a data perspective is that the digital representation of our projects is now important, if not more important than the physical product. It also means that we need to present and govern that data in ways that we haven't traditionally had to address. And to further complicate that picture, we now need to seamlessly integrate data sets in ways that we've never had to do in the past. The driving, so data driving driven insights, our leaders are demanding more and increasing differentiated information to make strategic decisions based on data analysis and interpretation. While leadership driven, it also means democratizing our data, meaning making data accessible to as many people as possible within the organization, which means we need to make it both available and appropriately governed to ensure that we're making the right decisions across the same data language. Product syndication, as many of you know, the amount of and the variety of data that we are now being requested to syndicate to our data pools and our retailers has exponentially expanded over the past years. In addition, the sources of information are no longer ERP centering, but rather come from a variety of sources that do not naturally align to the formats of what our retailers are expecting. MNAs and divestitures, after a short slowdown, many, during the early stages of this pandemic, the MNA market has been in overdrive and that's predicted to continue through 2021. Inorganic growth through acquisitions has always been part of Parago in our strategy and our ability to quickly assimilate organizational ways to come down to managing and integrating our data. The federated data governance, as we all are challenged with how to do more with less and improving our operational efficiency, it's critical that we build and enable an effective data network. One that ensures consistency, but also empowers the business to be accountable for that data integrity. This is no longer a nice to have for organizations, but rather high stakes for almost any meaningful transformation. For us, this is really evident in our go-to-market transformation, where improvements in speed and effort to market heavily rely on this model. So how do we get there and what are our goals to achieve those? So from a data readiness perspective, making our data adaptable to the changing needs of our organization and external partners becomes very important. With my leadership, putting more focus on data and data analytics and the implementation of new tools, my team and the data need to be ready for change and become adaptable to the implementations without breaking processes and other systems. It became more important for our data to be standardized and harmonized globally, and the MVG tool implementation is helping us with that. From an organizational alignment perspective, with the data governance organization on the ground and working with our key business partners on data concerns and the data projects, we have started to gain more trust in our data. We focused on the most critical data elements, identified some quick wins that helped us gain the data trust, as well as gain more support for our program. That data visibility, wow, I'm sure some of you have been asked these same questions. Where is our data? How did it get there? Why does it not match? Who owns that data? And why are we using that data? These are all questions that came up as we started to focus on data analytics. We had to identify the who, what, where, and why around our critical data attributes. Documenting these answers was just as critical and we utilized the InfoJax Data360, which Dave will talk about in upcoming slides. Data architect has also become very important for us as we expand our systems and change our processes. And then change. This is an easy one, right? Not, but it can be achievable. Here is where your quick wins become important. Align your message to your audience. Enable governance processes to improve efficiency and reduce risk. Every leader wants simplification and savings. Put data statistics together to show how you can help improve their departments. Keep pushing if you don't succeed at first. They will come around and they will adapt to the change that you have. So now let me go down and take you through our approach and talk about our challenges and our goals within here. So everything starts, everything for us starts with a common theme. Let's call it our North Star. And that's focusing on the value of our data. Using that as a key to our approach, we've followed and really continue to follow an approach that has proven very effective for us. Identifying focusing on the critical data. This ensures that everything we focus on is adding value to the organization and we're working on the most impactful things. Dave and I will go through this, how we do that specifically in a moment. Clearly align your data with business objectives and outcomes. Once we've identified our critical data, we need to make sure that we clearly understand the business usage and ensure that our efforts directly align with the outcomes they expect. As an example, the way we approach a business outcome for data driven by the timeliness and availability of data will vary by the accuracy of that data. Build a common business language for your data. One of the common pitfalls for programs is a failure to share a common data vocabulary. We can have the best data quality, but if the people aren't aligned on what that means and how it is used across the enterprise, we have not set up for a long-term success. You'd be surprised that even things that seem very straightforward, like the definition of a brand or a customer, can be misunderstood and misapplied across the organization. Establishing an operating model of data collaboration. Once we're all focused on the most important things and are aligned around common definitions, the next step is to ensure that we all know how to work together effectively to define our critical data standards and our business rules and ensure we sustain the required levels of data integrity. Think of this as the playbook for data quality and data governance. Once you know the playbook, it's much easier to align the roles of individuals that should run those plays. A common pitfall I see in other organizations is trying to define the organization's roles for the data first and then trying to figure out how they can fit that together. They really need to be done together. Again, Dave and I will share more on this in a bit. Define a measurement to model that drives adoption. At the end of the day, none of the above really matters unless we can measure and demonstrate value to the business and the organization. Everyone who has started a data program measures data quality in some way. What most fail to do is tie that to the performance measures and metrics that have meaning to the business. Data quality metrics are meaningless to the business unless they have context to the things that the business really cares about. I'll now turn it over to Dave who will take you through some of the details around how we operate. Awesome, thanks Colleen. And we're getting some really good questions in the chat already. So you're generating a lot of ideation from the group. And really, I think we're gonna answer some of them as we go through these core components. So Colleen mentioned that there are some nuances to every program, right? And there's even nuances within programs and there's nuances as you pivot and shift and kind of evolve your data organizations internally and certainly as you touch external partners. I've had the good fortune of kind of working in this space for over 25 years now and being able to share a number of customer councils work very, very deeply, both as a customer and now kind of as a practitioner here for the last 20 years or so with a lot of the analysts, the gardeners, the foresters, the bluers of the world, et cetera. And as part of that, I've really come to the realization we've applied this Colleen and I successfully now four times at different organizations, you know, where she's kind of journeyed through. At the end of the day, there's four core components that all successful programs have, irrespective and agnostic of their good and market strategy, their vertical, or whether they're multi-national or whether they're a national organization. And really at the end of the day, what technologies they even have in place. And I'm gonna start in the upper left and Colleen and I are gonna double click on these and give you guys some use case examples as we go through. The first is a governance framework. And governance framework to me tends to be a pretty misunderstood term. I mean, you know, anymore I can't walk down the street with a poppin' into some sort of a, you know, a data conference that talks about a governance framework. But when you really boil down a framework, I think people misapply and misuse what a framework means. And Colleen are gonna talk about that here in a little bit more detail, but at top level, your framework cannot just be around your data. It cannot just be around the tables, fields, and systems. Frameworks that add value and that top-down driven approach with that North Star that Colleen talked about and ultimately what delivers efficacy for your program and allows you to get continued funding, continued support, continued momentum, is being able to tie that data in your framework to the processes that create that data, consume that data, rely on that data. The performance measures in your organization that rely on that data for actionable insights and ultimately the goals and objectives of the organization. And we're gonna take you guys through what a good framework looks like and what you should start be thinking about either irrespective of whether you're just gonna set one up initially or you're gonna have to kind of expand what tends to be more kind of a bottom driven or table field driven framework right now. The second piece of that is around operating model. And when we talk about operating model, what we mean is we mean the playbook that you're gonna run and then the organizational construct that complements that and does allow you to be successful. As Colleen mentioned previously on one of the last two slides, one of the big failure points we find in organizations is their first kind of call to action when they wanna establish an organization is to try to set up a team. I can't tell you how many rooms I've walked into with C level executives or data professionals or data leaders and they say, well, how many data storage should I have? How many data custodian should I have? What should that look like? And we really need to say, hold on, TV time out. Like let's take a step back. Let's first figure out what plays we need to run. Then and only then are we gonna be in a position to really get the right heads and the right hats and to be successful. And again, Colleen and I are gonna talk to you about that. You know, I saw a question come in from, and I apologize if I'm pronouncing your name wrong, Irfan, around how do you tackle that business to IT bottleneck? And that really is the key. So when we get into the operating model stuff, that is what breaks down that paradigm and allows you to operate effectively with IT and sustain that relationship. The last one is around a decision tree, right? And out of all these four core components, to me and Colleen knows this, decision tree is the most important. It is a don't pass, go, don't collect $200, you will not be successful and let you have decision tree. And Terry, you asked a question about how do you go about identifying important data to manage? The decision tree is the answer to that question. Every organization, every single one that I've worked with or consulted with that has had efficacy of their program has applied a decision tree in a very, very consistent way, we're gonna take you guys through that. And last but not least is ultimately a metrics model. And the way we think about metrics, and this has been adopted by the analysts now over the past four or five years, which I'm really, really happy to see is really more of kind of like three phased approach that really connects your data value and your data metrics to business value. And Colleen and I are gonna take you through that as well. You'll notice before we move on, I did not mention technology, right? So technology is an important aspect and it's an important component of any successful program. But without these four, what I'll call non-technology centric pillars, if you will, any technology is not gonna be a silver bullet and it's not gonna solve your problems. So with that, Colleen, why don't we kind of move ahead? And before we double click into some of these core components, the first thing I wanted to talk it about is as you apply these four components, it's really, really important, not just when you're initially standing it up, but iteratively as you go along to really focus on things that matter, right? So if I take the bottom of this pillar, right? We have all this data in our system. So even mid-sized businesses or small businesses, less than a billion, up to two, three billion dollars, let alone large organizations, tend to have a lot of data within their applications and their platforms. You know, upwards of 8,000, sometimes up to 30,000 different data elements. A lot of those data elements just talk to each other and they're just used to get the systems running, but they don't really do anything from us from a process and analytics or reporting a compliance perspective. A subset of that data, you know, it's typically in about that six to 8,000 data element range is information that we use to conduct our business, right? Whether that's business process operations, analytics, compliance regulatory issues, brand protection, safety, et cetera. But there's a subset of that data that really is used in our processes. It's either created by our processes, consumed by our processes, or managed within and across our processes and systems. A further subset of that is actually used to measure our business, measure business performance. So that could be our key metrics, our KPIs, which would be process performance indicators, but the point is there's a subset of that data within our processes, generally about 6 to 800 different data elements that really are used analytically to help measure and drive business performance. And a further subset, generally only about 100 or 200 of them really drive actionable insights. They're the things that the C-suite really cares about. What's critically important for us is that as we organize a program, establish roles and responsibilities, operationalize a program, we are laser focused on the things that fit up the middle of that pyramid. The things that the executives care about that are driving actionable business insights that are paramount and is having good performance measures that are critical to enable our process execution and then focus on that data first. Once we get that right, then I can elbows out and handle some of what people call the low-hanging fruit. Low-hanging fruit isn't of any value if it doesn't taste good. So we try to go to the juicy fruit in the middle that the executives care about, orchestrate that through technologies once we've had the other components kind of set up. And then once we've had that, two things can happen. One, you get a lot of followership in the organization, but you can prove ROI and you get a lot of momentum. And then two, by definition, you're gonna be able to scale that program out to the other data elements, other functions, other business units, other regions over time because you've proved it with the things that matter of the organization, not just the ancillary kind of low-hanging fruit. So with that, let's kind of dive into when we talk about framework, what that means. And Colleen, I think I'm gonna start here and I'll ask you to maybe provide kind of some use case examples, but when we talk about a framework, and I alluded to this earlier, there's really three important concepts. And we think about it as kind of a bottom-up, top-down, and middle-out type concept. And I'm gonna start at the bottom here. Your framework really needs to capture at a system table field level what are your critical data assets, right? And that can include a technical data dictionary, a business data dictionary, it can include data standards, it can include business roles for information integrity. The important part about that bottom-up is, in and of itself, it may be a value to a data team, but it's not a value to the organization without framework components that take me up to the process level, the analytical level, and ultimately the goals and objectives level. And what I really mean by that is, if we come in and we're trying to remediate a help desk ticket for a data issue, my framework better be able to tell me that if I make a change to support that help desk ticket, have I understood all the processes that rely on that data, create that data, consume that data? Or am I gonna break something as part of that process? Have I also identified and do I have line of sight and visibility into any analytics that rely on that data element? So if I change a list of value, if I change a usage of that, am I gonna blow up a KPI, am I gonna blow up a metric? And then ultimately now, when I talk to the executives, is that important data element for them and trying to drive some strategic initiatives, right? And I need to be able to go from a help desk ticket level to make it really simple all the way up to make sure I fully understand the impacts of any change before I make any change. Similarly, from a top-down perspective, when I'm sitting talking to C-level suites who ultimately are gonna provide the funding, they care about three, four, five things in my experience, every C-level executive. We need to be able to understand that when they talk about a goal and objectives, if they wanna have, ultimately they would improve like a KPI or metric like case fill rate or your bias or forecast accuracy, we need to be able to understand then what are the processes that create, consume and use that data and ultimately what are the critical data elements within that framework that are gonna help drive those outcomes for the organization. And similarly from a middle out perspective, very often we're as part of process redesign efforts, process optimization efforts or process breakdowns where data is a big part of that, right? We need to be able to start that process-level integrity layer, work our way up to make sure that we're not impacting any strategic goals and objectives, performance measures, and then work our way down to understand what tables, fields within our systems need to be addressed, need to be remediated, whether that's from a data cleansing perspective or just from a governance perspective. So conceptually, that's what your framework really needs to do and Colleen, maybe I'll ask you to provide some maybe examples about how you've applied that at Pargo and even other organizations. Yep, sure, sure. I'm gonna start from the top-down and use an example. So like most organizations dealing with COVID-19, our cash position is a high focus right now. If our CFO wants to improve working capital as a data team, we need to ensure that we're focused on the critical data that's supporting us. Following the framework, we know that working capital is primarily focused on accounts payable, accounts reputable inventory processes. We know the critical data and metrics that supports that process of these processes and can laser focus on the critical data element. Our finance transformation program is a good example of the middle out. As we optimize our financial processes that are in the systems, we identify and standardize our critical data. While doing that, we can go up so that we ensure we're positively, not negatively, impacting our key performance metrics and analytics and also go down to ensure the systems with tables, the fields that we pull data from or send data to are included and can integrate. The bottom up is the folks you're probably, most of you are probably familiar with, which David was referring to on the business team, if they want to have a new field or they want to have a new value, they wanna change the way a field is being used. In the past, too often, that change was made in a silo. With the best intent for sure, not fully understanding the impact of all of the upstream processes, the metrics and the business goals. If we were lucky, there were no impact, but far too often we find a small, harmless change made impact that we had no visibility into until it impact. I won't say that we have everything covered here, but we know how to address the problems we have today and are working through to make it work. I'm gonna turn it back over to David. Great, Kyle. Yep, let's go ahead and move on and we'll talk a little bit about how those frameworks are really initiated and it's funny, because Jacqueline just sent a comment over there in the chat about how intertwined enterprise architecture is to governance and I can tell you, Jacqueline, Colleen and I don't agree more and here's where the kind of technology comes into play. So we talk about that framework and we talk about it conceptually. Here's where technology is a big accelerator and this is a representation of kind of data 360 and the platform that InfoJax provides and that Colleen uses, but there's other platforms out there that can do similar type things. I mean, I would selfishly say ours does it a little bit better, but it doesn't mean you have to have our platform to really apply this in a significant way. So we talk about kind of that framework. We have different personas, different roles, whether it's leadership, whether it's operations teams, teams that are focused on data analytics and big data, you have kind of your data organization which tends to be focused on store ship quality, sometimes master data maintenance and then you have kind of your enterprise architecture and your IT organizations. The only way to do this effectively from a government perspective is to make sure that we can connect the dots. So when Colleen talks about kind of top-down business leadership, we need to be able to take those goals and objectives, work our day down to the business ops teams to make sure that the processes, again, that create, consume or lie in that data, the analytics teams that are really using some of those insights to really drive key performance measures and really monitor performance of the organization are lockstep with the data organization, the governance or quality organization and we all have visibility to the enterprise architecture that helps support that, right? So when we think about system of records, source of truth and things like that, which we're gonna go into a little bit more detail within the decision tree, here's where technology really does knit things together really well and I would say and Colleen will probably agree the more visual your technology can be, so the less technology centric, the visualization layers of this information here really allow you to communicate with your extended business community a lot better than without that. You can have a pretty symbiotic relationship between a governance organization and your IT organization, because you're kind of used to talking to that table field level, right? You really can involve and you can't sustain followership from the business community without a visualization tool that allows them to understand how things roll up to them or roll down from them, okay? So with that, Colleen, why don't we kind of dive into a little bit of the operating model? Okay, so I think here what I want to kind of emphasize on the operating model is that making sure that it's a living and breathing document. The organization and has their properties that will continue to evolve in our operating model for data always needs to adapt. If you build an operating model based on individuals and not a process, it will break as soon as those individuals move on. So operating models are really just processes. So some examples of various process flows that I've built out is, how do we establish a rule? How do we change a field? You know, and following who owns those steps and who has to be informed about those, how do we profile our data? How do we cleanse our data? And just remembering that the operating models need to evolve and change as the business actually changes their model. And then similarly, here's kind of where that intersection of kind of enterprise architecture and IT really take conceptually what Colleen talked about and make it really real and really actionable, right? And you can really accelerate your decision making within an organization. You can record why decisions were made. You can deep dive into do we have the right roles and responsibilities as a part of the process by really embedding your operating model, your workflows and your processes as part of your overall governance framework, right? So it's one thing to kind of take that governance framework and have an operating model that kind of sits outside that if you can intertwine those, right? And allow organizations when I have a change that needs to go to a business process owner that owns in order to cash process because we're looking to change a payment term that they care about a standard payment term that was really initiated by maybe a sourcing organization for a different purpose. The operating model and having it instantiated within your platforms allows you in a really seamless way for them to get the request, identify that they have to evaluate or approve it and then they can double click and then understand what's my current standard? What are the current KPIs and metrics that I have that rely on payment terms in this case? What are the tables and fields and systems that I have ownership and accountability for that might be impacted? Who are the business owners? And ultimately, is this anything that my C-level suite cares about right now? So in Colleen's example around working capital, payment terms are a primary driver for working capital, right? So now I need to understand that, hey, what we're gonna do right here is gonna have some impact on some things that we talked about at a board level and maybe we're at my annual report. So again, getting the playbook together is really important, but having that playbook operational and being tied to your data assets, your standards, your rules, your dictionary items and making it easy for the organization to do the right thing is really, really key to success, right? For organizations, sometimes that tends to be a bit of an evolution, but that should be in your roadmap to kind of get there. People generally wanna do the right thing with data. It's either that they really don't understand or we haven't made it easy for them to do. People for years have said that business is really reluctant to take ownership and accountability for data. And while I think that's true to a certain extent, I think it's true for maybe a different reason than most people think. I think it's because we really haven't made it easy for them to be participatory and made it clear on what their roles and responsibilities are as part of the process. And this really crystallizes and clarifies a lot of that information. So with that, we haven't talked much about organization. We talked a lot about the playbook, Kyle, and I know org is an important piece. So why don't you just kind of like, you know, put a cap on that and how once we have the operating model in place, how we really drive the right organizational model? Yep, yep. So we've emphasized around, as David was saying, around the operating model, right? But at the end of the day, it's ultimately about the people. And so as I think about building an effective organization, I would encourage you to think about a few things that have worked well for us. Have a clear delineation between the entities that provide strategic direction, usually your board or your council, and the ones that provide you strategic operation such as your governance and ensure that you have the right representation that all of your process areas, business entities, your regions, inclusivity is a key to sustained engagement across these groups. Think about how your data stewards should be organized. By domain or is it by process area? We found that domain centric model works the best because so much of our data is cross-functionally used across processes. And then defining your organizations, what your organization will not do is just as important on what they will. People not connected to your data program will assume that your data team will handle everything. And I just had that come up actually this week that I was given a task of a data and I'm like, whoa, what's time out? So I knew I had to have some correspondence with those entities. And we just have to clearly define that and constantly reinforce that message to our business partners. And then developing an ideal candidate profile is really a valuable aspect when engaging the business leaders and identifying those candidates for your program. David, I'll turn it. Terrific, thanks, Kyle. And Terri, now we're gonna get into decision tree, which is something you asked a question about earlier and this, you know, as Colleen knows, I mean, to me, this is the most important core component if you really wanna drive long-term success. And decision tree is really fairly simple. And I'm gonna go through this relatively quickly and I'm gonna ask Colleen, maybe you can come in and provide some examples of how it's driven prioritization and the identification of your most critical data at power go. But when we think about a decision tree, it's a repeatable process, agnostic of who I'm talking to, agnostic of domain, agnostic of business unit that I think about data in a very, very structured or repeatable way. And the first question within that decision tree is what data do we govern? What's our most critical data? So I'm gonna use an example as I talk through this that might make sense to everybody. So everybody typically has some sort of a customer record and most systems and applications have about 300 attributes you can use. Most organizations use about 125, 130 different attributes to describe a customer. When we think about what do we govern, the criteria to make something important should not be based on the data itself, but you're really based on criteria, right? The impacts are the outcomes of that data. So all organizations that apply this criteria effectively really start with what I'll call kind of core four things. If the data has a significant impact on business process operations or transactional execution, it's a candidate for governance. Two, if it has a significant financial impact, it's used to facilitate internal external reporting and has significant financial implications, it's probably good candidate for governance. Three, if it has a significant analytical impact, so NetNet, it's used as part of calculations, right? It should probably be a candidate for governance. And really the fourth is around compliance or regulatory type needs. If it's a critical data element for compliance or regulatory type needs, it should really be a critical data element. Other organizations beyond those four really apply things that are specific to them, right? So for a company like PowerGo, it could be brand integrity, brand protection, it could be safety, intellectual property, things like that. The point is is that you need to have a set of criteria that you apply to your data in a repeatable step after step process. And what you'll find is out of that 130 customer attributes, I might come out of that filter with about 35 or so that actually makes sense for governance, right? That fit in the middle of that period, right? They're actually meaningful. Then your next question is, well, how do I govern that? And there's really three ways to govern data, right? Actively, meaning at point of entry, right? So don't pass, go, don't collect $200. I gotta get it right before it gets into the system. Passively, meaning that it's in the system and that I compare it to other data or I compare it against criteria or business rules and I say whether it's good or not, or procedurally, right? And I'll give you an example of kind of what that means to make it real. So all of us have probably ordered some things through Amazon here recently, right? And when I place an Amazon order, I can go in and I can get my name wrong, I can spell Dave wrong, I can spell Woods wrong, I can have my street address wrong, I can have my city wrong. But if I put my city down with an incorrect zip code, finally Amazon's gonna say, look, Dave, I don't care if you don't know who you are or where you live or how to spell anything, I can't ship a package to that city with that zip code. So that's active governance. That says, stop right now, I need to get this right. Then at the end of my order, Amazon will come in and it'll say, hey, I see all this address information you ordered, I'm comparing it to an address directory that I have over here, which I know is high integrity. Would you rather use this address? That's an example of passive governance as we go through, right? So as I get through that application, I'm gonna then say, I'm gonna take that 35 different data elements, I'm gonna say, okay, 10 of these meet criteria for active governance, 15 of these are criteria for passive governance and 20 of these I can just have in a policy and procedure. Then I can say, okay, where should I govern that data? And until I get to that point in that, how do I govern decision? It's very hard for me to say where, right? If my governance strategy needs to be active at point of entry, I should govern that data in the systems of entry, in the systems of record, right? If it's gonna be passive, then I need to see what's the right system in my enterprise architecture that makes sense to compare data to execute those business roles, right? And then and only then can I have a fact-based, you know, non-antidotal conversation with the business to say who should own this data, right? If you try to say business person A, business person B, you should own this data without having context, they're gonna have a tough time. But if you go to them and say, hey, here's why it's critical. It supports your business process and your analytics. Two, it needs to be governed actively. Three, it needs to be governed in the CRM systems where you guys maintain that information. Then that ownership and accountability and adoption of governance is gonna take seed a lot better. And we found is that organizations that follow this decision tree are very, very productive and realize very, very rapid results as it relates to the value of the program. And maybe, Kyle, you can kind of talk real quickly around how you applied that imperative. Yeah, so of all the concepts that we have talked about so far is like Dave said, the decision tree has been the most important thing that we've used. It's a way for us to eliminate anecdotal conversation and focus on the fact that make the data important to a program. If I've learned one thing, not to ask the business what data is important, the truth is they probably don't know as Dave was saying, and you'll get 10 different answers when you ask 10 different people. So instead, focus on the criteria that makes the data important to them, we can identify and evaluate, you look at the criteria and always be focused on the distribution, I think the organizational value. And then we can talk about the terms of the business. As Dave mentioned, criteria will vary by organization. And these are the things that became very important to our stakeholders, right, for Dave. Terrific, Kyle. Yeah, and I'm gonna go to the last one here, which is really they're on their metrics model. And Colleen, I'm gonna go through this rather quickly, just in the interest of the time here, and I'll let you kind of button things up and we'll take some questions. I remember recording a bunch of questions here within the chat, but metrics model's super important. And here's what you need to think about as it relates to metrics that are probably different than maybe you've applied in the past, right? You need to start thinking about metrics on three levels, right? And those three levels really are at the bottom efficiency and effectiveness. And that really tells me at the end of the day, what am I doing, right? Then I have a set of other performance measures as relates to data quality, data integrity, data advocacy around performance and value. And that says then it expands on kind of what are we doing to say how are we doing and what's changing? Are we making a positive or a negative impact? And being able to delineate your metrics, your data quality metrics, your governance metrics, your information integrity metrics, in a way that really breaks into those categories is super important. And why it's important ultimately and where really the carrot is on top of the cake here is that you have to have metrics that say all this stuff we're doing from a data perspective, is it making a difference? Is it driving business impact at the end of the day, right? And really the way we look at business impact and most organizations look at that is really kind of with four different lenses. One, am I better enabled in my processes, right? Are my processes better? Do I have less business process interruptions? Are they more efficient? Are they faster? Are they done with less effort? Two, can I trust? And are my KPIs and performance measures and metrics are they in better shape? Do we have better information, whether it's from a timeliness perspective or whether it's from an information integrity perspective as it relates to that? The third tends to be more qualitative, right? And it's more, you know, do people feel better about our data? And they tend to be more customer satisfaction surveys. So if you guys have impulsive organization or if you've had, if you say, look, I hate the fact that our data stinks. It takes me forever to get things. I spend half my time writing reports. 40% of my employees' time is kind of inefficient. That's that qualitative piece. And the last piece is around project acceleration. And Colleen's doing a ton of that right now. But if you have this in place, what we found over time, these are kind of benchmarked against about 18 or 19 different organizations. Saw some SMB, small, medium-sized businesses, some large organizations. But if you have this framework in place that we've talked through before and you're using in its operationalize, you can generally get your IT projects done 28% faster. So less on time on budget, 28% acceleration in your delivery times. And the bulk of that is really in the middle. And your design phase and your implementation, your build phase, where you spend most of your dollars and have most of your resources. And what we found over time is that functional specifications are generally accelerated by 40% and 50% of your rework, obviously all done to data and ownership accountability and rules are really not needed anymore. So metrics really, really important but start thinking about metrics in a bit of a different way than you have in the past as we go through. So with Kyle, cause I know we're gonna, we wanna get some questions for sure. And I've been, I've been getting a bunch of these. Why don't you kind of take us through a summary here. Sure. Yep. So just a, so I hope you have found the value in the session and we'll get some of your questions answered here. So really just to kind of recap is, you know, make sure that you're adaptable to the change. Make sure you're aligned to your organization's priorities, making your data visible across, you know, and available to your business partners and embedding governance processes to improve the efficiencies in the rest. And then our impact, you know, we've implemented a fully operationalized data governance organization to support Parago Transformation Project. We've implemented tools for data management and data governance. And that's helped us accelerate and our implementation of our global capabilities. Constantly investing in your data organization via repeatable successes and demonstrating that value. It's very important. Having a speed at that table, reiterating the importance of data. And you're gonna sound like you're gonna be repeating and repeating, but do it, it becomes very valuable. So positioning ourselves for quick react, pivot, and evolve based on the business initiative. So some of my advice to recap is keep pushing. Make that difference in your organization by enforcing the criticality of your data in processes, technology, and people. Capitalize on your data failures. Years ago, somebody told me that it was crazy. I don't want it to fail, but really if it does, it's going to happen, capitalize on it. And really get that speed at the table. Thanks, and we'll start answering some questions. And great, and just a couple of things here. So when I did see some of the chat, so Colleen and I did add this slide with our contact information, our email contacts. So if anybody on the phone here would like to reach out or have any follow-ups to some things that we talked about here today, Colleen and I are both happy to kind of take those inquiries from you guys. And we will, because we did add another slide in here around organizational model that Colleen did a nice job talking through. And we did not get that to diversity. So Laura, we'll get that over to you afterwards and you can have that available to the team for sure. So, and I will be in our kind of virtual booth here for the next hour, because I know Maya, you were asking kind of, what we do as an organization. And we do do both the consultative side of the house, which is where I work a lot with Colleen. And we do have the software applications that help support that, which is part of our team uses as well. So Colleen, I've been chatting some, dropping down some questions here in the chat. And I'm going to shoot some out that I think maybe you take the first stab at. So Uma, they asked how long it took to get buy-in and how long did it take you to stand up, or operationalizing your data governance program? Yeah, so, well, it depends on which one I've implemented and which one I refer to. So for Parago, I will say, like I said, Dave worked with the Parago team before I joined this and really got the adoption of leadership. And that is your key. Getting that leadership adoption is very, very important because the rest of it just kind of comes naturally, almost naturally, I'll say, but you can get that. So, but if you don't have that leadership adoption, which is one of the areas where I struggle getting the program set up, is because they didn't bring the value. And they weren't sharing that value and understanding the value of the data and what it's driving for their processes and ultimately across the organization. So we didn't get that support. So that lengthened and took some time to actually get to which is about a year's time. And I think even from Dave all kind of rely on you, it was even, I would say, Parago, the individual that I've been working with and partnering since I joined, her journey has been a five-year journey to try to get this implemented and get the value to the leadership around that. It doesn't always take that long from a five-year perspective, but it really is just a matter of keep pushing and keep driving and keep bringing that value to them because they won't get it on the first prize. That's evident. They might not get it on the second try, but they'll start getting it the more you socialize it. And that's where it really kind of comes in to capitalizing, I say this, on your data failures, right? And where the data fails in bringing that up saying, okay, hey, this wouldn't happen if we had this in place. Right, exactly. And Colleen, I'll build on that a little bit because the journey for us a little bit here at Parago was first when Colleen wasn't part of the organization, I was working with the Parago team and the other individual that Colleen had mentioned, they had some executive leadership level changes. There was an appetite and a call to action with all this business transformation going on to do something about our data. And I can tell the audience here, the way to do that effectively at that C level is to really crystallize a message around four concepts. In every slide deck, every message you guys put in front of a team should answer the following questions. And if it doesn't, get it off your slide deck, right? So it should be, the first question should be, what is it? Why is it important? What do we need to do about it? And what do I need from you? That's the simple message. You should literally almost have a four-page slide deck that's your keystone deck that you walk around to executives that, one, say what is it? So you describe kind of the problem statement, what is data governance in a way that they understand? Two, why is it important? You need to tailor that slide based on your audience. What do we need to do about, be very prescriptive as the core concepts that we talked about here today, and then what do we need from that, right? That was really an important thing. Once we got that in place with that new leadership, it was about two, two and a half months or so, obviously not a dedicated effort, it's tough getting folks, a time on folks' calendars for them to say, yep, we need somebody to lead this organization. Who do you know out there that's really good, Dave? I say, no, nobody better than Colleen, and then we got that process started. So next question, and one I'll just clarify. I'm so sorry to cut you off, but we have hit our way past our 50-minute mark, and we have only nine, actually eight and a half minutes left to the next session. So we're gonna wrap up. Thank you so much. This has been an amazing presentation, and thanks to all of our attendees for tuning in and participating with claps and the chat, questions for these guys. By all means, Dave, Colleen, go back into the live event slide. You can find your session and continue answering those questions in the chat for the attendees to come back to. You can also connect with attendees directly on the live event page by clicking on their names and then sending some personal chat messages. With that, without, we'd also love the attendees to just quickly fill out the conference session survey at the bottom of the session page, and we will see you all in less than 10 minutes for the next session. Thank you so much, Dave and Colleen. Thank you, Laura. Thank you everyone for your help.