Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We'd like to thank you for joining us in the latest installment of the DataVersity Webinar Series, Data Insights and Analytics, brought to you in partnership with First Hand Francisco partners. Today, Kelly O'Neill and John Ladly will discuss keys to creating an analytics-driven culture. 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'll 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 and questions via Twitter using hashtag DI analytics. As always, we will send a follow-up email within two business days, containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce to you our speakers for today. Kelly O'Neill is the founder and CEO of First Hand Francisco Partners. Having worked with the software and systems providers key to the formulation of enterprise information management, Kelly has played important roles in many of the groundbreaking initiatives that confirm the value of EIM to the enterprise. Recognizing an unmet need for clear guidance and advice on the intricacies of implementing EIM solutions, she founded First Hand Francisco Partners in early 2007. John Ladly is a business technology thought leader and recognized authority in all aspects of enterprise information management with 30 years experience in planning, project management, improving IT organizations, and successful implementation of information systems. John frequently writes and speaks on a variety of technologies in EIM topics. His information management experience is balanced between strategic technology planning, project management, and practical application of technology to business problems. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello and welcome. Hello. Hello. I'm happy new year to everyone. And happy new year for me as well. And I hope everyone is staying nice and warm because some of us are enduring some winter weather right now. Yeah. Bracing in the storm, huh? You bet. You bet. And cold. Anyway. Well, we will warm people's hearts by talking about an analytics-driven culture. How's that? Better than chestnuts by an open fire. There you go. And today what we want to do is start to get practical also. You know, in line with the way that we try and address these webinars is there's a lot of talk about an analytics-driven culture, a data-driven culture. And we want to go through some thoughts and concepts that help to make this a practical endeavor in your organization so that it's not just theory, that you walk away with some tools and techniques of things that you can do to embrace analytics in an analytics-driven culture. As we go through that, we will do some level setting around analytics-driven versus data-driven. And we'll talk about what culture is and why culture matters. And so how do we then tie the two together, engaging our key senior leaders and stakeholders, and look at how a vision statement can help to articulate and enlist those members of your organization to participate in the analytics program. We'll talk about some specific best practices for communications and approaches to drive the analytics culture, and then of course end with a few takeaways. All right. So first off, when John and I were working on putting this together, we talked a lot about data-driven versus analytics-driven, and are they really the same? And we thought, you know what, there is a nuance that we want to start to clarify. Data-driven does get a lot of attention. Data-driven is ensuring that all participants within your organization, all of your data citizens, understand how important data is to your organization and how to leverage the organization's information and infrastructure. But analytics-driven is also important, too, and it is a subset of data-driven. John, do you want to talk about the view of analytics-driven and how you see that as a nuance but an important delineation from data-driven? Yeah, and it is an important nuance. And I would say that when you're reading literature and you're seeing boards like analytics-driven and data-driven, a good way to not get a headache is consider analytics-driven more of a subset of data-driven. Because data-driven means that you are emphasizing a quantitative approach to things. So even if it's not analytics-based, even if it's just a really good business intelligence query, and it provides some insight to help operate an organization, that is technically data-driven. Now, analytics, where we're going in and looking at patterns and we're looking at something that is predictive or prescriptive in nature, means we're adding another quality to our decision-making process. So not only are we looking at what happened historically, not only are we looking at reacting to obvious inputs that we're doing as management, but we're also adding this layer of prediction or prescription to our decision-making process. And that is where the analytics aspect comes in and that's where the analytics aspect will layer over or enter into this data-driven thinking. It's really important. This sounds like maybe we're dealing with, you know, how many camels on ahead of a pin-type philosophical things here, but it's actually really important because a lot of organizations are, you know, saying we're data-driven, we're data-driven, and really haven't taken the time to figure out what that means. So some attention to the semantics here is important. Back to you, Kelly. Yeah, and I would like to highlight this concept of the pattern also, and so it's bringing in the visualization component to create the story that is important from an analytics-driven perspective. All right. Well, with that, Clary, we wanted to start our polling question. Shannon, are you ready? So question is, how would you rate your organization's culture related to its being analytics-driven? So are you in Nirvana where you are analytics-driven? Is it still a work in progress? So you're on the path to Nirvana. Are you not so analytics-driven so you don't know where to begin? Or is this something that is just not part of the discussion in your organization? Because it's potentially so far away from what you're currently doing. All right. The Nirvana, work in progress with Clueless or not sure? Yep. Is this the part where I've only got three or four seconds now, but so I won't sing the jeopardy theme or anything like that. Very well. And then the number should be coming in here at any minute. My money is on not sure how to answer or somewhere. Most answers in the latter half there. Well, there we go. There we go. Well, there we go. And I was wrong. Yeah. So I think that this is actually great in the sense that the largest percentage is saying work in progress. So 38% of you had indicated that you're on the path to having an analytics-driven culture. Surprising, I think, to us, John. But I think that's great. So that means that not only is there a recognition of the importance, but there is a process and a path that is happening. And, John, maybe our goal for this year is to get that 54% of you all that don't respond to respond. So how do we make these questions more compelling? Yeah. Well, we thought about some type of while we kick that one around. We're going to have to have some type of incentive here. I don't know. Fabulous prizes. We'll have to come up with fabulous prizes. That's right. That's all there is to it. OK. Excellent. Well, moving on. Anyway, thank you for those of you that do continue to participate. It does, I think, give an understanding of where we are as an organization, as a community. So we do appreciate your involvement and your participation. So the other thing is, you know, it is the golden age of data. I mean, I've been in workshops with the clients for the last couple of days where we are talking about data, data protection, data privacy, the implications to the organization. It is a huge multifaceted priority for many, many organizations. But we still see challenges. And so those, I guess, you know, 38, those 62 percent that aren't down the path quite yet are probably struggling with some of these same issues that we see in these different organizations. It's, you know, technology driven. So we overbuy technologies that under deliver true value. We have data strategies and cannot execute on them. So these are things that we've heard from our peers in the industry, our clients, our would be clients on what some of their challenges are. John, any of these that you want to highlight that you've experienced specifically? One, I'm starting to see a lot more than I have in prior years. It's something that's been in the back of mind. But that's the cost of ownership. This is almost off our topic here, but your organization and most organizations are spending a lot more money than they have to on using data. And they're spending a lot more money on analytics than they have to. And the initial rush of, there's two factors working towards that. One is the initial rush of the glorious technology and the cool stuff we can do with this stuff, which is kind of fun and neat. A second one is the internal cultures in this tendency towards functional areas to do their own thing. But organizations holistically don't realize really how much money they're spending on this. If you have started to go, wait a second, this is just kind of crazy. And I sense as well here getting ready to head down the hill on that one. And if you want to be data driven, that's fine. Where that applies, though, is you can't be data driven with every single functional area having its own VAs and its own tools and its own database. That's not data driven. We're not going there. I mean, some organizations say, well, we're data driven because everyone has data analysts and they do all their reports and they have all the self-service and all that. That's not data driven because that's way, way, way too expensive. It's not sustainable. Just some organizations are figuring that out. We may even have some more on that later on in the year here where you might take a deeper dive into this topic later on. So anyway, that's my one-on-one to point out there. Yeah, absolutely. So some of these might be familiar to the listeners on the line today. It is hard. It's not easy. And so those 38% that are down the path, they've gone down that path with probably a lot of scars. So why don't we talk a little bit about how to address at least the culture side of this and the culture side to ultimately address involvement, participation, and alignment around improving data value and reducing that total cost of ownership. All right. Why does culture matter and what is culture? So culture is really the concept of the way we do things around here. And so it is that group of assumptions, beliefs, values, and behaviors that are consistent across an organization. And it's the way that you hire people. It is the way that people create your success. Sometimes it is clearly articulated. So, you know, HubSpot, this is a great culture code because it's clear. It's written down. It's printed. It is visible. Things like that. Other cultures might not be as visible from a documentation perspective, but are very visible from a behavior perspective. So Virgin Airlines. Also, we're kind of rehearsing this. We're talking about how fun it is to fly on a Virgin airplane. And it's such a different experience from any other travel that you have. So at Virgin, their mission is to embrace the human spirit and let it fly. Play on the words with fly, but they're really talking about spirit and that sort of thing. REI, my favorite store in the whole wide world. Their mission and vision is to inspire, educate, and outfit for a lifetime of outdoor adventure and stewardship. So again, it's ways of saying, you know, this is what our focus is. So whether our focus is flight, and we want to embrace the human spirit and make it fun, et cetera. It does really inform the way that people behave in your company. And it doesn't always have to be written down in order to create a strong culture. And in fact, sometimes the culture has nothing, the true culture of an organization has nothing to do with what is written down. So again, we want to make sure that we aren't just talking about culture as a documentation process, but truly a way to get people on board with what you would like to aspire to have as a culture and what you want to see as an analytics driven culture. So as we go through this webinar today, we will go from this highest level in terms of what is culture and why is it important and then get down into how do we make it a reality and how do we ensure that the way that people behave and the way that things are done around analytics in the organization matches what you're trying to accomplish and what we document from the vision statement. So as we're thinking about creating that culture, this is about behaviors. And within an organization, especially when we're on kind of the data side of the organization, we think a lot about results. We think a lot about metrics. We think a lot about what are we actually getting at the end result and we want to drive towards that result. But the reality is in what we have seen in terms of experiments and investigations and just our own engagement with clients is that if you don't address how people think about what they're trying to accomplish, their beliefs, then you don't optimize the result because realistically, people behave not only in an analytical way, but they combine that analytical process with their belief system and that is how they ultimately behave and that's how it's manifested in the culture. So I did adapt this from a presentation that I saw by a guy named Dan Barnett, who's a wonderful speaker, and he talks about this as being the result force. And the result force is engaging people from their belief perspective, which engages their limbic system, their fight or flight, their core kind of animal brain, if you will. So engaging their belief system so that then when they combine that belief with their analytical neocortex data-driven or analytic-driven understanding, then you will get the results that you're looking for and that that is really what drives the culture of the organization. And by getting people aligned around beliefs and behaviors, you can create a consistent culture. And through creating consistent cultures and consistent behavior, you will get the results that you're looking for. So the idea is, yes, you need to identify those results that you want, the metrics that you want to create, the impact of the organization, et cetera. But in order to get those results, let's not forget that we are dealing with people and therefore we need to engage their belief system and ensure that we are talking about the importance of an analytics-driven culture in a way that their belief system can adopt and align themselves with. And that, I think, is a really important concept. So what we're talking about in the rest of the presentation is really getting to drive to this belief, getting to rally around beliefs, rally around visions, rally around a purpose of why we are driving towards analytics so that we can then get the results that we're looking for. So this flow of strong beliefs in order to drive results is a foundation for some of the rest of the presentation. John, did you want to add any color here or some of your experience in which you've seen strong cultures either created or exploded? Well, the key with a culture, all cultures, almost by definition, are strong because the way an organization does its things. The key to moving towards data-driven is understanding how heavily your culture is hung up on the belief in behavior thing, which is where you get the, we've always done it that way, problem, right, versus starting to be a more thinking organization and applying a quantitative filter to your decisions and your actions. This is the reason that we have one question that came in here, and I can answer it at this slide. A lot of people complain about data silos. This is where your silos entrench themselves because this is where they believe that no one else can do it as good as they can. Their behaviors are geared that way, and to get them to break out of that is a major behavioral shift. And this is a very human, to reinforce Kelly's point, this is a very human psychological problem. A lot of folks will say, oh, that's politics. That's politics. It's not politics. It might have been politics to start, but once it's entrenched, it is now behavioral, and it takes some work. It really takes some work to get there. So you've got to keep this picture in mind as we go forward, really. And I think it's important to think if we want to get people moving beyond the, it's always been done this way, we need to change that belief. We need to change that foundational construct of this is the only way that it can be done. So what needs to happen in order to change the way that that core belief and the way that their system is engaged in order to behave in a way that leverages all of that great data and analytics that we're shooting for? Absolutely. Okay, so let's get into the how. Yeah, okay. So supportive executive is the first. Everyone talks about that.  A supportive executive is a lot more than just buy-in. You know, they understand and articulate the what and why the key there is the articulate of something. So if you're going to be analytics driven, you have a supportive executive that is very articulate at why this is really important and how to do it. And then perseverance. In other words, a lot of people are not going to understand what's going on. You know, you talk to someone and here is the results of a Monte Carlo simulation, right? And it tells us to turn this way in the marketplace. You know, half your meeting is going to be wondering what is that and why do I need to pay attention to this. So now we're going, we're getting into this picture I told you to keep in mind earlier. And now people begin to get nervous. The visible support is kind of obvious. Accountability is also kind of obvious. In other words, if you are going to be data-driven, then you've got to tie incentives and activities to that. And is the advocate for overcoming the resistance and of course it goes without saying you have to be an effective communicator here. We are seeing a lot of people in charge of these types of initiatives that have not been trained properly to be a true supporter of this type of program. We are, we, and this even goes up to the chief data officer or the chief analytics officer. They might be a tremendous data person, a PhD in mathematics, a tremendous statistical engineer, and they are abysmal, quite frankly, at rallying the troops to follow this. And so things just kind of fall off by the wayside. There has to be an emotional connection. And unfortunately, a lot of the personality types that get into heavy analytics and heavy prescriptive and predictive that are, tend to be mathematical in orientation and not folks that are used to having to engage this way. And they need to be trained that way. Just hiring a very capable data scientist and making them in charge of a program who's nowhere near a guarantee of success here. And that's not your supportive executive. You might have to have another executive actually being the sponsor of whatever the analytics officer is trying to accomplish. You know, the key here is to increase engagement, and that's on the next slide. Here, I'm sorry. Kelly, anything to add to that one before I ask you to pop to the next one? No, I just wanted to reiterate one of the things that you said is that not every executive knows how to support it. So I think one of the things that we need to think about as a team is supporting our executives also educating them on what it needs to be supportive, giving them ideas on what they need to do being specific, helping them understand what their role is and why their role is also important to the program. Yeah. This is kind of a, with that a couple of charts ago, I said keep that in mind. Keep this one in mind, too. I have a little saying whenever I'm with a bunch of new folks who are new to this and they say, oh, we have buy-in. And that's just something I just say to myself, to ground the enthusiasm to make it, to channel the energy of the group. And that is that buy-in tends to be bogus, all right? Buy-in just means that you vocally support something. But where on this curve of engaging in a change in an organization is buy-in. Buy-in is between awareness and understanding and a positive perception. That's where buy-in occurs. Well, buy-in is in nowhere near the activities that you need to institutionalize and internalize something new. And that's why a lot of lawyers say, boy, we had buy-in, but our sponsor never shows up. Well, your sponsor, you had buy-in, but buy-in is not what you want. What you need is engagement, okay? So the key here is to actually move people through these steps. You need a plan to do that. So with awareness, you know, contact is hello, how are you? I'm so-and-so in this and we want to be data-driven. And I'm oversimplifying this for the sake of our event here. Here's the concepts. Here's what it means to you. Does that sound like a good idea?  Yes, now you have positive perception. Now, here's how we have to get it adopted by the organization. So now you need to understand what's going to happen when you go from that limbic behavior to the neocortex behavior. What is your organization going to do? If you're deeply siloed, you're going to have an enormous amount of resistance. And you're going to have to move it silo, by silo, by silo to get the adoption. And that's going to be one battle at a time. And you know what? There is no way I can make that easier. That is what you have to do. There is no magic formula there. You know, other than the CEO or in firm counsel writing a letter that the government has said through some vast new regulation to change everything overnight, those are the only things that will get people to turn on a dime. Other than that, it's going to be a department at a time. But eventually, you will be able to recognize and measure that people have made this behavior their own behavior and say, well, of course, I've always felt that way. That's when you know you've made it. We have to formally move folks up this curve. Remember that buy-in can be bogus, okay? It can be misdirecting. Some discussion points. Kelly, anything to add to that? I'm sorry. Yeah, I just think that, you know, this is kind of an upward curve, right? And so the idea is that the purpose is to increase the engagement. And we bring this up during the executive stakeholder discussion, because for your executive stakeholders, they should be going one direction on this curve. And they should be at a target state of the institutionalization, meaning they understand and they know what it means that analytics driven is just how we do business. And then they should also internalize what it means to them personally, to their own group, their own division, and how it helps push the business forward, so that you have involved them and they do believe that it should be an analytics driven. Because they are a wonderful voice for the analytics program to ensure that they are extending their belief, convincing other people, evangelizing why analytics driven is important. So this is key to think about getting those executives along the path to not just adoption, but institutionalization and internalization of their belief. Okay. So how do we do that? What's the way to do that, John? Well, you need to train your leaders. And then you need to measure them. So what we addressed earlier were the characteristics. So a leader needs to be either hired to be able to articulate and rally the troops, et cetera, et cetera, that we talked about. And then they need to be assessed along the way. What we have here is a sample from a battery of questions that we think that leaders need to learn. So when we train a sponsor or the change agent of a data-driven or analytics-driven initiative, they need to be very clear about answering these questions. I'm not going to go through every one. You can review this and screen capture it and all that later on. But how does the analytics strategy contribute to vision and business strategy? If they can't sit down and say in a very concise manner, this is kind of your elevator speech you'll hear about once in a while, we are doing predictive analytics so we can do X and Y. And if they can't do that specifically and not wander off into some exotic conversation about data science and big data and stuff like that, then if they have to wander off into that big discussion, they're not well enough trained yet. Same thing with the major issues. They've got to understand what their obstacles are. How can you move up that curve on the prior slide if you don't understand what those obstacles are? It is absolutely unrealistic to ask any type of leader to move an organization from its current state to a future state without understanding what those major issues are going to be. You can't say we'll deal with them as they pop up, which a lot of CEOs say. That's copying out. You've got to kind of think these things through. Again, what will be different? Of all those questions, when you look at your role and accountability and all of those things, I think if you're going to answer one question, Kelly, and this is something that we ask all the time, what will be different? We're going to hire this consultant. We've got a new consultant on staff named Harry Potter. Harry Potter comes in, does a spell, information alias or something like that. Boom. You've got all the infrastructure, all the tools, all the metadata, all the sharp data scientists you have. Leaders, tell me what's different about this organization now. What's happened to the balance sheet? What's happened to the income statement? If you can reach that point, now you're on the way. Those are the kind of things to do. So if you want to know how you do this, be able to answer that question explicitly. Anything to add to that, Kelly? Yeah. We also use this as an exercise to compare across how different executives view the analytics program. If you use all eight questions, you reduce it down to three or four. The idea is by asking each of the leaders those same set of questions, you can start to identify consistencies as well as gaps because if you've got two executives who are potentially in a leadership committee, one of them may be the identified sponsor but the other one is on the committee and they have an influence. If they have different viewpoints on any of these questions, then that's going to impact your ability to drive to consistency and to really create the analytics culture because their viewpoint of what does it mean would be different. So I think this is an important question list for your executive sponsor but also across that leadership team. So food for thought. Yep. We have another instrument coming up here. Oh, yeah, there we go. I'm sorry. So now we wanted to talk a little bit about other sorts of stakeholders within the organization. So, John, did you want to go through this stakeholder, guys? So these would be kind of your non-executive stakeholders. Yeah. We want to eliminate slightly. Go ahead. Yeah. And again, this is just, again, this is something you can screencap if you want to go through this again. Call up the recording of this and all of that. And that is, you know, first of all, understand what a stakeholder is. That it's not anyone that has a stake in it, right? That's the Chiefsburger definition. But it's someone who's going to be affected or jobs role will be affected by this data-driven initiative or analytics-driven initiative, which in some organizations could be almost everybody, right? And what's their role in that, you know, since they are affected, how are they affected? What's their role? Why will they stand out in this activity? Then you get to predict how they're going to react. Remember I said, and going back to what Kelly, I said, keep that picture in mind that Kelly went through about what drives culture. I jokingly tell people once in a while, this is where you get to gossip about people. You know, if you did this to so-and-so, how are they going to react? And they go, well, they're going to hate that. Perfect. That's just what we want to hear. You know, then we can work with that, right? Why will they react that way? What are they worried about? And then what do we need from them? And then how should we get them on board? Now, these teams like really logical questions, but you actually, again, you need to have a formal, you know, we have a formal organization change management practice in First San Francisco. We have engineered this because you have to. You can't just do this extemporaneously. We have to ask these questions or people will miss personalities. We'll miss some of the challenges and things like that. So the next thing is with everybody, I want you to have your leaders on board. Now you have to understand your stakeholders. And then, of course, Kelly will stop here, let Kelly weigh in. And then the next slide we'll launch off on the engagement strategy that we work with here. Yeah, absolutely. And I think that you can see kind of as the questions go left to right that they become very tactical. And this should inform your planning. This does translate into activity. So this does translate into project deliverables and that sort of thing. So understanding what you need from a stakeholder and then determining how to work with them. Those should be recorded within the project plan. So I know that analytics are programs, they're ongoing disciplines, entities, et cetera, but things are delivered in a series of projects. And so there needs to be time allocated for getting what you need from a stakeholder because generally it's not just a single email. Many times there's a lot more effort associated with that. And also engaging with that stakeholder does take time and should be allocated for that level of relationship management and communication is extraordinarily important and needs to be accounted for. And so the reason that we think about this is because there are only a few, well, there are only a certain number of hours in the day. There's a lot to do. And it is important to allocate your time across that stakeholder community appropriately. Otherwise you will not be optimizing those hours that are spent with the different stakeholders. So unlike the idea that the stakeholder marches nicely on this upward sloping curve, the reality is as many people don't, and you will come across stakeholders within your program that in fact go backwards or jump off the curve, right? So we want to make sure that we're identifying how we engage with them based on their ability to influence the success of the program and the impact of the program to them. So if we think about this as a quadrant and we have stakeholder influence going up the left-hand side and impact or interest in the program across the bottom, that stakeholder interest can be thought of also as how does the program impact them? Maybe they're a consumer of the analytics output. Maybe they feed information or they structure information that feeds into the analytics program. So that would be their involvement or interest. And the idea is that you want to plot those stakeholders across these quadrants to prioritize them. So do they have a lot of influence? And do they have a lot of interest in the program? So therefore they're a key player. If they are, we put them in the upper right quadrant. Maybe they don't have a lot of organizational influence, but they've got a lot of interest. Well, we want to make sure that we're showing consideration and fostering that interest as well. Those folks that have a lot of interest in the, or have a lot of influence in the organization, but don't actually have a lot of interest. In fact, maybe these are your obstacles or these are people who are fighting you for budget. You still need to meet their needs because guess what, if they're influential in the organization, they may actually influence a key player and you don't want a key player's interest level to start to wane either. So by plotting the different stakeholders on this quadrant, you can formulate a plan on how to engage with them. How do they fit into the communication process? Do you work with them individually? Who influences who? Sometimes you want to start mapping lines of influence. And if you know that some of the people that are in the meet their needs category have a great influence on one of the key players, you want to be aware of that and incorporate that into your planning accordingly. So this is a good way to prioritize activities around stakeholders and make sure that you are managing those stakeholders in the most efficient and effective level based on their ability to drive success or impact success of your program. Anything you want to add to this, John? No, no, that's well done. Great. All right. Well, so those are some ideas around stakeholder management. So now let's talk a little bit about the visioning process. The vision statements are images of the desired future, rich, inspirational, evocative, et cetera, et cetera. And so the idea is that that vision statement is what inspires and starts to impact the belief of your stakeholders and your community in general, because that vision statement should be the emotional part of it that starts to articulate the importance of the program. And so, you know, here's a sample. So data and information will be our main driver of organic growth. Data must enable the easy measurement of ROI, allowing for application of advanced analytics. So one could argue this is long, absolutely. But the idea is that you want to start talking about importance to the organization, so growth of the organization, measurement of programs and projects, and allowing for application of advanced analytics. So what this says is that this organization, they're trying to move more into the sophisticated and really advanced level of analytics, as opposed to doing more simplified analysis processes. Any end-downs of vision statement or even the process to get to a vision statement? Well, the process, you know, vision statements or vision statements, you can find a thousand ways to, you know, facilitate a roomful of people to get to a vision statement. But remember that you're building this vision around data-driven and analytics-driven. What does that mean to your organization? So the process to get there is fairly conventional. The result might be earth-shaking. I just wanted to ask, just to have everyone read that first sentence in that vision, data and information will be our main driver of organic growth. Now, we do a lot of work with a lot of companies, and we don't make much of this stuff up. These things are always right, Kelly. They're kind of boiled down from real examples that we've genericized or something like that. And we have worked with three organizations in the last year where their vision statement had something to say along these lines. One of them is doing pretty darn well. The other two adopted this as a vision, threw it out to the management team, and the management team immediately spun off into the ditch because they had no idea how to switch their main driver of organic growth, which just would get more customers, right, or lower overhead in their area or, you know, in some companies just buy back stock so the stock price goes up. Now, all of a sudden, you have this sea change of vision bumping up against whatever the corporate vision is. So this is a really important exercise to do. You might want to say something like this, but your organization might in no way, shape or form be ready to handle anything like this, and you're going to have to back away from that. So the person that's driving being data driven or, you know, even if it's a CEO, the CEO needs to be told, right, that you can't say this unless you're really ready to do it. You know, think about what that one sentence could mean to your organization, those of you that are listening to this. Think of what that would mean if all of a sudden your data was your main engine of growth. And you're all the people that send in the questions about data silos. You're probably going now and that wouldn't work, right? So, you know, again, it's not so much the vision statement, but an analytics driven vision statement really, really important because that will encapsulate and set the principles out there that you will follow as you move forward with your analytics driven efforts. Absolutely. So let's just put that into context a little bit. So the vision statement is really kind of the first step in this process. And so this is a five step process to create a communication and a way to align an organization around an analytics driven culture. And so you're absolutely right, John. You can't just start, stop at a vision statement and create a vision statement that has nothing behind it. And the idea is that this framework both helps to create the communication around what are we doing about making that vision a reality. And it forces the thought process and conversation around how do we fill it out in the sense that you can have a vision. The vision also needs to be supported by a purpose. And that purpose is why are you executing the vision? What is the impact to the organization that that vision will have once it's achieved? And then what does the picture of the future state look like? What will we have accomplished? How will behaviors be different? What are we expecting to see as a result? And this starts to evoke what people can internalize in terms of something that is visual. And they start to see how will the future be different than the current and of course how will it be better because we've articulated the purpose. Now to get more tactical, then that picture, there is a plan that supports that. That plan is what the organization needs to do in order to create that analytics program. And the plan could be a strategy document, a roadmap, a project plan. So depending upon where you are in building out your goals, that plan can be either high level or very strategic. But it needs to be good enough to articulate to people, this is our vision, what we're trying to do, why it's important, what the future will look like. And this is how we're going to get there. So the vision needs to be supported with the how. And then the participation, which is who is involved, what is your role in the program, etc. And you can see here that the vision and the purpose are targeting the belief component of that result force. So that you can really get people to change their belief around why this is important to the organization. And then the picture plan and participation starts to drive to the exact behavior that you're looking for in order to get to the results. So this is just a framework to create a communication structure, but also to create decision making in a planning process so that it is effective and it can be implemented and it's just an empty vision statement. John, anything to add before I go to the next slide? Because of the delay in unmuting, no. Okay, great. And we're just, we want to make sure we're also leaving time for questions. I think in our end of year webinar, we really came up to the end. So apologize to the group for that. So this all leads into the communication process. So that vision, purpose, participation is the content, but then there's the whole process to communicate it out. And so the communication plan is a really important component of your overall analytics program and your overall analytics planning process. Again, it is time consuming. There is effort associated with it. There is collaboration and coordination associated with it. And so it is important to account for it in your planning process. And it is an easy thing to leave off the plan, but it is important to allocate time, energy, resources, and therefore financial funding towards it. So just a few things on this. Always start it early. More communication, the better. Recognize that there is value in repeating. The more you repeat your vision, the more people will start to recognize it, understand it, ask questions about it, and hopefully internalize it. Remember to customize your messages by your stakeholder group because what is meaningful to one organization or one per individual person is not going to be meaningful to another person or another part of the organization. And this is also where communications can become time consuming and needs to be allocated for in terms of your planning because that customization of messaging is very time consuming. And then, of course, don't be afraid to test your messages to make sure that they are resonating with your stakeholder groups in the way that you do want them to resonate. John, anything to add? Real quick. Communications plans are plans. They're meant to be executed. I don't know how many people will call up a year after we're there and say, how's it going? And I'll go, oh, we have a trouble with all this. And I go, yeah, your communication plan. And they get it out and go, so which events have you done? Well, we only did, like, the kickoff meeting. Well, okay. Follow your communication plan. Try it. Because, I mean, worse is going to happen is you're going to push, you're going to bump into some resistance and someone will tell you to slow down. But communication plan is meant to be executed, not just done. It's just something I've been picking up here in the last six months that, I mean, we do it, maybe read it, maybe prove it, and go, yay. And then it says, have a big event in six weeks and the event never happens. Well, okay. Well, why do we do the plan? So execute the plan. That's not so much adhesion material. It's more advice. Sorry to sound like your dad, but that's advice. Right. That's right. That's right. And we mentioned this in one of the bullet points is that you need to speak to the language of your stakeholder. And so many times I'm translating what you see from a data value perspective into. And this is really important because what you are tracking and measuring from a data perspective in terms of quality metrics, redundancy, you know, speeds and fees. You know, number of reports, number of algorithms, et cetera. What they're going to care about is how have you helped me push my business forward? Because you've rationalized my customer base, I'm better able to execute marketing campaigns and my hit rate has increased significantly. Because you have provided me with the appropriate segmentation for this customer base, I'm able to launch my new product more quickly. So this is the way where you translate the data and analytics components into value statements for your business. Very important to do that. Anyway, in the end of the time, let's just go quickly to some approaches here. John, did you want to share some examples? Yeah, we had here's where, you know, the first organization transitioned to analytics driven started out with the sandbox and the sandbox was actually buried in a business area. The organization was not one to embrace new technology things. Lots of reasons for that. A deadly soup of IT doesn't get things done. Some of it was true. Some of it was not true as well as what I call the hero culture of someone learning Excel and doing a heroic report or something like that. So anyway, this organization not into a big centralized data science program. So they started with a sandbox in a business area and some visible results came out and it made some for some profitable actions, but they advertised those. And they said, look what this data can do. Now, there was immediately some antagonism because IT said, well, it's our data, you know, we're the data folks. Why are you doing this? We have a data warehouse kind of stuff. This other group says we're not doing this to make you look bad. We're doing this because we did really cool things with data. So what they did was they intercepted this antagonism and started to force themselves up this engagement curve to get everybody thinking together. And that meant building out stuff we've talked about before in this series, data governance. And they had stakeholder training and they had sponsorship training.  I am the second or third in charge in the organization. Oh, no, you do have to go. Have you ever implemented a data driven culture before? Show that to me on your resume. No. Okay. Well, then there's going to be some training here. They're working through these organizational changes. Another one is shift gears to manufacturing. And manufacturing companies tend to be somewhat entrenched in how they do things. But, you know, along comes Internet of Things. And all of a sudden data is everywhere. And data does become a potential source of organic growth.  Data governance and business alignment became a buzzwords day in, day out in the vocabulary of everybody in this organization. Because they were, and gradually they internalized that the next wave of organic growth in their industry was not just making bigger machines that they were already making or better machines that they were making, but doing stuff with the data that those machines were generating. So, guys, five minutes to the top of the hour. We can get to Q&A for the folks. That's right. So, let's just quickly. So, John, do you want to just take 30 seconds and go through this slide? I'm happy. I will just go through the orange boxes. All right. All companies have business strategies. There's two business strategies there. One of those strategies must, in an analytics driven organization, those strategies are enabled by analytics. And you can see there analytics is a subset of data driven, which is a subset of overall EIM or data management. So, analytics have to be supported by managed data, which has to be of quality of data suitable for the task at hand that has to be governed and certified. So, for someone to say, well, just because we're doing data analytics, that means we don't need data governance or data quality, that doesn't stick. That will not hold together over time. We use this chart a lot to kind of show people that this is an integrated problem that organizations have to deal with. And that was almost exactly 30 seconds. Let's go on to my top 40 training showing up there. So, what can we do about it? So, we've broken this down in terms of what can you do in 2018. So, as a senior leader, understand what it means to be a good sponsor. As a manager or a team lead, you can clarify your meaning, translate that analytics into action. So, as a team lead, you can get your team moving and you can start to communicate upward all the good work that they're doing. And you can also empower them in order to deliver that work. The sharing of the stories is really critical at that level up to your senior leadership. Individual contributors, you have a huge role to play in all of this. Because if you don't share your stories and your successes, who's ever going to know about them? So, tell your manager, your team lead, get an audience with your senior leaders so that you can communicate your successes and share the lessons learned. And then, of course, everyone can educate themselves about what is analytics, what are some of the successes out there, what are some of the changes that organizations have seen as a result of well-executed programs. And think about how can you bring something to the table that helps your organization forward in 2018. Q&A. Yes. Kelly, I'll ask the question. You take the first shot. We've got several questions, but there are two themes. So, first of all, the cost and return of a data program. So, you're going to be analytics-driven, you're going to be data-driven. How do I do the ROI? That's the first question. Yeah, and so this, absolutely. And I saw this question come in. Hi, Toni. Nice to take care of from you. This is a really big question. I'm trying to think if we had any webinars in the past where we- Yes, we have. Yes, we have. Okay. So, I'm going to maybe, Shannon, on our follow-up, we can send some links to those webinars. And those might be great resources to answer this question, at least from a first step and from a second step. We can spend some time with you in a more detailed way. But really, it is looking specifically at productivity, enhancing the way that people get questions, answers, get reports generated, execute operational efficiency. And there's one other thing. I'm going to add one other thing to it. Pretend that your next source of organic growth is what you're doing with your data. And then use the same exact calculations you do if you were doing the ROI on a new product or a new service or a new business. All right? Do it exactly the same way. Just pretend you're going to monetize your data. And that exercise right there will get you 40% down the road before the rest of the other metrics that our other talks have talked about. So change a mindset and do an experiment. So the next question is, executives use analytics and data-driven as buzzwords, but the organization decisions are not in line with the buzzwords. How do we deal with that? Kelly, you can take a crack at that, and then we'll finish up. Yeah, absolutely. And to them, that chart that we talked about just a couple of slides ago, the one that's how analytics-driven, data-driven, and data management are aligned. So that sort of flow or line of sight is a good way to start to link other decisions, other organizational projects, programs, and identify how they can and potentially cannot support analytics. So there's many instances in which organizational decisions conflict with the analytics program. And part of having a kind of cross-functional group of guides or call it a steering committee can help identify where those conflicts arise. And that's also a place in which your senior executive and stakeholder can start to be your advocate and break down barriers and identify where those organizational decisions are not in line with it and identify how to get them in line or what can we do to manage that conflict because sometimes it's an appropriate conflict. John, did you want to add to that? No, I think that was really good. And in the interest of time, Kelly, unless you have anything else, we can just turn this back over to Shannon. Thank you. That's good. Thanks for listening. Thank you both for kicking off the New Year. It was such a great topic. And thanks to all our attendees for attending and for being engaged in everything we do. We just loved it. And just we hope you can join us next month when we discuss the very hot topic of data, simplifying data lake and modern BI architecture. I know that's going to be a really good one. And I hope, just a reminder, I will send a follow-up email by end of Monday for this presentation with links to the slides, links to the recording, and then all the additional information. So I'll include a link into the past webinars as well for you all. And I hope everyone has a great day. Thanks, John and Kelly. And thanks to everyone. Thanks, everyone. Bye-bye.