Thank you very much, Vicky. Welcome everybody, to data monetization for retail. My name is Max. I'm based up in Edinburgh, and I lead the solutions team within InterWorks, EMEA. In the solutions team, we work with several different types of engagement or several different practices within InterWorks. So we kinda match our solutions that we are recommending to our clients in, whatever fashion they, may be coming to us with with help required, mapping those to the various services and products that we offer as a data analytics consultancy. So today, we're gonna be talking about one of the types of engagements that I really like. They're they're they're kind of fun to think about, and they're often very fun to to kind of implement and and really build a suite of products, so that we can enhance the relationship we have with our clients or our clients have with their customers. So we're gonna be talking about how we can monetize data today, and, how we can make the most out of the data that we all have as organizations. We're gonna look at several different ways that we can do that and several different considerations that you may have as an organization that will help you get started along that path. A couple of different things that we're gonna talk about today in terms of, you know, what BI tool we might use in terms of do we use Tableau or Power BI or ThoughtSpot or do we use something that's maybe open source? Lots of the concepts that we're gonna be talking about today are fairly interoperable and interchangeable. So there's there's we'll we'll call that out as we go, but, we're gonna be thinking about the overall kind of solution, in its kind of purest form today. And as Vicky mentioned, please drop any questions you have in the chat or use the QA function, or get in touch outside of the webinar, and we can pick up with you another time that's convenient. So data monetization. Whether this is a concept that you're looking to form an entirely new business around, or maybe a new initiative you think you can have, that could add value to your suppliers potentially or the different brands that you work with or or just your your customers in this, b to c sort of type of solution here. Data monetization offers, potentially new revenue streams to organizations, also can offer a method of generating enough revenue to pay for a solution or a product that is gonna enhance the experience, increase stickiness of clients, reducing churn, and give them a better sort of opinion and feel for your brand generally. Why would I look to get another product in in the similar space as this one when I get so much valuable insights out of the, the the the analytics that have been provided me by, your organization. So this can help promote, you for your customers, could help increase supplier engagement. We'll look at an example of that very shortly. And as I mentioned, decreased churn and maybe even catch up to the rest of the market that is providing these sorts of insightful, data analytics capabilities. So you're now gonna be sitting on equal pegging with people that have already got something that's, that's really exciting and and causing your customers to think twice. So today's session, again, we're we're gonna be somewhat too agnostic. We have lots of experience in this space. Again, there's this Tantal, there's Power BI, there's ThoughtSpot. There's lots of providers. But, again, as I mentioned, we're gonna be thinking about the different mechanisms we might want to consider, whether it's cost models or architectural concepts to really make sure that this is kind of real in your eyes, rather than just a theoretical, theoretical prospect. So first, some words about InterWorks for those of you who might be new to, to who we are. We are a data analytics consultancy consultancy that has been helping our clients with their data ecosystem since nineteen ninety six. From strategic advice to day to day support, we provide our clients consulting solutions to, resolve issues and improve their internal and external data practices. Our solution tend to grow organically from the needs of the industry. So since nineteen ninety six, we've seen BI move from BI one point o to two point o to now this sort of BI two point eight space. So if you want to kind of ask us about anything around the world of data and analytics applied to either, front end visualization, maybe databases and modern databases, how they can change and how you can move to one or how you can get help with your existing architecture, then then please feel free to get in touch. Moving to cloud has been one of these things as well that is is suddenly, or over the last few years has been taking up more and more of organizations' IT spend and, and and sort of cognitive load. It's gonna be something that is, that becomes more and more prevalent, on top of where it already is, which is which is quite significant as we prepare for the world of AI as well, which is very much gonna be cloud first as, as we're seeing. So, whether we're adopting government's tools or building new databases, please feel free to get in touch with us and ask us our advice. We work with lots of small and large firms across the globe, some of which you probably recognize on the screen here. We also partner with products that we think best represent value and the best product market fit, and they all fit a particular niche whether it's ETL or ELT, data visualization, BI, data storage and warehousing, for instance. So we'd be happy to advise any tool selection considerations you might have, if you're thinking of adopting a new technology. I think our track record here kinda speaks for itself, so we'll go through this in too much detail. We are Tableau's original service partner, so we'll mention Tableau a little bit later on, and one of the first with Snowflake as well. So many accolades on screen. But, yeah, hopefully, this this gives you confidence that we have done this before. So what we're gonna talk about in today's session will be the why, the what, and how data monetization works in various different circumstances. We'll look at a few different examples of, companies where they've succeeded in this area. We'll talk about some very some some circumstances where we might actually be involved in data monetization, in the products that we use every day. And then we'll look at a couple of different, explicit case studies of where people have been doing this successfully, outside, or more of a business to business context. We'll get into some of the technical bones of things like embedding beyond and analytics to fully utilize your brand, and take some credit for the solution. Also, people might actually implement data monetization and just put them natively inside the tools that they're using, but they might already be using in the organization with Power BI. Here's some Power BI reports. Go to Power BI dot com, and you you will give you some credentials. I think that's sometimes missing the trick, by embedding your analytics inside a custom website, CMS, for instance, then you can you can get maximum value benefit from that brand recognition and also unlock some extra features that we'll talk about later is how you can optimize that monetization process. We'll also look at things like a tiering process, or a tiering structure for pricing where we may have a bronze tier that might be available and kinda lightweight that's available to all your clients and customers. And then maybe as you move into the silver tier, you might ask for a little bit of, of of revenue in order to open that tier up as well as then the gold tier might unlock even further, capabilities. And we'll just be spending a little bit of time to get our brains thinking of what those capabilities could be and who you could leverage them to, to incentivize people moving to that higher tier. So your data is valuable and your customers need exploratory insights. And that's why you've come to this webinar to monetize your data. So you have valuable data. What do you do with that valuable data? Is it possible to open that up up so that your customers can get access to it in order to to generate some some of the benefits that you're already generating internally and some of those insights so that they could use your service better or they could, they could stay longer as a as a customer? So we're gonna look at some examples as promised. So there's two different types of data monetization that are very common. The first one being indirect monetization. It's almost expected nowadays that software providers share back our metrics with us, similar to how every software at the moment has a sort of AI button. Right? So there's not many products that you can see that has none of that kind of metadata analysis that's coming back at you at some point within those products. So there's three that I wanted to point out here. The first being Spotify wrapped. Hopefully, everybody remembers Spotify wrapped for a moment. It'd been six months ago now, almost the start of December when this comes out. And I don't know if, like me, you're maybe on a several different WhatsApp groups with your pals. And on, the in the first week of December at some point, I think it's around the fourth or the fifth, you tend to get a flurry of people sharing their Spotify wrapped with you. And to be honest, it gets a little bit annoying by the sixth and seventh of December. You're like, alright. You you just listen to the music that you like. Don't have to bring me into it. So Spotify Wrapped is definitely, something that increases the, the the the usage of Spotify, and it's a really big selling point to a lot of people, especially when you compare it to the other products that might be in the market. I'm sure Apple does an equivalent one. They probably call it something slightly different than on Spotify, as you can probably tell. So this allows people to kinda summarize their favorite artists this year, point out things that are kind of interesting. Oh, I didn't realize that I listened to, Taylor Swift as much as I I did. So, yeah, there's there's there's some really nice things that kinda come up as part of that as well. Some analytics back that might say, you know, this new player on the market, you've listened to loads or, these are your favorite podcasts, and maybe you might try checking out this person that aligns with several things. So there's a bit of machine learning in there. There's a little bit of just general analytics in there to show you the play times and how much you've used the product and reinforce how much that product is giving value to you as part of that process, making you not consider maybe moving over to one of the competitors. Also, one of the great things about Spotify, is the social nature. That's kind of one of the pieces that they're really selling to clients, the ability to create playlists and interact with, your peers and your friends, to be able to share things and join jams and all these sorts of things. So it's very much, wrapped into the ethos of Spotify, and it increases that penetration just before Christmas where people might be thinking, well, I might get, you know, my auntie a Spotify voucher for for a year, something like that. So, again, this is indirect monetization. We don't pay for Spotify wrapped, but we do pay for Spotify. And, definitely, I would imagine, maybe people not consider moving to one of the competitors, especially at that kind of time in the year where people might be considering their different subscription services. So very much strategic and, and a case for indirect data monetization. So, again, the difference here is indirect would be you're not explicitly asked to put your credit card details in for this exact piece of functionality, but we are using your data to share it back with you. Likewise, we've got, operating system usage data. Again, this is kind of meta information about how much you're using your device or devices. And it's showing us that and in this case, this is a iOS operating system. It's showing us out of the ten hours that you use your laptop today, maybe how much of it was using financial tools and productivity tools versus social media and maybe other tools that you might have in there as well. So, yeah, this is providing us a screen time overview generally broken down by themes, dates, times, and you can see an increase or decrease in screen time, over the course of the day and the week. And then finally, Google Maps is this kind of overt, you know, usage of your data is being stored. You're being tracked everywhere you go, but it's giving you a little bit back so that you can say, well, I've been to these twenty nine countries and these three hundred and seventeen cities and two thousand two hundred and one places. So it provides loads of content around where you've been to be able to track your history and maybe retrace your steps to plan your next holiday. So the monetary value in these cases is not in an explicit paid service that you receive, but rather in reinforcing the brand and the current product that you're that you're using. So when we think about direct monetization, again, the retail sector, it's an example, that I I keep getting messages about. I'm not a member of, the Strava pro plan, but, Strava is one of these examples where you've got a fantastic free tool that you can use that is tracking you and allows you to show your personal best and kind of rank you against other people that have done the same route and so on and so forth. A great, exercise tool. But there's an additional, ability to kind of upgrade again into that kind of annual model, that monthly subscription, that there's several different kind of plans for as you can see here. And a large part of that is the increased insights, that you can see as part of that tool. So they're using this as a big selling point. And this is not to say, again, when we talk about data monetization, that data has to be the sole offering of these frontier tiers and generating that direct revenue from your customers. But it can genuinely be a very big selling point for people, especially as they want to improve in a certain area, and this helps them optimize whatever area that is, whether that could be, you know, selling things on your platform or running faster. The the the the the concept is the same between both of those. So in Strava, you can filter your segmented leaderboards, for instance, by age, gender, weight, and see full rankings, maybe not just the top ten. So, again, this is kind of talking back to the fact that, actually, some functionality is being deliberately kept out of that free version so that it can be offered to, to people that are are paying that, that company for the service, which is a is a nice idea. So let's look at some examples where they align with data and analytics for, for maybe tools that we're familiar with in terms of Tableau and others, but also in the space where we might just want to embed dashboards and give them to our customers, from a b to b business to business perspective. So the first one I want to talk about today is, available on on tableau dot com as a bit of a kind of overview of, of Ocado's Beat product. This is a tier based subscription. For those of you who don't know Ocado, they, supply, supermarkets as well as customers, with their, with their goods, such as, you know, in this case, I think we've got some, detergent brands on the screen in the the dashboard that we're looking at here. So Beat by Ocado is a supplier interface where they can log in and they can see real time analytics. We need real time analytics for primary sales, to supply chain and product availability of the brands and so on and so forth. There's lots of different offerings as part of this product, whether it's, you know, voucher codes and who they've been used or maybe point of sale, initiatives that may be working or not working within, stores. So, Tableau is the back end of this, and this is, a, again, a portal where as a supplier of very liquid, let's say, or let's let's take a just a generic, detergent brand. That's a supplier or generic detergent brand. And you can log in and see how maybe I compare to my competitors in that market, be it fairy or finish or personal, for instance. So, this gives me massive amounts of, capabilities for optimizing my supply to Ocado to say, maybe my price is not comparing, but my marketing is better, and we want to really go hard on the marketing. Or we can strategize how we're gonna increase our sales in a particular area, maybe geographically or maybe in particular stores, or, based on particularly new products that we think might fit a niche. Maybe a smaller SKU or a larger SKU might be something that people, are are needing based on the competitor analysis that we have. So as part of the beat product, we, helped a lot in this, this initial rollout or the I think it was the phase two rollout. This is a couple of years ago now that we, that we worked on this. And I, was a was a part of the implementation team of our curated product as well as a a rebranding and kind of working with the experience team, at InterWorks to kind of go through the dashboards and optimize them and make sure they all fit together in a nice sort of confluent way. Everything, didn't look like you were embedding, you know, dashboard a in the middle of a completely unrelated page. Everything feels like a singular product, and you don't really see where those borders are, are are kind of when the dashboard's jutting up against the external interface as a kind of window frame. It all kinda looks like one picture, which is definitely something that I would recommend, especially as you want to increase the value and you want to go out to more and more people. We have seen in various different circumstances where, clients might just, use existing dashboards and maybe apply filters, and there's a bit of a sort of jarring interface between the two. As well as that, we also recommend having a site map delivered fairly, early in the implementation process if you're embedding or even creating a, non embedded interface. You want to make sure that you're hitting those requirements that your customers have, and you can use that to then generate a rollout plan, maybe looking at different phases of delivery of certain dash certain data sets to kinda tick off all the core functionality that you expect, and, again, maybe in different tiers as well. So this was built using Interworks' curated product, which I'll show you later. It's a content management system for analytics, which can be used as a branding portal for external users. And, again, that's, an option as well as maybe building your own or there are other competitors in the market in the same way that the different BI tools are available for, for the embedding process. So this was designed with the Intworx experience team and it offers a tiered based approach which we're gonna be discussing, shortly as well, when we think about different ideas for how you can, increase revenue by incentivizing people to move up into higher tiers. The second example that I've got here is Simply Get Results. And we've worked with Simply for a few years a few years here, and they offer a right skilling platform, for their customers. So business to business again. And this is identifying, four employees, looking at upskilling opportunities to maybe move into a different role, thinking about ten years and your, job security in particular roles where you might want to change the direction of your career. As as somebody who works for maybe a large organization, you have options in terms of maybe moving to another team. In this case, it's a great tool for those large organizations to say in these areas, again, maybe geographically, we're seeing that there's more developers or there's more, content designers, or maybe even data administrators that are required in particular, locales or for particular jobs on the market. So they use lots of really interesting data in order to create this, upskilling portal, and it's very interactive. So you can say, I've got these skills. What skills would I need to go into this role? And, again, this is using the Curator product. We're gonna talk about licensing very shortly, where, we might consider usage based licensing to underpin that analytics rather than a role based license. I'm gonna touch on that in the next slide, so I'll leave that there. And and, yeah, it's it's a it's a really good tool that that simply gets get results portal. And, again, we're we were proud to be involved in in their implementation. So how does it work? Again, I've used an example here of, a Tableau cloud, instance. There's a very, very high level, architectural diagram on the right hand side here where we can see that we've got two sites, that are being used within Tableau Cloud. And, again, this this this can be, the same for any of the other tools that you may want to leverage here. The reason we've chose Tableau Cloud here as an example is that that might be being used internally in this example initially. So you may have Tableau. You've got skills in Tableau. Your, developers and and data analysts own and create and edit content on Tableau every day. So they use that for internal analysis, and that's a great reason to consider using Tableau for your external analysis as well. It's a hugely flexible tool. I've been using Tableau for years, so I'm very much singing from from the my own experience and and the input here. And that means that you've got the ability to kinda create and style your dashboards with the functionality that you need, for your external use case as well. You don't have to use the same tool. You could use two different ones, but the portability from one to the other may, may may diminish when you do something like that. So if we are looking to use an external client external facing solution and an internal solution with the same underlying BI tech, then we would definitely recommend having two different sites in order to, in order to facilitate that. Why, you may ask. Why not just put everybody in the one site? That feels like it might be easier. Well, there's there's a couple of reasons for that. The first is just generally governance. So when you're logging into one versus logging into the other, you want to make sure that there's a bit of a separation there so you can be very clear on what tenant you're using at any point because you may be affecting the external work or the internal work, and it's very much easier there to kinda make sure certain people are licensed on certain sites. Also, connecting up your authentication domains is gonna be simpler there to say this is our external identity provider or bank of users, and this is our internal bank of users. And we don't expect any crossover really ever. There's nothing that internal people and external people should be sharing and looking at at the same time. If there is in a strange scenario, then we would expect that to be quite limited. And, again, if it's entirely that, then we might recommend actually, not what your business is. The typical consulting answer, It depends. But, generally, this is our recommended, recommended approach when we're using internal and external data analytics within our organization. It also opens up the possibility for Tableau in particular here to have different licensing SKUs, different license models between the two products. So within Tableau, there are three SKUs which I'm gonna get onto very, very shortly, or three major types of how we are built by the by the product. And, generally, if we have everybody on the one site, the one tenant, then we have to choose one of those, and one might not work for the other. Internal analytics, we generally find that a relatively small compared to external analytics number of people. Our internal employees, InterWorks, we have, I think, fifty consultants in in Europe or thereabouts. So those fifty consultants may be looking at time sheet analysis. They may be looking at what they have planned in the scheduler for the next month, two months. They might be using that internal analysis regularly. They might be logging into that platform and using the dashboards every day. We would expect several times a week in that instance. Right? External people, generally, when we talk about data monetization, are kind of the opposite shift in that kind of balance and that seesaw. There's usually lots and lots of people that use the tool relatively infrequently. So you may have twenty thousand client, resources that maybe wanted to log in or or or clients, customers generally, or representatives of the customer organization. Let's put it that way. So people maybe twenty thousand people might want to log in. Maybe half of them ever do. Maybe five thousand use it once a month. Two thousand use it, twice a month, and we can kinda see a distribution like this. I do think that would be irregular, where people won't be able to use it, but they don't necessarily use it every single day. It's quite a common, shape. So we don't want to be billed for the person on an annual all you can eat basis to say, I want that person to be able to log in as many times as they want. They can log in as many times as they want, but we know in reality, they might not log in hugely frequently. So a usage based model in that case would be a lot more optimal for us as an organization that are being charged by these products. So in that case, we might buy, a hundred thousand impressions. So when somebody opens a dashboard, that might count as one impression. So we might buy a bulk load of impressions at the start of the year, and then we can drill down on that for a different, cost model and different way of, of of of pricing. Again, it depends on the kind of distribution of how many times people are gonna be expected to log in, but that's generally the way we think, we we we find is gonna be most optimal for for paying for these services. So a little bit more on Tableau's pricing model here. This is, some some content from from Salesforce. The owners of Tableau so we recommend and this is something that we work with the, with our with our clients very regularly on picking the right pricing model for your solution. In Tableau's world, there's a few different options here. We've got the usage based model, UBL as it's kinda turned, and that's exactly as I just described it. You buy a bunch of impressions upfront, and those impressions will be drilled down over the course of the year. Similarly, in other in other tools, they might only have that usage based, ability, or or or cost model. There may be discounts available for external embedding. It depends on that tool and the way that they license that product. The more classic or the older version of, of of pricing, is the role based model. I say older, it's just the the traditional model of how we would purchase Table licensing in this case. So both TableCloud and server, you would pay a certain amount for a viewer, a certain amount for a creator, and a certain amount for an explorer. And depending on their range of capabilities, so use of Table Desktop or Table Prep, for instance, they're gonna be on that higher tier, of, of of capabilities, and that then increases the price per user per year. And then finally, there's also the option of having a kind of core based license, which is gonna support a certain number of users, until, against certain number of concurrent users, let's say, so that if everybody logs in at the same time, you're gonna have a large number of cores that are gonna be required by that product. And, if you have a relatively low number of people that are actually using the product, then that means you can have fewer cores, in the underlying server so that you then don't have the performance requirements and constraints. So it depends on the number of concurrent users there, and you'll get charged, based on the number of cores rather than the number of people. So, again, allows you to have huge numbers of people that can log in, but you might open that saturation that not everybody does it at the same time. So there's a bit of a balancing act which one of these solutions works for you. And, again, other tools will have different pricing models. So understanding those pricing models before getting into these, maybe a proof of concept for instance or, a pilot of one of these tools, it's gonna be something to really understand how this scales out, as as you offer these products to, to your customers. So we thought about a tiered approach here. So a multi tiered approach could be considering, maybe three tiers we've got here. There may be more than maybe fewer. It might just be that you have a free tier and then you might have a paid tier and that's it. Kinda like Constava's case. Right? In this case, we've kind of just thought what's the most intuitive for people to kinda understand and get their head around this concept. Let's split this into bronze, silver, and gold. It could also be standard insights and insights pro. We've got the capabilities of branding this however we would like, obviously, which is which is a nice experience and, and thought experiment. So in our bronze tier, this is the one that is maybe out of the box. So when you log in to the client, or sort of the customer portal, of, your website, for instance, there may be some things in there to track previous invoices, to to have a look at the current products that are supported or the things that are on the website at the moment, maybe, alter or delete the web pages. There's probably something in there at the moment, that we can kind of bolt this onto, or we can create an entirely new offering as part, as part of the the data monetization solution. So that basic offering is gonna be something that could potentially be free or it could be a nominal fee, that that get unlocks, basic functionality. Maybe there's a few dashboards, five or six that are looking at kind of content that you would expect as a customer, or or or things that are they're useful, but kinda leaves you wanting maybe slightly more. When we get into that silver tier, we're gonna get additional benefits. So there's a that level up that we kinda suggest. Have you considered these features that might be very useful for you and optimize again the way that you use our products and services? And then as we go into that gold tier, we're getting that really advanced capabilities that might even surpass the expectation of our clients, and, and and hopefully, it's gonna be really compelling, for customers that are looking to, that are looking to, gain more and more capabilities from their data to optimize your product. So we'd expect, again, the kind of length of the bar here is probably gonna be similar to the number of people or number of customers that you're gonna be expecting on each one of these tiers, because, potentially, the bronze offering might might be enough for a lot of customers, but the gold is gonna be the one that's gonna be the most the most compelling. So how does that break down? Again, this is some ideas. These are things that we've worked with our cost our our clients to, to to kinda bottom out in terms of the, the the capabilities that we might be wanting to unlock or lock depending on what stage of, that kind of product tiering we are at. So in the bronze tier, again, some ideas here, if we think of a a a business to business retail brands like and similar to to Ocado or something like that, then we might be showing just your data. So here's how your, your, detergent brand is selling, maybe across the country or, in particular stores. But it's very much descriptive analytics of here's what's happened over the last, let's say, six months. So there's there's the ability to say, this is only your data, and this is only six months worth of data. So really any mechanism here, we've got the ability to tweak. We can kind of open the pipe. We can close the pipe. We can, add new pipes. We can we can kind of remove pipes. We're really drawing that that pipe, analogy as far as I'll go there. But we've got the ability to really increase or decrease the functionality as we want. So in this bronze tier, we're thinking about what is really the must haves. The stuff that's gonna intrigue people and maybe it gives us that kind of go to market go to market strategy to say, these might be limitations for you in the first six months. We we can alleviate those by providing a free trial of silver or maybe you got a two week trial of gold, or discounting year one in order to kinda can upsell, those those additional tiers. So we might have also biweekly extracts here. So our data is gonna be fresh as of every fortnight. So, again, how does how how are we gonna increase that? Again, I imagine over the next couple of slides, you can see how we can improve that. And then we've got some generic styling and brand, very much your company's, company's style, very much part of their product. Again, for people that are gonna be logging in every six months or so, this this is gonna be absolutely fine. And, it's gonna reinforce your brand, to your customers. In the silver tier, again, this is moving up a little bit. I've got some ideas here as to how to, incentivize people to do that by additional functionality and features. So we might have more analytics and statistics available. Right? So that's kind of obvious. You've got, I think we said, five dashboards in our initial, our initial offering on the bronze tier. Maybe there's ten now. Again, I'm kind of making these up. There might be fifteen, however you want to do that. But these are gonna be the things that are not necessarily just the as is or the descriptive analytics. It's gonna be something that is gonna incentivize people to move up into that level. So, potentially, you've got things like benchmarking so you can maybe see an anonymized and aggregated view over your competitors. So the average detergent product in Gloucestershire is selling at this much. You're selling at this much. So you can see how you compare to your, your competitors in, in in in those circumstances. So, it might also in introduce twenty four months of historic data that we have there. So, I mean, we've got four times the amount of data that we had in the bronze tier. And maybe we've got daily extracts. Maybe we've got things that will be refreshing every day so you've got up to date information and you can drive, you can drive action quicker. Maybe it's not really slight, but you could trigger alerts to somebody to say there's been a dip in sales last week or expand on that kind of functionality as well. One thing that we hear often, in these use cases is the ability to download data from the the the portal, essentially. And you might want to hold that back from the bronze tier again just so that that silver tier is gonna be more, justifiable and useful because your comp your customers are gonna have their own analysts as well. They might want to pull that data down, in order to then enrich their own analysis, and and maybe transform that into a data, a data warehouse or something similar. So, again, lots of additional features we could be adding here, but we want to keep some back for that gold tier. Right? So this is your premium offering. And in this case, we're, likely gonna be generating revenue from these clients, and and revenue that will allow us to dedicate maybe some custom expertise to them to maybe create a dashboard for them once a year or, maybe apply some, bespoke brand stylings to those dashboards to say this is the Acme dashboard. Acme are are are really are really using this product a lot, and it's generating a lot of value for them. So let's create a dashboard and work with those Acme analysis in order to to create the optimal dashboard for them. And maybe they get two, three weeks a year of, of of designer and development time in order to create those. We could have customer branding in there. Again, we could split this into either another tenant, or we can use some of the features of our content management tools that might, make the site look a certain way for somebody from Acme to log in. So client logos and different color schemes, different fonts, all these sorts of things can make it feel like it's their offering. Again, this kind of, monetization and embedding and analytics that's kinda being passed down to Acme as their own tool at this point rather than being a centralized kind of supplier offering that's that's, that's obviously part of your company's offering. So there may also be competitor analysis in here where you might be not looking at the benchmark aggregated. This is the average in lost air for detergents, I think I used the example of previously. Maybe we're actually looking at the exact detergent providers there, if we're allowed to do that, which, again, has not been a problem, in the past when we've seen this, where you can actually say you're the second, there's one bigger liar that's the first, and it's by this much. So maybe that's a smaller distance or a larger distance than it's expected as a delta in between them, and you might just want to push that a little bit harder to kinda knock them off the top spot. So, again, that competitor analysis is something that's really enticing, in in the retail sector generally, and it's data that we have, so we may as well diverge that, in any way that we feasibly can. We might also, open up new functionality, Excel downloads, similar to the CSV, but maybe in different formats. We might also have PowerPoint report builders, again, something that we'll talk about when we talk about Intuit's curator shortly. But this might be features and functionality that allow people to schedule PowerPoint reports to be sent to them every day or two days, and this is something or even week probably. This is something that will allow us to go to multiple dashboards, set filters, and then have that scheduled to us in a PowerPoint template that we have, we've created previously. So additional functionality, there's the data side of things as well. So how those pipelines work and how data is ingested into your BI tool, all of these things we can tweak and we can optimize for your, incentivizing of these tools to to your, to your customer base, not just the dashboards. I think that's one thing we hear quite a lot. We say, well, what what's the gold dashboard gonna look like compared to the bronze dashboard? And, again, there's just there's other things we can think about here. It's not just the content itself. So talking of embedding. So embedding BI is something that doesn't necessarily have to be a data monetization example. We've actually worked with loads and loads of customers, who may want to embed BI, into their internal analytics. So there's one portal where people can log in, and they might see Tableau. They might see Power BI. They might see Faultspot dashboards all in one central location, which is where we came, up with our, our our tool Curator, originally built for Tableau, and now it offers functionality for several other products in the space as well as being able to embed other tools and, any any content from another site that, that that you, organize and manage. So why would we want to embed analytics internally? We want to embed things where where analytics kind of where they're used, where they're needed. So that might be in something like Salesforce, for instance, where, you're maybe looking at those Salesforce leads and you want to, you want to maybe have some more information on each of those individual opportunities that lead to your contacts so that you can see what they've done recently and you feel comfortable developing and showing that in Tableau. So that can be, relinquishing the the requirement for somebody to log out of Salesforce and open a dashboard on a different tab and maybe move them side by side. So you want to meet people where they are, and embedding is a great way of doing that. Perhaps ticket management, for instance, that system allows you to, you may be scheduling next actions on a particular ticket, and you want to see that something has maybe been closed four times before, or or maybe you've got in touch with the person four times, and now you think it's probably about time you you can close it. This gives us customizable interfaces that we can thread inside those tools to give us more insights. We can prioritize and we can focus content by embedding internally. So if we have new colleagues, for instance, and they're struggling to understand the various different statuses and places that they need to go to in internal tools, how much PTO have they, accrued since they joined, what's their sick pay allowance, what are the policies for both of these? This can give us one interface where we can direct all of those new starts to in order to show all the analytics that they may have at, they may have, the requirement for over the next few years as well as maybe embedding some of these other concepts and and different portals inside that tool as well. So we can focus this down, but we can also offer things like custom navigation. Sometimes these tools that we end up using, can be quite complicated, especially in terms of our dashboarding tools. So sometimes it depends how you set these products and services up or at least these suites of tools up. But we often see an environment of one of the big BI tools where there's hundreds of different projects. Governments is a bit tricky. We need everybody to be able to see everything, but now everybody can't see what they need because can't see the wood from the trees. So, we I've actually worked on a project with, with Curator, a while ago where, a, bank really wanted a kind of c suite or or upper echelon kind of reporting tool where they can really slim this down as as people who obviously need to be able to see as much data as they can. They want something that's gonna be nice and succinct so they can log in to that regularly, and they don't want to have to learn an entirely new tool where there's buttons and wizards everywhere that you can, that you can kind of say, when should I click that? What does that button do? It can strip all of that back if you design it in the right way. And then we can, again, we can offer customized navigation so that regardless of what project or folder structure or workspace, an object is a dashboard or or a different type of analytics offering, we can just combine that into a custom navigation to say, these are your end of your reports, these are your marketing reports, these are your sales reports, and we can just structure it in any way that we want to and maintain the permissions that are in our BI tool. When we're embedding externally, that's a little bit different. So, we obviously are gonna be applying a lot of navigation and a lot of branding so that we can really reinforce our brand to our customers. So we want to build trust. We want to build transparency with our customers, and we're sharing the data because of those, those, requirements and objectives. So we want to hammer home our brand. We want to make sure that everything is gonna be looking and feeling as we want it to, and we're not even gonna mention that this is actually embedding a Power BI dashboard, for instance. It's gonna be smooth and it's gonna be seamless within our offering. And again, we've got the upsell of additional context so we can make sure that maybe we have more instructions and all the things that we talked about on the different tiers. And we're gonna solidify that brand awareness and and make sure that if people are maybe considering churning or there's other competitors out there, we're really nearly going with the value of our brand and of the offering that we have within our suite of tools or even in our in our tool. So, again, like I mentioned before, Interworks Curator is built for this, this this this style of engagement, and we've done it several times by implementing data monetization, solutions with our customers. It's not the only option out there. Obviously, there's lots of different content management tools, but I thought we would just kinda spend a second to talk about embedding analytics in the context of Interbox Curator to see what the art of the possible is and what advantages, that has in a term in terms of kind of build versus buy. We do see quite often as well when people are looking to build their own content management system, they kind of underestimate how much time that this might take. It sounds to some people like it would be very simple, and some people, it makes them a bit squeamish. And I can understand both of those perspectives after kind of looking at a few different examples of where this has worked in the past. Often, we do underestimate how long the authentication permissions process management on the back end is gonna take. But generally, Curator is a great way of getting off the ground quickly, I would say. You can trial it out very quickly and easily, and then it's just a case of enriching it with styles and functionality and content that you want to show to your end user. And by trialing the product out quickly and kind of looking at that proof of concept or proof of value early, then you can just build that as a foundation and say, right. This is our offering. We need x y z dashboards, and we would like to have this additional functionality. Let's investigate that a little bit further rather than kind of reverting off to building something from code from scratch. So it offers flexible white labeling. As you can see here, these are all built within Interworks Curator, so you can kinda make it look in any way you want it to, which is really useful. Again, we do recommend having someone with kind of brand expertise and, graphic design knowledge in there to make the most out of it, custom icons, making sure it aligns really tightly with your digital marketing experience, and, and assets that you might have within your organization. It was built for Tableau, but there's lots of other tools that that it supports as well as I mentioned, and provides extra functionality like the PowerPoint scheduling I mentioned earlier, global filters and parameters, loads of really, really cool stuff that that is only available through its integration with the rest API of the underlying tools. Often, yeah, it it does offer a flexible hosting as well. So you can host yourself or it can it can be a a software as a service offering, and it's quick and easy administration. There's no coding required. This can be, you know, huge for organizations that maybe don't have web front end developers internally. You don't want to have that code heavy system where, yes, it works for now, but do people understand how the GitHub repository works and how we merge and branch the code and making a change over here is gonna actually bring something over here. So the fact that this is a no code solution can help organizations get started without that, without that hurdle of having to hire one or two front end developers in order to work on this project over the next few years. So again, fast tracking. It's a lot quicker to kind of build, sorry, buy rather than build in this case. And again, there there are other content management systems, that you can create to in order to embed and and procure in order to embed Tableau and and, and the other tools that we mentioned previously. It's a consideration. So, again, build versus buy. The big thing I think for us is that the the the costs, in terms of, building yourself are often underestimated, I would say. It takes a lot of development time. It takes a lot of knowledge to be able to tick off a lot of these things around authentication and security, as I mentioned, Dashboard management, that's kind of maintenance costs and operational costs from a day to day perspective. JavaScript API parameters, I mean, it depends on the software that you're using to embed, but, generally, there's some really good developer forums out there and developer elearning facilities to kinda give you lots of structured information about how this back end integration and embedding works between the two tools. But also the design flexibility here. If you want to change that color or change that icon, how do you do it? What's the what's the process, and and who can own that and who can do it, and what skills do they need to have? When you look to buy a tool, again, like Curator, and this can be owned by analysts rather than owned by developers, which is a big difference. Again, there's lots of reasons why that might be, might be advantageous to have analysts owning this, but, ultimately, it's their content as well. So they understand the use case for using this dashboard over this dashboard, and they can plan the interface around that knowledge that they have as well. It's quick, and it's easy to install. More time available for designing and iterations. And there's native integrations rather than building up those connections to those tools and interfaces by scratch, or even, you know, by digging through underlying code. And we've got fantastic support team as well. So sales pitch done. Let's have a look at data monetization when it comes to, a visual interface that we may want to use for, our web front end. So this is a demo. Hopefully, you're not seeing a blue screen with the word curator on it. Hopefully, you're seeing, a black banner on the top and then a white page underneath that, and a drop down list that I've just clicked. If you're not seeing that, then please share because I've shared the wrong of the screen. So, in this case, this is a demonstration that I will drop into the chat very shortly. In fact, I'll do that now because I will forget otherwise. So this page is publicly available. It's just a nice little example of how we can think about some of the concepts that we've talked about throughout the last hour. There's several tiers that you have available. And in this case, we've got a basic user and a pro user. So we've got the two sort of tiering systems. And don't worry. You don't have to know a password to log in to this. We can just click on basic user, and that's gonna pop in the password, and then we can log in to our data monetization example. Now this isn't styled as beautifully as some of maybe our retail demonstrations or or or our financial demonstrations and microsites we have for the tool. And if you're interested, just get in touch, and we can run that as a separate session. When we log in as this basic user, we have a terms of service feature. Again, you can enrich this with extra content rather than just looking at dashboards within this product. This terms of service might just allow people to read a little bit of boilerplate to say, yeah. Well, you have said that this is okay, and then you sent it to somebody else or you maybe exchanged your credentials on Reddit or something like that. You can kind of write everything in here that you want to guard against and protect against. So by accepting terms of service, as as the offering, as the offering opens for the first time, you can tick any of those kind of boilerplate boxes. And then we're welcomed into the full product here. Now we can see that there's not a lot of content that we have available to us. And, again, that's kind of by design. There's navigation that we have at the top, fully featured navigation in terms of our, KPIs. We've got that terms of service again. We've got things like an upgrade form, so our ability to upgrade into that secondary and tertiary tiers. And then there's some more information that we've threaded in here, around kind of instructions and things that you could learn more about, in Curator as well as requesting starting a trial. So a lot of these might link out to separate work, separate websites, Rhonda. So this is obviously the capabilities are available, to yourselves if you were to use this as a data monetization platform. So it doesn't necessarily have to only be dashboards here. We can embed other tools as well through my frames or JavaScript APIs, and authenticate people using single sign on so that everybody is, authenticated on all their different tools as they log in to Curator. We've also got things that are really simple, but actually just make a big difference like the ability to search across all the content. Like I mentioned for the financial example that we had earlier, if I'm, you know, CEO of a large investment bank, for instance, then I know that I'm looking for something to do with portfolio management, but I can't quite remember what the string of phrases were. And I don't have time to go through all the hundred different projects that are in there. So very simply, you can just look across everything that might be available within that platform from, PDFs that you can download all the way through to visualizations and, and and, dashboard assets as well. So, again, sometimes the simpler things are the most impactful, and we can certainly see that with, with with, Curator. So then we've got, our basic level dashboards in here. So we can click, your account to get some additional metrics here if you needed to. So if I click on this dashboard, we can see exactly how that works. So there's firstly, because this is the first time I've logged in here, we've got instructions. So the first thing might be a kind of pop up tutorial for the customer to stop them phoning your help desk, where we might say, this is how you use this dashboard. This is a Zresol better. So I might want to click on here and have a look at these are the different KPIs. These are the toolbars. Okay. I'm comfortable with it now. Don't need to show me that again. So now that I'm in the dashboard, I can then use, the Tableau dashboard as it's intended here. And everything, again, in this very lightly black style, black and white dashboard here, everything, sorry. That's not right. Everything within my red rectangle that I've drawn here is coming directly from Tableau, and everything outside of that is the content management system that you want to embed Tableau with. So we're seeing in this case, again, there's not a lot of brand, brand sort of recognition or kind of reinforcement here, but we do have, nice smooth borders between the two so that you're not necessarily seeing that drawing up against, the, the the outside world of the content management system. So if I just quickly log out as the basic user, let's log in as the pro user and see how that ends, that that implementation and that interface changes. So if I click on the pro user, we can see this maybe akin to someone who's getting the silver tier. So now when we log in, we can see we have more dashboard. We've got different over overs. We've maybe got, different content that we might want to be able to leverage from a navigation perspective as well. There might be searching for your data, so be able to align with, new functionality, being able to kinda Google the answers to your questions as you go using a completely new interface, in this case, like ThoughtSpot to be able to use natural language to be able to query your data and build your own analysis as you go. Maybe something a little bit more kind of ad hoc analysis that you're not gonna save. You just want to know the answer to the question, and you want a different interface to be able to do that. So, again, lots of food for thought, I think, when it comes to embedding and their web capabilities outside of our BI tools that we might want to be, embracing or at least considering in order to make data monetization a success. So I just want to touch before we go, three minutes left. If any of this was interesting to you. So if there was architectural conversations that you wanted to, kind of flesh out a little bit more, if there's maybe tool choice conversations that you wanted to look at, as well as potentially thinking about the sorts of content that are gonna be more useful in your kind of go to market strategy or as you roll out the bronze tier, the silver tier, and then the gold tier in in in six months or something. How do we structure those projects, and how we recommend from a technical and a kind of business and implementation point of view. If any of that is interesting, then please feel free to either get in contact with InterWorks via the, contact form online, or also, you can email one of the people that you've been working with at InterWorks as well, and we can discuss any challenges you may have, think about maybe trials of new products and things that we recommend in this space, as well as just mapping out the strategy that you have have for data monetization within your organization, and, how that looks over over the next year or so as you make it reality. So I hope you found that useful, and I hope to speak to you all again very soon.