Goal of this, the solution spotlight, is for us to share, a deep dive in particular topics that we think are really interesting or valuable in your success with data and analytics might be we go and grab one of our customers or clients to share the story of our journey together and the successes that they had, tips and tricks, particular topics, and, of course, If you have any ideas and things you'd like us to cover, hey, Rob, will you go cover data governance or you will cover this or whatever? Absolutely let us know what you'd like to hear we've got a q and a section in the webinar that you can put that in there if you like. You can obviously email or contact us separately a different way. If during this presentation, you have questions about what I'm talking about, by all means, put them in the Q and A as well. I've actually got a ton of content that we're gonna zip through today in our fifty five minutes. So I'll try to get us some time at the end, but most likely I won't be able to cover a lot of questions. So by, if there's anything that you do wanna cover by all means, happy to jump on a call, and and chat with you outside of the webinar. Keep an eye out for this. The next one we're gonna be doing is how to drive real value, with data science versus allowing it to be a buzzword. That'll be the one that we do next month. But first, let me do a little bit of an introduction. I am Robert Curtis. I know a lot of you, recognize a lot of you on the chat. For those that don't, I'm the executive director for Interworks for Asia Pacific. I've been in it at Endoworks for gosh. Fifteen or sixteen years somewhere in that realm. I've been in this role for, I guess, since Asia Pacific, our little opera operation over here was founded. So I've gotten a chance to work with a lot of different companies and talk with a lot of different folks from all over Asia, as well as Australia, New Zealand. So if this is the first time meeting, nice to meet you. Into works, we are a people focused consultancy. And what does that mean? It means we are we live by this particular mission statement. And that is we look for the best people to deliver the best work for the best clients. It's really just that simple. We believe in building long term relationships with folks rather than cashing in on a single deal, and letting the chips forward, they may. We believe in developing relationships with our customers, with our partners, so that we can together grow and prosper fairly simply. What do we do? Well, we do a lot of things, but if we were to to steal it down. Broadly speaking, we do strategy, which is sort of underpinning everything that, that we think about. We're gonna be talking a lot about that today. We do analytics. We do data. We do infrastructure and platforms, and we do enablement, for those things. So building communities, building data warehouses, helping you guys do data science or analytics, stabilizing your platform. All of those types of things in the data and analytics sphere, we're very narrowly focused, but we're great at what we do. These are the things that we do. We are global. Of course, I'm here in Melbourne, but we have folks in Perth, in Sydney, in Singapore. Those are the people that I look after. And then we have people all over Europe and people all over North America. Very quickly on our partners. We don't, have a lot of them. There's actually a lot more that we could be partnered with, a lot of folks come and ask us if we would. But we we prefer to stay best of breed. We don't have a particular allegiance to a stack. So while there are things about Microsoft that we like, we don't buy into the whole Microsoft stack. Same thing is true for Salesforce. There are aspects we like, others that we don't fit. We don't fit for us. And so these are the things that we do. Informatic is a new one, that is, new to this this particular presentation. So we just started relationship formally this week. So that's some news. So let's get started. We're gonna talk today about the five keys to a modern data strategy. And we'll we'll sort of define exactly what I mean by that. I'll I'll sort of give you a framework on on how we're gonna talk about it why it's important, all these sorts of things. And then I'm gonna go, into the weeds and a couple different things. And this is This is my experience. This is my perspective. So, obviously, you could probably get a different take on this if you talk to any other folk out there. But I think in terms of my experience of doing this for almost two decades, these are the things that I tried to distill down into the quintessential most important things. Determine whether or not you're gonna be successful. But let's start first by defining why strategy is important. It's not a buzzword. This actually matters. And these are the results of poor strategy or the poor execution of it. All of these numbers come from Forbes and other tech and industry journals, but eighty five percent of data projects fail, eighty five percent. You are less likely to be successful than you are at failing. That is a sobering statistic. And you can see As you go down these bullet points, that there are are increasingly more dire things. Every two to three years organizations change data strategies, which is about the time a CTO or director or manager leaves the organization. There successor inherits uncompleted or unsuccessful work, and then they completely change strategies, try to either bring in their tools that they like or their direction or just try to fix what they inherited, which means the hard truth of this is is that most organizations, not all, but most organizations are in a sort of a, a whirlwind, maelstrom vortex of constantly changing strategies and changing directions, which means genuine success or any sort of economy of scale or or building an actual full culture is actually very difficult to achieve. So this is where we go to cry. That sounds horrible, Rob. You've just ruined my week. I have good news, though. Once we understand why they fail, then we could start to fix it. So why? Why does strategy and how how does strategy most often fail? Well, there's a several things. I'm gonna give you some that I When I wrote this list, I was thinking of very specific incidences and projects that I was working on, that we came and inherited and fixed for for different companies. So these all come from real world examples. I will keep them anonymous, of course. So a lot of times people get focused or fixated on the problem. This thing doesn't work. It's not doing what we need to do, but they never actually fully define the solution or the opportunity that that problem presents. Or it might be AI. ML. These things are exciting. People are talking about them. And our CEO says, let's go do it. So we go do it. But we don't have any concrete ways that we can use it, but we're investing in it. It might be focusing on costs without looking at value. This tool is expensive. This tool is cheaper, but do they do the same things? Are they gonna give us the same benefit oftentimes they don't. Setting targets without setting tactics, it's like saying, in in two years, we're gonna land on the moon. Okay. Thank you. I'm out, and then the person just leaves the remote. Well, we have to figure out how we're gonna do this. And so you need to figure out tactically, what's the road map to get to this grand impressive ostentatious target. Investing on an answer, a single answer, but not the cap the the, capability or the, capacity to ask more questions And in a way this is related to the first one, here's a problem. We answered it. Great. Oh, no. We have follow-up questions, or we have other questions, but our solution was so narrowly defined. That we didn't give ourselves a framework to derive additional value. That's a, limitation of narrow thinking. Starting a new project, but not finishing it I see this all the time, and then whenever you see an organization with more than one data warehouse, this is inevitably what happened. They started it with a lot of gusto, but driving through and finishing it is really hard. It gets left in a half completed state. And some other group, has frustration, so they start with gusto, and they start something too. So there are a lot more ways that strategy can fail, but these are some common ones, ones that I pulled directly from from my experience. So let's talk about a modern data strategy. And what I'm gonna do is sort of give you guys some guidelines in terms of how we're gonna talk about it. I'm trying to deliver this. I'm trying to deliver this in a way that it is, accessible for business people, so we're not gonna go super technical. We certainly can go into deep dives If you guys wanna go seek technical after this webinar, I've got a whole host of data architects, solution consultants, whatever it is that you wanna do dive into data architects, picture data science, whatever. Today, we're gonna try to keep this focused on business value. We're gonna keep it simple. We're gonna focus on critical success factors. There's a ton of different things that could bring up. But we're gonna try to reduce the noise, and we're really focusing on maximizing ROI as soon as we can. So we're not we're we're gonna be put in forward a strategy that can be executed in weeks and months, not years. So here are the five keys. Spoiler alert, here they are. Purpose priority, platform, people, and promote. Now there's a lot inside of each of those. Let's start with the first one. Purpose. So when I when we're talking about purpose, I'm talking about what we're aiming at, but also the process in which we decide what we're aiming at. And so you can see some bullet points here. We're gonna go into some of these in a little bit more detail, but centralized decision making, you can't group think strategy, you will never get anywhere. Somebody or a small group of people eventually did say, Everyone's got stuff that's important to them, but we're gonna decide the order in which we approach it. Everybody has tools that they like, but we're gonna decide what's best. So I do believe that it has to be a smaller group that, again, listens to the needs of the enterprise. Recruit executive sponsorship. You can have the smartest people in the room, if they don't have budget or mandate from the organization, they're not gonna be able to do much. Start with specific use cases. We'll go a lot into that, and the next ten or so slides, assess the risk of doing nothing, and establishing metrics for ROI and success. So let's get into this. We'll start with specifically how decisions are made. So, again, this is my experience. You could go read a half dozen, organizational theory books, manage books where you want, but I'll give you sort of my real world experience, which what I'm calling my armchair organizational theory. So you have a goal here And the goal can be accomplished, in a number of different ways, by number groupings of people. I am guilty of this first one, which is sort of this lone wolf. I personally feel far more empowered to just go and do things most of the time. It's after my mini children are asleep, and it's around nine PM, and I finally get some space to myself. And I start planning my, you know, my how I'm gonna take over the world. As a lone wolf, you could be hyper focused. You are limited in the ability to actually make change because you're one person. And because you are hyper focused, it's very easy for that lone wolf to gradually start to stray off on things that are far more important to them than they might be to the enterprise. But a lot of times, most initiatives start this way. Particularly initiatives that are started by accident and then become something more formal later. The Wolfpack, When a initiative starts organically and then is formalized, it's oftentimes formalized into a working committee, like a Wolfpack, or multiple lone wolves can sort of form together, but like, I'm frustrated with this. You're frustrated with this. Let's join powers into Voltron, and we'll have more ability to do stuff. And that's true. They still have a high degree of focus. They can still be very productive. They can be more productive than a single lone wolf, of course. And because we're a pack now chasing the moon, chasing our prey, whatever, I'm beating this analogy to death, We have, a framework in which we have to make compromises so that our goals align with group goals and that means it's more likely to inter to align with the enterprise. Now if you add too many wolves or if you just sort of take a snap out of any organization on any given day, you end up with your herd of cats. Now this has a really bad connotation, but I don't think it should because this is sort of the natural state of when you have large groups of people together. They struggle to focus. They all sort of have their own little things. Cats are great hunters. They're really good at what they do. But they work in uncoordinated bursts. But if you give them a framework, patterns, boundaries, They can be quite effective, again, working in uncoordinated bursts. But we have to make sure that they understand. These are the areas in which you're allowed to play. Too many cats, a lack of focus, no boundaries. You eventually end up with a swarm of mice. There's no focus. Oh, the sound is gone. Okay. I'm back now. I'm not sure where we left off, but based off of the screen, I'll just keep going. So what arises when you have total chaos And what it is is a a breeding, a breeding ground of frustration. Everyone's going in their own directions. There's not a, Okay. Thank you everybody. I think I'm good. No direction, no productivity. People working against each other. No mission. And what happens inevitably when you get to a swarm of mice is somebody gets frustrated enough to just start doing stuff again. And then you sort of ended this little lone wolf Now you don't have to start in a cycle like this. You can pick and choose how you wanna approach solving that goal at the middle. And if you remember at the very beginning of this section, I said, recruit an executive sponsor. So if we take an executive sponsor and then bundle that together with a wolf pack and a herd of cats, I think that is what I'm re recommending is sort of the best way to sort of approach this. So your executive sponsor sets your vision, we're going to do something really important with data. That's gonna be intrinsically important. Does the executive sponsor necessarily have the technical understanding to die growth to to to create the strategy? Probably not. They're much more high level than that. But they will approve the strategy. They'll then set a budget, and then they'll make sure that there is accountability from the top down. We then get to our COE, our center of excellence, which is your cross section of really, really technical and skilled leaders, that represent the different domains where you have to be successful. That might be data, data engineering, community enablement, whatever, analytics These folks create the strategy. They develop the plan and the tactics once that strategy is approved. And then one, when that strategy and that plan is created, then they actually go build the foundational elements and the patterns that they can then turn over to your user base, your herd of cats. And then the, the cats, your user base, Those are the people that identify all the interesting and new use cases, and then they filter them upwards. They're the ones that are driving ROI because there's the numbers there. You might have a thousand people in your organization. They can't all be on the C. We, but they can all be contributors to your data effectiveness, your analytics, your community, the users, the use cases that you're developing. And I believe, strongly in self-service. We'll talk about that a little bit more in a second, but I believe that Governance when coupled with self-service starts from the bottom up. So accountability top down, governance, bottom up. And what do I mean by that? That sounds crazy for some folks. Which means take the base most essential elements of governance, the KPI. A metric, a measurement, and put the users in charge of that. Is this number right? Let's go level up. It's the logic right. Okay. Let's go level up. It's the worksheet. The dashboard, the workbook, the data source, all of those things increase in terms of value. And the entire we'll talk more about this in a second. But the user base starts the governance from the bottom up. Where I have seen people do analytics most effectively or data most effectively, it's in a model like this strong executive leadership. Clearing the way for the Wolfpack, your COE, to define the goals, to build the plan, and then a community that embraces the patterns that have been put forward. So how do people invest? Most of us are probably data professionals. There's probably very few CEOs on this call. If there are, hello. Reach out to me. I'd like to talk to you about how we can help you. But as data people, how do we convince business people to invest in our ideas. Well, we think about technology very differently than they do. This It's how data people think about technology. That's not just a fire hydrant, my friends. That is a gold fire hydrant. Isn't it cool? Look at all the chains and little things that are on and just imagine all the cool things I can do with this fire hydrant. It's much better than our old fire hydrant. Business people don't think about technology like that. They don't care about the features. They care about this, problems, solutions. How do you fix that? And if I can convince you that that golden fire hydrant will put out that dumpster fire. That is how people invest. So just a big reminder People do not invest in solutions or brands or technologies. They invest in solutions. Not Gizmos. That's a big, a big difference in the terms of the way we think. And a lot of times, your their data may not be ever going anywhere because you're thinking, how do I sell it to me versus how do I sell it to them? Speaking of selling Upwards, the risk of doing nothing thing. This is a really powerful way to sell a data strategy upwards. And it's one that we don't often think of. If we don't do anything. What's the risk? Well, most of us, like, if we do this, look at all the opportunity that then gives your executives the ability to say, well, we can put that off because I've got some burning stuff that needs budget or my attention now. Maybe I'll put that off for next quarter or for next year. But if you define it like this, legal risk, regulatory security privacy, these things make headlines every day. We don't wanna be a headline. There's risk if we leave our system as legacy, or we leave these problems unaddressed. Don't be a headline. This is really powerful. It's one of the most powerful ways you can sell upwards in terms of helping your executives understand the purpose of your data strategy. As an example, these are some of the biggest data breaches in history. Now I didn't wanna say the biggest Australian ones because we might have people from those these on these calls. So I'm just trying to be polite. We don't wanna name or shame anybody, but these are real problems that that result in a loss of brand equity, financial problems, penalties from the government. There's so much things that sort of result from not making sure your strategy is iron tight, particularly on all those particular categories of risk. So to finish off with purpose, we kinda talked about these things, but establish metrics for ROI and success, which is basically another way of saying, define the rules of the game, and then keep score. Where you do good things, let's acknowledge them. Where we don't, let's document them and then take the knowledge of what we just learned we did wrong or could have done better and put them back into the strategy. So either way, we are winning It's very easy for us to get busy and ignore all the things that we need to be thinking about, and the process in which we're doing this, the learnings that we just had, and formalize those. Not for my benefit, but for everyone else's benefit, for the people that come after us. Priority. Now that we've got, now that we've got a sense of who's doing what and how we're gonna do it, Let's start talking about those use cases that we defined. We're gonna prioritize them, and then we're gonna build a queue on how to go tackle them. This is pretty straightforward. But you may come with some sort of very baseline metrics in terms of saying, what's the value of solving this and what's the difficulty in doing it or the effort? And you might define that a couple different ways. You might say, this is value because we can productize our data and sell it. Or there's an external use case, or or we're saving money by doing this, or there's an urgency. We have to have this done by next quarter. Maybe this is important to your executive sponsor, and giving that person a win means we have the ability to go and do more things. On the flip side, effort How complexes is to get done? Do we have a low chance of success? Should we start this after we start other stuff because there's interdependencies? Are there key personnel that aren't available right now? Access these things. And again, you may expand these out to where it's more than just value versus effort, but let's keep it simple. So you may say, these are super valuable, and these are super easy. Well, that's sort of a no brainer when you think about it like here's where we start. Basically, the idea of this slide is is start simple, maximize on high value low effort things and then work your way down the list. And inevitably, additional things will be added to this list, and you can then work on reprioritizing. But as you are building things, you are building a bigger foundation. So you're seizing the value today while investing bigger a bet better foundation, better intelligence, better way of doing things as you approach more difficult things and expand things tomorrow. Click on this. So prioritize your use cases, build a group prioritization. Now I'm gonna use a very tri overuse phrase, but in this one, it's super important, particularly as we think about these first two keys, purpose and priority. If you fail to plan, you plan to fail. A lot of people buy the tech. They install it. They get it going, and they haven't put the effort into thinking about what comes next. Start with very simple use cases and get going. That will make your executives happy that you can show value in weeks. Versus quarters or years. I see this all the time where people get caught in this really big task or project that they're trying to do. And it's harder than they thought. They didn't think about everything. They didn't account for all the variables. And then they get stuck eating up all this money and not showing any results, start small, start with high value easy items. Let's go to platform. There's a lot here. We, as Interworks, are really, really in favor of best of breed, tools. We really like agile tools. Because, again, we wanna rapid prototype. We wanna start kicking goals immediately. And best of breed means that you've got the ability to be agnostic with your data sources versus I'm in this particular ecosystem. And, oh, my gosh, I've got a data source over here that isn't a part of it. Now we gotta do backflips to get that guy in there. Self-service analytics is worth the effort. I've talked to multiple customers that say it doesn't work. It's a joke. Like, well, does it mean that it doesn't require work? It doesn't mean that it's not hard, but it's certainly worth it when you get it. Centralize your data. A lot of times what you'll see with organizations is they'll they'll take this person from accounting and that person from legal and this person from sales, and they'll put them into a little workshop and say, just ideate together. Because you guys are gonna see all the different aspects of the business together, and just by putting people that focus on different areas in the same room is gonna create value. This is true. The same thing is true with your data. If your safety data is sitting next to employee data or whatever, obviously integrated and properly controlled and secured, there's gonna be value that you find there. We built a massive data warehouse for an international company, and their entire goal was take all of our datasets, get them into the same place. And, get really smart people and get them looking at the data. And it's just been this idea factory ever since. Which means you have to have user friendly data and also means performance always matters. Slow data is unused data. So let's build into this idea about user friendly data. Let's pretend very simple little architecture here that this is your architecture. And let's say that you guys do a lot of data lake sort of activities. You don't use your warehouse as much as much as you just dump stuff in there and, like, you know what? We got really smart users. They'll figure it out. They'll get the data they want. They'll do all the logic. We're here to just stage the data. Well, the problem with that theory is is that if you don't spend the time here when you're extracting, transforming, and loading, or extracting, transforming and loading, ELT, ETL, whatever, to get really nice curated data with business logic, the semantic layer, all of that here as much as possible, then that means you're gonna have your users do it here And that is going to fail in a number of different ways. It could be they're doing it every time they need the same logic. So it's inefficient. It could be they're not using the right data because they don't have your bird's eye view of it. It could be they've arrived at different calculations. So this department a different number than this department. Ultimately, your best effort, if you had ten hours as a leader of your data and analytics business? Nine of those should be spent curating your data. That is the best and most important product that you can give to your organization. Empower them to do self-service, But the easiest way for them to do self-service is with really polished, easily understandable and interacted with datasets. They can take it and integrate their own little worksheets or streamline it to be whatever extract they need to do to run their particular use case, but just giving them accessible data. That makes sense to the business is the number one thing that you can do as a product of your time. Yes. The other hour out of the tin can be used to productionalize really cool dashboards that the community dreamed up. Yep. We'll take that one. We love it. Best data, best performance. We'll throw it into our prod, projects. But curated data, essential. Now when it comes to choosing your toolset, which is an essential part of strategy, what tools are we gonna be using There's this adoption curve here, which I think is useful, because there's innovators, early adopters, and then there's this chasm, did this technology get liftoff, has it become less of a darling of the tech media and has it become foundational to the way technologists actually do things? I've got some basic rules for you, and and I'm gonna do this in a very risk averse sort of way because as a as as your consultant, my goal would be I want you to get the best value from your investment as early and as often as possible. So when it comes to innovators, Test and POC this only. These are very early stages, and there could be some really cool stuff in there. But it's probably not prime time ready for enterprise. Does it have all the pin testing, the security certifications? Does it integrate well with this tool and that tool and this tool and that tool? These are things that they have to develop as they mature. So test. When you get to the visionary, I would say look for these to solve edge use cases that other things aren't solving. Or if you're a smaller organization and it's easier for you pivot than it is if you're a fortune fifty company, then you could potentially go and adopt these things if you think it's the best fit for you. With care and caution, do your diligence. Once we get to the other side of the chasm, you're in a much safer place to to adopt enterprise level tools. The earliest, of course, would be the early majority. It's proven. It's growing. It's got brand recognition. There's probably a lot of talent out there that you can go find to to build those capabilities. Once you get into the red, this is the safest. It's got full maturity. If you wait until you're here in the skeptics, Are you getting the best life cycle of value? Your business may be fairly conservative. And he's like, yeah. It's more important for us to be one hundred percent safe. There's no bugs. There's all this community and everything built around it. But ultimately, I think you'd probably be better off going a little bit earlier in the orange or the that way, you you're adding years to the amount of time that this technology may be useful for you. So if we were to broadly assess the market is one person's opinion. Snowflake has crossed the chasm a hundred percent from a data perspective. When they had the largest IPO in history, that was pretty much a, you know, the the the the bell ringing that, Snowflake is here. It's real. It's got massive amounts of interest, massive amounts of value. As you go further, Amazon Redshift or the Microsoft products or some of the other things, that's when you start to get to more established, and then you can go even further. I won't be mean. I won't put any laggards on the screen, but there are, platforms that sort of try to pull your data in, build your semantic layer for you, and then it makes it hard for you to get out. That's where I would sort of in those laggards. That's a in my point in my viewpoint, that's a dying methodology. That is a model that is not looking like it's gonna carry for it. From the ETL ELT space, your data pipelines, This is a very exciting time for this because there's a lot of really up and commerce that are really exciting. The great thing about Matillion firetrend and DBT is they all work really well with Snowflake. So it's because Snowflake is an established technology, you can use ETL any one of those tools to solve particular use cases that you've got with some confidence that you're going to get value out of, and these are all relatively inexpensive compared to legacy tools. Informatica, obviously, it's the big player in that space. They do a lot of things. As the big player that does a lot of things, are more expensive compared to some of the consumption based models. So there's other things you'll wanna weigh here. Analytics, dataiku, and thought spot. Those are both partners of ours. They're emerging very exciting technologies, data science, natural language query, Tableau and PowerBI. They might even be a bit more red than orange, but very established players. Now these aren't the only things that you would use to sort of make your technology decisions. Here's some others. Cost, how agile is it? Is it easy for us to, Is it easy for us to install it? Is it easy for our users to understand how to use it? Which use cases does it solve? Can we find people that that can do these things? How performant is it? Do we have good relationships with the vendors? Are they good at support? Is it in the cloud? Is it prim? Etcetera. There's probably a dozen more things you could add to that. So guidelines, there's a lot here. But the big one that I wanted to call out is you sort of read through these. Is own your own data. There are a lot of organizations, businesses that will say We'll store your data for you. We'll curate it for you. We'll do all the integration, and we'll give you five reports every month. Now this is sort of the marketing way that that agency sort of present this will integrate all of your Facebook, LinkedIn, your social media, and then we'll give you access to a curated data set. And then he gets really big for you talking about ERP CRM systems, but they own your data. They've got your data. They've got all of your business inside of their application. And if you decide to get it out, and it's probably a good thing to get into your own data warehouse, you know, onto blob stores or s three buckets, whatever. So you own it. I see organizations come to the realization that could take years to get themselves out of that. So if you can avoid it now, do it. Own your own data. It is so incredibly valuable in ways you haven't even thought of yet. The other thing I would say is understand how to negotiate at the bottom there. A lot of clients don't understand what motivates vendors. They're like, I have two million dollars of licensing with you. Why am I not getting a great deal? Because you're not spending any new dollars. So you understand what would motivate the salespeople, you're gonna be able to get a better deal. And people like consultants like us, like Interworks, we know all of that. We get to see it every day. So we can help get you the best deal. There's a lot here though. People. How you use people within your data strategy is critical? Because ultimately it's people that do things, it's people that make decisions. It's people that drive value. The the tools are amplifiers. They are means for which people to go and derive data, to go and derive value rather. And so when we talk about the cost of people, unfortunately, every headline that you are seeing is telling you accurately that competition for resources has higher, costs are going up, and there are fewer people coming into Australia. With technical. Now, of course, hopefully, that's changing COVID and other things. But there is a compression on talent and, and and, thusly, an increase on cost. To make matters even worse, because of this competition, recruiting, actually, I was I didn't expect this as I was doing the research for this, is double what it cost a year ago. It used to be something like ten k. Now it's almost twenty five k. Just to get a resource, let alone their salary. So, again, this is where I go to cry, but the good news is actually this, The idea of the great resignation, it's true, but in a historical context, it's not as bad as it might sound. And, again, this is a a a sweep of the entire Australian market, so it might be different for your industry or sector. But this right here is actually still pretty good historically, which means investing in your own people is still a really safe Great bet. One, they know your business. Two, if you give them opportunities to grow their technical talent and take on more responsibilities, They're more likely to stay with you. And then you can backfill the roles below them at a lower cost, a lower investment, a lower amount of talent needed, in terms of their skills. And the tools that are available today, whether they are data science tools, or their analytics, or data, or ETL, They are far more accessible now for data savvy people, maybe than traditionally data trained university people. The grid, those are still gonna be a very important component, but you can recruit from within and scale them up. We do that as interworks all the time. Both internally, as well as for our customers. So building cohorts, grabbing your users and dividing them into different groups. This is important. And this is an examples, some examples of why it's important because then we can address them uniquely on what they need. Every cohort, you need to define what's the goal that I want this person to do and how they're gonna output their work, how they're gonna interact with data, what role they're gonna play in governance? Then how are we gonna train them? And if they want to go up to a higher cohort, what's the pathway to make it easy for them? Let's incentivize it. In fact, Again, this is something that we do all the time. We see it work. Self service and governance. It's eventually that we would have to talk about this. When I say self-service, and I talk about governance, most IT people have this in their mind, the wild, wild west. Terror, panic. Oh my god. Users's doing whatever they want, whenever they wanna do it. In other words, it's IT nightmare fuel. This is not what self-service governance like. This is not what governance looks like. This is not what self-service looks like. It is not what both of them combined look like. Instead, we're talking about delegated governance. The most technical IT people are still at the top of a pyramid, but the point is is there's a bottom of the pyramid so that the people at the bottom, bottom up, can start to worry about the the smallest units of your analytics and data, the KPI. And as you work your way up, you can get more sophisticated. And, again, when we do our strategy consultations, we build these pyramids for you so that we can show you exactly what the different cohorts need to own. As there's escalating administration and responsibility. Keep it simple. Use the framework of your tools to embed your security so that people don't to rely on common sense as much. You can't see this thing. We coded it that way. So thus, that's one less thing we need to worry about. Use the tools to embed your security, but make sure everyone understands the role. The last key, promote. So I'll I'll I'll be briefer on this one, but I really wanna spend time on a couple of things. And the the primary one is this idea of a data culture. I think for some people, this is like Narnia or fantasy land or middle earth. Like, yeah, yeah. Data culture. Yeah. Yeah. We got that. If you think you have it, you don't have it because it takes a lot of work, just like self-service takes a lot of work. But I promise you If you actually spend the time to build a data culture, it is it is literally like a superpower. When we go and work with organizations, and sometimes we help grow organ stations into this data culture. It is amazing how the entire workforce becomes this innovation factory. It is possible, but you've got to set your strategy and you've got to invest in people. You've gotta reward people gotta give them good data. You gotta give them access to it. You've gotta reward them for doing good things. But if you can get there, it is one hundred percent worth the effort. But you gotta have a plan. It doesn't just happen. There are some things that we do to help, build data culture so we have curator which is a portal that allows all of your different analytics assets, even different tools, power bi, Tableau, whatever. Oops. Probably gonna shoot for it again. But, help you do that. We have pro, which is a rewards program where we invest back into your customer success, and we do this for free based off of if you invest your licenses with us or your projects or whatever. And we take our specialists and say, let's build a community. Let's make you better at data. Let's make you better at the community of data. And then we have a bunch of other things like Keepwatch, which are basically support service that we do so that you guys don't have to worry about that. And you could focus on the strategic stuff or the project stuff versus did so and so press the button to upgrade this quarter? We didn't do that. Dang. We're behind. So we could take a lot of that stuff off of you. Lastly, celebrate your wins. This job is hard. It's it feels like a continuous sprint or at least a accelerated marathon. So when you do amazing things, we solve this problem. We saved the money this. We did a data science thing, and the model proof that we just made four million bucks, celebrate them. Documented, let the people that were responsible get in front of your community of practice and share it. Where it's permissible and allowed, tell that story externally. You will have a thousand vendors lining up for you to tell the story of what you amazing things you did with, with their software. But take the time to enjoy it because, again, this is a hard this is a hard job. So we can help. Interworks can help. If we think about the four types of projects, and I got this from Eddie Obing. I'm sorry if I'm not saying his name correctly, but I like his framework. It's basically how well we understand the problem and how well we understand how we're gonna solve it. So there's this matrix. So this first quadrant walking in fog, we don't know what the problem is, and we don't know how to solve it. Making a movie. We know how we're gonna solve it. I we have tools. But we don't know what we're solving. On a quest, we know what we wanna solve. We know the problem, but we don't know how we're gonna do it. And then finally, the project status that everybody wants to be in paint by numbers. We know what we're gonna do, and we know how we're gonna do it. Obviously, projects are most likely to fail on this side of these quadrants, and they're most likely to succeed on this side. That's pretty obvious. So we want to be here. We want every project to be attained by numbers. Ah, we know how to do this. This is easy. So if we wipe this clean, how do we guarantee success given that three of these have increasing levels of challenge? Well, that's what folks like interworks do because we do this type of stuff all the time. And so if you are walking in fog, don't know what you wanna do, and you don't know the tools you're gonna renew. But you know you've gotta do something. We have a product called an SVR, and I'll talk about that more in the slide. Basically, what that is, strategy, vision, and road map. Let's talk about what you're where you're going. Let's talk about where you could go. Let's talk about your strengths and weaknesses. We can define, dismiss the fog. Making a movie. Well, great. You've got great tools. We can help you define use cases in what's possible. We can compare what the industry is doing. We can compare our previous experience and say, have you thought about this? Let's do that together. On a quest, well, we could help map out the milestones and various tasks on what you know you need to do. You still know how to get there. We could help get you there. And then even on the paint by numbers, we have a team of very experienced consultants. We are not the biggest by far. We are not Deloitte. We're not Accenture. But we try to be the best. I went and figured this out this year. Our our consultants have on average sixteen years experience. If you saw my camera on, you'd see a lot of gray hair. I'm I'm on the other side of that number, unfortunately. It would be great to be young again, but Very small elite technologists that are very experienced, and we can empower and embed these learnings into your team as we're going so that you're better for it versus creating a never ending cycle of dependency. So if you think about this, this is actually the sequence on how you might actually think about starting a project, you start with where do we wanna go? What are we doing? What's our opportunity? You then might say, well, let's start defining our use cases. Let's build a plan to solve that use case, and then let's get going. So that sort of all underpins on the idea of an SVR, which is that strategy, vision, and road map. And I wanna spend a little bit of time just kinda showing you what that is, and then we'll conclude with this. And if there's any questions, I'm happy to take them. So an SVR, this is something that we do. It's two to three weeks, but we have in-depth hands on workshops that we do with your senior leaders could be your executives, could be your data and analytics leaders with our senior strategy team. So I myself do these all the time. And we cover every single aspect of what we think is gonna be important. And you may have it completely locked down. You may, like, Rob, Our infrastructure is amazing. We got this. We got multi cloud. We got great. Let's go review it. Document it. Make sure everything's great. And if it is, well, then now we've got this document, and we could show people in the future of your organization what you guys have done. There may be areas you're not proficient at. Our process, our people. We haven't thought at all about how to productize our data or how to go out into the place and find data that's been productized that we could really benefit from. We distill all of these conversations together, and then we build a road map. And that road map is strategic goals, tactical steps, things that you guys can do today, things that you guys might need to think long and hard about, things that we could do for you, things that you could do for you, a very detailed way of going forward after doing a three sixty on your data, your analytics, and how all of those things are to relate with your organization and your people. It's honestly the the most fun I have is to consult getting to talk with organizations like this. And sometimes, little insider tip here. Sometimes we do have to play marriage counseling. This department wants to do one thing, that department doesn't. And so we are actually skilled at coming to collaboration, to coming to conclusions where they can work together. That's less fun sometimes. Okay. So the five keys, purpose priority platform people promote. There was a lot in there. Again, I apologize for the disruptions record this. If I have to, I'll rerecord it nice and clean. And then we'll share with you guys the recording. And if you want the deck as well, Just let us know. I'm happy to share. Thank you for your time. Even with these disruptions, I was able to still save about five minutes. Let me just take a peek, and see if there was anything in the q and a. There is nothing in the q and a. I don't see any questions other than folks very nicely telling me that they cannot hear me sometimes, but it seems like we solved that problem. Thank you. Giovanni is our marketing person, so she's sort of the brains behind this operation. She posted in the chat for our next, little solution spotlight, you can click on that link to go and register. Otherwise, Thank you so much. If there's anything that we can do to help you or you're interested in learning more about an SVR, please reach out. We're happy to talk. Thank you again.