 Hello, my name is Shannon Kemp and I'm the Executive Editor of Dataversy. We'd like to thank you for joining this month's installment of the Monthly Dataversy Webinar Series, the Chief Data Officers Agenda. This month's leading industry experts from Silicon Valley Data Science will join Tony Shaw to discuss the Executive Guide to Data Strategies, sponsored today by CEA Technologies, the makers of Irwin. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them 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 CDO data strategy. And if you want to chat with each other and make comments throughout the presentation, feel free to click on the chat icon in the top right corner of your screen to engage that panel for you. And again, we'll collect questions in the Q&A section in the bottom right-hand corner. And 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 our moderator for today, Dataverse CEO Tony Shaw. Tony is responsible for the business strategy of the company and its subsidiaries, all of which conduct educational conferences, training, and publishing activities focused on the area of enterprise data management. You can meet Tony in person at our two upcoming co-located events in Washington, D.C., Enterprise Data World 2015 Conference in Expo, March 29th through April 3rd, and our second annual CDO vision happening March 30th through 31st. And with that, I will turn it over to Tony to get us started. Hello and welcome. Well, thank you very much, Shannon. Welcome, everybody. Looking forward to a good discussion today. Before we get going, I'd like to just send my best wishes to my colleague, John Ladly. John normally joins me as co-host of this monthly webinar series. He is, unfortunately, under the weather today in the metaphorical sense. I guess there's a lot of folks out there today who are also under the weather in a more literal sense, so we wish you well also. And I hope that you come through things. Okay. We've got this event coming up in Washington, D.C. at the end of the month, and all our contacts in D.C. at the moment are home with Snow Day. So best to everybody, anyway. It is a great pleasure that I introduced to you today some folks from Silicon Valley Data Science, Julie Steele and Scott Kurtz. We won't spend too much time in introductions, but I did want to provide some context here. It's the first time that Silicon Valley Data Science is joining us in our webinar series, and they're one of the pioneering and elite consulting practices in the data science field. I'm sure they will mention this in a moment, but Julie's research report on the Chief Data Officer was recently published by Riley, and I think she's going to show you how to download that a little later. There's also a nice brief Data Strategy position paper on the Silicon Valley Data Science website, which I enjoyed myself. I certainly recommend it to anybody else to go to after the webinar. So let me just reiterate, Julie has in fact written many reports for Silicon Valley Data Science, including that Chief Data Officer won recently, and Scott offers in-depth experience working directly with enterprises on implementing data strategies. I'm delighted to have them both with us today, and I think Julie is going to get us started. So Julie, take it away. Thank you so much, Tony. I think I might be having a multiple difficulty with our slides. I don't know if everyone else can see them, but I am not seeing them in front of me. I'm seeing slide four currently. First, there was Data Stick. Yeah, would you like me to... I may need to refresh or ask someone else to drive the slides. Okay, I see what. Shannon, why don't you take over? Julie, you may need to hand the... My apologies, Julie, here. Let me take your controls again. Yeah, okay. And let me... Do you see the slides now, Julie? Do you sleep? No, I'm not seeing them yet. Okay. All right, we'll drive. Okay. I don't see them either, Shannon. It's completely blank for me. Can we get a shout out from attendees as to whether they're able to see? Sure. Folks, just in the chat or the Q&A... No. Okay, it looks like... No. Okay, we've got a lot of responses. Thank you. Maybe early if you can't see them, we should have asked for that. But I can see them also. Julie, what I'm going to do... My apologies, everyone. So some can see, some can't see. So, Julie, what I'm going to do is I'm going to close this slide panel down. And then can you share your... Do you have the slides open on your desktop? My apologies, folks. We have this done in advance. Sorry, just one moment. Sure. Can you upload them again? Yeah, I'm not seeing a place to reupload them. My whole left side of the WebEx is completely blank. Okay, so let me close this. So I think we need to reupload, Shannon? Yep. Rather than screenshot. All right, Julie, you have controls again. Okay. I apologize, folks. I thought everything was working. By the way, nice to see so many folks attending this program who are going to be in DC at the end of the month. Please find me when you're there and introduce yourself. I recognize many of the names, even if we haven't met in person. So, please, in the process of reuploading. No, sorry. I'm seeing the introductory slide, but I still don't have any way to upload anything or to move the slides back and forth. Should I just sit WebEx and re-log in? So, you don't see share? You can't share in file? No, I don't have anything like that. Okay. Maybe Scott heard? Yeah, let me see if I can upload it. Again, my apologies, folks. I want to get this. So, I have the share my desktop share file. Is it share file that allows me to upload? Yep. Okay. All right, let's try that. Okay. It says it is loading the file. Okay. I see a new tab on my screen. All right. Okay. So, can everybody see the Silicon Valley title slide? I think it's working now. Julie, do you mind if I see it? Yep, I can finally see it. Looks great. Yay. All right. Let me close back to you, Julie, and we'll get started. Again, my apologies. Sorry about the confusion there. Sure that we get that sorted. Great. Thanks, everyone, for bearing with us, and I'll just make a brief clip that this is exactly why you need a strategy. This technology doesn't always cooperate. But thank you, Tony, again, for those introductions. And as Tony mentioned, I recently authored a report for O'Reilly Media called Understanding with Chief Data Officer. I interviewed over a dozen CDOs in organizations representing a broad range of industries, including Wells Fargo, the RNC, the Federal Reserve Board, Seattle Children's Hospital, Samsung, and a whole lot more. So some of you joining us today may also be CDOs, or maybe contemplating hiring a CDO for your organization. And so I hope that you'll add your thoughts to the comments as we go through this conversation today. But I wanted to take just a few minutes to share some broad themes from that report in order to frame our larger discussion about the importance of data strategy at the executive level. And then at the end I'll show you where you can get the report if you want to. So without going into too much of a history lesson, I think it's fair to argue that the so-called big data began to enter the executive consciousness with the various regulations about how personally identifiable information should be handled. A number of companies in government, in finance, in healthcare, and other sort of heavily regulated industries were the first in many ways to become acutely aware of the value of data, largely because of these pieces of legislation or these industry standards and the need to address compliance efforts around them. More recently, advances in tools, platforms, and the web have made data so plentiful and so accessible that companies in industries like retail, telecom, politics, insurance, et cetera, have found that it is a ready resource for the creation of new revenue streams and for better optimization, better workflow. So we've seen here as well a shift from a product-centric to you to more of a customer-centric to you. Anyone who's ever walked into a Starbucks and handed them your card and gotten a coffee back just the way you like it without having to order it or gone to your grocery store and gotten coupons for exactly the brand of cat food that you buy, it's familiar with the shift to the more customer-centric to you, where the data you generate goes not just to inform about products and product markets, but about individual customers as whole people. So there's this kind of shift happening that's making it more enticing for companies to use data in a different way as well. So it's not a perfect mapping, but you can think of this carrot and stick effect as loosely matched to data strategy and data governance, and of course look at much deeper into the data strategy side of things in just a few moments. But because of these two things, the promise of new services and efficiencies and the danger of security breaches and the obligation to follow these regulations. Many companies have begun to appoint chief data officers to oversee these aspects of the business, which are more and more crucial not just to the IT department and the technological side of things, but also to the inherent objectives and the value that the business as a whole is looking to create. So briefly, I want to cover what the CDO actually does in many cases. It definitely varies from company to company, and you pretty much find as many implementations of the role as there are companies who have one, but there are three important themes that I want to bring to your attention today. The first of those is centralization. So as we said, the goal these days is often to establish what's called a 360-degree view of who each customer is or patient or voter. But whether you're talking about, you know, voters or patients or customers or whoever, internal data is almost never enough by itself. So part of the role of the chief data officer is often to go gather data internally but also externally and to always be looking for additional data sources that can help create this surround sound view of the customer. So integration and aggregation are much better terms than centralization when we're talking about what happens to the data. And very often the pre-existing data-generating systems are left intact. So it's not necessarily about centralizing data as much as integrating or aggregating it. But I chose this word centralization because the theme at work here in the role of the CDO is bigger than just the data. It's also about centralizing the way company priorities are determined when it comes to data-driven projects. And that part of the role is equally important. That process of gathering, centralizing, and determining company priorities often means saying no to people. No, we can't address your project until two years from now. And that has to be done in a way that won't make enemies. So that ability to integrate, aggregate, centralize is a really critical aspect of the role. The second important theme is evangelization. So the CDO often has to work with product owners and managers from across the entire organization as well as with other executives. So as we were mentioning with the centralization priorities, important skills that here include diplomacy, political skills, and even what some would call sales abilities because the CDO has to set those central priorities and then sell that vision across the whole enterprise. And it's something that reaches down into every corner. You can in some ways think of the CDO's team as the central nervous system here. They're not the muscle, they can't actually move the body, but they connect and communicate everything back to the brand of the operation. And so finally, the third theme I want to cover here is facilitation because to continue our nervous system analogy, the CDO team's job is to lower barriers for everyone else in the organization to be able to act, to be able to collect and use data intelligently, agilely, and in keeping with that central vision that will drive business value. The ideal CDO in short is one who makes better, more efficient actions possible for the rest of the organization. So the themes we've just described here are all fairly broad and require a diverse set of skills. Some of the typical challenges of the role include both the technical side and the business concern. The obvious challenges of working with data from different places from inside and outside the company that of course comes in all different formats. There's a lot of bridges to be built there and architecture that has to be considered. But there's also the business question that ultimately this is about driving value and if you don't have good business questions, it doesn't matter what kind of technology you have as a joy about a girl who is the CDO for the City and County of San Francisco set. And then of course there's also the challenges of working with people. We saw there's a heavily political side to this and sometimes we like to say people are the hardest part of technology. So just to underscore, if there's one thing that you should take away about the role of the CDO, it's this, that while technology is inevitably involved in working with data, the defining goal of the CDO is not technological, but business oriented. And the ideal CDO exists to drive business value. So with that context and that framework, I'd like to turn it over to Scott to talk more about how to drive that business value with the Sound Data Strategy. Great. Thanks, Julie. So I'd like to take a few minutes and talk you through the Silicon Valley Data Science approach to data strategy and the way that we think about data strategy in general. So many of you are probably familiar with what I'll refer to as a traditional or a conventional data strategy, the way we've thought about data strategy for the past couple of decades. That's really primarily about data governance. It looks at things like how do you maintain control of your data? What are you doing with respect to security or retention policies or data cleanliness? And those are all important concepts. But as Julie mentioned earlier, those are really aspects of data governance and when we talk about data strategy, we actually mean something profoundly different. So if you think about data governance, what you're really doing is you're looking at various things that you can do to data. When we talk about data strategy on the other hand, we're really looking at things you can do with data. These are the things that represent the opportunity for your business, your ability to grow your business by attracting new customers or targeting VIP customers or automating the way that you do things to improve your operations. These are all the business goals that data can enable and that's really what our focus is when we talk about data strategy. So at SCVS, we fundamentally believe that the value from data strategy comes from the opportunity to blend business and technology together. And Julie mentioned the quote from Joy, Banna Guru earlier, about having good business questions. This is exactly the kind of thing that we're talking about. The linkage between those business questions and then the technology that actually provides the underlying platforms to help you answer those questions is really the most important thing. And if you can't articulate the relationship that technology and business have in your organization, then you really need to revisit your approach to data strategy. So when we approach data strategy at SCVS, this is one of the couple different offerings that we have where we work with clients to develop a data strategy with them. We take this very business-focused starting point as our primary way of engaging with customers. And in order to help talk you through that process, since we obviously can't talk about the data strategies of our clients, I'd like to introduce an example that we can carry through to introduce a bit of consistency. So to be very clear, Petsmart is not one of our clients. The article that I'm linking here, and I believe Julie is going to put the URL into the chat session so everybody else who will inevitably want to go read the article while they're listening to me can go ahead and do that. It's actually an article from a retail industry news site that's about a year old. And in it, Petsmart's CEO talks a bit about their plans and their ambitions for things that they want to do with data. And the reason I use this article is I really like some of the points that David Lenhart makes in this article. It's the level of conversation that we often have with business leaders at the outset of a strategy project. So I think it's a good starting point for a public example that we'll be able to talk about without compromising the secrets of any of our clients or picking on one of you as an attendee to share a business problem-wide that we work through together as much fun as that would be. So as I mentioned earlier, with any data strategy, we start with the business. We start by asking the questions that matter. What's driving your business? Are you trying to enter a new market? Are you trying to grow your business? Are you trying to optimize operations? What are the things that are keeping your board members up at night? These are the real goals of your data strategy. And the data is ultimately just a means to that end. So if we go back to Petsmart for a moment and coming through the comments from Petsmart's CEO, there's a couple of different high-level goals that stand out right away. They want to improve their ability to connect with pet parents in a personalized way. They're looking for new and interesting ways to grow their most valuable customers. And there are others as well, but I think these are a couple of good examples of the starting point that might lead to a data strategy for them. From there, we have to start breaking it down. We have to make the leap from strategy to tactics. How are you going to achieve those aspirations? Maybe you need to get down into the details and understand how you can predict demand in a particular region or personalize your customer experience. This is the point at which you really need to expand your imagination and think beyond how you're just using data today. Yes, you're bound by the art of the possible, but it's at this stage that you can really start looking for the problems that the business needs to solve in order to move forward. So in the case of PetSmart, they very clearly need to look at ways that they can capture and use customer and pet data. They have aspirations to deliver personalized recommendations and marketing offers to their customers in various touch points. And they're interested in changing the way that they track and share real-time inventory to make their online experience as seamless as possible. So these are the kinds of imperatives and objectives that we end up talking about with clients. And some of them will be addressable with data, and others won't, and that's okay. And so as you work through the process, you start to focus on where you can actually get the value from data. So even though we're beginning with the business, even at this early stage, working together is absolutely a required element. If you have the technologists and the business leaders together when you start working on your data strategy, you have the opportunity to get common consensus on what are the drivers, the priorities for the business, the basis for the strategy that's going to be essential later on. If you've created things together, you have a shared sense of creation, you also end up with a shared sense of ownership, and then it becomes a journey that you're on together as opposed to something that gets thrown over the wall to the technology organization to implement. And as we have these types of workshops with our clients at the beginning of a data strategy, we find that they often lead to some really interesting aha moments that just come from having the participants talking to each other in the same room. Oftentimes it's the first time that these two groups come together to have these types of conversations at that level of abstraction, at the end goal in mind of how can we work together to improve the business. So from there, next we have to consider what's the role of technology in making this possible? And this is where the technology leaders really need to take the reins because now we're stepping a little bit deeper into the technology world where it requires specific data and analytics expertise. There's a required element of exposure to and understanding of a really rapidly changing landscape of technology. So what we do at this point is we work very closely with technology folks to help describe the use cases. So for each objective, what is it that you actually build? We want to help clients understand the journey of getting from why you're doing something to what you're doing and now finally to how will you do it? So for instance, we might talk about use cases around customer segmentation to help discover VIPs or how you can build models for predicting product demand. For Petsmart, based on the goal that we talked about a moment ago around delivering personalized recommendations, we can imagine a number of different use cases related to product recommendations such as recommending new products based on past purchases at point of sale or something else that got mentioned in the article was the ability to recommend upcoming store community events based on customer preferences to help engage and become a more active part of the pet community where they operate their stores. So it's at this stage that you'll start to notice that you have a lot of different use cases across a number of places and now is the opportunity to start looking for opportunities to exploit patterns and take advantage of opportunities for reuse. So whether it's through looking for opportunities to build a common platform to serve a number of different needs or producing a data service to make sure the data is accessible in a number of different places or even condensing things down to technical workloads or a specific piece of services or customized in different places, you have to look across all the use cases in order to find these. What are the most common workloads you can make use of? So for instance, if we're talking about a business that requires social media sentiment analysis and customer product intelligence and maybe call center transcript analysis, all of those could share a common technical platform related to whether you're talking about point of sale recommendations or you're talking about community event recommendations, you might be able to rely on common recommendation engine that acts on top of customer data to produce different types of recommendations for different scenarios. So that might represent one common workload that you could build once and then customize to help you solve different use cases and focus on different parts of the value as you're working through this. So now that you've decided and you've even started to look at how you might build it more efficiently by looking at patterns and reuse, there's lots of things you can do. But one of the most common questions that we get from clients is what should you do first? How do I get started? That's probably the most common question that we have when we start talking about data strategy with clients is they have vague ideas of things they could do but they don't know what to focus on first. So in a data strategy engagement at this point, part of the thing that we do with clients is help them prioritize their efforts. Not all projects are created equal. So just picking one off of a list isn't really the best way to get started. You can often fall into the trap of what I consider to be the worst death a project can die. You've built a successful proof of concept. You've shown that a technology can be used to solve a particular business problem. It's working. You can demo it to everyone. You've run beta tests but yet you've solved the business problem that nobody cared about and so it's like shouting into the wind. So what we focus on at the priority stage is figuring out what matters most to the business. And it's really important here to remember that what you're prioritizing are the business goals first. You're not prioritizing projects and you're not even prioritizing use cases. You're prioritizing the goals that you have as a business and then you start to think about how you can tie projects back to those high level goals. And a project doesn't necessarily have a one-to-one correspondence with a business objective. So for instance, we might have a project for Petsmart that says build out a real-time personalization engine for their online store. And that addresses a couple different business objectives. It helps them deliver personalized recommendations and marketing offers but it might be part of that effort might also require building out some capability to track and share real-time store inventory because if you're recommending products that you don't have in stock, that's not a very good recommendation. So you'll end up hitting on multiple business objectives at a time when you structure your projects correctly. Beyond just the business priority, there's a number of other dimensions that you need to think about as you're figuring out where to get started. So this is oftentimes more of a technology conversation than a business conversation, although not always. But there's other things that you need to consider like do you have the right skills in place? What's the relative level of difficulty for the different problems that you're trying to solve? What are the complications that we might run into when we try to deploy this system? By going through these other dimensions, it helps you identify early where you might find quick wins but also where you might find black holes of effort that you can fall into and never come out of. And then finally, one of the other important things that we look at at this stage is the ability to be data-driven. That's really the point that we have. We're encouraging our enterprises to be more data- driven and everything that we do. And we force this into our data strategy approach as well. So for instance, you might have someone in the organization that's convinced that image processing is the most important technical capability that they need to build out. But if that doesn't line up with the business priorities, that's going to come out in the analysis that you do. So this is really about overcoming your assumptions, overcoming any conceptions that you have, rooting out people's pet projects that they might have, and doing so with real data, helping them understand why you don't want to focus on image processing first. It might be an important capability, but there might be three other things that you need to focus on before that. And because you actually went through this whole process together, you've got a shared sense of priority. You understand the reason that you're headed down this path. And as a result, hopefully you can make those kinds of shared decisions without them turning into major political battles. So the goal of any data strategy is really to identify a pipeline of projects that you can execute to take you toward your goal. So the last thing that we try to do with any data strategy is to build a roadmap that gives you a sense of the direction that you're headed. So we go back to the combined notion of priorities. We understand the additional dimensions that come in by looking at technical complications or even business complications, other efforts that are already underway. And we use that to build a single roadmap that can fuel agile development with an endpoint in mind. So at SCDS, within the way that we approach data science as well as the way that we approach data engineering projects, we're very much strong advocates of agile processes because we believe that they help you be a lot more flexible and reactive to the realities of your business. So when working through a roadmap, what we're trying to do is take the set of business priorities at a snapshot in time and build out a roadmap that is this pipeline of projects that you can then begin executing. But when you have a roadmap, there's this tendency to think of it as your 24-month plan that now you're going to put your strategy away and you're just going to start charging down that road. The reality, though, is that there's a lot of traps that you can fall into when doing that. And even if you're using agile as your mechanism for building any one of those individual projects, that may not be agile in the macro sense. So one of the last caveats here is that when you're thinking about your strategy, you have to make sure that it's flexible. You're doing an analysis at a single point in time, but what you're really doing is laying the groundwork in a framework for a repeatable process that you really need to be undergoing on a regular basis. How regular really depends on your business, how fast it's changing. But this will give you the ability to respond to changes in priorities within the business. Maybe your business strategy has changed. It allows you to respond to changes in the market. Maybe there's a new competitor. It allows you to respond to new changes and possibilities with technology. So if I think about all the various new technology developments that have happened in the last 12 months alone in the data and analytics space, things that we would have said were impossible or difficult or we would have had a particular approach in a project 12 months ago might look very, very different today based on the realities of technology and the way that things are maturing in the marketplace. So you really want to take the opportunity to reassess your priorities, reassess the difficulties and reassess plans as often as it makes sense to based on the realities of your business and treat your strategy as a living document that actually guides you down that process but without laying it in stone. The last recommendation then is that you need to make sure that the strategy that you create is actionable. And part of the reason that we follow that path from business objectives to use cases to workloads and then start to describe projects from there is that it really forces you into thinking about the actions you will take based on your strategy and gives you a clear notion of how you're going to execute that strategy because if you can't articulate what it is that you're going to do on day one after you've finished your strategy then you don't have the right strategy. Certainly you need to work within the realm of the possible and you need to be able to respond to complications and be practical about what you can accomplish and what you can't accomplish and in what period of time. But if you're clear about how the strategy leads to action and you've been honest in the roadmap that you've created all of that ends up coming out as a watch and you end up with an actionable strategy that actually helps you achieve those business objectives that we started with at the beginning. Let me sum up the process here in a few simple concrete steps. At the very beginning of any strategy you should be identifying your business objectives before you even begin to talk about technology. Next you do need to go from those business objectives to the tactics that you would employ. A lot of those objectives will be very pie in the sky, very high level, difficult to articulate what you're going to do to achieve them so you have to lower your level of abstraction to talk about the tactics. When you're doing this resist the urge to fall into separate business conversations and separate technology conversations including all stakeholders in the conversation will make your life so much easier as you continue down this road. Next look at how technology supports the use cases that you'll need to build. Get your technologists involved at all levels. This is not just a strategy exercise but get people brainstorming and thinking about how technology can support the use cases to help you achieve those goals. Next because no one likes to build the same thing seven times and we all enjoy the opportunity to take advantage of patterns reused to lower cost and improve reliability and all of the other benefits that you get from that. Look for those opportunities early and make sure that that's based into the process you use to develop your strategy. Next prioritize the possibilities that you have to figure out where the right place to start is. If you're not sure where to start go back to your priorities. If you think you have a project in mind but aren't sure whether it's the right thing to do test it against those priorities. If you can't articulate why you're doing what you're doing first then you're not ready to start. Next define your roadmap with an endpoint in mind even though you're working in an agile fashion at the end of this process you want to have a solid repeatable platform with a sustainable architecture and so you need to think with that endpoint in mind and make sure that as you're building you're getting closer and closer to that endpoint. And then finally please don't think of your data strategy as something that you put in a binder and put on a shelf that begins to collect dust. This is really something that you should be relying on and referring to frequently. One of our clients that we did a strategy with earlier this year has told me on several occasions that when they get into situations where they're confused about whether or not they're still working toward the goal he pulls out the strategy and he goes back and he looks at some of the prioritizations that happened that the executive team all agreed on and tests those questions against the priorities they agreed on and then asks themselves have the priorities changed? So the opportunity to leather, rinse, repeat and update your strategy on a regular basis is one that you should absolutely be taking advantage of as you move forward. So with that recap I will turn it back over to Julie to talk a little bit about some of the other aspects of CDO and other things that we have to offer from SCDS. Thanks, Scott. That was a great overview of how we approach data strategy and why it matters so much. I just wanted to circle back to the CDO because some of you having heard all of the elements that are part of a successful data strategy may now be contemplating hiring your own CDO if you weren't before. And I just wanted to address a couple key concerns there. So the first thing you really need to do if you're considering hiring a CDO is to know why you want one. As I mentioned at the beginning there are several aspects to data at the executive level, one being governance and regulation and compliance, and the other really being new revenue opportunities, the new customer-centric view that today's data makes possible, and some of the other optimization efforts around that. So here are the list of questions that I recommend that you ask yourself if you are contemplating the need for a CDO. So are you part of a regulated industry? Do you need to move from being product-centric to customer-centric? Are there products or services that you could add to your offering? Or could your current processes and outcomes be optimized? Not saying they're not good right now, but are there ways for them to be even better? And finally are there insights in one part of your company that could benefit other parts of your company if they were shared? And could a CDO help break down some of those barriers for you? Of course, you also need to find the right person to fill the role. And as we mentioned before, there's a long list of diverse skillsets, you know, everything from technical jobs to business savvy to the political and sales skills. And of course this is an executive level position, so we're talking someone with the requisite experience there. And if this sounds a little bit like a unicorn to you, you're not that far off. There are very few fully qualified people in the world right now and a lot more companies than there are people who would like to hire them. So while universities are now adding business classes to their data programs and vice versa, today's graduates obviously won't be ready for executive hiring until long after tomorrow. So we're facing an inevitable gap during which companies must be even more diligent about preparing properly for the hiring process. You really need to map out your priorities and your goals and understand which skills need to run deep in the studio that you hire and which can be learned on the job or acquired through collaboration. And according to Russell Reynolds, we're now in a very large spike of interest in this position. So there are some things that you can do to make your organization even more appealing to people who are qualified candidates. And the first is to know again why you want someone in this role, but the second is to prepare yourself for change. Even change for the better progress, as we often call it, is by its nature highly disruptive. So you have to be ready for that. And if you are and you can demonstrate that to the right candidate, then your company could wind up on par with Amazon and Google and other companies like that that are using data to disrupt their entire industry and really to kind of shape the future. So I just wanted to put that front of mind as we continue this conversation and then to share some resources quickly before we go to the Q and A portion. The first is the CDO report that I've been mentioning. You can find it here at these links and note that we will also be posting these slides after the webinar and we'll tweet these links as well. You'll be able to find them pretty easily right after we're done. And second is the data strategy position paper that Tony alluded to at the beginning. This has much of the material that Scott covered today including that data strategy checklist at the end. And this is also available for free at our website. So please find that. And with that, thank you so much for your attention. It's been a pleasure to talk with you today and we'd love to take your questions down. Okay. Julie Scott, thank you very much. First of all, for the presentation, I'm going to invite everybody on the call now to leave your questions in the Q and A section which is the portion of the screen on the right hand side at the very bottom. Just feel free to type your questions in there and I'll translate for them and prioritize. Let me get things going though. Since we're talking about the role of the CEO and we've had a presentation based on strategy, I'm reminded of Drucker's premise. He said that structure should follow strategy or strategy defines structure with something along those lines. Can you tell me, you know, in the experience that you've had, we always get a lot of questions about what the right structure is for either the CDO within an organization or the chief data officer organization itself, you know, how should that be structured? I'm wondering if either of you have come across that question very much and is there any sort of an answer that you can provide in general without knowing what specifics I get? Yeah, I think that there's no one single answer to that. But one of the things that we have seen is that as we help companies understand what's possible with the technology, they realize that there's process change and organization change that needs to follow along with that in order for those things to be successful. And I've seen different companies go in different directions and not just at the CDO level but all throughout the data organization, people placing data scientists embedded into business, people creating centers of excellence. I haven't seen one answer that I feel is a perfect fit for every organization. Julie, I don't know if you disagree on that, but that's been my experience. Yeah, I do disagree slightly. I think it's true that there are many current implementations, especially when it comes to CDO. I've seen them reporting to the CEO but also to the CIO, chief information officer or even to the chief financial officer in some places. It's true that your mileage may vary. It depends on the needs of your organization and the structure of your organization. There are some large enterprise companies with hundreds of divisions and they might have multiple CDOs. They might have one for, for instance, I know that AIG has divisional CDOs that then reports to sort of one central CDO. So while I think it's right to hesitate to be too prescriptive, I would say that if you need a default, the correct default would be for the CDO to report directly to the chief executive officer. I think that it's a mistake to think of this as a technological role and to lump it in with the CIO. There is, of course, some overlap there, data being a very technical pursuit. But I do think that ultimately this is about having business value and there should be a very short link between the chief executive officer and the chief data officer. So again, you know, your mileage may vary but the defaults are powerful and I would say that is the correct default. Yeah, I'll just reference some research we did at the end of last year ourselves and we found that the CDO is often reporting into the chief operating officer as well. That was a very common linkage. All right, so thank you very much the folks who have submitted questions. The first one here is how does your approach work within government agencies? Any experience there? We haven't worked directly with government agencies at SCDS but I'll pull from historical experience and say that while there's obviously different complications in budgeting and the notion of a business objective is very, very different for a government agency. I do feel that you can still use the same approach and instead of looking at goals and themes at the very beginning that are focused on growth, often, and maybe I'm being a little pollyannish, the goals are more rooted in altruism. What is the purpose of that organization? What aspect of the population is it meant to serve? You still need to have those high-level strategic imperatives even within a government agency that drive why you're doing what you're doing, why do you get up in the morning and what is the value that you're providing back to your citizens. So I think from that perspective the early pieces work well. Where I think we're a long ways off is in thinking about common platforms, common services and reuse across agencies. I think that there's probably a few agencies that I've been familiar with that have the level of sophistication in technical platforms that they can support across agency interactions in the way that would be required to really do shared services. I think that there's a fair amount of work to still be done there. But I think the approach still holds well. Okay. I'll come back to the previous questions. The most recent one is a little bit of a continuation of that discussion. So as the CDO role develops, pardon me, the chief data officer develops the highest level data strategy, how do you recommend they address the challenges raised by various business functions who want to continue to create and manage their own separate data strategies? So I guess how do you find or see the CDO role in something unifying those or standardizing those data strategies, I guess? Sure. I mean every organization is going to have politics. I think that ideally the CDO after they've crafted that central data strategy or that central list of priorities again has got underlined with the participation and the buy-in from many kinds of stakeholders in the organization. Hopefully there's, you know, if you've done that process correctly and you've included people along the way and invited their input, there's ideally not too much convincing that's left to be done. To the extent that incentives are misaligned or that process hasn't carried out in such a way that there is not organizational buy-in, then yes, the CDO will often spend a large part of their time having those conversations. Sometimes you repeatedly end at length to show why it's worth everyone's time to quote unquote play enterprise baseball. I think if you've created those incentives and those strategies and those priorities in the right way, then there is a compelling case to be made about how a rising tide can list all parts of the organization and data is most powerful when it's used in aggregation. But again, politics is definitely part of the job. Having said that, going back to the idea of hiring, one of the most important things you can do before you hire a CDO is to lay that kind of groundwork so that they don't spend the first two years of their job justifying why they're there or trying to convince people in other parts of the organization to play ball with them. So a big part of what you can do to lay that groundwork is to develop the use pieces and show why this is a good thing, show why this role is necessary and desirable before they get there. Scott, did you want to chime in with anything there? No, I think Julie captured that one. I'm happy with letting that go. Okay, and I agree with everything that Julie mentioned there. One other thing to add, again, referring to some of the things we did late last year, and I'm sure you guys found this too in your conversations with various CDOs, is we learned that change management was the single biggest unanticipated challenge that CDOs ran into across all of the folks we interviewed, trying to bring around either colorful change or individual business divisions and changes to adapt to the new strategy. It's the single biggest challenge that folks faced. So I think that's all consistent with the question that was asked. Okay, a question here. Other than business strategies and business plans, what other documents would you recommend be reviewed when gathering business objectives? I think part of the exercise is reviewing documents and looking at the business strategies, looking at the business plans, looking at the projects that are underway right now, where are your large investments, and going back to the business cases for all of those. A lot of this, though, comes out by asking executives, how do you spend your time? Talk to the CEO. Talk to the CIO. Talk to the SCOO. What absorbs your time? What absorbs your attention? What keeps you up at night? And driving into those kinds of questions will often surface objectives that no one's put on paper, but it's in the back of everyone's mind. The other thing that we often do is we'll go through and gather business objectives with executives, and then we do a lot of deep diving into what's really going on within different organizations in terms of how they use and rely on data, understanding where the gaps are, and things like that. And that grassroots process often helps us surface objectives that the business didn't know they had. So, for instance, there are objectives that are maybe hampering other objectives, and they're not front of mind for a CEO, but they should be. Or they're front of mind for the CIO, but not for the CEO. So there's a lot of back and forth that goes on there, but really asking what keeps you up at night? Is it a great place to start at that strategic level? And then start breaking them down. Look across different areas of the business. Do you have objectives that represent all of your various business lines or business divisions? If you haven't captured all of those, then perhaps you still have gaps that you need to fill. Okay. We've probably got enough questions to run through the end of our call today. So just to caution anybody who might be thinking of charming in at the moment, try to get a couple of quick ones, although all of them are sort of lengthy answers. This is a common one, though. We see this come up a lot. How do you qualify the value of strategy? I'm not sure that there's a simple answer to that question at all, but how do you qualify the value of data strategy when it's hard to measure return on investment from something like that? Well, you're right. There is no simple answer to that. I'll say the simplest answer that I can give is how much have you spent on failed projects? How much have you invested in projects that didn't take you to any realizable business benefit? That's one half of it. And then the other is, assume you have a data strategy and you can answer the questions, those strategic questions of how you're going to grow the business. Oftentimes, I'll find that businesses have goals that they want to grow a particular business line by 40%, for instance, in the next 18 months, but have no idea how to do that. The value in the strategy is in giving you a path to find that benefit, and you can clearly articulate what 40% growth looks like in terms of your bottom line. And if you don't have a really good answer for how you're going to achieve that 40% target, then that's where the value of your strategy comes from. But oftentimes, we just admit up front that this is something that shows its value over a longer term as opposed to the strategy is complete, here is your roadmap and this is what your return on investment has been. It's a much more complicated discussion than that. Sure. I think you answered this through the course of your presentation, but there's a couple of questions that I think you're trying to get a little bit more specific here. They're asking, what levels of management should be involved in data strategy determination or in the data strategy conversation? I think you answered that by saying that basically all the relevant stakeholders, regardless of where they are, but maybe you'd like to put your own interpretation on those questions. Yeah, so I'll say that there's a collection of people that come together to make an effective strategy. The first are your high level business executives that can set direction for the company and tell you what that direction is. The next are your technology leadership, the director, senior director level people that work with a CIO or work with supporting different business units that can articulate the technology needs of the organization. They need to be involved. And then later on down the line, when we start getting into that use case level, you've actually got to dive much deeper into the organization and start to talk to the people that are involved in making things happen, whether that's technologists that are going to build different pieces of the platform so you understand the existing technology environment, the skills that they have, what's easy and what's hard for them from a technical perspective. And similarly talking to product or project owners on the business side, who can give you the context of what work is underway, where their data needs are being met today, what things they would like to be able to do to impact the projects that impact the businesses that impact the bottom line. So there's no one level that you need exclusively that you can exclude all others, but it's involving the right people at the right phases. Certainly involving a data analyst and your head of infrastructure when you're talking about the business questions isn't always necessarily the right way to go. But keeping as much of the technology and business conversation linked together as you go up and down that chain. Okay. So I think we have time for a quick answer to this question, complex though it is, but I think it's relevant to almost everybody in the call is that for somebody who aspires to become a chief data officer, is there a recommended career path? The person who asked this question is currently a senior data architect working on data strategy for a large university. Is there a recommendation for how to get to that CDO role eventually? So again, the current role models are pretty diverse and there's no consensus about any one way. Also there is such a gap in availability that there are very few perfect candidates out there and it's more about making the right marriage between the skills that you have and the skill set that the organization needs. So some organizations will look for people who are deep on the engineering side but can think in a business way. Other organizations will need people who are adaptive solving business problems but are fluent enough in technology that they can work with the requisite stakeholders there. I think whether you're deep in engineering or in business, the ability to work with all different kinds of people at all different levels of the organization to make your case to be diplomatic and to juggle competing priorities I think is always essential. And of course just domain expertise can go a long way too. So those are the four pieces of the pie I think that I'll hold hands and I'd say evaluate where you are now and which of those pieces you need to acquire or develop and there are multiple possible ways to develop each of those but just try to learn what you can as you go and then make yourself available. I'd say again there's such a need right now in such an interest that I think candidates will emerge from all different kinds of places. All right. Well I think that's a good place for us to wrap up because we're out of time. I'd like to thank Julian Scott again for the presentation today. I know Julian you'll be with us in Washington DC later this month. I look forward to meeting your colleagues from Silicon Valley data science as well as well as many of the folks on the call. Shannon I will hand back to you and my thanks to everybody who's been with us today. Thank you so much and Julian Scott thank you so much and thanks to everybody for being here on to get to everything working in my several years of using WebEx. That's the first time I've ever seen that. First time for everything I guess. And just a reminder I'll be sending out a follow-up email with the links to recording and links to the slides of this presentation by end of day Monday. Again I hope to see you all in Washington DC at the end of the month for EDW and for CDO vision. Hope everyone has a great day. Thanks Shannon. Thank you Julie thank you Scott. And thanks Tony. Thank you. Bye bye.