Thanks, Vicky. Great to have you along. Thanks for joining. Not entirely sure how many people we've got signed up just yet, but, plenty enough. And, yeah, glad to see that there's people excited about hearing about data strategy and, enough to give up some of your valuable time to come join me on this hour. I found over the last few years that this is a super hot topic and one we're talking about to a lot of clients. And, yeah, I'm genuinely excited to share what we're seeing in the market with you and, hopefully, kind of guide you towards some successful strategies and things that we've seen out there. Today, we are gonna be coming coming through a couple of bits. The role of data strategy. And, we're gonna be then sort of moving into this idea of debunking some of the pervasive myths that we've been hearing in the sort of BI space. We're gonna be discussing some actions that led to positive outcomes for organizations that we've worked with and their data strategy, and we'll walk through a couple of use cases. And finally, at the end, we're gonna give you a couple of slides that you can screenshot, just some sort of high level summary slides that might be good takeaways for you. But before we get into all of that, we'll have a little section on InterWorks and, I guess, me as a as the as the presenter here. So let's talk about what it is we do. And, if you're sitting here not having heard of InterWorks and you don't know what we do, then this section is for you. If you've joined a few of these webinars before, then, this will be pretty pretty familiar to you. So I'll start with InterWorks, and we're a people focused technology consultancy. We work across IT and data and analytics. And that means that we really seek out clients that we can foster that trusted advisor relationship with, and importantly clients who are excited to get into the action with us. It has to be a partnership. And when we get to that partnership phase and we are able to bring in our talented consultants and work with those excited engaged clients, then we can produce some pretty stellar outcomes. And, this is our idea of best work. Some of the clients that we work with are big names you will have heard of before and some of them perhaps not. This is by no means an exhaustive list of either client or partner. But as you can see, we work with a really broad range of technologies, and that allows us to provide, you know, a true best in breed solution for a broad range of client industries and really tailor everything to fit their needs. And what's important to us is that we love to work with people that fit the description of best clients, and we wanna do that using the software and platforms that we consider to be best, in terms of technology today. And that combination has been a winning model for us for many years now, certainly for the for the twelve or so that I've been with IntraWorks. And that kind of brings us to the, the last but not least section, who am I? So I'm James Austin. I'm the services director for EMEA. I'm based in Bournemouth down in the south of England. I've been working in BI data and analytics for fifteen years or so, twelve of them with InterWorks, and, I love the flexibility this role affords me. I can talk to clients to solve problems on large scales, but I also get to dive right in and get my hands dirty with the front end applications. You know, that that balance of working with clients to evaluate tools and then using those tools in the wild has, kept me here for that long. And I'm really, really noticing that conversations at many levels, both exec and down with the technicians, it often starts with that kind of idea of data strategy, which is after all why we're here. So let's get started. We're gonna be talking about the role of data strategy, and there are three elements that we're gonna bring into this. And, you know, data strategy enables organizations to to really leverage the value of their data while, I guess here's the challenge, whilst maintaining the secure controlled environment that many organizations absolutely need. And, you know, those two are sometimes are at odds. So we're gonna work our way through this idea of what is the data strategy, and why is data strategy important, and what are some of the common challenges that we see out there? So let's start with the definition. What is data strategy? A data strategy is a long term plan that defines the technology, processes, people, and rules required to manage an organization's information assets. It's from that little known organization called AWS. There's lots of bits in that statement which I really like. You know, the the the technology, the processes, the people, that is very much at the core of what we live by. I also like this idea of information being an asset. You know? Thinking think of it and treating it as a product, as an asset, I think can really help with the way that you start thinking about it. Staying on that theme of what is data strategy, there was a Harvard Business Review done, and their their their thoughts were that there's two components or two core components to data strategy. There is the offensive side of things, that's analytics data to providing flexibility and growth, And then there's the defensive side of things where we're there to ensure that the right controls are in place. So I'm gonna dive into those in a bit more detail because I think that's a really interesting, way of looking at a strategy. So if we start with offense, this is the idea of thinking about flexibility and growth and you know supporting business objectives, increasing revenue, profitability, customer satisfaction, all of those outbound metrics, designed for growth and for improvement. You know, a lot of the time this is around operating more efficiently or faster decision making, increasing our revenue streams, and we often find that that type of strategy is focused on by industries like retail or marketing or digital product companies. I mean that's by no means exhaustive, but when you look at the type of analytics that these industries focus on, it is offensive. And sometimes before we arrive and get there and help them with the reporting, it genuinely is offensive to look at, but, it it it's offensive in terms of its strategy, and its vision is often quite forward looking and rapid and, you know, designed around increasing the the the organization's revenue or customer base or whatever it's needed. So that's the first half of it. That's the offense. On the flip side to that, we have the defense. You know, ensuring compliance with regulations, using analytics to detect fraud, building systems to prevent theft, and we're talking about a lot of rules and governance on the data processing. Really thinking about modern security systems and making sure the proper access is there, that data is not being seen by the wrong people, and, all of that governance is in place. And often looking beyond the analytics to the full data strategy, the way the data is viewed is fundamentally defensive. So we would see that in organizations like or industries like the the, you know, financial industry, insurance, health care, government. The natural focus for a heavily regulated industry when defining a data strategy will be based on defense. Now when data loss or leakage just cannot happen, when incredibly detailed regulatory reporting is required just to get a license to operate. The defensive aspect of a strategy becomes really, really important. But there has to be a balance between these two. An effective data strategy needs both elements. It needs the offense, and it needs the defense. And, having that front and center of mind when you're starting this journey can be can be really important. We'll we'll come come back to that theme a bit later. Sticking with this idea of what is data strategy, I've got a couple of questions up on screen at the moment, and they're not easy. Right? When when you start sort of beginning that high level journey and you've got to answer questions like, what business problems are we trying to solve? Or do we have the data to solve our business problems? What do we need to do with the data to solve our business problems? A huge one is do we have consensus around the data and around metric definitions? Those those kind of things can be such blockers to an organization, and it can mean huge delays in implementation when you've got to, you know, find that consensus across the organization. It can be a big push. But if you have a solid answer to these questions, then, honestly, you're well on the way to building that data strategy. There's there's more, though. You might also want to think about, you know, what control systems do we need to ensure our data is trustworthy and secure, Thinking about that defensive side of things. Do we have the technology? Do we have the skill set to execute our vision? If you don't have those, come chat to us. How much time do we dedicate to an initiative? How should we prioritize each element of the sequence of the data strategy to make sure that they build off of each other effectively? You know, it is it's not a straightforward thing. So a quick poll, if I may, just to get you guys all in there. You've been listening for a little while, so just to get your opinion on things. Does your organization currently have a data strategy? Vicky, if you're able to fire that open. Done and done. So we're starting to get some results in. I'll leave that poll open for a little bit longer, so everybody who would like to vote can do so. And then I will share the results with you shortly. What do we do in the meantime? Can you sing for us, Becks? See, again, this is my notion of having the hold music off. I've got, the Chuckle Brothers going around in my head at the moment as kind of like a, you know, emphasis to to vote. So we're on sixty eight percent. So I am going to end that poll and share the results with you. So looking good. We've got around forty eight percent of people who say, yes. We do have a strategy. Thirty five percent say, no. We don't. So well done for joining this webinar. This is the first step. And then sixteen percent who say I don't know. So they're not sure if there is a data strategy. So, again, well done for joining the webinar. Oz, back to you. Yeah. Thank you. I mean, they the the five of you, word, probably familiar. On on the the don't know section, it's it it's genuinely interesting that there probably is a strategy, but, you know, if it's not advertised and it's not organization wide, then it's really not a strategy. So, maybe that's something to feedback to, to to to the leadership team if you're not part of that leadership team. And if you are, maybe it's something to to consider. Awesome. That was fun. Thanks, Vex. Okay. We're still on this theme of what is data strategy. So, you know, I think what I was trying to get to with that big long list of questions that you might want to go through is data strategy is complex. You know, it it's it's not an easy thing, especially for a big organization. In fact, in fact, for any organization to understand what the priorities are, and it does require technology and process and people and rules to make sure that the business goals are looked after. It's a complicated thing, and, the things that we'll discuss today are just some of the examples that we've seen and maybe some ideas of where you can get started. So that kind of moves us on to the why is data strategy important. And one of the key things whilst I was doing research on this is this idea of really being able to leverage value from your data. And it's value whilst maintaining a secure controlled environment, that offensive and defensive balance that you have to maintain. But if you can get it right, it is incredibly powerful. And then there is the ability to diagnose your organization's goals and solve the business needs. So here we're looking very high level, and and trying to really build that strategy. But if that's done well, it will really lead to a reduction in technical debt. There's fewer systems, fewer disparate silos of data, and much higher effectiveness in your data. That's both short short term and long term. So well, well worth doing. So what are some of the common challenges? Well, I've put together a couple here. We're we're gonna move on to, some sort of areas where InterWorks helped with some of these things, but organizations often struggle to put together the strategy. And it's because it is complex. There is a lot of areas to it. I mean, we saw with that big big sort of double slide list of things that you might want to consider. But here's just a couple of sort of little little things that might be floating around your head. The idea of, you know, where where do I get started? Is it the people? Is it the processes? Is it the technology? How do I get the right data in place? And what do I need to do to get leadership buy in? And I think that is one of the key ones. I think without leadership buy in, a data strategy is never gonna work. It has to be organization wide. It has to be thought about as a joined up piece of the puzzle within your organization. And, if different areas of the business have different data strategies, it's never really gonna work effectively. Okay. So we're gonna see a few real world examples of data strategy challenges. We're talking about this sort of theme of what's the common challenge. So here's challenge one. The client had been very technology focused. You know, they they were very focused on the vendors that they were buying from, the technology, and that led them to being actually surprisingly narrow minded about the complete strategy. You know, it was very much technology first. And Interweb's worked with them over many years, but the start of it was helping them to first define the questions that were being asked before creating the strategy around the existing platforms. So it was that taking a step back and looking at the real defining question, the business goals, the OKRs that we might be interested in as an organization, And starting from that point, knowing that the platforms and the the technology was already in place, but making sure that we took the higher level approach and brought those two things together. And do you know what? As it happened, the technology choice was absolutely sufficient. It was just the lack of depth of vision that was the problem, but building that into a sort of complete data strategy, meant that they just went in leaps and bounds, in in in their ability to use the technology that they had, but also the vision of how they could use it. Challenge two. The client did not identify and prioritize all applicable systems that were needed. I mean, this is this is absolutely key. Half half the organizations that I speak to don't know what their estate looks like. They don't know where the data sits for all the, you know, all the different areas of the business. They don't know what tools are in play. And, again, taking that step back into works was able to help them audit all of the data systems and define where what data was contained in each source, and honestly it was game changing. This allowed the client to understand how the data tied together between all of those different systems, and we we hear the phrase governance an awful lot. In this case it was about observability, discoverability, and when we guided them through making those choices, we ended up sort of talking about cataloguing tools and things, which was completely out of their world vision at that point. We we had a client who was incredibly focused on the automation within the business. They were looking for efficiency beyond all else, and, it was very much thought that automation would solve all the business problems. It's a it's a it's an interesting viewpoint. You know, there there's a lot of it that you couldn't argue with in a one on one scenario when you sort of looked at the individual bits, but as an overall strategy, it really wasn't very successful. So Interworks dived in and helped them understand that it really is only one piece of the puzzle, and a system of controls needs to be implemented to ensure that you've got the security and the reliability around all that automation. Because, you know, if it's just left to run, you're not always sure what the output is. Interesting. No. A couple of challenges there. Hopefully, they may maybe one or two of them resonated with you. Pop some, pop pop some feedback in the chat if, if any of that was sort of, ringing any bells. But that moves us on to section three, which is quite a quite a fun one. We're gonna be debunking myths. Great word. Debunking. This idea of separating myth from fact as far as data is concerned. We're gonna look at three different areas here. We've got this idea that more data always equals better insight. Let's just pull the data in. Let's go go crazy, get it from anywhere. We can just we we can store it. Storage is cheap now. We can store data for nothing, and and and that's bound to be, you know, good for us in the long term. Myth number two, AI can replace human driven data strategy. Interesting one that we'll come to in a second. And myth number three is this idea of a single unified platform solves all data challenges, and that platform might be Microsoft or Amazon or Google or Salesforce or whatever whatever your organization is tied into. And that there's a subtlety to this one where, you know, a platform might have multiple tools. Now we're talking about multiple tools or just all multiple platforms. But we'll we'll come to that one in a second. First up, more data always equals more insight. But we've found that the reality is that data quality is more important than data quantity. Time and time again data quality is more important than data quantity. And if you disagree please use the q and a or the the the comment section, fire fire some info over. But that's certainly been my, my my sense of it at the moment. Sorry. I haven't actually been monitoring the the chat. I'll I'll take a pause and read the, read the comments in a second. We are all good, Oz. You carry on. I'll chime in. Fantastic. Thank you. Thank you. Looking into that reality in a little bit more detail, the takeaway is is to focus on the data points to answer the business question being addressed. Just pulling in all the data really might not help to to answer the business question. The business question might well be answered with ten rows of data. It doesn't necessarily need ten billion, you know, every single recording station per second across the globe, you know, being aggregated up. Sometimes you can get huge insight from relatively small amount of data if it's relevant to the business question and if it's clear and the quality is high. Secondly, implementing a system of controls and data governance that ensure reliability and consistency, and that is far easier when the data volumes are small. And finally ensure data is processed in a manner that is digestible by end users. You know, having a huge great dump of JSON files in a in a big big data lake that people can't really access or understand or define, it doesn't help. It's got to be digestible by the end users. And I'll end I'll I'll add to that digestible and add timeliness to that as well. It's gotta be done in a timely, digestible manner. We worked with an organization, and we helped them prioritize the sort of cleaning and analyzing the existing data objects, the pipelines and the metrics, and and the ownership, implemented a data mesh strategy. And, we we implemented that at that low level before bringing in some of the new store systems that they were really keen to get in, into their platform. And it was it it was definitely revolutionary for them. We we managed to get all of that data in such good quality that they were able to then use some of those rules to bring in the new stuff, and, you know, it gave them a very flexible start point that they could then grow and head to. Nice. Data myth number two. AI can replace human driven data strategy. I mean, yeah. It can replace analytics. Data strategy is it's a tough thing to define anyway, let alone get AI to, to do it in its entirety. And I I truly believe AI is a fantastic thing in the world of analytics and data. It it it's it's game changing, but it's here to compliment us. It's here to be a tool for us to use. It's not here to replace our strategy making. So a couple of takeaways on this one is that we need our own intelligent thinking to ensure that the correct input is going into the AI tool, right. You can't just load in rubbish, rubbish in, rubbish out kind of methodology, if it's not being trained with the right information it's not going to be useful to you as an organisation. And AI does not ensure data privacy, and it does not ensure a lack of bias. There are definitely still some areas of AI that are fundamentally problematic to to a data strategy, anyway. And, you know, we can absolutely utilize AI to aid our decision making. I think it's fun I I yeah. In terms of the reporting and the the data processing and the discoverability side of things, AI is great, but, it's a tool to be used. Another quick client story. Yeah. It's a common common question that we're getting. How should AI be introduced into our into our, ecosystem? We our competitors are using AI. We need to be we need to have it as part of our data strategy. And I think that the start point was helping them understand that AI was not going to replace the human driven strategy, but how it can be used as a tool within your organization. And lots of the vendors are already using heavy AI complements, whether that's, just as a sort of an assist or whether it's a full sort of build from the ground up. The the the tools are there. We're there. We're we're here to use them, but not to let it replace us. For example, if you're in marketing, AI could help you do sentiment analysis or segmentation, but you're absolutely gonna need a marketing human to train and retrain the models, validate the segmentations that are built, and any biases that you see provide business strategy alignment to other departments. You know, all of that is absolutely good on either human. If you're in, say, supply chain, could AI help you manage traffic, you know, optimize budgets, predict maintenance for fleets. Maybe? Will you need a human being to provide the contextual oversight? Handle the edge cases, manage complex decision making? Absolutely, right, it's it's not a replacement. Interesting one. Our last myth, a single unified platform solves all data challenges. I think the reality is that effective data strategies always include multiple tools. It says often there, I think always be is the reality. No. Yeah. Okay. We'll we'll we'll stick with often. I think this is a really attractive myth for execs and the leadership team to hold on to because vendor lock in is so strong that it's very tempting to always pick the tool that is inside that platform. So we've got to be prepared to identify and integrate tools that address specific problems. Maybe you're having just a fundamental lack of adoption into analytics across the organization, and you need a much more user friendly front end. It needs to be natural language, so they just want a prompt to be able to type and get a result back, and and that doesn't exist within your platform. Go look for another tool. Focus on long and short term when you're selecting a tool. You know, there's the there's the scalability aspect as your company grows. There's the reliability. There's the cost. There's the future road map of that tool. Lots of things can play a part when you are thinking about which vendor to to to dive into. And then reassessing your organization's tech stack based on the changing business needs and problems. You know, you're inevitably gonna need to reassess regularly, and maybe the tool that was fit for purpose five years ago is very much not fit for purpose now. Last little client story, and, actually, I mean, this is more of generalized one, really, but, many companies fit under the same umbrella of, is it right for us to have multiple tools? Should we have Tableau and Power BI running alongside each other? Is it the right strategy given the tools and technologies that I already have? Does my data strategy fit that tool pairing? Do I have to commit to a single data or analytics tool? And and these questions are maybe a little bit more subtle and less vendor lock in and more sort of, can I get away with just using ABI tool for all of my analytics needs? And and the the reality is more and more we are finding that it has to be a blend of tools. Maybe they would all sit under the same vendor, but very rarely. We worked with a higher education organization who wanted a they wanted a custom digital product for their, as a front end for users to navigate. They wanted it sort of to be day in day in day use, and they were finding a lot of challenges along the way. And we discussed their sort of options, and we ended up bringing together seven different source systems or seven different bits of technology to make it all work. We had InterWorks Curator as the sort of white level white labeled client entry point, and then we had six other bits of technology all doing different things. You know, data integration, storage, processing, visualization, webhooks, all of these different aspects of it to build that single client portal. It was, not obvious at first, but, you know, by applying design thinking to the data strategy, and working with the client to help them discover what they needed, we came up with this successful data and analytics implementation. And, you know, at the end of it, we created that idea of Interworks best work. It's it was, fun fun journey. We've gone to section four, this idea of driving results. Vicky, you start okay on chats and polls and things? Caught caught you off guard there, didn't I? I? Do you know what you did? My mouth went to sleep. I apologize. You know, we're still all good. Wait. Were you halfway through eating a cake? Now you know I'm on a diet or so let's not go there. Okay. Sorry. Back to the matter at hand. Let's discuss what what what drives results. So organizational alignment and clear vision. This idea of offense and defense that we looked at earlier, the offense being the flexibility and growth, supporting business objectives, increasing revenue, profitability, customer satisfaction, tend to be focused around, the customer focused industries. And then we've got the defensive side of things, the controls, the compliance, the regulations, fraud and theft, you know, strong regulations around an industry. And those two themes often have a, an overriding emphasis on a single company. So I was interested we're gonna go go through a few polls now, so Vicky, be be ready. This one, I I'm interested in. So thinking about that offense and defensive side of things, which does your organization fit under? So a poll here, which does your organization fit under? That idea of offense or defense. K. The poll is now open. Lots of answers flooding in. So we'll give it one more moment. It looks like we're coming up to nearly half of you have answered, so thank you so much. It really helps us sort of steer the direction of the webinar for the remaining twenty minutes or so. So I think we've got where we've got to, and we have got pretty much an even split there. So sort of just over half saying they are more offensive and just under half saying they are defensive. No one's offensive around the other day. Yeah. I I'm am I surprised? I guess I I guess not. It's interesting to see, though. Thank you thank you for the polls. I I I wasn't quite sure which way that would would land, but, yeah, roughly fifty fifty, fifty four percent on the offensive side of things and being aggressive with, you know, build building a strategy that helps you grow and deliver, versus the sort of regulatory side of things. But, very good. Thank you for that. A little takeaway on that sort of organizational alignment, the clear vision thinking. That they've distracted you, what whatever you choose or whatever is currently in play, it has to be business driven. It's gotta be fundamentally built from the type of organization you are and the type of organization that you want to be. Cross functional collaboration is of paramount importance, it is absolutely vital. You can't build a data strategy from the IT perspective only and ignoring the the the needs of the rest of the area. And even even as much as, you know, procurement can't have its own data strategy compared to sales or or HR. And then we've got this idea of, you know, tailor your data governance and performance metrics to your specific use case. Data governance is gonna be our next conversation, which is sort of, the the segue there. Yeah. Let's, let's skip over to that. So looking at data governance as a pillar of success, What does what does this look like? Data governance is a framework for managing the availability of data, the usability of data, the integrity of data, and the security of data. And governance doesn't always have to be as rigid or stifling as maybe that sounds. We find that governance can help aid in lots of the data strategy goals, like data accessibility or quality or data security. And these are or these certainly should be shared goals across the organization. So this could mean that implementing people and processes, assigning ownership and stewardship of the data objects and accountability, could really help a lot of the data strategy. And this might be something like, choosing a data a data catalog technology, like, Alation or Atlan or Calibra, and that can help with the sort of data discoverability side of things. Or it might look like a data lineage platform, like Monte Carlo, or taking us through sort of a semantic layer and and basing a a lot of its around sort of data mesh principles of the ND, the the the the data creator as overall responsibility for it. And perhaps you're concerned that you can't trust the insights on the dashboard that you get emailed. Again this might be a data governance challenge, if you can't coordinate the single source of truth, the metrics within the organization that are being shared out, again maybe this could be a data governance challenge. And all of these concepts are becoming more and more personal, and it can be helpful to start thinking about which elements would be most useful to your organization. So let's let's start sort of high level and say, does your organization have a data governance plan in place? Bonus, if in the q and a or or or chat, you let us know which element of data governance you're most interested in. We do love a poll here at Interworks, don't we? This this this this webinar is especially poll heavy, isn't it? I'm I'm It really is. It's really useful. It gives us an indication of of what content we should be put doing to put together in the future, you know, if it's a priority for the majority of our our customers or or people that we're working with at the moment. So thank you very much to everyone who is participating. Like I said, we do use this information. Hopefully, you see more tailored content coming in the future based on your responses. So we've got a good answer rate. So I'm just gonna share those results with you now. So if you can see, fifty eight percent of you said yes, with the rest of you saying no or not sure if you do or not. So thank you for taking part. Awesome. We have one more of these little kind of, pose a question, I'll wrap up, and then we will, look at the lessons learned to, finish off this webinar. So probably another sort of five or so minutes to go if, you guys can can stay with us for that long. Okay. The sort of takeaway on the data governance side of things is, you know, using data governance as a pillar of success and how to drive positive results in your company's data strategy. Define organization policies for data ownership, quality and usage, I think that's really, really key. And ensuring that testing is implemented where necessary, audits, and verifying business logic, and check that that is iterated on regularly enough that that business logic still applies today. It's very easy to set these things in place, and six months, a year, two years, I mean it's amazing how quickly time flies by after we've, selected a vendor and decided on a strategy, but how quickly are we iterating through that and working that iteration into the development of our processes. So what we're talking is iterative development, you know, that kind of agile type methodology to allow teams to create processes dynamically and then refine them over time. And as those processes are developed, it's gonna help with better understanding of your data and better understanding of business problems. So this is quite a last scene poll, I promise. A bit more of a subtle one, and maybe you don't have a strong opinion on this one, but does your organization refine data processes as you learn more about your business? And all of these questions have sort of been subtly slipped in there to help you think about not just data strategy as a single thing, but as a fairly complex, relationship across the whole organization. So refining the data strategy and making sure that, the, you know, the the the processes and the, people are in place to do that is part of it. Sorry. I'll shut up and let you get it onto the poll. Wonderful. Just one more moment, but we do have a clear winner here. So sixty one percent are saying, yes. They do refine the data strategy, and then forty percent are saying, no. They don't. I make that sound incredibly negative there, didn't I? I'm not never gonna be a good news anchor, I don't think. Thank you everyone for taking part. Okay. So the idea here is that, you know, you're you're trying to build piece by piece rather than shooting for the whole thing in one project. If you're building this strategy from the ground up and, you're you're able to build it, there is no point trying to, wrap it all up into a huge, great, very detailed process because that will change over time, and you're far better just going for that small wins, regular wins, and, build upon that. You know, the the strategy as a whole can be formed, the ideas, but the processes underneath it, I think, have to be refined. Keep pulling in user feedback because, ultimately we have a data strategy to serve up the data to business users for their needs. And if something in the data strategy is breaking, then that data is not going to flow all the way down to the business user, and, you know, that that feedback loop has to be in place. And then, you know, you've got to identify the changes that have gone on within the within the, the the systems recently. And, you know, bottom line is you're never gonna get it right first time. And even if you did get it right first time in six months, the landscape's gonna look totally different. Cool. I'm actually going to just skip these because I think it got a little bit old heavy. So I am just gonna kinda rattle through a couple of slides and go for the review session. So we have gone through four different sections, and I've kinda tried to summarize those into a couple of takeaway slides for you. So, get get your screenshot ready. And, this is a sort of takeaway slide number one. We've got three lessons that, overall, we have learned from innovative leaders across the across all the organizations that we've worked with. And lesson one here is that organization alignment is crucial for effective data utilization and data strategy. You've got to have buy in from everybody. This means that across departments, as well as across leadership, and leadership down to analysts and business users, everybody's got to be bought into that same idea of the data strategy. Lesson two, quality data governance helps build trust. And with that trust comes reduced data risk and foundation for growth. It really does allow business users to feel like they have a source of truth for their business needs and that something that they can really rely on to make accurate decisions. And lesson three, leaders who take an iterative agile approach often see better results than those trying to overhaul everything at once. I mean, that's true of many many things not just your data strategy. Build on successes and tackle one challenge at a time. There you go. Takeaway slide number one. Takeaway slide number two is a quick six pointer on the path to data strategy. So you need to align your teams around a shared vision, kind of similar to that lesson one. You need to invest in strong data governance principles, kind of like that lesson number two, And use quick wins to demonstrate value and build momentum, kind of like question three. But based around that is this balancing the offensive and defensive strategies that we talked about. Prioritize scalable, flexible, and future proof data architecture. Short term and long term vision there. And then, and this this last one last one I like, consider treating data as a product. If you start thinking of it as a product that your company creates, often you start thinking about it in a much more sensible way. You might even find that you can monetize that data. And, there there there can be a lot of big wins that come from that mind shift of data as a product. Fantastic. Over to the sort of general general polls, general general questions out to me. But, yeah, honestly, any questions, I would be, be interested to hear from you. Thank you so much, Oz. We've got approximately seven minutes left. So if you do have any questions, please feel free to ask them. As we said before, you've got the chat facility. You've also got the q and a, which you can find located at the bottom of the screen. If you have no questions, it just goes to show we are experts in our field, and Oz has done an absolutely terrific job in presenting today. We are more than happy to answer any questions as they come after. You may find in a couple of days that you want to refer back. Please reach out to us. You can go to our website, w w w dot interwax dot com forward slash contact. We'll be happy to answer. We just had a question in. So how would you suggest management and governance for data stored in multiple cloud data warehouses? Yeah. Good good question. It's it's very straightforward if you are happy to introduce another tool into your ecosystem. So there are some really good governance tools that are around discoverability and observability, so making sure the quality is there and understanding where that data comes from. It doesn't all have to come from one place. So I mentioned tools like Alation and Atlan. They are absolutely fine with reaching out to multiple cloud source systems. Also combining that with some on premise source systems that you just, for one reason or another, can't get up to the cloud. So it is no problem. You've you almost don't need to worry that these things are in desperate places. It's it's quite straightforward to put together. Wonderful. And for those without data governance in place yet, what would be the first three or five steps to implementing a data governance policy? Well, that's a big question. I'm gonna save save you here, Oz. We, Michael, we have got lots and lots of blogs and a data governance white paper that we have written. I'll be more than happy to send you those links, or I can pop them in the Slack channel now if we've got time. If we move on to the next question, please can you provide some examples of an offensive data strategy? Yeah. I I am gonna very touch quickly touch on Michael's question and just say that actually building the strategy first, so a lot of what we've talked about today and understanding what it is that's important with governance, you know, is it, making sure that data is readily available to end users for that idea of an offensive data strategy. You know, is the discoverability and the availability the key component of your business strategy, or is it a much more defensive regulatory audit style implementation that's that's gonna be more important. So build the data strategy first, and I think a lot of those governance questions fall out or a lot of the focus of the, the the governance will fall out, and then you can start understanding whether it's semantics or observability or discoverability that's more more interesting. Please can you provide some examples of an offensive data strategy? Yes. I I mean, to be honest, when you see a standard sales dashboard, you're fundamentally looking at an offensive beta strategy. You know, that this is something that is there. The analytics that is, pushed out to the end of it is predominantly there for growth and improvement and, and to to to build the business. So the data strategy around that is all gonna be supportive of that. It's gonna be things like making sure that the data is processed quickly and timely. So a sales win comes straight through to the, to the desk and people know about it. It's gonna be making sure that data cleanliness and accuracy is there. So a lot of these things cross over, but, yeah, the the offensive side of things is often around, growth and improvement. Wonderful. Looks like that's it for questions. I have popped some useful links for our governance white paper and some blogs. And for those of you who aren't familiar with it, we've got a little bit of a world famous blog. So if you do have any questions or you're looking for some answers, you probably wanna head over there first as a resource. And as Oz has just popped up on the screen, we do have, some information about our data strategy offerings, that you might be interested in having a look at. We're more than happy to help where we can. So I think we're gonna wrap up today's session. So, Oz, thank you so much for leading, today's webinar. I do believe we do have some information on Alation, but, Austin, we can certainly get you some tailored content through. Wonderful. In which case, Oz, thank you very much. Thank you very much everybody for joining us today. Thanks, Hope. That was good fun. Enjoyed.