Alrighty. So let's get going. Thank you for everyone that has registered and attended. I see a lot of familiar names, in the, attendees list. So, nice to see you all again, and I see some new names. So, nice to meet you. We'll get into the introduction part of this in just a second. If for whatever reason you've want to review some of the things we talk about or you've got some colleagues that you like to share this with, we will put a post or a recording of this presentation on the interworks dot com website. There was a USA version, and then this obviously is one for Asia Pacific, which is a little different. So just give us maybe two to three days business days, and then we'll get that posted on the website. We will send out an email so that you'll have the link. So if you wanna share it or review it for whatever reason you wanna have that in your library of content, it will certainly be yours to do with as you wish. So let's get going. We are gonna talk about data strategy myths and realities. What is effective and what is not? There's a lot out there in terms of what you should be focusing on. Some of it is going to bear fruit and some of it might might waste your time. So let's jump in. We're gonna start with a little introduction. I'm Robert Curtis. I'm the managing director for InterWorks looking after Asia Pacific. I'm based here in Melbourne. I've been working with InterWorks maybe seventeen years. And once you start to get into the double digits, it all starts to run together, but a very long time. So it's been a pleasure, working with, the great team here at InterWorks as well as all the customers and partners that we get to work with. A little bit more about InterWorks. We basically do three things when it comes to data. We do strategy, which is exactly what we're gonna talk about today. We are gonna be talking about, solutions to some degree, but we certainly help build data pipelines, governance, data warehouses, analytics, data science, the full gamut of things that you can do with your data. We help build those with our customers. And then after we get those things built, we help continue to support you. That might be supporting the application, supporting the solution, or helping you build a user community that can take full advantage of the great things that you have, that you have built. And some of those things that we build, this is a bit of a wheel of all the things we do. The stuff in green is sort of your user facing type things, and the things in purple are the things we kinda do behind the scenes with IT. That is self-service analytics. That is AI. That is building data apps. And all of that is centered around sort of those concentric gray circles, which starts with unified data, getting your data in a usable state in a single platform that power all of these great ideas and initiatives you might have. Protecting that with governance and culture, aiming it and directing it with strategy, and then building support and training for the entire organization to maximize all the things that you're going to do. So, really, we do end to end data. We've been in business, InterWorks, for more than twenty seven years experience. What would that be now? Jeez. We're almost going on twenty nine years experience. Oh my gosh. Time flies when you're having fun. From a very small little startup, we have grown to be a big, big player in the data space of seventy five, maybe even seventy seven now of the Fortune one hundred, our InterWorks customers across analytics or data or, infrastructure and platforms. We generate, a lot of interest in our industry leading analytics and data blogs, something like three and a half million page views per year. So some of you have had, access and read some of those articles. I would recommend for those that haven't, go give that a look. You can find all sorts of how to articles, strategy conversations, what's happening in the industry, what that means to you, security risks, all sorts of stuff. And we have a lot of clients around the world in almost every industry vertical. One of the things we've gotten a lot of awards and honors over the almost thirty years we've been in business, but one of the ones that we're particularly proud of is Forbes, a couple years ago named twenty five small giants that, despite their size, was significantly more impactful than you might expect, and we were one of those. InterWorks was one of those. So we were really, really proud of that. We're also really proud of some of the amazing clients we get to work with. Some of these are folks that are here in Australia or more broadly across Asia Pacific. So those are the folks that I get to work with, and some of these are some of the names that we like to share from other places like the United States or Europe. We're a global organization, InterWorks. And we like to work with a lot of amazing technologies, and you can see a sampling there. Snowflake in particular is paramount to our, customer success stories, and so we use that quite a bit. Okay. Enough of the introductions. I think we've got a whole bunch of people that have joined us. I've been kinda talking a little bit about who we are, so now we can get into the meat of this. Facts and myths. Let's get started. The first thing we're gonna talk about, in our presentation today is really the role of data strategy. We probably have a very diverse group of people that have joined this call. Some of you might be folks that are data architects for data strategies forefront in your everyday consideration. Some of you might be data engineers that are just new into the industry. Some of you might be executives. Some of you might be in analytics. Some of you might be some platforms people that are looking after infrastructure. We're gonna try to zero in on exactly what data strategy is so that we have a working, definition that we can all access. Then we're gonna go into submits, and these are things that we commonly see as consultants, as thought leaders, and as your strategic partner that companies are doing that maybe aren't generating the return that they're hoping. Then we're gonna talk about some things that we do know that really, really work well, and then we're gonna summarize all this from lessons learned. I've also got a little, cool next step for you, for those folks that are interested. So let's jump in. We're gonna start with the role of data strategy. So we're gonna have some content on the screen here. I'll try to talk you through this so that you guys can read while I talk versus me just reading it to you. But, basically, it's what is data strategy exactly, why is it important, and what are some common challenges that folks share. We're gonna start with a little bit of a definition, and we're pulling this from Amazon. I'm gonna put on my reading voice. Data strategy is a long term plan that defines the technology, processes, people, and rules required to manage an organization's information assets. And the key parts there are those those really five things. A long term plan is we're building a vision. It's not tactical. It's not the next week or quarter. We're talking twelve months, twenty four months, five years. We're talking technology, which is the platforms. We're talking processes. How do we do things in a way that we are predictably successful in mitigate risk? People, how are we going to engage the people in our organization? How are we gonna engage the people outside of our organization to get the best return? We're gonna build rules around it so that it is standardized, it is safe, and it is efficient. You can kinda pair this into two basic motions. Harvard Business Review calls this offensive strategy or defensive strategy. I've talked a lot about this for anyone that's been on our previous webinars. I talk about this as a push pull dynamic as well, And I think you can see, parallels in how different organizations want to center their data strategy based off of an offensive strategy or a defensive strategy. Think of this in terms of sports. But also you could use this exact same parallel on how you would sell Upwork to an executive to get them to buy into your big ideas that you need to do with your data, offense and defense. So what does that actually mean? By the way, I I was remiss. We do have a q and a. If so if you look in the, the Zoom, display, probably at the bottom of your screen, there'll be a little q and a button. If you have any questions that you wanna ask, I I probably won't stop in mid sentence to answer the question, but I might have five to ten minutes at the end of this. So if you do have questions, chuck them in the question and answer. And if I can't get to them, I will certainly reach out after the webinar and give you a a perspective or maybe pulling some other experts depending on the type of question you're asking. So use the question and answer if you've got anything you wanna chime in with. But back to the offensive part of here. So what we're focusing on is how we how we grow, how we focus on the return. So for instance, increasing revenue, profitability, or building customer satisfaction, growing our customer base, making our products more accessible and more attractive. That's the offensive side. And you see a lot of industries or perhaps departments inside of your organization really focusing on this, like, retail focus or people that are doing marketing or or people that are building digital products. And that is we want to use our data to get in front of more customers and drive more value for them and thus drive more value for us. Go, go, go, grow, grow, grow. On the flip side is a defensive strategy, and it doesn't mean that you have one or the other. There are just certainly elements that you're going to have to have. You you must have of both. But DeepInce is really about mitigating risk. So for instance, ensuring compliance, making sure that you are in line with the regulatory requirements of your particular industry or your department, using insight and intelligence to to see if there is fraud or risk or if you've got potential security breaches or all of those sorts of things. And this is the stuff that really keeps the the executives awake at night. And you see a lot more emphasis in the defensive strategy in strongly regulated industries like your banks, those folks that do insurance, any health care. We have a lot of health care clients. I'll toot our horn for a second. We were recently given the, the Snowflake Health Care and Life Sciences Industry Competency Certification. So that just proves that we get to work with a lot of folks in health care, whether they are private, semi private, public, or not for profit, and they are very, very focused on a defensive, data strategy and government, obviously. This is where risk presents a substantial, I should say, the risk of data breaches or or, a lack of insight or control on what's happening with your data presents an untenable risk versus embarrassment. Now, again, we gotta really reemphasize, you need both a defense and an offensive strategy. You have to have those coordinated. So in terms of your data strategy, where's a good place to start? Well, I'm a big believer, and we're gonna reiterate this several times, that your business needs to be driving the why we are doing things. You need to be thinking of a term in terms of ROI. And it could be we're increasing our return, or it could be we're minimizing the risk or we're minimizing the investment. But either way, that ratio improves. We did an entire webinar last year of how to maximize your data ROI, and we talk a lot about how to calculate that and how to then mess with the variables so that you can optimize it. So that's a webinar. If you're interested, happy to share that with you. But you gotta start with what problems we're trying to solve as a business. And that problem might be we are worried that we have a lot of customer information, or it might be we really want to reach more potential, vendors as we do a b to b wholesale, product marketing program. Offense and defense, defense and offense. So what other things might you might you consider versus your starting point? Well, our is our data trustworthy? If we give it to our users or we sell it onto our customers, is it actually something that is valuable? Is it going to give good insights to then drive business actions? Are we prepared technologically from an application standpoint, from a platform perspective to do all of these things? You're going to have a queue of projects and priorities, that you're going to, organize. And do you have the time outside of all of the manual things that you have to do or the other things that you've got to do to get the data ready to actually go and pursue these individual initiatives? And how do you prioritize all of these things in this haze and this cloud of all these important things that have to be done with all the fires and urgent things that always pop up day to day to day. This is all part of managing a proper data strategy and just generally business strategy. So where to summarize this more simply, data strategy is very complex, and it is a moving target. You can spend a lot of time. You could bring in your big four, grab all your senior executives, and map out a plan for five years. But realistically, every six months, you're probably gonna have to go and do little revisions. New technology is released. Wow. That means a lot of the things that we didn't have the ability to do, maybe we can. Or a competitor is doing something and we're losing market share. We gotta adjust. Or maybe something regulatory changed, we gotta adjust to that. There's always gonna be little things that affect either your platform, I. E. Your technology, your processes, how you're doing things. Can we do them more efficiently? Is there AI assistance in doing that? The people that you're working with, do we not have the user adoption we anticipated? We gotta revise our strategy to to do that better, as well as the rules and processes and goals, that you are going to do to make sure that all of those things are effective. So it is complex. It is organic. It is something that you have to constantly, reassess. So why is all of this important? Well, hopefully, this is something that's fairly intuitive to most of you folks skiing being that I've been a lot of you do interact with data and data strategy on a day to day basis. But simply said, data is a massive it is a valuable, valuable resource. I've got tons of quotes from industry leaders and thought leaders from around the world that say this is the most important thing, the most important asset that an organization has, in their entire organization today. So making sure that you use data to drive value, to build a vision for what your future, is going to be in your market or how you're gonna service your customers, whether you're private or public, and doing it in a way that is secure and controlled and safe, that is the heart of data strategy. And, really, data strategy is business strategy because of how important and how prevalent data is. It also gives you the ability to diagnose your organization's needs and how to solve those problems. And if we solve them with data versus intuition, then you are you are setting yourself up for something that is likely going to be more successful versus having to oh, that didn't work. Let's try something else. And by building the infrastructure that's gonna allow you to source more of your important business decisions with data and with all the things that come with data, automation, self-service, computer assisted decision making. It is going to reduce the cost of decision making, the technical debt, and all the infrastructure that goes with it and improve the effectiveness of all the things that you're doing. There is an economy of scale that comes with data for sure. The problem is is that a lot of organizations really struggle to tie all of this together. For instance, if I said, let's start with the business needs. I have a lot of engagements where I get talk with organizations about what they should be thinking about with their data. And a lot of times, they said, we don't even know what questions are the ones we should be solving. We don't even know what questions we've got. We just know this person over there does data, and that person's never accessible because they're so underwater. If we could get that person more time, maybe we'd start to discover some of the things we could do with data. Or maybe there is a huge gap between sales and business focused executives and the engine room of IT that there's not actually a a good conversation that they can have so they understand what the opportunities are. So there's a lot of ways organizations never quite get off the ground to build a consolidated and unified vision of how data strategy can impact the organization. I'm gonna answer a quick question here because I do think it's timely. Sean, we do record these, and we will share the recording with you via email within four or five business days afterwards. If you've got clashing webinars, you are safe to, catch up on this one later, although we will miss you. Okay. Let's move on. So let's get into some of our myths. We're gonna we're gonna focus on three particular myths, and here they are. Thought it'd be fun to put mythological creatures on there. We're in data. We're allowed to be nerds. Right? So the myth number one there is more data always equals better insights. More data is more good. AI is AI, and AI is everything. I'm sure you've heard executives say something similar. And a single unified platform solves all data challenges or the one ring to rule all things. There's another name for that that I'll save when we get to that particular myth, but these are these are myths, and we're going to bust them. So in terms of more data equals more insights, for anyone that is on the data engineering side or the data architect, I'm sure you already understand that this is a flaw. Data quality is way more important than data quantity. More data, particularly data that is irrelevant, that is, filled with errors, is not structured or modeled in a way that is useful to the business, just means you have more things to more garbage going into all the different things that you're powering with your data. And this is particularly true with AI. AI has to be carefully trained on great data. Otherwise, you're going to get all sorts of unintended consequences. Focus on data points that answer very specific business questions. Yes. Collect as much day as you can and throw it into a lake. Very cheap nowadays for storage. But the only stuff that should be making it to your warehouse or your more curated data sources, your semantic layer, should be the stuff that you know is actually focused on driving business value, which goes back to your priority list of use cases that you're trying to solve. A system of controls and data governance, that's obviously critical to ensure the reliability and consistency of the answers that your data is producing in whatever downstream application you're doing, whether it's self-service or reporting or data science. And you gotta make sure that, in terms of your data quality that you are building something that is usable by nontechnical people. Your business users know the business better than anybody. So we have got to get them data that makes sense to them. So that's why, a semantic layer, business logic, whatever you wanna call it, has to be so perfectly tailored to their needs that they can take it and run with it versus having to rely on IT people, build reports for them. We we've talked about self-service analytics and the value of that in numerous, webinars before this one. An example. We can't share names with all of our clients. We we have to have permission to do that. Some of them even give us permission to do case studies. So there's a bunch of case studies on the interworks dot com website. But we do wanna share some stories, about some of these examples. So we had an organization, a client that we're working with that was focused on cleaning and analyzing existing data. So they wanted to say, listen. We've got a data source or a data source or two. We're trying to solve a particular use case. Let's make this data great to solve this use case versus the traditional approach of let's get all the data together, and let's all boil it all at once and make everything nice, and then we can let the u organization solve whatever use case they want. Hit those organizations never get anywhere. These guys focused on taking existing data, making it usable, making it great, which allowed them to, one, build competency very quickly because now we had stuff going through the pipes. We had people doing stuff. We understood where the processes were slow or bottlenecked or inefficient. And the I the entire data ops cycle was improved as well as immediately contributing value back to the organization, which gets those executives that are on the other side of that IT divide understanding, oh, there is value in data. Let's look at the next thing we can do. Another myth, AI. This is obviously in the headlines with DeepSeek and all the stuff that's happening, but AI is the answer to all things. AI can replace humans and can and specifically replace data strategy. Well, obviously, that's not true. AI is a tool in your tool belt. Just like self-service reporting, just like a data warehouse, just like ETL, all of those things help human beings make good decisions. AI is exactly that and no more, at least not yet. Maybe AI can teach AI, but we're a generation or two away from that. Human intelligence or human beings are necessary for understanding, one, where we wanna go and for also building the AI and making sure it's got the right data. AI cannot do that. AI also takes whatever we give it and then does its best to try to model it and come back with answers. It can't look at and say, I shouldn't have this information, at least not predictably so. You could build AI to help you look for that, but you have to build the AI to look for it. AI itself cannot do it. It also doesn't know if it's being biased because of bad information or whatever. So human beings always have to be engaged here. And when we're thinking about the four pillars of AI, which is something that we talk a lot about in our webinars, I've got a white paper coming out very soon, we are talking, strategy, data, governance, and people. And people and strategy, which are the beginning and end of those four pillars, are what really make AI effective. Human beings need to decide what's important, and human beings need to understand how to engage with AI to use it properly so that it's adding value. And there are a ton of examples of where AI did not add value and, in fact, added very humorous headlines for the people not in the organizations that it affected. So AI has limitations. We have to understand that. And this is a pretty broad client story, but I have talked with numerous organizations all across Australia, across multiple verticals. And inevitably, a executive will go to a conference and hear how great AI is and then come back to the organization and say, solve something with AI, which is ridiculous. Right? It's like saying, here's a saw. Go saw something. What am I sawing? What am I using the screwdriver for? Give me a give me a goal, and then I can figure out how to solve it. So human beings always need to be front and center in terms of the decision making we're doing. Technology is there to help them make better decisions. And sometimes that decision making might be, here's why this data is interesting. It also might be, rather than you spending time dealing with a thousand entry level questions from a customer, AI can do that for you so that you can then deal with the second and third level questions. But it's ultimately trying to accelerate and augment human beings versus AI being everything always all at once. That doesn't mean that's always true. Who knows? In twenty years, we may be Wally relaxing on a beach with universal income, and AI does everything for us. But today, AI is meant to be a compliment, a tool in the tool belt. Our last myth, a single unified platform solves all data challenges. And there's a tricky one because if you ask this in a slightly different way, I would disagree with this myth being false. But the way that it is written, it is indeed false. Effective data strategies often involve multiple tools. Now I told you that I had another name for this. I I call this sort of the Lord of the Rings fallacy, one ring to rule all rings. There is another way that you could call this, and I'm this is gonna be an unpopular opinion, particularly in Australia. But this is the Microsoft fallacy. Meaning, all I have to do is get Microsoft, and they say they've got great tools at every stage of the data cycle, I'm done. Problem is they don't. A lot of those tools are very legacy, and some of those tools are amazing. Some of those tools, Microsoft knows is bad, which is why they cleared the way for Databricks to come into their ecosystem while they try to correct and fix. We have an approach, InterWorks, of taking best in class at every stage because of the interoperability and extensibility of data, particularly when you start to think about, the ecosystem that we're in today. So Tableau came in, best in class tool, data agnostic. And when you compared it to the Power BI of ten years ago, Power BI had very specific cube specific type, data requirements. As a result, the rest of the industry in analytics became far more, agnostic towards data and far more useful. Snowflake came in as the first native data platform and and was extremely, versatile, which then brought in a whole bunch of modernization and rapid changes to the traditional data providers as well as competitors. Now there are a lot of things you can do on these platforms as they expand more and more, the capabilities. Snowflake is an example. They are you've got Snowpark. You've got ice or you got iceberg. You've got containerized, warehouses where you could run AI models of your choosing from their market. But there's a ton of stuff, and there's gonna be even more stuff. They've got a an acquisition that they did, last year sometime, I think, in q three, where they're gonna be bringing in the ability to do some more transformation and pipelines. This is all great. This is all useful, but we always have to keep in mind is, is this solving a problem, and is it solving it the most efficient way? Snowflake looks like they're batting a thousand. Some of these other tools, particularly legacy tools, it's hit or miss. There are some really great things on some of these platforms, and there's some big misses. Regardless, even if I were to say, there's a particular vendor that is amazing from top to bottom, there are still other things that you need. For instance, let's take Snowflake. Snowflake, as of this moment, does not have an analytics tool. It's got the ability to do some data science, but does it have the ability to do all the things that you need to do with data science or with ML or with AI? No. It doesn't. There are governance requirements that Snowflake doesn't have. So regardless of what your particular vendor of choice is, the data life cycle and all the controls that you need around it require a lot of expertise. It requires a lot of skills and thus requires a combination of tools and vendors to make sure you've got everything that you need. This is something that we do quite a lot. We take a look at where you are. We take a look at what your requirements are and where you wanna go, and then we start to pick the tools that are best fit for you versus us saying you need to change to fit into this tool because we have a particular tool we like to sell. We do Databricks. We do Snowflake. We do a lot of things. So we try to find the tool that best matches what you need. Some takeaways. I think you need to have a life cycle of the tools you select. I think the life cycles are different based off of what problem they are solving. If you're looking at a data warehouse, I think you need to be thinking a minimum of three year commitment, probably more like five. Ideally, if you can get seven out of it, even better. Analytics tools, probably a bit of a shorter lifespan. You often will run hybrid tools. So you might have some of the organization using, say, Tableau, another part of the organization using Power BI. You might have some embedded analytics in terms of, an API that you're using like Salesforce or some marketing stuff that you've got. ETL tools are far more, use case specific, so you might have three or four of them depending on what you're trying to do. But be prepared to have an organic, champion challenger approach to the tools that you've got in your environment. You're considering ease of use. You are considering, the speed of adoption, the feature set, the future road map, and the cost. Those are all things that are critical. Some client stories. So, again, these are some questions that we hear a lot of customers ask. I think there is a a lot of sense in looking at the life cycle, of a piece of software. I think I did a webinar on this ages ago, but there's sort of this challenger state, early adoption, maturity, and then sort of your legacy, sort of this natural ebb and flow, depending on what kind of organization you are, for instance, if you're playing more offense or if you're playing more defense, the size of your organization. If you are, SMB, commercial, enterprise, or strategic, that will affect where you would be most comfortable picking off of that lifespan. If you are, for instance, a small digital native, you can go for challenger tools that are right on the cusp, of being well known across the market. You can make those investments because you're quite agile and you have very low risk. Whereas if you are a huge multinational, you probably have to pick established winners that are in that mature phase, even if they're near the end of their mature phase because you have such a low appetite for risk, and it's gotta be properly enterprise. There cannot be that's in the road map or we don't have that security certification yet. So all of those things factor in. So when we talk to customers, try to help them figure out what their greatest strategy, we we take all of this stuff into account. It's very bespoke. There's not a cookie cutter answer for any of this. Let's talk about some things that actually really impact results. These are the things that are in terms of the fact, admit, these are more of your facts. So fact. Your data strategy must have organizational alignment and clear vision. So whether it's offense or defense, your business, your executives, and your technology folks all need to agree this is the direction we are going, and we are going to build, a platform that supports solving these use cases to then get us these results. And these are the ROI expectations we have. You'll decide as a part of that conversation, do we wanna play a little bit more offense than defense? Do we wanna make sure everything's locked down and controlled because we're a bit we're we we have a a greater degree of risk, or if we wanna go and grow. And we can be a bit agile in the way we approach, some of these controls or governance. And, again, these will change over time. You might be more defensive in a little bit. You might be more offensive in the next year. You gotta have these conversations continually. Some takeaways. Again, data strategy must be business driven. I will drive that point multiple times. You have got to have cross functional collaboration, which means your executives have to buy in to the ideas that your business people and your IT people, your data specialists, collaborate on. Another way to think of this is your center of excellence. You have to have appropriate stakeholder representation for everyone that is affected or affects what your data is going to do for you. So your governance people, your your, enablement folks, power users from departments or from particular groups that are really, really close with the data, oftentimes that's marketing, because they are so data rich. And you have to tailor all of this, particularly your governance and how you measure this to the specific use cases that you're trying to solve. Governance looks very different for, let's say, a government health care provider than it is for a private information broker that resells all this information to customers. You can see how quickly your entire perspective on how you govern your data would have to change. So, again, it's very specific, and you have to have the conversations. And stakeholders from across the organization will bring that unique full spectrum view of the things that you might not think about given your particular area of focus. Data governance is a framework. I think a lot of people look at data governance as like a tax you have to pay to do, data. When you're doing it correctly, it is a value creator. That is difficult to get to, but you have to have those conversations. You have to explore how data is helping, rather how data governance is helping. And there's a lot of automation to make it far less painful, particularly, like, in the area of master data management or cataloging or data quality. AI can really assist those workflows to make that much, much easier nowadays. Data governance is a pillar of success. So, again, you have to have there's two aspects to to data governance. I'm gonna restart this slide for a second. There's two aspects. One is, what is our data governance that we're doing? Okay. I'm a just plug that into this particular application. I can do users. I can do groups. I could do permissions. I can set my RBAC model so that these people get this data and these people don't get that data. That's all the really technical stuff. And while that is cumbersome and task oriented, it's actually not the hard part. Now the people that have to do that would be like, oh, contraire. But the hard part is actually getting the policy figured out because then you have to take business reasons and business people and marry that up with what is possible from the technology. And then everything that's not possible from the technology has to be built into an evangelism scheme of change management and user based communication, which means you have to teach people the governance parts that you can't lock in and automate into your machinery. That's the challenging part. And we get customers coming to us all the time. Like, we don't know what we don't know. We don't know what good governance looks like. We don't know other than the vendor saying that we can do these things with clean rooms or whatever, what we should be doing. And so that expertise is actually the part that's hard with governance. The second bullet point there, testing and audits, just making sure that every assumption you had is proven in fact so that you minimize that risk. And you should probably test scenarios as well, just like you would if you had a recovery window. Our entire system goes down. Can we get it back up in an hour? You should be thinking about that with data. Because as an asset, it's probably got greater risk associated with it than maybe losing, your cloud host to your on prem, whatever it might be. Iterative approaches to the development process. Hopefully, nobody is still thinking waterfall when it comes to building a data strategy and a data platform. You will never get anywhere. You have to do this use case by use case in a rapid start, rapid prototyping, agile way. So whenever we talk to an organization, we try to figure out what the big goals are, what their limitations are, what their strengths are, and then we put forward a list of tools that we think are gonna get them there cost effectively and most efficiently. And then we grab use cases and start solving them. So we build the foundation, and they're like, okay. Marketing needs this or finance needs this or your supply chain, this is a big problem for them. Let's go solve it. And we solve use case by use case. And as we solve those use cases and get more and more great high quality data into your data warehouse accessible to your users, you can see your data estate is growing. And And so use case by use case, the benefit, the the rising tide that floats all boat is improving. So that's what we mean when we say iterative approach. And as you do this, you will get better at it, rather than trying to do it all at once, phase by phase, and then discovering after phase three, oh my gosh. We have to redo the entire phase one because we didn't know what we didn't know. And we only learned that because of phase two and three. Some other takeaways for iterative approaches. Again, build piece by piece. You cannot boil the ocean. You cannot eat a pizza in one bite despite what my five year old tries to do. Doing this in an agile sprint format means that you're able to collect feedback as you are building in very rapid sprints, which means the refactoring process is painful and immediate. And then obviously keep that engagement with the business so that you can update systems. It might it might be actually physically updating, but in the cloud now, it's probably more, hey. There's a new feature coming out or there's a new requirement that's coming out that that that we now have available to us so we can actually update processes and features that we're using and how that then affects our data strategy. Leveraging BI tools to extract insights. A solution that is not used is a useless solution. It doesn't matter how great the data is in there. And, ultimately, which really, really drives the return on data investment are the users that do stuff with it. And the way that most business users interact with your data estate is through a BI tool. That could be Excel, which is a BI tool, a limited BI tool. It could be Power BI. It could be Click. It could be Tableau. It could be Sigma. It could be ThoughtSpot, whatever. All of those have strengths and weaknesses. All of those talk to particular types of users. And so you need to have some way for your users to engage with your data because that will then give you a whole bunch of great ideas and insights. One, this is the factory for building use cases for more data. These guys found something. They have an idea. Now we have another use case that we could put in our prioritization queue. It'll also say, oh, wow. These guys have a metric over here that doesn't match up with the metric over there. We need to make sure everyone is using the same definitions. BI will help you find that stuff. It also helps you, one, obviously, it's gonna help you find actionable insights using data to improve the business. It'll help you find errors. It'll help you find bottlenecks, process inefficiencies, all of those things. But think of it as the grass roots factory that's going to make data more efficient and give you more use cases. I don't know if there's much more I need to say on this slide. So let's review what we learned. We're forty five minutes in, so we should be wrapping up with maybe five to eight minutes left. So here's a high level view. If you wanted to screenshot something, this might be the best slide to screenshot, but these are three things, three high level findings from this conversation. Fact. Organization alignment is crucial for effective data utilization and data strategy. Everybody needs to be on the same page. Everybody needs to be rolling in the same direction. That is leadership all the way down to your base business users. It is your power users in the business. It is your IT folks. It is your data architects. It's your cloud architects. It's everybody. Everybody has to have alignment. Data quality is way more important than the quantity of your data. And to get to good quality, it takes a lot of people, takes a village to raise a child, it takes a company to have great data. You need business users saying this is what we need from our data. This is how we think about these metrics. You need data stewards to then capture that into the semantic layer. You need engineers and architects to get that data to them in a way that is highly performant, clean, effective, and consistent. Rapid prototyping, agile methodology to what scrum, whatever you wanna call it, iterative approach is far more powerful, and you can get far more learnings far more quickly, and you can actually start to get wins on the board. And when it comes to these big data projects, momentum is everything. If I go to a board of executives and I say to the CEO, I will get you value in two years, but you'll be you'll be amazed. I will never get that project approved. Whereas if I say we can get your entire strategy lined up in about three to four weeks, we can then build all of your applications, most of which are gonna be cloud. We can get those up and running in another two weeks, and then we can start solving the problems that are making your leaders, your middle management, ineffective or less effective or can really drive value. So from start to, finish or the first solved use case, we're talking three to four months, that is something that gets executives excited. And you can only do that in an iterative agile approach. Tackle one business challenge at a time, get the wins, get the learnings, get the success. Another way to think about this is this is your path to data strategy. Strong governance, shared vision, quick wins. Data is a product. AI is a tool. Balance your offensive and defensive strategies to make sure you're creating value but mitigating risk, and think for the future. Flexible, scalable, future proofing your architecture. So in terms of what we do from here, we've got a little offer for you. If you're looking to accelerate your data strategy or vet and proof, a strategy that you've already arrived on and you want some expert third party opinions like InterWorks. We have a really simple, little exercise that we call a DART or a data and analytics review and tactics. That's part of a bigger strategy that we would call an SVR or strategy, vision, and road map. But, basically, what the dart is, it's a very simple exercise. We do it in a few hours, but we just talk to some of your stakeholders and we give you a bit of a maturity grading on five key elements, which is your culture. Think Think about that as your users, your analytics, your data, your governance, and your platforms. And across those five, we're then able to assess your AI readiness. So for instance, if you've got great data, high quality data, but no governance, your AI readiness is going to suffer because AI requires very strict oversight and governance. It's something very easy to do, and we can give you an actual physical report that that that visibly shows, your maturity across these five frameworks and your AI readiness. So for anyone that is interested in doing that, contact us. Or if you have other questions or you wanna talk about pipelines or building communities or analytics or dashboards or whatever it is, again, we do end to end data like we talked about at the beginning of this presentation. We are happy to help. We have future webinars coming up every month. We are gonna be leaning quite heavily into AI and automation and data and strategy and governance, over the next. So keep an eye out for that. You obviously can go to the interworks dot com website and see any things that are growing. You can always see us on social media. We like to post those things there. And if you are on our contact list, then we will probably mail you saying, hey. We've got this great webinar we'd like you to, attend. So scan that right there. Reach out. Happy to help. We've got about ten minutes. So if I didn't get a lot of questions in the q and a. So if you do have questions, you can pop them in there. I'll maybe give thirty seconds. They're just sort of spitballing here to see if anything pops up. Otherwise, I'm happy to let you guys go back to your day and chat with you guys the next time. Let's see. I think we're in the clear. Alright. So just as a reminder, this webinar will be, we'll we'll send you a link in the next, say, three to four business days. And if there's anything that we can do, let us know. We're happy to help.