 Hello and welcome my name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We would like to thank you for joining this Data Diversity webinar, Effective BI Portal Design Patterns to Drive Higher User Engagement, sponsored today by Metric Insights. Just a couple of points to get us started, due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A, or if you'd like to tweet, we encourage you to share highlights of questions via Twitter using hashtag Data Diversity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And just to note, Zoom chat defaults to send to just the panelists, but you may absolutely change it to network with everyone. And to find the Q&A or the chat panels, you may find those icons in the bottom middle of your screen. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now let me introduce to you our speakers for today, Mike Smithman and Marius Moscovici. Mike is the VP of Sales and Marketing at Metric Insights and has over 15 years of product and marketing experience in the business intelligence industry. He has helped bring analytic products to market with senior roles at Seagate Software, AIM Technology, Tealief, Extero, and Good Data. Marius is over 20 years of experience in analytics and data warehousing. Marius is the CEO of Metric Insights, the leading provider of a BI portal that helps organizations organize their BI environments and ensure users are getting the actionable data they need. And with that, I will give the floor to Marius to start today's webinar. Hello and welcome. Oh, you're muted. Let me unmute you. Okay. Can you hear me now, Shannon? I can. Okay. Well, great. Well, thank you, Shannon, for the introduction. Welcome, everyone. And today, we're going to talk about the number one problem that I think plagues BI environments pretty much universally. And that is poor engagement. So, you know, typically if you were to go in and you looked at your environment today, I would bet you that whether you have a Power BI or Tableau or MicroStrategy, it really doesn't matter what BI tool or stack you're using. If you've had your BI environment in place for at least a few years and you go in and you look and say, geez, of all the content that's been published inside of the catalog for these BI tools that I have, how much of it is actually getting used? You know, how much has any engagement in, let's say, the last six months? You might be surprised, or perhaps you won't be surprised to learn that usually less than 30% of that content is being actively used by anyone. And then if you ask the similar question of all these BI tool licenses in my organization, how many are actually actively used by users, typically less than 50%. And in fact, these numbers actually pretty good for most organizations. We did an audit for one of our customers recently and we found that less than 10% of their content was being used and about the same amount of usage was happening from a licensed utilization perspective of the BI tool. So this is an environment with thousands of pieces of content, thousands of users. And really, if you think about that, it's sort of shocking that 90% of that content was things that nobody really wanted to use and the issue with engagement was so severe that hardly anyone was really using these very valuable BI tool licenses. So given that this is a serious issue, if you were to go and say, well, okay, let me ask my users, what's going on? Why is this happening? What would they say? Well, their answers you would hear would be things like, I just don't know where to find the information that's useful to me. Obviously, if there's so much clutter in the environment, so much content that's not useful, then it's hard for people to find the content that actually can be useful that they can take action with. Or you might hear something like, well, I know that I have the information there and maybe I even know which dashboards to go to. But when I have a question to answer, my information is kind of buried across many different dashboards. Some of the information I need is in this particular tool and this dashboard and then there's this other dashboard I need to look at and then oh, sometimes it's this stuff in the PDF or Excel, I just got to span this wide surface area of content in order to answer sometimes what feels like a simple question. Or you might say, well, because of these dashboards are so complex, there's so many filter values, there's so many layers and tabs. It just takes a long time for me to get any insights from them. The insights there, but it's not an easy process for me to engage with it and answer the particular question I have and get the value. Or a very common complaint you might hear is, well, I just don't really get the data in the dashboard. So I see the says sales, but how is sales defined here? Is it the same way that it's defined in these other dashboards? Does this use the newer definition that our business group just agreed on last month or is this used based on some kind of obsolete definition of sales that was effective three years ago? You know, what am I really looking at? How do I know I can understand it? And if I'm seeing different numbers, which is often the case across different dashboards, different presentations, how do I know which one's the trust? How do I know that this number is certified and trustworthy and this other piece of content is maybe a little bit more suspect? So how do I reconcile this? And these are the things that you'll hear kind of irrespective of the environment from the people that would be consuming the content, but they're not. And at the end, if you think about it, if you look at all of these statements and what do they really mean? If you were to boil that down, what they're saying really is very simple. They're saying that the juice is not worth a squeeze, right? The level of effort that they have to go through in order to get value out of the analytics is not consistent with the actual value that they're generating. So consider for a moment sort of a typical journey that the user goes through, maybe going into your behind environment, looking at final answer to question. They want to be able to try to do some analysis or maybe they're just trying to understand how is my business changing today, right? Just even that simple thing and I want to be able to go in and figure that out. Well, they're going to go through this whole effort to find that out. They might open one dashboard and say, oh, well, okay, that's not the right dashboard. This is a wrong version. Okay, let me find another one. I know I found the right version, but this one has numbers pretty much look the same as they did last week when I looked at them. So nothing's really changed. I didn't get any value out of that. Even though I went to open it. Let me look at another dashboard that maybe looks at the numbers, a different KPI that I'm interested in. And what does that say? Well, that didn't change either. So I'm spending a lot of effort as a consumer typically using dashboards. And the reward isn't there initially. Now, maybe over time, after I spend maybe 10, 20 minutes looking through all the different analytics that are at my disposal, slicing and dicing that data, looking at different slices, I might notice, oh, my sales in the Northwest is a little below today. What's going on? Let me drill down into that. And I'll use the dashboard to dig into that and I'll discover what this product is driving, this drop off in sales. Oh, it's happening in this particular channel. So I can go through this journey with the data. Once I kind of find something interesting to dig into as a user, there's something that really requires my attention, that requires me to do something. I can dig in with the BI tools and eventually using the analytics that are available to me. In most cases, I can actually get some value. I might figure out something that I need to take specific action on based on the information that I saw. But the point is that this journey, it takes a really long time. It could take 10, 20 minutes sometimes. And the typical user, the typical user in your organization, the truth is there may be willing to give you one or two minutes a day. That's all they're going to spend kind of looking through. They're BI dashboards. And then if they don't see something useful there, if there isn't something generating value, if they're helping them answer a question or helping them understand where they should look to be able to take some action, then they're just not going to continue with the journey. They're not going to continue putting in effort. And not only that, they're going to conclude after doing this maybe a few times that juice is not with a squeeze. So I'm not getting enough value for the reasons that we just described. And so I'm not going to come back. I'm not going to come back and look at the BI analytics. And hence, you get that low engagement. Hence, you get that lack of real value from the hard work that goes into building the data pipelining and the assets and creating the visualizations. And this is what happens really continuously in every organization. So how do we solve this problem? What can we really do to generate engagement and not only generate a kind of a one-off basis, but create sort of a compelling experience for your users such that engagement is sustained. So users are coming back to the analytics that are generating value for them time and time again. That's what we're going to talk about in this presentation. So here are the key principles that you need to adopt to make that happen. The first step is to take the chaos that is represented by most of the BI environments and generate order from it. So I'm reminded here of a, I don't know if any of you on the listening have watched this, but back about maybe almost 10 years ago, there was this really popular TV show. It was called Hoarders. I think it was on maybe Lifetime or some cable network. And this whole show was about, they would follow a individual who their house was just filled to the brim with junk. Like, you literally could not get from one side of the room to the other. And the entire premise of the show is that producers would help them declutter their environment. They would help them let go of all the junk and during that show, they would transform their home into something that was like a really functional pleasant place from this completely congested, terrible environment. And this is exactly the problem that many BI environments have. If you go look in your BI tool, and if in fact was a 30% or 90% of the content that is sort of clutter, right? You're either way, you're creating an environment where somebody coming into that for the first time, maybe they're new to the company, they've just got to bump into all kinds of things that are obsolete, duplicative, slightly different variation of something else to try to find the thing that actually is going to generate value. So it's just, it's chaotic. And what you want instead, is you want much more of a museum experience, right? Like think of a museum. When you go to the art museum, it's not like they just throw all their paintings up on the wall in some random way, right? It's a, you go to an exhibit and it's their best artwork that addresses that particular theme or topic. And not only that, but it's carefully organized and curated in a way to maximize the impact on you as the audience when you're consuming that work, right? They look at lighting and placement and all these things and think about how you're going to guide you through that experience of consuming the art so that you get the maximum impact. It's the same thing with your BI environment. You need to have a way that everything is ordered so that you can get value in a way that is engaging to your users, right? The second key principle is that you need to have a compelling user journey that's based on trusted content. So, you know, a compelling journey is the key piece. So all too often, one of the key mistakes that we make in BI is we'll build a dashboard and we will not really think about the journey that the user has in consuming the data. You know, that is how are they using that information? What questions are they trying to ask? What are the things that come up day in and day out for where they really need this information to help them make their best decisions? And then have we designed the portfolio of assets that they have? Because there's usually more than one dashboard. All the different assets they have, are those things accessible to them in a way that they can navigate that trusted content and support their user journey, right? Support the place that they want to go to. So this is key, shifting that paradigm from a how can I take this data set, visualize it, slice it and dice it so that you can answer as many questions as possible with it, which is the traditional dashboard paradigm. Transforming that into more of a, okay, how do I take this particular persona and the way that they use data and create an experience that guides them through that journey in a way that they can answer the question effectively? That's the key distinction there. Then the third key aspect of it is that that journey needs to be accomplished quickly, right? Going back to the juice being with a squeeze, you've got to get your users to value very quickly because if you don't, they will give up and you'll never have a chance to generate value. So what does that mean? That means minimizing the number of clicks that a user has to satisfy the journey, minimizing the number of times that a user has to change context between one visualization and another, minimizing the number of dropdowns and filters that they've got to navigate to actually get to something meaningful. Looking at all those things and thinking about how do I take them from what they're looking to for to the actual answer into something useful in the shortest possible way and in a way that feels pleasant and engaging to them, right? And then finally, this is equally important to all these ones above. You need to integrate or weave data literacy into the experience of consuming analytics. All too often, the experience of a user consuming data going into any BI platform is that they'll open up a dashboard and they'll see numbers and they really don't have any idea what is the context for those numbers, right? It's going back to the museum example. You go into a museum, you look at the artwork, there's a little plaque that explains that, aren't you? It gives you an idea of, you know, maybe tells you something about the artist, maybe tells you about the context for that artwork in terms of, you know, when it was created, who paid for it? What were the issues that were happening historically at that time that motivated that particular piece? How does it fit into the overall genre that you're looking at, right? So there's all this effort that comes into giving you contextual understanding of the work so that you can really appreciate and understand it. Same thing applies to BI. When I look at a visualization, I need to understand what are the numbers? What do these mean? What's the definition of this particular term? Who signed off on this definition? Is this something that the analysts define on their own or is this an enterprise-wide term for how we measure sales or churn or is this particular cost or things that I don't know? Where can I find out more about those particular rules? Because if I don't understand that context, then I don't really understand what I'm looking at and I can't make the right decisions with that visualization. So data literacy and context, just as important as the data itself. So with those kind of principles in mind, in the remaining part of the presentation, we're gonna talk about practical things. So how do you take these principles and how do you actually put them into action? Create something that is going to be, you're compelling to your users and it's gonna make these principles come to life. So let me talk about some of the pieces that you'll see and then Mike will show you, in a demo, he'll show you a little bit about how we've brought those to life in our product. But the important thing here is not the solution itself, it's more understanding the irrespective of what technology you use, whether you've metric insights, some other portal, build something yourself. If you're going to be successful in generating sustained user engagement around your BI platform, then these are the kinds of patterns, design patterns, if you like, that you need to apply. So think of it in that context rather than any specific tool. So first of all, from a consumption perspective, it's very important when we talk about journey, right? So journeys are personalized. Somebody in your organization, when they look at their data journey, that journey is gonna be defined by the role that they have within the organization, what they have to do day in, day out, how they use information, the kind of decisions that they make. So that's going to be, maybe there's obviously groups of people that share the same journey because they have the same role within the organization, but the journey must be personalized to that role. So if you are looking at a solution and the solution is just, here's all my content, go at it, then you've already failed in creating a compelling experience that's gonna generate engagement before you've even gotten out of the starting gate because it's not a personalized experience. So having a landing page where I have my content, I have my visualizations, et cetera, I have ability to get the context for that content, help things of that nature, critically important as a first step. Another key thing to identify, I understand from a design pattern is that more often than not, people are overwhelmed by the amount of information that's at their disposal. So even if you were to declutter the environment, and you say, okay, now I've really got these set of visualizations that these users should be using because it's the valuable, it's the good stuff, right? It's a certified content is really good. Even if you were to do that, you're still dealing with a situation where because of the complexity in most of your business environments today, people are working maybe with a dozen dashboards, right? There might be a dozen different between dashboards, excels, PDFs, things like that, a dozen, sometimes two dozen pieces of our assets of information that contain valuable insight that could be relevant to that particular role. So you can't expect any given person who's not an analyst, who's just a regular business user to go in and open up a dozen or two dozen pieces of content, flip through filter values, and do all the grunt work to figure out what's really going on, right? You've got to bubble that up, right? You want to be able to bring up just the KPIs, just the high level numbers. And not only that, you want to alert them, let them know, bring them in, grab their attention for those things that require attention. So if I have two dozen visualizations measuring all manner of KPIs that are interesting to me, but today there's three or two numbers that have actually moved in a significant way, those are the ones I want to look at. So don't make me go through those dozen visualizations, discover what they are, tell me upfront, here's the three numbers that you should look at today and then show me what those numbers are, give me understanding of how they're different from what they were yesterday or a year ago, whatever the right reference period is. And then of course, I'm going to jump right in and dive into that and get me into that dashboard where I can roll on my sleeves, dig in and really understand what's going on. So taking that top down view where you're going from the high level numbers down to the dashboard rather than dumping your users directly to the dashboard, absolutely critical if you're going to drive engagement and sustain value from a portfolio of content that is available to users. Then another key component is understanding that, yes, there is a journey and the users are going to come ask questions. Sometimes you created that experience but maybe there's something I want that's outside of that path. I want to step off my regular path. I want to find something else. Search is very, very important. So again, if I'm going to change my journey, making my get the value very quickly, again, as the principle, to do that, that means I need to have search that allows me to find content effortlessly. It should be as easy as Google and it should be as smart as Google. You need to be able to bubble up that content in a way that I'm not going to be given things that are not relevant. Things like how much engagement content has us factored into relevancy. I can find the content that's highly rated. I can find content based on different tags. There should be many different ways for me to work and find that content and it should feel effortless and intuitive to me as if I was searching for something in Google where there's a lot of intelligence behind the scenes to make that process easy. So that process of discoverability is very important. It's also important to understand that discoverability extends to more than search in a simple way, in the sense that you have to be able to make certain content visible to users that maybe they don't yet have access to. So we all have security models and a particular BI tool may be only accessible to a certain group of users and that's fine, but you ought to be able to say, hey, I'm going to allow my content to be the metadata around this content to be visible to a broader set audience. They can discover it and then they can request access to actually see the data. So having that kind of mechanism, very important to make sure that content doesn't get lost or that people don't request duplicate content to be created. And then integration of metadata. So we talked about the museum example, the context, data literacy. Metadata is what generates data literacy, right? It's what provides context. So provide ability to understand this visualization, what kind of enterprise KPI doesn't measure, is it certified, who certified it? When was it certified? All of that kind of information needs to be organized and presented with the data. There's no sense in having a data catalog somewhere, a relation or a caliber or name your data catalog of choice where you're keeping all this lineage information and definitions and glossary. And you've taken all this effort to consolidate and bring all that together and put it there. If that information is not integrated and woven into the experience of users consuming data, right? I mean, how are you gonna get value out of that if it's only the analysts that have it? You need to be able to make it visible to the end user of the analytic because that's how everybody's consuming the content. So with that, I'm gonna turn it over to Mike and he's gonna show you examples of what we actually mean by these. What does it actually look like when you create one of these design patterns in an actual solution? Mike, I think you're still on mute a few. No, I'm good, just kidding, I'm excited. So, yeah, thanks, Marius. So as Marius said, I'm gonna take you through sort of what some of this looks like in the metric insights environment. But again, technology aside, how might you go about sort of peeling back the layers to solve some of these issues and remove some of these barriers that your end users are having as it pertains to sort of engaging with BI? Not to belabor a point, but the challenge we're really trying to solve is that BI environments in large organizations continue to be very fragmented. We probably have multiple BI tools. We've definitely got things in SharePoint with spreadsheets and documents. There's a fragmentation of BI across the organization. And today, typically what we expect users to be is kind of somewhat of an expert in each of the tools so that when they go to, if they figure out the dashboard they want is probably in Tableau, we require them to go to Tableau and navigate an organizational structure of sites and projects to find that particular dashboard. And then they figure out something else is in Cook Sense or Power BI and they have to go there and navigate through that. They have to log into each of those technologies. And it doesn't take long and it doesn't take much of a barrier to be put in place where people just stop doing that, as Marius said. And so we need to start layering on some pieces to the pie here in terms of solving that problem. And the first is kind of removing the complexities of that fragmented environment. So while you're looking at here is the concept of putting together sort of a catalog, a portal of content so that users have one place to come and access and interact with BI regardless of underlying source. So you need to be able to have a centralized catalog where the key content gets published. In this case, you're seeing these tiles in the middle of the screen here representing certain reports and dashboards coming from different tools and technologies that we wanna publish within to the catalog. And we're organizing that content in then a single sort of navigation paradigm whereby, again, I don't have to know how things are organized and arranged in the underlying tools because reality is that's often different and tool dependent. So step one is making sure that we can consolidate everything into a single space. When we consolidate that information, we also wanna make sure that we are adding the necessary context that Marius spoke about. So this is a whole other sort of area of discussion but as we publish content out to our users, we need a process that almost enriches that content with the necessary metadata, wherever that might be coming from, that gives them the necessary sort of information they need to either decide this is a report that is gonna be useful or not, or if it is, what is sort of the context behind that report? And obviously that's things like making sure it's named and has an appropriate description, but it's also things like understanding ownership, who's responsible for it? How is the content classified or categorized because that ultimately could determine how I use this piece of content? Is it internal? Is it publicly facing? Does it contain PII sensitive data? Because that's gonna dictate ultimately how I use it. And also kind of what are the ingredients of this piece of analysis? What metrics does it contain? How are those metrics calculated? Who owns the definitions of those metrics? So that again, if I go and look at this piece of content, it is clear what it is I'm looking at and how I should be using it. And clearly, if I drill into this and I can then interact with the live application. And again, it shouldn't matter whether this is, in this case, Tableau, whether it's something coming from Power BI, whether it's a spreadsheet we have up on SharePoint, the experience should be a consistent one for our users. Now, even if we centralize things into a catalog like this, as Marius said, if you look at the majority of consumers of information in your organization, they're probably going to give you a minute or two every day to focus on data. And if we can't sort of answer some questions in that time, they're just gonna pick up the phone to the BI team and get those questions answered. So what we wanna do is really take that content that we now have centralized. We're obviously gonna layer on our permissions and security model. Ultimately, we wanna craft what Marius referred to as a journey. We wanna craft an experience for a particular type of persona in the organization. And I'm gonna kind of just pick one here as an example. Maybe we've got sort of the exact, the senior management level persona. They're generally looking across the business. Sam, in this case, has a particular focus on sales and marketing. But it's really tracking stuff across the organization. We are an environment that has multiple tools and technology that contain BI. And so there is a lot of content that from a permissions perspective, Sam has access to. And so what we wanna do is basically take what he has access to in the portal and craft an experience for him that is gonna capture his attention as quickly as possible. And in a lot of cases, that starts with a more welcoming experience than typically you would find if you delve into individual BI tools or even a catalog like we were just looking at. We want something, design is important. It's an important piece of this process. We want something that, this is just an example, but that is branded to your team or your organization. We want something that's familiar. We want something that's gonna be attractive to this user and entice them into delving into things more. Ultimately, from a functional perspective, we wanna make sure that a welcome page, a landing page like this, a storefront, if you like, to the content is gonna get them quick access to the most relevant content that they have. So think of designing, again, a sort of welcome page for each of your personas which is structured and branded in a way that is going to appeal to them. And in this case, what we've done is, on the right-hand side, we've included any help or knowledge-based articles or FAQs that might be interesting and help Sam in his journey here. On the left, we've taken the critical elements out of the catalog that we want to basically share and organize in a quick way. So with a single click, I can get into that content. So maybe there's some sales and marketing reports that are critical to Sam out of the catalog. Maybe he's curated a set of personal favorites within the catalog that he looks at often. And typically the first thing we wanna do is give him quick access to that content. Now that content is probably coming from multiple technologies, as we spoke about before. So some of the marketing stuff is in Power BI, others is in click. We run our Salesforce reporting and Tableau. There's probably a host of different technologies on the backend. And I don't wanna have to flip through those technologies. I don't even wanna have to flip back and forth through the different tiles here to access that. I wanna give this particular user a navigation paradigm that irrespective of tool, lets me flip through the key folders of content that have been curated for me or that I've curated myself so that I can go from Power BI to Tableau in this case or to any other spreadsheet or tool that might contain BI that's interesting to me. Yeah, it's a small thing, navigation. But again, if you think about what we ask people to do today, not only are we asking them to navigate for a set of content in an individual tool, we're asking them to do that across technologies. Again, even if it's you have a single BI tool but you also have spreadsheets and documents up on a file share or a SharePoint, they still have to take the time and effort to go to that. So a welcoming experience, a curation of the most critical content that hides the complexities of the underlying technologies and where that content exists because Sam doesn't care ultimately where we're creating the reports. You'll notice something as well that as I navigated through those technologies where it essentially has to do a single sign on as well for it to be effective. So I also don't wanna have to remember my logins and passwords for individual tools because I can never remember. So we should take care of that for the user and let them seamlessly flip through those reports as we were just looking at. But again, as Marius said, even if I take the time and effort every day to flip through that curated set of content, nine times out of 10, not much is typically changing in the data. It relies on a certain level of scrutiny to be able to go into that content and check it every day and make sure I'm not missing anything. So something you also wanna think about doing it's a capability on the metric insights platform is to take some of the key performance indicators that exist within that body of content and almost bubble it up as a set of leading indicators that we can help to drive behavior. And what that looks like in this case is maybe there's a set of metrics that Sam is interested in and we can bubble up to his welcome page, which of those metrics or how many of those metrics are under performing on this particular point in time and allow him to drill into that to get a good view of what's going on across the business. And so you're seeing different groups of metrics here in the middle, probably likely being sourced from many different technologies, certainly many different dashboards on the backend. And we wanna present that in a way that very quickly he can scan this. We're doing basic analysis against those metrics to highlight ones that are in the red in this case. Very quickly I can scan the metrics and figure out what's going on. Also what Marius mentioned is, a lot of times we build content to answer every potential question that might be asked of it. And that really materializes in building dashboards that have many different filter combinations, many different ways that you can slice and dice the data, which is good because we don't need to create many sort of pieces of content to answer those questions. But again, the typical user is only interested in a certain slice of data often or slices of data. And so when we present things like these metrics, we should be taking that into account. We should be automatically pre-filtering content for the user. So in this case, if I'm responsible for a region and a particular product set category or product type or channel, I should be able to come in here and get a pre-filtered view of my world and be able to see immediately without doing anything, zero clicks where I should focus my attention. In this case, there's three metrics that I potentially want to drill into. I'll park that for a minute. The other thing you'll see on this page is we talked about sort of trust and quality of information as well. One of the hardest things in BI is to gain the trust of your business users. Probably one of the easiest things in BI is to lose their trust when they find issues with the data. We've all been in those meetings where we spend an hour arguing about the data rather than what it's actually supposed to be telling us. I would argue that we don't lose trust necessarily because there's issues with the data. Reality is data is never 100% correct 100% of the time. We lose trust because we don't tell people about those issues. So as you think about the journey, you need to be factoring in data quality, checks of data quality that automatically can communicate to users like we're doing here at the top with this announcement when there are issues or potential issues with the data and then communicating that for a user who has no time in the first place, you're not gonna upset them and lose their trust by convincing them that they should go and drill into this and spend time analyzing it when at the end of the day there was issues with the data. They've wasted a lot of time. They're gonna give up and not come back again. So communication as part of the journey is key as well, particularly when there's potential issues. But let's assume this issue has been solved. We've presented Sam with a set of pre-filtered metrics here, it's highlighting metrics where there's potential issues that he might wanna drill into. So we've gone from a bunch of metrics down to three in this case. And as I said before, it's likely that these metrics are gonna be coming from different data sources. So if perhaps there's a number of regions I'm responsible for and I am slicing and dicing the data, but then I'm drilling into a particular metric, it should be a seamless process to take me from that high-level metric into the appropriate dashboard. In this case, Tableau pre-filtered to the size of data that I was checking that metric for. And again, a minimal amount of clicks, a minimal amount of interaction gets me to a level of detail in that body of content that I have access to in the quickest time possible. So yeah, again, think of this as just one fairly simple example with some key components, right? Make the experience welcoming, make it familiar, make it a minimal number of clicks to get to content that is gonna be useful and lead the user through the data in a way that lets them filter it down to something that is consumable every day in a few minutes so that they're not having to monitor a whole load of stuff from a report's perspective, which in reality, never happens. They pick up the phone to the BI teams to get their questions answered. So we wanna make so that the barriers to using this stuff so low that it is easier to use it rather than pick up the phone. Final piece I wanna touch on is this concept of search as well. So the journey takes me down my familiar path every day every so often I'm gonna have questions for content that I don't look at on a regular basis. And if again, the barrier to finding that question and finding that content and getting those questions answered is too high, my default action is I send an email or I pick up the phone to the BI team to get that question answered. Even though nine times out of 10 there's probably a piece of content there's a metric out there that could answer it for me. So as Marius said, we want to create the Google type experience for search in our BI so that we entice people just to run a search because it's the quickest way of finding stuff. So the way that materializes in metric insights is having an ability to search across that catalog of content that we've been talking about. So as a user, I can come and enter search terms. In our case, we're doing some natural language processing against those search terms. We are looking across the metadata that we have around the content within our catalog. We are recommending common search terms as you enter them in. As again, you might expect in Google, we're recommending common and highly ranked content that relates to those search terms. But if I run a search, again, similar to Google what we're doing is applying an algorithm to that metadata and that search term to say, okay, what is the most relevant content for that? And it's taking into account obviously looking through the metadata, looking through where we find those search terms within the metadata, but also allowing you to filter it out by things like data source, by category, by data classification like we spoke about before, by a whole other topic around certification of content, what's been certified by our teams, and also looking at things like engagement with the content. So ultimately, what do I want when I search for something like sales analysis? I want the most relevant piece of content that we have around that, and typically the most relevant piece of content that has high engagement associated with it because it probably means that other users are finding that content useful. As we search across the catalog here, it's obviously taking into account security and permissions that we have in place around our BI content. So the default sort of behavior should be bring me back results of content that I have access to, but equally, and hopefully we have an example of this, yeah, equally I should be able to run searches, or let me back up, equally as a publisher of content, I should be able to publish content that has security and permissions enforced against it, but I should have the choice of making that content discoverable to a wider user base in case it's something that they can find useful. So in this case, running a search, I'm looking for some procurement reporting, you'll see a dashboard at the top there, which is giving me the metadata around that dashboard, but it has blurred out the image and put a padlock on it on the right-hand side there because my current security permissions means I don't have access, but there should be a seamless process then say, okay, I know this dashboard exists, well, let me request access to it. What happens then is gonna be dependent on your organization. It might simply email the owner, or it might open a ticket, or it might even assign them to the appropriate groups if you want that level of autonomy, but as a user, I'm not picking up the phone saying, can you create me a procurement dashboard because I know one exists already and we don't need to replicate that. So yeah, in summary, organization of content in one place, creating compelling, familiar, quick journeys with that content based on a persona, and we've just looked at one very simple one today, but hopefully it gives you an idea of sort of the concept behind this and making sure ultimately that users want to engage with the content, want to search for content because that's a quicker way of finding answers and having to pick up the phone to the BI team. So I'm gonna pass it back to you, Maris, to maybe wrap up and then we'll see if we've got questions. Thank you, Mike. So just to, I mean, I think we, you saw kind of the next examples that Mike gave and how to apply the patterns, you know, how the kind of pulling in just the things that are useful, not every piece of BI content into a curated catalog kind of takes the generates order from the chaos that's out there. You saw how, you know, one example of a specific user journey defined with trusted content, with certified content, with things that people understand, you know, that they can use. We should, he showed you some examples of how you can apply design patterns, understanding the journey to get to users, get users to value very quickly, right? So being mindful about that. And then also you saw how data literacy is kind of completely integrated into the experience in order to have people understand what they're looking at and make the right decisions with that. So with that, I will turn it back to Shannon and let's see if there's any questions. Thank you both for this fantastic presentation and just to answer the most commonly asked question, just a reminder, I will send a follow-up email to all registrants by end of day Thursday for this webinar with links to the slides and links to the recording. So diving in here, you know, if you have questions for Marius and Mike, feel free to submit them in the Q&A portion. But I wanted to grab one that came in early from the chat. Let me just see if I can find it here. So is there a data management framework you follow for data management? Is there like the DMA, DMBAC or DKAM? I mean, it's not, you know, we're generally speaking with in terms of our focus, it's been largely around more of the visualization aspect of the BI governance. So, you know, our philosophy is that it needs to be, you know, whatever data for management framework we have and we're not, we don't have a specific opinion on those frameworks better than the other. I think it's really a question of what really fits and suits your organization and your approach. But whatever you use as an overall framework, it's just very important to consider BI governance as part of the solution, not, you know, what we see the problem being more often than not is that a lot of energy goes into the management and governance of the data itself, right? Which understandably so, it's very important, right? So you've got to make sure that the data is properly governed, that the right cataloging takes place, lineage, all that, you know, the governance is critically important. But organizations sort of fall down because they don't think about the visualizations through which users are consuming that content. You know, the last mile, the data's got to get you users. How is it getting to your users? And how, and what's the framework that you're using to think about that part of the problem and solve it in a way that provides these key principles, you know, that generates true engagement, right? So a lot of the energy, frankly, and money that's spent in putting together those, following those data management frameworks and putting together these really, great environments where the data could be really clean, et cetera. But a lot of the effort could be wasted if we are not effective in doing the delivery in that last mile that gets that information to users in a way that they can consume it. I love it. So can you integrate Kaliber data catalog to Tebo dashboard? Yes, yeah. So what, you know, and we didn't, you know, you're asking us sort of some more product related stuff and, you know, this was more of an educational presentation, but it is possible to get data out of Kaliber through the Kaliber APIs, glossary definitions, key terms, things of that nature, and integrate them into dashboards that are sourced from Tableau. We have a publishing workflow that supports that. And, you know, if you're interested in that, just sort of reach out to us separately and we can give you a demo of that and show you how that works. Very cool. So is there a general rule of thumb for how long curation takes for each dashboard? I'm sorry, there's a general rule of thumb for how long, how long is your duration to bring a dashboard in? Yeah, curate with a C. Oh, curation should take for a dashboard. I'm sorry, I did not understand that. So I think, you know, it varies on the dashboard, but I think in that process as well, it is actually very important that you don't have a heavy curating process. And in most organizations, because of the fact that they're at least the ones that we deal with, where there are large organizations that have many assets out there and many teams working potentially on creating content, you've got to be able to have a process that's distributed. Right, so it has to be both lightweight, lightweight in the sense that it's not, not that it doesn't do what it needs to do, but lightweight in the sense that it's easy to follow. So it does not put a huge strain on the person doing the curation. And then at the same time, meets your minimum standards of what you said as an organization for what does it mean for content to be published. So, you know, in other words, do you, should it have a description? Should it have an owner? Should it, what's certified? Should you tag it with key terms? So you come up with kind of a set of criteria. So that's upfront work that needs to be done to define what that is. And then what you do is you define a workflow for onboarding that content that is very light touch. Right, so that enables you to be able to bring that content in with minimal number of clicks to do the curation process. So to kind of go back to the answer to the question, it should only ultimately, after you've designed that process, it should only take a few minutes of time to curate a dashboard and move it on. You should have synchronization process where all of the content in your bag tool is available to be able to be brought in easily. I use it, somebody selects, the right person selects what content should be onboarded based on their knowledge of that. It goes through the right hands for the certification. And if it's a simple onboarding with a single user doing the curation, that should just be a few minutes. If it's a process where multiple parties need to be involved, maybe the business user needs to provide some context and the analyst needs to add some metadata and the data governance team is to certify. If it's something like that, that might take a longer, but it should still be only a few minutes per resource who's involved in the process. And you should have that mechanism also be sort of alerts, exception driven so that it's very easy to, eyes as a user know, hey, I've got some content, new contents out there. I need to go look and see if it should be published to the portal. I can go click, click, click, click, publish it to the portal, done. And that's very important because as we all know, as well-intentioned as we are, if our governance practices are such that they require a whole lot of manual effort by a whole lot of people, they're going to fall down. People are not going to comply and you're not going to get success. I love it. So is this more of an internal tool or can I use it to display to external customers? We have both, it's used both in both scenarios. So we have some customers that are using it internally for and, you know, to enterprise wide. And then there are customers that also use it to be able to show information to their customers or partners within their ecosystem. So it can be deployed, depending upon your use case and what you're trying to do. And again, I think, you know, technology aside, you know, a persona that you should be thinking about journeys for is probably a customer persona or a customer persona that isn't necessarily internal to the business. You know, the same applies to them. Again, forget the technology for a minute. How do you deliver data to your customers in a way that they are going to be happy with it and feel like it's useful? That's another persona you should be thinking about. Yeah, that's a great point. So it doesn't really change at all the questions that you ask or the approach. It maybe it's even more important if it's customers because, you know, they're, you, you definitely, they might be paying for the service and you want to make sure that they're, that they're getting value very quickly. So, you know, I think we could have a whole web and are on the next question. So, you know, let me see what you can do in just a few minutes here. You know, what is, what is data literacy? Is it's a fancy new, is a fancy new concept? What does it solve? And why is this term emerging now? Well, I mean, I, yeah, that's, that is a, you're right. That's a whole webinar or even a series of webinars on that. And it means different things, right? Again, it's so funny depending on the context in which you ask of it, right? Often historically, I think data literacy, when people talked about it, they really were thinking about and maybe continue to think about the analyst, right? Who's using data. So I, as an analyst, I want to go use a data set to be able to answer a question. How, you know, data literacy is what is the, is the, you know, is the degree to which I can understand which data should I use, which tables, how the data that can answer the question, how are those, is that data represented? You know, what, where does it come from? What are the rules to use it to pull it together? So it's that body of knowledge that enables me as an analyst historically to be able to use data. But largely, I think that's an incomplete definition in that data literacy needs to apply across the entire organization. And we just need to look not only at like, you know, what is a, what does an analyst need from a literacy perspective, but really thinking about what does the end consumer of data need in order to be able to understand the, the information that they're looking at and make a proper decision associated with that. So, so there's, you know, people, they're oftentimes, classically, people try to solve data literacy through trainings, through awareness, through, you know, let's teach you how these things are in place. There's tools that keep glossary definitions, things of that nature in different places and they make them, connect them to those resources. And I think that the, the only, from a practical perspective, the only, I would say that the only thing that's really valuable is what I can get when I'm actually consuming the content, right? Cause that's the point at which you've got me and you got my attention and you, and you know the context with which I'm working. If you just point me to glossary, what are the odds that I'm going to go consult that glossary when I'm looking at my, through another tool, my visualization, right? So, so, I mean, I don't think I'm answering the question at all, but data literacy because of this broad topic. But I think the way to think about it is more in just answering that very basic question of what does your user need to understand about the data they're looking at in order to properly interpret the information and make the right decisions with this information, right? And how are you presenting that information to them, right? And do you have a publishing process for the content that ensures that just, just like you're making sure the data is correct when it's published, very important, right? You should also be making sure that the necessary documentation, context, tagging, et cetera, it's present with the visualization at a sufficient level. So a typical consumer of this visualization has everything they need to make the right decisions looking at the data. Perfect, I love that answer. I'm sure, again, we can explain on that forever. You know, there's a lot of questions here about what you connect to and not connect to. So even if you have a page of things that you connect to or if you want it, I can include that in the follow-up email as well that goes out and we'll get those questions answered. You know, how is it beneficial to analytic producers or is this product's target audience exclusively analytic consumers? Yeah, that's a good question. So I think the key is to create an ecosystem where you're serving the needs of both. So an analytics producer would be using the search, for example, that Mike illustrated to you. We need that kind of capability to make sure that they're not creating another visualization that's where there's something very similar to what already published out there that's been certified, but it's been maybe done by a different group. So the discoverability, the ability to search into metadata and find things that are maybe close to what you're looking to create, to be able, ability to see, we didn't really showcase that part because we were focusing more on the consumer in this presentation. But in that, from that search, you could hit a lineage discovery where you can see what is the lineage of that particular object. So that persona, that analyst, publisher, wants to be able to see all that and have all that context and deciding what they should create next. The other benefit that they need to get out of this kind of ecosystem is the key diagnostics that tell them how the analytics are being consumed, who's consuming it, how sticky is it? What are the trends happening in that so that you get that vital feedback cycle? What do people think about it from a scoring perspective, from an MPS perspective? There are a number of aspects that we just didn't have time to go into because we're not germane to just the pure engagement level, but apply to the producer where to give them the tools to be able to then refine and tweak what they do on an ongoing basis to get better and better at creating content and publishing content that is in fact driving user engagement. So that's a whole other webinar, but it is important to understand that this ecosystem needs to support both parties, not just the consumer. Yeah, I would simplify and say, you know, to the BI consumer, the BI producer, there is nothing more frustrating than getting phone calls and emails asking for content that you've already created and that's already out there. And that's typically what happens today. And the reason it happens is everything we've been talking about. So the value in taking the time to document and publish the content in a more effective way that removes barriers to consumption is going to play into the hands of the analysts who can then spend time doing stuff that they should be doing and creating more valuable analytics in the way that Marius spoke about. Well, thank you both for another amazing webinar, but I'm afraid that is all the time we have for today. And thanks to Metric Insights for sponsoring another webinar. And thanks to all our attendees for being so engaged in everything we do. We'll get the extra questions that we didn't have time to get to over to the team to get to some answers. And again, just a reminder, I will send a follow-up email by end of day Thursday for this webinar with links to the slides and links to the recording. Well, thank you all. I hope you all have a great day. Thanks Marius. Thanks Mike. Thanks everyone.