We're gonna talk about five ways to improve your return with less investment, specifically, in the realm of data. So how to get more by spend and, spending less. And there's a lot that we could cover here. So let's just get through the formalities. I'm Robert Curtis. I'm presenting today. I'm the managing director for InterWorks looking after Asia Pacific. I've been with the organization eighteen years now. So it's been a it's been a minute or two. I've had the opportunity to work with hundreds of companies all over Asia Pacific, primarily in Australia, helping them with their analytics and data, whether that is, working across strategy, solutions, or supporting them. We do a lot of things for our clients. Perhaps imperfectly represented here, there's so much sort of trying to figure out how to diagram this. But, basically, it also sort of centers on unifying their data, making that data governed and usable, building a culture around it so that we can maximize adoption, painting a strategy on how we're going to use that data, supporting and training the organization, and then all of these things around it, ETL, data quality, building data apps, Gen AI, data science self-service, we do all of it. And we're really focused on building solutions versus selling tools. We will sell you tools, of course, because a lot of these tools are really helpful. But But if we don't factor in everything, enablement, change management, support, and ultimately how you're gonna be successful, then ultimately what you're buying is shelfware. So we try to focus holistically on everything, which is what we're trying to represent in this diagram. A little bit more context about us, we've been in business for twenty seven years. I think that might be twenty eight now. I might need to update that, bullet point. Seventy five of the Fortune one hundred are our customers, so we are global, and we have a massive footprint with some of the most complex datasets and companies on the world. We have a blog, interworx dot com, generates, I think, nearly four million page views a year now on analytics and data and governance. It's kind of amazing. We have thousands of customers across every industry vertical, public sector, private, you name it. And we had the honor being labeled or named a Forbes Small Giant. So twenty five companies globally that punched far above their weight, and we were one of those folks. So we were very proud of that. We have lots of customers that we work with. Here's an example of some of them, and we work with a lot of great partners. And I know some of our partners are on this call. So nice to see you again. So let's get started. We've gotten through the sales pitch, and we've given ourselves plenty of time for some more folks to make their way into our our little friendly webinar. So we're talking about, how you can get more for less when it comes to your data investment. We're gonna focus on five particular themes, and then there's a lot of stuff in those themes that we'll talk about. But let's start by building a little bit of a state of play. We're at a very unique time right now globally. The cost of entry to getting into data solutions is going up, which means another way of saying that is there are more technologies that are needed. You must get on cloud, to do a lot of this stuff. The resourcing is more expensive. So the cost of even getting in and solving data problems is more expensive. Cloud and AI investment is naturally growing because of the massive opportunities they present. That's a large driver of how these costs are going up as well as the associated cost, talent, and all that other stuff. But IT spending overall is actually decreasing. It's flattening out. It spiked a little bit during COVID. There was a massive acceleration. You can imagine companies needing to modernize very quickly because no one was in the office anymore, and they were kind of operating blind without that desk to desk, you know, conference room conversation. So there was a lot of investment, to modernize. Within, I think, one year, most companies try to stay take seven years forward in terms of their data strategy. Economic challenges globally also make this quite challenging. Inflation, all of the different things that every company every every country is experiencing, Estrella being a particular focal point, because of our dependence on China and the USA, and both of those economies not doing very well. All of that is to say, don't get depressed. But as data people, it's more critical than ever that we can prove that we are driving great results for the money that we are allocated to do so. We have to prove that there's real genuine value here. And the thing that's tough about any ROI calculation is exactly how do you calculate stuff. Like, how what is the value of a dashboard? Well, there are ways we can think about this. Let's start with, the cost of investment, the denominator. We're gonna break this into two components, the cost of a data solution. And the easiest way to say, you know, some direct cost for your data solution is, the licensing cost. And we'll talk about the different licensing costs in a second that you might experience, but it could be per head, per device, consumption based, usage based, fixed cost, term based, whatever. Those are direct costs. And that's probably the main thing that IT departments stay awake at night worrying about. And a lot of times they are a cost center. They inherit a lot of other people's costs too. But there's also a people cost, and it's really important to call this out because people are largely what we use to make these solutions work, to build them, to maintain them. And you might get a deal on the cost of a tool, but you're spending more time and energy and resources on the people side to make it work. So we wanna look at this holistically. The cost of investment's largely these two. You could add more to this, of course. But the unifying force, when you think about, investment, the thing that is going to make this better for you or to lower your cost is being more efficient. Being more efficient in how you procure the technology in which you're doing it and more efficient in the way you enact those things with the people that are looking after them. So in terms of efficiency, one we wanna bring costs down, which is why that down there. We don't wanna make efficiency down. We bring cost down with efficiency. So, again, buying exactly what you need from licensing versus overbuying, optimizing the usage for your consumption based tools so you're not spending more than you need in terms of the credits or whatever unit of, transaction you have. The cost to maintain these items, you could be paying someone to do it. InterWorks has a service called Keep Watch. We maintain these services for you. Or it might be your people doing it. Either way, there is a cost directly or indirectly. And the cost to build, these things, that might be deployment, that might be actually getting things out. Is your tool harder to build in, or is it efficient? Those things are all part of your investment before you get to see benefit, the net return. And we're gonna break this in to three components. Now there's again, the net return on on any sort of data or IT investment is is a little bit harder than the cost of investment. That's normally a balance sheet item, but the net return is difficult. So we're gonna focus on building data products. And data products can be very wide. It can be operational products, which is your essential reporting. If we don't have data or a nice warehouse, we're still gonna report on these things because we have to know so they'll appear in Excel spreadsheets or whatever. Operational reporting. Analytics. This is a much broader, group here, but it could be your traditional dashboards. It could be empowering our team to do self-service, but it could also be experiments. And this is where your stuff like your data science, your machine learning, or your Gen AI would be in that. We're gonna test hypotheses that are either going to lower the cost of investment or it might be creating more value, but it's basically, let's see what's possible and use data and technology to verify our assumptions. We also have external products. And, again, those are the ones that are the easiest to say, I have a line item in my balance sheet that's saying we are producing x. We are selling our data or we're selling the products of our data externally. And it might not be a direct transaction, but we might be giving it to retain customers. So there's a lot of ways you could think about external products, whether it's embedded or or whatever. It might be a marketing tool, and then you can use the leads generated to then go down the sales conversion funnel to then justify that from a very concrete, expenditure. The other ones, operational and analytics, are a little bit harder. You could, of course, say we built, an automation which saved us x number of hours per year, and this was the salaries to that were required to go do that. That's a very easy calculation. You could also then put on top of that, here was the opportunity cost, of us having to maintain this, and now those people are freed up to do other things. That's the whole point of self-service. If we can empower the organization to do a lot of their own data discovery and interrogation of data to solve problems, then our centralized team doesn't have to do that, as well as we have a lot more people that are a lot closer to their particular areas of the business adding value via the data product. So the unifying thing here, on the denominator, it was efficiency. On the net return side of this, on the numerator, it is adoption. Are we getting people using our data? Are we getting them using our data products? Or are they using our tools to create their own data products? The more we get usage, the more people are doing things, the better we're going to increase that net return. So some of the key things about adoption, how quickly these tools add value. So you build me a tool. Does it take me a day to find something useful, or can I do it in fifteen minutes? That's gonna increase the adoption. It's gonna increase the value. Do I have a data culture that I'm building that is going to encourage adoption, that's going to challenge the things that I'm building to make them better? Another way to think of how you calculate ROI is what is the alternative? If they had nothing, what would that be? And and a way that a lot of accountancy sort of look at free services is, what would someone pay for it? So if you were to go to an organization and you say or a department and you built them a dashboard, well, what would you pay to use this dashboard if it was a subscription? A thousand dollars a month? Ten thousand dollars a month? Is it perfect? No. But you're not gonna get a perfect number when we're talking about return. But it is a way to sort of help start qualifying quantifying it for for however you wanna start projecting those costs and and value out. Direct revenue, obviously, external facing stuff is much easier to do that. If you we have customers that have data products and they sell direct access to their data feeds. That's a very easy way to say there's a line item. Opportunity creation. Did we create a new line of business? Did we create a new product? Did we create a new customer pool? Those are really clear ways to say we are getting more people using stuff. Now one thing to think about is, this isn't, so much as something that is adding to your return, but it would go in the numerator side, of thinking about risk here. And that's if you have downtime, you obviously have less adoption. People can't be using tools while things are down. So you have to make sure you've got uptime across everything that needs to be, visible to your users, internal or external. So this is kind of a long way to kinda get to this sort of view of it. This is the way I think about data ROI. What products are we putting out there? Are we getting people using those products? Are we improving on those products to make them as useful as possible for all the different types of stakeholders we wanna support? And then what is the cost of us doing that? That's the cost of licensing. That is the cost of people. It might be contractors, consultants, whatever. And then you can then start to evaluate, are we doing what we need to do? Are we adding genuine uplift to our business? And if you can master this type of calculation, you are master selling to your executives. So we're gonna talk today about this calculation, this formula, but we're gonna specifically focus on the bottom part of it. We're gonna talk about efficiency. We wanna spend less so that that value then naturally becomes more valuable. By the way, we have a webinar chat. And if you've got any questions or anything that you wanna check, just chuck it in there. And if I happen to glance at it, while I'm covering the point you're talking about, I I might divert. Just say, hey. Here's a question. I misstated something. I can correct it. But I'll definitely try to circle back around at the end of this. And, hopefully, we have time for a little bit of q and a, and I can answer any questions you guys might have. Alright. So five ways that we're gonna drive better efficiency. And these are very broad, but automation is probably the most obvious one. So when we talk about automation, the easiest one is that we're gonna take stuff that people do and we're gonna let machines do it. That is obviously the flavor of the day right now with generative AI. Generative AI can replace human interactions with processes in a way that no other technology before that could do because you can teach generative AI near near, replication of human intuition, which an obvious example of that is language. Or you might be taking something that has already been automated and you are improving it. We are making it less expensive or we are making it faster. And if you make it faster, you are making it less expensive because of the human cost. Other ways that automation might be helping you is building better infrastructure. And I'm gonna put the next bullet on because I wanna talk about both at the same time or building better data. If you invest in better infrastructure and data, that opens up more solutions to you, which then opens up more opportunities to automate. As an example, you must have great infrastructure and you must have highly performant data to do anything in the near real time space, which then opens up the opportunities to automate things that maybe human beings are having to do. We have folks that have venues and they have very tight looks at inventory in near real time. We have folks that sell tickets and these shows sell out very quickly. So near real time is super important to them. And if you're going to build that automation on that reporting or those analytics, you gotta invest in your infrastructure and your data. Leveraging generative AI for decision making, I mentioned that already. I've got a nice stat about exactly how much time and energy people are putting that into. Another thing that people don't think about with automation as much is, yes, you are investing in something, but what is the opportunity cost if your data engineers or your data architects or your business users are doing the, quote, unquote, human glue of making something work? Could they be spending their time on other stuff? One of the previous presentations that we did in this series, we do a monthly webinar. So if you're not seeing that, come to the website, sign up. You'll get the the, notifications, is we did one where we're looking at BI operating models. And one of those is the centralized report function or or what we call colloquially the report factory, which means you have a lot of business people that are only report consumers. In the Tableau parlance, they are viewers. And the centralized team, the most capable, IT people, are your data workers, and they're the only data workers. So they not only build data models and they do curated data layers, they also have to build the dashboards. Giovanna, who's, helping us on this, just posted a link to the previous webinars, and you can find this particular one there. And the the and there's a lot of great studies on if you have your centralized IT team actually doing the reporting and analytics for all the different stakeholders, where they're gonna get all of their stuff slower, which means their decision making time is delayed. But, also, their eyes, these IT people, these data workers' eyes are on things that they have to be doing for users that we could probably allow them to do at minimal amounts of enablement instead of the stuff that only they can do, which is building great data products or building great data sources for other people to do their analysis. That's a tough one, but opportunity cost is probably the number one thing that automation is gonna help you with. Automated self-service, I feel like I kinda covered this just in the previous point. But, again, if we can build data pipelines that get people the information they need when they need it, that then empowers them to go and look and explore and solve their own problems. And, again, AI plays a huge part here in LA enabling people that are not technical at all to build their own dashboard, to generate their own SQL, their own scripting. And then finally, obviously, if you're automating and taking human beings out of it, the quality of the results you are going to get is going to improve because humans produce errors. And I talk to organizations of tremendous enterprise sizes, global organizations, and there are still components where they have people hand typing in data. And you can imagine their data quality, it suffers. So I've got some stats here, that speak for themselves. I say that, but I'm still gonna speak as I show you these stats. Fifty percent of the work today can be automated. That is a tremendous number. If you just think about an organization and each person can contribute two thousand hours of time and they've got a hundred employees, half of that time could be automated. Just imagine the cost savings they could be making as well as the other value that that workforce could be providing instead. It's a astronomical number. I bet you anything that as AI gets better, that number is going to grow considerably. Eighty eight percent of small business owners say that automation enables them to compete with larger companies. We are a global company, InterWorks, but we're only about three hundred people. Automation is critical for us to compete with the big four consultancies. Eighty five percent of data is unstructured, and unstructured means it is not something that is very easy for traditional analytics tools to deal with. We are talking about video, sound files. We're talking about images, scans, all that kind of stuff. AI, generative AI, allows that data to go into automation, whether that's pipelines or whatever, that then means that we've got a massive amount of our data estate that we can automate rather than human beings having to do that. We've got a customer that does toll roads, and they had to have human beings looking at license plates for people that weren't their customers so that they could then key it in so that they could bill these people. Now they have AI doing that for them. We have another customer that is doing a lot of shipping, and they've got a lot of cargo stuff. Now they're gonna use AI to read those cargo freight can whatever that manifests and then make that into to actually actionable data in a more structured format. AI is allowing automation on a level unparalleled because particularly because of the eighty five percent of it being unstructured, unreadable until now. Sixty nine percent of managerial tasks could be fully automated by the end of twenty twenty four. It's an astounding number. And, again, that number is only gonna go up. And this is perhaps I think, as we think about how generative AI is going to unlock more opportunities for automation, including how we interact with our customers in a frontline sales or support capacity, Projected spending on AI in twenty twenty four alone, five hundred billion USD. That number is undoubtedly going to double within two years, if not sooner. Automation is a massive, massive opportunity, but you don't have to go and buy twenty five million GPUs to go do it. There is automation and AI being integrated into the tools you're using every day that can help accelerate this. Efficiency opportunity number two, consolidation. And I really mean consolidating the tools. So when you're thinking about what this actually means, I encourage you to think about in terms of the data life cycle. So data starts at one place and has to go through all these steps to get to the consumer. Think of it like panhandling or digging for ore, panhandling for gold. I mean, like, panhandling on the side of the street. There are pans that have to sift through the sand, and then they find gold, or they might find gold as a part of a rock. And then there are all these steps to purify it, polish it, make it market ready, and then get it to people. All of those steps require tools and intervention. I am not a proponent of a single stack because what you get with a single stack is convenience largely over quality, where you might get one great tool, but a lot of hacky stuff that they keep putting new names on, and and and repackaging even though they're not great. And some of this technology that's out in massive technology brands that do full stack, some of this technology is fifteen year old on prem stuff that has just been forced into the cloud and given new names. I won't name names, but you can probably use your imagination. But I do think that there are innovative companies that are finding areas of strength to expand the ways that they can help. We are a massive believer in Snowflake, and they are a perfect example of how they went from a data warehouse to a data platform, how they are expanding, containers and and Snowpark and to do all of the data science and AI and ML. There's a ton of stuff that they are then using the strength of their platform to be great at. And there's other tools that do this too. Each tool that you have represents time and energy to deploy it, to maintain it, to negotiate it, to upgrade it, all of those things. So the fewer tools you have, assuming that they can add the value, then obviously the less investment that you have to have. You also have to think about how these tools intersect, and it's there that you're gonna find your weaknesses. If if you've got seven tools to do your data pipeline and they've just passed from here to there, like, you've got let's say you got a SAP, and you have to have some sort of a data connector to get that information out, and then that goes into your ETL tool. Or maybe it's just the the the, the e and the l, and transformation is done by another tool. And then it goes to your data warehouse, and then you have a last mile data prep. And then it goes to your every point that there's a handoff, there's a governance risk. There's a security risk. There's a privacy risk. There's an efficiency risk. So dealing with all of that is incremental to actually doing the work. The technical cost of tool proliferation, again, this is teaching people how to use these tools. This is, the support cost, all of those things. The adoption cost. How do users use these tools? If you've got, let's say worst case scenario, you've got an organization with very three or four really strong, departments. I think in the business in the BI operating model webinar, I think this was the hybrid where you've got a centralized team, and then you've got, let's say, your customer team, and you've also got your finance team, and they've been allowed to go and source their own tools. You actually see this all the time as crazy as it sounds for those folks that are in a very centralized environment. Where do users go to find their reports? Where do users go to find their data? It is incumbent organizational knowledge on where to go look. No catalog is gonna keep up with that sort of change, particularly across all of those different tools. There's governance risk, obviously, with tool proliferation. Centralizing your tool selection is probably the smart thing to do, but I would say you have to decentralize the use cases that you're trying to support. So you have to have a community of people outside of your organization and empower them to find value and to use the products that you're going to create with it and create their own products and participate in your governance strategy. But I'm a big believer in let's pick the right tools for the organization and then teach everybody to use them. One method that we use is what we have we call this the solution essentials framework. And so we try to distill down the essential components of what somebody needs to be good at data. And and sometimes these are very broad. And what we do is we try to map out where they're currently at. Okay. So you've got SQL on prem or you've got Power BI or or you're using Spark or Python or you've got Dataiku here or whatever, and just try to map out where they are. Is this a modern tool that is low maintenance, that is low cost, that is highly effective, and has a plan for the future in terms of how it's going to grow? And then how many do you have? If I were to tell you that you could probably get two tools, let's say three tools to cover this entire wheel that that would perform excellently, that would certainly be better than thirty five. And that those opportunities for efficiency are there, again, keeping in mind the use cases that you are trying to solve. But this is how we think about it. Number three, optimization. We overwhelmingly now have a trend where you are paying for usage, And that by be metered, that might be consumption based. Metered is you have a certain allowance that you can go spend. When you're out, you gotta go and renegotiate. Consumption base is you can use whatever you want, and we're gonna charge you for it, which is think of it as uncapped. And so optimizing the way you are using these technologies is a key ingredient to keeping your cost down. The biggest mistakes that we see as consultants is people don't shut these tools off when they should. Oh, no. Phil left the, triple XL virtual warehouse running over the weekend or over the Christmas break, and we just got a twelve thousand dollar bill for a single query. The other one is the queries are, not running efficiently, which brings us to the last point. It's really a thing of user enablement. Are the people using these tools in the best way? And if they're not, let's teach them. There's a myth, I think, that, the consumers have or the customers of these tools have, and it's this idea of usage versus users. We wanna limit our costs of consumption based pricing. But I don't think you should think about it that way. It's easy for me to say that because I'm a consultant, but I'll I'll explain why. If you are driving a lot of results for your users and your users are engaged and you're using these tools efficiently, then the cost of your usage is offset by the value that you are creating. It's the ROI calculation. If you're using them inefficiently, that's when you start to get sensitive about costs. But if you're using them properly, then you're only paying for the great results that you're producing. That's a myth, that I think most people fall into, and you gotta think about value when it comes to consumption based pricing. Optimized, of course. The other myth is this idea of platforms versus people. There are a lot of tools out there, and, again, I won't name names, that have a lot of promise, and they you can probably buy them for ten percent, fifteen percent, twenty percent lower cost than some of the mainstream stuff. But, again, not factoring in the cost of people is a huge mistake because you might be saving ten percent on the cost of some tool, BI tool, data tool, whatever, but then you have to have double the amount of people to actually make this thing work. You are losing your ROI calculation. Yes. There is a line item on a bill that that might be a little bit lower, but the effectiveness of your tools, the overall ROI calculation of you as an organization, as a data team, has significantly been impacted. That's a myth that you gotta be very aware of. Costs include people. It includes the time to do all these things. I couldn't find a more recent, chart, than this one, which really is, I don't know, somewhere in twenty twenty three or maybe late twenty twenty two, they did this. But I liked it because what it does is it shows you just how prevalent consumption based pricing is in terms of the tools you're go you're going to be looking at. Just just from twenty eighteen to now, that number has more than doubled. It looks like maybe even almost tripled, and I think that number is going to go up. And you could see people here actively testing and will test, etcetera. But consumption based pricing, particularly on the cloud, particularly with a data rich environment where they can track everything down to a single query, this is the future. So you have got to adapt and optimize the way you're using it. There's not really other alternatives, to be honest, particularly when you get into the data side of the house. Analytics, you find a lot more of the traditional pricing. But when you get to ETL, data, governance, a lot of that stuff, it's it's very much usage based. Procurement. For the vendors on the call, I apologize. I'm going to give away some of your secrets. Hopefully, we can still be friends. But how do you get the best deal when you're talking to vendors? It's not what a lot of people think, and I'll explain why. One of the best things that you can do is when you ask for your deal. Every salesperson that works at a vendor, and I mean vendor is someone that is a supplier software, so whether that's cloud based data platforms or software you a BI tool that you log in and you you install on a desktop, all of those salespeople have quotas that they have to meet annually and quarterly. So if you can get them something that's exciting for them at the end of the quarter or their or their manager or their country manager, they are more likely to listen to your demands, particularly at the end of the year. That's the most juicy time, because they get paid off of their performance. This is sort of the trick of sales. Timing is super, super, super important. So when they say, hey. It's in the quarter. I'm giving you the best deal. They are not lying to you. You will not get that deal a week after the quarter ends. That deal will go away. Timing is super important. Another thing that people get confused about is they might say, I've got ten million dollars of services or rather ten million dollars of licensing with this company. Why aren't they giving me a great deal? It's because most salespeople are compensated on growth. It seems counterproductive from a customer perspective because, like, but I'm spending so much with them. Why aren't they being nice to me? Well, they have obligations to the street. They have obligations to the market. They expect to grow. Their shareholders expect them to grow, so they put a priority on growing. So if you can come to them and say, I can grow by fifteen percent, you will get a much better deal than saying, I have a lot of renewals, but I'm staying exactly where I am. Or if you're like, I wanna recontract, and I wanna lower the amount that I am spending, you will not get a lot of happy salespeople that are bending over backwards to help you because you're not helping them. As silly as that sounds, understanding the motivations of your of your vendors will get you a better deal. The number of SKUs. I'm buying a lot of different things. Some people care about this, some don't. When you're in a usage base, it's really more about the credits or the units that you're buying versus the variety of ways you're using them. Where that can help you more is if you put your hand up and say, I'm happy to be a customer testimonial for you. I'll show up at your global conference. I will do webinars. I will let you be a part of a I I will be a part of a white paper. And if you have more SKUs, you have more ways that you can talk about how they've helped you. That is interesting to, vendors. And, again, you're helping them, so they're going to help you. And, again, there's a lot of different licensing models, and all of them have little tweaks and things that that sort of influence them here or there. This is where somebody like us really come into play. As a value added reseller or or in the market, we just call ourselves VAR. I'm gonna talk about this again when we get to step five, item number five. We can help here because we understand leverage when it comes to our vendors, and we can do a deal that is going to make you happy and make them happy. And there's a lot of things that we can do, to help incentivize this. There are services and package and bundle deals that we can put in with these. If we resell on your behalf, it's going to give you more opportunity to be successful. And things like that that we've talked about in previous webinars, they're no secret is, us doing your deployment for you for free, us giving you a bundle of services hours, or us giving you, consulting hours, us giving you enablement for your team so that you can hit the ground running. These are all things that value added resellers into works in particular really focus on because we want you to be successful. We want you to buy great tools, and we wanna have great partnerships with our partners. So it's a win win win for everybody. This is what value added resellers do. It's in the name. We add more value when we resell. So here's an example of a whole bunch of licensing models. As I mentioned, the metered consumption, those types of things are very much on the rise. They are very good for cloud based consumption. It also means you can start trying out these tools for next to nothing and then get comfortable with it, see the value in it, and then scale. And paying for something that you're getting really great value at at scale is a win. Again, it's that usage versus users myth. There are still tools that are doing perpetual, though not as much. There's not many that are doing lock to a device, I. E. You have the ability to use this, but any user can come. It's we're a data rich environment, so most of that transitions over to user licensing. There's also still capacity based licensing. A lot of analytics tools do that, to some degree. But understanding the licensing model of the vendor will let you understand the best way to negotiate, and a VAR like InterWorks can help you do that. Funding. What is funding? There are a lot of programs vendors put together to help you guys be successful, and they want to they want to encourage you to try their tool. And so there is POC funding that a lot of companies will give in certain situations and certain conditions, that will help you get started and and and offset some of the costs in building these things. Because, again, like us, they want long term customers. Long term customers mean a healthier amount of, opportunity. It also means if they can get you in the door and you like it and you are kicking goals and you can start to add more workflows and use cases to the tool, you're gonna grow your usage. You're getting value. They're getting revenue. Everybody wins. And so the way they do this is they have funding programs. They are very specific to the vendor. There are there are particular things that different, technology software providers are interested in. Some of them are, let's get them as a customer. Some of them are, let's get them away from a competitor. VARs like InterWorks can help you understand what those opportunities are and when best to use those cards. Overwhelmingly, it is the hyperscalers that are the most interested and have the biggest amount of money to give you. Meaning, if you can take a workflow onto Amazon or Azure, there's a great chance you are going to get some of that back to you to help you with consultants or subcontractors or any sort of credits, to help offset your initial costs or some combination of everything. There's a lot of money that people are willing to invest in you that we would call marketing, to get you guys going and get you guys successful. Having that understanding means you have a lot more ways to negotiate. It may not be a lower cost, but it might be investment into your success. Are you eligible? It is very highly determined what you're trying to do, who you're trying to do it with, and when you're trying to do it. Some of these are time capsule, like, we really wanna give away this funding by the end of this financial year. Some of it might be open ended. But the important thing is how to have these conversations. And, again, you can bundle incentive programs. I mentioned that hyperscalers are the ones that are probably the most eager and generous because they've got the I mean, they just own the market. Anyone that competes anywhere on data, whether it's governance or a data warehouse or ETL or BI, they're on the cloud, so the cloud wins no matter what. AI on the cloud. But all of the vendors also have their own forms of this. So you can find a way to say, okay. I wanna do a data warehouse, and I wanna do data science, and I wanna do BI, and I wanna do it on this particular cloud. All of those vendors know the game. They all are alliance partners, and they'll say, what can we all do together to give this person the best deal because we really, really want to grow. We wanna add more customers. So you can make all of these guys work together. In particular, you can have a VAR also work together. And as you start to see, I get this benefit from a, b, c, d, e vendors. The VAR will say, I can round all of that out and put the glue in between all these pieces and make it all work together, which brings us to value added resellers part two. We have a program that we call Plus. It used to be called Pro for those folks who've been in the program. But, basically, anyone that resells through us or buys their vendors through us, we have a very coordinated program, that we've been doing for years and years and years to support you. That might be supporting your data initiatives or your solutions. It might be building user communities and individual users through mentorship and coaching. It might be helping you assess your strategy and the immediate tactics on how to get there. We're just trying to add, I e, the word plus, to the investment that you made in your technology so that it can be successful. An example of a report card, that we give to our customers looks like this. We might say you are qualified with this level of spend with us. You spend a little bit more. We bridge we bring you into the interconnect program, which is us helping you support your data community and really building a culture of data data driven decision making. The next little tier there would be let's go and assess where you are and understand your goals, and we'll help you map them and get to them. Again, we work with customers all the time across all styles, all scales, and all verticals. We can very clearly see this is where you are on the journey, and these are the things you need to think about. And then as you start to get more and more, like, you start to add a little bit more licensing or you start to add other partners that we can qualify you into this plus program, then we can start to customize and be like, alright. We'll help you build this. We'll teach these people this. We will do these things for you. We'll take this burden off of you. It's very customizable and open ended. But these are the types of programs you can get when you leverage your buying power, either with vendors or with VARs or with both to get the best deal for yourself. I've had a lot of really shocked customers say, I just can't understand why they're not working with me. I'm like, you're not growing. You're not using funding. You're not using the things that are inherently to your advantage. So I apologize to my vendor friends if I'm putting stuff out of you know, releasing the cat out of the bag, but I think it's important for you guys to know this, our customers to know this. So those are the five things. Some other things that you might think about, we have products that can help here, on any of the topics we covered, any of the five. So we have SVR workshops where we're really focusing on strategy and painting a vision, and that could be reducing the cost so that you get more value. We can also help you assess tools and make recommendations to fit your use cases and then help you get the best price on those tools. And then, obviously, we can do something what we call BI as a service, which is you paying us a monthly subscription, which then we give you the opportunity to uplift your team or take some of the burden off or round out maybe capabilities that you don't have yet so that you can do this cost effectively. And by doing it in a sort of a monthly subscription, we're able to give you the best version of that in terms of pricing too. If any of these things are exciting for you, contact us. We got a QR code here. Scan it. I think it takes you to a calendar or to a contact us form. Fill it out. We would love to chat with you. I would love to chat with you. You've got great salespeople all over Australia. The worst thing that you can do is take this knowledge, find some ideas in there, but just never do anything about it. So if nothing else, reach out. Thirty minutes, hour. Happy to ideate with you. But all we wanna do is make sure that we're helpful. And if you find us helpful enough to invest in us, great. And like I said, with the plus program, we'll certainly invest back in you. Hopefully, you've had lunch or you have time to have lunch, and we'll see you guys for the next one. Thank you very much.