 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining the latest installment of the DataVercity Webinar Series, Data Insights and Analytics, brought to you in partnership with First San Francisco Partners. Today, Kelly O'Neill is on vacation, but John Lavey will be joined by guest speaker Anthony Alvin to discuss top five priorities for an analytics leader, sponsored today by DataWatch and Looker. Jessica points to get us started. Due to the large number of people that attend these sessions, he will be muted during the webinar. For questions, we'll be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DI analytics. As always, we will send a follow-up email within two business days containing links to the slides, links to the recording, and anything else requested throughout the webinar. Now, let me turn it over to Rachel from DataWatch for a brief word from one of two sponsors today. Rachel, take it away. Thanks very much, Shannon, and thanks to everyone for attending today. So we're excited to be able to sponsor this webinar. And we wanted to share a little bit about what we believe at DataWatch. So DataWatch has been through a lot of changes over the years. And the way we see things now, having been through many different market cycles, is that data intelligence is actually key to overcoming a lot of the challenges we're going to talk about today. And that's something of a new term. But I think it has a really important meaning to any of us and really all of us who work with data on a daily basis. If you think about what it means to get smarter about data-driven decision-making, a lot of the very basic challenges we have with data-driven decision-making are about where to find data, how to understand that data, the quality of that data, the timeliness of that data, the veracity of that data. And that veracity, that trust factor, is hugely important. And it's one of the things that really should be motivating many of us as users to participate in governance initiatives. So, of course, as analytics leaders, we also want to build our teams. And we want to put them in the driver's seat when it comes to analytics. But that requires that we do a few things. First of all, that we understand who the users are and what their perspectives are. So, those users come from IT, they come from data science where they're very comfortable with scripting and writing their own code. But many of them, with a lot of the value and insight into the core workings of the business, come from a non-technical background but have very, very valuable contributions to make to how the data is interpreted. So, one of the things we really focus on is providing tools that are fast and easy, whether it's accessing data, decoding data, cleansing data, creating predictive models for data, or visualizing data. We want to make it easy for people to work with. And we want to make it easier to bring people from IT together with people from any part of the business to really bring all that value together. Because that's how we believe you get results that matter, these business insights that are impactful. Because if you think about it, any time that you've listened to different perspectives across your organization, you may learn something new, whether it's about the data and why it's stored that way, or it's about the business and why the business records the data that way. For example, as years go on, fail structures are very common in the change. It isn't really correct to always compare one year's data to another if there have been hierarchical changes in the data. You're not comparing apples to apples. That won't always be visible from a system perspective if the field names haven't changed. A business user will have that insight. And of course, efficiency becomes important because when you do find really impactful insights, the next thing that happens is you're expected to maintain them and continue producing them. So you want to have the ability to easily update them. You want to have the ability to automate them, to keep them going really on autopilot to feed, whether it's traditional business intelligence systems, operational dashboards, or systems further down the line. So for those of you who are trying to figure out which way to go in this rather fast-changing environment, we want to be the partners that help fuel your business. And we want to do that in a few ways. First thing is help you get at the widest selection of data. Wherever it is in your enterprise, even if it's sitting out in PDF documents that you've only got that one person who understands, we've got the capability to help you unleash that data, make it very usable. From a trust perspective, we want to give you an infrastructure where you can not only securely and appropriately share data within groups, but have that data approved by subject matter experts, implement governance, partner with IP to get access to challenging data sources, and make sure that you're working with the right data that you can trust as often as possible, or at least have a good idea what level of trust you can have in the data you've found. Because we all know that one of the primary rules of business is you will have to make decisions within perfect data. It's just better if you know which data is imperfect. And of course, last but not least, bringing more minds to the table. Everyone in your organization is there for a reason. Bring them to the table by giving them an interface they can work with, by letting them add the values that they have in understanding your data, whether that is technical, whether that is structural, whether that is business context. But also understand that people move on, things change, people change roles. And so we have a layer of machine learning that runs in the back end to capture some of the tribal knowledge that's in your organization and help you to reuse that and transition it from user to user. Now, we're all pretty comfortable with the concept of a chief data officer these days. And we all know that our industry is spending a whole lot of money as well as time and energy really on data and analytics. In fact, I think IDC is estimating it at about $200 billion annually by 2020. And we're all getting much more comfortable with the CDO role. We're also getting more comfortable with the diversity of data. And the fact that everyone who appears on this slide may access any one of these data sources. They may be interested in streaming data. They may be touching the lake. They may be bringing document data. They may be in need of some predictive. So many more of us are producers today as well as consumers compared to what we were yesterday. And at DataWatch, we think we bring a lot of value to making this a reality for more and more users at every level of the organization with our DataWatch Panopticon offering, which is streaming data visualization. Our DataWatch Swarm offering, which gives you self-service data preparation with governance, with machine learning. And the most recent member of our family, the DataWatch Angus offering, which helps you do very self-service oriented predictive analytics. So I thank you all very much for your time today. Rachel, thank you so much. And Rachel will be joining us for the Q&A at the end of the presentation if you have questions for us. And now let me turn it over to Lisa for a word from Looker. Let me just pull up your slides here and make sure we got you going there. Lisa, take it away. Thank you very much, Shannon. And hello and thank you for including Looker in this important conversation. This is one of our favorite talk bits to talk about. And by the way, I'm Lisa Daniels, and I'm the VP of Demand Generation Marketing Operations and Marketing Analytics at Looker. And I'm happy to say that the folks here at Looker wholeheartedly agree that a chief analytics or a chief data officer or however your data leader is is a really key and important role for companies today. Companies have so much more data than they've ever had before. A strong leader who is able to prioritize is deadly important. And this is why. At Looker we've had the chance to talk to many, many people over the last few years about how and why they use data, why they need it. And we're hearing that more and more people need to get access to data simply to do their jobs. Engineering, marketing, sales, customer success are just a few of the teams we talk to on a daily basis. And everyone is asking for more data to do their jobs better and more efficiently. The big question then is how do I get access to my data? We frequently hear about two common behaviors that result from the challenge and the difficulty of getting access to data. People have to wait in line to talk to a data analyst to get the data they need. And this, of course, is frustrating and can prevent people from making timely business decisions. Or here on the right side of the slide, people get so frustrated in the time delays that they go rogue and just use whatever data they can get access to. It may be a subset of data. It could be incomplete or outdated data. Or it could be third party vendor data that is just really, really siloed. This, of course, causes different departments to have different answers to similar questions causing data chaos. Either way, people are not getting the data they need to do their jobs and this, of course, is bad for companies and prevents people from making smart and efficient business decisions. Much of the problem is caused by the way companies organize, architect, and access their data. The traditional approach creates deep silos of departmental data. That's because departmental data is unique and it's rare that a single person or team can understand and create the connectors between company data. But in reality, companies today do need to make those connections in order to have a strong and thorough data story across their customer's journey and their customer's lifetime. So after a few years of doing this, of course, we have found that those data leaders, those chief analytics and data officers are the best people to pull that company data together so that everybody can be more efficient. That CAO is able to develop a strategy that benefits the entire company rather than just maybe feeding the loudest department. This strategy, of course, can include self-service access to the data, an integrated data setup that reduces those lengthy waits for data, and then reduces that departmental data chaos. And of course, Looker is here to help. Come check us out at Looker.com and see for yourself. We can easily get a demo for you or we can even let you try Looker out on your data, your own data, and you can see what we can do with your data. So thank you very much. And you can always contact me, Lisa, L-I-S-S-A, at Looker.com. Thanks, Janet. Lisa, thank you so much, and thanks to both DataWatch and Looker for sponsoring today's webinar. Again, Lisa will likewise join us in the Q&A section if you have additional questions for her. And let me now introduce our regular speaker for today, John Labley. 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. And with that, I will turn it over to John to get today's webinar started and to introduce our guest speaker, John. Take it away. Hello and welcome. Well, thank you very much. And let's just get into the topic here. We thanks our sponsors. And today we're going to talk about analytics leadership. We've talked about architecture in this series. We've talked about success factors and all kinds of stuff. We're going to start to talk about now kind of the organizational aspect, the roles and the responsibilities aspect of this thing. And we have a very astute, highly qualified guest speaker who I will introduce here in just a moment. The key here is that we have evolved to a, we have evolved over time to a new type of role. But if we have evolved from, in a way where nothing, this is not brand new, it is evolutionary. So when we talk about an analytics leader, we're talking about an evolution from all the way back in the 1960s where you had operation research, the statistician in the actuarial type function. And these are folks that did a lot in a ledger. They did stuff with slide rules. They did stuff with old record keeping machines, which eventually began to be called computers. And, but they had a very specific function. Now that function evolved as a business element with the arrival of some fairly convenient tools like SAS, SPSS and a whole bunch of other types of things like that. We had a burst of something in the 90s called data mining. But again, not too specialized. We just kind of had some specialized people. Then all of a sudden analytics is a business. We start getting a lot of internet data. We want to do stuff with it. And now we're in the cloud and big data era. So now we have analytic officers, chief analytical officers, chief data officers, et cetera, et cetera, et cetera. So in that context of evolving, we have now evolved some new leadership traits. And our guest speaker today, Anthony Alguin, will be talking about that. We're going to have a bit of a conversation and go over those five priorities that these leaders need to have. Anthony has had a lot of really good experience. Yeah, I think he'll be the first to tell you he's been pretty fortunate to be on the bleeding edge as I have on a lot of really neat stuff the last several years. He has a book coming out on, related to this topic. And he has been a chief data officer and has been heavily way down in the weeds on data and cloud and big data and analytics. And I'd like to just, Anthony, hello, how are you doing today? John, thank you for that great introduction. And yeah, I'm excited to be here. This is the topic that I know is close to both of our hearts. And I'm excited to jump right into it. I know the next 45 minutes is going to go by very quickly. Well, yeah, it is. So we're going to just dive into it. We are going to allow 10 minutes for questions. So we really have about 34 minutes and 30 seconds here to talk about stuff. So let's just get right in. The first priority here that we want to talk about is evangelizing the importance of being data driven and what that means. So Anthony, just tell us what you think of that. We've got some bullet points to help folks kind of remember this, and please talk about this, the first priority. Right, and I think the first thing to understand, and I think the obvious thing for a lot of folks is that when you do create data analytics capabilities or any kind of data driven activity in an organization, I think there's an inherent knowledge that we need to market that to people inside the organization to get them to actually use it to drive some sort of business improvement. And I think that though that's an important early step, that's not even the first step. The first step is to really understand if we're an organization and we have this notion that we need to do something with data, what does that mean? What is data driven even mean to an organization when it comes to trying to build these kinds of capabilities? Because I think a lot of organizations, we're at a point now, and I liked that slide that you had a couple ago where we had kind of how things have evolved over time. We're at a wonderful place now where executives inherently understand data is important. They realize that something's going on with this stuff and they need to do something about it, and yet we don't know what that is. And that's where we're trying to figure that out. And each organization's relationship with data and what they're going to try to leverage data to do as an organization is going to take on a pretty individual and context-specific relationship with each individual business. And I think that that is the thing we always have to remind ourselves, especially because we, as data professionals, can get very in the weeds. Like you mentioned, we get very in the weeds with a lot of the stuff that we're trying to build. We can instantiate data governance or we can go build a data warehouse or we can put in all these amazing analytics. But if we don't connect what we're doing to actual business outcomes, to improving that top-line revenue or decreasing costs or managing risk, then we're going to be kind of floundering out there just trying to hope that our stuff matters. If we orient to that very deliberately and very directly, then that message will be heard throughout the organization from the CEOs on down. Yeah, I think this is very different than what, say, a statistician had to deal with or what we would call, I worked in a company once where we sold our data, but it was back in the 1980s. And of course, technology was much different and all that. And we had to specialize people and we had to sell, but it was a very product-based model. Now you're bringing that all internally. These roles, you just can't be a mathematician or a statistician. You have to be able to communicate to others and bring an organization up from a prior state to a new state, right? Well, absolutely. And good ideas aren't enough anymore. A good idea that's not operationalized doesn't add any actual value. So that's the thing. We have to connect these big insights and thoughts and capabilities, whether it's artificial intelligence and machine learning, this bleeding edge of things, or it's something as simple as an operational report. If it doesn't lead to some sort of change on the business side, then it doesn't add any actual value. There's got to be something. There has to be some coaching in this somewhere. It's a constant conversation that I have when I talk to folks out there in deployment land. Which gets us kind of to our next topic, which is, all right, we've got to evangelize. We now know maybe what data-driven is for our organization. And we need to have some coaching. We need to do something new. How do we go about that? Right. And I think that if our first priority was around kind of orienting what we do so that it's heard by the business, the second priority is really understanding what the business needs and what is possible from our perspective. Our data folks know what's possible. Our business folks know what's valuable and what's reasonable in terms of being able to take that kind of action that we were just talking about. And so really it's about us bringing our insights of what can be built from a technology perspective or a data analytics perspective to the business that needs to do business stuff. And it's really about getting that two-directional communication. One of my least favorite terms in an organization in this space is this notion of business requirements. Because what that connotes is a business telling the data or technology people, hey, build this for me. I need this thing. And what that is is a one-directional communication. It's a, I have a need to build this for me. Instead, it should be, let's listen to that. Let's understand whether we're on the business side or the technical side. Let's understand what the other side knows. Let's have a little bit of empathy, pause for a moment, and understand where their perspective is. And then together, bring your disparate pieces of insight and understanding together so that you can collectively move something forward. There is no success independent of one another, whether we're on the technology side of the fence or the business side of the fence or we're a data governance professional. At the end of the day, if we don't work well together and create something as a overall organization, then things are just going to start to crumble and kind of fall apart. Yeah, one other aspect of this I'd like to explore is just the activity of doing the alignment. You know, you're talking about, we have a lot of smart people that know what we need to date, to know what we need to do with data. And then there's this aspect of it's not a build it and move on proposition. And what I see very often is a list of so-called requirements and they're labeled as business requirements or business goals and objectives, but they're really just kind of technical requirements. We want to deliver data more timely with transparency. We want to have a certified copy of the truth somewhere. We want to use analytics to do really cool stuff or some cheesy branding of a program or something like that. But I do see a lot that when you see something based on those types of quote-unquote objectives, you have a tendency then to click into this build that didn't move on because you deliver those technical capabilities, deliver the warehouse, check, right? Data quality, check. Install some stewards, check. All right, what's next? Well, that's not how this stuff works. That alignment to what the business value is, that's your key to sustainability with this stuff, I think. Right. And that's where I think of that as treating symptoms. You're going through this check the box mentality as you just explained. And that is a relatively shallow approach to doing things with data, what you really want to be thinking about. And instead of saying, here are some symptoms, I want you to solve these symptoms, what we really want to be thinking about as a business, okay, what could we do better? What could we use data to do that we can't do today? And the real engagement is around saying, okay, if I didn't give you this thing you're asking for, so if you see that list of symptoms and you say, if I didn't do this, what's your alternative? What would you be doing if you don't get this? And then if we can identify what is that potential business outcome differential that can be created by doing these kinds of data things, now we can orient towards how do we make that delta as big as possible versus how do we check all of these different boxes because somebody said that was a box that needed to be checked. Yeah, I got a lovely note and lovely questions last week from someone who's got to do an ROI for their analytics and data governance project, right? The boss needs the ROI on it. What's going to happen? And they're struggling because all of their reasons were these technical, all these objectives and what's the ROI on data quality? What's your ROI on transparent data? Well, there is no ROI on those, right? This is doing stuff for the business. This is no different than building a new factory fairly. Right. You're always at a level of abstraction from that ROI and you could make prediction on how you may influence those things but by itself data very rarely outside of when you're directly selling it, data very rarely is only one step or move from that business value proposition and that's where tracking an ROI can be very difficult but it doesn't mean that that exercise is unwarranted. I mean I think that if you want to justify a data effort of any kind, you need to be able to communicate to those that are going to be providing that funding or that support. Hey, here's how this effort becomes valuable someday. Here's what it goes through and here's the order of magnitude that we think we can achieve and then you get to be held accountable to that. Exactly. There you go. Well, you know, accountable is a good word to segue to the next role which is developing competency because accountability has been a stranger to a lot of data folks over the years and now all of a sudden here it is. We've got a different kind of generation of folks that we're developing. Yeah, you know, when we think about the analytics competency and we think about how do we, you know, kind of get out of the game, start doing this. You know, I think about my time as a CDO and in the public sector, especially it was, you know, my mantra was let's stop talking about working and start working. You know, you don't ever add any value just by having a meeting about something. So let's go and try to build something that can lead to even a small change in business outcome and see and learn from that. And, you know, while we're creating incrementally a little bit more and a little bit more business value through our activities, we're going to be constantly learning and constantly improving and building awareness and doing all of these things that we need to build momentum and teach folks how to do this. And I think that if we look at the job market right now for analytics folks and for people that are trying to build these teams and organizations, there's not enough people to go around. I've seen some really creative things that organizations are trying to do in terms of training up people they already have on staff or building out skill sets that, you know, folks weren't even thinking about five or 10 years ago because the demand is just everywhere. I mean, there's not too many positions in most corporate environments these days that don't rely or produce data in some way or another. I really like the last point there about going to marketing and finance. That's where you're going to find those people to develop them, right? That's right. And that's where, like, marketing is not the first thing a lot of folks think about when they think about, you know, great data analytics people, but think about what marketing does. They use relatively poor quality sets of information to make some sort of decision, and they deal with a lot of ambiguity and somehow have to make it all make sense. And so they have some intuitive understanding of things like data quality and just governing of data and data definitions and things like that because they're all over the place. And they've been doing market research and quantitative analysis for a very long time. You couple that with finance folks, which is a lot easier logical progression to think about is that they're dealing with very detailed numbers. They're dealing with, you know, balancing of accounts, transactions all over the place. Naturally, they're going to have a good skill set of data built up through that career path as well. And so, really, I find most of the time, especially when I'm doing data governance or data quality initiatives, things that aren't necessarily about building a data analytics technology capability, those are the folks that I'm going to go talk to first or the finance and marketing folks. Yeah, yeah. It's kind of funny how things have come for a circle. Back in the day, data processing, which is what it was called, reported to nine times out of 10 the finance functionality. Right? Sure. And because you had to have data controls and everything was batch and linear and things like that. And I'm beginning to see more and more dependence on finance yet again for the exact same type of skill sets for this. Yeah, definitely. And another thing, and this was particularly the case in the public sector, internal audit groups, especially large organizations, tend to have developed some data skills because they had to compile large data sets to do some analysis. And you may find people that have built up some capabilities there, usually not quite as structured or as advanced as some of the things that you'll see in marketing and finance. But that can be another good place as well to try to elicit some folks for these kinds of efforts. Yeah. Well, then the next priority, we could, we could talk all day about this stuff. So you've got the people, we were aligned with the business. And by the way, if you have any questions for our panelists or our sponsors, please feel free to enter that question in the Q&A spot there on the right side of the screen. We endeavor to answer all of them. And as many of you know, if we can't get to them, we will write down the answer and that gets posted as well. So don't be shy, please add some questions. So we have talked about evangelizing, aligning analytics with the business. We've got to get our people somewhere, but we have to develop this sophisticated competency. Now let's make sure we get something done and delivered. So talk about this one, Anne, to me. Right. So I think that it's really about taking action and finding a way to push things forward. And I think it's really about saying to that mantra, let's stop talking about working and start working. We need to create these analytics and make them happen. Talking about making them happen doesn't make them happen. And the key for me is recognizing that as data practitioners, and I'm sure in the people that are attending this webinar, there's a wide range of roles from data architects to data governance folks to technology people all across the board. And I would like us to think less about what is it on paper that my role is supposed to be. And these are the functions that I've been asked to perform. What we really need to be focused about is how do we push to those business value objectives and recognize that it's up to us to make those happen. And we may not exert direct control or influence over the entire chain, but if we're not thinking about it holistically, then nobody's probably thinking about it holistically as it pertains to leveraging data to do something of value to the business. So we have to recognize as data leaders, we need to recognize that it falls to us to make these kinds of things happen and to intersect what we're producing from data analytics capabilities and get those aligned with a business strategy and the business operations of our organization. And John, I know you have some really good thoughts on how you tactically plan or how you pragmatically approach business strategy and data strategy and don't overdo that roadmap. So I would like the attendee to hear your perspective on that piece. And everyone knows I have a perspective on everything. Yeah, we've done. I've done, you know, before first San Francisco with seven San Francisco and probably all on and off until I pull the covers over my head roadmaps. I mean, that's kind of the number one artifact that everyone wants. Where do we get started? How do we get started? Well, let's talk about that. A roadmap of where do we get started? How do we get started this day one? You know, what I call Monday morning is very tactical. But then the statement works as three to five year. Okay, fine. Look, in modern 21st century business, five years out. Oh, come on. All right. You can't ask the data governance team or data management team to do something with a five year horizon unless they're sitting in with the board of directors. All right. They've got to have that level. They've got to be speculative after 18 months or two years. But based on what's in the annual report or whatever, you go ahead and do that. What I urge people to do, though, is make sure that you don't spend the money on super, super details. All right. Get yourself, the first three to six months, literally a day by day checklist, punch list of what you need to do. All right. Then after that, kind of like at a project, summary project, plan level. And after that, everything's going to change anyway. But to quote George Patton for a paraphrase because he's key cursed and I can't do that on a webinar. But, you know, the reason you have a plan is so somebody can change it. All right. That's why you want to have that three to five year horizon that all things being equal, this is what we have to do. But you really owe it to your organization, to your theme, Anthony, which is do some work. All right. To show them what that work is. If you come, I just looked at one a few weeks ago, someone called and said, we would like you to come in and help relaunch data governance. Well, why? Well, we had someone in there. And I looked at this project plan from a firm. I'm not going to name names or anything like that. But it was 18 pages of Gantt chart of very lofty things that everyone knows has to be done anyway. There was no work in there. There was just, you know, install a capability, install a capability type level stuff. You don't want to do that. You want to show them what are you doing to move the needle. And that also precludes, I'm sorry to say this, foundational projects. All right. We're going to install the glossary and we're going to load it up. Now we're going to do this. Maybe in 18 months we're going to start doing something for the business. You're dead. You're dead on arrival. Sorry. I'm not going to work. And I've seen it enough that I don't need to hesitate and say words in such a bold fashion. You have had it. You can actually get anywhere. Delivering insights. Like you said, 80% of the solution and then the last 20%, you and I have both been down there in the trenches and rolled up our sleeves. Change has to happen and you have to make the change happen, right? That's right. And I think, you know, going back to your last point, don't be afraid to test the null hypothesis, right? To say, hey, what if we do nothing here? Do we really need this thing? What's the value that's going to come from this thing? There's only a finite amount of resources that we have for anything in our organization. Let's not just assume because data governance is supposed to be an important thing or whatever that thing might be. Let's at least deliberately analyze what our perspective is on that so that we can defend it should we choose to go down that path. Let's not accidentally find our way down a path. Let's be deliberate and thoughtful and measure and get better at these things. Very extreme example. Just recently finished work with a lovely organization. They're going to do great things. But without going into the detail so we can move on here, the recommendation to them was don't appoint any data storage. Don't start a data quality program. Don't put in all the trappings of what you would call traditional data governance management. Go out, do some projects. All right. Because you have no credibility. And no one had done anything bad. It was just a way this organization had evolved through mergers and acquisitions and things like that. So the point was there is no context to do all this fancy stuff. So don't do it. Just get something done. Let's, boy, we could just do this. Anyway, moving on though so we can have time for questions and stuff. Is this, now that we can, you know, got the people and we can evangelize and we can communicate and all of that, where do we make the money? Where can we find things? And this is another one. You and I will tag team on this panel, right? And, you know, you go ahead. Go first and I'll kind of talk to the little picture here over here on the right. Sounds good. And, you know, I think monetizing data. Data monetization, I think it's obviously there's some importance in data monetization. But I also think that there's an incredibly kind of inaccurate view by a lot of organizations that this data monetization is some sort of panacea of value is just waiting to be untapped. Like, you know, there's gold in them, their hills, right? And that's, I don't know that that's always the case. Like we think that, you know, there's people out there that really want our data all the time. And yet we have more data in this world than we know what to do with under most circumstances. I look at most client organizations and having not enough data has never been the problem. It's always we have so much data. We don't know what to do with it or we need help to find where the value is or what have you. I don't know that a lot of organizations exist out there saying, you know what I could use? It's more data. That's the thing. That's the secret ingredient. And so I think we need to temper some of our expectations on where those data monetization opportunities actually lie because really getting into that second point on this slide, it's really not about the data itself. It's really about simplifying the buyer's journey to the data driven business outcome. That's where the value is. It's how do you add, how do you create more money? How do you save costs? How do you manage risk? That's where value exists. So if I'm thinking about what do I contain in an organization data wise that may prove useful for somebody that is trying to create business outcomes with data in some sort of fashion, then the closer I can get them to that actual value, the more value what I'm providing has and therefore the more monetizable that would be. And that really isn't about selling data and say, here's some raw ingredient for you. It's really about saying, here is a service. Here is a way to get your journey easier and that's something that you would presumably be willing to pay for. Yeah. So when we talk about monetization and when we were putting this material together for those of you that are listening, we're talking about how deep do we go into how you monetize your data. Now, there's been great books written about this recently. A lot of talk about it. Anthony said, John, I saw a presentation you did at some point in the past and we found this slide. The origins of this slide, which speaks directly to the modern analytical area of data monetization, was created in 1999, Anthony. That's when this slide table started. And we used the word monetization on the top of this and we said we weren't going to use it because no one would understand it. But people have caught up with it. Because we're going to increase value because if you're on a board of directors, value to the board of directors is the stock price is better. There's more wealth. The balance sheet is starting to look pretty good. So it's the same thing because monetization is really improving the business outlook or the organization's financial picture, whether it's saving money or new products or new services or whatever. If you're out there thinking about this and you think, well, we're going to monetize our data and someone says, well, I don't know how we're going to sell our data to somebody else. That's not all it is. You can monetize your data by improving a process. Just do stuff faster or better. Improve your quality. You will make money by using your data to improve your quality. And that is a direct alignment, which is what this analytics leader is looking for. Now, the next one is the competitive weapon. We need to be smarter than our competitors. There's a lot of commercials. If you watch golf tournaments, which I do, which people say I also watch Painter Eye, but I find that exciting as well. But there's several brokerages out there that are just calling at each other like crazy in the marketplace. And they say they know more about each other. And that's all having worked for some of them. There's a real competitive awareness of what their competition is doing there. That's monetizing your data. How about making a new product? Well, that's the classic where we're selling our data to somebody else. But then there's the intellectual property aspect of it, which is you just take what you know and make your products smarter. AI and machine learning is really pushing that way a lot. There's the enabler, which is you can monetize your data by just allowing your employees to offer a better service at the point of contact with the customer, right? And then managing risk, which has come into this list a little bit later than the other ones. But look what we've done with risk management and compliance being the big driver, right, of data governance here in the last 10 years. The point here is that the analytics leader needs to be operating with this table in their head all the time, right? They need to be thinking about how can I monetize my data, but it isn't just simply putting a bunch of stuff in a big database and coming up with some brilliant report that has the word Eureka at the top of it. As we said, it's the very first priority here. What does data-driven mean to your organization? Well, it's what are you going to do with stuff? You can monetize opportunities to monetize the data. So with certain clients, we'll just run them through this entire drill if we can have a bunch of senior leadership in the room. It's because sometimes they just don't know, right? They just don't know. Yeah, and I just want to emphasize kind of what you just talked about and why this was so memorable for me. I mean, because it was a couple of years ago that I had seen this and you give this talk and go through these. And I think it's so important. And this is why this is kind of that last priority that we're talking about today is that data monetization happens both from an internal loan perspective and an external perspective. So what I was talking about initially was a lot of that kind of externally facing, which I think what people say, data monetization, a lot of the times are thinking, okay, how can we monetize it directly? Be that one level of abstraction removed. What I love about your list of different ways to monetize data is that those apply internal or external. Those apply whether we're creating services inside an organization to improve our ability to go to market as a business or as a service outwardly facing to other clients, whether it's businesses or consumers, we can help them gain direct value by using this data. And they apply both ways. And so it's a great cross-section of ways to think about using data that will have monetization impacts. You bet. You bet. So we're moving into the final stretch here. So let's just talk about our key takeaways then for our topics here and then we'll turn it over for some questions and answers here. Let's see here. Business value, it really is all that matters. Now I do have a question on that one, Anthony, if I may ask. You're in public sector. The business value is somewhat of a difficult concept when you're funded by taxpayers or grants or nonprofit or something like that, right? So for the people in my audience that are maybe in a government position, how does this statement apply to them? Yeah, that's a great question. And as somebody who has spent a little time in the public sector, and this would really apply to nonprofits at anything, it's just anything where your denomination of value isn't necessarily in dollars. So if you're a nod for profit, you're not necessarily looking to maximize dollars as the barometer of your business success. So I'm going to be looking at what is the amount of social good that I'm trying to achieve? And this would apply in a government sector in a lot of cases as well, saying how do I achieve that mission of my organization if it is not a purely profit-motivated enterprise? And I think that it's an important distinction to make too because I think there's a lot of organizations out there that really believe as part of their mission that there's more to what they're doing than simply dollars. And I think that should be factored in to how we're trying to leverage data because it doesn't have to be denominated purely in numerical form and a dollar's basis. It can really be that business value can be created in other ways, but you can definitely break those down into a top line, a bottom line, and a risk management function. Okay. Now, the second one, do something. All right. That kind of gets back. The foundational projects aren't going to help you much. That's really the bottom line on that one. The partnerships, how do you build a partnership between data tech and business who in many organizations aren't real crazy about each other and don't invite each other to their Christmas parties? Right. That's the thing I try to advise, especially because I do a lot of work on the technology side of that equation. Stop saying no before you listen to the problem. That's a good place to start is to say, okay, let's try to build a little bit of empathy and realize that everybody's job is hard. Everybody's trying hard. Let's make that a baseline assumption and then say, you know, maybe I'll get a little bit of help for the things I care about if I'm willing to help them out and, you know, see where those things go. And I just say, you know, try to hit the reset button on those relationships as much as possible and just try to help one another out and see what you can build together. Okay. Then we talked about momentum, which is good. We talked about being an agent of change. Our, and I think our sponsors will be able to, I think we're just, I'm going to ask them to maybe weigh in on some of these. Our sponsors, they don't want to sell products that don't flourish and don't get used to their fullest extent because that ends up being a black eye on them, even if it really isn't their fault, right? And being an agent of change and building momentum and earning the tools rather than just going out and buying stuff, just to buy stuff, but having a reason to buy it and knowing what you're going to do with it, that's really, really important. That makes things, that makes things very, very sustainable. And I was going to say, here's our question and answer period because we're right up there now to the question and answer period and I would like to ask the first question to our sponsors is, I would like each of them to take a minute and talk about how do they address that when they go into an organization that is looking for the silver bullet or the miracle and I'm going to hand out my money and you're going to deliver this and everything will be absolutely wonderful. What is it they're looking for and what to say or not, or not, I mean, there could be a philosophy of, well, we don't get that deep, our specialty is not to do that, but I was just wondering if they wanted to weigh in on that. So how about Lisa? Are you out there, Lisa? I am. Hey, John, how's it going? Okay. Good. So it's been a great conversation listening in on that today and so many of the points are key to how we look at data being data-driven. We very much want the customers to be using it and we very much look at the use cases of how the data is being used so that we can figure out if we can help them do it better, maybe through sharing with each other or sharing with them how we use the data internally. So we very much do marketing analytics with our tools and our data here at Looker and it is very much expanding beyond just one group, but it's being able to connect multiple data points together. So we very much want our customers to be leveraging it so we do what we can to help them. Of course, it's an ever-growing effort, never enough resources to help everybody, but we're doing what we can from a best practices, community standpoint. And we find, of course, that if there is that central team and they're helping the other teams be self-service and self-guided, they can all come back together and have fabulous growth loops where what the plan was is changing because the data is changing or where we are getting the data is changing and the business is changing. So I like the idea, it's a never-set-it-and-forget-it. Right, absolutely. Rachel, what are your thoughts on that? And you'll need to unmute, I believe. Rachel, you need to unmute. Okay, well, we'll come back to Rachel here in a minute. Rachel just sent us a note. She's having an issue with her microphone. So we'll just get back to her. Here's a question for you. I've always wanted to say who it's from and where they are, but we're not supposed to do that. But we'll just make something up here. I think this is from Edna in Cleveland. All right. You were at CTA, you know, public sector, and you talked about evangelizing. How did you do that there? Give us some hints and tips on that. You know, it really wasn't that much different than anywhere else I've been. I was fortunate enough in that organization to be at a pretty senior level. I said at the same level as the CIO and had really direct sponsorship from the top of the organization. And so I had a kind of soapbox that was pretty easy to connect with folks that were interested in influential and leveraging data analytics. And as bad of a rap as the public sector can get sometimes, when you're dealing with buses and trains and thousands of people, millions of writers, you need to be data-driven. And the organization had some pretty antiquated processes to be data-driven with, but they definitely cared about using the information that was available to them to improve their operational processes. So for me, it was really about getting connected with those other leaders in the organization, listening to where their challenges were and helping them identify, based on what I knew I could do with data, how we might be able to influence the things they were struggling with from a business perspective, whether it was operational issues, whether it was staffing. There was a huge issue with bus drivers not showing up for their shifts, for example, and how do you manage that? There were countless ways we could leverage data in the place. It was just a matter of finding where were the receptive folks and where could I help them in a way that would translate to dollars in savings or service improvements for the organization. Okay. So we have another question just across here, and I think we can both take a run at this. And again, I can't say where or from, but I can say something like Glenn in Atomwa. So long-time listener, first-time caller. Okay. I work in an organization where there are a lot of people who have worked for the company for 20 or even 40 years. Change is difficult for a lot of people. Do you have any suggestions on how to promote data analytics and the use of new tools? Right. So I'll take a quick stab at this one, but I think it's, you know, the fact is that change is always hard and in large organizations that people have been around a long time, but the inertia is that much more. I think it's really about trying to not promote data as the separate thing and trying to just say, here is a tool that can help you, that can help you get your job done a little bit easier, a little bit faster, and it's not, you know, try to diminish the threat aspect of data and just use it as yet one more tool in the arsenal to get your job done. You know, if we can take that somewhat servant-leader approach of saying, here, how can I help you, and they see that that's genuine, then you'll get a lot further than if you say, okay, data analytics time, get with the program or get on out. You know, that's not going to work, and that doesn't work anywhere, I don't think. Yeah. I think that's a great point, Anthony, and this is Rachel. I finally managed to get myself straightened out there. But one of the things that we see all the time is also just having some consultation to understand what people are doing and how they're working so that when you present these new tools and these new opportunities, you're linking it to problems that matter to them because most people in the organization, regardless of how inertial you may feel they are, typically have some motivation of getting their jobs on being successful, and that's a basic human motivator. Yep. My add on to that last one is don't promote data analytics and new tools. Promote the solution that they deliver. You know, how can I help you? And we're going to help you with these tools and stuff because they're new and they're cheaper to use than they ever have been before and they're more powerful than they ever have been before. But you do not promote data analytics or a new tool for the tool's sake. You know, we got this great thing. I'd like you to use it. That's not going to help. Yeah, people don't need new gadgets. No, we got more gadgets than we can swing a cat at. All right. How does a new chief data analytics, whatever has to learn a large organization, successfully drive data governance across many mission areas, find staff? Who are they looking for? Where are they looking for it? I would like Anthony take about 30 seconds on this one and we'll let everyone else weigh in on that one and then we'll be about ready to wrap. All right. I'll try to come in on the under on this one. The key thing is to start small and start somewhere. You got to pick your spots. You can't just have this blanket approach to accomplish everything at once. Try to find an initiative that's underway that you can add value to. Latch on to that, improve that, reach out to more departments, more initiatives, and go from that perspective versus saying, my job is to create an enterprise strategy so darn it, I'm creating an enterprise strategy. Start somewhere, make an impact, build on that. Okay. What about finding the people to do it? Where do you find them? Well, I think we talked about that a little bit in the session. Look at marketing. Look around you. Find who in your group is receptive to trying to help. I'd rather teach somebody some technique and skills that when they really care about trying to do this stuff versus finding somebody who has a skill set already and convince them that this is a good idea. I think that the people tend to gravitate to this because they recognize that there's value in it and they want to be a part of that. Okay. Lisa, 30 seconds on this one. Sure. I think one of the key roles would be going to that point we were just talking about the data adoption and if it's somebody already on the team who's really good at helping other people and showing them the way or answering questions and teaching them to fish rather than giving them fish, data fish. I think you want to really think about who on your team is going to be like spokespeople and people who are actively helping your organization. So really think about those folks who are going to help drive adoption and not just answer the question for folks but help the people go find the answers themselves. Okay. Rachel? So, I mean, Anthony and Lisa did a great job on this. I totally agree with how you find the person you know, look for that person who's the helper, who knows stuff, the guy. But from a governance perspective, people get scared of the concept of governance a lot of the time and starting small is absolutely right. I think who you find plays a lot of role in the acceptance of governance when you look at it as a value add. This is a subject matter expert who's going to help you have a greater level of trust in what you've got in the data that you're working with. So I think like we mentioned before, talk to people about how it's bringing them a solution, how it's adding value to something they're already trying to do that matters for them. Not about you're going to add oversight and control. Well, very good. Well, thank you very much. My last read on that is start inside. Don't go out and hire people for something that's going to raise eyebrows. Try to find and train internally. That's usually what works in some of my practices. And with that, we've reached the end. I'll turn it back over to Shannon and we'll wrap up. Thank you, everybody, for these great presentations and thanks to our attendees for being so engaged in everything we do. We just love it. And just a reminder, I will send a follow-up email by end of day Monday for this presentation with links to the slides and links to the recording. So thank you, everybody. I hope everyone has a great day and thanks to DataWatch and to Looker for sponsoring today's webinar. And enabling us to continue these series. We very much appreciate it. Thanks, everyone. Have a great day. Thank you. Thank you. Thanks, everyone. Thank you.