Alrighty. Let's get underway. So welcome to our webinar. We are talking about, your data maturity. We are going to do a little bit of a hands on workshop here. We've got some questions we're gonna ask and survey of our friends that are are joining us. This won't be exhaustive. Obviously, we only have about forty five minutes or so to get through all this content. But I wanna give you a sense of, how we do these exercises and what kinds of questions we ask as well as to give you a sense of how you can measure it. I am Robert Curtis. Let me just grab that slide forward. The managing director for Asia Pacific and InterWorks. I've been with InterWorks since about two thousand, so I've been here quite a long time. I have gotten to, to meet several of our friends that are joining the webinar, and maybe there are some new friends we're gonna meet for the first time. So nice to meet you. A little bit more about InterWorks. We basically do these three things. We do data strategy, solutions, and support. So those are obviously big umbrellas, but the whole point of this is we are trying to help our customers through the entire data life cycle from envisioning what the future state could be to building that value and then continuing, and sustaining your growth and your ROI. A little bit more about us, we've been in business almost thirty years at this point. I think now seventy seven of the Fortune one hundred are InterWorks customers, so we've gotten to work with the most complex and most extensive massive amounts of data that you can imagine. But that doesn't mean we only work with the largest companies. We also work with, smaller businesses. We're really looking for people that are looking for long term relationships with their consultancy. We have a industry leading data blog, which gets our closer to four million page views per year. That is quite impressive considering we are talking and tackling all the nerdy topics like ETL, semantic layers, visual best practices, all the stuff you can imagine. We have thousands of clients across the world in every industry vertical. And a couple years ago, Forbes picked twenty five small giants, and we were very honored to be one of them. We are global, but we are still very much a local consultancy with a local actually, we have a local consultancy field. We have a few hundred employees, around three hundred or so worldwide, so we are able to tailor our approach very specifically to our customers. Speaking of our customers, we like to ask how we're doing, and we're happy to say across three different surveys, quality, service, and relationships, we scored a ninety eight percent on each of those. So, again, just more proof that our commitment is to your success. And finally, a little bit more on our solutions. I said we like to sort of help lift you up and support you through the entire data life cycle. This is what I mean. So if you think about strategy as being a great starting point and then building foundations of your platforms, how you think about and implement your governance policies, data architecture, and how you're setting up those platforms to make you successful to then add value through analytics, AI, and data science, growing and sustaining that momentum through your community, through the individual users, through enablement and training. And, of course, we can also help take the burden of application support off of you, which includes things like break fixes, uptime, patches, all that kind of stuff. So we do all of it or some of it. Again, we we build bespoke solutions based off of what you need us to help you with. So without any further ado, let's get started. So today, we're gonna be talking about five maturity frameworks. And this is a snapshot of a service that we offer called a DART, which stands for data analytics review and tactics. So we're gonna be doing a little sliver of this. It's much more extensive to actually deliver a full DART. It takes somewhere around a day worth of effort. We're gonna be trying to condense this into about forty minutes. Our five maturity frameworks that we think are essential for your success in in gathering ROI and doing great things with data are culture, analytics, data itself, governance, and platforms. These are foundational. And when you look at the sum of these platforms in your maturity and expertise, it gives us a really good idea of how ready you are for for productionalized a or sorry, AI solutions. And you you you can kind of see that we start from a business perspective. And as we go right across the screen, these get a little bit more technical, getting pretty technical. I should probably swap data and governance, but, they get a little bit more technical, which is exactly the kind of relationship that you're gonna have inside your organization as your business and your IT folks come together to create a center of excellence to make sure that you're achieving all the great things that are possible for your organization. Why is maturity within data and your data capabilities important? Well, quite frankly, it's efficiency. We can reduce costs, which means we're spending less money to do it. We can achieve more things with less tools, with less people, with less time, and with less money. That's always a positive thing. It's more governed and stable. So, again, less putting out fires means we can spend our time doing other things. And if there is a legitimate crisis, we can be more efficient in the way we respond and repair. When you're when you're talking to your c level folks, efficiency and risk mitigation and cost savings, that's where you wanna start. The next category is value creation or opportunity. And the opportunity is when you are mature, you can bring more users into your platform, which is the number one way to measure ROI, user adoption. You can solve more use cases, and you can solve them in more creative and expanded ways, which gives you more options to do more things. When we think about AI readiness, obviously, this is sort of building off of the previous two, but you have to be mature in in several things. You have to be, competent, and excellent at several things to do AI without an unacceptable amount of risk. We have several webinars that we've done on AI and how to be AI ready and how to introduce AI solutions. I would recommend going to the into works dot com website and looking through some of those. In those, we talk about your, what your organizational risk profile might be and what types of AI use cases are appropriate for you today. Obviously, the further we go in the future, the less risk there will be overall. Obviously, the biggest thing with AI is the opportunity that it, presents to us. So I'll give you some stats that I pulled out of the headlines. Fifty percent of the work today is able to be automated. That's probably gonna go up as we see more agentic AI being introduced across different workflows. Eighty eight percent of small business owners say automation enables them to compete with larger companies. It is a massive level of the playing field. Eighty five percent of data is unstructured, and you require AI to really help you get value out of that. These are sound files, videos, image scans, all that type of stuff. Sixty nine percent of material managerial tasks can be fully automated by the end of last year. Probably need to update that slide. Which means there's a bunch of stuff that we we are doing right now that we don't need to, and we could be focusing on other things. Certainly part of my everyday. And our projected spending for AI in twenty twenty four, we could probably somewhere towards the end of this, you could actually lock this number to an actual, five hundred billion USD. Billion with a b. That's a lot of money. So AI represents a significant transformation in the way we do business. Even more so, it is a significant transformation in the very culture, globally of how we think about work, learning, everything. Tremendous opportunity. So to unlock that opportunity for your organization, you need to be good at five key competencies, which is what we're just talking about. But let's go into a little bit more detail onto each one. So when I talk about culture, what I'm really talking about is how engaged is your community with data. This is the best way again, as I said, and I'll say this a thousand times. I have an r I have a an ROI webinar where we specifically talk about this for sixty minutes that you can go find. All of our webinars get published on our website, so you can go look at them after the fact whenever you like. Building a long lasting self sustaining engagement with your user community is the number one way to measure ROI. Think of it as a grassroots innovation factory of people that know the most about your business. That is a tremendous asset, probably only on par only only maybe equaled by the actual data you are collecting itself. I have seen organizations go from no culture and grow through a single person solo to an aspirational data culture to really inspirational, where people are getting they are building off of the ideas of other people, and there is a innovation and excitement that takes root. And it is transformational in terms of what you can do with your business. Very exciting. So some of the things that we cover when we talk about culture in-depth, and we have strategy services to really go into these, is how how are you using your BI tools and how does that intersect with the process and people that are doing it? That's your BI operating model. How are you how are you facilitating self-service, whether that's self-service analytics, maybe it's self self-service data discovery or data preparation or maybe even further up into data engineering. There are tools where you do not have to be a battle tested data engineer to really start putting together some of these pipelines. Use case discovery, how you're enabling the different cohorts in your community so that they can continue to improve skills and maybe even graduate to more technical cohorts, how you're supporting the request for assistance or for analytics. Again, how do you measure user adoption and how are you empowering your leadership, from the executives, and understanding what what what's actually happening in the engine room, to a step lower than that to the center of excellence and how you're getting and facilitating all the conversations of the different subject matter SMEs that need to contribute to that success. This is how we think about data culture. And there's more. This is just sort of wedding the whistle. By the way, if you do have any questions, we do have polls that we're gonna put up so that we'll get you guys to be the interactive part of this. But if you have questions that aren't related to the polls, which are very specific questions in each of these five frameworks, chuck them into the chat, chuck them into the q and a, and I'll hopefully have ten or so minutes, maybe five if I get a little bit verbose to come back around and see if I can help. And if I don't, I will email you after the fact and certainly follow-up and and give you your help there. Let's move on. Analytics. So analytics is technical. Culture and analytics, there is a big intersection because a lot of analytics can be done by your community. But when we're talking about analytics in this framework, we're really talking I guess the easiest way to say this is what kinds of questions can you ask and get answered, and can you productionalize those answers? So we start from we we don't have any insight into our business, Or maybe I can only look backwards. And for those folks that are on the in analytics inside, that's reporting. Or maybe I can have insight into why these things are happening. That's analytics. Maybe I can look ahead and do forecasting, predictive, prescriptive analytics. And that's when I start to really unlock what's really capable now in this sort of third wave of analytics. So some of the things that we think about when we talk about analytics and we have these conversations with your organizations, what types of tools are you using, what kind of tools is your community actually using, where does your organizational logic reside, is it in an Excel sheet, Is it in somebody's head? Is it in a workbook in Power BI? Is it in the semantic layer inside of your centralized data repository? What is the ratio of power users to your community members? How long does how long does your community have to wait before they get analytical insight? And how much automation are we doing to help people arrive at those? These are all sorts of things that we're thinking about when we're thinking about analytics. Hopefully, that clarifies between community and analytics because I know sometimes those definitions get blurred. Let's talk about data. Most important asset of any organization. I'm a huge believer that your reference architecture should start with unifying your data in a central repository, and only slightly less than that is everything you can do to own your semantics or your business logic, you should. Should. There is a historical trend that people moved away from that seems to be coming back where vendors are trying to do lock in with your semantic layer. I won't mention any names because some of them are our friends. But if you can keep your business logic in your control versus having it reside in a third party application or on the other side of an API, I would recommend you do everything to own your data. So we start from none. We go to foundational, intermediate, and advanced. What are the things that you're able to do? And the great thing with cloud native, data architecture and data platforms is that they can easily integrate expanding sets of features, containerized LLMs, RAG, search AI, those types of things into the platform without you having to do a lot of extra work to maintain the hardware, do the upgrades, do all the expansion. It's a very exciting time for data. So when when we think about this, we're thinking about what's your primary tooling. What are you using? Is it in the cloud? How are you moving data around? How are you transforming it? Where are you transforming it? Does it have to go back into the l ETL tool? Does it have to go somewhere else? Does it have to leave Australia? Can it happen inside of your data warehouse? What's your data operating model? In other ways is what is the process in which you productionalize your data products? What are the centralized data capabilities of your organization? What are the responsibilities back to your organization? Do you have distributed data capabilities perhaps by function or department, maybe by geography? What's the availability of your data? How are you using your data platform to better ensure risk, rather, security and minimize risk? What does your RBAC model look like? All of these things are important when we're thinking about data. Governance. What's funny about governance is that this is simultaneously sort of a dirty word. No one wants to pay for governance. Governance people are often hired as a checkbox. I'm sorry if there's any governance people on this call. I love you. I support you. I see you. But when an organization says, oh, we need governance. Hire somebody to do governance, and we'll consider it done, when in fact that is nowhere close to being accurate. Governance is an enterprise effort, and it takes a lot more than one person trying to assign metadata to fields to actually say you've got good governance. We, I wrote a white paper on the five top data challenges for enterprise organizations, And we wrote this white paper by querying everyone we could talk to. So whenever we went to, a conference or a convention or in our email drops to our customers or any prospects we could talk to would ask, what are your biggest data challenges? And we accumulated something like, I don't know, two hundred and fifty responses. And the number one answer by a margin of double what number two was, people struggle with data governance. We were surprised. We thought people would say, we wanna do AI. Or a a couple years ago, we can't keep good talent because people are it's so expensive now to hire data people. It was governance. And it makes sense when you think about the explosion of data and how big the data estate is getting. We have no idea really what's in there if you don't have good governance. You need observability. You need catalogs. You need all of those things to make sure you are wrangling date, your your data for the most opportunity, but also limiting the amount of landmines that are gonna explode. Oh my gosh. There's a data source in our lake that has PII information in it, and we didn't know it. Which is why when we measure your success with governance, we're talking unaware. I'm unaware of the problems I've got. I'm unaware of what people are doing in my organization for good or for bad. I'm aware. I'm aware, but I'm reactive, or I'm proactively monitoring and forcing, compliance. That's what we've talked about when we had this scale. Some of the things we talk about, again, I mentioned them. Catalog and discoverability, quality, observability. Do you have well defined roles and policies for each? What is the primary tooling you're using for governance? One thing about primary tooling in governance is there are a lot of tools that sit outside your workflow. They have to be integrated into your workflow. So if you can stage data and automatically start to feed into your catalog and assign metadata, that is a sign that your governance and your catalog and all the things that you're doing are gonna be intrinsically valuable versus we've done our processes. Now we go do governance over here in this corner. That is a sure way that you're gonna end up with shelfware. How aware of your governance policies is your organization? So that's change management. That's evangelism. That's making governance and community work together inside of your culture maturity framework. What are your team's capabilities in doing governance? Last and certainly not least, we have platforms, which is where all this stuff sits. The stability, the security, the resiliency of your infrastructure. Obviously, a lot of the stuff that you have is probably on cloud. But, again, just take a quick drink here. Tip. Whenever you're, presenting, have some tea nearby. It helps. If all of your stuff is on cloud, that does not mean that that the the battle is over. You still have to think about particular risks to your organization. Do you need greater than ninety nine point nine percent? Some organizations might say yes. For instance, if you are selling tickets to the Australian Open, you cannot have a blip in the middle of the first thirty minutes that those tickets go on sale. Or if you are running a network of hospitals and you've got primary care, employees dealing with actual real time, situations. They can't have a blip. So then you start to think about multi multi cloud, tenancy, all of the extra stuff, the layers of security that you're putting on cloud. Do you need a private VPN? Those types of things. Or if you're at the less complex scale, how are you speaking to cloud with your on prem things? There's a lot of things you can think about that you should think about. Most often people don't think about platforms until something bad happens, and then they go all in. Hopefully, you're not one of those organizations. Some of the things that we talk about when we talk about platforms, infrastructure, security, scalability. Can we grow without having to completely remake our environment or a lot of manual effort? Interoperability. Do these systems talk to these systems without a lot of work? Are we performing even under peak, even with high concurrency? Is are our platforms compliant with our governance policies and regulatory expectations? Are we innovating and making them better and more efficient, more cost effective? These are all things we think about. I think that's enough of a preamble. I think it's time to get into our little self assessment. So what we're gonna do is is I'm gonna ask you a question for each of these maturity frameworks. And and when we actually do this as a part of the DART, we ask ask way more questions than this. But for the sake of us doing this in a nice little functional forty five minute hour webinar and then being able to look at the results together, we're gonna keep it very simple. So what I'm gonna do is I'm gonna ask you a question. I'm gonna have it on the screen, and then I'm gonna share a poll. And so I'd love for all of you guys to click on your answer, and then I'll get the results back. I'll enter it into our little spreadsheet if everything technically goes as it's supposed to, and then we'll have a result at the end of these five questions to sort of give you an example of how this all works out. Of course, I'm not taking your response. I'm taking an aggregated average. So the the results could be quite interesting. Alright. Let's give it a go. So we're gonna start, with how we measure this. So I like to use this, and each one of our frameworks is gonna be mapped on an axis. So you can see culture is the axis going, from the middle to the top, and we'll work our way around in a little clockwise circle analytics data governance platforms. And as we score, we're gonna end up with this sort of spider web area chart that will give us a sense of how effective we are and how it leans into one or more frameworks or the other or the overall spread will give us a good sense of maturity. The good way of doing it this way obviously, there are way more complex ways you could do it. But doing it something simple like this is that you can come back in twelve months and do a very similar exercise, and your executives, your people that are not involved with data, can visually see the changes. So it is a great little exercise just for the, the communication of how you guys are maturing. Once you get to a certain threshold of the scores, remember the scores are from zero to three, I will award you a star out of five for your AI readiness. So we have to get two or better to get a full star, and we wanna try to get five stars by being excellent at all five. Great. Let's jump into our first question. I'm gonna read it, and then I'm gonna share a poll. Let me just make sure I got the poll ready. Great. Culture. So our first framework. Here's your question, and then I will share the actual poll. How would you best describe your analytics user community? A, we don't have one. B, it happens whenever they think to have a conversation. That could be around the water cooler. Very ad hoc. Or c, we bring in all of our users and we have a presentation that we talk to a lot of people all at once. And maybe that's quarterly, maybe it's monthly if you're ambitious. Or d, we do this by cohorts. So we've got data stewards over here, data engineers over here. We've got our business users over here, maybe our executives, our power users for analytics tool. I'm gonna launch this poll. So you should see that now. Great. We've got folks answering. This is great. Ad hoc coordination is taking a very strong lead. This feels like a horse race. This is fun. And it looks like it is sort of settled that we haven't had everybody give it an answer. So if we could just get a couple more responses. I will in the poll, I'll I'll summarize these all for you at the end so you may not get to see the results right away. Poll ended. Great. Let's go to our next question. Analytics. Here's your question. What type of analysis can you productionalize? Now this is important. Productionalize, meaning automated, performant, reliable. So we can't productionalize anything right now, or I can look backwards. What happened? We just made this sales in this quarter and this region last year. Why did it happen? Okay. Well, so we had these sales, and this is why. Or d, we know we can use our analytics to think what's gonna happen next or what we should do next. I'll share that poll with you. There you go. Oh, this is a tighter race. Alrighty. Couple more people responding. We had more responses on this one. Thank you very much for your participation. Alright. We'll end that one. Oh, we got another couple people that chimed in at the last second. Great. I'll end that one. Thank you very much. I would love to do an aggregate average, but instead, this is just giving me sort of a winner by percentage. So I'll just take the winner by percentage. Maybe afterwards, we'll take the overall scoring and figure out what the, average is. Great. Next question. Data. What tools are you using for ingestion and transformation? A, we're not really using any tools. We're just doing custom scripts or very uki yucky legacy ETL. B, open source with limited automation. This is your Python, for instance. C, modern, ELT or ETL tools with pipelines. Or d, fully automated modular with integrated workflows. I will share that all with you. There you go. We're seeing a bit more sophistication here. Awesome. Thank you. Couple more answers coming in. We have a draw. So if somebody else could just chuck their answer in there to help me break this draw. Otherwise, that might give us a half point one way or the other. We're doing a half point. Thank you. Okay. Next one. We are now on to governance. How is data quality monitored and improved within your organization? A, we don't. We we it ends up in reports and we're like, that number is not quite right, but we just sort of wing it. Or data qualities are data quality issues are addressed reactively. We see it show up in the report. Hey. Can you go fix that number? Because I know that that's not right. Or, c, data quality metrics are defined and tracked for critical data, critical data only, so not for the entire data estate. That also mean that your data quality metrics, are inside of your systems. Or, d, a proactive continuous data quality improvement program is in place. That means you probably have a dedicated tool to it. It also means that you're probably doing some form of master data management. I will launch that question for you so you guys can weigh in. This is most decisively so far, in terms of all of our responses. K. Alrighty. Let's move on to our last one. Platforms. How is data encrypted in storage, processing, and transmission? Some of our data is encrypted. Data at rest is encrypted. In the warehouse, in the lake, encrypted. But when we send stuff out and back, not all of it. Or, c, end to end encryption is in place for the data at rest and in transit. Or, d, encryption is fully automated, real time audit audited, and there is strict compliance and enforcement. And, obviously, if we do this with your organization and we we have more questions, we'll be able to go you may not say, well, what what do you mean by d or c or whatever? And and we're kind of a little bit of both. We can work those questions out. Obviously, we can't do that, in this environment, But there is your poll question. This one is a very tight race. Got some people that are deciding. Oh, somebody picked the best answer. Good for you. Okay. I'm gonna score this one, say, decimal because we are pretty divided. We'll go like that. Okay. I'm going to end the poll now. Great. Thank you for your participation in that. So now what we're gonna do is we are going to score this. So I've recorded the results. I will now go in and do this directly into my little deck here. And let's just cross our fingers that this works as seamlessly as when I practiced it. So there's that, that one. And once I finish, it'll all pop up on the screen at the same time. That that is how we scored. We almost came in bang on one point o, one point o, a little bit more capabilities with data, and, of course, we were talking specifically about ETL. Governance was pretty, strongly in that number one spot there, and platforms was, was one point three. Now if I were to look at this and I go, this is a a an an amalgamation of a lot of different people responding. So some of you might be around these marks on some of these questions and really strong on others. But because overall, we have ones to one point five, if I were to look at this, and read it, there's not anything that is particularly strong in one direction or another. I would say that this is an organization that is probably early on in their journey, and I would expect that where they would grow very quickly, very fast would be it could be one of two things. It could either be the culture grows really fast and that they found something that is cool and a lot of people start solving problems with data. And normally, that is how cultures, rather, that is how organizations grow in data. You've got a very progressive department with data like marketing or finance that starts solving problems, and they go find their own tools to do it. And as they start kicking goals, then the enterprise or IT says, you know what? We should probably bring this in and and start to help them and put some framework and support and guidance around it. And that's generally where you see analytics and data start to spring up. For more traditional organizations, so organizations that are, say, very Microsoft focused, you might find very strong platforms and very strong data and very strong analytics. Power BI is a great tool, but Power BI is also not the most user friendly. It can be complex. DAX is hard to use. Copilot, that's obviously helping that. So if you were to see a very low culture score but very high platforms analytics and data, for instance, I would I would probably guess that's a Power BI organization. Whereas if you had really high culture, really high analytics, and really high data, but maybe lower over here, then you could probably say this from a from an analytics perspective, this looks like a Tableau environment. You're gonna have a lot of really passionate users, and they can go off and use Tableau because there's if there are data sources of any variety, they can kinda just get going. But based off of what we see, we see do we do see a little bit stronger in data. Platforms is obviously a little bit more stable. But in terms of our AI readiness, you can see no, no, no, no, no. We're really not ready based off of this result before we could start thinking about productionalizing AI models. The thing we would wanna do first, based off of what we're looking at is I would say you'd wanna bring your governance up to speed to a two as quickly as you can because that's going to make your data secure, performant, and when you start to train these models, more far more predictable in what you're going to get. And, again, if these are external facing models like customer support or whatever, that is critical to avoid brand damage. The other thing I would say is it seems like your culture is either going to be Excel based or siloed, power users. So we wanna try to get more people understanding the value of analytics and then uplift our analytics tools to help empower them to do that. So you can kinda see how these things can be quite useful in drawing tactical assessments. It's a great way to start off any sort of strategy engagement so that you can say, listen. This is what we're seeing right now. Let's talk big picture. Let's talk vision and future state. Let's go implement that stuff. And in six months, twelve months, come back and do this again. And, hopefully, then you start to see, improvement in the areas that we're targeting. Hopefully, that was a useful exercise. What would we recommend for what might be next for you? Let me advance that screen there. Well, we've got some opportunities for you. There are three that fall in really nicely to this conversation. Obviously, I've been talking about the dart, and we're offering the dart, to any participant of this webinar for free. So we would do the exact exact same exercise, but guided with more like eight to ten questions per maturity framework, and we'd be bringing our SMEs to help really frame those questions so that there could be a deeper learning and understanding. We would then produce a a a full report based off of the DART to give you an assessment and next step recommendations. We're offering the DART for free. Next to that is the spec and select. So if you're thinking, you know what? This on prem SQL Server is not cutting it, or, gosh golly, these analytics tools that we're using, Excel, maybe aren't as sufficient for our needs, we can help you find what might be. So when we're doing a spec and select, we're looking at your needs, we're looking at costs, we're looking at your capabilities, the data sources you have, and then marrying the right product to what your organization would best be bit benefited by. And then the big piece here is the SVR. This is the full strategy, vision, and road map exercise that we do. It's basically two to three weeks of very, focused whiteboarding that we do across all of the different domains. We're talking people, processes, vision, data, infrastructure, all of it. And then from there, we come back with around fifty to sixty pages of strategic ideas, tactical fixes, and and we cover everything. Highly recommend that. It is amazing to come and visit our customers, and we've done this number of number of times, six months, twelve months, two years after we've done the SVR, and to see the success they're having and all the recommendations that we're like, we really think you guys should do this and coming back and they've done it. It is exceptionally fun to, to do. I do a lot of those personally. So this is what we're recommending. Again, remember that Dart is available and free. After this webinar, we'll be reaching out and sharing you ways for you guys to take advantage of that. If you want to get in contact with us, please scan this code here, and we'd certainly be love we would love to have a conversation on how we can help. It might just be an hour of spitballing. Obviously, we're not gonna charge you for that. We'll give you all the help we can, but we just wanna figure out how we can help. That is everything that I wanted to cover today. So great news. We've got ten or so minutes before I'm gonna let you go to answer any questions that you might have. So no one has submitted anything yet. Let me just check a look at the q and a and make sure I'm not misspeaking. Yeah. No one has submitted anything yet. So if you do have questions, chuck them in there, and I'm happy to answer them right here, live for everybody. And if you don't, well, I wish you well, and I'll see you guys next time. Question. Perfect. This is from David. Can you elaborate on your semantic layer and lock in comment? I can. Let's pick on, an older, an older company. Companies like SAP and other companies whose names start with s as well, they want to get you into their systems and then help you build your semantic layer or have you build their semantic layer using their proprietary tools so that if you wanted to abandon that product because they've raised the price or they've added seventy five more add on modules that you have to pay for, you have to recreate that logic from scratch in a new platform. Whereas if you take a tool like, say, Snowflake, all of the data sits on, you know, in s three buckets, blob storage, and everything else is just SQL. And and Snowflake and Databricks and companies like that have even gone further to try to make it to where there is more flexibility on what tool you use for what workflow or or for what process. So there is iceberg tables, and all this sort of stuff where you can get that data out, and you're less tied in for life, into that particular platform. So I would say own your semantic layer for that reason. And when you look at these organizations, like SAP and some others, you'll notice that their retention rate of their customers is in the high nineties. A lot of that might be because people are having great value and good for them. It is also because it is very difficult to get people out of those platforms. And we do that stuff professionally, and Torex does. And I'm telling you, they are very, very time intensive. That's what I mean. Hopefully, David, that, that made sense. Any other questions? Great. Rowena, our results for governance was a one point five. What do you recommend further developing to bring your organization to a two or a two point five? I'm gonna scroll back to the governance description in our deck here. Thank you for that question. Right here. So, what we wanna do governance is really about making sure, we are minimizing our risk, and that risk could be we have data that's not being used to its full advantage. It could be that there is data that we should have deleted and not retained. It could be that we have data that is not secure, or we have policies and procedures that we have not effectively communicated to our organization or our report builders. It could be that we don't have catalogs that people don't know what's actually usable. They don't know where to look, so there's a lot of wasted time. These are the types of things that I would recommend doing to help improve your governance score. Again, you might be really good at some of these, and you may not have thought about some of the others. So, specifically, I'd really wanna talk to you about it. But, for instance, if you are bringing in data to your centralized data platform, and as a result of that staging, you can automatically build a data catalog, and then you're using AI that's been trained to generate metadata for that catalog, And then you could start to use all of that to then start to do data quality checks that is in the workflow of data and ETL and self-service, which means that your that aspect of governance becomes far more a part of the assembly line. It becomes far more a part of the daily routine versus, alright, everybody. Hold your breath. We gotta go do governance for an hour, and then we can get back to doing stuff. That's the easiest way to make governance intuitive and beneficial versus a chore that people get to at the very last. Hopefully, Roona, that answered your question. If you wanna talk more about governance, happy to reach out to one of our sales folks. When they reach out to you, just let me know, and we're happy to jump on a call. These are great questions. Let's see if we got time for one more. Any other questions? I'm looking at both the chat or the q and a. Still have a little bit of time. Otherwise, you can always reach out after the webinar. You can find me on LinkedIn. We'll reach out after this. Again, remember that this webinar is recorded. So if you wanna share this with other folks, the link will be up in a couple of days. I have stalled long enough. I think we are done with questions. So thank you so much for joining today. I hope you found this useful, and we will see you guys next time. Thank you.