Build an AI‑driven Dashboard

Key Takeaways

Hello everyone and welcome to today's session. My name is Reese and I'll be a moderator for today. We are a couple of minutes out from starting. We're just waiting so everyone has a chance to join before we properly go live. As you will have seen in your email inbox, if you'd like to join in live with this session, there is a little bit of setup required so that you can get access to the Omni platform. The link to do so is in the session resources. So, if you have a look in the video description and the pinned link in the chat. We've got a little three-step process that you'll need to complete before you're able to get access and code along with us live. However, it's pretty straightforward. So, yeah, you will have time to do it at the start of the session. We've also got the slides that we're going to cover in there as well. So, please do check out the pinned link in the chat or the session resources link in the video description. They're both the same link. So, yeah, go check those out now and please do continue with the setup if you haven't done so already. For anything else in terms of registering for this session, if you haven't done so already, you can scan the QR code that's on screen now. You can also check out the link that's in the chat as well. You can also head over to datacamp.com/webinars where you'll find this session as well as all of our future sessions as well. This is we're pretty much at bang in the middle of our AI agents in the enterprise series. We've got plenty more sessions coming up next week. So, please do check those out. I'll drop a link so that you can check out the series. Later on. But for the moment, the main link that you want to be clicking is the session resources link that is pinned in the chat and at the top of the video description. So, please do check that out. If you have any questions or notes throughout the session, let us know in the chat. We're going to be answering your questions throughout the session as well as at the end of the session as well. We've got a couple of folks from Omni to help you out with any technical questions that you might have as we go. So, please yeah, don't hesitate and get involved in the chat. For anyone that's new, thank you for joining. We are just about to get started with today's session. We're just waiting so everyone has a chance to join before we get going. There is a little bit of setup if you want to join in with us live today. Everything that you need is in the session resources link and [music] the pinned link in the chat. So, yeah, those are your best place to go if you'd like to join in live with us. However, if you do just want to watch along and catch up with the recording, make sure you register for this session. You can scan the QR code on screen now. You can find the link that I've put in the chat and you can also head over to datacamp.com/webinars as well. If you have any questions or notes, let us know in the chat. And yeah, please do like the video if you enjoyed the session. It'll help recommend it to more people like yourself. Brilliant. I think I will hand over to Richie now. But yeah, please do make sure that you check out the link pinned in the chat and in the video description for the setup info, the slides and anything else. Brilliant. With that, I'll hand over to Richie and yeah, we'll get started. Hi there, Data Scamps and Data Champs. This is Richie. Welcome back to the Agents in the Enterprise series. So, we've had a lot of fun so far this week. We've been messing about with Open Claw, building things with A 10 and Pinecone. Yesterday, we learned about AI use cases in learning and development. We've got more sessions for you next week. But today, the focus is on business intelligence. So, we're turning our attention to using AI for dashboards. So, AI is both useful for helping you design and create dashboards. It can also help users understand the dashboards you've created. I feel this is perhaps the biggest shift in business intelligence in recent memory. And today, we're going to make use of the Omni BI platform to build our AI-driven dashboard. So, Omni's one of the perhaps the newer generation of business intelligence platform. So, some very exciting stuff to see. Our guest is Peter Whitehead who's a solution engineer at Omni. So, we go straight to the source. Welcome, Peter. Hello. Thank you for having me. Yeah, great to have you here and what I must say what a great mustache you have. Likewise. You know, I always love to have a mutual mustache. Absolutely. I guess anyone in the audience as well, if you have a mustache, please do let us know in the chat. So, Peter works closely with Omni's customers to help them build out BI solutions and he's got over a decade of experience in both analytics and data engineering. So, he's had stints in Google's Looker team and also at Astronomer. So, with that, please take it away, Peter. Awesome. Thank you, Richie. Well, I'm really happy to be here and introduce you all to Omni and help you walk through building out a AI-driven dashboard. Before we get started, please if you haven't signed up, please take a look at the registration link in pinned in the chat, also in the description. You'll have time to go through the setup. It is really easy and you can start coding along with me as we're building out this dashboard using AI and other other interactions with data directly in Omni. So, to get started, we're going to today build an AI-driven dashboard and as I said before, let me jump over to here. We're going to go go through a little setup. This is going to help you get set up so you can follow along today. It'll be really really good to have everyone actually going along as we go through a pre-built dashboard on an Airbnb data set. But then also creating this ourselves. And you'll have plenty of time as I go through the intro with Omni. So, what we're going to cover today. We're going to cover Omni as a platform. So, I will teach you who Omni is, what we are and how we approach analytics. We're going to learn how to interact with a dashboard, a workbook in the underlying semantic model of Omni. We we have a layered approach that I'll get to in in a little bit in the intro. From there, we'll start the code along and we'll start actually building content um together. So, we're going to start on a pre-built dashboard and see how we can ask questions of the data. We'll move into our workbook environment and start from scratch and recreate that dashboard and you know, personalize it, make it our own. And then finally is we'll you know, utilize AI directly to make AI summaries on the dashboard and see how we can put AI throughout this entire dashboard. And then finally, we'll also edit the model, all that good stuff as we're we're we're building this out together. So, what is Omni? Omni, we are an AI analytics and business intelligence platform. We're helping customers small and large you know, get the most out of their data. If that's with the leveraging our semantic model to have AI conversations, being able to interact with data in a traditional sense as well. We are built on from decades of experience within the data realm. You know, as Richie said, I came from Looker myself and our founding team also came from Looker. Our CTO actually was the first commit to the DBT project. So, we have a ton of experience within the modern data stack. And then we also are backed by some large large of those industries as well with Snowflake, Databricks, Google all investing into us. We just raised our Series C funding. So, we definitely have a place in the market and we're excited about our growth trajectory going forward. Now, Omni, how we approach data. As I said, you know, we are built on top of a semantic model. That semantic layer is where data teams can define their metric definitions, their business context as well as security parameters of who has access to what rules. Now, this is very important in the age of AI because we've seen how text to SQL is great for fast and loose questions, but if you ever need to make data-driven and business decisions, you know, you want some governance backing that in so you can trust in the accurate results. And we've seen this in the market and we've seen how users are getting the most out of our data through conversational AI backed by that semantic layer. But we also trust that you know, it's not just a one-way street with natural language. Users sometimes want to take over the wheel and actually start interacting with the data you know, in a traditional BI sense or even a traditional Excel sense. So, that's why we've brought all of these modalities of interaction with data into one platform. If I want to start my exploration of data with conversational AI, I can do that. And then what I can do is pop open um my workbook environment, and I could start interacting from a traditional point and click sense. And then maybe I want to, you know, open up a spreadsheet and use Excel functions. I can do all of that within one window and then finish it off by modifying it, maybe creating a window function in SQL. That's really where Omni shines is interacting with data in specific ways or all the ways for all the different personas across an organization. So, how we approach data and how we built um our product. We've built it with this layered approach. At the bottom is the SQL database. So, we connect directly to a SQL warehouse where, you know, you might have transformed your data from DBT, all that good stuff, and it is set up in a star schema or in the AB schema, however you want to model it out. Then we have our shared semantic model on top of it that allows the data team to define your business logic and metrics in one central location. But not only is it defining your business logic, it's defining your business context because this is an extremely important as we'll show in the the code along today of like if I'm asking questions of the data, I need it to be able to answer it correctly for our business. As an example, you know, different organizations have different definitions of what a customer is. If I ask me, show me my recent customers. Pretty vague question. Without that context baked into the shared model, the AI model doesn't know exactly your definition of customers and what it needs to present back to you with that SQL query. So, that shared model is the metrics layer, the business context layer, security controls as well. And then on top of that is the workbook layer. And this is what we'll get into today when we're building our dashboard. It is built on top of the foundation of the shared layer, so everyone starts from the same governance standpoint. As you're you all are logging in right now, you are given a dashboard. All those dashboards are baked on top of the shared model. And then within our workbook, we can modify that shared model as like the foundation and building blocks into it. And then finally is each of the dashboards um are going to be built on top of that workbook. So, each dashboard has their own workbook attached to it. Great. Um Hopefully everyone is logged in uh right now. Um so, I'll I'll I'll I'll move into Omni and we'll kind of walk through what that is. Um and then Omni is very very easy to understand, I I believe, like especially from the conversational analytics standpoint. So, you'll be able to catch up if you're still logging in right now. So, we'll start with the lay of the land. This is the Omni homepage. Right up front and center, you're going to be able to see our natural language entry point. We've seen many customers use this now as the entry point into any data questions that they can ask. You know, we even have those those bottom little uh quick questions that you can ask like what what can I ask? Show me some data. Where it understands the context over what you have access to cuz we can permission this out on a user level. Say some users might not have access to certain tables or they might not have access to certain columns where the AI model can't actually show you those. Now, our AI agent, we call him Blobby, um is going to be able to answer us quick questions of the data. So, we can get in insights into what's underneath there. Over here is always how you're going to be able to navigate back home. So, if you start going to a dashboard or workbook, you can pop this open and you can navigate back to the home and get back to this homepage right here. Within the hub and shared with me files, this is where you're going to be able to find uh your dashboard that we're going to be working on. So, we'll see the the first dashboard and then we're going to start recreating it. So, in shared with me actually is where you'll find one once you've created um your user and logged in, each of you should have your own dashboard from there. And then if you wanted to start from scratch yourself, you can always hit this new tab up here and you'll see an option to jump into a model and start creating uh a workbook from scratch. Great. So, let's navigate to that first dashboard right here. So, if you go to shared with me, you will find a brand new dashboard built out. Everyone's going to have the same dashboard. We've replicated it for everyone as they're logging in. Now, what we're going to do is we're going to navigate to this dashboard and we're going to start asking questions with AI. And then what we'll do is we're going to we're going to navigate to the workbook and see what lives within that workbook. So, let's do that right now. So, here it is. You can go to shared with me and find that dashboard. I'm actually going to go to my documents cuz that's where mine lives. And I'm going to pop this open. So, here's what the dashboard looks like. You know, it has some high-level KPIs right here, some, you know, histograms, line charts, bar charts, map maps, all that good stuff. Now, with Omni, each one of these is a standalone SQL query to the underlying database. It's current constructed using that semantic layer, and that semantic layer then issues the SQL query down to that underlying database. You know, we are in a at Omni database agnostic. So, if you're sitting on top of a Snowflake warehouse, Databricks warehouse, Postgres database, all of that's going to be compliant and able to connect directly to Omni. So, now that we we're looking at this dashboard, what I might want to do is ask a follow-up question. As of right now, I do not have um a filter or anything on top of here. But, you know, I have my AI agent down here at the bottom. So, I can always ask a follow-up question on my dashboard or I can even ask it to summarize my dashboard. Say if I was um an executive at a company and I just wanted to say, hey um summarize this dashboard for me. Since our AI agent, Blobby, knows the context of your business, it understands, you know, what metrics are important to you and what you're looking for so that it can actually give you a summary in a, you know, what what matters to you. We're here, it's going through the dashboard and actually telling you what it's finding. You can read this like a a newspaper. It can even present you follow-up questions you might want to ask. Where I can ask it, okay, how does I can occupancy vary by room type? And it can give me that. So, the AI agent is a great way for us to get, you know, follow-up questions here, especially for those users who um are are onboarding at a company or who might not be as um data savvy. They can come in here and just continue to follow up and iterate on this. Rather than only having a a path of like in a traditional BI sense of drilling into the data, I can always get into finer grain detail or exploration right here with the agent. Even doing things along the lines of, you know, a follow-up analysis, like a deeper dive. Like this chart right here, available trends. Like what is causing these big decreases in the availability right here. I want to know that. So, I can click these three dots right here on the top right of this chart where it says tile options. I can open that up and I can ask a follow-up question right here. So, not only do I can select the AI assistant down here, but I can do it specifically for certain visualizations. And right here I can say, what is causing the large de- creases in availability? And we can have our AI agent go off, do some research, pick apart um, you know, what may be causing this as it's building our query right here. Looks like it's uh doing some analysis. And this can be going through an agentic flow where it's passing results between queries as it is right here and figuring out what might actually be causing this. Seasonal demand. Great. All right. Now, what we want to do is see how this dashboard is actually constructed. What actually makes up this dashboard? Now, that is the workbook. Each dashboard is comprised of a workbook and tabs within that workbook. So if I wanted to open up the workbook and see where these these uh these uh SQL queries are created, I can hit this workbook button up here on the top right corner. And that will navigate us over into the workbook environment. Now, this workbook environment is very much the traditional BI that you might be used to. Over here on the left-hand side is the field picker. It's going to be comprised of measures and dimensions. Um I'm going to go to this listings by room date so I can see a dimension. The dimension is, you know, a dimensional value in your database, like a column in your database. Or it could be one that's derived or calculated. A measure is going to be an aggregation over those dimensional values. So think about sums, averages, counts, all of that good stuff. Now when you select one of these, so let me edit this right now. I can you can see I'm now in a draft. So changes I'm making here live only in this draft so I'm not affecting content for other users. Um if I wanted to instead of looking at listing count, maybe I wanted to look at average um revenue for that. I can simply just grab that average revenue and deselect listing count and my chart has now updated with those new measures. We can see the results here. It's now looking at average revenue count and I can even see the SQL underneath the hood that is being issued by me doing this. So this is that govern metric. We've defined it as it's an average of this dimension right here. Okay. So let's start from scratch. So what I'm going to do is let's hit the Omni O on the side over here and go back to home. Now what I'm going to do is as I said, we've seen a lot of the um ways users are starting to interact with the data and enter into an analysis is via our chat interface. So we can just ask a question of um Blobby, our AI agent right now, and say uh can you show me the average um average Let's see what I wanted to do here. Maybe the average uh nightly price, unique hosts, average revenue, and total listings. Something like that. Just I want to see a a smaller sport of uh metrics. And here we can then actually just have a pure natural interface or a natural language interface. So here we got a uh visualization. Um it's going to give me a summary of this. I can continue the conversation here. Or what I can do is I can go up to this chart right here. And I want to I want to use this chart, but I want to modify it in a bit. So I can see I have a couple options up here on the top right of this visualization. One is if I want to see the query details. So with our AI, you always have that human in the loop and the way the the option to debug what's happening. So I can see what's actually happening right here. What fields are actually being used in this query. I can ask it to explain itself. So if I wanted our AI agent to explain why it chose this, we can do that. But what I might want to do is actually I want to take over the wheel and actually modify this. If you've ever tried to have AI do something like, you know, hey, move that chart two pixels over that way, it's kind of frustrating at times and sometimes it'd just be faster if I was able to do it. So I just selected that and I'm going to modify this here. Now, let's get this chat window out of the way. So now I am uh in the workbook environment and I'm going to see I have a visualization, but I have multiple results here. So I'm going to recreate that first visualization that we had on that chart. So let's go back to the chart and what I can do is hit this add button. This is what we call our KPI visualization. We have a lot of options here where I can have a number, a comparison chart, um even a line chart baked right into this or even if you had images or text, things like that. So let's add a brand new number. Um this number, now that we're selected into this, I can scroll to the right over here and I can choose which um value or measure I want to be displayed right here. So now I have that unique host directly in there. I'm going to add another one. Let's add that average listing or average revenue and let's add another one. And then we can have that listing count as well. I can change the style if I wanted to. Maybe put this in the center, align it a little better. And then even if I wanted to change the text sizes, I can do that. So now that we have this visualization and we want to start recreating that dashboard that we saw, all I need to do is go up to the plus dashboard button up top here and I can hit create dashboard. And I'm going to say Peter's Oop. And I it gives me an AI summary version. Well, let's just use the AI summary version cuz that looks good. It even gives you a suggested description. So I'm going to save that. And now what we are is in the edit mode of the dashboard. You can see now I'm in the draft mode of this dashboard. You can see how I can add charts or I can add filters, controls, all that good stuff that we will get into. Um I can also see that we can modify this dashboard. I can move it around. So let's put it over here on the right as we did before. And there we go. Now I have a nice KPI visualization directly on my chart. Sweet. So if I wanted to continue to add charts or tiles to this, I can always just select this option right here to add it. Or I can go back to the workbook and add a chart by adding a brand new tab. Now you can notice that we have each tab is going to be named with an emoji and just query. This is how you uh differentiate them. You can always right click on this and rename it. I'm going to rename this KPI chart. Save it. Now this one I'm going to rename to um I believe the next chart we can do um available days. Something like that. Now, in Omni, we can either start directly from SQL if we wanted to to create a visualization. If you had a CSV locally on your computer, you can always upload that into Omni and we can start querying that directly. Or we also have a spreadsheet UI. But I'm going to jump to this topic. Now the topic um is going to be a collection in curated data set of joins that you can interact with. So each of these tables is comprised of those dimensions and measures, but we've predefined the join paths to these tables within our model already. So you can see I already have two joins to this inside Airbnb table, to the calendar table and the review table. Great. So let's create that chart where we wanted to look at available days. And let's look at the date field. I'm going to grab that date field. Here we are. So we can look at the chart. Maybe I want to filter this to be um on a different date. So let's add a filter. So to add a filter, you can go to the date field over here on the left side and select the kebab menu right here. From there, I'm going to hit filter and the drop-down menu is going to allow me to select in the past, between, before, on or after. So I'm going to say this is going to be on or after. Let's go back to maybe November last year. And now we have a chart with those drops, everything like that. So, what I want to do is I like this chart and this is what was used in the previous dashboard. But, what if I think maybe with those drops I might want to think about having a 7-day moving average. Now, there's a couple ways we can think about doing this. We can always add a trend line um in here where if I want to add a reference line, I can um put it in a not this one. >> [snorts] >> Leave it in No. Oh, forget about that one later. Um or we can have an Excel calculation. In Omni, we have a one-to-one syntax match with Excel. So, if I wanted to, I could hit a column to the right. So, let's just go up here and you can see the kebab on this column right here and I'm going to select um add a column to the right. Now, this is a one-to-one syntax match with Excel. So, if I wanted to, I just hit equals and I can start writing my Excel formulas, everything like that. Now, if I wanted to do a rolling average, maybe a 7-day rolling average of availability. I can go down to the seventh column or seventh row. I'm going to double-click into the cell and you can see with the cursor now in the cell, I can then start typing. So, I can hit equals average and then I'm going to select B1 colon to B7. And then once I close that out, I can hit enter. And now let's double-click into the title and you can change the name of the column. And I'm going to call this rolling average. So, now that we have that rolling average, I can move back to my chart and I can go down here on the options on the right-hand side and I can scroll to the bottom and grab that rolling average and I can drag it up into the Y axis with the available days as well. So, now I have that rolling average alongside my available days. Awesome. Now, the next thing I want to do is let's create that map visualization. So, that was a really cool heat map and we want to see that alongside um these visualizations as well. So, let's grab that. So, let's add a new tab and we can change the name just so I have know what's going on down here. Um let's call this heat map. And we'll save this. And again, I will go into my topics. From here, what I can do is I can see I have a location field. Now, Omni does support lat long. Uh we also support, you know, um zip codes, everything like that for our mapping types. So, what we can do is hit latitude, longitude. And this is a row-based value for listings. So, now I can go to the chart. I can just choose map. The map region I want though is going to be a point map cuz I want a heat map and this should be projection. We go over to Amsterdam. Zoom in. Mhm. Do I need to uh I need to add the latitude and longitude. Latitude longitude. There we go. So, now I have the latitude and longitude on here. You can always it looks like we've hit a row limit here. So, we have more than a thousand results. So, we do have a row display limit here. It's we were still query the entire data set but we're only displaying a certain number of rows. So, what I can do is always go I can go to this limits option on the top right and I can up this to um say 20,000. Done. And now this is going to query this again and looks like we got all of our rows. So, size, maybe we might want to adjust this size to something um along the lines of um They're pretty big. Maybe five. That looks a little better. As you can see, when we highlight over these, we're getting a tool tip. And it's just the latitude and longitude. Maybe I want to learn a little bit something about each of these listings as as I highlight over them. So, what I can do is then just start grabbing dimensions as well. So, maybe I want to know the um hosting name. Maybe I want to know um the listing or the rental type. Um maybe I want to know nightly price. Um and maybe I want to know their um average review or something like that. Now, what we can do now that we have those fields um in here, you can see we have this tooltip option down in the bottom in the op chart options over here. So, I can scroll up and put these tooltips in here. So, I'm going to get rental type in there as well. We can get nightly price in there as well and average score. So, when I hover over some of these, you can see that I'm seeing, you know, who the host is, the short-term rental or the rental type which is short-term, and the nightly price. So, you're getting a little bit more detail about each of these um listings directly on this map. Great. So, now let's see how our dashboard's turning out so far. Looking pretty good so far. So, let's uh maybe modify this right here. We got our heat map down here that I like. Um Now, I think we going to do maybe a couple more. Um let's add, you know, a listing by room type. Now, um let's go back to our workbook. So, we've seen a couple modalities how we can create visualizations where it was we use our point-and-click UI to create this visualization. We've used Excel functions as well, but you know, you're still going to be able to interact with AI directly in the workbook as well. So, if we go over to these sparkles over here on the left-hand side, I can select that sparkle and I can ask um our AI agent a question to just like show me um total listings by room type. And now it's going to construct this query on my behalf. You know, I have started using this more and more just to get me to a starting point. So, I don't need to spend, you know, 15 clicks to get to the the analysis I want. I can just have it give me that starting point and then I can start building on top of it. Maybe instead of a bar chart, let's, you know, just let's grab the bar chart as we did before. Now, we have room type here. Um we can also do something like maybe pivot. So, like add a stacked bar chart here. So, if I go to we're on uh rental type now. I can then select the kebab menu on the side and I can pivot on this. And this should break it down as you can see long-term and short-term. So, not too many long-terms um but what we can do is look at the chart and we can even stack this a little bit. Oh, maybe we want to stack it on Uh stack it on room. No. There we go. No. And then we'll go rental type as the colors. There we go. So, here we are. We now have the small sliver of long-term up there as well. Great. Now, the other thing we can do Oh, I'm going to actually update this name before I forget. Um listing by room count. >> [snorts] >> Now, the other thing we can do is we can start um you know, as I said when I was talking about the the layered approach of Omni. Omni's shared model, as we can see, this is exactly what this is right here. We have the shared model. This was blessed by the data team. They've defined these metrics for everyone to use. But me as an end user, I might need to, you know, make a subtle change or maybe create my own metric. Um so we can do that right here within the workbook. For example, maybe I wanted to look at um average review score. But I only wanted to look at the review score by a specific room type. Maybe I wanted to look at the review score of all the private rooms. One way is that I could filter on this here, but it might be a metric I want to use all the time. So, I'm going to go over to the average review score metric over here. I'm not going to select it yet, but I'm going to grab the kebab menu and I'm going to go down to the modeling portion and I'm going to select duplicate. I now have a copy of that metric, which I can modify and say average review score and change the name. Now, the way I'm going to change it is, as I said, I want to look at only review scores for private rooms. Now, to do that, I can add a filter within my measure. Now, that measure then would be a aggregate over a case when statement. So, I'm going to add that filter here and I'm going to search for that room type field. So, let's grab that room type field here. And then I'm going to select private room. There we go. And I'm going to update. Now, within this field, what we can do is do some modeling as well. Like all of the things we're doing right here is generating the semantic layer on the back end, which is great. Um because some people prefer to have that GUI interface uh and some people prefer to be in the YAML code. So, we're really those different personas right here. And I like to think of this more of that build as you go workflow. I want I don't want to be switching context between codebase, exploring the data. You can do all of your that mode out or all those right here. So, let's call this average review score uh for private rooms. Um I now have that. I tell what view it wants to be. I can change the description if I want. So, average overall review score across listings. Um it's going to be across private room listings. I can change the format if I wanted to. This is us being able to modify the drill fields. So, whenever I drill into an aggregate value, it is a query that makes up what that aggregate value is. So, you can modify what you want to actually see in that follow-up query as well. So, let's add this. And now, you'll see that this should only give me reviews for that average review. You know, nulls for everything else. So, we've just created a metric on the fly that we can use. So, I'm going to remove this. I have my average review score for private rooms. I can now have my average review score as well. So, you can look at these measures side by side. Um and maybe we want to look at this by um property type. Great. Maybe let's look at the and Cool. Let's look at this maybe as a a table visualization. And what we can do is actually have this as a table visualization here. And you can set up some conditional formatting. So, if I wanted to do something like um have it on a scale. Or here we can either just like have it across rows. Um or what I can do is apply that color scale. Maybe I just want average review score. Um and I wanted to see that scale across this. Or here maybe the max is five, the min is zero or min is it's a little pretty high. So, maybe the min is three. What it should show how it is average score. Give it a min. Should be four. Give us a little bit more. There we go. Great. We'll call this average review. Now, the next thing I want to do is create an AI summary for my dashboard. Now, Omni has an AI summary visualization. So, let's say maybe I just want to have an AI summary over just hosts and like their their properties. This is a great opportunity to let the AI do its thing. It's very great at taking in results and presenting back uh you know, a summary or whatever context you provide for it and explaining the data set. It's really not uh as great as like looking at a raw schema and then coming out with a query on top. That's why we bake context into the model. But we can also do things like if I wanted to just grab a host, let's grab over here on the left-hand side um maybe a host name, a host neighborhood, um if they are a super host, let's look at their um nightly price, maybe property type, rental type. And then let's look at, you know, um the total estimated revenue, listing counts, and then average review score. We have a lot a lot of details from these hosts. Now, I can go to chart and I can select this option on the right-hand side that has the sparkles on it. This is our AI summary visualization. Now, it's going to take in those results that we just created and actually provide us with an AI summary of those results. Now, I'm going to actually up the limit here so we can have um all of our hosts in there. Um but right now, it's just taking them in and giving you just a plain summary. It has no additional context over what it is. So, it's a very stock just like, here's what I'm seeing. If I wanted to add additional context, I can then add that additional context right here. I can have something like explain like a pirate. You know, have the AI explain who's the best host like a pirate if I wanted to. Um or here it should give me uh ahoy matey or something along the lines of that. Or I can even have it um like do some fun stuff as well to make our dashboard more presentable. Maybe instead I'm like, um uh show me or tell me about the top three hosts. Also, highlight the worst re- reviewed host and some fun emojis and markdown visualizations. And then here, it's going to take that context I'm providing it and actually give me, hopefully, a much better AI summary that I think would fit perfectly into my dashboard. So, here we go. Top three hosts, worst reviewed, and it gives me a fun visualization right here. So, let's go down to our dashboard and finalize some of this. So, we have a few things here. Uh maybe I want to move this over a little bit. Um let's add uh we'll see if we want this or not. Um we have our AI summary, which I think would be great to have up top like as maybe a um headline or just a quick re- overview that we can have. So, I'm going to move things around to make sure we get that. And then I'm going to move this over. Perfect. And then finally, move this one over here. And maybe these ones over here. So, an easy way for us to make this look a little bit better. There we go. Starting to come along. Now, not quite this exact same as the last dashboard, but I think this is this is great. And it gives us really a rich, you know, um visualizations uh and like just like appeal as we're looking at this where I can see different types of charts. If I wanted to look at this at a glance, I can understand the dashboard right here. And always I can once I publish this, I can then see that AI summary as well. Now, the last thing I might want to do is maybe add a filter on top of this where I can go up to the add button up top here. Let's add a filter. Maybe I wanted to look at just like a neighborhood filter so we can focus in on certain neighborhoods specifically. Um I can grab that neighborhood filter. I can add it. >> [clears throat and cough] >> Now, you can see that it's added to the top here. And you can actually control which visualizations listen to that filter. And even say if this was a date filter, you can make it more of a generic date filter where maybe one visualization um uh was filtered on booking date while the other one was filtered on listing date. So, you can change it across the board. Cool. So, now let's add that filter there. And we have this good to go. I think this is a perfect dashboard. Um let's publish it. So, let's publish this dashboard here. And now everyone can see this live and out. Now, really to take it one step further is that we can then um you know, make this dashboard even more of our own. In Omni, we can theme our dashboards as well. So, what I wanted to do was I wanted to make this even more AI driven. Um you can import JSON uh theme copies. Um and so I took the DataCamp website and I decided, "Hey, let's make a DataCamp uh website theme." So, I go back to my dashboard and that's what I did. I made this in 1 minute where if I go to file, I go to theming, I now have this DataCamp uh dashboard theme where if I hit edit, you can see if I wanted to import a object to import it in with all of the colors, everything like that. We can see here that I have the generated theme from it. And I can save this and apply it to my dashboard so we can make it even a little bit more of our own as well. So, here we are. Brand new dashboard. Maybe I need to go into dark mode with those um some of those. There we are. All right. That looks Great. So, um we've covered a lot today. Uh in the slide deck I did we kind of missed out on a couple of these slides cuz I was so excited to get into the product, but we are sharing this slide deck out with you. Um you should have it, but also we I am going to leave some leave leave behind exercises if you want to, you know, take another dive in here, learn more about Omni, and try to find some answers to some questions that we have on here. Um I've I've I've put the answers directly in here as well so you can see if you can hit those hit those questions right. But then we also um left some follow-up resources for you as well. This has links to our documentation, um our training guides so you can follow along with other videos on how to create dashboards, how to work in the workbook environment, and some of a lot of the functionality I did not cover today because there is quite a bit I did not cover today so um I'm excited for you to learn more there. Um and then also um I left behind our weekly product demos and blogs. Um Omni as a company, we we love to develop out in the front with everyone. Um so, even right after this call, I'm going to our our team's all-hands call where we have engineering demo every week all the new features that they're building. So, we want to showcase everything new and exciting that we're building out to all of our uh customers and potential prospects. So, um we'd love to pre-order the blog in there and see what we're doing. Um all the fun stuff. Great. So, um we covered a lot today. Um I wanted to just check to see if there's any more questions in the the uh the chat that I can touch on today. Like I can go back into the workbook, the dashboard, anything like that. Uh brilliant. Uh thank you so much, Peter. Uh that was a lot of fun. I love the uh the DataCamp theming of the dashboard at the end. That was remarkably easy cuz I think that's often a sticking point is, you know, you make a dashboard and then someone in marketing is like, "Oh, you know, you've got to do the proper official style whatever." And you spend ages changing all the colors. So, I love that that was instant. Um and I also love the chickens in the background. Uh that was very relaxing having uh some animal noises going on on behind you. Oh, yeah. They're uh they go crazy in the morning. Nice. Um okay. So, uh we've got time for a couple of audience questions. Before we get to that, I've got a couple of questions for you. So, I think maybe the secret sauce here was around the the semantic layer. So, just to make sure everyone's understood this, do you just want to go through like what the semantic layer is and why it's important for building dashboards on top of it? Totally. Um so, that's very important. So, the semantic layer like as I said when I when I covered um where we came from, a lot of us came from Looker and Looker was a tool that was built directly on top of LookerML, which believed in governance around not just just governance around business logic. Now, in the age of AI like business logic is important. It is it is extremely important for everyone to be reporting on the same metrics. Like if two people come to the uh uh a meeting and each of them have defined, you know, sales revenue in slightly different ways, there's going to be an argument. But having trust in metrics on the back end for every user to have the same same metric definitions is extremely important. But now even when AI is involved, it's extremely important for AI to choose the same metric. We don't want to risk AI hallucinating the wrong metric definition or joining in the wrong table so things are slightly off and who's making the right decision. Like that is really the the importance of the semantic layer in in today's day and age. Absolutely. I mean, uh just even counting how many customers you have is like different teams wanting to define it in different ways. And like simple things you think would be simple uh can often be a point of confusion. So, I love that idea that you've got that one definition and that's going to help the AI get the right answer. Yeah. And And as I was showing how we created the the uh metrics in the workbook there, we can also add context directly in there as well. Um so, like you start thinking about democratizing the context building into the the semantic layer as well. It's not just relying on solely the data team. It could be the marketers who are sitting closest to the business who understand what customer acquisition cost means to them and why it's important to them. So, then everyone across the business can share in that same importance as they're asking questions of the data. Okay. Nice. Uh I also have a question about the um the chat the the AI chat you have to query what's going on in the dashboard. Now, I know some of the answers said uh this is generated by AI. You need to verify the accuracy. How does that work? How do you generate How How do you verify that you got the right answer and what Of course. And now And that's that's really where I think about like the take over the wheel. Like always being able to pop open into the workbook so you can either, you know, explore the data a little more, verify it's giving you the right answers, or you can, you know, take a look in And most of that is the the auto-generated summaries are what you're like verify with AI. And it's fun that even um here on this title, um if I edit this, the summary one, you can hide or replace the um disclaimer to verify. My viewers know. So, it's like we want to be very transparent about what is AI generated and what is not. And I mean, I think we just need knew it's AI is non-deterministic. As as as like it's generating summaries as well, like we want to make sure people know we have the tools to verify this within the workbook environment. So, we're going to let you do that as well. Nice. Yeah, cuz if it gives you the wrong answer, then that's a bit of a disaster. So, I like the the right way to to go back and check things. All right. And one more question on that. So, if you're getting lots of questions from your users, that seems like it's going to provide good information on how you need to update the the dashboard. Like if you've got 50 people asking the same question, you might be like, "Oh, well, I'll update the dashboard." Is there a way of collecting all that information, those chats, and for the dashboard developers to to get feedback? Yep. So, our admins also have access to the analytics page over here where we can actually see, you know, the AI query responses where I can see who's asking what. We can export this, and you can then actually We've started seeing customers actually take this and use it as um uh model refining and context building. We can see what users are asking, what's important to them, and actually push this back into our model as well to improve the actual AI responses. Okay, that's that's good you've got that that feedback loop in there. Yeah. So, I think we don't have a the ETL process running just yet for the what everyone was asking today, but yes, it'll be directly in here. All right. Maybe you like administrating dashboards as a whole separate webinar then. >> [laughter] >> Yes. Yes. Yes. All right. So, we've got a question from Don. So, Don says, "Do you have to define all your metrics before you can use them, or can you just use columns of the table as metrics by default?" Great question. And yes, you'll be able to just use columns from the table by default. So, if I go over to our view here or our workbook here, like we can use the topics. Like that's a curated approach and we think like what is going to be best in the long term. But by all means, you can also just go browse all views and fields, and this is just the raw columns if you want to. If I wanted to start from here, I can just start grabbing fields, and this is just going to be the raw database columns. And then you can even start just using this area to start then building on top of the raw database columns, and that's how you start building that shared model directly here because if I made a change to this, maybe if I wanted to add an aggregate, maybe a count distinct here, you can see how this works. We have a Git workflow where now I have model state model changes staged in here. So, I can just start from scratch with nothing, and then just start creating them right here on the fly, and then promote them to the shared model, and Omniwill generate that model for you. Okay, that seems pretty straightforward. Nice stuff. All right. So, with that, we are at time. Before everyone dashes off, I want to say next week we've got four webinars for you to continue the Agents of the Enterprise series. So, we've got a session on creating GPTs on Monday. On Tuesday, we have a session on Agentic data science with Serve. Wednesday, we've got a session on Oh, I can't remember what it is. What's Wednesday? Oh, yeah. So, Wednesday we're doing putting agents into production, and Thursday we have a session on how AI can improve enterprise search. So, please do sign up for those, and if you're not already, come back then. All right, thank you once again, Peter. That was a very fun session. Awesome. Thank you all. And thanks for all the questions. And hopefully you enjoyed the the demo a lot. Wonderful. Yeah, great stuff. It was a pretty fancy dashboard that was created in the end. So, excellent stuff. And yeah, thank you to everyone in the audience who asked a question. Thank you to everyone who shared with us today. I will see you all again then.