 Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Dataversity. We'd like to thank you for joining the latest installment of the Monthly Dataversity Webinar series, Advanced Analytics with William McKnight. Today, we'll move with discussing organizational change management for data and analytics driven projects. 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. We'll be collecting them via the Q&A section or if you'd like to tweet, we encourage you to share your questions via Twitter using hashtag ADV analytics. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note the Zoom chat defaults to send to just the panelists, but you may absolutely change this to network with everyone. And to find and open both the Q&A and the chat sections, you can find those icons in the bottom middle of your screen for those features. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session and any additional information requested throughout the webinar. William, just FYI, we're seeing your speaker slides right now. Oh, okay, yeah. And let me introduce to you our speaker for this series, William McKnight. William has advised many of the world's best known organizations, his strategies from form the information management plan for leading companies in numerous industries. He has a prolific author and popular keynote speaker and trainer. He has performed dozens of benchmarks and leading database, data lake streaming and data integration products. He is a leading global influencer in data warehousing and master data management and he leads McKnight consulting group, which has twice placed in the corporate 5,000 list. And with that, I will give the floor to William to get today's webinar started. Hello, and welcome. Hello, and thank you, Shannon. Welcome everybody. Welcome to a very special edition of advanced analytics. This is going to be a little bit different than most of the others. It's going to be less technical. Well, at least from a technology perspective, I will be sharing a lot of specific tips and techniques for shoring up this very important aspect of the projects that we do. This is about data and analytics driven projects, which are the bulk of the projects that I work on in that I assume that you work on too. And frankly, are the bulk of the projects in most enterprises today, at least to some degree. So whether you're building a data warehouse or whether you're enabling targeted marketing or fraud detection or the like predictive maintenance, etc. This is for you. And this is going to address something that I feel is very important because it's left out too much of our projects. Now, I'm not going to be sharing with you a methodology that makes sure that every single person is on board with your project when you go to production. That's just not possible. You just will not be able to move at the speed necessary of that that the business dictates today if you account for all those contingencies. However, there is a prudent level of organizational change management. I hope that you walk away understanding what that's going to be for your current and your future data and analytics driven projects. I have noticed that myself on projects and I've been on many projects. I'm continually on multiple projects of this nature. And I noticed that I just sort of gravitate to those things that are necessary, but people don't seem to want to do or really have the capacity to do. Well, I hope that to give you some some level of capacity here today, but it's really just a start. A lot of this is less about learning though it's about doing it's about getting out there in the projects is starting to kind of work the situation to make sure that your wonderful projects that you're building and I know they're wonderful are actually going to be accepted by people and ultimately that will be what makes it deemed a success. Not the fact that we build something great, but the fact that it gets accepted by the organization. So have you been here, you develop a great project, and the organization responds with no bueno do not pass go. Did you talk to Bob and accounting about this. Did you account for this XYZ contingency that that you know you never you never asked me about that so I'm just not sure about it. Who's on board with this project that that I can, you know, kind of group with and and and see myself, you know, accepting this project. So there's a lot of reasons why you get to know I used to get a lot of knows until I learned a lot of them I learned later were really soft nose. They were more kind of deflection kind of nose where oh thank you for thank you for the information and we'll be in touch kind of thing right. So, unless your process is 100% automated, you're going to need people to buy in, or you're dead on arrival with your project. And I'm going to go into the who the how and the reasons why there is this resistance to change, which is what we're doing with data and analytics projects right. These are among the most important projects for the company. And so, by their nature, they will involve organizational change. So, what is this organizational change management that I'm talking about. Well, I like to put a little definition on it here at the start. Organizational change management is the people side of change. It's how to facilitate people from current state to future state with high technology adoption and usage. That's right. We're talking about using what we build we're talking about using the data. We're talking about using the analytics talking about using the AI. So I want you to understand how your stakeholders are looking at their problem. And we want to be addressing how they look at their problem. Organizational change management is a focus on the people aspect of the activity that you're doing and having stakeholders part of the current state analysis and the solution. So we're not developing it. And when we go to production, we will kind of spring it on the organization spring our O.C.M. on the people at that point. It's an ongoing process. And frankly, you really want these people these stakeholders to be involved throughout the project from early inception all the way through production, at least some of them. And that will certainly, you know, fortify your ability to have a great project, a successful project. So why resistance. And by the way, your resistance to your projects that you're building now could come from multiple of these. And it could be different for different people for sure. And people could have multiple of these issues with the project. Right. I'm going to start with change just pure change changes hard. We got a lot on our minds. We're very busy these days. And, and now you're introducing let's do things a different way. Well, even if it's going to be a more efficient way even if it's going to kind of put aside a lot of the tasks that people really don't like to do anymore. And they would like to add value. There are people like that. It's still change. And there's still a turnover to that new new way. And it might be a resistance to the turnover time. It might be the timing is not right for them. It might just be I like, you know, I might complain, but I like what I'm doing now. ROI concerns. This project you did is it really bottom line best for the organization? Well, you've already sunk a bunch of costs into building it, right? Probably the majority of the costs for the project are already sunk, but that doesn't stop some people from bringing up ROI concerns at that point. And frankly, I really hope you've developed a project that produces bottom line ROI to the organization at least, at least some time in the future, if not short term. And so these are all valid things that we need to address. Credibility. Who is presenting the change? Is it somebody with credibility in the organization? That brings a lot of good knowledge of the organization. So that shows that they've made a productive change to the organization. Or is it someone that's really hell bent on, hey, use this technology. This technology is good for you. You must use this technology. We have to be, you know, quote unquote data driven and et cetera things like that. The terminology that you're using can be quite confusing. And let's face it over here in the data and analytics world. We have confusing terminology and we're used to it to more or less degrees, but our users are not generally speaking. They're getting better about it, but generally they're not. So we have to be sure that we're speaking to them in their language and communicating the ideas that way. The organization just may be an organization that resists change. And that's the culture. And maybe it's, it's kind of about there's a lot of governance that needs to be in place. And maybe there have been some elements of that that have been missed along the way that people care about alignment with values is what you're introducing aligned with the values of this person that is that needs to accept the project. Or maybe you're focusing on the process too much or the technology too much and not on the outcome. And maybe you're, you're introducing something that we think is easy to use, but they don't think it's so easy to use. They don't see the ease of use that we that we see because we know things and we're used to things. And so they may not feel like they have the competency so that's going to that's going to create barriers that's going to raise the barriers to accept it. So, you are going to need some patience with this we are talking about people we're not talking about a computer that we can go and reprogram overnight. We're talking about people and people change at different paces, however, I have found that most of the time, people will come along with the good change that we're trying to make to the organization. And some people are early adopters, some people are later adopters and we'll kind of get into that. Remember, change feels hard when people don't feel confident. So, to that last bullet, if people don't see how they're going to be able to incorporate your changes, they're going to feel less confident and they're going to resist. Now, we are stepping into a world of artificial intelligence, right? This is the next big wave that most of us are going to be working on in the next decade for sure. Now, and I've kind of established this in other in other webinars, but this requires strong organizational change management, even stronger than what we have now. I mean, just think about it, go to most people and say we're doing artificial intelligence, and that does get their attention. I'll put it that way, as opposed to a few years ago when I when I would say, oh, I'm working on analytics, an analytics project for so and so. Yeah, okay, that's nice, you know, but if you say artificial intelligence, that does raise certain fears. And these are some of the, these are some of the realities of AI. It's our responsibility to get ahead of the fact that AI is coming for the company. It's AI is going to create a lot of efficiencies, it's going to enable a lot more capabilities. We all know this. We know this on the call, but other people don't necessarily. But it's our responsibility as an organization to get ahead of that. And you may want to get some C level involvement in this, some C level backing and so on. Hopefully, you have that if you need it for your very important project that you're doing within the company. And I would say a lot of people are mostly people are too hesitant to call in those markers from executives to, to, you know, encourage the change that's going to be necessary. Remember, we are paid here at this company for what the customer is willing to pay us for. And it's our duty, really, to be ahead of AI to be as efficient as possible. And this is certainly something that moves us in that direction. In the absence of information, some people will go to the worst case scenario. I heard my friend's nephew lost his job to AI. Have you heard of refrain like that yet? You certainly will, if you stay in this world, you certainly will. And what do you do with that? I mean, I'm not, I'm not fully aware that AI is going to, the projects that we're working on now is going to cause a lot of job loss. And I think organizations are going to move more carefully than that. I hope your project is moving more carefully than that because there's a lot of value add that people can still be doing in the world of this initial AI launch over the next five to 10 years. You can also ask, what do you not like about your job? Well, you know what, that's probably the thing that AI is going to work on, what you don't like about your job, because it's probably the, the most mundane aspect of the job that AI is going to be picking up. Replace the fear with positivity. I know, easy to say, but we must be positive about AI, positive, be positive about the possibilities that it creates for the company to be sustainable. And for all of us to be sustainable in our roles and in our expansion of our capabilities. Gain the respect by listening. We want to tell so much. We want to go, we want to be one way way too much. We need to be listening more to our users all along the way. We want to huddle with people that are like us and we get things done that way, but somebody needs to be listening all along the way. And don't forget, AI has a certain ethics component to it that we won't get deeply into today, but that's part of the conversation that you need to have. Yes, we're doing AI, but we're doing it with ethics. And here they are. And I think that really helps in my experience, that really helps to communicate that, hey, we thought about some other things other than making the technology do things. AI represents big change to the organization. Again, not a little change, big change. Organizations implementing it have recognized the need to make significant changes. People essentially don't like change. I think I've already established that when you add AI. That's going to make the issue worse if you don't get ahead of it. So I'm really throwing up the caution flag here. As you step into your AI related projects because once those words get kind of seeped out into the organization and, you know, rightfully so we could be getting ahead of that right. But it is going to create more barriers to change. And I think that's going to be a huge thing that companies are going to be working on. And it's going to make headlines over the next five to 10 years. So I'd like to educate a little bit here and make sure that we're on top of it. We need to be demonstrating how it can help the company instead of having the fear grow with people thinking it's going to hinder them or even worse replace them. In order to enable this cross-functional collaboration, provide a more robust and up-to-date IT infrastructure and manage new risks that can jeopardize trust in AI. Education is the key. It's the key for AI, but it's the key for all of these data analytics projects, really. When I give you the techniques a little bit later for organizational change management, a lot of them are grounded in education. Educate in many ways, early and often. And different people respond to different types of education. We're not talking about classroom education. In a lot of organizations, people don't have time for that anymore. Of course, we're all virtual. For the most part, people are still virtual. So that means we're not going to get together and have that benefit, if you will. Dispel misconceptions. And I threw up the one about my friends, uncles, whatever, lost his job to AI. That's what I heard. Well, probably not true, but dispel misconceptions about what AI is and what it isn't in the organization. This includes everyone. Your executives needed it just as much as everybody needs it in the organization. Focus on explaining why the change is being made instead of emphasizing the technology. And this is true throughout. Make it a shared learning process rather than a cute change. You can learn from them too, by the way. Remember the listening that I talked about before. Be open to learning. Be open to... I don't have all the answers here. I may not be providing exactly what you need. I'm open to hearing what you need and making sure that we gear it as much as possible in that direction. So now let's talk about the key areas of change by information management and discipline. Information management is broad, all-encompassing. We all probably have our list of the three to ten things that it really means. So I've got about four here today that I'm going to share with you. I think most things will kind of fall under those buckets in terms of the projects that we're doing out there. But hey, you may have to find your way into one of them with your project. But first, does project deployment always have to be like this? Well, we didn't account for the mountain here. It's a virtual train wreck. Now again, I'm not saying let's account for every contingency and make sure everybody's on board the minute we go to production. No, not talking about that. Talking about doing reasonable things to make sure the project is overall deemed a success. And so project deployment does not have to always be like this. Think about your past project deployments. I'm still amazed. Well, I'll go to a company for to do a project and I'm starting to learn about their software development lifecycle. And I get three or four different answers in regards to what it really is. And so I work a lot on that. I work a lot on the path to production and things like this. Organizations, yeah, if your organization is like that, you're not alone. I guess that's where organizations are today. They're too busy with some other things to make sure that everything is tick and tied and moving along smoothly. So it's hard to make things smooth in an organization. So let's do our part to make sure what we control is as smooth as possible. So data and analytics driven projects create organizational change. They create automation. They add value to the organization that must be trumpeted, must be declared early and often. And the product of the project itself needs to be shown to be really a culprit in adding that value to the organization. It can't be an afterthought. It can't be, well, you know what the data scientists, they accessed the data and did the algorithms and did all the work. Well, yes, but who built the data lake? So the data lake itself, that's a huge part of the value of whatever that data scientist did. So let's go together forward. Real time all the time, yeah. Organizations are moving to real time and they're moving to all time where there's really no downtime anymore. And a lot of people are still kind of stuck in the well, there's a batch, you know, we can account for that. No, not really, not so much anymore. This does require job changes and new roles. And these, I must emphasize, these must be fluid today. These must be fluid not only with us on the technology side, on the build side of things, but also on the side of the users and how they're doing their job. And so we must influence the job changes. We must influence the new roles. We must influence the types of people that we're looking for in the organization. And all of this across the board, it's not just about technology. We're not strictly beholden to whatever happens to be on the other side of that fence. We need to be influencing it. The incorporation of more and more and more. It's always more, it's more data. It's more data scientists, it's more analytics. And there seems to be no abatement to any of this in the near future. And of course, artificial intelligence, as I just mentioned. These projects require organizational transformation. We might think, oh, it needs the right data, needs a good database and good technology. And we talk about all that here on advanced analytics. And of course, yes, needs all that. But there's also the people risks that must be attended to, right? Present great opportunities, but also poses significant implementation risks. We encounter many risks that are people related, which must be managed for a successful implementation. Projects will always involve people. Now, in the future, future, future, there might be fewer people involved in the projects that we do within organizations. I don't know. But for now, and for the foreseeable future, whatever people that these projects affect, we need much more than the right strategies and good planning. And because of the implementation, many of the implementation risks are associated with people, the changing of mindsets, attitudes and corporate culture. We must provide direction and vision for the transformation and be bearers of hope to their people. Make the people feel like the change will provide them with great opportunities and become servant leaders to our users, which I always call our internal customers. And next slide. Okay. People risk require attention on data analytics driven projects. Leaders may not be aligned with the transformation. Departments may feel they have little no input. Employees are concerned about their jobs. Corporate culture is there, kind of overlaying everything, causing everybody to be resistant to change. In many ways, the larger the company, the more that is a real possibility. Possibly have limited energy. We have other projects going on. There are simultaneous rollouts. People are kind of overwhelmed right now with the amount of activity coming from the data and analytics teams and the data science teams in many organizations. So we must take that into account as well. This is all the more reason why we need an OCM organizational change management layer in place in our SDLC that we just do. And we think about it. We think about maybe doing more or less, but we just do certain things to help make sure that the projects are successful because any setback, any setback is like what happened yesterday here in Dallas. We had, we finally had rain after about, I don't know, 40 days and it came in buckets for about a half an hour and 1500 flights were delayed or I guess canceled across the country. You know, it's just that kind of delayed effect, that domino effect, if you will, of not doing the right things. New and changing job roles that require organizational coordination. Yeah, it's no small feat to be changing job roles within the organization. I mean, I'm talking, going down to HR and changing the job spec of the user community. Of course, with their, with their approval, with their, with their knowledge and so on. It's just all part of it. Change readiness and organizational impact assessments can provide insights. So that's what I want us to think about our organization. How much change do we need? What are we doing? You know, what kind of project are we doing? And what might that mean for us? So what kind of project are we doing? Well, maybe we're building a data warehouse, data lake, BI analytics project, something that kind of falls into this category. We've probably all been around these projects many times. I've spent the bulk of my career on these projects. And I'm going to give you a paradigm here of use, accept, think and contribute for each of the types of projects. What do we want people to use? What do we want people to accept and not question over and over again? What do we want people to think about? How do we want to change their mindset? And how do we want them to contribute to the project? Not just take for their job. I don't mean to take in a pejorative sense, but not just kind of take away data, but how can they contribute to making sure the project is successful and grows for their, for their, grows into being able to do more for them and the rest of the organization. So for data warehouse related kind of project, we want them to use the data warehouse, not the old ways to get data. And that's just simply pull it. And this doesn't have to be more complicated. You used to go there to get data. Now you come here to get data. We want to accept, we want you to accept the data, not question its quality or completeness. Wow, we could have a whole advanced analytics webinar about data quality, as a matter of fact, we have. And we will again, because it's that important of a topic. But we want to be sure that we are communicating and that we actually did bring the data to a quality standard for purpose. And we want them to think of other uses for data in the data platform. We only know so much. And I find that my users, when they start to use the data warehouse, they start to grow their capability. They start to think, well, you know what, I can use this data for this. I've always wanted this data. I didn't know it, but I always wanted this data in order to do this other thing. And so bring those ideas. You know, none of us thought about it when we were building it, but it's supposed to be a leverageable platform. So leverage away. Contribute. Contribute data and transformations to the data platform. What I hate to see, and unfortunately, I still see it a lot, is a great data warehouse in place or a good enough data warehouse site, say, in place. Or data lake, as the case may be. But the users, quote unquote, will just come and take data away to Excel or something. That's where the real work is done. Thank you very much for the data. I'll be back when I need another data dump from you. That's not what we want. That's not very efficient. Excel is not the greatest BI tool. It's the most popular one, I suppose, if you call it that. But you're missing out on way too much by going that route. So I won't get off on an Excel sub box here, but I will say that things could be better than that, than that approach. So we want them to contribute data and transformations to the data platform. What are you doing with that data over there? Let me do that for you here so that you don't have to do all that. You can be a great user applying this data to your business problem. OK, master data management, something near and dear to my heart, near and dear to a lot of your hearts, right? So this is something that we focus on here at Advanced Analytics as well. We want people to use the master data and the new business process, as the case may be, to generate and update master data. Simply put, simply put, accept the new business process. Because a lot of times we're changing how master data gets generated. We're certainly changing how it gets distributed. We want that acceptance of that thing. We want them to think about how to grow the MDM base of data because with MDM, I won't belabor it, but you do a kind of subject area at a time. And this idea of MDM can grow into many, many, many subject areas that you can't even think about when you start your journey. It's something that I love it when the habit sinks in to the organization. And when they think about new projects, when they think about new data sets, they think about MDM. They think about doing it in that very leverageable way. We want people to contribute data and their process to MDM. A lot of the processes are just reverse engineered business processes, but into the MDM workflow. And I guess that's okay for starting, but we want to optimize it, but we need to get those processes regardless. So we want them to contribute that. Now, hold back, all right? For big data and data science projects, we want people to use big data for business analysis. Big data is this whole new set of data sets that organizations have historically not used, historically been able to get away with not using that and just sticking to relational alphanumeric data to run the business. But we're kind of past this, I think most of us on this call are past this idea, but not all our users are. They're not past the idea that they need to be using big data as well for business analysis. So if they're not doing that and they could be for their job, then that's something to work on. Accepting the big data collection process, thinking about deep methods of data usage, not shallow methods of data usage, not everything's a dashboard, everything's a report. Automatically, you know, knee-jerk reaction, oh, new data, I need a report on that. Let's stop and think about all the possibilities that exist and contribute your own data needs to big data and data science. Finally, for artificial intelligence, we want people to use their distinct human advantages, know what they are. And AI has always stood on the shoulders of human intelligence and will continue to do so for the foreseeable future until it takes over or whatever happens there, I have no idea. Anyway, we want people to accept that AI is part of the company's future, okay? We want people to think in terms of AI, not just BI. This is a common refrain you'll hear me say. You know, people ask for BI, people ask for those reports I mentioned a minute ago. Reports and dashboards, reports and dashboards. What about AI? What about AI? What are you trying to do with the reports and dashboards? Maybe AI can do that. AI can do that very thing today and contribute how AI algorithms can grow their effectiveness, become a partner in this journey to AI as an organization by contributing some ideas that way. So, people will go through different stages of change. Our brain responds with dopamine when presented with new things. And that's what we're doing. We're presenting their brains with new things. And so in an organization, you're going to see pre-contemplation, preparation, so they're starting to turn the corner, action. Okay, they turn the corner. They're taking direct action towards the goal and finally maintenance. It's there. That's sort of the holy grail. This is very similar to the stages of grief. I'm sure most of us have heard of those, right? Denial. Let me see if I remember them. Denial, anger, depression, bargaining, small levels of acceptance, and then big acceptance and then I'm shouting it from the rooftops. I'm so happy about it. So, it's very similar to that. And I'm not saying we're bringing grief, but according to their brain and their dopamine, it may feel that way. Okay, so we need to take care of that. And where do people come from in terms of their ability to accept the change? Well, it's important to know that people are going to come from different perspectives of change. Some are going to be out there innovators, early adopters. Some of them are going to be a pragmatist, conservative and skeptic. A lot of people start on the right side and move to the left. And in terms of who they are, it's where they are in their journey. And some people move fast like a day or two. They get it. They're primed for it. And some people move slow. And it might take months or even years to get people across the chasm to being an early adopter, to being an adopter period, actually, at that point. And there's been a lot of studies on this. And I find it fascinating. Anyway, organizational change management for the data and analytics project. So let's get to some of the tactics we can, we can do to take care of all this mess I've been talking about. All right. So we can either do OCM from an embedded or centralized way. And I like to embed it. I like to have control over it. But some organizations already have a centralized team for things centralized project management, centralized security, centralized, shall we say SDLC? Okay, well, you know, we can work within those kinds of organizations as well and do a centralized organizational change approach. I would say most organizations kind of by far maybe 75% don't have a centralized team for organizational change management. I believe this will grow. I believe more organizations will take it on centrally because they want to be sure that all projects do it. But it can be embedded or centralized for now, embedded in a project to support that project focused on the project. So when I'm doing when I'm building the, let's say the data lake for predictive maintenance, et cetera, et cetera. That's what I'm focused on. I'm focused on acceptance of that project. And there can be a tendency when push comes to shove within projects and we know that happens to neglect OCM and push it to the side and put all possible resources in building. That would be a mistake. That would be a mistake. That would be something that you will pay for later. And so I want you to take care of that early and often and not neglect it. The centralized team is going to be in support of multiple projects, maybe part of data governance or another other organization, maybe part of your centralized PMO. You know, I like the orientation to projects, but you can do it different ways. So how much organizational change management to do, I'm going to give you the give you the things to do in a minute here. And you might say, wow, that's really overkill or that's not enough. Well, okay, I expect that people are going to think both ways about what I'm going to present. But I want you to think about your project. And these are the factors and how far out you are not going to make it a complete science because it's not. But if the further out you are on the spider graph, the more OCM you're going to need to do. Will business processes change as a result of this project? Are there a high number in the highest relative to your organization? If it's a large percentage of your organization's people, then it's high. A high number of stakeholders with the potential to be unsupportive. I'm going to throw that in there. How widespread are the organizational implications just here within the department? No big deal. Or, wow, it's going to affect a large part, a large swath of this organization. It's going to create new projects. You're creating new ways to deliver. That's big. Or jobs changing. How about the organization? Has it proven that it's used to change? Not the talk, not the, oh yeah, we're all about change here. But what's been the reality? Are they good with change or not? So the further out you are on these five factors, the more of the OCM you're going to have to do. Now, the first category for me for you is stakeholder management. Managing people one-on-one. Managing people. Objective. Identify and address stakeholder concerns. The results? We want the business leaders and staff supporting the changes simply as possible. So what do those activities look like? Identifying stakeholders, assessing them. Yeah, red, yellow, green, something like that. And finally influencing them. So you need to know who's going to be, who seems to be. And I find that most of the time when you spend, I don't know, two minutes thinking about, you know, somebody that's an important stakeholder, you kind of know whether they're on board or not, whether they're red, yellow, or green with this project. And so that's what I'm asking you to do. This doesn't have to take a long time in terms of the assessing part. Now the influencing part, now that could take a while. You want to be consistent in your message. You want to have common messaging in all your presentations about this project. You want to have messaging that is a forbearer of the future, not just a current state. And it's presenting that future in a way that stakeholders can find their way in that future. And they know what they need to do or what they're going to have to change to in that future. There are different dimensions that you're looking at when you are assessing the stakeholder. How do they appreciate communications? Some people, it's, you know, can you hang on after this conference call for five minutes, I want to tell you something. And for other people, it's, no, schedule time a week in advance, 30 minutes, and give me your best, right? What are their key issues? And usually that ties back to the role they play in the organization. What is their current status with the project? Again, I was mentioning this before, are they red, yellow or green? What's their desired status? I would say nobody in the red that's an important stakeholder, of course. But I wouldn't want them all in the yellow either. Okay, so ideally everybody's green, gun, hoe can't wait. But the reality is some people are going to be, I'll wait and see. I'll wait and see how Joe in accounting reacts and I'll act accordingly. I'll wait and see if this is a success or not, you know, based on holding out as long as I possibly can, and so on. What is the desired project role? What are the actions that we desire out of them? How can they manifest their acceptance of the project? How do we get them to kind of make a, not necessarily a public pronouncement over the loudspeaker or anything, but how can we get them to make a tacit acknowledgement of the project? Because when people do that, they tend to want to double down on whatever they do, because it would be change to then step away from that approval, right? And change is hard before we establish that, right? So we want to get them to show approval in a public way as public as possible, except for here. All right, so let's get into step number two. So we've done stakeholder management. Step number two is going to be broad communications. So you had the stakeholder management, which is all the one-on-one stuff. You got a plan for that, right? You got a plan for taking care of those stakeholders one-on-one, but how can you soften the blow across the board through broad communications? The objective here is to build organizational awareness and a commitment to the process and technology changes. The desired results are companies' commitment and support to implement change vision. Now, a company doesn't have feelings. A company doesn't tell you how it feels, but you know what I mean. I mean kind of the majority of the people, the important people, et cetera. This is all going to address the big changes too, by the way. This isn't going to address the fact that, okay, Mary doesn't go to the FALC cabinet to get this piece of data anymore. It's right here in this dashboard. This doesn't address that. Okay, nobody but Mary cares about that. But it's the things that the whole organization needs to care about. Look at the new capabilities. And frankly, yes, I'm delivering something that has ROI. Great, but it could have much more if people bring their ideas to the table. And that doesn't happen if they don't know what's going on. So I want to see this and I'll give you some tactics in a minute here. But one other thing to say about this, this is why before in the presentation, I was talking about, you know, getting people to think, getting people to think about what it means to them, getting people to contribute, contribute to the project, not just it being a one-way street. So this is a sample communication plan. I won't belabor it, but there are a lot of things on the table here. Scheduled meetings, face-to-face meetings, demonstrations and presentations. I love this. You might be surprised if you haven't done this in terms of how many people will come out to see a demonstration of what the future is going to be like. And this gives them a chance. And frankly, this is what we want to do. We want to give them chances and chances to raise their hand and say, oh, but what about this? We want that early. We don't want it late. We want that early. And that doesn't happen unless we're educating or showing through demonstration what this is going to be. Remember, some people are going to react to change favorably through the written word and short and sweet, and some people are going to want to see a demonstration before they get it, before they even pay attention to what you're talking about. And finally, stuff like all hands meetings and the broad meetings. Can you get on that agenda for five minutes? Can you get on that agenda and show a demonstration, a quick one? You know, it's that important, right? We are working on important things, right? If you agree, then we need to act like it. Okay. Number three organizational training. So this is to train effective company team to use the new business processes and align roles. So this is the chance to change job descriptions. This is the chance to make sure even at a one-on-one level, people know what's going to be expected of them when we go to production. And maybe it's a soft rollout. So they also get to know kind of when, when things will change. Can't be tomorrow. Can't be tomorrow. Has to be a soft blow. Has to be, you know, have to respect their need to transition whatever they need to transition. So get ahead of this game. And finally, I have some suggested work products for you. This is all stuff that I just sort of went through here in one place. So remember three categories of things that we can do for organizational change management in our data and analytics focused projects. We can do stakeholder management, which comprises the analysis and a plan and obviously execution of that plan, broader communications. We need a communication strategy and plan. I showed you something and we need that organizational training. Now, whenever we talk about training, there's all this stuff wrapped up into it. Now potentially wrapped up into it. If you're doing something on a broad basis, one-on-one, yeah, you need to do some of these things, but maybe not some others, but there's understanding people's needs for training. And it's not just training about their new job and so forth. That's obviously part of it. But how does their mindset need to change? How is the organization changing such that they need to think differently in order to be a great part of the organization as it goes forward? So I always try to seek that in. I want people to be successful. That's my bottom line. I want everybody to be successful. And we all know that it requires change. And so we've got to be servant leaders here to help everybody move forward and be successful in the new world. And this means when you're training, figure out your approach, your curriculum, your materials, your delivery, evaluate the training effectiveness after the fact, identify the impacted job roles, specify job changes, job transition and planning in the event that it's necessary. And frankly, this is all becoming more and more necessary as time goes on. And we get more complicated with our projects. So in conclusion, OCM is essential to organizational analytics transformation. Choose the applicable work products and their level. Don't push it off until the very end. Be proactive about it. Insert them into your plans. Insert them into your backlog. Everything that you saw in the prior slide, put that on your backlog right now. And you can pull it off when it's appropriate. And frankly, if you've already started the project and you haven't done any of this, it's probably appropriate in the very next sprint. Focus on stakeholder management, broad communications and organizational training. Make the soft or real tangible part of an action we're in a framework. And it may sound right, but the soft stuff can be the hard stuff. And that's what we're here to learn about today. We talk about the hard stuff, the hard hard stuff on all the other webinars. And I enjoy that as well. But this is sort of a reality. And here's another look at reality. Don't forget your plan might be the straight and narrow, linear approach. But reality always has its ups and downs. And but hopefully we're getting somewhere. We're trending in the right direction towards success with the project, which will mean success with organizational change management. And that brings me to the end of my formal presentation. Shannon back to you to see if we have any questions. I am lots of questions coming in. If you have questions for William, feel free to submit them in the Q&A portion of your screen and just answer the most commonly asked questions. Just a reminder, I will send a follow up email for this webinar by end of day Monday with links to the slides and links to the recording of this session. So diving in here, William. This came in super early. So you comfort some of this, but let me just, let me just read the comment and see if you have anything additional you want to add. I hope you will have a response to, a response slide to the resistance points. I have seen many of these and would appreciate to know strategies to overcome the resistance stumbling issues. Yeah, I hope I have addressed that. That was important for me today to do that. So I gave you some real tactics, some real things that you can put in your plans. We talked about, you know, how different people have, you know, they come at it from different perspectives. They're looking at, you know, their personal situation as well as the organizational situation, right? So we have to understand their perspective. As I mentioned, we have to understand where they are on the cycle of moving towards acceptance and the pace that they're at. You know, are they going to be okay if we leave them alone or now do we need to get a little bit heavy-handed here with some of these OCM tactics with them? So, yeah, all that stuff. From this perspective, I have given my best on that question. Perfect. So as an organization which is low on data literacy, do they have to hire a data literacy education firm to build data literacy within the organization? Well, it depends how low we're talking about. I mean, I think it would have to be pretty low. And frankly, I don't know very many organizations where I would personally as a consultant make that recommendation. Now, but that does beg the question. Well, is that because you, William, are playing that role? You know, and sometimes, yes, I guess I am. Because I am a lot about education. I feel like it's very important as I stress today. So I'm going to look for ways to educate. And if you want to say that I'm promoting data literacy by doing that, then that's fine. And yes, I think this kind of is similar to the broad-based communications that I talked about as one of the pillars of an OCM strategy. And yes, you must address the data literacy. You know, somebody has to have kind of the head on the swivel, if you will, looking at the short-term and looking at the long-term. This is definitely, this is a long-term thing. And this will soften up the organization for its ability to accept your projects and accept AI projects and accept the projects that frankly they need in order to be competitive. If there, if some of this need to be data-driven, it's already sunk in and evidenced by the executive team. So I'm always looking for that. I know that my projects are going to be more or less smooth sailing based upon that factor. So it is important. So with respect to ethical implications, any thoughts on whether industry is doing, will do enough to address unconscious bias, to include addressing cultural nuances from your perspective? What steps would you consider? Do you consider would facilitate addressing such? And do you foresee increased input by ethics professionals? Yeah, I think I see a lot more of this, a lot more attention being given to ethics. Sometimes early in the AI game for an organization, sometimes after the fact when they run into ethical dilemmas in terms of what they're doing. So unlike what I was saying about the data literacy person, I'm a little more, I'm a little more keen on there being at least someone who has it in their job responsibility to be looking out for AI ethics for the organization. In terms of unconscious bias, I suppose this means within the data that can, that certainly, that certainly happens. It has a lot to do with the makeup of the training data and what we're doing with that data. But I think that there's a lot of emphasis on this today and I'm feeling very optimistic that we can get over, we can get past this problem of the unconscious bias and data time will tell. But I think organizations do need to address AI ethics pretty much now in order to be on the correct rung of the AI path. So how important is data valuation to calculating ROI? Can you recommend some data valuation resources? Data valuation. I don't know if this means to be putting an actual dollar value on data which some companies do, some companies don't, certainly not required yet in any kind of public statements, although as I mentioned, I think last month, I think that that is going to happen in the next decade or so. So I don't really know quite what is meant by data valuation today in this context from this question or if they could add a little more. Maybe I could address it better. Give us a little moment here to extend on that question. In the meantime, let me move on here for a second. From an HR perspective, any thoughts on how an organization better includes HR in the OCM process? Wow. I love the question, by the way. I don't find too many HR organizations, frankly, are forward enough thinking in this way. But I think they should be. I think they, you know, the OCM can come from different places like I mentioned. And frankly, I didn't mention, I should have mentioned, that it could be a hybrid. It could be, you know, you do some, you do, you handle some aspects of the OCM on a project level. And then there's some centralized, at least direction setting for overall OCM and some requirements, maybe some, you know, quote unquote, auditing that happens. Could it come from HR? I haven't thought about that. But as I sit here and think about it, I think, yeah, that would, that wouldn't be bad at all. I mean, we are talking about people change and progressive HR organizations are not just about hiring and benefits and moving people around, but they're also about the future of people in the organization and how they need to be thinking. And the things that really projects need to do to ensure that, you know, we're all working cohesively. So yeah, I like that. I'm to the data evaluation question, William, that is assigning dollar value to it. Yeah, yeah. Okay. Yeah. I think the question went on to say, do I know anybody that does that? Well, we, we've done it. I'm not going to say it's, it's a, it's a well-developed discipline. You do your best with it. But I, I don't, I don't know too many organizations that are actually trying to get ahead of that and do it that way. There may be some reasons for it, maybe selling the company or some such thing. I don't know, but I don't, don't much to add on that. And she goes on further to say, I'm speaking about, you know, managing data as an asset and understanding how data driven OCM can improve this. Okay. Yeah. All right. So, um, what I'll say to that is we want our projects to be delivering ROI to the organization, not some general concept of over data driven and that's going to, that's going to boost us by, you know, 10% and they were in the, our stock price or whatever like that. No, we want our projects to be delivering ROI. And that's what it, that's what it's all about. So if OCM is contributing to project success, great. If it's not, not so good. It must. It cannot do like a lot of data governance, frankly, still does, which is sit on sideline and kind of sit on that mountain top and say, do this, do that. It organizational change. It has to be there. It has to be there. If sleeves rolled up actually making sure that these things happen and that the project is successful and the project drives ROI. Again, it's not that I say again, because I've, I've touched on this in other presentations, but the data warehouse, the data lake, these things don't drive ROI. That's all the investment. The returns come from the usage of that stuff. So what is the usage, track the usage, understand what the ROI of the project is. In the entirety, including all the resources that I have to go into making it happen. Well, did it produce increased sales? Did it reduce expenses? And it did, or did it make us make it possible for sales to come more efficiently in the future or for expenses to be lowered more efficiently or lowered better in the future? Did it do that? What did it do for us from the bottom line? That's what I'm looking at. So that's what, you know, becoming data driven means you're creating great data projects that do that very thing. We've got just under three minutes here. I think we got time for one more question to slip in, you know, with the increased integration of AI thoughts on its impact with respect to data literacy across an organization. Data literacy is still behind where it could be for helping organizations be successful. It's definitely an area that you want to put some attention into in one way, shape or form. But I don't think it comes again back to an earlier response. I don't think it necessarily comes from an outside kind of program. I think it's custom, if you will. I think, I mean, I think AI is just making data literacy harder because whereas in the past, there's a lot of people out there in organizations that are still getting literate with the idea of using a report or a dashboard. And now you're going to say, well, there's these algorithms that can analyze, you know, all the data in the world to bring us the perfect answer to this mundane question. And by the way, do that a thousand times a day without human involvement. That's a big leap. So I think that AI will face severe data literacy challenges and that's why I emphasized it in this presentation in the first, right at the beginning of the presentation, I had special section on AI because that's going to kind of make or break organizations. That is perfect. That brings us right to the top of the hour. Thank you so much for another fantastic presentation. Also, Kudo is going on in the chat here for this. And thanks all the attendees for being so engaged in everything we do. Again, just a reminder, I will send a follow-up email for this webinar with links to the slides and links to the recording by end of day, Monday to everybody. Hope you all have a great day. Thanks, y'all. Thanks, William. Thank you. Bye-bye, everybody.