Name: Data with Depth - Good captions
Uploaded: 2025-10-06T17:31:14.561Z
Duration: 55 min 4 s
Description: Data with Depth - Good captions

Transcript for "Data with Depth - Good captions": your media analysts, your dealer partners, and your dealerships, using that power of Domo and Domo everywhere. And this customer saw a 21% increase in lead volume, $3,000,000 reduction in tech debt, and $6,500,000 in identified customer value. So, Mike, thinking about your question, I think that this is a good call out, you know, reduce your tech debt and increase your customer value. That'd be a great proposition as a CTO, I would think. So what's next? What are we thinking about with using Domo to help these folks further? We're thinking about using some of these tools inside of App Studio. And as as Catherine mentioned, the AI agents now, how can we use this to help those folks activate even faster with the tools that are inside of App Studio? So on the left here, you can see we've got some simple, some simple analytics for folks to see some real time data about their consumers and these different audiences. We've got a quick easy form on the left that allows them to resubmit where they'd like to target. And then on the right, we're exploring how we can use agentic, the AI agents inside of Domo, inside of the workflows in order to push that data back to platforms like Google, Facebook, etcetera, to be able to, quickly and easily try to reengage those audiences where it makes sense. And speaking about reengagement, we're also thinking about using the same type of workflow to be able to help them with one to one customer messaging and in retargeting with their with their media assets that they already have out there. So they can think of this as reaching out directly, via email or, targeted campaigns, or how we can reuse some of these assets, or when does it make sense to resend an ad that we've already put out there we've already spent money on. And then finally, again, using one, platform like OneMagnify. App, we can embed that. We can have a white labeled experience that provides that branded familiar experience, allows these Domo powered insights to look like your existing tooling rather than a separate tool. So we're providing that enhanced user experience using those personalized views, with secure and personalized data access. And so, if any of the things that we've talked about today, sound exciting to you or if you're looking at maybe, enhancing the data that you already have, no matter where you're at in that that data journey that we're talking about or that customer journey, we would be interested in talking to you. So if you could scan this QR code that we've got on our screen here, we would love to connect with you and get you in touch with somebody on our team. Give that a second, and I'll stop sharing. Alright. There we go. Okay. Mike, did we wanna go over to the q and a at this time then? Yeah. I think we'll bring Savanah back on to lead us in that. Awesome. Yeah. So we do have some presubmitted questions, but we do have one in the chat. So while we run through these, please get your questions into the q and a so we can get them answered. Let's start with the one in the chat. So for what use cases or functionality would you use a third party data lake maybe, unless lack is a term that I'm unfamiliar with. Third party data lake, Databricks, or Snowflake instead of the Domo data warehouse. Yeah. I think, I'll jump in, Jason, if that's okay. I think, I mean, I think the primary use case is is if that's what you already have, and that's what makes the cloud integration, product of of Domo so powerful. You don't have to. It's you you just keep what you have, and then you, bring it over, to activate it in Domo, and you don't have to have to migrate. I yeah. I would say that's the primary use case. I mean, there's probably other situations where, you're trying to do something very 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 warehouse, whatever your architecture is, and you don't have to worry about migrating all of that. You just, you know, bring it into that solution with Domo. Yeah. I think about, recently, I saw some folks were doing some really cool stuff inside of Snowflake with the Cortex AI. And, what they're doing there is they had, kinda years worth of historical, PDFs that they were parsing out and getting some information out. And, that work had basically already been done within the tool within Snowflake, so that's work that's already been done. They've already delivered some some tables and some information, some data that they would like to use. We could then layer Domo right on top, take that data that we're looking to share or even, just a subset of that data that we're looking to share with particular partners, put use Domo Domo everywhere to deliver that data right out of your existing investment, through Domo and be able to do that personalized branding, the white labeling, and, again, embed it all within your website, within your analytics tool as a way for folks to access that data, and and just get more more out of the investment that they've already made with those existing tools. Yeah. Those are all really I mean, that it's spot on. I think one thing one of the reasons I came back to Domo was because of our partnerships with cloud data warehouses. I spent the first, you know, six years at Domo selling enterprise selling in the enterprise space. And, to tell a CIO that you need to move everything out of AWS into Domo was, like, the biggest mouth drop I ever experienced. And, I thought, you know, until we partner, we're never gonna grow exponentially. 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 ground for your data warehouse strategy. And so if you have and, Catherine, you kinda hit on this. It's like, where is your data? Right? And what Domo wants to do is we wanna bring the tools and the applications to your data. 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 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. And in some cases, where you have terabytes of data rather than bringing that in and pay you know, here, don't tell my boss I'm saying this on a webinar, but paying paying Domo for every single table that you bring in to the back end, instead, you can very easily pay for that con that compute within the Snowflake or the Databricks environment to visualize that app that your application or your end use case. And so, Steve, I'm not sure what your role is at your company, but if you're the CFO, there's 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 will tell you that. But but from a from a cost standpoint, you'll actually save a lot of money, if you do this strategy as well, where 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 know, cortex and AI models. A lot of these big cloud data lakes and data warehouses have models. You can bring your own model. You can do all that work there, and then we just wanna activate that into your business. You don't need to move the data. So that that's the only thing I would add, in terms of that as well. And I think Savanah it's really cool tools that they're building within, workflows with the, AI agents and things like that. You know, there's a lot of really neat use cases, I think, that can be activated there. And I think that the ETL, as we were showing in the earlier slides there, we did some modeling. You know, we're doing some modeling within, Databricks. Databricks has got a tremendous amount of power for, what I would think of in my head as, data analysts that are very comfortable with code and with very comfortable with doing those transformations directly through a, like, code type of interface. But you might have a a team of analysts that are at different stages or at different levels and having a tool like Magic ETL or some quick transforms can be handed over to some folks that, you know, are more comfortable with a GUI based editor or doing some of that, analytics with in in combination with the the AI tooling that's inside of Magic ETL. I see that as another huge area of opportunity, where you're not, you know, giving folks the the access to that core area where they might not be super comfortable or, you know, maybe they're just not ready for it. But you have an area where you can do some quick easy transformations on the data, hand those out to various members on your team. 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 and show different folks. And I think also just from, like, a, an outward facing perspective, showing a bunch of slides of code to somebody and saying, look at all the great work that we've done, might not be as easy for them to understand to see and, you know, the magic ETL canvas 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 sense at the end. So I think there's a lot of power that comes into that tooling that, sits on top of your existing infrastructure and, with some of the great things with the MedGTL, again, being able to do some transforms and things, directly integrated with those warehouses. There's some really great unlocks there too just for data observability, explainability, and data data democratization to getting it out to everybody within your organization. And we did have another presubmitted question. I think, Mike, this one will be towards you. We have different cloud tools like GCP. How is Domo different from it, and what specific feature of Domo is used for convincing us to move to Domo? Yeah. Thanks, Savanah. I actually just copied and paste the question in so the audience could see it as well. And it's kind of in the same vein. And I just spent the last five years at Google Cloud selling Google Cloud services, and so I I do feel like I have somewhat of a an ethos to speak to this. Well, first off, we're not asking you to move from GCP or from any of the cloud services to Domo. So if if that's the conversation, we're we're having the wrong conversation with you. And and please reach out to me, and let's have the right conversation. Because, ultimately, we're partnering with Google Cloud, and we have the same amplifier built on BigQuery. And so everything you've seen for Databricks and Snowflake, you can also do on BigQuery. And the advantages of doing this is you can run all of the Google large language models in BigQuery and to power your Domo applications. We talked about Cortex on Snowflake. You can also power Gemini on Google Cloud and BigQuery and activate that within Domo. We have a very strong partnership with Google Cloud. We we just launched on their Google Cloud marketplace, and we're very excited to actually partner with them where you're really using Domo from an application standpoint. So that means dashboarding, visualization. Catherine mentioned the embed piece. We really want you to be using Domo in your applications powered by Google Cloud, or powered by Snowflake, powered by that data warehouse 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 is really where that where those companies excel the most of it. And so I'm I'd hope you know, when when we think about a big cloud data warehouse, though, like Google Cloud, there are I remember them showing me the thousands of SKUs I had to sell as a seller. It was a little overwhelming, frankly. And so are there some overlap? Absolutely. But, you know, ultimately, we're not trying to convince you to move anything from Google Cloud to Domo. We really want you to use our tools to have that cohesive one data strategy on your cloud. Hopefully, that wasn't too long winded, Savanah. And I don't know if Jason or Catherine have anything to add. I think hit it all. Yeah. Yeah. And I think the the data exploration piece of it, is is really huge. Domo makes it very easy for you to take huge massive tables that exist out there in your, you know, your Google Cloud platform, your Databricks, your Snowflake, and quickly and easily understand them, visualize them, and transform them into, insights. You know? And that's that's where I think Domo really shines is, you know, it as a as a person who's been inside of a snowflake, you know, I can, run some code, you know, select this from that from there and show somebody from the business, like, look, here's all the data. It lives there. 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 in and really see where these things are. So I think that there's a lot of, opportunity there to have, a lot simpler exploration inside of Domo of those, existing assets inside your cloud platforms as well. And give your business access to it as well, which can be kinda challenging. I know, thinking about data strategy, 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 of Domo, give them access to one very specific view of the data, and allow them to then, do their do their own reporting on it. Okay. Thank you. Last call for questions. I have one last one that was submitted. 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. Okay. So this question is for everybody, and I do promise it was presubmitted. I'm not just making it up. What do you like to cook at home? Jason, we can start with you. Oh, for me, it's, the eggs. I mean, just simply scrambled, over easy, whatever. I've started to, understand how to use a stainless steel pan recently. It wasn't something I use. I was you know, I will, there's some people out there that are gonna hate this, but I was a big fan of the nonstick Teflon pans for a long time because it was easy, simple to use. But, 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. I'll I'll jump in. This question smells like it's a John Lee question. It's true. Does. Right? I love cooking too. You know, my we bought a La Croquette, pot. And since I bought that, I'm I'm loving making French onion soup. And my my kids, they absolutely love that. And also, anything I can cook in there because going from the stovetop to the oven, it's just genius. Also have a Traeger, so there's like a hard second would be, you know, smoking meat on the Traeger. But, yeah, it's it's, it's definitely a lot of fun to to cook those meals. That's a fun question, Savanah. Yeah. Actually, I'll, pile on with the, like, crusette. I love that for caramelizing things such as, I've got a great pasta recipe, a go to that, 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 wonderful. And then I think my go to is just a good piece of fish, and a cast iron on the stove, and then finish it in the oven, and it's perfect. You're making me hungry, Catherine. Yeah. Sometimes a lot of times I use this. So I'd like a question, Savanah, that popped in from McKeaney open. Oh, perfect. Do you wanna take that, Mike? Sure. What is the best model to use with Domo AI? Is Domo GPT the best in Domo? Man, this is a this that is a very contentious question to ask, Akiny. Every every person that owns a model will be upset. You know, I think one thing the way that we look at this is we really want you to bring your own model. Right? And so there I wouldn't say that there is the best. You you can actually choose what model you feel is the best. And so all of that when when you're in the ETL transformation framework where you wanna run a model against your data, today, you you actually hit a drop down and you can bring in various models. I think one advantage of this is you can test it out yourself. So if you're if you're wondering is the latest model from OpenAI or Claude or another model that I wanna try and train in there, do I wanna use it? Domo GPT came about primarily for a cost savings plan. Right? I mean, we have extremely brilliant, data scientists that that have have put this together. But but, ultimately, it's it's us being economically smart to let you run some modeling against your data with Domo GPT. I don't know that we've actually benchmarked it against any of the other models, where I know a lot of Google and OpenAI, they have benchmarked their models against each other and said, hey, we're the best multi model model model model for this or etcetera. 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 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 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 and you wanna 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 console. And so what I would say is we have definitely, you know, put the flag in Switzerland where we where we don't wanna make a claim that one is better than the other. And I also think that there's various models that are better for specific use cases too. And I'll stop talking as a sales guy. I'll I'd love to hear what Catherine has to say as a technologist on what she thinks. But that that's my 2¢. Yeah. I mean, I I would just reiterate everything you said. It's balancing costs. They all are gonna have, you know, different, cost implications, of course, with what is it that you're trying to do. And, you know, as Mike said, every model, every LLM is a little bit different, trained on different data using different methods. So it's gonna each one is gonna be a little bit better, for different tasks. So I 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 for your use case, balance budget, and then experiment and see what works best. But, yeah, I think the biggest thing here is what Mike said. You you have so many options there, within Domo, which is pretty amazing. Mike, do you remember that chart from a long time ago that Domo had where it was kind of, what's the best, visualization to use in any use case, and then it kinda had branching paths off of it. That's what this question makes me think about. Yeah. We should oh, I'm sure if, if Chris Willis was listening to this, he's probably already got something modeled up to, like, have the have the data model tree visualization. Actually, that's not a bad idea, Jason. Run with that. But it is funny because I don't know if you remember Jeremy Morris. He he works now with as one of our customers. But he he was so big on this. Right? As a data scientist, like, what is the right visualization? And and he he as a business consultant before I got into sales, he was like, you need to read Tufts because if you if you're choosing the wrong chart for the wrong visual, I'm gonna smack you, you know. And and so there is there is a bit of, you know, strong feelings in that area. Yeah. So I think the the overall is, you know, I love the the way that we can all coalesce around the same thing. What are you trying to do? You know, that's the real question is, what are you trying to do with it? And that that outcome is gonna drive what your choice of tooling is on the way to that outcome. I've seen, folks on our team too when they're asked that same sort of question, go back to, you know, maybe maybe it's not a cloud model that you need for that, but there is this really great, data science algorithm that is machine learning that we can utilize to get you to that result, 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, you know, we can help you find the right tools to use to get to the desired outcome. Amazing. I think we are all out of questions. We can wrap it here. Big thank you to Catherine, Jason, and Mike. We really appreciate you all coming and talking and sharing all this important information and super cool information. And thank you everyone for joining us today. We, hope to see you on our next one. Awesome. Thanks for having us. Yeah. Thanks a lot for having us. Bye, everyone. Bye.