Alrighty, let's get going. We're gonna talk today, from a strategy perspective, we're not gonna focus on tools. We're gonna be speaking more at a business value perspective on five ways AI is changing data governance. So let's just jump in. So I'm Robert Curtis. I'm the managing director for Interworks looking after Asia Pacific. I do most of my time now on strategy, delivery leadership, thought leadership, and governance, consulting. I've been with Interworks for about eighteen years, somewhere in that number. I can't remember a time when I didn't work for Interworks or whatever that means. I've been here for a long time, and I'm based out of Melbourne. So for all of those new faces, it's nice to meet you for all of our returning friends. It's great to see you again. Just a little bit about Interworks first. We are a data consultancy and we focus on data strategy, data solutions, and supporting your your data platforms or your data, solutions. We've been doing this for more, I guess, almost thirty years or thirty years on the dot. And we are global. We are all over the place. I look after Asia Pacific and so we are all over Australia. We have offices in almost every capital city in Australia. A little bit more about what we do. If you think about data as a life cycle, so we do strategy coming down to building platforms, governance, policies, frameworks, implementation, your data, getting that prepared from an architecture engineering perspective, performance perspective. Coming down to adding value in solving problems that could be analytics, AI solutions, data science, and then sustaining and helping you grow as those things are put into place. And that could be helping you support applications or processes, building up your user community or training individual users to get the most out of the solutions and tools that you've added. We do all of that. So we're an end to end data solutions provider. Yeah. So we are at twenty nine years experience, so more than twenty seven. Apparently, I need to update this slide. We have a lot of experience focusing on data and analytics exclusively. Seventy five of the Fortune one hundred around the world are Intoworks customers. We get to work with some of the largest, most complex datasets with some of the largest, most interesting clients all the way down to the SMB folks. We're not looking for a particular type of customer. We're looking for a type of customer, Meaning, they're looking for somebody that they can partner with on the long term. We have an industry leading blog. If you've not gone to interworks dot com, I highly recommend that you do for those folks that do. I get a lot of rave reviews about all of the content that we put out there. Three point five and trending up million paid views every year about data and analytics. You can imagine we've got thousands of articles out there from everything from data to analytics to ETL to last mile data prep governance, how to do permissions on your platform, all kinds of stuff. And we have eight thousand customers across every industry vertical that you can imagine of every size. I think in twenty twenty or twenty nineteen, somewhere in there, Forbes listed a twenty five small giants or smaller companies that punch way above their weight. And as you can see from the credentials above, are certainly one of those and we were honored when Forbes officially recognizes us that. Alright. Let's get started. So, let's kick this off like this. I wanna spend a little bit of time talking about data governance, and you'll you'll understand why as we sort of work way through this, because I think a lot of people think of data governance as a lot of different things. And then and the thing is, it's tricky about this is they're probably all correct. And data governance has become way more of a conversation recently. And we'll talk about that a lot more detail as well. We did a survey over the course of a couple years. And I published the results of those surveys into a white paper. And what the survey was was asking folks, data leaders just like yourselves, what are your biggest data challenges? And we thought we knew what the answers would be because we talk to customers all the time. But we were surprised. The answers were not what we thought they'd be. Just a little throwaway here. We were asking people we weren't we didn't do any sort of formalized survey with any sorts of, you know, governance around it. So take it for whatever it is. There's a lot of conversations and and questions and surveys and that kind of stuff that we aggregated into a position paper. But the number one thing that we found. Well, these are these are the bottom seven. So user enablement, people worried about getting their users up to speed. Tool selection, are we finding the right tools? By the way, we have services to help you do that, regardless of whether it's analytics, data governance, automation, and getting the most out of, the technology that can make you more efficient, users adopting your tools, building a data strategy and a vision of where you want to go, resourcing people that was far more of a problem, right around the great resignation and COVID than it is today. But the number one thing by a margin of almost double whoops that way is governance. And we were shocked when we got that result. But overwhelmingly, people were having stress about governing their data. And so we started to ask, what are the unique trends that are making governance go to the very top of the list? Not just the top of the list, but overwhelmingly the top of the list. Let's start by understanding what data governance is. It probably helps us to build a bit of a working definition. But here's the problem. Data governance is actually an umbrella of a lot of different things. And you might think some of these things are in data governance, and you might think of these some of the areas in data governance as belonging to something else. I spent a fair bit of time trying to find a neat little graphic that would be sort of an industry standard for me to say this is data governance. I couldn't. I found a lot of different iterations. So I took one, and recreated it here for you that had, that popped up in a couple different books. And I think the thing that was the tiebreaker for me was the idiot's guide to data governance. It was in there as well as some other places. I don't know if I would draw it this way, but let's use this as sort of a shared definition. So here's data governance. What is actually a part of it? So we've got data quality. We've got architecture. This is one of the areas I'd say, maybe it influences architecture, but architecture is probably its own thing. Data modeling and design, data storage and operations. So retention policy, deletion policy, that kind of stuff. Data security, that is obviously governance. Data integration and interoperability, how do we govern the way those, different tools and systems work together? Documents and contents, this is probably where your lineage and data cataloging goes. Reference and master data, making sure that if data changes in one place, and that's the most important place, that it reflects through all the other data sources where people might discover it. Your data warehouse and BI as well as metadata. So depending on your viewpoint, that might be everything that you think is data governance. Or like me, you might say there are some that are very strongly data governance, and then there are some that sit next to it. But I'm trying to illustrate a point that data governance is a bit of a nebulous concept, but we all know kind of by the smell test whether this is governance or whether this is not. What's interesting about this is that it's gotten way more important more recently. But we still don't have a definition, so that's my attempt to try to consolidate all of these into this little statement. The point of data governance is to create data or data assets that are trustworthy, consistent, compliant, appropriate, and valuable. And so if data governance is a umbrella of different activities and skill sets and technical domains, that's the goal of what we're trying to do with it. Now, data governance is quite hard. According to Gartner, and this was a twenty twenty four survey study that they released data governance projects and initiatives are very difficult. The rate of failure is extremely high. It's at eighty percent. So one out of five projects is deemed a success. That is not too different than data projects generally. Data projects generally are also around that mark. And so you would imagine that there's probably a compounding effect. If a data project is hard, governing that data or building an initiative that you can measure as a success is even trickier. But the base of it is, is that data governance is quite hard. It's hard to define and it's hard to be good at it. But as we saw in that survey, it's no longer it's okay to fail at data governance because it's not super important. It's the nice to have extra bits that we do and we're not super busy trying to drive value. That's no longer true. Data governance has gone from eventually to essential. And we see that in the survey, people are stressing about it. But why? AI. AI is driving the urgency. But the cool thing about this is that AI is also help solving the problem. So when we talk about five ways AI is driving data governance, we're going to look into those five ways right now, we're really talking about two factors. And for those folks that have worked with me or heard me speak on anything, I use these two factors quite a bit. And this is this push pull dynamic of change. And you could use the same methodology of pushing and pulling in terms of building buy in for an initiative or selling an idea. It's certainly something that is at the forefront of what I do every day. If I build a strategy or I build a governance framework or whatever, I'm using a push pull dynamic to convince the different types of cohorts that they need to they need to listen to this, they need to action it. And those push pull dynamics are this, Quite simply, a push dynamic is saying you can't stay here because something bad is going to happen. I'm going to push you off this spot. And a push dynamic is risk mitigation. You can't stay here because if you stay here, something bad will happen. So we need to move to mitigate risk. Now overwhelmingly, if you are selling upwards to the sea level, that is the best way to get your project prioritized. If we do nothing, something bad will happen. On the flip side, a pull dynamic is come over here. I'm pulling you to something because there's good stuff over here. That's value creation. And so if you can do a push pull dynamic in any of your ideas, whether it's strategy or a use case you're trying to solve, that is the most compelling thing. We need to change, and we're gonna change over here in a way that's gonna be beneficial. So we're mitigating risk, and we're creating value. That's the most powerful way you can do it. AI is having a push dynamic. You can't stay here, but it also is giving us a pull dynamic. So we're gonna break these down into two pushes and three pulls. So here's our push dynamic. First, let's talk about the data estate. The data estate is all of the data you have available to you in your organization. And that that might be augmented if you've got external data that is useful to you that you are bringing into your data estate. I went and looked, and there's studies on this, on how much of the data estate people actually use. It's nowhere close to one hundred percent. And if you think about data lakes and data warehouses, you already intuitively know this. There is some data that we just drop into blob storage or S3 buckets and that data just stays there. We'll call that cold storage. And then there's data that we actually work on to make more usable. It's modeled, it's curated, our users can actually start doing stuff with it. That stuff is what goes into our data warehouse. And the whole idea of a lake house, a data lake is sort of the combination of how dynamic you can make those two elements on cloud platforms. But it takes time and energy to, quote, unquote, warm up data so that it's it's servable. Think of it just like a restaurant. Your data lake is your freezer, and your warehouse is the hot plates that are ready to go to your customers. That's data that is ready to be used. So in terms of the percentage of the data estate that most organizations use, on average, it's about thirty two percent. And that's from a study by MarketLogic. From my experience, it's less than that. Most of the customers I talk to, it's probably closer to ten, ten ish or fifteen percent in terms of all the data that they get from their websites, their ERPs, their CRMs, the transactional stuff, the event logs, all of that kind of stuff. So we're only looking at a very small percentage of data that we actually are using. And that number is only the the data itself is only getting bigger all of the time, which means that percentage is only gonna get smaller if we don't do exceptional efforts to increase it. Here's a fun stat that you can drop at parties. Ninety percent of all data that is out there today was generated in just the last two years. That is the exponential rate that data is being generated. Is all of that data super useful? No, but there is certainly data out there in your data estate that would that would either one create a lot of value for you a pool or it is a potential landmine of something bad that could happen. Push. So there's risk and there's value in the broader data estate. Maybe there's PII information you haven't classified that's sitting out there. Maybe there's some useful insight to your business that will make you more efficient or unlock new revenue. We don't know cause it's in the lake. It's in the cold storage. The challenge that we've got is that creating curated usable data sources with that business logic in there that's got all the good data quality that's de duped and all the things that you have to do is a very time intensive exercise. Most of the time, data engineers are some of the busiest people. And if you're part of a centralized IT team, those data engineers are likely doing non data engineering things just to keep the lights on, and they never get a chance to build products that then can extend everybody's capabilities as much as they are fighting fires. This is one of the reasons why the data estate is so small. And again, I would say for the customers that we get to talk to, it's smaller than thirty two percent. So in the past, data governance was done simultaneously with data engineering. So data source by data source, again, very human effort. We're going to classify this, we're going to put metadata on it, we're going to put it into a catalog, we're going to make sure that it's secure, and we're just going to hope everything else in the data state is pretty safe. Inefficient, but so long as we only needed to have our users as long as our own our users could only see this thirty two percent, it was manageable. And the risk that was out there was sort of locked in ice and we didn't have to worry about it exploding. So that bit could be considered quote unquote governed. And even today, I see people at varying levels of success governing only that small portion. Here's the problem. LLMs, chatbots, and other AI solutions don't need to just look at that thirty two percent. They actually can leverage way more stuff, and they can do it way faster and more effectively than a human being can, which means larger parts of the data state now become part of the concern, far more than what we can curate. So if AI then gets into doing all the great things that it can do, it then makes everything potentially inside. So we could be at a hundred percent, probably not, but you very well could be depending on your business and your data and what you wanna use AI to do for you. So that then unlocks the frustrations and fears that the data leaders were saying to us. Data governance is my number one problem. We've got so much data and it's growing that I don't know what's gonna explode. I don't know what boat we're missing out on. There could be another revolution in my industry that I'm not seeing because I can't see all my data. It gets worse. I promise eventually I'll get to the the pool dynamics and then we'll have, you know, rah rah backslapping and be really happy about things. But we have to go through the worst bit first. And this is point number two, RAG. So if this is your data state and use thirty two percent of it and your data state is increasing all of the while, Retrieval augmented generation, it's an AI technique that can go outside of your environment and go look at other data and pull it into your models and then extend, expand, improve the responses that you get. We as an organization use chatbots and perplexity and things like that to make our jobs easier, make our workflows easier, and it goes to the Internet and goes and gets goes and gets answers. But AI can also look at other things that are sort of that would be considered standard data. And of course, here I'm talking about unstructured data. It can look at PDFs and pictures and videos and sound files. And again, that's something that probably isn't even on your radar for most folks to think about how do we govern that. So let's re envision exactly what this data state looks like in the context of everything else. It's actually quite small when you start to think about all the unstructured data that we might have in Box or the other file storage systems or content management systems that you have. All this unstructured data or might be actual data that's just sitting out there. Because remember, we're a for as much as we try to do with Snowflake or Databricks, whatever is using, there's a vast amount of Excel desktop level data. AI has the potential to see that and has the potential to use that. But wait, we're also talking about the Internet. And there are trillions of gigabytes of information out there. And if you use RAG, and I'm telling you it's valuable, it's worthwhile, it's something that you should be thinking about, you've got to think about how does AI use that in a way that we can govern it. That's govern it for our internal use so that we're making sure our business users are getting the best responses. We're using it in a way that is ethical but even more important, oh my gosh, if we're using this externally we've got to be a hundred times more careful. And this type of thing, Rag, is available on all the major data platforms, Snowflake, Databricks, BigQuery, and more. It's very, very powerful. It's very, very useful. But governance becomes that much more difficult. It becomes that much more essential. That's the bad news. This is why governance is now at the top of our list of people's heartaches and headaches. We've got to solve governance. Oh my gosh, we've got to do something. The good news is that there are three different things that are pulling you guys forward. AI is actually making this a little bit easier. Let's start with ELT. So ELT is different than ETL. Elt is extract load and then transform, whereas sort of the old school extract transform and then load meant that there was this middle step. And from a security, from an interoperability standpoint, oh, the data has to go somewhere. So from our source system to our destination, it goes someplace else. And then we do all of our transformation there. Well, with cloud computing, cloud platforms, it's best to just get it straight in there and then leverage the versatility and scale of the cloud to do your transform. So that's why ELT has become the acronym, for the modern app because everything is going to cloud Databricks, BigQuery, Snowflake. The challenge is is that we want to also start to do things to help us understand what data we're pulling in. So if we're going from a lake to a data warehouse, I'd like to get some governance put in there. And often cataloging the data and all the stuff that you do as a part of cataloging, classification, profiling, all that stuff was a separate workflow. It's not it's not E L C T, or EC LT. Cataloging is its own little effort. And most organizations, particularly of a certain scale, have data architects that sit next to the data engineers. And then over here in a different group, they have governance, which is more attached to end users, enablement, and that kind of stuff. The teams don't even sit together. That is a massive problem. That is why cataloging and all the other sorts of data governance stuff that would happen as a part of an ETL process often or ELT process often happen in different work streams or don't happen at all. I know one organization that had something across all their datasets and all their fields, something around the line of forty thousand different fields across three different warehouses. And their approach was and this is an atypical, I'm not picking on anybody, was to try to go and write human definitions field by field by field to try to start with the most important data sources and then work their way down. That is like counting sand on a beach. Because you can imagine by the time you even get halfway through, there's gonna be more fields, there's gonna be more data, and the way they're using those fields have all changed. So you're never actually going to get through. The good news is is let's go here. Is that there are tools where you can build your catalog as a part of load. So as you are ingesting as you are doing ingestion, you can automatically use AI to start building a data catalog. This happens before transformation. So that means all the work that individual users, your data engineers are traditionally doing, it doesn't stop you building a catalog. You don't have to wait until it's perfect. You can actually catalog as it comes in. So you go a separate workflow down here to cataloging, and then the transforming data continues on as it would. This is extremely powerful. There are tools that do this. I think Informatica does it the best as sort of a loaded system. And then they're able to use AI to learn all the things that you are trying to do with your catalog, as well as your data pipelines, by the way, and then use machine learning to try to help you do it next time so that there isn't as much human intervention and AI can start to do these things. Are other tools that I think do this, but from an end to end solution that includes ingestion, transformation, integration, and all the data governance, I think I think Informatica, the IDMC and cloud version of Informatica does this the best. It they've I know Informatica has a brand of being sort of your grandfather's ETL tool. They have completely rebranded and rebuilt their platform, so it's worth looking at. But we're not here to talk too much about specific tools. Let's go to the next pool. So AI is helping us automatically build cataloging and data lineage and profiling classification as a part of the ELT process. Great. Thank you, AI. That makes my life a little bit easier. We can also do automation inside of governance. So again, this was the diagram that we had before of all the different things that AI that, that fall under the umbrella of data governance. What's great here is that AI can augment all of these. Now I would say there are different levels of maturity, and so the examples I'm gonna talk through here, really quick. I'm back. My headset dies if I don't hear any sound. It's one of those unusual features that they built into my little headset here, so it died there. But I'm back. So I'm gonna go through a couple of examples of of stronger maturity, but AI is augmenting the workflow inside of the tools that the people are doing with all of these things. So if you're on Snowflake or Databricks, or you're in Informatica, or you're in Matillion, or Fivetran, or whatever, they're building AI workflows and augmentation inside of those platforms, copilot type augmentations to make your job easier, and then to predictively figure out what you're trying to do and then help you do it next time with less intervention. All of these things are being augmented right now. That's that is a separate version of AI that you building up a your own LLM to solve some business problem. All your vendors are trying to solve problems to make your job easier. So let's go into some examples. So data quality. AI can continually monitor for errors, inconsistencies, duplications, and can auto correct them in the trigger alerts when it does find them. That's available today versus you having to do it as part of building a pipeline. Metadata, this is something that's super powerful and super useful, particularly if you want your data to be discoverable and tagged and classified. With training, AI engines can generate an update and normalize your metadata from source, lineage, ownership, and more in real time. Very powerful. Something that we have had to do in the past manually and has never been done or maintained. This opens up a whole new world in terms of what metadata can actually mean for how you use business, particularly from a self-service standpoint. Reference and master data. AI can use fuzzy logic and pattern recognition to accurately classify tag and group master and reference data, as well as help you maintain that data. So if you've got a data source that has these three fields, and that's the master data field, but other, data sources around it leverage that field. If something changes, we've got to make sure it's up changed in the master and the master data source. AI can help you manage that. Documents and contents, again, we're talking about, lineage and cataloging and that kind of stuff. This is super powerful when you start to think about how big your data lake is. It can help you identify, classify, and tag data based off of content and context. Or in other words, if you are worried that you've got PII information or data that needs to be protected because of regulatory compliance standards because of your business or industry vertical, AI can help you look for things that look like that data and identify them based off of a scoring, then you can go and deal with it directly. Very powerful, very useful. And from all of the folks that end up in the headlines because they're they bought a breach or misuse of data or misuse of customer information. This is stuff that can spend you can spend thousands of dollars on to save you millions as well as headlines. So there's a lot more that you can do in terms of the automation of governance. The other thing that's really exciting here is not just automating, but making governance intelligent. AI can make data governance proactive and smart. So it's not just us putting rules in place to automate, which on its own merits is super powerful, super useful, super efficient. But we could actually make data governance intelligent. So again, we are we talked about continuous data quality integrity checks. It looking for things that need to be fixed, potentially alerting you or even better, just going off and fixing them based off of the training that we've done for it. Predictive risk identification. Hey, this thing doesn't match everything else. I'm identifying it. Take a look at it. And that could be from logins, could be from RPO disaster recovery type frames, it could be data quality, it could be in a lot of different venues. Automated policy enforcement could be RBAC, it could be a lot of different ways that we're sort of making sure people are participating and are protected within the automation that we're building into permissions and assets and things like that. And advanced data data classification and protection. Again, let's let AI take the the the word or the workload off of us and propose classifications and protection. And then this is probably the most exciting one, I would say we're every use case is probably not at maturity for this one, but anticipatory anticipatory action and sentinel monitoring. So AI sensing that there's a problem AI sensing that there's a next step to do, and it will it will flag it and then potentially even go do it. Again, audit logs, records of everything that's happening. You want visibility and all these things so that you can understand what's happening. And then again, every model, every AI, you have to go through that machine learning process where you are training, validating, maintaining. So a lot of crazy stuff that's that twelve months ago seemed like science fiction. That stuff is available today. That stuff is available on lots of different tools today. One of the things that we do when we are sourcing tools and vendors that we want to partner with is we're looking at exactly this type of stuff. Is it best of breed in its usability, its cost, its efficiency, its performance, and its future roadmap with AI, and the roadmap and feature functionality that it's got today. I already mentioned this, but it's worth calling out again. If you are looking at legacy tools or you're thinking about doing a transformation to really uplift the value of your data tools and your data and your business, we help people do that every day. I would love to work with you to see if we can help you as well. So in terms of a summary, we have a eighty percent governance failure rate. And in the past, data governance failed so often because it was seen as optional. In the old days, we only used the data state that we actually curated and built data pipelines for and got ready for dashboarding. So all the other stuff was just locked down in the vault. Yeah. There was probably bad stuff in there. Yeah. There was probably good stuff in there, but no one was using it, it doesn't matter. That's not true anymore with AI. It was a human effort. And again, just that story of forty thousand fields that we're trying to manually define because, oh, this field, that field data stewards are having to do it, at least then we're delegating it out. But again, it's a human being that's trying to contextualize this versus allowing an AI to give it a first crack at human beings improving it. And again, it was outside of the data workflow, we bring data in, we transform it, and then load it or in the more cloud centric, process, we extract data, we load it, and then we transform it in our cloud. Governance was not a part of that workflow traditionally, it can be now. And, of course, as we grow, grow, grow, grow, grow the data estate and our interest in data, external data that we are doing through sharing platforms or data that we're sharing with other people, the Internet, unstructured data, it becomes prohibited to try to do this at scale, particularly when it's manual. AI has changed everything. AI makes this more achievable. AI makes this more efficient, and AI unlocks more value. So again, that push pull dynamic. So hopefully this has given you guys some ideas. It's given you guys maybe some pause for consideration. How are we doing governance? Are we doing everything we can to get the best value out of our data? And are we protecting ourselves from the risk, that data might present, particularly if we add AI solutions to it? And AI is inevitable in terms of how it's going to be implemented in your organization. There's just too much value in it otherwise. The good news is we can help. I'm gonna talk about two different things here. And hopefully, you find both of them something that's that's pretty useful. The tricky bit about building a data governance framework is that it is bespoke. We have a lot of folks that think if they buy a governance tool, they have a framework. And that the governance vendor, the people that are selling you the tool, will come in and say, this is how you need to do do you need to do governance. They can't because it's so specific to your business that they can't tell you how to do data governance. They can tell you how best to use your tool to operationalize and automate your data governance framework, but it's very difficult for people that don't have a strategy view to tell you how to do your governance. That's where we come in. We do strategy. We do data governance frameworks. We'll go through the steps to talk to stakeholders from the users, from your executives, to the management, to your technical teams, data, platforms, all those folks, and get an understanding of goals and objectives, an inventory, what's actually in your data state and who should be owning it. Then we can build policies and author standards. Then we can assign those roles and structures to the people in your organization. So there's accountability. Without accountability, there is no governance. Then they will build plans to implement. That might be policy. That might be evangelism. It might be change management. It might be the tools themselves. We then go down to the users and build plans on how we're going to train and engage your users to buy in. Most people see governance as this cumbersome process that prevents them from doing good things. We want them to understand it as something that is empowering because now you can do things without fear of breaking something or getting yourself in trouble because this framework is there to protect you. And then, of course, how do we monitor it, make sure it's effective, make sure that it's being adopted, and how do we improve it? The one thing we know about data and data technology is that every six months, it gets it's gonna get changed. It's gonna get flipped on its head. I saw some reports that, artificial general intelligence, the big, big bang of AI, is two to three years away. And then after that, who knows? Artificial superintelligence, when is that gonna come? Our industry, our data industry is changing rapidly. And so you have to have an organic document. It cannot be written in stone like the ten commandments. It has to be a living document. And so we'll help you how to figure out how to monitor and iterate. So we'll do all of these steps to get with you collaboratively to make sure you're getting the best version of a governance framework. And again, this is a business strategy document that then gets applied to technology, to data, and to the systems and applications that use it. We'd love to talk a bit with you about how we can help you do this. The second thing is something that we're gonna give away for free, and this is what we call a DART or a data and analytics review and tactics. Now it's not just data and analytics. But again, when I say how are you doing your data? I mean a lot of different things. Plus, we have to think of a clever acronym. I'll give Kathy McGregor all the credit for the DART acronym. But what this does involve is a lot of different things. And we want to give it to you for free as part of our commitment to helping you guys on your data journey. So what a DART is, is it's really an assessment of how well you're doing across five maturity frameworks. That's culture, like your community of practice, your end users. That's the analytics tools. What are your capabilities? Can you do, visual best practices? Can you do predictive prescriptive analytics? Your data. So again, we're talking about the warehouse, the platform. We're talking about ETL pipelines, all that kind of stuff. Your data governance, which is what we talked about today. How are you protecting your data? How are you making it trustworthy, valuable, appropriate? That definition that we used at the very start of this. And then your platforms, or in other words, infrastructure. Are you resilient? Are you performant? What what is your strategy cloud versus on prem? We go through a survey on all of these, and then we're able to give you a score, a visual score, based off of how you performed. Each of these we rate on a scale of zero to three. And then we we sort of charted on a spider web graph. And based off of those results, we're also able to give you an indication of how ready you are to productionalize AI. And that's important. Productionalize AI. We don't want you Yes, you could do an AI solution through tremendous effort and then get it out there. But you don't have the data that's going to grow with it, because the data is not governed, the data is not structured, the data is not modeled, Or we built something with AI, but because we don't have strong culture, we're not actually solving a problem. We're just doing it because it's a buzzword. And now that we've launched it, no one's using it. You have to have all of these things at some level of maturity to do AI effectively. And what's great is doing them well is in itself a benefit. So we'll work with you guys to figure out what those look like and then give you guys tactics that you could achieve over a three, six, and twelve month framework to improve those. We've done these quite a bit. We love doing them. It is a great conversation piece. We'd love to help you too. So if you have any questions from today or you'd like to contact us about your governance, your data, your analytics, what you wanna do with AI, or you'd like to take advantage of the DART, there's a QR there's a what are these? A QR code? Blanking. Scan that thing, and that'll take you to a contact us page. Otherwise, you can just respond back to to us. I'm sure we've been in contact. We'll be sending out the the recording of this webinar within a couple of days of of today, so you can simply reply back to that if you like. Hopefully, you found this session useful, and hopefully, there's some things that have got you thinking about what you guys wanna do with your data strategy going forward. Again, certainly love to hear from you. If you've got other ideas for things you'd like us to talk about in future webinars, I'd love to hear that too. Alright. Well, thank you so much. And again, reach out. We're here to help. Thank you so much.