We're gonna talk today about the five biggest data challenges and how to solve them. There is a backstory in terms of how we determine the five biggest data challenges, and there's a lot more reading and in in material we can give you as a result of how we collected this information. But before we get into that, let me introduce myself. I recognize a lot of the folks in the chat, so most of you probably know me already. For those that don't, my name is Robert Curtis. I am the managing director for InterWorks looking after Asia Pacific. I'm based here in Melbourne, Australia. I've been with InterWorks combined somewhere around eighteen years. So, while I have had a few other jobs, this has basically been the majority of my career getting to work with customers and clients like yourselves. For those folks that I'm meeting for the first time, a pleasure. For those folks I'm seeing again, very happy to be in your presence again. A little bit about InterWorks. We do a lot of things, but broadly, we can kinda use the next two slides to sort of frame that. So, basically, it's strategy, solutions, and support, within the context of how you use your data. So that might be building up data warehouses or figuring out how we solve problems using your data. A more detailed way of looking at this is this little overly complex wheel. But in the top, you've got sort of your front end applications or things you can do with your data. These are those green squares. The bottom purple are the foundational things that your other side of the house, your IT folks might be doing. And those are all situated around large important global or both sides of the house working together to build the idea of unified data governance culture strategy and support training. So we do all of those things. We do end to end data solutions. A little bit more about us in terms of how long and what we've been doing. Well, we're we've been in business for twenty seven years. Seventy five of the Fortune one hundred are InterWorks customers. That is a tremendous amount of experience and pedigree and expertise, one that we, are very, very proud of. Our blog that we write, generates somewhere between three and a half to four million page views each year just talking about data and analytics and and the challenges folks like you encounter every day. We have eight thousand different customers. And a few years ago, Forbes recognized us as one of the twenty five small giants. So small organizations that have greatly, greatly out outperformed the size of the company. Lastly, we'll talk about some of the technologies that we work with. We really love Snowflake. We really love, Informatica, for instance, and then some of the customers that have given us the privilege of sharing that information that that we get to work with them. And, of course, the many, many more that, would prefer to remain anonymous. So let's jump into this. We are talking about data challenges. And over the last two to three years, we asked you, our audience, our customers, our prospects about what your data challenges were. And we did this as a bit of a a survey. Now when I say survey, I am not saying that this was a super scientific way. As much as it was us being at a booth or us getting you in around a breakfast table to talk about data and challenges and giving you guys surveys or the ability to answer or rank your challenges. And then we wrote everything down, and then that is how we sort of compiled all of this. But if you wanna know a little bit more about our method, basically, every time we got into a room and got to talk to customers like we did in these events across Australia, or every time we were at a conference, and we would have these cards, multiple different cards as well as blank cards that you could fill out. What are you struggling with? What are your big challenges with data? Of course, we did the health care of it, so we're trying to do something fun for health care, so we did the thermometer that time around. But the whole idea was just getting a sense of what you guys needed help with, what you guys were struggling with, and where the biggest opportunities were for you to solve problems. And as a result, we got three hundred and more responses. And this is just from Asia Pacific, so this is something that we've done quite locally. So those folks that are here from Asia Pacific or Australia in particular, you are talking to your peers. In fact, your responses, you might see up there on the board at one of those events. So our initial impressions I'll I'll give you I'm I'm not going to delay too much longer what the actual top five or more are, but I just wanna give you some sense. We did collect three hundred, results, and we got a chance to talk to people that were different types of organizations, so public versus private, supply chain, mining, energy, financial sector. We got to talk to a lot of different people and a lot of different roles within the organization, analysts all the way up to a c level person. We had a intersection with a lot of different people. And what was interesting was regardless of who or where, the answers were very strongly consistent. So, yes, I've got, the server results across three hundred, but if we disaggregated those by Perth versus Sydney or by public versus private, The answers shuck out almost exactly in the same rankings. That was very interesting for us, not one that we would have predicted. And the other thing that we wouldn't have predicted was that the number one answer was not what we were expecting. We were expecting a number of different things that we get to talk about more directly. But when we actually shook it out, it was overwhelmingly the number one that made us really look at the way we were thinking about our business and the things that we wanted to do to help support you. So in that in that regard alone, that was really useful for us, and we pivoted some of the things that we wanna do to help. So without further ado, let's talk about the results. As I mentioned, the top one response is so clearly the number one response. You can see it's nearly double, anything beneath it. So to make this fun, let's start from the bottom and work our way up. Number five was automation. Ten percent of our respondents said that that was a primary challenge for them. User adoption was fourth with thirteen percent. We've got tools. We want users to use them. Strategy, where to go, how to get there, what we need to do to be successful along the way, fourteen percent. People resourcing, sixteen percent. There is some context. And when we ask this question, had we asked this question five years ago, maybe that question would be lower. We'll talk about the influences and factors that maybe accelerated that to number two. And the number one response, which was a big surprise to us, was governance. Thirty percent of people, again, across role or cohort or vertical, thirty percent said governance is their number one problem. It's their biggest concern, the things that keep them up at night. That is our challenge. So when we think about how we're gonna solve these things, we gotta understand what they are, why they're valuable to spend the time and energy on, and then how to actually put something that's going to be effective. I'll give you the next two. The other ones start to get really into the long tail, but selecting the right tools is an example of, number six there, nine percent, and then user enablement. How do we get the our users upskilled in all the things we need them to do given the explosion of all the different technologies and tools that they might intersect? We're not we won't talk about those, but I just wanted to give you sort of the the top clear seven. Once you get past that, everything kinda trails off into almost bits and bobs, single answers here or there. But governance was a surprise to us. Why? Why in particular was governance such a surprise to me as a consultant that's been in this game for twenty years? Well, I'll tell you why. Woah. It's very hard for me not to do that in a Keanu Reeves. Woah. Because governance of all of the different things that we see our customers, our our our clients working with, it's the least that people wanna talk about. It's not exciting, generally speaking. Now there are gonna be some governance professionals on here, like, what are you talking about? It's amazing. It's so exciting. I talk about it all the time. Most of us don't. It's the least budgeted. It's the thing that people have to spend money on versus what they want to spend money on, and they spend as little as they can get away with. And oftentimes, it's not nearly enough. Oftentimes, it's the least amount of work that people put into making sure that their data and analytics are successful. And it's oftentimes as a result of the top three the least successful. I've given some pretty sobering stats about data projects, analytics projects, and how successful are they they are, rather more accurately, the rate of failure, as well as how often tools actually are able to attain, ROI, and they are very sobering. Something like eighty percent of data projects fail. When you put that into the context of a governance project, a data governance project, those are substantially higher still. And it's because, one, they are not a focus. They are not properly budgeted financially, and they are not properly budgeted with time and energy. And there's more to that. When we get into the actual governance of what it is and why it's important, we'll get into a lot more of of some of the other particulars there, but that was a surprise for us. But the big thing that changes why governance has so quickly risen to the number one is it's big data, it is self-service, it is AI. All of those things are uniquely valuable, but all of those things pose a unique risk if you are not tightly governed across the entire enterprise. But we get ahead of ourselves just a little bit. We're gonna talk about all of these data challenges. We're gonna explore why they're important, and we're also gonna go and look at how we potentially could solve these. Now I could give you the the big four. Well, you need to build a ten year strategy, and you need to revolutionize x y z. That's not gonna help you today. I'm I'm gonna try to give you things that'll help you in the next days and weeks, as well as giving you something to shoot for. And I did it at any time, of course. I'd be remiss if I didn't say this. We do these types of things all the time, so we're here to help. So let's get cracking, shall we? Shall we jump into the first one? Governance. The tricky one about governance in terms of how I wanted to break this down for you, it is a massive umbrella. So I tried to distill down some true aspects to it that we could say this is an operational definition that is broad but specific enough to cover a lot of things but still be useful. So I came up with this definition. An umbrella of different but related domains, data governance is the management of the data life cycle, so how data lives within your organization from production, maturation, curation, and then sometimes deletion. For the security, quality, and usage of data in line with your organizational values and goals. So that last part's important too. We're not just talking about security, but we're also talking are we using the using data and and employing tools that use data in a way that is in line with where we wanna be, who we wanna be, and the relationship we wanna have with our customers and our employees. All of those things become a part of it. And, again, you could start to throw out you know, we're talking about data catalogs, lineage, observability, quality, all of those things, security, privacy, regulatory, all of those things fall under governance. But we talked about in a couple slides ago why governance is so important and why it's worth the effort in particular. Well, data is exploding, and it's only continuing to get more and more and more data. I say this stat a lot because I love it. So if you've ever heard me speak at a conference, I'm sure you've heard me say this. But if you were to take the sum of all of human history, all of human intuition and knowledge and data, and throw it into a book and say that represents the sum of everything we've ever produced as a species, We would produce a second volume in a year. That is how quickly data is exploding. That is the Internet. That is the Internet of things. That is AI, generative AI, that is human beings being able to react online in a digital presence and us capture everything. It is all of that and more. There's also just more people with more money looking to do more things, which brings us to self-service analytics. This was a huge boom that that was kicked off with Tableau some fifteen years ago, and it's only getting better. Self-service was reporting, then it became analytics, and now it's becoming semantics. It's very, very exciting. But the more tools we're able to give into business users' hand to make them into data workers, the more transformative and powerful that can be. But we have to have governance to do that. It is mission critical. At perhaps the most exciting, but yet undeveloped thing that is even more critical for governance is generative AI. You're gonna see this as a theme throughout this entire document because every conversation we have about people having challenges, the ultimate goal is we wanna kick goals with AI. We wanna do amazing things with AI, and we can't do those things if we don't have x y z, and governance is forefront among all of that. So this is the promise or the reason why or the value of governance. Why do people struggle so much? Why is governance so hard? Well, honestly, I could fill this entire conversation this entire hour conversation with just this topic. So we'll try to keep this at high level. So I've got I've got three reasons why. Why is this a challenge? Well, it's very time intensive. Another way of saying that is it's very manual or it has historically been very manual. You wanna build a data catalog, you better have some full time people thinking deep thoughts about how to define fields. Metadata, not easy to automatically produce. There are a lot of things people have to do that require human intervention or traditionally have required human intuition to define and make usable. Uncultivated skills. We talk to a lot of customers, and they'll have one of the big tools. They'll have Alation or Calibra or Informatica or Atlant or any Data dot World, any number of different tools. And they're like, listen. We got a lot of great support from the person that built the tool, and they were very happy for them to transcribe our governance policy into their tools, but they couldn't help us build a governance policy. That's the part that gets really hard. We don't know what we don't know. We don't know what good looks like. We could go invent it, but we don't wanna invent a new thing every time we're trying to accelerate a particular domain. Where governance intersects with strategy and vision, that's quite difficult. And the tools have been lagging. Meaning, are the tools an accelerator, or it gives you more options? And if you're looking at a blank canvas, most of the time, it's very hard to start. It's this writer's block of governance. Yeah. There's a lot of stuff I can do in that tool. I don't know where to start. I don't know what to start with my policies. And and then at the same time, where we're at right now, I need a transition phase. I don't even know how to diagnose what we've got to get to the spot where we need to get to. These are all things that make governance really hard. The other thing I would say is this, is governance is not the same answer depending on who you're talking to. If you're talking to somebody that is building Power BI reports, governance to them and how the policy and processes and procedures and expectations and accountabilities need to be translated to that person is very different than a data steward or a data architect, which means governance now has to be cut and refined by cohort as well as tools and goals and domains and datasets. Very, very tricky. So how can we help solve this? Well, I'm a big believer in the old adage that you hear carpenters say measured one or if measured twice, cut once. So I'm a big believer of thinking a lot of deep thoughts about stuff, particularly before you get going. And if you have some help in framing where to spend your time and energy to think, I think that makes it that much more focused and that much more accelerated. Delegated governance. You cannot take a traditional IT approach, a centralized BI approach to governance. Everybody in your community has to own part of that, which means I think you have to have a delegated approach. If a u at the very minimum, think about it like this. A user looks at a metric. They didn't create the dashboard. They don't source the data. They don't prepare the data, but they look at the they look at your numbers. Their responsibility to say, I know that number's not right because I'm a lot closer to this part of the business. Something is wrong. And it's my job and our delegated governance strategy to say, we need to go check those numbers. Is our logic wrong? Is our data wrong? Is it outdated? Whatever it might be. That's a level of delegation all the way up to the people that build these reports, that build the datasets, that manage the platforms, that build the pipelines all the way up. Everybody has to have a hand in pulling the rope in the same direction. If you really well articulate that, then you spread the load, and it's not one person in IT that is losing sleep over all the things that can break. I also think you need to be thinking quite, proactively in how you take away as much of the human element and build that into rules inside of tools. So there's a lot of little phraseologies that you're gonna hear me say. Like, when we talk about cohorts, we talk goals and roles. When we talk about this stuff, we talk about rules and tools. If you can put permission access into your analytics tools and you can put permissions and and access and security and all that stuff into your tools, that takes out a lot of the human level of governance. We still need to delegate governance. People still need to feel accountability and ownership of what they're looking at, where they're engaging. But be smart about how you select tools based off of how it's going to make governance easy. Sometimes people don't think about that. All they think about is the bottom line or this has some flashy thing that solves this use case, but we have zero governance or we have a whole bunch of silos that we can't see into. There's a lot more that we could go into, when when we're talking about governance. We'll we'll talk at the very end of this whole presentation, I'll give you guys some ideas of where you can start as well as the actual full white paper that I wrote. And and I spend a lot of time on governance since it's overwhelmingly the number one answer. Last thing I'll say on here. There are people that wanna do AI to solve use cases, like customer use cases. We wanna give customer support, and we wanna, accelerate this thing. But the easiest way to use AI to great effect now is to use it inside of your BI and data tools because they're being integrated in there by your by your providers to accelerate. And you you can see this very famously in Power BI with Copilot or Fabric. But you can also see this in your governance tools. As an example, Informatica has all of these different things that they built into their platform. So you can do ingestion, you can do integration, but you can do cataloging and MDM and quality. And if you start to bring in, if you use Informatica as your ETL tool just by staging it, Informatica will give you the opportunity to build a catalog because you've done everything you need to do to start to organize all of these fields, and then they will augment that with what they call CLIR. Now all these different tools have different iterations of this, but one one of the reasons I bring up Informatica is just the completeness of solution rebuilt from the ground up with AI in mind. So if you use AI quite creatively or use AI rather in the scheme of accelerating automation inside of your tools versus the use cases you're trying to solve with your tools, that is a massive advantage. So I think there's another way you could probably look at how you're sourcing and selecting tools based off of how easy it is to keep them running or to get them working. Like I said, we could spend a lot of time on governance, but we're twenty five minutes into this webinar, and I need to get to the other four. People resourcing. So supplying the organization with critical talent, the critical talent that is necessary and vital to implement change, enterprise level, organizational transformational change using data to do it, and then supporting all of that stuff after you deliver. Super important. It is a lifeblood of innovation, qualified, helpful people. Overwhelmingly, what we need, what we see from the industry is people need data engineers and then architects. There's other people that they could use. Obviously, we analytics, are always useful. A great analyst is worth their weight in gold. But if you open up or talk to any customer or any of the people you're thinking about, what can we really yeah. Yeah. It's probably data engineers. We need as many data products getting out to our users as possible, and they are in short supply. Emerging cloud technologies. The more stuff that is going out of the cloud, if you just think about the Snowflake ecosystem, they started off as a data warehouse, then they became a data, it's called platform where they can do lake and warehouse. And now they've got AI and ML and containers and all this extra stuff. More and more stuff is happening on the cloud. However, the AI battle for supremacy, all once all the dust dust settles, it'll be the hyperscalers that are still winning. They own the market. AWS, Azure, and to its lesser extent over here, Google, those are the people that are always going to be because they're where the battles where the game is played. They own the arena. So everybody needs that type of skill set. Every organization needs cloud architects. And then, obviously, it is very exciting. Again, I said this would be a theme. Generative AI and the people that can then train these models and deploy them and and and monitor them and then really get value out of them, those people, while it's hard to find them and we probably don't know exactly what the best resume looks like as we were figuring out how all this stuff is going to work and to make it productionalized, those are very, very valuable skills that people are really interested in. So in terms of why people resourcing is hard, this is a sobering look back on the last few years, particularly in Australia. Well, there's a high demand of skills. We we already know that. A lot of people, a lot of organizations, every organization needs a data engineer. They need an architect. They need analytics. It does not matter if you are health care. It does not matter if you are retail fast moving consumer goods. Everybody has data and everybody needs to wrangle it. So that that has not changed. COVID has made it a massive problem, at least for this country. Because for about two and a half years, our supply of international data skilled workers was gone. And we were we were barely scraping by with what we had coming in. And now you take out all of Asia, you take out all of Europe, you take out all of the Americas, and we're we're we're we've got a growing economy, we've got growing needs, but a finite number of workers, which led to the great resignation, meaning ridiculous amounts of money, sometimes thirty to forty to fifty percent more than what the market could stand, were being thrown around. So people were leaving. It was very hard for a lot of organizations, particularly from that midsize down to keep and retain talent. And so we are still catching up. Even though our borders are open and we're sort of post COVID, knock on wood, we still have a significant shortage in terms of people resourcing, which is no wonder why this is number two. Whereas maybe ten years ago, it would have been in the top five, but maybe not so pertinent. We we just came out of a very painful period. So how do we solve this? Well, I could I'd love to say, well, let's just tell the government to let in a lot more qualified skilled data workers. Well, that's not a realistic how are we gonna know who's who? So we have to be a bit realistic. So the answer that I see a lot of people use is subcontractors. And I think this is I think this can be, an unhealthy relationship. I was gonna say an addiction, but that's kind of a strong way to say it. Subcontractors, in my experience, the reason that I'm I'm I'm sort of equivocating here. So subcontractors have the have have sort of a they're a cross between consultants and they're a cross between full time employees. They're across from consultants because they are probably more expensive than your normal employees, and they come with outside skills. But what they don't have that consultants have that's probably a little bit more is that they're not there to focus on a particular thing as much as they are to be told what to do. And I'm not trying to be mean to all subcontractors by no means. But a lot of the times, when you have an organization, just think about it, You guys hire four data engineers. They show up on day one and they're waiting. Okay. What do you want us to do? We want you to build that thing. Okay. Great. I'll go build that thing. Versus a consultant like InterWorks, for instance, we we have a conversation like, this is the way you guys need to do this. It will save you time, money, energy, and you'll get better value out of it. So they're not subcontractors are not known for being thought leaders. I'm not saying that no subcontractor is a thought leader. I'm saying as a group, they're probably more there to help you press buttons and give you a longer reach to your ideas your ideas, which is why I put a con I put a question mark there. If what you need is subcontractors, by all means, knock yourself out. Another way that you might think about doing this is building a really robust enablement strategy so that you can have the people in your organization take one step up in terms of their technical capabilities. So your nontechnical business users, let's teach them to do a little bit of self-service, which then the people that were doing self-service, let's call them the report creator, now they can go up and come data stewards. All we have to do is teach them a little bit about basic data preparation and how we govern those datasets. And then we can start to push everybody up a little bit so that eventually we start this factory of producing more highly skilled data workers as they go through the the the the process of forming and shaping data people. If you build a robust enablement strategy, they will continually be improving, and you don't have such a dependency on outside talent, whether that's subcontractors, consultants, or hiring. You can always go and hire the business user versus the technical user, and There's generally more business users available at better cost than there are your data architect. Whereas if you're preparing data engineers to fill that role and then backfill them all the way down the organization, you now have an asset that is going to benefit you for the entirety that you maintain it. I'd be remiss if I didn't give you guys a a short term or low cost solution. We built something called dynamic demand based off of exactly what we see in the market right now. But the goal was to give you guys access to high class, high level consultants with a range of different skills as you need them, but give it to you at a very cost effective way. And the goal there was you guys commit to, say, five days a month. We then talk about what you wanna do in those five days. We might say this month, we're gonna give you four data engineering days and one analytics consultant because we think that's what you need. Or maybe you're going hardcore analytics. Okay. Let's do five. But by paying them in a subscription format, we can lower the cost significantly, but still give you the full access to an entire BI team, whether that's platforms, architects, technicians, analytics consultants, data engineers, data architects, data scientists, whatever. Something to think about. We've had a lot of customers, take us up on that either at the commercial level or the departmental level. We need more people, but we don't have the the ability to go hire all the people we need. So we're happy to take them in doses. Number three, strategy. This is something near and dear to my heart. I, in my role, get to do a lot of strategy consulting with customers. I love doing it. I love going in and seeing all the great things they've done and then seeing where they have challenges and opportunities and help them map the rest of the way there or identify new things. So strategy is a planning exercise on how we're gonna go forward. We have to think about the vision. Why are we doing this? Why is this important to us? What values do we wanna carry with us? The road map, where are we going to go, and what are the big points along the way, the milestones? And then when we get there, what is our future state going to look like, or what does it need to look like so that we can ensure we're the most successful? So it's a transformation project. It's a destination. It is a way of traveling. All of those things are strategy. These are very highly dynamic times, not just from, a technology standpoint or whether or not we have people and resourcing and all that kind of stuff, But the economy is very dynamic, so we constantly have to be keeping an eye on strategy so that we can get to where we wanna go and we can do it in a way that is predictable. Data is extremely powerful. Everybody in the last five to ten years has been hearing that this is the new goal. This is the new oil. The most important resource you own is data. And I'll tell you, own your own data, which is another strategy point. And, of course, generative AI. I wouldn't be remiss if I didn't have generative AI on here. Every my marketing people said, hey. Talk about AI. I'm like, don't worry. I've got it covered. We're talking about it. So strategy is hugely important. You don't know where you're gonna go, if you don't have a plan. If you don't have anything you're targeting, you're sure to miss. You you you've heard all the sayings. Right? So when we're talking about strategy, why is it so hard? Well, one, everybody has day jobs, and taking time out to think deep thoughts about where and how you're gonna get there is extra to all the other stuff you've got to do. Also, there's a lot of noise. A thousand different people could tell you this is the answer. Only a couple of them are right. So understanding what's distraction versus what's essential can be challenging. And are you if you actually pull people into a a week long whiteboarding session, are you actually producing something that's useful, or did you talk a lot about some things that we could do, a lot of the problems that we identified, and maybe even identified, hey. This is where we wanna go. And then when you're done, like, oh my gosh. We didn't actually define any tactics on how to get there. Overwhelmingly, when I come in after a big four consultancy and we're like, okay. Can I see what they propose for the strategy? I'm like, yeah. This is right. But they didn't give you any direction on how to get there. Like, there's a whole bunch of things you have to do to even get started versus loan, hit this huge target they put ten years out for you. That's not useful. That's the easy part of strategy. So in terms of how we would solve it, I'm a big believer in in thinking strategically, but but working tactically. How are we going to get there? And it's okay to say, I understand the first three steps, and the next three steps, we're gonna plan to be adaptable. Now you have to embrace that. You have to develop those problem solving skills, but it's perhaps smarter than waiting till you have all the answers to get started. Don't forget why you are doing this. Why are we setting a strategy? Why is this important? When we have a destination, why do we wanna be here? Why does this help our customers? Why does this help us? We do a very short and succinct strategy exercise, which we call a strategy vision roadmap. It's basically two weeks. And we've we are laser focused on data analytics, people process, vision, strategy, all of those sorts of things within the data context. If you're thinking about monetizing your data, if you're thinking about AI readiness, or you're thinking about automation, optimization, selecting new tools, building everything greenfield from scratch, getting to the cloud, all of these things within the purview, and we talk about them with customers all the time. And, again, when I say we've got seventy five of the Fortune one hundred, that's the real value that I'm talking about. We get to talk to the huge international companies all the way down to the mom and pop shops. And from those, there are kernels of wisdom all over the place. And so when when we get to work with our customers, we are looking and listening to what you need, and then we're overlaying all of the great things that we've learned that are gonna be useful for you, mapping them to milestones and keeping a vision of why we're doing this. User adoption. When it comes to spending money on BI projects, there's three things. This probably could extrapolate out this more, but we'll conveniently say three things. And most of the time, people spend money on only two of them. And that is the platform, I e, the technology, perhaps the process. Okay. Here's what we're gonna do. Here's the project in which we're gonna build it, but very rarely did they spend money on people. Now that we've got this thing, are people using it? That's important. Empowering users to adopt the tools that you provide for them, that you've built for them, and all the data, that has been populated into it to create these great analytics and reports. That is the number one thing that indicates whether you are getting ROI, whether you are getting the return on that investment. You can spend all the money you want on data science or AI or machine learning or analytics or whatever, but if no one uses it, it does not matter. And this is honestly one of the biggest fallacies that I see in most organizations. They do not think about people. Well, we've seen, oops, we've seen how powerful self-service is. It is transformative if everybody in your organization can think and make decisions with data. Not easy. It doesn't happen organically. It doesn't happen by hope. It is a well coordinated executed campaign. It is a marketing strategy as much as a technical one. Enablement is cost effective. It's easier to teach someone you've got than to hire a consultant, to hire a contractor, or to hire a new resource. And, again, the ability to teach is an asset. And if you've got the curriculum and you've got the the structure to do this, it will vastly benefit your organization. I one of the people on this call is a a friend that I've had over there in Perth, and his whole role for years and years was making sure his community was engaged and was doing great things with data. Cannot overstate how important that is. And, of course, the value of a data culture, which is sort of everything above and more. Do we value data? Do we see the opportunity with data? Are we using data to solve problems? Are we encouraging and celebrating people that that score wins with data? Are we constantly thinking about the next thing that we could do, the next tool, the next evolution, the next use case that we could solve? That is a skill to build it. It is a skill to maintain it, but it is a tremendous asset once you get it there. So let's talk about why. Why is this a challenge? Well, I think a lot of IT people or the people that are sourcing, procuring, and setting up these things don't understand human incentive. We bought the tool. This is the tool. Why aren't they using the tool? Well, a lot of people might, but you have to understand why they're using it. And if you wanna get the other fifty percent, I think most BI tools, you're you're averaging thirty to forty to fifty percent adoption, which means there's a massive amount of people that aren't. You need to understand why they want to use it. They probably wanted to solve problems. Do they have the Excel sheet? Well, how are you gonna beat that? You You gotta think in terms of like that. You gotta think in terms of how you're gonna make it easier for them to use this more convenient. Change management. It might be a well thought out tool. It might have all the things, but if nobody knows about it or you're not consistently evangelizing, I e, in your community of practice meetings, in your formal center of excellence, conversations, and strategy sessions, in building a a community portal where everybody goes to the same place and you can put all your announcements and all your new things and all your whiz bang videos about all the cool datasets we just released, people won't know about it. Again, this is a marketing campaign as much as anything. Also, you have to continue to work at it. It is not built and done. It is not a monolith in which you've constructed in the lobby of your of your building. It is a garden. You have to weed the garden. You have to you have to fertilize it. You have to water it. You gotta make sure that you are getting fruit out of it. And if you don't sustain it, it will wither and it will go away. Several of you have probably worked in organizations that used to have this that don't anymore. It is something that must be going on and on, and that is a challenge because people have to be dedicated and focused on doing it. It cannot be your your your job on nights and weekends. So how do we solve this? Well, I'm I'm a big believer. We already mentioned this roles and goals. If you are going to understand incentives, you've got to understand what their goals are by the types of role they've got. We need data people to do these things. We need analytics people to do these things. We need the business community to do these things. We need our executive leaders to do these things. Let's translate that into what's useful for them and then build a curriculum to empower them to do those things. Everybody in your organization at the minimum needs data literacy. We then build on there by cohort. I did a webinar. I think it was the last webinar. And and, hopefully, Giovanna, who's, our marketing person that's on this call, jumps on here and helps out with this link. But I just did a, a webinar on BI methodologies and how the different methodology select is going to help or hinder the goals you have for your business intelligence. They functionally are going to support different types of operating models. It's important. If you wanna go learn more about what I'm talking about, centralized methodology versus distributed or hybrid or whatever, I go into that over a course of an hour. Thank you, Jean. She's got it there in the chat window. So you have a look at that. Again, also, give us a buzz. Happy to talk about it too. We've also built, another product that we called interconnect. And what we've seen as a result of people not really putting the energy into their people to build user adoption and really maximize ROI is we've seen that the community effort or the emphasis of data culture is something that people talk about or don't understand or talk about but don't get to. So we thought, listen. We're really good at this stuff. So we built a product where we would basically help outsource it for you, and we we called it interconnect. So the idea is is that we will work with your center of excellence to help frame the conversations you need to have and give you guys outside opinions. We'll help you build your community of practice and sponsor and organize the types of things you need to be talking about every month or every two months, however often you wanna build your bring your community together to do the show and tell about the grid great stuff that this user has done or the announcements, new datasets, new governance policies, to organize your vendors to come in and talk about here are the next round of features. Here's how AI is gonna intersect your tool. Or or us, InterWorks, come in and say, here are best practices on this amazing cool thing, whether it's using color to tell stories or or or how AI is gonna make building a semantic layer easier. We have found a tremendous amount of support and excitement about this. So I wanted to mention it here because we can help you. We can actually do the community building for you. Lastly is the InterWorks Data Academy. Again, I talked about roles and goals building enablement plans by cohort. We do that. And, again, if you start to think about how this bundles together, we can package it as a monthly, or you can pay for it upfront if you want for the year. We don't care. We're very flexible. All we wanna do is just have the opportunity to help. Automation, the last one we're gonna talk about today. This is a broad one, but I'm putting it in the framework of data. So the opportunity to reduce workloads, drive cost efficiency, and provide better experiences for users and customers because we have we have removed the dependency on human beings and instead inserted intelligent machines to make this for you. Opportunity in new tools. So there are always going to be automation opportunities as tools get better. Automating automation, that's a tricky way of me saying learning when and how to use automation for maximum effect versus potentially unexpected consequences. And then, of course, everybody take a take a drink. Generative AI. Woo hoo. That's our buzzword for the day. Generative AI will be massively important for automation, particularly as it starts to replicate human reasoning at computing scale. I've done several presentations, but I think we did a webinar recently about AI readiness. And we talk about why generative AI is so attractive. And it's the automation part of this that is really, really, really, really fun. So why is automation hard? Well, it's understanding when and where to use it. I like to use this idea of an opportunity queue, which is you taking everything that you could potentially solve and rank them in a couple different categories. Think of these as use cases. How hard, is this to do? Is it easy? Is it impossible? And what's the value? That's the other axis. What's the value? Is it is it something super, super valuable, or is this a nice to have? You put those two answers next to every use case, as many as you can think of, and then define them by those two characters characteristics. And then you sort them by easy to do but highly valuable, and you start there. And the benefit of that is you're naturally going to adopt an agile approach where you have releases. This one was pretty valuable. Easy, easy, easy to do. Bang. Went on the board. So I think people struggle with where to start. And I think oftentimes, it's not made by ease or by value. It's made by a fiat. Executive wants this done. Like, wow. That would not be the one we would start with because there's a lot of stuff we need to learn before we go do that really complex and tricky one. And it's gonna take us a long time. People will be wondering, what are you guys even doing over there? Foundational requirements. Before you can get to automation, particularly when we're talking in the context of data, you have got to have a well functioning data platform. Your data has to be curated. It has to be functional. It has to be secure and governed, all the things we've talked about to this point. You have to do those things before you can be effective with automation. Machine learning. Teaching these models, feeding them the right data, monitoring them, observing the results, all of those things take time and energy. And, again, those skills are in short supply. So a lot of times, people are doing it that haven't done it before. And it's easy to make mistakes, or it's easy to to to spend so long trying to get prepared for fear of failure that you don't actually get off the ground. So there's a lot of reasons, and there's there's way more than this. How to solve? I'm a big believer in tools, particularly when it comes to automation. You don't need to I I know folks may not have tremendous amounts of budget or whatever, but with a lot of the tools that we're talking about now, cloud based data platforms, they're integrating a lot of automation opportunities, whether it's Document AI, etcetera, etcetera. That's a Snowflake tool. There's a lot of stuff that are pretrained that you can start to use. And and we've rolled out Document AI for a a shipping company ship freight in, freight out. I'm probably not using the parlance like they would. But they use that after failing multiple times of trying to get something to recognize cargo, cargo paperwork or or or slips that sort of say this cargo is going from here to there. But we had great success using the latest tools. So I'm a big believer of look to the opportunity of the stuff that's coming out because it's using the best tech, the best models, the best opportunity to succeed. Data pipelines, this is the easiest place to automate. And this sounds silly, but if your engineers if your most technical resources are spending times manually doing things that should be automated even as something as innocuous as the data pipeline, orchestrating it and making sure everything flows through as per normal and doesn't need your oversight, those people can then go spend time doing other things. But if they're constantly stuck troubleshooting and putting out fires, like building data pipelines or figuring out why data doesn't work, they can't then go innovate. So simply taking stuff off of their plate gives you more time to automate and then dipping your toe. This goes back to the opportunity queue. Again, I'm a big believer, start small with obvious, measurable value, and then build upwards. You will have immediate results. Those are really results will give you immediate learnings, and you will get better and smarter and faster at it. Those are the five data challenges. We're almost right on time. So I wanted to give you some next steps in terms of what's next. All of these were written into a white paper. I wrote it I think it's somewhere around, I don't know, eighteen to twenty two pages. Lots of pictures and graphs if you don't wanna read. I promise. So if you wanted to download the white paper, it's right here. Just scan that little link there. There's a little lead form. I think we ask you to give us some of your data challenges as well so that this whole process is self replicating. But then you've got full access to it whenever you like. It's PDF that you can download. Would love to know your feedback on it. I've got a a a queue of other white papers I'm gonna be writing. I'm in the process of writing an AI readiness one, self-service data culture building, that kind of stuff. So would certainly appreciate your feedback. In some in terms of some of the recommendations, we talked about some of these. I'm a a lot of these do cover strategy. So I mentioned the SVR, which is our strategy, vision, and roadmap workshop. Some of the folks on this call have done them. Some of them are about to do them. I I can see some of the names of people we've got queued up already coming up in the next weeks and months for us to talk about strategy. We love doing this. And, overwhelmingly, we found that this is some of the most powerful stuff we do for our customers, is really just defining what's possible and talking about where we can go from here. We also do something that, we don't do as good a job of advertising, but we we we help people figure out what tools are gonna be best for them. So we can assess the marketplace. We can make recommendations. We can build a framework to help you assess and evaluate these tools so that you can find the tool or series of tools, a suite of tools to solve the things you're trying to do. And because we get to do this all the time, we have a lot of experience in figuring out, one, the right features and functionality, two, how this intersects with an overall governance strategy, and three, how best to negotiate for these tools to get the best bang for your buck. I've also mentioned a couple different tools or services that we provide that roll up into this idea of BI as a service. That was that interconnect or our dynamic demand or the data academy. BI as a service was our attempt to try to make this really, really easy for folks to get a lot of acceleration, to get a lot of progress when it comes to data and analytics. And we're really flexible here. We're just trying to be as useful as possible. So you could take one or all of these. We could scale them a little bit bigger or a little bit smaller based off of what you need. But the idea is you pay a low cost monthly subscription, and we either help your users, we help your data workers, we teach your people, we support your community, we support your platforms, whatever it is that you need us to do across these services. We just make it easier for you guys to then make decisions and to really drive value with data while you take a lot of the heavy lifting and the hard stuff and let and let us do it. So if there's anything in here, whether it's BI as a service or an SVR or just wanna chat and say, hey. I liked a lot of the stuff you said or I quite frankly disagree with what you think about x y z. Reach out. That little QR code will take you right to a contact us page, and you could say, hey. I wanna talk to Rob, or I definitely don't wanna talk to Rob. I wanna talk to someone that knows what they're talking about. Whatever it might be, we would love to talk to you. Thank you so much for joining us. As it says there, the path does not end. We would certainly love to talk with you. And if nothing else, please come check us back. We do these every month. We try to pick something interesting and exciting. I hope you have a lovely day. Stay safe out there. Stay warm, and I'll see you guys next time.