 Alright. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager at DataVersity. We'd like to thank you for joining this DataVersity webinar, How to Achieve Trusted Data with Business First Approach to Data Governance, sponsored today by Precisely. 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 by the Q&A, or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVersity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note Zoom defaults the chat to send to just the panelists, we may absolutely change it to network with everyone. And to find the Q&A or the chat panels, you may click on those icons found in the bottom middle of your screen. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and any additional information requested throughout the webinar. Now let me introduce to you our speaker for today, Cameron Ogden. Cameron is the Vice President of Product Management at Precisely. He joined Precisely with the acquisition of InfoJix where he was responsible for growth, innovation, and go-to-market strategy of the full InfoJix product portfolio. With over 18 years of experience, Cameron has a proven track record of delivering enterprise data solutions for companies across all sizes and industries. And with that, I'll give the floor to Cameron to get today's webinar started. Hello and welcome. Awesome. Thanks, Shannon. And really appreciate the music leading into this. That was great. I think all meetings should, I think, have some sort of music, some sort of good vibe to get the meeting going. So I saw folks joining from Malaysia, really all over the world. So I was about to say Good Tuesday to everyone, but Good Tuesday or Wednesday, that everyone had a tremendous weekend for those of you that are just coming off of a long weekend. And I'm really excited to be able to talk to you all today about an approach that we've seen to be a gigantic mover and a difference maker in the terms of success or really, I won't say failure in data governance programs, but really challenging data governance programs. What's one of the big or two or three of the big takeaways that we could discuss over the next 45 minutes that are difference makers when we see data governance programs be successful. And I assume that many of you on the phone today are interested in data governance or have some sort of stake in your own organizations or perhaps in your professional development and career. But for those of you that might be tangentially involved with data governance, or just have a role in working with data, I think that there are going to be some good takeaways here as well, such as how to communicate the value and calculate the ROI for data, how to link data to business objectives and initiatives, how to prioritize the data that matters in the analytics or the insights that matters, how do we establish really data governance with a backend of certifiable data trust or data quality. We'll talk about all that and a lot more Alec at what Shannon had said, please feel free to put your comments into the chat. I believe this is my third or fourth data diversity session that I've given over the past couple of years and again I'm just really happy to be with you all here again today. So let's go ahead and jump in and talk about why do we even need to think about business first anything, whether it's business first governance or analytics or data quality or integration or data management, you name it. I think it's almost has become a bit cliched at this point to say, well, we have to make sure that we are delivering business value or we have to make sure that the business is really taking custody of the program. But what we see in what you see some of the statistics here on the screen really articulating is that data programs are having some fairly significant challenges in getting adoption from the business and really demonstrating the value of the program over the long term. And we'll talk about how programs get started in an initial sense, but how that initial trigger begins to fade if we're not able to clearly articulate what's the value and what is the impact of the business over the long term. Four out of five governance initiatives fail to deliver the expected outcomes Gartner found when they surveyed hundreds of companies a couple of years ago and ran a pretty astonishing survey. And really what the benchmark there was was against the initial business case that was developed for the governance program or the governance initiative. So four out of five programs failed to meet the target measure or metric that was articulated in that initial business case. About two thirds of data citizens or another term for that could be analogous to business users, data knowledge workers, just folks in the organization that work with data every day, but they might not necessarily have the specific data expertise or be technical users, but two thirds of the data citizens and organizations don't know how data governance impacts their role. They understand the data governance is important, but they really don't know what's in it for them and why they should care. And about three fourths of all data leaders struggle to calculate the ROI data governance projects. And I'm sure that you all have seen similar challenges in your organization where the executive leaders and the executive sponsorship in the organization, they get conceptually why data governance is important. They understand that data governance is going to keep the company out of the news in a negative way. And from a PR nightmare perspective, but they really don't understand what's the value that it's driving day to day. So all of these cases can be avoided if we take a business first data governance approach. And that's really what we're going to talk about here. But what we thought that we would first start is to talk about a typical governance story. And this is a story that from a precisely point of view, we have the advantage of working with many different organizations, hundreds of organizations and how they build their governance programs. And there's a typical governance story that we hear play out and that we see play out over and over again. And many of us, me included have sat in the seats of data governance leaders and directors that are building these programs and then have moved over to either working with products building products or into the consulting side. And usually the story starts like this, there's some sort of inciting event, there's some sort of initial trigger that the business has around founding a data governance initiative. Now you could say, well, you know, we've been doing data governance for years, Cam, in our organization. We have two or three people that are doing nights and weekends work where we have spreadsheets, we have, you know, kind of casual meetings, but it's not really formalized. Well, this applies here as well. This could be the catalyst where leadership says, you know what, Cam, we know that you and your team have been trying to build data governance from the ground up. And we now have an initiative, a data privacy initiative, or we are standing up a data analytics function, a center of excellence around data analytics and insights, or we want to improve our capability on how we get to speed the market more quickly. I'm just giving you an example of any number of use cases here, but there's some sort of inciting event. And leadership says, you know what, we're going to give you the capital, we're going to give you the funding because we need to build this data governance function. So the data governance team gets formalized. And let's say that I'm the director or I'm I'm put in charts to lead that team. And it could be a team of two people or it could be a team of 20 people. It really doesn't matter. But I'm pretty savvy in knowing, hey, you know, I need to get the business involved. Ultimately, if this program is going to be successful, it's not going to be my team that supports this. We are going to be a force multiplication team, or we're going to have data stewards that are going to help the business. We're going to be great liaisons to connect the business with it, but ultimately the business needs to get involved. So how do we do that? Well, we're going to start to set up meetings and we're going to ask the business to give their input and opinion on business terminology and glossary definitions and business rules and data standards, because ultimately, we want them to be able to make the decisions they need to be able to own the data. So we go about starting to do that. We kick off data governance council meetings. We have representation from all the business reporting lines, process ownership. We have our architecture team in the room. And the business users, they come to those meetings and initially they're excited because they're asked to lend their knowledge. And they will go along with it for a bit. They'll help with definitions and approvals and with ownership. But in the back of their mind, they're really starting to wonder, wait a minute, how does this help me do my job? And is this an additional thing that I'm now being asked to do that is not a part of my core job? Am I just doing this to be a helpful person? Is there actually some sort of executive mandate that I'm going to be compensated for? Am I being measured on this as a part of my role? These are all questions that where if it's not really clear, what they do end up coming away with is, well, hold on, I get it. I now have a data firefighting team in the state of governance team. I have a data governance group now that for any issue that I have with data, I can just call on and say, hey, Cam, I got this issue, go and take care of it for me. So the data governance team, we all of a sudden start to get a lot more issues because there's a lot of attention. There's a lot of quote unquote collaboration from the business who is now coming to us and telling us all the issues that they're having with their data in exchange for their knowledge of rules and standards and terminology. And that data governance team, what ends up happening is it starts to lose focus. I start to lose focus as a data governance leader in the initial business case that was developed around that inciting event. And I start getting really focused on tactical correction of the data and firefighting of the data. And the business users eventually say, you know what, these meetings are a waste of time. All we're doing as we're talking about more issues, we're arguing and we're squabbling over definitions and terminology. I don't see how my knowledge is being leveraged at all to improve ultimately the trustworthiness of the data. I don't understand how my job is getting any better or easier or how the data is enabling me fast enough to the level of my expectations. And then eventually that executive leader that funded the program dips his or her head back into the sessions, calls me into their office and says, hey, Cam, how are things going? And ultimately, you know, the measure that we had to that we used to found this data governance program on that initial business case, why am I not seeing the return on that? Can you help me understand what's the value that we're getting? And if I don't know any better, I'm going to point to the number of meetings that we've had and I'm going to point to the glossary terms that we've developed and I'm going to point to the business rules and data standards that we've gotten alignment on. But at the executive level, we're at the strategic level, they're thinking about things like, well, how does this help our top line growth? Or how does this help us with cost savings? Or how does this help us with our speed and market initiative? Or how does this help us get in front of our data protection activity? So this is what we want to avoid. And really the question is, why does this happen so often? And I'm sure that there are many of you that are here in this session today where you're nodding your head and you're saying, you know what, I have something that looks very similar to this in my organization right now. So that's what we're going to talk about today is how do we build business first data governance that avoids that trap and gets business users adopted into the organization where they can clearly see how it helps their job. From a data governance leadership perspective, it helps us get line aside on what are the capabilities that matter that I need to prioritize first? Because as we all know, the data is not getting any less. It's not reducing day over day. Certainly the speed in which the business expects the supply of data is not being reduced. And essentially, tackling the data governance challenge is just going to get more and more complex. So how do we get in front of it and make sure that we're consistently delivering value for the business? And the first thing that we need to be able to do, there are really three takeaways that we're going to talk about in today's session. But the first thing that we need to be able to do is successfully link the value drivers that the business is already invested in that the business already cares about. We have to be able to link those value drivers to data governance and vice versa. We have to link data and data governance to our business objectives and value drivers. And before we talk about value drivers and goals and initiatives, let's just make data super, super simple in the way on how we would explain this to anyone who is not even a part of our own organizations. Let's imagine for a moment, and many of us here in the US came off of a long weekend, I'm based in the US, came off of a long weekend this past weekend where I'm sure many of you got together with friends and family. And if you have a conversation with your friends and family and if you work with data, they probably don't have a real clear idea of what you do. But ultimately, they do understand how information is used. And if we think about how information is used within businesses just in general, it really gets down to three different ways. Data is used to minimize risk in the organization. And you see examples here of data protection initiatives. It's to prevent risk, fraud, anti-money laundering. It's used for data privacy. It's used to ensure safety, to get in front of regulatory compliance, internal reporting, Sarbanes-Oxley. These are all examples of how data can be used to minimize risk. We often talk about this as a set of defensive use cases. We are defending how data can potentially be leveraged in a way that has a negative impact to the business. In the middle column, data can be used to make decisions. So that's the second way on how data and information can be used. And this is really more of an offensive case, really more of a growth minded use case around data. So data can be used for analytics and insights to get better insights into NPS or Net Promoter Score. Essentially, that's just a fancy way for saying, do our customers like us or not? Help us to get insights into our website traffic if we want to grow, cross sell or upsell or figure out how to expand our customer reach. Target marketing campaigns, how do we hold on to customers and reduce our churn, get a 360 degree review of the customer. All these are examples that many of you, I'm sure, have in your organization, whether it might be part of your visibility or part of your job role or not. But I'm sure your sales and marketing and growth teams are focused on how they leverage data for these kinds of use cases. And then the third area, which is a bit underserved, it's a bit overlooked is how data can be used to really improve the business from an operational standpoint. Things like optimizing working capital or capital that we have on hand to essentially get more out of our resources. How we can enhance our customer care processes, improve product traceability, facilitate mergers and acquisitions. These are examples here as well. And essentially, this column is all about if you think about your core business or your institution, what is it that it is made to do? What is it that it aspires to do better than anyone in your region or in your country or in the world? This is the core business function and data is absolutely used as a part of the operational integrity or the operational success for that core business function. And so we can think about how data is used that way as well. So we just think about it super simply, data is used typically in one of these three ways. But here's a challenge from a data governance perspective. The same data, the same data set, the same data asset can be used across all three. So let's just say as an example, we have customer information, customer address information or account information that has a data protection impact. It has an impact or it has a role to play on how we get a 360 degree view of our customer. And it has a way on how we improve our customer care. And so from a data governance perspective, imagine being in a meeting where we have the head of compliance or the head of our GRC team to the left. We have our head of marketing. We're a head of sales in the middle. And then we have our customer management business process lead over to the right. And they all have an opinion on how the data needs to be taken care of, what the hygiene of the data should look like, what are the data quality dimensions, how much they want the data to be shared and accessed versus how much they want it locked down and controlled. And the data governance team, the data stewards and so on are in the middle of trying to negotiate, how do we prioritize the capabilities against the business objectives that we have for this data. So if we take one more step with this and break this out, we're going to go top down. So let's say that if these are the various ways on how we leverage data within the organization, let's think about this on the various business goals that we would have that have an impact on data, which is what you see all the way to the left. And these are just examples of business goals that not only that we have in our organization here precisely, but also that you all have in your organizations and almost every business in the world is thinking about things like, how do we improve the personalization of services or products that we send to our customers? How do we increase our sales and revenue through faster speed to market? How do we provision those services or products more quickly? How do we increase user productivity with our own internal employees by getting them access to data more quickly? And how do we reduce our overall supply chain cost? Certainly over the past couple of years, that's become a harder topic in commodity. The next column over each of these goals have organizational stakeholders. So that's the next layer of complexity. Each of those stakeholders have their own expected outcomes for the data and how it drives improvement for their business. But these two purple columns that you see highlighted on the right, that's where as a data governance leader, I need to be able to think about how do I tease apart all of these priorities, these business goals that have competing initiatives and stakeholders that come from different parts of the business and different expected outcomes that I'm going to have to show how I'm driving improvement against so that I can make them look good in their roles because then they're going to be on board. How do I really prioritize the data governance objectives and capabilities? And what we do is we start to think about what are the value drivers that each of those business goals have and the expected outcomes? And then what needs to happen from a must have and from a nice to have perspective in the capabilities that I need to provide both from a people or I should say all from a people process and from a technology standpoint. Now the people in process is correlated here with the data governance objectives. So I need to establish a common view of trusted customer assets if we just look at this first column. Unless it's isolated on this first, I should say row here instead of column and look at how we might build out one of these examples. If I'm building a net new program that is focusing on delivering value as fast as possible to the business, I can take this goal of improving the personalization of customer goods and services. And one of the things that I know that I have to do, I must do is I have to establish this trusted view of customer data assets. I have to know what customer data I have and where it is. So in order to do that, we can think about this in the way of must have and nice to have or as a good friend of mine says pain killers and vitamins. For those of you that like to go out and do a walk or like to work out or exercise, you have no motivation to go out for a walk or to exercise or to get up and move if you're in pain. If you have a sprained ankle or if you're recovering from a knee injury or if your back hurts, suddenly the appeal of going out for a walk just does not seem too enticing. But if you don't have any pain, then going out for a walk seems pretty nice and you can start to get more healthy and then you can start to enjoy the benefits of exercising. So it's very similar of the painkillers and the vitamins that we think of within the organization. The painkillers are things the must have that we need to do in order for this goal to be real, for us to realize this goal. So when we think about establishing a trusted view of customer data assets, there are a couple of things that we need to do in our painkiller column. Number one, we have to centralize what are the customer data assets that we have that we use for marketing for promotion? Well, that's task number one. We know that we need to do that. Do we know what data that we use? What's the critical data that we use from a reporting standpoint? Is it address information? Is it credit information? Billing information? Market segmentation information? You name it. We have to be able to get a lens out of all the data that we have. What's the data that we use for marketing and promotion? We'll talk about data prioritization in our second takeaway here coming up. Number two, do we understand where that data comes from and where it goes? Forget about the tools for a second. This isn't a tool conversation. This is just around capabilities. Do we understand where it originates from? What's the entry point of that data in the organization? Ultimately, what's the demand? What's the consumption downstream? That's really important as a must have. If we don't, then that data pipeline is always going to break. That's where we experience the pain. The next thing, do we have approved governance ownership indicating that the data is certified for access and use? Certified in this case just meaning that someone has said, hey, this data is trustworthy for use. If we don't, then we're going to use the wrong information for personalization. Again, that's a pain. Then the fourth thing is from a trust perspective, do we have metrics that indicate that the data is accurate or it's consistent or it's timely? You name it. If we can start to think about the painkillers here as the must has, then once we get those building blocks in place, we can start to overlay the bonus items here, such as these items that you see here on the right, which tend to lend themselves more around automation, more around intelligence, more around advanced capabilities, more around tools and technology. The mistake that I think new data governance leaders make or organizations that are taking this step for the first time is they constantly look to the technology as a silver bullet. They're constantly looking for a data quality monitoring tool, or they're looking for an AI or ML tool to catalog all the data that they have and to put a semantic layer on top so that you have a catalog of all the data that you have in the organization. Instead, we need to be thinking about what's the key data that's needed for our customer goods and services, and then think about what is the pain that we need to remove, and then eventually once we get those building blocks in place, think about what are the nice to have that we can layer on top as we build our capability for a business goal. So let's now talk about next on how we prioritize the data that matters. As we start to think about what is the goal or objective that we have in the business from a top-down perspective, what are the things that the business wants to solve for, we can then start to think about what is the data that matters to the organization. And this is where having a focus on what matters becomes immensely important because if you look at this pyramid, essentially what this pyramid is saying is that most organizations for any reasonable size and scale, out of 100% of the data that that organization owns, it doesn't matter what store or what format it's captured in. It could be IoT, it could be in cloud, it could be a mainframe, it could be a database, it doesn't matter. But out of the 100% size of that pie, only about 40% of the information is actually used to run the business and conduct daily operations. 60% of it is obsolete or it's archived or it's just not used to run the business but we keep it on hand because we might use it later. So right away, only 40% of it is viable and only about 10% is used to use for KPIs, key performance indicators or performance measures or some sort of analytic, which is another way to say only about 10% of the data that we're capturing is being used to make some sort of decision around changing the business strategy in a way that's conducive to how we want to run the organization. And then only about 5% of that is really actionable, that is used at the strategic or at the organizational level. So 5% of it really relates to those four or five value drivers that I showed a couple of slides ago. So from a data governance perspective in a world where the data is not going to slow down, where the complexity is not going to slow down, we need to think about how we really focus on that 5%. And the way that we do that is we again take our top-down approach on what's the business goal that matters. In this case, we have a stakeholder here that says, hey, Cam, we need to personalize our outreach to our customers so that we reduce churn. We got to stop treating our customers like they're just a generic customer and we have to instead reach out to Kevin or Joe or Cindy or Tammy and say, hey, we understand that you care about these sorts of things and we want to make sure that our outreach is more personalized. So in order to do that, we need to think about what is the critical data that drives that personalization? This is what we talked about earlier. It really only comes down to about 40 or 50 different data elements, things like name and address information, market segmentation, credit, shipping, billing information. We have to make sure that that information is being taken care of and that we are building high data integrity for that data on a consistent basis. And once we understand what's the critical data that aligns to that business initiative or objective, we then can start to build our next capability, which is how do we get lineage and insight into where that information flows through the organization? And then also, what are the data integrity rules that will let us know that that information is 100% trustworthy, that you see in the top right of the screenshot here, and it is available for business use? What would be a bit of a misguided approach is if we tried to tackle this for all data that we owned at our organization. So there's no possible way that I'm going to be able to catalog and keep up with all the data that we're onboarding on a day-to-day basis, keep track of the lineage, govern all of it, and make sure that that I am validating that it's trustworthy in whatever way that I would define that. These critical data attributes that you see here to the left will rarely change. These are going to be the core customer critical data elements as a part of this Goal or Initiative. But as you can probably recognize, they're also the same data elements that are going to be important for getting a 360 degree view of the customer or improving customer care or improving our marketing or growth efforts. So by really starting a conversation with the business on what is the top 5%, this can help us get line of sight on what is the data that matters and why. And this takes us to our third and final approach for building a business first data governance program. And then we're going to make sure that we leave plenty of time for Q&A or feedback through the back end of the session. But we're often asked, hey, how do we really build the value case for data governance? How do we build that initially? And how do we show that on a consistent basis, on a sustainable level based on who our stakeholders are? And we first of all have to think about, well, who are the stakeholders of our data governance organization? So we really need to think about, you know, there are many different stakeholders in the organization across, if we just make it simple, strategic, operational or business process in tactical levels. So the folks that are working with the data hands on keyboard or closest to the data on a day-to-day basis. And each of those stakeholders, this is not a hierarchical thing on where they are in the organization, but each of those stakeholders have a different vested interest in the data that they use on a day-to-day basis. Again, none of us are doing anything on pen and paper anymore. We all have hands on keyboards. We're all data consumers. We're all data citizens. But the way in how we use the data matters very specifically to my job and to my stakeholders' jobs. So we have to make sure that we're putting into context how are they thinking about the value of data and then going a step beyond that, the value of the data governance program. And we can tie this to measurable value or return on investment in a way that resonates with each of these levels. So as an example, if you just take a look at this middle layer, one particular value metric that we might have, and a value metric that we mean in this case, are ways on how we can measure the value of a capability or the data that is used within each level. So if I were to take a step back, at the strategic level, if we go out and we talk to almost any C-level in any organization, they're looking at things like key performance indicators or customer sentiment. What's our growth opportunity for customers based on the survey feedback that we're getting from our customers and net new prospects? How quickly can we accelerate projects according to our business objectives? At the middle level, they're looking at things like data quality and number of touches that we have in the timeliness and availability of data and cycle times and our data error percentage, which is what we see here. And tactical certainly can have a number of value metrics here as well. These are all just examples. You have your own ways in your organization. I'm sure on how you would measure the value of data and data governance and data quality in your organization. But if we were to just take a data error percentage, what we mean by this is what's the amount of time that we spend on correcting data errors? Where are the percentage of data errors that we have that ultimately translate into cost and effort? So if we were to take this example value metric, and it's just an operational metric in the way that we're categorizing it here, let's build a little business case for it. So the way that we do that is we think about what are the variables that go into this case? Well, for every data error that we have, let's say, as an example, it takes two people to fix it, one person to report it and another person to go and fix it. Let's assume that it takes 10 minutes to fix the error. So if we know exactly what the data is, and we know exactly where the error is, let's say that it just assume, take an assumption of it takes 10 minutes to go and fix. And we can look at what is the fully loaded cost per person? Now, this would just come from an average salary level for whoever are those two people that are correcting the errors. The fully loaded cost, an average cost, a benchmark that we can use is $2.50 per minute, or I think that translates to about $150,000 a year. Certainly you can move that down or you can move that up depending on what the internal cost is. And if we have 10,000 customer records in our organization, then that means that the total cost per error is $50 US. So we just multiply the variables to people times 10 minutes, or you could say 20 minutes times $2.50 per minute. What that does is if we take an assumption and say that if 25% of the errors can be avoided with a governance program, so we have 10,000 customer records, and if 25% of the errors associated with those records can be avoided with a governance program, that gets us down to 2,500 errors. 2,500 errors and $50 in savings gets us to $125,000 in savings and US dollars. Now, I'm using example variables here because these variables need to be plugged in for any organization that we work with or that you work with in your organization if we use this value metric. But what this starts to do is then we can take that initial $125,000 benchmark, and we can look at how do we pick benchmarks that resonate and showcase a benchmark estimate, a conservative estimate, and then the value tied to the specific governance strategy, which is what we're showing over here to the right. So using benchmark data, just the generic data that I believe that we pulled out from Gartner on the amount of time that it takes for data errors to be resolved, 10 minutes to believe it takes two people, fully loaded costs, you can use that for your country or for your region. That benchmark comes out to $20 per error to fix, and that translates to 125K in savings. But the way on how we would stratify this is, well, let's take a conservative estimate. Let's assume that it costs us $10 per error. Well, that's 62,500 USD. And what if we just take a crawl, walk, and run approach? We're not just going to solve 25% of all errors right from day one. Let's say that we resolve around 20 to 30% of them. So our value would be 50K. This starts to give us our levels of the business case. So at a baseline, we could expect to save about $50,000 if we really do our jobs well, and if everything goes according to plan, the top end that we could expect is $125,000. And this is just one example. I want to make sure that we're showing a very specific example, because oftentimes, as we've seen sessions being given on creating the business case, the specifics get a little bit lost. And so I know that this might be a little bit more detail, or perhaps a bit complicated to process, but for those of you that enjoy this sort of thing and getting into the details on constructing the business case, this is for you. And you can then pick benchmarks that resonate different value metrics across their strategic operational and tactical levels, and then make estimates for each of those value metrics. So for a data governance leader, if we go all the way back to that initial trigger, that inciting event in our initial story, and my executive comes to me and says, hey, Cam, I want you to formalize this data governance program. And our initial use case is making sure that we can prevent a bad event from happening around a data protection type of scenario, a data protection event, or a data privacy event. I can say, great, what else? Because I have an opportunity to also use that same information to grow the business, get better with the analytics and insights, and also to improve how the business runs today from an operational standpoint. And I can start to layer out different value metrics on how the data governance program can drive value with the organization. And then once I have these business objectives or initiatives through that exchange and that conversation, that's when I can then start to build the data governance capabilities and prioritize the data that matters that we talked about in section one and in section two. I'm taking this slide, and we have genericized this slide to protect a customer of ours that uses this as an example with this approach. But ultimately, what this allows us to do is to create a value story. So these value metrics come together at each level to tell a complete story. And this is an example of how they had some tactical value metrics, which really are the inputs. They look at things like the number of catalog assets that they had collected and the terms that they had defined and the quality rules that they had developed. And what they did is they said, we've cataloged when they went into the report out meeting to the progress meeting with their C-suite, they said, we've cataloged 10,000 supplier data assets. And we've defined the top 50 critical supplier terms. And we've worked with your business teams to align on the key rules and policies for each. And based on those critical supplier terms, our data quality is showing at 90 plus percent accuracy and consistency for supplier spend data. But how does this really matter to you? So what then what? What they were then able to say is, as a result, our supplier data setup process has decreased by 25%. And we're able to identify the top 20 vendors, 33% faster for our contract renegotiations. And we've increased our full-time employee productivity by 20% due to data self-service. And as a result in X amount of million, and again, protecting the number here in savings for the organization. And then what they went on to do is to proceed in engaging with other business initiatives and business objectives, as their initial trigger here was around supplier spend, spend analysis out of the procurement or out of the supply chain side. But this data governance team was then given the funding to grow the initial governance organization from being a supply organization with a supplier data steward, and a supplier data governance team to grow it into customers and materials and into the finance space. And that data governance program has been running strong for several years now. So this is an example of how one customer of ours has put this in the practice and how they're able to create this value story by translating the different value metrics across multiple levels, in this case, the tactical and the strategic level. So that's about what we wanted to discuss today. And before we open it up to questions, I just wanted to do a quick recap. When we talk about business first data governance, it really starts with the ability to link data governance to the business goals. Oftentimes, the misstep that we see is that we go to the business and try and talk to them about why they should care about business rules and policies and data stewardship and having a common glossary and centralizing a data catalog or you name it. And ultimately, what we need to be able to do is to be thinking about what are the initiatives or objectives that they're already invested in that are part of their core daily jobs and figuring out how governance is going to make their job easier or faster or safer or whatever the value metrics are that they ultimately use to communicate progress and performance in their role. We also talked about how we can think about prioritization from a capability perspective in the role of thinking about must-abs or nice to has, painkillers and vitamins. And then the second thing that we talked about is how do we get priority on the data that matters? Not all data is created equal and out of 100% of the data that we have in the organization, we need to be able to focus on the top 5%. So instead of thinking about the 95% of data that we have that is non critical to the business, how do we really focus on the top 5%? We talked about our customer example of identifying the top customer personalization data elements, being able to then stratify that out with a lineage view and then backing it up with data integrity rules metrics to communicate trust. And then the third thing that we talked about is how do we build value across three levels? Not only thinking about our stakeholders in the different ways on how they articulate and think about data value within each of their respective levels, strategic, operational, and tactical, but also how do we formulate the business case in taking first some benchmark assumptions if we don't have any benchmark data internally, but then using that as the lever to build conservative estimates and then tie out the value metrics to the specific strategy and initiative that we have in the data governance organization to give us a bit of a spread. We talked about the $50,000 USD spread to $125,000 USD spread as an example. So overall, I hope that you've been able to at least take away one thing from this presentation on how to think about not just data governance, but data quality, data analytics, data management, or just data in general and how to connect it in a more meaningful way to the business and articulate the value that it has to each of your organizations. So this has been helpful today. And at this point, I think Shannon, we'd love to open it up for questions from the group. Cameron, thank you so much for this great educational presentation. It's been so good. If you have questions for Cameron, feel free to submit them in the Q&A portion of your screen. And just to answer the most commonly asked questions, just a reminder, I will send a follow-up email by end of Thursday for this webinar with links to the slides and the recording and anything else requested. So this question came in pretty early, Cameron. You know, what's your take? What is a data leader? Can you give examples for roles and positions? Yeah, that's a great question. So a data leader, I don't think about data leaders as being hierarchical at all. If you are a change agent for how you are using data in your organization to make a positive impact and make a difference in your data leader. So I think about it more as someone who, and we think about it here precisely, as someone who can make a positive impact for how data can be used and leveraged in your organization to drive some sort of great outcome for the organization. So leadership in that case is someone who is brave enough and courageous enough to be the face, to be a leader within your sessions, to provide feedback, to look for opportunities on how to drive improvement and make impacts in your organization. So the punch line there is anybody can be a data leader. It just is starting with wanting to make a difference in having a passion, having, you know, the care and drive to make a difference in your organization. I would say that anyone that is on this webinar that obviously is taking a vested interest in data would be a data leader. Thank you so much. And, you know, and this next question kind of gets into that in any data governance topic, we talk a lot about the people, right? You know, based on your experience, any thoughts with respect to balancing multi-generational perspectives, especially when dealing with legacy issues? Well, yeah, that's a good question. I mean, a little bit ambiguous on, you know, on the multi-generational issue. So I don't know if that is more of a more of an age thing, or if it's more of a, you know, legacy systems versus kind of moving from, you know, on-prem to cloud, but I'll just kind of take a stab. I think, you know, what it comes down to is you have to think about whoever you work with in the organization, what do they care about? So in a way, you have to be a steward, you have to be a facilitator to delivering value to the things that they're already invested in. We talked about that a lot in the presentation. It is much easier to tie the things that you care about to the things that somebody else already is invested in and cares about. So you have a conversation of where I can say to you, Shannon, hey, Shannon, you know, I'm thinking of, I think about the data-versity crowd, they're really focused on becoming more educated. They're focused on becoming more knowledgeable about data, what trends are occurring within the market or just within other organizations. They are, they want a network. They want to get better within each of their jobs. What they don't want to do is they don't want to hear a sales pitch. They, you know, don't want to hear boring content. And so what we try and do is we create, what we try to do through the purpose of this session is create something that's valuable for this audience. And I would say that that's analogous to anyone that you deal with in your organization, regardless of their position or regardless of their generation. Now, there are many ways on how you can get someone to care. Really, the two most fundamental ways is people care because they want to or people care because they're told that they have to. And the best way to get someone to care is to work with them on something that they want to be a part of. So we always try and look at what are the different interests and what are the different, I think, initiatives and things that our stakeholders already care about regardless of what their background is or, you know, where they sit in the organization, business IT, you name it, and try and attach our cause to how it's going to help their day-to-day role. I hope I answered the question, but please, if out there, if you have any, whoever was the asker of that question, if you like any follow-ups to that, just ping me offline or ping our team offline, we'll make sure that we can have some good follow-up with you. Cameron, I think that was perfect because she clarified that agency or seniority she was talking about specifically, which you address. So yeah, so going back to the data governance committee that is set up to tackle data issues and opportunities, how do you keep stewards that are not so motivated by a specific business case of improving churn rate? For example, the steward that captures the data or processes at downstream may be the one to say this is a waste of time. Do you only include directly involved stewards and citizens? Yeah, so good question. Let's think about it as rings, like rings in a tree for a moment, or if you were just thinking about a target or a bullseye. So in the center of that bullseye or center of the ring of the tree, concentric circles, let's just say that that's where our business objective is. So whoever is the owner of that business objective, and in this case, let's say it's the CRO, our chief revenue officer or head of sales. That person really, really cares about customer churn or around top line growth. That's their core job. Well, one layer out from that might be the sales team. And then one layer out from that might be the group within IT that manages the CRM database for that sales team. And then one group out from that might be the data integration team that provides data that flows into that CRM environment. One layer out from that might be someone who is making sure that that data is integrated and is replicated properly, that that data is fit for use. So to the point of the question, that's a great question. That person might not have this ability to the center of the target. They might be four or five, six different hops away from it. It doesn't diminish their role. Their role is to improve the health of the efficacy of the data as it's being integrated and just as hypothetical example that I'm using. But ultimately, I think everybody wants to, I'm going to generalize here for a moment, everybody wants to feel like they are meaningful and important to the organization. Everyone wants to know how their job matters and ultimately how they can contribute to the growth of the organization. And that's the way to do it. So for that individual that might be kind of heads down focused on, you know, ensuring the health and the efficacy of the data, it's being able to give them the steps on how ultimately they're driving. They are playing a part in driving one of the most fundamentally important initiatives for the entire company. Now, they don't have to worry about growing top line revenue. But what their job does is it eventually trickles down where if they don't do their job appropriately, then we have bad data in the source that's being replicated to our CRM environment that's being used to engage with customers that is affecting our churn rate that ultimately winds up on, you know, the investor report or on the street from a Wall Street perspective or on our balance sheet that is reviewed on a monthly basis by the C suite. And I think all of us have a role on how we impact the business great or small. It doesn't diminish anyone's role. Some of us play a very specific role. Some of us play a broader role, but not as deep, but we all play a role. And I think it's being able to give line of sight to how that customer data steward plays a role. What's the brick or couple of bricks that they put in the wall that help the business achieve their goal or target. And getting visibility to that is really important. So if we're not able to articulate that to our team members, then that's something that we need to raise as part of our data governance planning sessions and ultimately the leaders that we're reporting to so that we can get that visibility and communicated to our teams. Again, so many great, you know, questions on personnel and yet another one, you know, dealing with the people, you know, I would love to hear some recommendations on how to assign ownership for attributes with cross functional impacts. We have a small handful of key master data attributes that several groups collaborate on to identify and assign a value to a specific product or customer. Our teams are so nervous to volunteer for ownership. So how can we identify one owner or can data governance ever own it? And how can we inspire teams to own the data? Yeah, that's a great question. So I'll try and answer this quickly and I'll try and be succinct with this, but this deserves certainly a broader conversation as I'm sure this person has found out. So first of all, ownership is not a set it and forget a thing and neither is data governance. You know, I think data governance, data management, data quality, any activity with data, we should constantly be thinking about how are we benchmarking? How are we testing? How are we learning? How are we improving? So the most important aspect is being able to take a step. That step can be a six inch step or it can be a giant leap. But the most important aspect is that we don't get analysis paralysis and start to argue about who the right owner should be. Oftentimes it typically starts with who has the most to gain from or perhaps if it's a defensive use case, the most to lose if the data values, if it's a value driven field or if it's a string field. If the rules were changed on how this data is maintained or created or deleted, who feels the biggest impact? So from a customer management perspective, if it's a customer account field and all of a sudden we change our formatting rules or our formatting logic, who within the business feels the most pain from that? And that's typically a good place to start because that person likely feels the most ownership and cares the most about if the underlying data were to change without them knowing about it, without having to say, then they would feel the most pain. It would throw off something from the business, it would throw off some sort of report. So that's a pretty good place to start. Sometimes we think about that on who are the data maintainers? So who is creating the data, hands on keyboard or updating the data when it needs to be changed? Certainly there are downstream impacts as that data gets maintained. Rarely do we think about who is typically responsible for or for improving the data quality from a data governance and stewardship perspective. The owner, as we talked about in the presentation, really should reside in the business on who feels the most business impact. So there are a couple of ways on how to tease out that conversation, but that really starts to play out when you build out your data governance organization in your operating model. And that's something that we have a lot of experience in kind of negotiating how that relates to the unique organizational culture and structure. So I'd like to have some more conversation there then certainly reach out. We can give you some helpful tips with more context after this session. It's a great question, though. Yeah, it could be a whole webinar, right? It could be a whole webinar. Yeah, maybe webinar number five. Yeah. So we have time for a couple more questions here. We've got about seven minutes left. So Cameron, data governance teams from a resource perspective, do you see most teams you work with staff with a blend of IT and business partners, or are these teams typically made up of mostly data, excuse me, my questions just moved on me, typically move it mostly business partner resources? Yeah, that's another good data governance org question. I'm really liking these kinds of questions because that tells me that data governance organizations are being built within the companies or institutions that you all work for, which is fantastic. That means that there's progress and that these debates are all healthy. It means that we're building up these functions in the organizations and there are stakes for success. So most data governance organizations that we see have a mix of business data and IT, I mean business, data, comma, and IT. The business roles or business sponsors that we typically see would be like a business process, a workstream lead. So someone who is responsible for, let's say, a new product introduction or for a product company, obviously, commercialization or account management or supplier spend. Other business representation that we see is a business analyst, so or business data analyst. From the IT perspective, it's important early on to have representation from architecture and perhaps specific DBAs because they know where all the skeletons are buried, so to speak, on how the data lands within the source, how it's maintained, how it's structured. They typically are pretty close to the data in early or emerging data governance organizations. And then the group in the middle are your data governance data management and data quality teams. Now those labels that I just used could sit anywhere in your organization. So you might say, well, Cam, our data governance team sits in IT or data quality, all our IT organization does our data quality work. Actually, I was talking with a customer last week where they throw the switch on data quality remediation because they don't trust anybody else to correct the data within their reports. Then they trust themselves. So the way how this particular organization had it is they will tell the business on when needs to be corrected. And the business has access to certain systems to go and correct the data. Some of you might be putting your hand on your face and thinking, oh my gosh, it sounds like a nightmare. But this particular company has made it work and they're looking to evolve that structure. But I think in general, just to generalize with the 200 or so people that we have here on the webinar, we do see emerging programs having a mix of business representation, data representation, folks who are just thinking about improving the quality and efficacy of the data, data governance, data quality, data management, and then the IT organization. So data infrastructure, data architecture, DBAs, perhaps data engineering also sits in that side if not the data team. So hopefully that gives you some helpful context. But I'll caveat any of this of if you have follow-up questions, we're happy to answer that offline or after the session. Thank you. And given the increase of data privacy protection regulations, would you provide insight on best practices with respect to data governance and the integration of tools and organization options to use? Yeah, I think the big, I think, focus point there is you have to use tools that really fit for purpose for compliance and regulatory. There are some organizations that can get by. It's not the right term. There are some organizations that can benefit greatly from having data governance capabilities that serve a variety of data governance use cases. We talked about compliance and reg and analytics and insights and operational. So we see a lot of organizations that are using an existing data catalog or they're using an existing data governance platform that is not designed specifically for compliance and reg, but is being leveraged for compliance and regulatory purposes based on the needs, based on the initiatives. Oftentimes compliance and regulatory events have very specific criteria that they need to meet. Articles that need to be followed if the auditor walks in through the door, figuratively speaking. So it really is important to do an assessment on what are the capabilities that this compliance and regulatory event is asking for and what is the acceptable criteria for this event. From that, what are the tooling capabilities that are needed? Do we need data lineage? Do we need to have some sort of automation from a governance workflow perspective? Do we need to show measures and metrics improvement in our data quality data? Do we need to have monitoring in place if our certain data assets fall below an acceptable data quality threshold? It very much is specific to the regulatory and compliance event. And if you find that your existing investments in tooling doesn't support those capabilities, then I think you have to look into specialized tooling. What we see more often than not is that most customers can use data governance solutions, certainly precisely has their own data 360 that can support compliance and reg and analytics and insights and operational use cases. But it really is fit for purpose on what is the compliance regulatory event and the specific criteria that the auditor or that the internal GRC team is looking for. Cameron, that's perfect timing as that brings us right to the top of the hour. Thanks to you for this great presentation and thanks to all of our attendees for being so engaged in everything we do. We'll get the additional questions over to Cameron and precisely, thanks to precisely for sponsoring today's webinar. Just again, reminder, I will send a follow-up email by end of day Thursday for this webinar with links to the slides and links to the recording. Thanks, everybody. Thanks, Cameron. You bet. Have a great day. You too.