 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining the latest installment of the DataVersity Webinar Series, Data Insights and Analytics brought to you in partnership with First San Francisco Partners. Today Kelly O'Neill and Joan Lally will discuss CDO versus CAO. What's the difference? 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 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 DI analytics. As always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce to our speakers for today. John is a business technology thought leader and recognized enterprise information management authority. He has 30 years of experience including planning, project management, implementing information systems, and improving IT functions. John writes and speaks on a variety of topics and enjoys sharing his expertise on strategic planning, data governance, and practical technology applications that solve business problems. Kelly O'Neill is the founder and CEO of First San Francisco Partners. We're actually working to get her logged in right now. We have a little technical issue there, but she's on her way. She is an information management consulting firm. It is a veteran industry leader, speaker, author, and trainer. Kelly is passionate about helping companies leverage the value of data, empowering them to drive insights and information decision-making and improving results. With that, I will turn it over to John while I get Kelly logged in. Here she comes. Hello and welcome. Well, hello, hello, hello. And I can see Kelly going through the process now to get in. And in the meantime, I'll get us started until Kelly joins. Good morning, everyone. Well, there's Kelly right now. Yay. How are you all? Can we get started? Yeah, we are started now. We're up. Kelly, Shannon, why don't you turn control back over to Kelly. Kelly, you're a little faint, so maybe move closer and then we're good to go now. Fantastic. We just said hello and did the intro, so let's just start talking about the videos. We're going to keep going. Absolutely. Let's light this candle. Yeah. So this is hopefully going to be a really dynamic discussion today because there's been so much change in the industry recently with new roles and new titles and who does what and making sure that we're not stepping on toes or overlapping or anything like that. So what originally drove the conversation is that we thought that it would be valuable for you all to have a conversation around the chief data officer role and the chief analytics officer role. And as we started constructing the slides and pulling all of this together, we realized that there's other roles that are popping up left and right. So what we're going to do today is we're going to talk a little bit about essentially the data-driven evolution of the C-suite and what is driving that. And then we will drill down into the chief data officer, chief analytics officer, but we'll also consider some of the things like skill sets that are needed and why they are needed. So hopefully that'll be interesting for you all. And then how do you manage within those environments? Where is the overlap typically? Where are those gaps that you want to watch out for? What are some organizational structures that work and that don't work? And then how do you want to ensure that everyone is successful in their new titles? And of course we will always end our conversation with some key takeaways. All right. So how did we get here? Let's first talk about what's happening in the industry. Well, as you all know, the world is becoming data-driven. Everything is getting digitized. So whether it's from our phone conversations, our WebExes, what we present to clients, the way that we input information and order products online, the way that manufacturing lines track their effectiveness, everything relies now on data. And the digitization of assets, the digitization of information so that it can be consumed in an automated way and through computers. And so of course that's changing the way businesses are run, all businesses, big businesses and small businesses. And so to become more data and digitally driven, the C-suite is of course looking at how do we need to restructure in order to address these changes that are happening in our market, in our industry and therefore need to happen in our company. And this role of the Chief Information Officer is either changing along with it or is being complimented by new roles such as the Chief Data Officer, Chief Analytics Officer, which will be our primary focus of today. But you all are also seeing new roles pop up as a Chief Digital Officer. As well, the Chief Information Security Officer is now getting a louder and louder voice as security requirements are becoming more pervasive across all types of channels because of course data is both of an asset and a risk. So how does the C-suite evolve based on the way that the industry is changing? And so what we see is that first was the creation of this role called a Chief Data Officer. And the whole idea was to leverage data as an asset to move the business forward. So to drive the corporate objectives that are already identified within the boardroom and within the C-suite. So to take that strategy and turn it into a data strategy. So this means innovating existing processes. It means looking for new opportunities for competitive advantage and it means things like data monetization and identification of growth where there wasn't an already identified growth path. So it is truly becoming a business driven role. Now typically it oversees data capabilities. So if their role is to take the business strategy and translate it into a data strategy then it means that what are the data capabilities that we need within the organization in order to execute on that? So typically these are called things like data governance, master data management, data quality. It could be standards around data integration. It could be standards around data modeling. And it could also include sometimes business intelligence, data science and other sorts of analytics driven capabilities as well. So it can be quite a broad role this chief data officer. And the reason that it is called a chief data officer is that typically it will lead a dedicated group of data professionals that focus on these categories that we talked about. I've seen in the past where sometimes it does start with say just one person and their right hand person. But over time the reason that it ends up in the C-suite is because it actually does lead an organization. John, do you have any thoughts on that before we move on? Just to clarify as we're going through this, I think that probably a lot of people are thinking, well, our chief data officer, we have someone with that title and that's not it, or we have someone that does that and that's not their title. So just going forward, we're setting some guardrails around a chief data officer as a C-level type position. And the same thing as we'll see here in a minute on the analytics as well. There will be other people in organizations with top day jobs or senior data positions that we are talking here today. Our context is a C-suite type position. Excellent. Yep. Very good point. So that gets us to the next one is what to look for in a chief data officer. I'm looking at my sheet here. It's my turn. All right. Obviously, it's obviously is a level of data literacy. You say, well, that's obvious, but we've seen a few circumstances where someone has been appointed and then grown into the position and have discovered that this whole world of data management and data governance and advanced analytics is out there and gone, holy smokes. I didn't know this was here. Other key there is this optimization of the data supply chain. And we've talked about this before. On the road to analytics, data has to go through a lot of steps and stages and just focusing on that back end analytics output can get you into some problems if you don't have a good idea on how to run the entire data supply chain. We've talked about that a few times already. So the person coming in has to have a very broad orientation across data life cycles. The understanding of data value and monetization concepts is proving to also be important. We've seen some positions where the monetization has been giving me new products that are based on data. And really there's been many, many other opportunities within the organization to save money on the data monetization side of just saving money or lowering expenses and they've overlooked those. And lastly is a real business orientation. This is not a position for someone who is focused entirely on technology and the latest and greatest items to go by and install and hardware and software and networks and cloud services, et cetera, et cetera. The critical skills we see all the time obviously communication but the ability to communicate some pretty difficult concepts to the peers. Remember this is a C-suite position. So there will be C-suite peers to this position and very, very often the CDO is in a position where nobody knows why they're at the table and they have to explain that and then justify the continuance of their position. So they have to develop an organization and that brings in change and there's a change leadership component which is extremely important. And probably when you see a successful CDO, you see a very successful change program and really good leadership skills there. And the ability to get pieces of the organization to collaborate that didn't collaborate before. That's another high skill. And then, of course, if you get someone that's done something like this before at a very broad programmatic level, that's terrific and it's crossed a lot of functions. There's not a lot of people out there with this type of experience. So a lot of folks are promoted into this position or lobby to get into the position or recruited into it and do tend to grow into the position as well. We'll move on to the next one then here, Kelly. Let's see. Sure. Yeah. I mean, I think one thing just to add to that, though, is that if they're promoted from within the organization, demonstrating prior success in any sort of change initiative, any sort of cross-functional program is a great thing to look for because many times the person who is the candidate for the CDO is the one that identified the importance of data in, for example, a cross-functional multi-geographic SAP implementation as an example, right? And so these types of people have great skills within your organization and have identified and worked on data components of other sorts of programs and therefore have developed this understanding and skill set. So just when you're kind of looking across the organization, just another thing to look for. All right. Back to you, John. Okay. And thanks for that addition. Now, similar to the CDO, the analytics office that you're coming in should have roles, have some very specifically set out roles in there. And in our context here is they focus on the data analytics operation to drive the analytics-driven decision-making. That doesn't mean, again, that there aren't CAOs out there that are in effect a CDO. But for the sake of our conversation here today, we have to draw some boundaries. All right. They have to be an evangelist of analytics and vision and strategy because when you start to get into data-driven and predictive analytics type outputs, you're going to have an eyebrow raised when you say the data says we need to take the organization this way. Obviously, that's going to take somebody with some real marketing and sales skills. Typically, this person is over business intelligence and data science and analysis of all other types of business insights. It's nice that they are. We do see a handful of organizations. It's a decent fraction where business intelligence reporting is in another area. It's nice if it's not, but it is what it is. Key, though, is if you are going to do analytics in the advanced analytics predictive type sense, machine learning, AI, those kinds of things, the person really knows how to do business with that type of output. They're also looking for opportunities. They're looking outside the organization and what's going on in the industry. They're always on alert for what can we do? What kind of analysis can we run? Very, very much a detective, very, very much someone who loves a forensic approach to things. That's the typical type of role you see is they've got this roll up your sleeves and go find some stuff out type of role. Anything to add to that, Kelly? Yeah, I just wanted to highlight that outside the organization viewpoint. One of the things that they look for outside the organization is not just analytics capabilities and best practices, but data sets. What data can I bring into the organization that's going to help my data scientists that's going to provide me that needle in the haystack that I'm looking for? And so that, I think, is an important, I guess, well, difference in the sense that that is, I've seen that done much more proactively by someone who is a chief analytics officer or equivalent. So, yeah. Anyway, we're keeping going with you. I'm trying. Yeah, there we go. There we go. Okay. So what to look for? Baseline skills, obviously, again, the data literacy, the business orientation, but a really good understanding of statistics and analytics. This is not a position to bring someone in and have them not have that awareness. I suppose it could be done with a really bright individual, but think about it. You're recruiting people who speak day in, day out in the language of the statistician or an actuary or someone like that. And that's a very specialized type of language. Exploiting opportunities in AI and machine learning, that's another very specialized learning. Now, on top of those specialized skills, communication, collaboration, they go hand in hand. Same way with the chief data officer, you're going to be building communities of interest within an organization based on some type of cross-functional output or cross-functional program. If they've got success doing it before, that's terrific. Again, there's people out there that have been successful. They're already highly recruited. Most likely you'll promote someone in and help them get to where you need them to be. But if they've done a really nice big day to warehouse or an analytics effort somewhere, then obviously that is a super, super help. And once more, what is data value? What is data monetization? What do those really mean? Being aware of looking for opportunities for those types of positions. Again, that kind of a forensic mindset and looking for opportunities. Super, super important. Kelly, you're off to the next one. Yeah, just a quick one. I think a big difference from the chief data officer slide is the way that we are treating the supply chain. So in this one, we're talking about, of course, they need data literacy, and they need to have an understanding of the data supply chain, whereas the chief data officer, because they essentially will be accountable for the quality of the data supply chain, they need to be a lot more capable of managing to that level of quality, as opposed to a chief analytics officer who is kind of at the end of that supply chain, but if they don't have an understanding of the data supply chain, they might end up running their analytics program on a poor quality input or a poor quality data. So it's important that they understand what it takes to get there in order to be of the best quality. So let's look at this in practice. Go ahead. Yeah, I think this was mine, and you've got the next one. So the CEO, I guess, Kelly and I have both seen this the moment that all C-levels DRED comes back from a conference with a grand idea. And we're going to monetize our data. It's not exactly a Dilbert or a Dilbertian, if I can make up a word, situation there, but what does happen is where do we have opportunities? It's a legitimate thing. How do we monetize our data? And around the table, all of the C-levels are going to have various thoughts enter their head right away. The CFO will be, what's this going to cost? Is there an RI on it? Typically, the CIO will, in today's world, and we'll talk about CIO as information officer here in a little while and what happens along the way for that. But in today's world, a lot of times it will be the technology or the applications or infrastructure for something like that. In the context of our conversation today, though, the CIO is going to be thinking, well, let's align that to the business strategy. Let's make sure that we can, whatever we think of, is aligned with strategy, that we don't get ourselves into maybe something that's new or not in our core competencies or not in line with our market or our strategies. The CIO is going to have that more specific thought that, well, it's my job to do the advanced analytics in the analysis. And when you talk about monetization, that might be a new product or it might just be streamlining the organization a little bit and we can make money off of that. So what enters our minds is now, at a table like this, the conversation is going to be, this would be the ideal conversation, right? Everyone's going to put all this out on the table and then we're going to go from there. Now, depending on your organization chart, which we're going to get to here in a little bit, this could be driven by the chief data officer. We would want it, in an ideal world, want it to be actually driven by the chief data officer. But in a lot of organizations, chief analytics officer is also going to take a look at those types of opportunities and maybe some things that they can do and look for support from the chief data officer. So that's kind of one conversation that can be had when you have these positions up at the C level. And I'll turn it over to Kelly here for comments on this and she's going to talk about another type of conversation that can be had at the table. Absolutely. And we would all hope that the Cios are asking the question around how do we monetize data. But I think what's really happening is that the CEO is asking other business questions and it's up to the CDO and the CAO to figure out what that means to data analytics. So let's say this year the CEO says, our goal is to grow 25% through acquisition. What's our strategy? So then that needs to be translated into what does it mean from a data perspective and analytics perspective, et cetera. So a reaction or a response from the chief data officer could be that, well, we've optimized our data capabilities and our data supply chain. So now we can more easily ingest and leverage a new organization's data to truly get value out of that acquisition. So this is setting up the data infrastructure so that when there is an ingestion of new customer data, new employee data, new product data, et cetera, it can be done smoothly efficiently and therefore at the lowest cost possible. Maybe from a chief analytics officer perspective, they're saying, well, through our previous analysis, we understand the gaps in our products and where an acquisition could add value to our organization. So the chief analytics officer might be driving some of that forward-thinking viewpoint based on the analysis that they've already been doing to move the business forward in other ways. Of course, the CFO is asking about the cost of an acquisition and the anticipated benefit and I think that those responses by the chief data officer and the chief analytics officer do talk specifically to both that cost because it's not just what are you paying to acquire another company, but what is that cost to merge the companies together to ingest the processes to leverage the employees and the customers, et cetera, and then how do we maximize benefit in doing so? And then the chief information officer has a similar viewpoint as the chief data officer where perhaps they're saying because we upgraded our infrastructure and we have clear architectural standards, we can now assess the kind of scale expected from the integration of a new company's IT department. So again, different questions, this question being much more focused on this business growth versus leveraging data, but each of these participants in the C-suite has a unique viewpoint and an opportunity to work together to answer the question that the CEO is asking in a way that will help the CEO put together the optimal plan from an M&A perspective. John, any thoughts on that one? Yes, but I have to unmute to offer them. The difference here, and this is, we very deliberately did these two scenarios. One is kind of strategic, right? First one is, well, we're sitting around, we're doing what C-level executives do, and then we're doing strategy. And that's one type of conversation. This conversation is a bit more operational or tactical. We already have a plan, we already know what the strategy is that we're going to grow through acquisition. Now, if we go by somebody, what are the consequences? Well, that's kind of a reaction more of a tactical approach, and it was that the conversation shifts slightly. The takeaway for me from this is a conversation is a bit more granular in terms of actionable things that can be done, but there's two new faces at this table. The companies have bought other companies many, many, many years before CDOs and CAOs come along. Now you've got an extra dimension to that conversation with these roles. That's a pretty good dimension in our data-driven world, which is how we started this conversation. So off to the next one then. Excellent. So what do these organizations typically look like? So as John had said early on in the webinar, we are really talking about direct line to the chief executive officer. So these are C-level roles, and they sit on an even playing field with the chief financial officer, possibly a chief revenue officer, a chief marketing officer, and a chief human capital officer. I'm not sure what the trendy name is for that. But one of the points that is really critical here is to having this C-level group be aligned to effectively create and sustain a data-driven organization where they're all equal seats at the table or at that round table that we just looked at so that they can have an equal influence on leveraging all of these different assets that the company has at play in order to best move the company forward per their objectives. So in this instance where they are all equal players and they all have an equal seat at the table, it is important to clearly delineate the roles of the chief data officer versus the chief analytics officer to effectively collaborate and minimize that overlap. Now I do see that we have a question in here that says, what do we do if we have a chief analytics officer type person who thinks he or she is a chief data officer? So if you guys would humor me and let me answer that at this moment, is that okay? I think it could be a good time. So I think in that instance maybe there is an opportunity for role clarity in the sense that there might be some confusion around what the scope of the chief analytics officer is meant to be and do they have a chief data officer? Maybe in this instance there's not a chief data officer and so therefore the chief analytics officer picks up that accountability because there's no one else doing it. John, did you have a viewpoint on that? Did you want to add to that answer? Yeah, some... We've seen two different scenarios with this. One is that there is a CDO there, right? We've both seen that, Kelly. But there's a poor definition of where the guardrails are between the two positions and someone will wait in and say, I think we need to go this way and there are some examples in the literature of some real serious friction because there wasn't time taken to separate these. So if there is a CDO in place and the CAO is starting to act like a CDO, that is a definite problem for upper management and they need to be alerted to that. If you have a chief analytics officer who has no CDO, either reporting to them or as a peer, then you need to find out maybe their job is to act as a chief data officer or the expectation has been given to them by senior leadership to act like a chief data officer. We have seen that several times where the CAO has come in and has been in essence the fact of chief data officer as well but just not have the title for whatever reason. So this might be something that proves ever asking this question is it's an issue or it's causing an issue. But then again, in fairness to that individual, there might be some input they are receiving that you have not heard. That means that they feel they have to act this way. The third scenario is they feel they have to act this way and they're just kind of stepping outside the guardrails. That's a whole other set of problems when an executive steps outside their authority. Sooner or later, the peers will get involved with that and that situation will, they tend to be self-correcting over time. So that's my additional thoughts on that question. Great. Thank you. And so maybe what could be happening is an organizational structure that looks a little bit more like this where the role of the chief analytics officer and the role of the chief data officer happen to be swapped. So that could be what's happening, not knowing the organization necessarily. I'm not 100% sure. But what we're seeing more commonly is that the chief data officer, because that's been around for several years now, is at the C-suite, and then their organization is delineated into analytics or usage of the data, and then enterprise data governance, enterprise data management, and other enterprise data capabilities that looks after the guidelines and the care and feeding of the data that the analytics team uses. So this is a way to ensure that there is the lens focused on data and analytics and is in fact a great way to get the chief analytics officer up and running and prove some success while that analytics capability is still developing. And then maybe they would get promoted so that that chief analytics officer does get a seat at the C-suite table versus being a level down or being on the other side of the CDO. So this is something else that we've seen that is quite successful. John, thoughts? John, hit your mute button one more time. I think you were trying to say something. There we go. I have to quit double-clicking. All right. One thing we saw is a chief data officer. Actually, not just one thing. I've seen this several times. That has been brought in from an analytics position and now has taken this up, this organization that we have here, but they still have mentally stayed back in the realm of the chief analytics officer and that's manifested by not understanding the whole data supply chain. Understanding that the realm of the authority is the rest of the data supply chain. So some of that goes wanting for attention at some point in time. So you can reverse those two titles and reverse that question and still have a dilemma in your organization. This particular organization chart, I like it because the CDO has control of the data asset in all realms of its use and exploitation from governance, analytics, BI and the whole thing. Another question has popped up. Kelly, I don't know if you want to kind of do what we did with the last one here. Go ahead and answer that one along the way. It's based on kind of this particular slide. If you see that pop up. I can read it if you want to tackle it now. If the CIO has the ability to execute, is that the one you're looking for? Nope. If you could only have one of... Okay. If you could only have one of the roles, which one would you bring in first? Boy, good question. Yeah. I got an idea. Go ahead. Yeah. I mean, my thought is I think it kind of leads to probably why you and I created the slide this way in the first place is that bringing in a chief data officer starts to get the discipline within the organization of being data driven. And without being a data driven organization, the analytics group can only have marginal success because if their inputs, if their data supply chain isn't optimized, then of course their outputs are going to be compromised. So I would say a chief data officer would be the best role first and then optimize the data as it is fed into business intelligence, data warehousing, some of these more traditional forms of analytics and then jump to the unstructured and more high volume data lakes, sort of data science analytics, and then you can bring in a chief analytics officer to really make that organization scale. So that's my viewpoint. Yeah. And that's pretty much where I am too. So we can move on. Let's probably break the slides together, right? Yeah. Yep. Okay. So here's another organizational structure to consider. And so we've talked a little bit about the chief information officer, but not necessarily a ton. And so here's an organization where the chief data officer, chief analytics officer, and chief technology officer map up to the chief information officer. And so, and then underneath the chief data officers, of course, enterprise data management, enterprise data governance. And under the chief technology officer would be infrastructure and applications, et cetera. So this is something that when there is a very successful chief information officer. So let's remember that the I in CIO does mean information. And so when there is a very successful chief information officer who is forward thinking and understands the value of data and analytics and isn't just focused on architecture and networks and systems, then this is something that can be optimized as a way to pull together all of the capabilities within that organization. Now the caveat here is that it's an absolute critical success factor that that chief information officer be completely aligned with the business goals of the organization and really engage in operational execution so that the purpose of the CDO and the CAO are not just for technology purposes, but that they themselves are also completely aligned with their business partners to ensure that the business goals of the organization are met. John, any comments or should I go to the next slide? Well, yeah, I pressed above because that's kind of where my comments come from for the next slide. If you look at the 13, you'll see that we don't really we're crazy about this one. It's because, you know, remember the beginning of the conversation is these are C-level positions. Now when you have this, you could have something like this where these are all C-level positions in a really enormous organization with some complicated, large strategic business units. So we're talking about maybe, you know, one of the top 10, 20 type organizations in the world. But even then you get into confusion and we've seen confusion along this way here. There really isn't a chief data or a chief analytics. There are subordinate to the CIO and it pushes data management, data governance down visibility-wise. It's a little too far for us to feel comfortable with that. So if you've got something like this going or you hear about it or someone throws it up on the wall as an idea, proceed with caution on this one. This is a tough one. It's really difficult to make this work the right way. And that's, I just, my comments are all based on this slide right here. So I'm at this point ready to move on. Move on. Very good. No, absolutely. Over to you. All right. So CDO, CAO, you know, basic capabilities under the governing of data, the managing and data, and the using of data. And so we just threw a simple model up here that when you govern it, you've got everything from your strategy through oversight and data directives, which are policy principles and doing what data governance does and managing the change around that. Then, of course, managing your data is the quality, the metadata, the data models, data controls, things like that. And then the use is where you would have your analytics API and reporting and things like that. A chief data officer is going to tend to have some oversight over all three areas, but really dominate and set policy and governance and management. And the usage of data must be responsive to and in line with whatever the governance and data management policies are. The CAO, again, by our definition, is really heavily embedded in the capabilities in the use data region there. And they may think, and this might be where that one or question came from, they have to dip into data management because maybe the data quality is not right, or they feel like they have to dip into data governance because people aren't following the rules, bound the data, we're getting too much external data in the company, et cetera, et cetera, and it's causing the analytics aspect. It's causing problems for the analytics aspect of the organization. And this is a legitimate concern on a lot of CAOs, we're seeing this now, is that they go, well, look, I've got this job to do, but what they're sending me is not worth, I can't do anything with it, or what I can do with it is not what I'm, I can't maximize my position because of that. So you do see a lot of CAOs want to reach into these capabilities without a CDO to be clear, they will need to do this. And that's probably where this other question came from, is the CAO has a job to do and the data that is being provided needs governance and needs management and they got to do their job, so they're going to do whatever they need to do their job. That's why we think that CDO should really be, first, obviously they have to collaborate here and obviously really clear definition of which capabilities are accountable to which individual across all of these enterprise information management capabilities and who, you know, it's mandatory to do a nice, good, specific job of defining who is in charge of what in this particular realm of business management. So anything to add? If not, Kelly, pop on to the next one. Yeah, no, I think that you're right, that that may be the diagnosis of why the question came up around the chief analytics officer being, thinking that they need to be the chief data officer. But the reality is, is the analytics group aren't the only group that use data in the organization. And I think that that's one reason why it's a more challenging organizational structure to have a chief analytics officer that has a chief data officer or equivalent below them on the hierarchy because data is used in managerial reporting, operational processes, other sorts of decision-making. So it's not just used by analytics. And if the data is only optimized for analytics, then they might be missing those opportunities to optimize the data for standard business processes. And as you were talking about this, John, I was thinking that, you know, it's kind of like when you've got the head of sales complaining about a product and I can't sell the product, the product's not working, and having the solution be, well, why don't you run the product organization yet then? So that solution doesn't generally come up. And I think that that's an equivalent that people should consider how it's not a good solution to put the CDO organization under analytics just because they might have some complaints around the data. Yeah, yeah, absolutely. Anyway, just a thought. Okay, next slide. Yeah, so here's some scenarios. We've kind of been hitting some scenarios already. Right? CDOs brought in to lead everything but analytics because the CAO was already there. Now, when that happens, you've probably already got some data management, some data governance going on. You want a smooth handoff of those functions back to the chief data officer. Hopefully the chief analytics officer won't mind that kind of activity. And hopefully the CAO finds it to be not too terribly disruptive. The chief data officer is hired, but they came in as an analytics person. But they're not quite there, so they fall back on what they know. And then the CAO takes over business intelligence and data quality. We have capabilities that have left the realm of the two positions that are supposed to be over these. And now we've got about a third party to the mix. Hilarity ensues, no, not really. It becomes a real battle as to who is supposed to be doing what. And it can get pretty contentious. So please try to avoid those types of things. Boy, another one that happens is the chief analytics and the chief data officer brought in and the job descriptions are almost identical. And then it's who's doing what. Two real sharp folks will sit down together and work it out and divvy it up and make everybody happy. Others may not be so eloquent and flexible in their work style. And then you have some conflict. And lastly, we have, and Kelly will talk to this a little bit, I'm sure is the chief digital officer, which is putting all things data, all things digital under this one position and obscures the line, the clear line between chief data, chief analytics. Maybe not. It's something that we see here starting to see more and more frequently than we did say a few years ago. So I'll let Kelly chime in on that one before we do the final stretch here. Yeah, I would agree. I've seen the chief digital officer come in at a level underneath, say the chief data officer, but I've also seen them be promoted above the chief data officer and the chief analytics officer. So you're right. I think that there's evolution of these roles. And, you know, we can't expect any of that to change. So just when we think things settle, there's going to be something new coming up. So maybe we should just go to the next slide because that's where we're talking about organizational success. So absolutely what we think in the context of this conversation is the, these are C level reports. If they're not chief, whatever's, then give them another title and cross your fingers that they have the authority to get things done. They've got to be fanatical about aligning the enterprise and a single data vision and strategy. You, what you really can't have is an analytics, obviously monetize data this way and the CDO having a different idea or pushing a different agenda to monetize data another way. Everything should be lined up. There's a lot of technology that will affect this. There's a lot of business impact on this. If data is an asset and you want to make it an asset and model liability, everyone has to be on the same side of the table. Another key aspect here is the CEO needs to really understand these positions. There are examples of these positions being created because some consultants said you need one and the CEO really hasn't had a lot of interest in it and that becomes a problem. Again, within this definition is that they are direct reports and if they're not direct reports, then they're not fulfilling the defined roles that we've set out for them here. That means they have authority. If they aren't given authority or they said, look, you're brand new, your peers aren't used to you, so everything you're going to be doing now is just a guideline or a recommendation until we can figure a way to convince the rest of your peers in the leadership room that we're serious about this. That's not going to work very well. In general, any CDO, any CAO pages the data literacy of the whole organization, pulls it up, makes the whole organization smarter about data and everyone benefits from that. Over to you, Kelly. Absolutely. Why don't we look at it? How do you create success without a CDO or a CAO? One is just recognizing that there is a requirement to have somebody as a top job, so have a top data person. That top data person could come out of more of an operational role such as a chief medical officer or a chief customer officer who are both key consumers of lots of data and they have authority over rationalizing and standardizing and optimizing the data within their categories. Or you can consider some of those data intensive roles, so head of data management, business intelligence, master data management, if it's a strategic program within your organization. I think that's one thing is to recognize that there may be a top data job that has just as much impact on the organization therefore should have the same authority even if there's not the appetite to promote them to a direct report of the CEO. Another way to ensure success is recognizing from a sponsorship level and maybe one of maybe whoever that top data job reports to essentially sponsors them to have the same level of authority and mandate as if they were a chief data or chief analytics officer. And sometimes this could be in a very effective chief information officer if they truly recognize the information aspect of their role. So it is completely possible to have success without a CDO, especially when you're in smaller organizations where adding direct reports to the CEO may not be possible at all. So in the interest of time, let's just go through our wrap up slide just because I think that there's still some discussion here around critical success factors in our best practices and key takeaways. So like John just talked about, ensuring that the chief data officer or chief analytics officer is at a level within the organization in which they can exercise authority. Now it doesn't mean that it's command and control, but it means that they are integrated into the business vision and execution of the company and that they really have the ability to identify resources, rally resources, and execute programs across the company. So it is truly an enterprise role. As we've talked about on multiple of these slides and gotten questions from our listeners, that clear accountability is absolutely critical. So whether it's making sure that the line between a CDO and a CAO is as small and dark gray as possible, maybe it's clearly delineating the role of the chief digital officer. But the idea is that you want these folks collaborating, not competing with each other. And this can be done through clear job descriptions, communication and clear reporting structures, having those people demonstrate how they collaborate together, having them be viewed by the organization as effective and partners as they work together. And then I think this last one, John, you probably want to also comment on is, when you're interviewing candidates, there's a variety of backgrounds that these folks could be coming from. So it'll be very important to truly interview for what is critical within your organization, based on your culture, based on accountability, based on the current state of data and analytics, to make sure that you are hiring the right person for the right job. John, anything to add before we get to questions? Just 30 seconds. The key here on this last one is there are, like anything else, someone might have a title and it looks like they've got this and they've done it. But because even your human capital areas aren't used to hiring these folks, you've got to really, really break this stuff down. Use a strict definition of the term of what you're looking for. Start with the ones we've provided. Make sure that this is or is not a C-level type position, if not, adjust accordingly. But make sure you use a consistent semantic to find this person or else you'll think you're hiring somebody or someone's coming in and it's not the person that you think they're going to be. So I think, is that it? I think we can go on to the questions, Kelly. Okay, I'll read one and then you can get to one here. And we'll do the 15, 30-second lightning round here, version of these things. If a CIO has the ability to execute most, if not all of their needs, but a CDO or CAO, usually, and when I read this, that's where my eyebrows went up, usually needs their own IT resources to properly execute a strategy. Where do those resources reside or are they doubled up? So I think one of the things that's becoming clearer in the way that company and organizational development is changing is there is a lot more matrix management. And we all need to become more and more comfortable with the fact that we can effectively run organizations in a matrix structure. So in this instance, that decision around whether the CDO or the CAO needs their own, quote, unquote, IT resources is really, to me, a question around, can you work together in partnership without a reporting hierarchy, number one? And number two, maybe you need clearer lines of delineation around what the roles of IT versus a data organization or an analytics organization is. And so maybe there's a lack of clarity around those roles. Yeah. There's also the tendency to not understand in some organizations that almost everybody has, quote, unquote, IT, right? And just because someone higher, somebody who is IT-ish in their job, but they're not in there, the CIO, that's not necessarily a horrible thing. An awful lot of communication to Kelly's point needs to take place to clarify that one. All right. Moving on here then. Does having both position apply to any organization? What characteristics do you need in your organization to really need both positions? So my initial viewpoint is basically just the size of your organization. So we did show an operating model where an organizational model where the chief analytics officer reports up to the chief data officer. So it may be that that is the head of analytics, not a chief analytics officer. So I would look at the size and scale of the organization and how data driven is your product and the way that you go to market. So a company that relies 100% on essentially selling data services or monetizing the output of analytics is going to have a different requirement than any factoring organization that's highly labor intensive. So I would look at those as criteria. What do you think, John? Yeah. You don't always need both. If you're going to have one, you know, a chief data officer, we already covered that. The reason you would need both is, again, that an enormous amount of opportunity to monetize your data where it's just going to take bandwidth away from the CDO for other aspects of managing the data supply chain and enterprise information management. So that, again, and that would be a really large organization. Moving on to the next one. This is a quick review of our topic, but I think someone might have popped in. Like, what is driving the trend and the need to create a C-level position for CDO and CSOs and CAOs? I don't know whether that's a typo or if someone was talking about security officer there. Anyway, we talked about what's driving it. It's the fact that we're a data-driven world. It is now everywhere. Kelly, that's kind of where we started. Absolutely. That's right. One more question in before we close. What are the pros and cons to this chief data officer and a chief analyst officer being the same person? For cloth? Sanity? Sleep at night? Yeah, exactly. That's my look. In some organizations, everybody wears multiple hats because it's a smaller organization. And in some organizations, that person is actually one, and they have delegated the responsibility of the other. So they are either more focused on the analytics personally, and they have delegated data, or they're more focused on data, and they've delegated analytics. So I would just gauge how much work would a single individual be able to do and handle. In the 30 seconds we have left, the last question, we're getting started with defining a data strategy. Sounds to me like we should emphasize putting these roles in place. Would you agree? I would agree not as a rule of thumb, but, you know, what capability, go back to the capabilities chart, what capabilities do you need, how much is what's going to be the workload across roles and responsibilities on those capabilities and go from there. And it was, we didn't have 30 seconds. We had 15. Carrie, one more comment, and we'll just, we'll just, I'll just, I'll turn it back to you, Kelly, for a wrap up and we're done. No, I think you answered it perfectly. So let your data strategy drive your organizational strategy from that perspective. Shannon, over to you. John and Kelly, thank you so much for another fantastic presentation and thanks to our attendees for being so engaged in everything we do. We just love all the questions that come in. Just a reminder, I will send a follow-up email by end of day Monday with links to the slides, links to the recording of this session. And we hope to see you next month on August 2nd for the missed promise of Hadoop and new and emerging technologies. So hope to see you then. Thanks everybody. Enjoy your day. Bye-bye.