Data with Depth | OneMagnify’s End-to-End Pipeline with Domo

[speaker_] Are we live? Hello, everyone. Happy Monday. Thanks for joining us this morning or whatever time of day it is.

[speaker_] I already see we have some people from Texas or New York.

[speaker_] Uh, let us know where you're joining us from in the chat.

[speaker_] My name is Savannah. I'm a product marketer here at Domo, and I'll be your host for today's session.

[speaker_] If you're here, chances are you already use Domo in some way, whether it's integrating your data, building visualizations and apps,

[speaker_] or crafting workflows. But today, we're excited to showcase how One Magnify brings together these pieces of the platform to develop a full

[speaker_] end-to-end data pipeline from ingestion, to transformation, to delivery that drives measurable business impact and better customer experiences.

[speaker_] So let me introduce our stars. We have our very own Mike Harding from Domo, who's our director of Domo Embed Sales.

[speaker_] He plays a key role in helping customers uncover true value for their business with Domo's embedded analytics.

[speaker_] We have Catherine Hayes here with us. She's a senior director from One Magnify where she helps lead a team of engineers focused on building AI-powered solutions

[speaker_] including custom apps, automated workflows, and AI agents. And finally, Jason Altenberg, also from One Magnify.

[speaker_] He's a seasoned business intelligence leader with more than nine years of experience in Domo, and has been nominated as one of Domo's most technical and impactful

[speaker_] users. So he knows what he's talking about. Uh, Jason, I wanna give you the opportunity to tell us more about yourself here in a second.

[speaker_] But first, let's start with a rapid fire Q&A to get us all warmed up.

[speaker_] No pressure. These should be easy. Uh, and everyone else, feel free to throw your answers at the chat.

[speaker_] We want to know. Um, okay, Jason, you ready? I'm ready.

[speaker_] Let's go. All right. Early bird or night owl? Oh, boy.

[speaker_] I used to be, uh, a bit of a night owl, but, um, life with kids has changed that for me a little bit.

[speaker_] Now I really appreciate the value of getting a good night sleep, so I'm slowly becoming a- an early bird.

[speaker_] Amazing. Movies or TV shows? Oh, um, for

[speaker_] me right now the, as I mentioned having kids, uh, right now Kpop Demon Hunters is huge in our household.

[speaker_] We love that movie so much we play it at least two to three times a day.

[speaker_] Uh, one of those songs, uh, is stuck in my head, and I've never even seen it.

[speaker_] So (laughs) I can't imagine how you feel. Um, okay.

[speaker_] The past or the future? Uh, focused on the future, um, really excited to see

[speaker_] the adults my kids get to grow into and excited to be a part of that journey.

[speaker_] Okay. And finally, I know I mentioned these would be easy, but Magic ETL or AppStudio?

[speaker_] Oh, boy. Um, for me, it's gotta be Magic ETL with all these amazing new features being rolled out by Andre

[speaker_] Anderson and other folks at Domo. Uh, just so excited for what's next, the future of Magic ETL in addition to all of the amazing

[speaker_] stuff that we're seeing today already. Amazing. Thank you. That's all the questions I have for you.

[speaker_] Um, let's get into the content. If you have... if anyone has any questions about the presentation, please put them in the Q&A.

[speaker_] We will get to them. Uh, leave your comments in the chat if you feel so inspired as we go along, and, uh, look for any poll questions that might

[speaker_] pop up. Uh, Mike, we have one to kick it off, do we?

[speaker_] Yeah. We do actually. Hey, everyone. Um, we really wanna understand where you feel like you are in your data journey in this as well, so

[speaker_] we're gonna- we're gonna throw a poll question up around that.

[speaker_] And then as Jason gets into the middle of his presentation, there's a specific slide around this that maybe we can have a good conversation about where everyone is

[speaker_] and how they're feeling about moving to the next stage of data readiness.

[speaker_] All right. With that, Jason, I'll let you take off, and I'm gonna jump off stage.

[speaker_] All right. We're gonna go ahead and, uh, share the screen here.

[speaker_] All right. Here we go. Um, so today, we're gonna be talking about,

[speaker_] uh, data with depth, and we're gonna be talking about how we're using Domo to be delivering at scale.

[speaker_] Uh, you've already heard a little bit about who we are.

[speaker_] Um, just wanted to call out real quick, uh, part of the, um...

[speaker_] I've been part of the, uh, Domo community to- now for quite some time, uh, where I'm a community coach, uh, was nominated as a Domo Sensei,

[speaker_] um, also as part of the Innovators Program. Wanted to do a quick shout-out to a couple folks.

[speaker_] Um, as a manager at One Magnify, my team, uh, Team Fiesta, couldn't do- do without you.

[speaker_] Hannah, Reuben, Shreya, uh, really excited to be able to work with you every day.

[speaker_] And then finally, uh, been also working with, uh, Domo for Good, so, uh, Ashley Pennywell and the Oaks of Righteousness, uh, on that side of things.

[speaker_] So if you have any questions afterwards about, uh, engaging with Domo with non-profits, I would love to, uh, get that over to Ashley or to answer those myself.

[speaker_] So who are we at One Magnify? Um, our analytics team, we are fully certified in technical and professional services.

[speaker_] Uh, that's all of our folks on our Domo team.

[speaker_] Uh, we have over,Domo projects that we've completed.

[speaker_] We are a seven-time Domo award winner, and we've serviced overcompanies with four published apps in the Domo App Store.

[speaker_] Here's a little slide about some of the things that we do.

[speaker_] Uh, we do major Domo consulting where we help folks with anything end-to-end implementing Domo, whether that's Beast Modes, variables,

[speaker_] PDP, trying to figure out SSO, all that and in between.

[speaker_] Uh, data strategy and governance, we've got some- we've got a dedicated team that focuses on data governance and helps folks with data strategy....

[speaker_] cloud data warehousing, where we can, uh, help you maximize your investment with things like Snowflakes, Databricks, or GCP.

[speaker_] And we'll be talking a little bit more about that part today.

[speaker_] Um, in addition to the Agentic AI and data science, uh, we're leveraging gen AI, predictive modeling, forecasting chatbots, and, uh, all things inside

[speaker_] of Domo to he- help deliver those enhanced insights. Uh, Domo Everywhere and custom apps.

[speaker_] So this piece we're also gonna be he- focusing on today, where we are able to develop solutions within your instance and then push those

[speaker_] out to folks within, um, white-labeled, very branded experiences. And then, of course, training and enablement, where we're able to help

[speaker_] our clients drive that curiosity and foster data-driven cultures within their organizations.

[speaker_] So today, really, we're gonna be focusing mostly on the cloud data warehousing, Agentic AI and data science piece, as well as Domo Everywhere

[speaker_] and custom apps. All right. So before we're gonna get into architecture and apps, we're gonna talk about the why.

[speaker_] Our goal here is to be unlocking, uh, the intelligence at scale.

[speaker_] We don't want those insights to be just useful for your analysts or your HQ, so it's gonna be for your entire ecosystem, franchisees, dealers, partners, distributors,

[speaker_] any external folks that might need access to your data. So what does at scale mean?

[speaker_] We're talking about hundreds or thousands of individual users being able to see that data that you're producing and working hard on in a personalized way.

[speaker_] So whether you're man- managing a national media plan or empowering some local teams, uh, our architecture is gonna be able to scale seamlessly.

[speaker_] And so that's what these next few slides are gonna be about, how we're actually making that possible.

[speaker_] We're gonna walk through data science and modeling work in Databricks, cloud integrations, um, how Magic ETL lets us move fast, and how App Studio is gonna turn that all

[speaker_] into, um, intuitive, actionable tools. So let's start under the hood of how we prepare and model our data.

[speaker_] Or sorry, uh, we're gonna j- jump over to, um, this piece, which is about first-party data.

[speaker_] So, uh, what is first-party data? Uh, first-party data is gonna be data that your company is collecting directly from your customers,

[speaker_] your prospects, or users. This isn't gonna be like scraped data or data that you're getting from another third party.

[speaker_] So this is gonna be directly from your own interactions.

[speaker_] Some examples of this might be website analytics, uh, mobile app usage logs, CRM data, uh, loyalty programs, purchase transactions, surveys,

[speaker_] or customer feedback. Uh, and you can see here, I mean, we've got some stats up here, but improving retention by% can have the same benefits as reducing costs

[speaker_] by%. So you don't need to cut costs to help you focus on your growth.

[speaker_] You can just leverage the data that you're already using today and that you're already working hard at collecting and cleaning.

[speaker_] So, and you've built a company that people believe in.

[speaker_] And so, uh, we at OneMagnify, our goal here is simple.

[speaker_] We're trying to drive ROI across every stage of your customer's journey.

[speaker_] This slide here is outlining how Domo sits in the middle of enabling smarter media performance through four interconnected capabilities that we're

[speaker_] working with. Uh, up top on the right, we're starting with audience creation.

[speaker_] Using both the first-party data and third-party data, we're building highly targeted audience strategies.

[speaker_] This means activating customer data to identify high-value prospects and reach those folks at the right time in their journey.

[speaker_] Domo is helping us unify these data sources, making it easy to segment and activate audiences across those platforms.

[speaker_] Next on the right is media optimization. We're using a framework that combines predictive and retrospective analysis.

[speaker_] Historical performance data helps us forecast which channels are gonna be ab- delivering the best ROI.

[speaker_] Now, with Domo, we can track media performance in real time and refine those future investments based on what's working.

[speaker_] Then on the bottom here, once we know who to target and where, we focus on what to say.

[speaker_] With dynamic creative, we're ensuring personalized messaging at every stage across the customer life cycle.

[speaker_] So it's not just about that personalization though, it's also about relevance.

[speaker_] Our creative adapts to customer behaviors and preferences. And Domo allows our teams to align that creative execution with audience insights, ensuring that every

[speaker_] interaction that the customer has with your brand feels tailored. And then finally, on the left here, we're closing that loop with intelligent reporting.

[speaker_] Domo's gonna connect that CRM data and campaign analytics to deliver real-time insights.

[speaker_] So we're empowering teams to manage their campaigns more effectively and respond quickly to those performance trends.

[speaker_] This helps us understand how we're engaging both with our known customers and with our new prospects so we're maximizing that impact.

[speaker_] So by orchestrating all four of these pillars in Domo, targeting, planning, personalization, and reporting, we're gonna be able to reach that right audience,

[speaker_] deliver the right message, and ultimately maximize return on investment. All

[speaker_] right. And so now we're gonna dive into what's under the hood, the data pipeline.

[speaker_] Uh, this is the technical backbone that's gonna help you get that data, uh, from your existing investments, uh, into Domo.

[speaker_] So at OneMagnify, we're building data pipelines and models, uh, in this example, using Databricks, using both the first-party and that third-party data.

[speaker_] Uh, we utilize, uh, medallion architecture, bronze, silver, gold layers that are gonna be progressively enriching, cleaning, and transforming that data.

[speaker_] Uh, calling back to that poll that we've got going on in the Q&A, uh, this slide here might look familiar to some folks.

[speaker_] Uh, but if it doesn't, uh, maybe you're earlier on that journey, or maybe you've seen a dozen of these slides and you're on the other end of that.

[speaker_] So what we're trying to pro- point out here is that we're building something that's efficient, scalable, and it's built for repeatability.

[speaker_] So this same pipeline can serve hundreds of, of dealerships, partners, business units without duplication.

[speaker_] Our data science team is using these gold layer tables in Delta Lake Uniform to build multi-dealer segmentation and audience definitions.

[speaker_] So what does that mean? It means we can identify clusters of customers by propensity, uh, equity.

[speaker_] Propensity, how, you know, are they going to be buying?

[speaker_] Equity, are we gonna need to invest a little bit more in financing these customers?

[speaker_] Um, or their life cycle stage. Where are they at in that buying journey?

[speaker_] Not just demographics like, um, age, sex, or race. So then by using Domo's cloud integrations, what used to be called Cloud Amplifier,

[speaker_] um, we were joking about this is kind of the, uh, the artist formerly known as Cloud Amplifier.

[speaker_] We deliver that model data directly into Domo.Uh, so this is a direct connection between those Databricks Delta tables and Domo data sets.

[speaker_] We're not working with any manual exports, and that gives us flexibility and speed.

[speaker_] Um, new data, new model, it's gonna be updated automatically and it's gonna be immediately visible in the Domo apps that you're building.

[speaker_] (clicks tongue) So, Databricks gives us that horsepower, that big data science, the pipeline, segmentations,

[speaker_] logic, and scalable architecture. Once that's in place, uh, Domo takes over as our activation layer, and that's where Magic ETL really shines.

[speaker_] And we wanted to point out some of the things that we're doing here, um, inside of Magic ETL to help with that analysis.

[speaker_] So, uh, it's built for speed. Our analysts are gonna be able to go from concept to working data flows in minutes, not in hours.

[speaker_] We're using it constantly for lightweight modeling, reshaping that data, uh, blending it across systems without waiting on, like, a full data engineering sprint.

[speaker_] Uh, everything runs in Domo's cloud, so it's fast and easy to iterate.

[speaker_] If you change your logic, you can see those results in, uh, instantly.

[speaker_] Um, on our left here, we can see that we're pulling those tables in directly using that cloud integration.

[speaker_] Uh, in the middle here, we've got a couple of tiles showing, uh, that we're masking audiences for Google Ads.

[speaker_] So later on, we're gonna show, um, some ways that we're activating with this data, and Google Analytics really wants us to have our data in a very specific way

[speaker_] and have the PII hashed. So we're just using a Python tile here, uh, to do that.

[speaker_] So we've got, um, you know, more than just kind of drag-and-drop ETL stuff.

[speaker_] We've got these, uh, Python tiles to run custom code right inside of that pipeline, whether that's through, uh, scoring, um, running regression logic, or en-

[speaker_] enriching for those external APIs, like that Google, uh, Analytics API.

[speaker_] And then down here in the bottom, we've got this clustering tile.

[speaker_] Um, this lets us group behaviors or customer segments d- dynamically inside that data flow.

[speaker_] Um, and then on the right here, you can see we're using, again, another Python tile to, um, enhance that data a little bit further and, uh, enhance with our

[speaker_] propensity, our segments, and things like that. (smacks lips) So our combination here, our visual workflow and the embedded code right inside of this workflow

[speaker_] is giving us that flexibility of having, like, a data science notebook, uh, but the speed and transparency of that ETL tool, so anybody can get in and take a

[speaker_] look and see what's happening behind the scenes. (clicks tongue) All right.

[speaker_] So far, we've looked at our data foundation, and now we're gonna move up into that application layer.

[speaker_] So this is where that data turns into action. So what you're seeing next isn't just reporting, it's gonna be tools that are designed to help your planners and decision-makers

[speaker_] actually use these- this data and these insights day to day.

[speaker_] (clicks tongue) So here's a great example of us visualizing all that hard work that we were doing in Databricks and Magic ETL.

[speaker_] So remember, we saw those audiences with their propensity scores and their customer equity.

[speaker_] Uh, here, it's all clearly labeled, segmented, and clustered. So by pushing all that work directly into Domo, uh, we're turning that data science work that

[speaker_] we did into a planning tool. Now the business, our folks out there needing to make the decisions, uh, they can make intelligent decisions about which

[speaker_] customers they need to target, how much to invest, and then where that high- the highest lifetime value really is for a customer.

[speaker_] (clicks tongue) Then we're talking here a little bit more about optimization.

[speaker_] Um, so here's where our optimization piece really comes to life.

[speaker_] So on the left here, we've got our demand curves, and these are showing us the spe- the impact of our spend by channel.

[speaker_] So on our top graph there, we can see the effect versus spend, so if we have some outliers, like we're seeing a lot of effect from our social media,

[speaker_] um, and we're putting very little spend into it, versus out of home, where we see very low effect and high spend, that allows us to make the decision to

[speaker_] change and get extra lift from that next dollar by investing it into, uh, more into that social media activity rather than that out of home.

[speaker_] And then underneath, we've got our demand curves that let us know where are we gonna start to see the diminishing returns from that investment.

[speaker_] And if all that sounds super complicated and a little bit hard to digest, uh, on the right here, we have a custom planning application

[speaker_] that we're using to make it so much easier. So instead of asking somebody to crunch these numbers by hand or analyze all this- this stuff on the left, um,

[speaker_] we built a media spend optimizer app inside of Domo, so, uh, just by- with just a couple of clicks, planners are gonna be able to see where you're overinvested

[speaker_] and where you're underinvested and where to spend next. Um, our app is gonna be able to suggest reallocations.

[speaker_] It shows ROI impact instantly, and even lets you run some what-if-type scenarios before you make any s- spend decisions.

[speaker_] So it's decision support that's built right into the workflow.

[speaker_] Um, and when I'm saying the custom or these, um, different types of scenarios, you can see here, uh, we are just selecting, in this particular

[speaker_] view of this app, we're selecting a quarter, we enter that quarterly spend, and we pick which optimization target we're going for and click Optimize.

[speaker_] And then this scrolls a little bit down there, but you can see for all the different channels that we've identified, you can pick, uh, you'll see the weekly spend,

[speaker_] what you can expect from that, um, what your expected profit, your conversion expected ROI, your awareness expected ROI, and your total RO- ROI is going to be from that

[speaker_] investment, along with insights that are generated on here to justify, um, what that all looks like.

[speaker_] Jason, can I jump in and ask a question on that before you move off?

[speaker_] Um- Certainly. I love that you're using the app, um, piece from Domo.

[speaker_] What, what does it take to deploy one of these into a customer's instance?

[speaker_] Say that I'm, I raise my hand and say, "Hey, I would love OneMagnify to create this media spend optimizer for me." Certainly.

[speaker_] Uh, that's a great question, Mike. So, uh, in thinking about that, um, I really think back to this slide that we were showing here.

[speaker_] Um, this is all built on Databricks in this instance, where again, we've got this pipeline set up....

[speaker_] uh, where we're taking that customer data and we are doing some transformation on it.

[speaker_] And as we said in that, kind of that poll question, uh, thinking about where our customers at in that journey is really, uh, gonna let us know how long

[speaker_] that's gonna take. If you're already s-... If this stuff all looks familiar, you know, we've already spent some time, we're doing some work doing, um, doing

[speaker_] master data management, then it's likely that we can come in withintodays and get something stood up and working.

[speaker_] Uh, it could take significantly longer though if that customer, um...

[speaker_] You know, you've got disparate data, things are not... Maybe not really lined up yet.

[speaker_] Uh, we might need to do a little bit of that work in the master data management area or maybe some, uh, data normalization.

[speaker_] Um, but we do have a framework that works there, a framework that we can come in and help you deploy, uh, within your, your organization.

[speaker_] I love it. And so, uh, in other words, if I, if I am...

[speaker_] I mean, so if I have all my data prepped and I have this medallion architecture already created within my cloud environment, in this case, Databricks, you could have that

[speaker_] deployed pretty quickly for my, for my marketing team to be using within a month or so?

[speaker_] Yeah. Yeah. Absolutely. And, uh, uh, not just, uh, limited to Databricks, that is the one that we're showing here, but, um, as I'm sure you know, cloud integration,

[speaker_] uh, has... It's, it's also another one of those awesome Domo products that we're seeing new features from, um, every day.

[speaker_] I mean, I think we were just hearing about the, uh, Oracle and the NetSuite, um, uh, analytics warehouse.

[speaker_] So, uh, any of those existing cloud platforms where we've got those Domo cloud integrations, if you've got your first party data in there, or, um,

[speaker_] some of your... Even some of your third-party data, um, or even you're thinking about, "How do we get that first party data into a platform like this, or even

[speaker_] into Domo directly?" Um, that's all stuff that we can help with.

[speaker_] But if you're already further along down the chain, uh, this is something that we can come in and, and deploy as a solution that sits on top of that

[speaker_] investment that you've already made. I will add to that too, um, yeah, the having your data foundation

[speaker_] obviously is an essential component. Some of the data modeling, um, here, the MMM, MTA, we can...

[speaker_] If you don't already have that, we can also help with that, and that can be done within Domo as well.

[speaker_] It doesn't require a Databricks necessarily. It's... But, but the...

[speaker_] Yeah, in terms of your data readiness, I'd say that is, uh, an, an essential component for us to be able to move quickly.

[speaker_] Yeah, absolutely. All right. I think we'll keep moving. Okay.

[speaker_] Um, and we'll be talking about the MMM and MTA here in just a second too.

[speaker_] So, uh, going back to this, uh, we were talking a little bit about, uh, the custom planning application that we have there.

[speaker_] Uh, moving into here, we're thinking about optimization. It's not just where you're spending that money, where you're spending the dollars

[speaker_] on that advertisement, um, it's also about when and how often you're spending that money.

[speaker_] Uh, in app studio, we've built some visualizations, and we've built, uh, part of our application around, uh, frequency, flighting, and media timing.

[speaker_] Um, this really is... We can think of this simply as we are letting our planners know if they're underexposing or over-saturating an audience.

[speaker_] So we can help you fine-tune your campaign rhythms. We're gonna be balancing that reach, the frequency, and the timing, so that your impressions are gonna be landing effectively.

[speaker_] Uh, this is the, uh... To, to put it simply, I think this is...

[speaker_] It's a difference between, uh, going out randomly and shouting your message or really orchestrating and taking the time to understand when you should be delivering

[speaker_] that message, how often you should be delivering that message, and, uh, what the effect is gonna be there.

[speaker_] And then finally, um, when it comes to proving that effectiveness, we're, we're bringing it all together here.

[speaker_] So, on the left-hand side, we've got, uh, at the strategic level, we can think about market...

[speaker_] Uh, the media mix modeling is gonna be showing... Or MMM, uh, media mix modeling is gonna be showing ROI across all of our channels.

[speaker_] This is online and offline. So we've got TV, radio, online display, social media, those kind of things there.

[speaker_] And then at that tactical level, our multi-touch attribution modeling is gonna tell us, uh, which digital touchpoints are driving results.

[speaker_] So our planners are gonna be able to adjust their messaging across all these different channels in real time as that feedback is coming in from your customers.

[speaker_] And then on the right here, our customer journey dashboards are tying that together, showing how audiences are actually moving across those touchpoints on their way to

[speaker_] conversion. So by embedding all three of these types of views into one application, our leaders are getting both that big picture strategy and

[speaker_] that granular lever, uh, granular level with the levers that they can push and pull, uh, without having to choose between which of these analysis see...

[speaker_] Which of these analyses they'd like to see. Um, and that's gonna bring us to the next part of the story.

[speaker_] Uh, how we're actually delivering all this through embedded analytics with Domo Everywhere.

[speaker_] And to walk us through that, uh, I'm excited to introduce, uh...

[speaker_] Or I'm excited to hand it over to Catherine Hayes.

[speaker_] Uh, she's been leading the charge on embedded delivery and is gonna talk about some examples of how our clients are making these experiences truly their own.

[speaker_] Thanks, Jason. Um, you can go ahead to the next slide.

[speaker_] Okay. Um, so yeah. Uh, basically, the next step is then we take that full solution that Jason just walked you through,

[speaker_] and then we can, um, deliver it directly to our end users through our embedded analytics platform.

[speaker_] And the one that we have, uh, is one magnify dot app.

[speaker_] It's, um, a web-based application powered by Domo Everywhere, which enables programmatic embedding of Domo content,

[speaker_] um, giving users access to curated insights where they're already at.

[speaker_] Um, embedded analytics through Domo Everywhere allows for embedding of Domo dashboards and apps.

[speaker_] Really anything that you, you know, can create that you...

[speaker_] A user can view and interact with, you can embed....

[speaker_] um, directly into your website or your application or your platform, allowing users to access this content where they already go for

[speaker_] their data and their insights. This, of course, allows for a truly white-labeled experience, providing a branded and familiar experience,

[speaker_] um, and allowing these Domo-powered insights to look like a client's existing product rather than a separate tool.

[speaker_] Um, this enhanced user experience means the user doesn't need to learn a new system.

[speaker_] They can access the insights through tool, through a tool they're already familiar with, um, which ultimately can improve user adoption and retention.

[speaker_] So instead of them having to go between different systems, build the insights in Domo, deliver it directly through your platform.

[speaker_] Personalized views ensure each user only sees the information relevant to them.

[speaker_] This means you can restrict access to specific dashboards or apps, um, to only certain users or specific groups of users.

[speaker_] This can also be integrated with your single sign-in, your SSO process, um, if, if needed, um, allowing for even more streamlined control

[speaker_] over user management and access. Secure and personalized data access means that not only do users only see the dashboards that they have access

[speaker_] to, but the data that powers those dashboards is also restricted and filtered to meet their specific requirements and use

[speaker_] cases. Programmatic filtering through Domo Everywhere ensures that each user only sees the data and insights relevant to them, ensuring

[speaker_] data security and privacy. And again, what we're looking at here, One Magnified app, that OneMagnified.app, this is our, um,

[speaker_] embedded analytics platform, but the, what powers this is Domo Everywhere, the ability to programmatically embed

[speaker_] Domo content in any website, such as this one, um, or your own website.

[speaker_] You can build your own custom analytics platform, embed, uh, your, your Domo dashboards there, or if you have an existing

[speaker_] one, same idea. I think you can move on to the next one, I think, Jason.

[speaker_] All right. Um, I wanna call it back to our host before we jump into this.

[speaker_] Do we have the results from that poll that we could look at?

[speaker_] I do think we need to stop sharing your screen to show the results.

[speaker_] Ah. Okay, let's do that real quick. Let me click the button.

[speaker_] I was unable to see it, so... Okay.

[speaker_] Well, that's really interesting. So it looks like the vast majority of people, uh, folks are...

[speaker_] and it's not surprising given that we have a Domo audience here, uh, that data is actively being used to inform decisions across multiple parts of the business.

[speaker_] And then, uh, right underneath it, we've got quite a few folks there too that are saying that they're delivering those data experiences to both internal and external users.

[speaker_] So this is actually a, a great spot for us to be able to help folks is that, um, as we mentioned, you know, it sounds like these folks really

[speaker_] do have an existing in- investment inside of their, uh, data infrastructure.

[speaker_] Uh, they're using that to inform those decisions across multiple parts of the business.

[speaker_] And so, um, yeah, this is really great to see.

[speaker_] And also, those folks that are down here, we've got a couple of votes where folks are still working to do those other pieces of it, and as I mentioned,

[speaker_] if, uh, you're looking at how do we better clean our data, orche- orchestrate it, get it ready for production-ready-type applications like these, we'd love to assist you with

[speaker_] that as well. Uh, maybe, Catherine, as a follow-up question on the Domo embed piece, uh, I...

[speaker_] you're a CTO with your long history in technical user, um, leader.

[speaker_] I'm curious, like, when you think about the buy versus build, so when, when we do an embed application, we're asking you to buy Domo Embed and deploy it,

[speaker_] um, and I think technologists love to build. Uh, what... walk us through your thinking when you, when you make that decision with something like this.

[speaker_] Make the decision to bui- build an embed? Or, yeah, if you're gonna build the embed or if you're gonna buy something like Domo,

[speaker_] what are some of the key factors on what you would do?

[speaker_] Oh. Um, I see. Well, I think, um... I mean, really, I think what we're talking about here is, is both.

[speaker_] You know, Domo as, as the, as the powerful engine behind the scenes that allows you to quickly create these dashboards and these insights, interactive apps,

[speaker_] agents, um, and then in, and then in terms of the front end, there's, you know, I think there's multiple

[speaker_] use cases. A lot of our clients have a whole subset of users where, again, they don't really want those users to have to learn a new platform, to have

[speaker_] to go in, log over here, log in over here, figure out where things are.

[speaker_] Maybe they're already going to this existing website or platform or application, and so you could just take these dashboards and include it in the place

[speaker_] where they're already going. Um, so I think that's really what we're talking about here.

[speaker_] So not necessarily one or the other, it's, it's both.

[speaker_] And you're enhancing the overall experience by, um, you know, giving your analysts and technologists

[speaker_] the power of Domo to create all these cool things and then delivering it in this white-labeled, um,

[speaker_] you know, u- and, uh, user experience that they're already familiar with to, to your users.

[speaker_] Awesome. Yeah, I'm- And integrated too, you know, like making sure that those users are only getting access to what they need.

[speaker_] Of course, you can restrict that in Domo itself, but it's even more curated to exactly what that user, um, should access in, in that embedded analytics

[speaker_] platform. Yeah. Great. Yeah, I find, I find people are like, "Oh, we'll just slap some analytics on our, on our portal or on our software

[speaker_] or something," and not really thinking about how much work goes into building an entire, you know, dashboard platform.

[speaker_] (laughs) Um, I think that's one of the advantages of the, of the white-label piece, and then I actually love how you're talking about you guys have built a lot

[speaker_] of these applications that can deploy very easily in- into it, and you can also build custom applications as well.Um-

[speaker_] Yeah. That's great. So do we wanna skip over and a-answer some questions now, Jason and Catherine?

[speaker_] I think- Oh, let's do that. I think I was gonna dive back into the slide deck real quick and we'll wrap it up- Okay.

[speaker_] ... and then we can jump over to Q&A from there.

[speaker_] Okay. Perfect. Thanks. All right. Great. All right. So we should see the slide five

[speaker_] or the section five here. Okay. (sniffs) So bringing it all together.

[speaker_] Um, and so here's where we're bringing this all together for that real-world use case that we're talking about.

[speaker_] Uh, so OneMagnify engaged with an automotive OEM, and this was a joint venture between them and their dealers, and we're contracted with a number of third parties, and we're

[speaker_] trying to provide that media out to those dealers. So again, uh, these folks need to make decisions about how they're positioning and distributing their

[speaker_] media, uh, out in the real world, you know. They need to figure out, where am I gonna spend my next dollar?

[speaker_] Is that gonna be on newspaper ads? Is that gonna be on TV ads?

[speaker_] Is that gonna be on Facebook? Uh, so we allowed them to help measure their media performance by partner, segment those audiences into

[speaker_] groups that allowed them to make better decisions, and then we provided that media optimization tool set that we were just talking about for those dealers.

[speaker_] So over here on the left, we've got our data management platform.

[speaker_] We can think about this, again, as your cloud data provider that's, um, giving you this, uh, backend or it's really where all your data lives today.

[speaker_] Uh, then we are able to, and as we mentioned, um, doesn't need to be in a Databricks, it can be directly in Domo using, uh, the tools inside of

[speaker_] there. We're doing that modeling, the multi-touch attribution, the media optimization, that media mix modeling, um, so we're doing that modeling and we're providing that set of

[speaker_] tools for media optimization, um, in there. And then we're taking that and we're delivering it out to your different audiences, your media analysts,

[speaker_] your dealer partners, and your dealerships, uh, using that power of Domo and Domo Everywhere.

[speaker_] Uh, and this customer saw a% increase in lead volume, $million reduction in tech debt, and $.million

[speaker_] in identified customer value. So, um, Mike, thinking about your question, I think that this is a good call out.

[speaker_] You know, uh, reduce your tech debt and increase your customer value.

[speaker_] That'd be a great proposition as a CTO, I would think.

[speaker_] So what's next? What are we thinking about with using Domo to help these folks further?

[speaker_] Um, we're thinking about using some of these tools inside of App Studio, and as, uh, as Catherine mentioned, the AI agents now, how can

[speaker_] we use this to help those folks activate even faster with the tools that are inside of App Studio?

[speaker_] So on the left here, you can see we've got some simple, uh, some simple analytics for folks to see some real-time data about their consumers and these different

[speaker_] audiences. We've got a quick, easy form on the left that allows them to resubmit where they'd like to target.

[speaker_] Uh, and then on the right, um, we're exploring how we can use agentic...

[speaker_] the AI agents inside of Domo, inside of the workflows, in order to push that data back to platforms like Google,

[speaker_] uh, Facebook, et cetera, to be able to, uh, quickly and easily try to reengage those audiences where it makes sense.

[speaker_] And speaking about re-engagement, um, we're also thinking about using the same type of workflow to be able to help them with one-to-one customer messaging

[speaker_] and in retargeting with their, with their media assets that they already have out there.

[speaker_] So then you can think of this as reaching out directly, uh, via email or, um, targeted campaigns, um, or how we can reuse some of these assets or when

[speaker_] does it make sense to resend an ad that we've already put out there, we've already spent money on.

[speaker_] And then finally, again, um, using one, a platform like Onemagnify.app, we can embed that, we can have a white labeled experience

[speaker_] that provides that branded familiar experience, allows these Domo powered insights to look like your existing tooling rather than a separate tool.

[speaker_] Um, so we're providing that enhanced user experience using those personalized views, uh, with secure and personalized data access.

[speaker_] And so if any of the things that we've talked about today, uh, sound exciting to you or if you're looking at maybe, uh, enhancing

[speaker_] the data that you already have, uh, no matter where you're at in that, that data journey that we're talking about or that customer journey, um, we would be interested

[speaker_] in talking to you. So if you could scan this QR code that we've got on our screen here, um, we would love to connect with you and get you

[speaker_] in touch with somebody on our team. Give that a second and I'll stop sharing.

[speaker_] All right, there we go. Okay. Uh,

[speaker_] Mike, did we wanna go over to the Q&A at this time then?

[speaker_] Yeah, I think we'll bring Savannah back on to lead us in that.

[speaker_] Awesome. Yeah. Uh, so we do have some pre-submitted questions but we do have one in the chat.

[speaker_] Uh, so while we run through these, please get your questions into the Q&A so we can get them answered.

[speaker_] Um, let's start with the one in the chat. So for what use cases or functionality would you use a third party data lake

[speaker_] maybe, unless lake is a term that (laughs) I'm unfamiliar with, a third party data lake, Databricks or Snowflake instead of the Domo data warehouse?

[speaker_] Yeah, I think, um, I'll jump in, Jason, if that's okay.

[speaker_] Yeah. Um, I think, uh, I mean, I think the primary use cases is if that's what you already have, um, and that's what makes c- the

[speaker_] cloud integration, uh, product of, of Domo so powerful. You don't have to, it's, you, you just keep what you have, um, and

[speaker_] then you, um, bring it over, uh, to activate it in Domo and you don't have to, have to migrate.

[speaker_] Um, I, yeah, I would say that's the primary use case.

[speaker_] Uh, I mean, there's probably, uh, other, um, situations where, um, you're trying to do something very

[speaker_] particular or your business requires a certain tech stack, but again, I think it's more just you already have your data lake or your data

[speaker_] warehouse, whatever your architecture is, um, and you don't have to worry about migrating all of that.

[speaker_] You just, you know, bring it into that solution with Domo.

[speaker_] Yeah, I think about, um, recently I saw some folks who were doing some really cool stuff inside of, uh, Snowflake with the cortex AI.

[speaker_] And, uh, th- what they were doing there is they had, um, kinda years worth of historical, uh, PDFs that they were, uh, parsing

[speaker_] out and getting some information out. And, uh, that work had basically already been done within the tool within Snowflake, so that's work

[speaker_] that's already been done. Um, they've already delivered some, some tables and some information, some data that they would like to use.

[speaker_] Uh, we could then layer Domo right on top, take that data that we're looking to share or even, uh, just a subset of that data that we're looking to

[speaker_] share with particular partners, um, put, use Domo, Domo Everywhere to deliver that data right out of your existing investment,

[speaker_] uh, through Domo and be able to do that personalized branding, the white labeling.

[speaker_] And again, uh, embed it all within y- your website, within your analytics tool as a way for folks to access that data, um, and, and just

[speaker_] get more, more out of the investment that they've already made with those existing tools.

[speaker_] Yeah, those are all really... I mean, that, it's tr- spot-on.

[speaker_] I think one thing... One of the reasons I came back to Domo was because of our partnerships with cloud data warehouses.

[speaker_] Um, I spent the first, you know, six years at Domo selling enterprise, selling in the enterprise space.

[speaker_] And, um, to tell a CIO that you need to move everything out of AWS into Domo was, like, the biggest mouth

[speaker_] drop I ever experienced. And, um, I thought, you know, until we partner (laughs) , we're never gonna grow exponentially.

[speaker_] I think part of the value of where we are today is rather than think of Domo data warehouse as your end-all, think of it as your pseudo holding

[speaker_] ground for your data warehouse strategy. And so if you have...

[speaker_] And Catherine, you kind of hit on this, it's like, where is your data, right?

[speaker_] And it... What Domo wants to do is we want to bring the tools and the applications to your data.

[speaker_] And so if you're sitting on Snowflake, if you're sitting on Databricks, Azure, GCP, we, you can use our connector framework to move

[speaker_] data into your data warehouse. You can use our ETL applications to transform that and then put it back in, or you can go directly out of your environment.

[speaker_] And in some cases where you have terabytes of data, rather than bringing that in and pay (laughs) , you know, here, don't tell my boss

[speaker_] I'm saying this on a webinar, but paying (laughs) , paying Domo for every single table that you bring in to the backend.

[speaker_] Instead, you can very easily pay for that con- that compute within the Snowflake or the Databricks environment to visualize

[speaker_] that app, that, your application or your end-use case. Um, and so Steve, I'm not sure what your role is at your company, but if you're the CFO, there's

[speaker_] a huge financial benefit of having this kind of data strategy, and don't tell the sales reps that I told you, "Don't use our data." You know, we, I, I

[speaker_] will tell you that (laughs) . But, but from a, from a cost standpoint, you'll actually save a lot of money, um, if you do this strategy as well, where

[speaker_] you're really just using Domo for the front-end. And, and Jason, you also hit it on the, on the nail, you know, hit the nail on the head with, you

[speaker_] know, cortex and AI models. A lot of these big cloud data lakes and data warehouses have models.

[speaker_] You can bring your own model, you can do all that work there, and then we just wanna activate that into your business.

[speaker_] You don't need to move the data. So that, that's the only thing I would add, um, in terms of that as well.

[speaker_] And I think Savannah (crosstalk) Yeah, It's really cool tools that they're building within, uh, workflows with the, uh, AI agents and things like that.

[speaker_] Um, you know, there's a lot of really neat use cases I think that can be activated there.

[speaker_] And I think that the ETL... Um, as we were showing in the earlier slides there, um, we did some modeling, you know, we're doing some modeling

[speaker_] within, uh, Databricks. Databricks has got a tremendous amount of power for, um, what I would think of in my head as, uh, data

[speaker_] analysts that are very comfortable with code and with, very comfortable with doing those transformations directly through a, like, code

[speaker_] t- type of interface. Um, but you might have a, a team of analysts that are at different stages or at different levels and

[speaker_] having a tool like Magic ETL where some quick transforms can be handed over to some folks that, uh, you know, are more comfortable with a GUI-based editor or

[speaker_] doing some of that, um, analytics with, in, in combination with the e- the AI tool, and that's inside of Magic ETL, I see that

[speaker_] as another huge area of opportunity, um, where you're not, you know, giving folks the, the access to that core area where they might

[speaker_] not be super comfortable or, you know, uh, maybe they're just not ready for it, but you have an area where you can do some quick, easy transformations

[speaker_] on the data, um, hand those out to various members on your team.

[speaker_] And also, I think that there's a really great opportunity there inside of, like, the Magic ETL to have shareable, explainable data flows that you can hand across

[speaker_] and show different folks. And I think also just from, like, a, um, an outward-facing perspective, uh, showing a bunch of slides of code to

[speaker_] somebody and saying, "Look at all the great work that we've done," uh, might not be as easy for them to understand as seeing, you know, the Magic ETL canvas

[speaker_] where you've got, you know, clearly labeled data flows that show along every step of the journey, "Here's what we're doing to your data to make sure that it makes

[speaker_] sense at the end." So I think there's a lot of power that comes into that tooling that, uh, sits on top of your existing infrastructure and, um, with some

[speaker_] of the great things with Magic ETL, again, being able to do some transforms and things, uh, directly integrated with those warehouses, there's some really great

[speaker_] unlocks there too, just for data observability, explainability, and, um, data, data democratization too, getting it out to everybody

[speaker_] within your organization. And we did have another pre-submitted question. I think, Mike, this

[speaker_] one will be towards you. "Uh, we have different cloud tools like GCP.

[speaker_] How is Domo different from it, and what specific feature of Domo is used for convincing us to move to Domo?" Yeah,

[speaker_] thanks Savannah. I actually just copied and pasted the question in so the audience could see it as well.

[speaker_] Um, and it's kind of in this same vein, and I just spent the last five years at Google Cloud selling Google Cloud Services, and so I do feel like

[speaker_] I have somewhat of a- an ethos to speak to this.

[speaker_] Um, well first off, we're not asking you to move from GCP or from any of the cloud services to Domo.

[speaker_] So if- if that's the conversation, we're- we're having the wrong conversation with you.

[speaker_] And- and please reach out to me, uh, and let's have the right conversation because ultimately we're partnering with Google Cloud, and we have the same

[speaker_] amplifier built on BigQuery. And so everything you've seen for DataBricks and Snowflake you can also do on BigQuery.

[speaker_] And the advantages of doing this is you can run all of the Google large language models in BigQuery and

[speaker_] to power your Domo applications. We talked about Cortex on Snowflake.

[speaker_] You can also power Gemini on Google Cloud and BigQuery and activate that within Domo.

[speaker_] We have a very strong partnership with Google Cloud. We had- we just launched on their Google Cloud marketplace, and we're very excited to actually partner

[speaker_] with them, where you're really using Domo from an application standpoint.

[speaker_] So that means dashboarding, visualization. Catherine mentioned the embed piece.

[speaker_] We really want you to be using Domo in your applications powered by Google Cloud, um, or powered by Snowflake, powered by that data warehouse

[speaker_] so that you're getting to use all of the- the power of Domo, but you're also keeping your data in one place and your data strategy

[speaker_] is really where that- where those companies excel the most of it.

[speaker_] And so I'm- I hope... You know, when- when we think about a big cloud data warehouse though like Google Cloud, there are (laughs) I remember them showing me the

[speaker_] thousands of SKUs I had to sell as a seller.

[speaker_] It was a little overwhelming frankly. And so are there some overlap?

[speaker_] Absolutely, but, you know, ultimately we're not trying to convince you to move anything from Google Cloud to Domo.

[speaker_] We really want you to use our tools to have that cohesive one data strategy on your cloud.

[speaker_] Hopefully that wasn't too long-winded, Savannah. And I don't know if Jason or Catherine have anything to add.

[speaker_] I th- think you hit it all, yeah. Yeah. I mean, I think the- the- the data exploration piece of it, um, is-

[speaker_] is really huge. Domo makes it very easy for you take huge massive tables that exist out there in your,

[speaker_] uh, you know, your Google Cloud platform, your DataBricks, your Snowflake, um, and quickly and easily understand them, visualize

[speaker_] them, and transform them into, uh, insights. You know, and that's- that's where I think Domo really shines is, um, you know, it...

[speaker_] As a- as a person who's been inside of a Snowflake, you know, I can, uh, run some code, you know, select this from that from there and show somebody

[speaker_] from the business, like, "Look, here's all the data. It lives there.

[speaker_] It's there." That- that's not as impactful and effective as them being able to open up Analyzer themselves, drag and drop some of their columns, and then really

[speaker_] see where these things are in it. So I think that there's a lot of, uh, opportunity there to have, uh, a lot simpler exploration

[speaker_] inside of Domo of those existing assets inside your cloud platforms as well.

[speaker_] Anything... And give your business access to it as well, which can be kind of challenging, I know, uh, thinking about data strategy.

[speaker_] Sometimes it's challenging to land those data assets or give an existing user access to Snowflake versus, you know, spin up a user inside

[speaker_] of Domo, give them access to one very specific view of the data, and allow them to then, um, do their- do their own reporting on it.

[speaker_] Perfect. Thank you. Uh, last call for questions. I have one last one that was submitted.

[speaker_] Um, get your question in now if you have it or I guess just ask it later, at a later time if you connect- can connect with them.

[speaker_] Um, okay, so this question is for everybody, and I do promise it was pre-submitted, I'm not just making it up: what do you like to cook at home?

[speaker_] Uh, Jason, we can start with you. Oh, for me, it's, um, it's eggs.

[speaker_] I mean, just simply scrambled, uh, over-easy, whatever. I've started to, uh, understand how to use a stainless

[speaker_] steel pan recently. It wasn't something I used. I was, you know, I will...

[speaker_] Uh, there's some people out there that are gonna hate this, but I was a big fan of the non-stick Teflon pans for a long time 'cause it was easy,

[speaker_] simple to use, but, uh, go- if I could go back, I'd- I'd be using stainless steel because I'm having a great time learning how to cook all over again.

[speaker_] I- I'll jump in. Th- this question smells like it's a John Lee question.

[speaker_] It sure does. Right? (laughs) Uh, I love cooking too.

[speaker_] You know, my... We bought a Le Creuset, uh, pot, and since I bought that, I- I'm loving making french onion soup, uh,

[speaker_] and my- my kids, they absolutely love that, and also, um, anything I can cook in there because going from the stovetop to the oven,

[speaker_] it's just genius. Also have a Traeger, so there's like a hard second would be, you know, smoking meat on the Traeger, uh, um, but

[speaker_] yeah, it's- it's, uh, it's definitely a lot of fun to- to cook those meals.

[speaker_] That's a fun question, Savannah. Yeah, actually I'll, uh, pile in with like the Le Creuset.

[speaker_] I love that for caramelizing things such as, uh, I've got a great pasta recipe, a go-to that,

[speaker_] um, you, has just a ton of shallots that you caramelize and you add a little tomato paste and it just, it's- it's pasta and it's

[speaker_] wonderful. Um, and then I think my go-to is just a good piece of fish, um, uh, in- in a cast iron on

[speaker_] the stove and then finish it in the oven and it's perfect.

[speaker_] You're making me hungry, Catherine. Yeah. (laughs) (laughs) Need lunch.

[speaker_] Time for lunch almost, so. Looks like we- Hi, Mike. ...

[speaker_] a question, Savannah, that popped in from Ekene Open- Oh, perfect.

[speaker_] Do you that ... Do you want to take that, Mike?

[speaker_] Sure. "What is the best model to use with Domo AI?

[speaker_] Is Domo GPT the best in Domo?" Man, this is, uh, this, that is a very contentious question to ask, Ekene.

[speaker_] (laughs) Uh, every, every person that owns a model will be upset.

[speaker_] Um, you know, I think one thing, (laughs) the way that we look at this is, we really want you to bring your own model, right?

[speaker_] And so there, I wouldn't say that there is the best.

[speaker_] You, you can actually choose what model you feel is the best.

[speaker_] And so all of that, when, when you're in the ETL transformation framework where you want to run a model against your data,

[speaker_] today, you, you actually hit a dropdown and you can bring in various models.

[speaker_] I think one advantage of this is you can test it out yourself.

[speaker_] So if you're, if you're wondering, "Is the latest model from OpenAI or Claude or another model that I want to try and train in there,

[speaker_] do I want to use it?" Domo GPT came about primarily for a cost savings plan, right?

[speaker_] I mean, we have extremely brilliant, uh, data scientists that, that have, have put this together.

[speaker_] Um, but, but ultimately, it's, it's us being economically smart to let you run some modeling against your data with

[speaker_] Domo GPT. Um, I don't know that we've actually benchmarked it against any of the other models, where I know a lot of Google and

[speaker_] OpenAI, they have benchmarked their models against each other and said, "Hey, we're the best multi-model, modal, model for this," or et cetera.

[speaker_] Um, and so what I think is exciting about Domo and what you can see is when you do a model call, if you have an agreement

[speaker_] with one of these companies, you basically just put in your billing ID and you continue to pay your contract and not Domo to use your

[speaker_] model against your data. Of course, we have a, a small token price for you to call that model, but if you have a big commitment with Google Cloud

[speaker_] and you want to use their Gemini model against your data in Domo, you can just wire that in and then you just continue to pay through your GCP billing

[speaker_] console. And so what I would say is, we have definitely ch- you know, put the flag in Switzerland, where we, (laughs) where we don't want to make a claim

[speaker_] that one is better than the other. And I also think that there's various models that are better for specific use cases too.

[speaker_] Um, and I'll stop talking as a sales guy. I'd, I'd love to hear what Catherine has to say as a technologist on what she thinks, um, but th- that's

[speaker_] my two cents. Yeah, I mean, I, I would just reiterate everything you said.

[speaker_] Um, it, it, it's balancing costs, they all are gonna have, um, you know, different, um, cost implications of

[speaker_] course, um, with what is it that you're trying to do?

[speaker_] And, you know, as Mike said, every model, every LLM is a little bit different, trained on different data,

[speaker_] using different methods. So it's gonna, each one is gonna be a little bit better, uh, for different tasks.

[speaker_] So I think it's really about what is it you're trying to do, do that due diligence with, like, what, what models might be most effective

[speaker_] for your use case, balance budget, um, and then experiment and see what works best.

[speaker_] But yeah, I think the biggest thing here is what Mike said, you, you have so many options there, uh, within Domo, which is pretty amazing.

[speaker_] Mike, do you remember that chart from a long time ago that Domo had where it was kind of, uh, what's the best, uh, visualization to use in

[speaker_] any use case and then it kind of had branching paths off of it?

[speaker_] That's what this question makes me think about. Yeah, we should...

[speaker_] Oh, I'm sure if, um, if Chris Willis was listening to this, he's probably already got something modeled up to, like, have the- (laughs) ...

[speaker_] have the data model tree visualization. Actually, that's not a bad idea, Jason, to run with that.

[speaker_] But it is funny because I don't know if you remember Jeremy Morris, he, he works now with...

[speaker_] as one of our customers. But he, he was so big on this, right?

[speaker_] As a data scientist, like, what is the right visualization?

[speaker_] And, and he, he... (laughs) As a business consultant before I got into sales, he was like, "You need to read Tuft, 'cause if you, if you're choosing the wrong

[speaker_] chart for the wrong visualization- (laughs) ... I'm gonna smack ya," you know?

[speaker_] (laughs) And, and so there is, there is a bit of, uh, you know, strong feelings in that area.

[speaker_] Yeah, it's... I think the, the overall is, you know, uh, I love the, the way that we kind of all coalesced around the same thing, what are you trying

[speaker_] to do? You know, that's the real question, is, uh, what are you trying to do with it?

[speaker_] And that, that outcome is gonna drive what your choice of tooling is on the way to that outcome.

[speaker_] Uh, I've seen, uh, folks on our team too when they're asked that same sort of question, uh, go back to, you know, maybe, maybe it's not a Claude

[speaker_] model that you need for that, but there is this really great, uh, data science algorithm that is machine learning that we can utilize to get you to that result,

[speaker_] uh, very quick. And that's one of the reasons I really like the team that I work with and all the smart people that are at OneMagnify, is that, um,

[speaker_] you know, we can help you find the right tools to use to get to that desired outcome.

[speaker_] Amazing. I think we are all out of questions.

[speaker_] Um, we can wrap it here. Big thank you to Catherine, Jason, and Mike.

[speaker_] Yeah. We really appreciate you all coming and talking and sharing all this important information and super cool information.

[speaker_] Um, and thank you everyone for joining us today. We, uh, hope to see you on our next one.

[speaker_] Awesome. Thanks for having us. Bye, y'all. Yeah, thanks a lot for having us.

[speaker_] Bye, everyone. Bye.

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