 Hello and welcome. My name is Shannon Kemp and I am the Chief Digital Manager of DataVercity. We'd like to thank you for joining this DataVercity webinar, Business Value Metrics for Data Governance, sponsored today by IDIRA. Just a couple of points to get us started. Due to the large number of people that attend these sessions, he will be muted during the webinar. For questions, we will be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVercity. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now let me introduce to you our speaker for today, Ron Huzenga. Ron has over 30 years of business and IT experience across many different industries including manufacturing, retail, healthcare, and transportation. His hands-on consulting experience with large-scale data development engagements provides practical, real-world insights to enterprise data architecture, business architecture, and governance initiatives. And with that, I will give a floor to Ron to get today's webinar started. Hello and welcome. Thank you, Shannon, and welcome, everybody. It's great to have you all here with us today. And today's topic is, as Shannon stated, Business Metrics for Data Governance. And this is definitely not a technical session. Generally speaking, I do a lot of sessions where we get into some pretty technical detail and that type of thing. That's not happening today. This is really going to be a session when we're talking about the business side of data governance. So with that, we were going to have that business focus, but we're still bringing in a few concepts just for a frame of reference as we're going through the different parts of this. What I'm going to talk about today, first of all, is just to set the stage and just talk about some general information around the state of the nation or the state of the world today actually in terms of where organizations are in terms of information capability and data maturity, particularly. Then what I'm going to talk about is just a few aspects of data governance that I'm going to use as we go forward in the presentation as we build out this business case for data governance as well. And then the important thing that we're here for today, what really is business value and how do we quantify it? Quite often we get caught up in discussions where people say to really drive forward a data governance initiative, you need to build a business case. Today we're going to walk through how we go through the steps of doing that and aligning it with what our business leaders are expecting to hear from us to be able to sell that business case. Again, the frame of reference for that is what is our organization about. I'm going to talk a little about what comprises a lot of organizations such as vision, mission, objectives of the company, as well as key performance indicators for your particular organization or industry and how they come into play as well. And then finally, how do I communicate that governance message? How do I get the buy-in internally to really start moving forward with it in the organization? I'm going to run you through an example based on a manufacturing background. I started my career in manufacturing and it's still one of the industries that I find extremely interesting. So I'm going to walk through the example with a manufacturing type of paradigm, walking through the actual financial business case for governance, and then I'll wrap it up with a few final thoughts. So when we talk about information capability, there was a study that a number of people have referenced in the past, and I think it's a very good one. It was a 2015 study by PWC Iron Mountain, which was called Seizing the Information Advantage. And they had some very key findings in that particular study, as well as some good categorization about positioning companies and their readiness for governance. So I'm going to talk about that over these next couple of slides. First and foremost, when you look across the entire corporate world, very few organizations utilize information to its full potential. When we look into that deeper, we see a number of different things. There can be deficiencies in some technical capabilities. There can be deficiencies in skills in terms of applying what they're finding in that information. And in a lot of organizations, there's still lack of a data culture that really needs to be over to come to be more successful on this. What we also see, though, is a lack of investment in value-driven information strategies. And what I mean by that is when you're managing your information and your data in your organization, you want to tie it into your organization's mission, your organization's strategic objectives, and those types of things, because that's where the business is seeing how value is driven. So we want to tie how we use information directly in line with that same value-driven proposition that our organization uses day in and day out. Very few organizations, however, really understand how to derive the maximum value from the information they have. And if that's not corrected, that really does erode the corporate value. And we see all kinds of organizations today that make their entire business model based on managing information. And those that also have tangible products to sell or other services and that type of thing gain a huge competitive advantage by utilizing information better than their competitors do. So it's a very important area that we need to explore. Some interesting categorization. Now, one categorization in the study took the majority of companies at 76 percent, which they referred to as the misguided majority. And they broke it down into several different things. There were companies that were well-informed but constrained in their ability to deliver things like governance or utilize their information effectively. And there are also a number of companies that are still uninformed and ill-equipped to really take advantage of information in their organizations effectively. Some symptoms of that in a lot of organizations, data can still be seen as a byproduct or taken for granted. And that means people don't really appreciate the commercial benefits that utilizing that information can give you. Other things are legacy approaches or constrained by some certain types of regulations that don't really allow you to take full advantage of the data. So that can definitely play a factor. And in general, quite often there can be either weak analytic capabilities in the organizations or where there are strong analytic capabilities. There may be a lack of a value focus. So there's a lot of analytics going on, but not necessarily on the things that are going to generate true value for the business. In some organizations, there's still a very low level of analytic capability in general. Other things that come into play is just the sheer data volume that we're dealing with in organizations today. So companies can be quickly overwhelmed by the volume of data that they have. So they need a way to figure out what's important, how do I prioritize it, and how do I act upon it. And then of course, some companies still view data as the domain of data architects. They really need to get that business ownership message across and say that the entire organization owns the data, and the data architects help facilitate that and help us to structure it, but the business itself owns the data. And again, in terms of governance, where there's failure quite often or lack of traction as it were in business data governance initiatives is where it's IT led rather than business led. It really needs to have that business focus and needs to be led by the business organization to be truly effective. And in terms of also analytic capabilities, what you'll find in a lot of companies is lack of a fully formed analytics capability that's really fit for purpose, where a lot of organizations are still doing analytics in different areas and quite often in spreadsheets or find themselves in spreadsheet held because they don't have that consolidated capability. Now we're going to go to the other end of the spectrum and talk about what they refer to as the information elite. And these are the top 4% of organizations that really have a handle and really leverage data effectively in their organizations. It's classified as proactive action and what that means is being able to use the information at their fingertips to diversify their business model and take advantage of different market opportunities. Looking at that data and analyzing it to improve their operating efficiency internally in the organizations and also identifying and implementing new markets and opportunities around that. In terms of tangible data value, this is always aligned to and linked to organizational KPIs. Every organization has key performance indicators that are important and really has the pulse of what the business and how they're succeeding. So taking the data and linking it to those KPIs is extremely important. Also, how do we exploit that data for competitive advantage? And that competitive advantage can come in many different types in forms. Again, gaining more market share. You could be more cost effective. You could do things like introducing or reducing things like delivery lead time. So you have better delivery or better service than your competitors or a combination of multiple things. And that's usually rooted in data to allow you to make the decisions to allow you to achieve those types of things. And of course, we also have to balance security and value extraction. So that means we do secure our data in those types of organizations and pay special attention to security. But the people that truly need it to make the decisions have the access to the data so they can extract the true value out of it. And of course, the holistic approach. Governance isn't an add-on. It's a normal part of the business and daily business operations. So stating that a different way, it's woven into the organizational fabric and part of that overall data culture that's needed. And again, the information strategy. The information strategy is again aligned with the business and it reflects the key business objectives stated in information strategy terms as well. And interestingly enough, when they looked at the top 4% on the information elite, they saw the highest concentration in the following sectors, which were healthcare, manufacturing and engineering. Why is that baton thought about that for a minute? And the reason for that is probably because those particular industries have actually had a very long history of being rooted in data, especially in healthcare with all the information that needs to be tracked about medications, patients, and those types of things, but also in manufacturing and engineering, where there are design parameters, lead times, detailed specifications that need to be kept track of just to get a product out the door. So companies that are used to dealing with volumes of information and see the importance often are able to use that as a springboard to really use it for competitive advantage. If you've seen one of my webcasts before on data maturity, you've seen this slide, and I'm not going to go through this in great detail, but this is exactly the same type of thing that I've been talking about and others have been talking about in terms of data maturity as well. And when you look at the four major categories that I've got here in terms of data governance, master data management as part of that, data integration strategies, and data quality, we have the full spectrum from initial through to optimized, and I've even got a classification for none because I've run into organizations that really have no data maturity at all. Generally speaking, most organizations are still down at a level one or level two that they really haven't attained what they can achieve in terms of data maturity. And when you look at it, a lot of the things that you see on this slide align with some of the statements that we saw in the previous slides, such as, is your governance still thought of as a project? Well, that means you've probably got a fairly low level of maturity and you're not really leveraging the information effectively, whereas on the optimized end of the scale, if you've got enterprise-wide data governance baked into the organization, you're going to be taking a lot better advantage of it. And that includes things like having things like data stewardship councils, data centers of excellence, and also your approach to how you actually manage the data in terms of your technical data integration strategies, as well as your focus on data quality as a fourth objective, where you've done things like try to consolidate those silos, having data cleansing at consumption in order to basically first then improve the data quality, but then emerging to the point where you actually have a prevention approach to making sure that data is accurate as you're first capturing it in the organization rather than cleansing it later on. When we look at the other dimensions of this, if you're at a low level of data maturity, your primary IT focus is probably still primarily on IT technology and infrastructure, whereas at the mature end of the scale, the focus is really on information and strategic business enablement. Again, directly in line with the things we've already talked about, but also risk is very important. You're at a very high risk if you're at a low level of data maturity, but a lower level of risk if you have a high level of information maturity, and that's exactly contrary to value generation in the business. Usually, companies with low data maturity have a low level of value generation, but those that can capture and act on the information have a very high level of value generation, and that all ties into this business case that we're trying to put together. So how do we achieve data maturity? Well, first of all, we need to have an information governance oversight body, and it needs to be comprised of all important functional areas in the business. It needs to have full support of senior leadership, and as alluded to previously, it's something that needs to be owned by the business, not owned by IT. This, in principle, is why we've seen the emergence of roles like the Chief Data Officer in recent years, because companies are recognized that it really needs to be business-driven, business-focused, and aligned with business objectives, so that's why the CDO role is extremely important in organizations. What we also need, though, is what we call a culture of evidence-based decision making. Information is a valuable asset, so rather than flying by the seat of the pants, you make decisions that you arrive at by analyzing the information, and you have the facts and the data to back up that decision as you go through and make it. And if our businesses are doing that, then we need to expect the same of ourselves when we're trying to make the case for something like data governance as well. We also need to have a protection of sensitive and valuable information, particularly with a lot of the privacy regulations today, but even so, the regulations shouldn't be what's driving us to this. These are the types of things we should be doing anyway with or without the regulations. We also need to make sure that we have secure access, but those who need the information to make the important decisions have the access to that information to do so. And also, when we're doing the data analysis, it needs to be fit for purpose. We need to be able to interpret and visualize it according to the audience that's consuming that type of information as well. And underlying that, what you really need to enable it all is a sound data architecture and enterprise architecture practice as well. And that means things like data modeling, what's helped you to map out and understand and classify the data, tied in with business process modeling and understanding your business processes, because that gives you context. It shows you how your data is created, how it's used, and how it actually makes its journey throughout the organization as well. The companies that do that successfully are in that top 4% that are really making achieving those gains. In terms of governance specifically, this is a representation that I've used for a while of the Dama Wheel from the DIMBOK. And I'm not going to go through this and talk about all the different aspects of data governance, but I'm going to key on a few different things today that come out of data governance in some of these particular categories. In particular, I want to talk about the importance of data classification. We're all overwhelmed by data and information in our organizations today. So we need a way to classify that data, starting with things as simple as doing classification between master reference and transactional data, prioritizing it based on things like business value or business importance. And that gives us the ability to divide and conquer in terms of going after the things that are most important, and those are going to drive the highest level of business value first and then making our way into the other types of data in the organization as we progress. In particular, what we're really going to be looking at is data quality of the data characteristics and those critical data elements, which are the ones that really do drive the essential business value or are extremely important to our organization. Coincidentally, a lot of those critical data elements are usually found in our master and our reference data as well as opposed to the transactional data. And again, we always have to accommodate the regulations with security and privacy. There are still some organizations today that think that governance is a program that you put in place to deal specifically with security and privacy regulations. Again, governance should exist whether those regulations exist or not. It's something that we should be doing on a day-to-day basis to derive maximum value out of the information so that our business can make the best decisions possible. In terms of classification, again, this is a very small snippet and this is right in the data model. So it should be baked right into our process. So if we're doing a design first where we're actually designing applications and systems and building data models to support it, we should build this data classification right into the design phase when we go forward. In a lot of instances, we're actually trying to figure out what's out in our data landscape so we need to bring the information back in through things like data discovery, reverse engineering and those types of things. But there again, the classification should be one of the first things that we do because that gives us the ability to figure out what's important and divide and conquer and take advantage of that in our organization. So now I'm going to take a change gears for a second and just talk about quality in general. And I'm not talking about data quality first. I'm talking about quality in terms of how we look at it on the business side of our organizations. So let's say I'm a manufacturer or another type of organization. There are certain things that come into play. The first is quality insurance. So that means the entire systems of policies, procedures and guidelines established by an organization in order to achieve and maintain quality. And that's usually quality in terms of delivered products, delivered services and other things that you do in your organization. Obviously data feeds into that, but this is really about what we're delivering as an organization and how we build quality into that. From a quality control perspective, that's a series of plan measurements that are designed to determine if quality standards are being met. So what you're really doing there is you're doing it to have a control mechanism to measure and say, am I actually attaining the standards that I wish to meet? And this of course comes a lot out of things like manufacturing. And then we also have quality engineering in circles like that, which means building quality considerations into design and to predict possible quality problems prior to production. And of course, this was a lot of the quality movement for manufacturing and everything that we saw in the 80s and early 90s when we saw really emergence of things like ISO standards and everything else for quality conformance. The thing to remember is quality assurance is not quality control alone. You really need to have that quality engineering baked in as well. One of my favorite statements that I've heard is that the fundamental principle of quality is that quality cannot be inspected into a product. It has to be built in. And that analogy comes directly from manufacturing, where when something comes off the assembly line, if you look at it and inspected, you can detect the defects, but you have done nothing to actually prevent those defects from occurring. To do that, you have to go back into your processes in manufacturing and make changes so that you can actually design that type of defect out of the way you produce the product itself. And it's also important to remember that the responsibility for quality belongs to everyone in the organization. Again, borrowing from manufacturing, and a lot of the principles that were put in place there, empowering individuals to shut down the manufacturing assembly line if there is something that's detected that's wrong with the products so that it doesn't make its way through the end. It's resolved right away and then things proceed. We need to have that same type of attitude and same type of philosophy when we're dealing with the information in our organizations. So with that, let's talk about data quality. What we're really talking about in terms of data quality is the degree to which data is accurate, complete, timely, consistent, with all requirements and business rules, and relevant for a given use. Taking that a step further to information quality is now saying the degree to which that information consistently needs the requirements and expectations of the knowledge workers that are using that information in performing their jobs, which means helping them make the right decisions in their jobs. In terms of specific use, how does it meet the requirements and expectations for that particular use as well? Because data still has to be fit for purpose in order to make the correct decisions. And again, these are the dimensions that we look at when we're looking at data quality overall. Accuracy in terms of are the recorded values correct and do they conform to what we're expecting in terms of real values? Is the data timely? Because it could be out of date. If you're making decisions on information that's out of date, it's as bad as making decisions on information that isn't accurate in the first place. How complete is the information? Is it part of a collection? Where information do we need to have to use it in conjunction with those other data elements to make a decision? Is the data consistent? In other words, are the data values the same in all cases that should be the same to make sure that we don't have redundancy or inconsistencies in the data? And again, the relevance of the data for the types of decisions that are being made, which dies in directly into the fitness of use for that data. Because the data to be fit for use has to be presented in the correct format that serves the purpose of the person that's utilizing the data and also stated in terms that that user understands. An example of that is product data. The way we present product data is going to be different for salespeople that are selling the products versus engineers or production workers that are actually designing and producing the products. With that in mind, part of data governance and part of how we can actually leverage value out of the information to build this data case is look for data quality improvement opportunities in our organization. And again, these are aligned to the business. So here's some examples of the types of things that we could be looking at. Maybe we're getting poor quality data from the source. And in particular here, I'm talking about things like we get bad invoices or we get bad expense reports or all those types of things. So our accounting staff spend 25% of their time reconciling the submitted invoices due to those inconsistencies. There's a huge opportunity for improvement there. Things like inconsistent pricing of products across regions which could result in things like lost revenue in a particular business area. In this example, an organization had that and they lost potential revenue of a million dollars in one quarter in their European market. Going back to my manufacturing routes, organizations that have inaccurate bills of material that can shut down a production line if you have a sudden inventory outage that you weren't expecting which can also cause production delays and all kinds of other costs. Things like multiple IDs for the same product that exist in different systems. That can lead to things like incorrect orders in quotes, particularly if they're prepared in different systems with different types of information around them and bad decisions that are made. Same type of thing, order processing. Multiple instances of the same customer across systems resulting in incorrect credit checks. Maybe you don't know the full credit exposure of a given customer because they actually have their information spread across many different systems. Things like inaccurate prescription recording which could be resulting in dangerous or even fatal drug interactions. Things like that get your attention in quite a hurry. Incomplete safety credentials tracking which could mean for instance if you're something like a mining corporation or something like that, having uncertified disaster rescue teams should disaster strike and you have to deploy that team. And things like incorrect engineering data that could result in something like an uncontained explosion because of incorrect manufacturing specifications in the product that goes out. All of these things have their root and information. Now things that are a little more generalized in terms of poor data quality implications, they may not be that severe or that specific sometimes. But in general, poor information quality can cost any organization the equivalent to 15 to 20% of their annual revenue. In a lot of organizations, that's a very big number. And that's based on a study that was done by the US Insurance Data Management Association. Generally speaking, when we look at situations like that, the low quality means a low level of efficiency in operations. And generally speaking, it's insidious. So a lot of those data quality issues are kind of hidden from data to work, but it just means you're not as efficient so that you're kind of grinding away and not able to execute as quickly as you otherwise could. Unfortunately though from time to time, those manifest themselves in terms of a small amount of bad data leading to a disaster of epic proportions. And some real life examples that some or most of you may remember. When data flaws happen as an example, we can look at things like the space shuttle Challenger. Believe it or not, that was way back in January 28th, 1986. The problem that occurred with the Challenger was the O-rings in the solid booster rocket that failed, which resulted in an uncontained explosion. But when we looked at it in terms of what contributed to it, there's always more than one thing that's contributing. From a data perspective, there were five categories of data flaws that could be traced back. Poor accuracy of data, incomplete data, data inconsistency, the relevance and fitness for use of some of that engineering data, as well as a number of other factors that came in from a business perspective that contributed to that overall disaster. Another one, which maybe a lot of you won't remember, but I actually still remember the TV coverage of this when it was going on, and this was the Q Creek Mine in Somerset County, Pennsylvania. What happened there is in July 2002, miners accidentally dug into an adjoining mining shaft, and the mine flooded with 50 million gallons of water. There were 18 miners in there, nine of them escaped, but there were nine that were trapped in the mine. They had to stage a very complex rescue, which took 77 hours, and that required things like drilling a parallel air shaft and an escape shaft. The interesting thing about this is that the company had actually been cited multiple times for previous safety violations, but when we looked at what actually happened is the maps and the information that the miners were using were undated and they were uncertified. So there again, one of the major contributing causes was data accuracy, completeness, timeliness, and again, the maps they were given were not fit for the use that they were actually using it for. So again, these types of disasters get your attention, but we don't want companies to have to go through this. We want people to be able to leverage and take advantage of data on a day-to-day basis. To be able to do that, we need to be able to convince the business leaders with a business case to put that together. So what we need to do is we need to tie things like data quality and data governance into true business value. The business value is defined as the benefit that will be realized from an initiative measured in monetary or non-monetary terms. Generally speaking, there are three basic approaches that are used. Revenue enhancement, looking at cost savings, and also cost avoidance, and I'll talk about these things. Competitive advantage also comes in because we've talked about competitive advantage and using information for competitive advantage. Generally speaking, when we use the information for competitive advantage, we can tie that back to benefits back into one more of these other three quantifiable categories. So we just need to keep that in mind. And when we're quantifying it, there are some typical approaches that are used. A lot of organizations will use a fairly straightforward approach, such as basic return on investment calculations before they look further, but you'll get more detail and more quality out of things like looking at net present value or things or payback as well. The types of productivity improvements we're looking to drive to align with our business strategies are things like improving the efficiencies, so things like lowering total operating costs, savings in labor time, savings in machine time, reduction of waste. Things like improving effectiveness, like better decision making and just better communication in the organization as a whole. Looking in all these different areas will help you find the types of information that you can focus on. Achieving higher performance, whether it's increasing quality that we've talked about, reducing accidents or downtime and minimizing equipment breakdowns because that always costs in terms of productivity as well as unexpected costs. And also just general better organizational health. Companies that are executing well and smoothly usually have higher levels of morale, improve job satisfaction, and a lot more of a cooperative culture among employees as well. Now, just as an aside, sometimes it's a little difficult to measure productivity, particularly if you're in a service organization rather than an organization that's producing a tangible product. But even there, you can usually express things in terms of benefit and cost ratios as you're moving forward. In terms of the cost avoidance, it's not the same as cost savings and we just need to be very clear about that. What a cost avoidance measures are actions that avoid having to incur specific types of costs at all. So it could be things like decreasing costs by lowering a potential increase in expenses through pre-emptive actions that you take. Examples of this are assuring that these costs are never incurred like soft savings that we're talking about, and they are things like complying so that you avoid regulatory penalties and fines. Negotiated produce price increase from vendors. In other words, they're not going to do a price increase to you or a lower price increase than they were planning because you have your information to negotiate with them based on the information that you have. The interesting thing about that is these types of savings never show up on an income statement or a balance sheet, and that's because you've preempted those costs from recurring, but you can use that amount that you've saved to project out what your costs or your revenues would have been to show you what the business value is that you're gaining. Other things, maybe it's done on a cost basis, but sometimes it's just expressing things in terms of elimination or deferral of headcount increase due to process improvements that you can make based on the information you have, or even being able to do things like rescheduling maintenance of critical equipment so that you avoid work stoppages so you increase productivity overall. Again, the metrics that we use to measure this is we want to use what I call smart metrics, so we want to be able to have specific measurements that we can measure and target the area that we're really concentrating on. Again, they have to be measurable, they need to have data collection that's accurate and complete. They need to be actionable, which means they need to be easy to understand, and you need to be clear in terms of charting your performance over time, which direction is good or bad in terms of the actions you take to act on that information, and it needs to be relevant. So don't try to measure everything. Try to hone in on the things that are important to actually make the decisions and ignore the noise from other irrelevant sources of data that may be around it. And it needs to be timely. We need to have information in a timely fashion to make those decisions. Now back to the three categorizations of how we actually quantify these things in financial terms. So we're going to talk about return on investment, the net present value, and payback. First, we'll talk about return on investment. It's a simple financial ratio that's used to calculate the benefit in relation to the investment costs that we made to retrieve that benefit. And then we'll talk about the cost savings. So we're going to talk about the cost savings. So we're going to talk about why it can be stated in a couple of phrases, one by comparing income to the cost of the investment, or if we're doing it from a cost savings perspective, comparing the cost savings versus that cost of investment. So as a simple example, let's say you have an initial investment to begin a product, say first year staffing and tools on some type of a program that I'm doing that cost me $300,000 at the outset. So the way I would calculate my return on investment is taking that 800K return minus that 300K initial investment and then dividing it by initial investment. That gives me a ratio of 1.67, which gives me 167% return on investment. It really is that simple. Of course, the hard part is figuring out what those estimated cost savings are going to be, and I'll talk about that in a few minutes. Net present value is extremely valuable, and it's built on a very solid principle of a dollar today is worth more than a dollar tomorrow. The dollar today can be invested and start earning interest immediately. So what that means is if we get returns later on in a cycle, it's really not worth as much to us if we state it just in the absolute dollar value. We need to discount that to reflect what, to take into consideration the interest or alternate investment that we could have earned on that. So the present value of a future payoff is determined by multiplying the payoff by a discount factor which is less than 1. What does that really mean? That means we have a discount factor which is the reciprocal of 1 plus that rate of return. So 1 over 1 in brackets plus the rate of return if that's in the first period. If you look in the bottom right, if I'm looking at the rate of return a few periods out, what I'm going to do is I'm going to take the present value is going to be that cash flow in that period. It's going to take 1 plus that rate of return, it was 10% 1 plus 0.1 which would be 1.10 basically to the power of n which is how many periods out. So if it's 1 period out, n would be 1. If it's 2 periods out, n would be 2 and 3 and so forth. That gives you the present value of that cash flow for that given period. So then your net present value is actually taking that information and taking it together universally. Things to remember is that rate of return can also be known by different things in different businesses. It can also be known as the hurdle rate or the opportunity cost of capital. These are all basically terms for the same thing. What we're really doing with net present value is we're taking the value of the money we invest now and we're comparing it to what the returns that we're getting over a period of time that would equate back to the same as today's dollars that we'd be investing to do that. So again, C0's cash flow in period 0 which is often the initial investment and cash flows is typically expressed as a negative value and then C1 through Cn, if you're doing it over multiple periods, are positive values which are the amount that's actually being yielded by that investment. I've kind of been talking about a one-period example, but when I go through the real example, we'll actually go through a five-year example to show you how we do this. And of course, net present value is the return. When you take that investment, the return less the required investment and that can be a string of investments and a string of returns that we actually work with together over a period of time. And now again, it's just restating that same formula. So the net present value example is an example is let's take that same type of thing. My initial investments again is still that 300K that I talked about and my savings are still that 800K that I talked about. In this example, my discount rate because I think I can get 15% for my money elsewhere is going to be 15% or .15. So for the one-period example I'm talking about, my net present value is that, taking that $300,000 outlay, minus 300,000, and I'm going to add to that the discounted value of that 800,000, so 800,000 divided by 1.15, which is 695,652. Therefore, my net present value is 395,652 dollars. Again, we apply these principles the same way when we're dealing with streams of cost because our discount factor will change based on how far out the expenses and the returns are when we're comparing those streams. We can calculate the net present value by taking the savings versus cost in each period, as well there may be an upfront cost like I talked about which we call period zero when we're talking about these types of examples. I'm not going to go through detailed financial examples like you would get in a finance course, but you could also see things like varying rates over time that are impacted by things like compound interest and market fluctuations that you're considering as well. The important things to remember is in terms of decision rules that are usually made by businesses is you'll typically accept investments that have a positive net present value and you'll accept investments that offer a rate of return that are typically graded in that cost of capital if you can bring that back. The last one is payback. And there are certain situations your organization may be looking for and an additional investment to pay back within a certain timeframe. That's what's known as payback. And that means that you're going to have a specific cutoff period upon which you're going to evaluate what the end result is. So in the simplest form, you just count the number of periods it takes before the cumulative cash forecast equals what that investment was. So payback formula is initial investment over the net annual cash flow. So things to think about on this though is you have to be kind of careful on it but you could also want to be more accurate. So you might actually discount those cash flows in future periods, just like we did on net present value before you determine what that actual payback period is. So it would be based on that cumulative net present value rather than just the absolute values of what those cash flows are. And then you need to be careful because if you set your cutoff period too soon, you could actually disqualify a viable investment or a viable program that would have greater returns later on that you actually may want to undertake. So you have to be very careful with that horizon. Now in terms of the business case, we want to do a few things. One, to state the business case, we need to know our organization. So you need to ask yourself questions or say, do you know things like, what's your corporate mission statement? What are the key performance indicators in your business? When you're communicating your message, who's the audience that you're trying to sell something like governance to? What are their interests? What are their concerns in terms of their day-to-day business jobs and look at it from their perspective? How are you going to be able to relate to them? And how do you want them to react? So when you present your message to them, how do you want them to think? How do you want them to feel? And what is it that you want them to do? You want to be able to put it together very concisely. So think of something like an elevator pitch in terms of how you would sell this pitch or at least engage in further dialogue if you had that elevator ride with the CEO of your organization. Maybe it's a two- or three-floor elevator ride, so you're going to have a very short period of time to make your initial pitch to talk about it. It has to be clear, concise, and effective to be able to do that. And again, you have to align the benefits of it, stated in align with those business objectives that that executive is going to be thinking about. There again, what you want to be thinking about is aligning it with things like your corporate mission if you have one, and stating that benefit measurement so it aligns with the achievement or improvement of corporate KPIs that you know are in existence. And things to think about because it can help you to try to sell it is if you were trying to sell or have a mission to the type of program, such as governance that you're trying to sell, what would the mission statement of that governance program be? That can help to put it into perspective as well. In terms of techniques during the pitch, again, the corporate objectives, knowing your audience. Sometimes you may need to give examples of the result of taking action and what it will do. Sometimes you may need to use metaphors to help people understand the message that you're trying to communicate. And sometimes you may need to ask rhetorical questions, but just like if you're in court and there's a witness on the stand, make sure you know what that answer is going to be before you ask that rhetorical question or you could actually backfire on you. And again, don't try to commute everything. Just the important points of what your business case is. If you're looking for, for instance, approval here, what you're looking for is to open that door for further discussion of the opportunity. And best of all, do your homework. Once you've opened that door, the proposal is going to come under scrutiny, so you need the information and details to back it up. In terms of that corporate mission, I'm going to talk about this just for a minute here. Organizations may have a vision statement. They may have a mission statement. They may have both. The vision statement is the dream of how does your organization wish to change the world? It's kind of your someday statement and it's usually something that's big, exciting, and compelling. Your mission statement typically should be something that is what's the organization doing to accomplish the dream. And like I say, sometimes they're merged into one statement. The thing to think about is what do you do, who benefits from it, and how do you do it as an organization? And think this is often thought of as your everyday statement. You do it as an organization every day. And most importantly, you should not state that mission statement in financial terms. The renowned management consultant, Peter Drucker, says the mission statement has to express the contribution the enterprise plans to make to society, to economy, to the customer. Mission statements that express the purpose of the enterprise in financial terms fail inevitably to create the cohesion, dedication, and the vision of the people who have to do the work so as to realize the enterprise's goal. Stated a little more succinctly, what that means is you can talk about the fiscal objectives such as revenue, costs, and those types of things, but those should go in your supporting goals and objectives for the mission statement, and they should be quantifiable and measurable, just like the things that we've talked about. Very quickly, some compelling mission statements. Patagonia, to build the best product, cause no unnecessary harm, and inspire and implement solutions to the environmental crisis. That's a motivating statement. Google's is interesting to organize the world's information and make it universally, successfully, and useful. Coca-Cola is to refresh the world, to inspire moments of optimism and happiness, to create value and make a difference. Akiya is to create a better everyday life for the many people, and this one I really like is Nike, and that is bringing inspiration so that if you have a body, you're an athlete. That's a very flexible, but very motivating mission statement. So now, let's take these types of things and go to our real life manufacturing example, which we'll wrap up in the next few minutes here. This is Hotshot Manufacturing. The corporate mission is to be the world leader in the industrial heating industry, providing the highest quality, safest technology for hazardous environments, combined with the best delivery and customer service in the industry. That's an all-encompassing, motivating and actionable mission statement. Some of the supporting objectives are increased market growth and share of at least 10% annually, continuous improvement in manufacturing and supply chain efficiency, inventory is viewed as a liability, not an asset. Why? Because it's basically tying up cash that can be used for better purposes elsewhere in the organization. So to deal with that, we want to evolve to just-in-time manufacturing with no pre-stock of finished goods. We want to improve inventory turnover to six turns a year and this is how we're going to tie it into data governance priorities. To be able to do that, we've identified some opportunities that we want to put a governance program in place as our first pilot project to really justify governance throughout the organization. We want to improve order lead time for suppliers or our accuracy of knowing the order lead time from suppliers. We want to improve order sub-polices in terms of based on the types of components that we're buying different algorithms like time period of supply, balancing part period balancing which is balancing order cost to carrying costs, and other algorithms that we use based on the types of components that we're using. We want to make sure that we can have more accurate work center cycle times to set up time and improve efficiency, reducing down time at those work centers and improve product flow throughout the organization in the manufacturing plan. We want to improve product data. We need to have better build material accuracy not only to help the manufacturing of the product itself but also our forecasting. And again, we want to improve our forecast rationalization because that helps us to forecast and also give delivery schedules to our suppliers so that we get the right components at the right time for the right products that we're building for our end customers. We also want to minimize or eliminate safety stock which is a buffer that we typically keep for stock of components because of those other uncertainties. If we have better certainty in the numbers we can minimize that safety stock which is just money that's tied up to just hold inventory for the sake of holding it. Our initial targets are very modest and this is also something that's very good about doing in a business case that have very modest targets. We want to decrease inventory levels by 5% annually and we want to improve general efficiencies by 1% annually. Think back to that earlier slide when I talked about the cost of data quality costing a typical organization 15 to 20% per year. Here we're saying we're only going to tackle 1% of that as part of this business case to show that the rate of return just on doing those types of things. Very quickly on the numbers, the annual sales of hot shop manufacturing was $100 million a year. Typical gross margin on the products was 30% which meant cost of goods sold was 70%. Yearly growth also to express in those objectives was that 10% a year sales growth inventory carrying cost in other words how much it cost you to carry something in inventory for one year if you did is typically 25% of the cost of that item. So it's a very big number and it's something that's fairly standardized in a lot of industries and that's a cost that you want to drive down. The current inventory turns in other words how often we turn over those components in our inventory is currently 25% a year and our discount rate or hurdle rate that we're going to use for this business example is 10%. Here we're forcing out the annual sales and this is independent of the governance program so we're seeing that 10% forecast of sales starting in period one through five. What our cost of goods sold is based on these things that we're talking about. What our gross margin is based on the things that we've talked about above and what our inventory levels are forecast to be before we make any changes. Now what we do is we quantify the cost and the benefits of our program. So across the top those yellow numbers that you see are that discount factor. I'm using that 10% so that means my factor in period one is that 1 plus 10%. Period two is 1.1 squared. Period three would be 1.1 cubed just like the formula that I showed you previously. That's our discount factor that we're applying to both costs and the benefits of the programs in each of those periods. For my data governance program I've basically said here's my staffing cost starting in year one are going to be $500,000 and I'm expecting that to increase 5% annually over top of that. I'm going to have an initial software hardware acquisition of $200,000 and a further acquisition in year four specific to this purpose and I'm going to have hardware and software maintenance basically 25% of that as I go forward on year on year. So my total program cost is actually that should be $200,000 in period one then $550,000 in period two working their way up. Now the present value of cost is just taking that yellow number multiplied by the factor in the top row so basically no difference in the period zero. $550 divided by 1.1 which is $500,000 again by the 1.21 the $575 becomes $475 it can't speak and so on and so forth and what I also like to do is I like to track what's that cumulative value over time because if I'm looking at what my break-even period is going to be later on then I can actually see that by just netting my costs and my benefits that you're going to see in a second. Something that's also very useful is expressing it as a percentage of sales so the cost of my program when I look at the overall sales is only costing just over 0.5% of sales so 0.55% of sales now the benefits the annual inventory decrease of 5% that's a million dollars in year one 2.2 in year two 3.6 in year three because we're saying that that's a cumulative benefit that we're going to keep decreasing it by 5% a year because of the gains we're going to continue to make. What's that done to my corporate objective of inventory turnover? In the five-year period just this measure alone is taking me from 3.5 to 4.67 turns on inventory by inventory levels you can see forecast out by period but importantly with that is by the reduction in inventory look at the carrying cost reductions 250,000 alone in year one accumulating up to 1.8 million dollars by the time we hit year five with a total carrying cost reduction of 4.8 million dollars by general efficiency which is just a meager 1% a year that I'm stating again starts at 1 million in year one through to 7.3 million in year two as I continue to accumulate the gains there for a total of 19 million again we take those total benefits and add them together but then we want to apply those same discount factors that we saw in that very top row so then we have the present value of the benefits then of course the net present value of our governance program which is what we're really after here is the green roll in the bottom the outlay of 200,000 in year one our returns in the following years resulting in a net present value of over 14 million dollars on this initiative the return on investment if we look at that formula is 623% through this initiative I can tell you right now that this the reality was actually even better than that but this was the pitch so what is the pitch again this is your elevator speech there are inconsistencies in our product supply chain and manufacturing data that are restricting us from attaining our corporate mission and objectives we'd like to institute a formal data governance program to address this with an investment equivalent to only 0.55% of sales we can significantly reduce inventory carrying costs increase turns from the current level of 3.5 to 4.6 over the next few years general efficiency will also improve dramatically an improvement of only 1% will drive bottom line improvements exceeding 19 million dollars over 5 years overall the net present value of the program would exceed 14 million over 5 years but a cruise returns immediately with net present value of greater than 600,000 in year one return on investment is forecast at $600,000 we'd like to start immediately as this will be the cornerstone for similar improvements in other areas as well that's a door opener and that's the type of thing that you want to put together to really drive together this business case for business governance final thoughts data governance is a business why because data governance is part of the business you need to run the governance program like a business you should set up things like a governance vision statement mission statement you might combine the two again if your organization does the same you want to have specific goals and objectives for that governance program and you want to align it with that corporate vision mission and goals just like this like the manufacturing example gave you you want to have smart metrics that are measurable that you continue to evaluate your progress against you want to be able to classify and prioritize your data for instance on things like data quality because that's going to be based on the business value and impact that you're going to bring through the initiatives you also want to focus on things that will give you competitive advantage over your over others in the marketplace as well again you need to remember that governance is a journey and it's hard work there's no easy button you've got to roll up your sleeves and really go after it hard and you may want to start small and grow such as a pilot project to really start demonstrating the value and build from there and last but not least is you really need to celebrate the success when you have successes in your governance program for two reasons again it's hard work so you need to reward yourselves but celebrating those successes also gets the message out that you have that success across the organization that was quite a bit and some interesting financial numbers to look at there and with that I know it's been a fairly long and I thank you for being with me and we'll open it up to questions Ron thank you so much for another fantastic presentation we just love it as always if you have questions please feel free to submit them in the Q&A section in the bottom right hand corner of your screen I know there's a lot of good questions out there so please feel free to start away to answer the most commonly asked questions just a reminder I will send a follow-up email by Thursday to everybody with links to the slides, links to the recording and anything else requested throughout so just to get that going that's been a lot of the questions today Ron is can I have the slides there you go of course those are only published as well as the recording so we're good there so yeah absolutely so how do you measure efficiency of data quality controls well in terms of the quality controls themselves again it depends on your organization itself right you need to tie it into tangible metrics so you really measure the efficiency of the controls by evaluating against you know how are you making that difference in your business and in terms of the types of measurements of course I kind of gave a manufacturing example here if you're in another organization like retail you're probably going to tie things into critical business things which also would typically evolve around product as well if you're brick and mortar also your locations and what you're really thinking about there is things like gains in your assortment planning and also forecasting and things like that so always tying it back to the business equivalent if that makes sense indeed so what are some strategies to help prioritize classify based on business impact the best thing I would do there there's typically some critical data elements that are used everywhere like I say in manufacturing your product is everything and how you manufactured everything so generally anything around master data to do with product in terms of its characteristics its bills of material the lead times those are the types of things that you would typically want to focus in on same with product and retail obviously customer data is very important as well so the way I would typically categorize it is you're probably going to have some major categories of master data that you're going to want to focus on first and they typically revolve around things like product, customer stores and locations depending on what you're doing and you build out from there because those are the things that participate in virtually every business transaction that you do is there any tool or website available to guide us through some of the formulas mentioned to give us the tangible numbers brought up at the end in terms of the best I guess in terms of the tangible numbers again you have to know your industry so the things I would do depending on what industry you're in if you don't know this already somebody in your organization probably does so say if you happen to be in an IT function but you're not in finance or something in your organization align yourself or find somebody in your organization that can state what the important organizational KPIs are to you and once you've got that you might find what those standards are or those targets are in your organization then look at industry websites in the same industry you're in to see what the industry is actually using for KPR targets for those same measures and then start to establish that as a baseline to measure against because then you're not only evaluating what you're trying to do internally but you also have that benchmark against what you are compared to the industry as a whole in terms of the financial calculations and everything like that pick up a finance textbook you'll find detailed exploration of concepts like net present value return on investment payback and a whole bunch of other things that you may not have known about before just by kind of looking in a finance book Ron I think we got time for maybe one or two more questions here do you recommend doing a maturity assessment and if so do you recommend a model or approach I think that a maturity assessment is something that is not a one time thing I think it's something that needs to be part of the overall governance program in other words that you do repetitive maturity assessments it could be a quantified assessment based on a specific type of maturity plan or it could even be qualitative even based on evaluating some of the factors that I outlined in that one slide to say how do we measure up and how are we making progress against this and I would typically do it on a minimum of an annual basis to see how you're progressing I love it are there comments on the alignment of the CDO and organizations records manager interestingly enough when you look at the chief data officer and you look at records management the records management I believe should fall under the purview of the CDO overall because we're not talking about just electronic data and that type of thing we're talking about any type of data that's used in your organization whether it's in relational databases big data stores it could even be things like documents, video whatever types of data sources that you have that all needs to be governed and managed so that should all be under the portfolio of the chief data officer obviously for records management you know that would potentially be a function right within you know that reports into the chief data officer as well there's a request here if you could go back to the slide with the model and that I found this all the time we have where that does bring us to the top of the hour there's some great questions unfortunately we don't have time to get to today but thank you again so much for this great presentation and thanks to all of our attendees for being so engaged in everything we do we just love it and I will get a follow-up email again as a reminder by end of day Thursday with links to the slides links to the recording of this presentation as well Ron thank you so much and thank you and thank everybody for joining me today and you've got my email address up there I'd be interested in hearing how your governance initiatives are going as well absolutely I'll be sure to include that in the follow-up as well thanks everybody thank you very much