Scott Townsend: Hello, and thank you for tuning into the session of Get Leverage. My name is Scott Townsend. I'm the director of marketing at Domo. Get Leverage is a content series where we explore how businesses are using data to overcome real-world challenges. Today, we're going to talk with a friend from DeGreed, an educational technology company. How DeGreed made the shift from self-service BI, or to self-service BI, and how that shift is helping them better empower employees and free up valuable data science and engineering resources. Our guest today is Mike Makas, Head of Analytics and Insights at DeGreed. In addition to his experience in BI, Mike has worked in customer success, consulting, product, and research, which positions him nicely to help us understand what it takes to bring these exciting new capabilities to life and share advice around how to improve self-service BI adoption, data literacy, and advance the data-driven culture of your company. If you have any questions during the webinar, there's a Q&A window on the right side of your screen. Feel free to ask questions throughout the session, and we'll do our best to answer them during the Q&A. Mike, thanks for joining us today. Hi, Scott. Thanks for having me. So, you know, as we've been preparing for this webinar, you've shared a ton of exciting information. I'm really excited to talk to you and have our audience learn from you today. Can we start just, can you share some background on DeGreed and give us some context as to why you wanted to implement data differently within areas of your company?
Mike Makas: Yeah, sure. So the greed is an upskilling platform. Many of us are probably all of us are learning every day, whether it's, you know, at work or on the Internet, taking different courses or content. And so the greed is a place you can go to constantly upskill. Any of you can get the app or your company can license it for your entire department or company. It's a place where you go tell it the skills you have, the skills you want to have. or that you want to work on, and then Degree will constantly recommend new learning for you. And so for a company that licenses, imagine getting all of that skill data to understand who can do what things, and then they can help better with career mobility to get people lined up for different jobs. So that's all about what Degree does. Now, how that intersects here is that my team gets to do all the awesome product analytics and all the cross-functional analytics that pull all the data together. And so that's where we use Domo to help tell the story to our company of what's happening.
Scott Townsend: Very cool. Very great to hear what you guys are up to. And it does make sense as a high-tech company that you'd have a lot of data coming in and that you'd want to use that data to help expand and improve the service. Can you share what were some of the initial problems you were trying to solve? Like what was the genesis or the main goal that led you to want to do data differently?
Mike Makas: Yeah, it's a really fun story for me because it's really put me in my dream job actually as well. So the catalyst for change and kind of the beginning of getting more serious with Data Decreed happened a few years ago. We were a young company, more of a startup feel, just racing, get our product out there, focusing on sales, focusing on renewing clients. But ultimately, we would be in some situations sometimes where employees, client-facing employees like salespeople or CX people, might share things that maybe they heard someone else say, but they didn't really ever read or get the answer themselves. People at the company weren't always certain on who to go to for answers, or they were asking lots of people, so it was creating a bit of noise. And then with a lifelong learning culture that we have, employees are always constantly craving more and more information. So that was kind of the background and what got us to be a lot more serious. And so I approached... our CEO, our chief product officer, and our chief of operations and said, you know, it's time we get super serious. We put things in employees' hands and let them kind of do better for employees and solve this problem of people getting different answers from different places. And so we got a lot more serious about our data, and we knew if we made this leap – we would do a few things, and at least these four that I would share with you. For employees' sake, if we got much more focused on getting the data that people needed, we would help educate the employees so they could then become better consultants, better partners with our clients. If we picked the right tools and technologies, as our company has been continuing to grow really fast, we'd set up for scale with the right data tech stack, the right data ecosystem to be able to grow into. And then the company was constantly talking about being data-driven, but we knew as data professionals that the first step of that was getting them more data-informed. So we had to get them the answers, and that's what we were after first, to get them to be more data-informed so we could be on the path to be data-driven. And then the last part for us that was a big deal is our culture. Like I said, it's a lifelong learning culture, people always constantly learning. So they're always super curious. So we knew if we did all this right, these would be the things that we solved at Degree. And so we went about and said, we've got to start, we've got to put together, you know, a business intelligence data visualization engine to kind of get people to get their data in one place. And this was the process that we went forth. Putting on like my consulting hat from my past days, we did some basic research. We found the vendors that might have benefit for us. uh we have and we sought out to evaluate eight different options so we ran kind of like a light uh procurement rfp process we sent out a questionnaire to each of these eight uh we put together a time and calendar to get a demo done from each put them all on like a schedule so we could compare we put together some scoring criteria for the questionnaire and the the demos we did down selection processes throughout and we and um And as we down selected the final three vendors, we had data ready. So we put together a sample set of our product data and had two business apps ready to connect. And so for the last three that we looked at, we were able to compare apples to apples and compare them equally. And then through the process, I'd say one of the key things that we did was that we pulled in the business, like coming from a change management background, we pulled in people to evaluate that would be using the tool. We pulled in the heads of engineering that would help us get things set up. And we pulled in the executives that would need to help sign off on the expense and the moving forward. So they were constantly engaged and they were committed and involved with the decision. And then as this process finished up, obviously we're here today, but we picked Domo. And some of the obvious things that I would share, they were awesome. were probably the moment for me was we spent weeks trying to get together with our data engineering folks and writing to an API to Salesforce and getting our account data fed in and never actually got that successfully done. But when we were through this process, trying out different vendors with Salesforce alone, I put in my own user credentials, my own username, password, and was able to pull in all of our hundreds of thousands of account records in minutes. Literally, I think if I remember, it was 18 minutes. So at that moment, we kind of had the aha light bulb of this might really work out great. And then the second part is still the piece that I love to talk about all the time. is with Domo in the App Store, it really allows us to incubate, and it keeps us agile where if somebody approaches us with any different project, we look it up and see, you know, is there an app, is there a connector or a data set easy to plug into? And this helps us try things out extremely fast in that minutes example that I gave versus trying to have somebody do some custom engineering work that you need to do to make it permanent anyway. But the incubation that Domo provides has been great, and we still do that today a year and a half in. And then the third thing that really helped Domo stand out was the way that creating charts was really intuitive. Obviously, everybody in the business that's doing data visualization has different charts, but the one, the key theme of feedback we got from the different people who evaluated is that the creation process was just simple to business users. A lot of the tools can do different things, but some of them are more technical oriented. Domo just made it super simple and intuitive. The last piece I'd wrap up with and kind of like what's made us successful here is in this year, specifically with the pandemic, like what's helped us get much more leverage is this year we've been able to everybody in the company just point to Domo as the place to go get your answers. And that's really helped anchor us this year with everything changing all the time, but it's still that you can get your KPIs, your okay results directly from Domo. And then now that we're, our company was mainly remote before, but now that everybody else is, it's just been a supernatural fit. And in the sake of being remote, one of my favorite things is that Domo has an awesome mobile product. And so anytime we create charts of dashboard or any type of visuals or results, They're automatically produced on mobile. And I love seeing when we look at our own stats of how we're doing, like half of the executive team is the top mobile users. It just makes us happy putting those out because we know that's the right group we want to be targeting. And then the last piece kind of reinforces the app store stuff I already spoke about. But we really don't have met any limits yet. We're able to make all kinds of connections, whether it's getting data into Domo or even sending data out from Domo. And then one last thing I would close on here. is that, you know, getting so much more leverage with Domo. They've been a great partner, and recently we've been working on a new that's not released yet, but we're almost done with a Slack integration. So you can meet people in the flow of work, and if you want to send a link to something, it expands and gives them, like, a snapshot of what's going on. So hopefully these are some examples of, like, what's really worked for us, how we got here, and what we're doing now.
Scott Townsend: Thank you. Thank you, Mike. That was really informative. So in the beginning, you were trying to provide curiosity and confidence to the employees to help let them explore data and make more informed decisions based off of that data. And I love the speed, the ease of use, the ability to really iterate, so that agility, so you can incubate new ideas and then Maybe never would have thought, but now that we're in this pandemic, the mobile, the ability to support remote working has really, really helped. And I'm so thrilled to hear you say all that. Rewinding back to this decision-making process, with your consulting background, I imagine you had some really specific criteria that you were judging Domo against other options because there are a lot of options out there. Can you talk a little bit about some of the criteria that was most important during selecting Domo? your data NBI solution. And now that you've had some time to sort of use Domo and see some of the things that, that Domo brings to you, are there any criteria that you didn't, that you didn't have on your list, but you're so thankful now that you've got that maybe you could educate some of the folks on the line, the things they should be looking for as they're constantly reevaluating their, their solutions.
Mike Makas: Sure. So on the Domo, evaluation process the we tried to make our evaluation really well-rounded where i think we part we partnered with our engineering folks so there was there was definitely an emphasis to you know does it have all the connections does it have these security type features but we wanted to do a lot more so we did those were all included but we wanted to make sure that Data is all about who's going to consume it. Can they answer questions? Can they move the business? And so we added to our questionnaire and to our process and to our evaluation to make sure that there was a level of simplicity and that there was not only the necessary requirements of can you connect, can you do A, B, and C, can you do all these data transformations, but we also had this, at the end of the day, does it work? And when we produced something, was it just simple? And that's when we got down into the mobile part really stood out as like an advantage that we didn't even necessarily look for right away. Kind of to answer the second question you had, it was easy through the evaluation process once we discovered that it was like just did what we needed it to do. So we didn't really have to dig too much further. But I would say that we kept a good balance of our evaluation of technical requirements versus like business ones. Is it actually going to meet the needs and are people going to use it? Because we believe at our company, I'm big on trying to democratize the data. I have a belief that within a few years, most people that are hired like at our company would have a certain level of SQL or technical skills. So we are on the path to open up our BI data visualization tools to the whole company. We focus a lot of our time on like a data dictionary so that anybody could do it and they don't have to wait for our team. I'd say the answer to your question is yes. making sure your valuations like really well rounded. And it's not just all about the tech part, but more about the business part is going to be very useful.
Scott Townsend: Thank you. That's that's really helpful. So I've heard you talk a little. You mentioned a few things about data driven culture and how you're improving that data driven culture within degree. How has implemented implementing Domo change the way you think about and build your business at degree?
Mike Makas: It's the best answer that I'm actually proud of is it's helped us change the conversation where I've been at our business now four and a half years. And for a lot of the beginning time of that, we're asking the same question, but you're asking it like 50 different ways. It was like all about usage. And it was like, is my client using this or how are they doing against this? And it was 50, 200, 300 different variations of the same question about monthly usage. And so there was just significant energy spent on exporting data, pre-DOMO, exporting data, pivot tables. People are getting really good at that, right? Like pivot tables and looking at all different ways and then sharing it with each other. So you'd have all these different spreadsheets just on like monthly usage. And so I'm really proud that we've kind of shifted the conversation to you still need that. Like that still has to happen. But now with Domo, I'll share in a little bit some of the ways that you can begin to look that up. We now have thousands of charts in Domo, but that's all self-service, where people don't have to go ask, they don't have to wait for engineering to get a custom report, but we've now shifted the conversation to much more of our different product services or features or offerings and helping get into two directions, either Like, okay, our like questions are like, how are we moving the needle for the business or specifically in a product analytics space of like, why specifically is this customer and these users, are they coming back and using our product? And so it's been a really fun to watch that journey of like, I need 12, you know, different versions of usage. And now it's like, we're in, we're in new chapter for a few down, we've matured. So it's been fun to watch.
Scott Townsend: That's great to hear. You said that you'll show us a couple use cases, ways that you use Domo. Why don't we go through those? I know you've prepared a few use cases for us. Can you walk us through them? Yeah, that'd be great.
Mike Makas: So first I'll explain to you the three I've pulled. Happy to take any questions on different ones. I've tried to pick ones that won't get us in any trouble with sharing the screen and looking at data. the three I've got for you that are very popular at our company. So the first is a cohort comparison, we call it. I come from a consulting background, so oftentimes clients always ask to be like benchmarked or they think they know how they're doing, but they wanna see how they compare against their peers. So this is our version of that and how we do that. The second is classic KPIs and usage trends that they should be able to showcase some of what Domo offers. And then the third is a little bit different, something people might not expect, that tribal knowledge in your company of kind of like who is on what team and how do you find people. So let me share my screen and take you through these. Okay. I'll give it a second here to come up. And you should be seeing on your screen there, I'm going to go through this cohort comparison first. So what we do here is for all the folks that service clients, whether it's our client experience staff or someone in sales, what they can do is come in here, and I've already done it in advance of logging in, but I've picked, you know, maybe show me all the clients in the tech industry, show me all the clients that are between 5,000 and 75,000 people. So I've already kind of filtered it down so you can kind of start to see how it works. And then we take all kinds of cuts of the data. Now, I'm not going to interact with this too much because it will reveal all our client data, but I'll tour you through. So on the far left, you'll see in the last six months, these are how clients would compare. So it's viewed anonymously so someone in CX could then export this and work directly with the client. It can kind of give them that report card to help drag them along of areas they're excelling they should be proud of or maybe really need to give them a boost and maybe something they need to partner better on. The second one's the same type of story, but most of you that are out there with some level of SaaS product or something has to be implemented. It's like, how did they do the first X months? So we took our usage and carved it by month one, month two, month three, six, nine, and 12. And then we start digging into our specific product. So you can see here kind of that one hour in the middle of that company is really excelling. We get in our business, we're in learning. So a common question is how many things are people completing? So this breaks that down by how many and then of what type. And then we go through another like 15 different charts, but I'll call out a few. As I said, our product is where you can come in and upskill, rate your skills and see how you're doing. We see a comparison in the middle there of different skill counts across clients and what type. We have some social realm in our product where you can follow someone just like on a LinkedIn or Facebook and then they could follow you. So this shows you like an average count to see kind of in this industry. So then they would know how they compare. And then when you get into certain features, all your data experts know like an average doesn't always tell you the answer. So we look at things like the distribution of the max and min or the median to see how they're doing with these different things. So clients could kind of gauge how they compare. This has really gone over really well, because imagine doing this in something like Excel. You'd have to take this data out. You'd have to then join it all together, use different pivots for these different dimensions or variables for your portfolio. But we've now, with pulling the data together and then providing the right filter sets, kind of automated this and made it really fast for the client-facing teams. So this has gone over really well. We added this probably every month with new data sets.
Scott Townsend: The second thing I would share – You were showing some of the drill downs and that those charts are all interactive. Do you see a lot of interactivity from the business users, or is it that the analytics team and the BI team are still supporting people through this? Can you talk a little bit about how these are going to be used?
Mike Makas: Yeah, so this next example answers a lot of that. So we try and produce products that are – more general that could be reused in different ways so this example shows um it's a different dashboard but it's going to answer the question so this shows you our usage activity kind of from generally from our portfolio but not to reveal the secret sauce of degree i've kind of pre-filtered it to just a specific region and just a specific stage of clients so you can kind of see a taste of it so we would provide this and then maybe somebody that works in that region would do the same or somebody else. So as you scroll through here, you can see some examples of, well, I just want to see the enterprise clients. So you can see as you filter that in any card, it automatically then filters and cascades across to all the other charts in the same dashboard. Or maybe this is so noisy that it's got so much data, you just want to distill it down to maybe show me this year. So you could come through and pick let's say just the last two years just with like the slide of a mouse and we know people are doing this um for specific things they need maybe they can't like you always get a lot of the same questions of show me this calendar year or the last 12 months everybody wants it their way so we try and produce things that people could interact and consume there's a little bit of teaching up front sometimes but overall they tend to share these little tips and secrets once they get going And so in this example, you see something like, you know, the average use, you know, the new users by month, comparison over time. We're looking for seasonality in charts like this. return users versus new, and then down to the weekday, and then this one kind of closes out here with region of where people are from. So then they could drill into, well, show me the people mainly just from Argentina, and instead of having to go re-pull data, you have it right here by just clicking on Argentina, and then it rebuilds everything here from that user base. So that would be an example where we kind of meet them in the middle so they can adapt to them.
Scott Townsend: Got it. Got it. So where is all that data coming from? Can you show us, maybe lift open the hood a little bit and show us where this data is coming from and how you are able to manage that data?
Mike Makas: Sure. Yeah, that's the part that gets exciting, and we have a team that does that as their job all day, every day. So I'd approach it from two angles to kind of give you a tour. So if we went into a specific data set, so let's say this one here shows you new user activations by month and breaks it down by account segment, you can drill into – um i think my favorite answer with all of our business stakeholders is you can drill into it like so people always say i need the raw data i want to drill into this and i say just click on it and so they can then click on the data and then they could click into here to get the more data from this specific dimension i won't click on it to not reveal specifics of the data here but the answer like that we're going for is if i go click into the data set i can then start to drill back into the data warehouse end of it. And so this shows you from this data set how many cards are being powered, how many people have access, what's going on. And this is the part that's the real value here. So a lot of times people debate of, you know, where is this data coming from? How is it different? You can look. Now, remember, we started a chart that was showing monthly activations, and then we drilled in to look at the data set. And then we can see This is the data set underneath that's powering that card. And then where did it come from? So it's immediate transformation is the join of a BI layer table from our data warehouse. Microsoft Azure data warehouse. And then with our customer's data set is joined together. And then so where does that come from? So you can start tracing it up the tree. to understand not only where did it come from in the lineage, but then you can look into the things of like when did it last update and how fresh is it or how old is it. So you can get all that data just by clicking in. then it's there oh that was the one way and i promised you another way too so here's a different way so when i first meet with like business stakeholders i can kind of you know look from the top down you know the 55 50 000 foot view and this shows you connected into into domo these are each um pillar column or data based cylinder here represents a data source So these are set up by the different applications or sources that you can see in the spinning wheel here, like Twitter or LinkedIn or HubSpot, our data warehouse, Google Analytics. All these are connections to feed into Domo. The height is represented by the size, and then the ones that you see that are lit up in the screen, those are updating right now while we're talking. And then over here, you know, managing this whole team and this whole tech stack, you can see kind of how many total rows are in Domo so far for degrees use. So hopefully that gives you like two different cuts, Scott, on the different view of how the data all gets there.
Scott Townsend: Yeah, that's super helpful. So getting the data connected, I'm seeing a couple of questions coming from the audience about, you know, what does it take to connect data? Is that something that, you know, some people are saying I've got a small company, other people are saying I've got a very large enterprise company. Can you talk a little bit about connecting that data and what does it take?
Mike Makas: Sure. So I'll kind of show you. I'll give you like a teaser because this could go into a thousand different paths. But to connect data directly into Domo, you go into their app store. So let's say if we wanted to connect in maybe some JIRA data, I'd go in, see if there's a connection pre-built for JIRA. And then you have a few different options with really popular apps. So you might have like a connector, and I'll come back to this in a sec, or you might have like a pre-built dashboard that just lights up for you. So if you go into Jira, if I wanted to get this data and pull it through and set up a pipeline, you'd go in, get the data, and you'd plug in your in the JIRA example, your JIRA URL and your username or passwords, tell it how often to run, tell it which specific data set or table to take. And then it would, depending on how big it is, sometimes they run as fast as like 45 seconds. Sometimes they might take like up to an hour, depending on, as in everyone else's world, how much data you're pulling in. And then it would set up a recurring data feed for that app. That'd be the default way with many popular apps. It's almost got over a thousand different connections or apps in their app store. That's the part that's given us a ton of acceleration for not having to figure these things out with the examples like ADP, JIRA, Medallia, Salesforce, and more. If they didn't have it in their app store, you can either work with them on a services agreement to get it in, or you can help write the API yourself. So there's a lot of options. I think that should have answered the question on how to get data in, at least at the tip of the iceberg level.
Scott Townsend: Yeah. Thank you so much for that. And I didn't mean to derail you from your use cases.
Mike Makas: No, it's a great question. It's definitely one to cover through. This last use case, then we can round out with this one, is I'm certain that many of you that work in software or even matrixed environments, if you go into your HR system, we use an ADP, and you see your, like, let's say if Scott was my manager, people see Mike works for Scott, but it doesn't mean the four different teams Mike or Scott work on. So we've brought into this concept of kind of the matrix org and the software development to see kind of who to reach out to for different questions. And so we've taken all of our product and engineering teams and put them in kind of a grid. And then for demo purposes today, I pulled the column where it had personal names, but just imagine each row here is a somebody, a person. And so let's say we needed to sync up with the content and curation team. I told you degrees in the business with all this learning content. So we would easily click here, and it's just like a great fast pivot table, but with tree maps to kind of guide you. And this shows you everybody on that team, what their role is, what state they're in, and how long they've been here. Or maybe you have a situation where, like, I need to get a hold of all the product managers we have. And so I could do the same thing and I filter down this list to all the product managers. And this, I was actually surprised when we created this, I thought like, okay, yeah, that's cute. But you would be shocked at how many people need to get ahold of a team or a certain role. So this is a really hot one for us that gets taken. And the reason I wanted to share it is it's like something simple. It's often done on like a Google sheet or Excel. We gave life to it with these kind of face plates of a tree map and it just really took off. So this is a really hot page for us that people use all the time. That covers the three use cases I was going to show. I don't know if any other questions came in, but I'd love to talk about like the results and now that we're here, like what we solve for.
Scott Townsend: Yeah, there was one more question. Someone asked about on-premise data. Do you, so I know Sodoma does have connections into on-premise databases with write-back capabilities. Do you have any of that or are you all on the cloud?
Mike Makas: We take advantage of both. So we mentioned through introductions and through part of the call that my team also runs our product analytics. So I don't know exactly what they define on-premise, but our own degreed product on our own cloud data warehouse, we pull in. So now I know that's cloud, so that might not be the answer they're looking for. But if it's a system you can reach, a database, Domo's pretty agnostic. They have different connectors for where Azure migrating to Snowflake. So those would be our main focus. But I know there's others that you can connect to or that Domo can help you with. But I don't think they're not specific to on-prem or cloud or specific brands. It's more like, how do you get the data in? The services group has been great on helping us make it happen to however we need to get it done.
Scott Townsend: Got it. Thank you. So thanks for sharing those, those three use cases. That was great. And it was, it was nice to see you go a little bit deeper than just be, you know, beyond the dashboard. Um, can you share how, how did you expect to use Domo at degree compared to how it's being used now? So thinking about that evolution, um, can you describe that evolution and what do you think drove that evolution? So the, um,
Mike Makas: I think I expected it, you know, if you asked me a year and a half, two years ago, when we were turning the corner, getting in, partnering with Domo, I expected more of the realm of the usage type of KPIs and OKRs for the companies. And it's really evolved into that and more where we've kind of done a few things. We're now like cataloging our product of tell me which client has this setting on, this setting off, or tell me what of our customers subscribe to which product. It's really been the go-to self-service place to answer questions. And we take the lens of try and answer questions like two or three things we try and take the lens to like if a lot of people need to know it's going to help them do their job better let's get in front of it and let's answer it or there's something people haven't asked yet but we knew if we answered they'd be able to do their job better or more efficient or faster so that's kind of like how it's evolved so originally i thought we'd be more in the space of just like business kpis but we really started changing And then there's been some other fun projects we did. So this directory thing is one thing. And then through our launch, we kind of had an aha moment. I'll share here shortly with the tips for all of you that are going to start rolling things out. What we actually did are... an employee map. So you would think like that's not a lot of business value, but we used to have a lot of pre-COVID, a lot of people that traveled. So they'd be in a city like who lives near here or who lives in my area. And that's really taken off. So that'd be part of the answer I'd say is how we've evolved. Like I wouldn't have probably guessed we would have been doing like an employee map versus like a business KPI, but there's been a lot of value. We try and cater to like a business answer, helping people with efficiencies, but also making their life as an employee experience better. And so we've been hitting the mark on those different areas.
Scott Townsend: Thank you. So you've been talking a lot about self-service capabilities, and it's clear that self-service BI is something that's really important to you and is helping the team at Degree. Can you share a little bit about how Domo's self-service capabilities have given your teams a significant bump in confidence and decision-making?
Mike Makas: Yeah, so it's kind of as expected with the data that's there. Instead of having people to request for a certain report and then everybody compare, the confidence that we've gained is at all levels, at the executive level, the management level, and then the individual contributor level, they can all cite and say, well, I pulled this number from this place in Domo. And generally, I mean, knock on some wood, but we haven't really had challenges with any dueling data people get like this. If I need this answer, I go here and this is what I get. you might run into challenges of like they looked at this segment or these different clients, but ultimately you can always go back and say, well, what did you do? How did you pull it? Where did you see it? But it's the confidence has been in that, you know, this super fast, we launched in June of 2019. And within days, you know, I guess I'm lucky that the executive group were the faster consumers. They were all over it. And so they kind of set the standard of like, you can go get answers and here you go. But I would say that that's just helped the self-service journey and that people go and get their answers because it starts with our leaders. And I'd say I would link that back to the advice I was giving on the search process that we got them involved before we started. You know, we kind of painted the picture of where we're going and got them involved from that point.
Scott Townsend: Got it. So you've eradicated that conversation about whose number is the right number. And instead of arguing about that, you're able to just focus in on the same number. And now what are we going to do?
Mike Makas: Yeah, I mean, there's always specifics, but generally we definitely eliminated that dueling data situation of it's not from this person's spreadsheet, it's like from this page. So the only debates that we witness that are extremely rare would be like somebody's misinterpretation. And so how we've settled is we've also published, we call it data dictionary, and we have a hard rule. Anything that gets shipped in Domo, is documented, the dimension or measure is documented in our dictionary. So if somebody debates something, we can point to that and say, this is the definition. What are you debating? And it's been really solid.
Scott Townsend: That's cool. The data dictionary is a really smart example of building your data culture. So with more people and self-service BI, more people accessing data, can you share... Can you share your approach on governance and compliance? Or can you talk about how Domo helps create confidence around governance? And if there's any compliance or privacy that your business has to deal with, customer data, things like that. Can you share a little bit how Domo is helping you there?
Mike Makas: Yeah, the way we leverage Domo is same with the branching off the last segment with the dictionaries. we don't only define the terms, but in any chart labels or descriptions, we specifically cite them so that there's the governance from the chart, from the consumer back to the source. It's very clear like on what it is, where it came from. We don't have, I'm fortunate so far in our journey that we don't publish any of our products back to customers. So we haven't had to deal with the, the full cycle of the data coming out and back to customers that's handled by our product teams but we are embarking on it now of leveraging the same warehouse and charts to match up on it but i would i would anchor that com the comment and answer for you that we we re-leverage our dictionary and put it into domo in the descriptions and then when we get into different questions we leverage the the feature that i was screen sharing with the the lineage uh back to the data sources
Scott Townsend: Okay, so there was a question about the data dictionary. Is that stored on-prem? Is that in Domo? Where do people go to access the data dictionary?
Mike Makas: For our data dictionary, we link it from Domo's homepage. Currently, we're in a Google document, but we've outgrown it, and we're moving it to a website, and we also leverage in our data warehouse DBT documentation, so we're going to marry that up there. It's something I've been trying to partner with our Domo services group of, like, how that could be pulled forward with any kind of – API or XML to put that adjacent in Domo, but right now it's a secondary product we maintain. It's not a product, it's a Google document.
Scott Townsend: Okay, thank you. And another question that came in is, does the fact that data is coming in from so many different sources into one place increase security and governance? Is that a factor at all?
Mike Makas: Well, we all have to be, you know, On top of security at all times, instead of being reactive, I'm proactive and partner with our security folks all the time. And any big data sources, we make sure they're involved so that we're in a good spot. For our organization, the conversation has never been simple, but it's been easier because we've elected from day zero. that what sources we pull in, we're limiting our PII. And so we don't have to jump through some of those hoops, but I know meeting other Domo customers in the past, they do different things with some of the other features and products that Domo offers. So for us, we mainly manage it at the data source and not bringing in certain data elements so that we maintain a low risk profile. We are getting into new chapters and putting steps in to put it into our warehouse first, then into Domo to manage it that way. But I would generally say that there are certain offerings. We haven't leveraged them yet.
Scott Townsend: Got it. Got it. So you mentioned that Domo helped create a deeper level of trust in the data throughout the organization. As a result, how has collaboration changed amongst your teams? Yeah.
Mike Makas: we the i would give it two answers so the the products people bring to us and service are um you know more research um inspirational curious versus like i need these basic answers so it's it's accelerated us in that curve and then the second part that we enjoy now at this chapter is that when we launched there was like we had a two data teams, data engineering and analytics insights. And we originally had like eight admins that did all the creation. And so we've changed the game where when people come to us with, if they have any realm of data skills, whether it's background, whether it's Domo or Power BI, Tableau or SQL or any technical skills that could show that they could maybe help, Instead of saying you got to wait and we'll do it, we put them through what I call a Domo primer. And we take them through our process. We get them up to speed in Domo. And then we give them their own editing rights. And then we just have processes on the back end to monitor what they're building and to make sure they're following our process. But I would say as far as collaboration, that's been a key component. forced to galvanize us all together where the company sees us as their partner versus like us versus them. They look at it like when they come to us with a problem they want to work on, and then we invite them into the club, they get really excited. And so we've had a few people that have really jumped in to the deep end of the pool and built out stuff that we either wouldn't have done, we didn't have time for yet, but it's been a fun part of the journey to watch. And Domo provides you the different levels there, whether it's you give them cert, permissions or there's a ton of activity log data that you could um i don't like the big brother stuff but like you could see what they're doing like you could put alerts on to see if if they're producing stuff so you can go check it like if if they followed all the rules you gave them so we go on at our company more of a trust process but then we monitor
Scott Townsend: Got it. Got it. It sounds like you're in a great position within the organization. I love to hear that partnership and that trust that folks have for you. In your opinion, what has been the most surprising insight gained from using Domo?
Mike Makas: Like as a fact and insight and output? That'd be hard. I'd probably want a little more time with that one. But I think I would answer first with I'll just shape it as specific, like, product setting answers so I don't reveal anything about us. But, like, just to see, like, simple things of, like, did you know X percentage of clients don't have that on? Or, like, when they do have it on, it correlates to, like, a higher or lower score. That's, like, the opposite that you'd expect. I won't give, like, teeth to that one because it'll get into our specifics of product, but it's been more of the obvious stuff that you wouldn't expect when you're just doing a simple thing of looking at, like, a client database settings that are on and then how they are performing. And it's been an aha of, like, really? Like, we need to look more into that. I would say that'd be the top thing I know even people recently. But then the other... end of it not business related but is the fun one is like the employee map of like oh i didn't even know that many people live near me i can just like they can you know maybe get together for a holiday party or like schedule a lunch like those have been fun to like give back to the my co-workers of they might live within 30 miles somebody because we're so remote but had no idea because they're in a different team So that's been a joy to watch that connection happen that in a physical company, you might naturally get a like, oh, that person's cube is next to mine or office is next to mine, but we're able to help complete that circle in a purchase.
Scott Townsend: That's really cool to hear. So do you have any advice for the folks who are logged on? I think you might have a couple more slides. Can you give us any tips?
Mike Makas: Yeah, sure. So I would leave for those of you either diving into like a BI, data viz rollout strategy or a change, a migration. These would be my top three tips that I would give is the first one. We were wildly successful because we partnered with our loudest team. And I say that with open arms because I came from that team. But find the top one or two metrics that like they're going to want to ask for 12 different ways or 100 different ways. And we partnered with them and we delivered and we delivered it in 50 different cuts in ways. And that made that Loud team like a proud Loud team in anchoring back and making Domo successful. So that would be my top tip is to embrace that partnership that's right in front of you. The second one was I kind of already revealed this with the maps, but identify something that every employee would benefit from. So I think a lot of the times all of my – fellow data folks on the on the call uh you know we steer often to her like select data projects on select topics because like that's what someone wants but i've tried to go out of the box a little bit and we came up with the map thing because we said what could we deliver that every like 80 to 90 percent of the company would log in and check out and take advantage of and come back And we landed on the map, and it literally took us like two weeks. We connected ADP data with some Domo map capabilities, and you just take zip codes in counties and states, and it just dropped the pins there. And that was a really awesome win. People were loving it. They checked it out. They visited it once in a while, and now we almost have competition between states of like which states it's more high in the U.S. or countries in Europe, which one has more hires. And so like they can look it up on their own anytime. And the third one I would say, with our domain, with data, you can work on stuff forever and continue to perfect it, but we tried the opposite. We said we're going to launch as fast as possible. I think when we signed and got started, it was like 42 days later, we launched with like 10 basic data sets, and I think it was like 80 charts at the time. But we launched as soon as we could, and then we committed – to following up and like iterating listening to feedback and making things better and we use the data that domo gives us of like what's the more popular charts to gauge like what people were using to go figure out where to invest our time so these would be my top three tips there's obviously more you could do but these are very actionable and easy to follow and then i think like i would i would uh go ahead
Scott Townsend: I was going to say that's really helpful. Someone had asked, how many employees are consuming your dashboards?
Mike Makas: Oh, that's a great question. I actually wrote down one of my sticky notes here to make sure I cover that. So I'm super proud in my role that 90% of our company has logged in to use Domo at least once, and 60% of our company uses Domo every month. And so 60% of the company routinely using our work products feels pretty good, and we know we're adding value. And we do different surveys to find out what specifically they need, what saves them time, how much potential money, would it save them? And then also what we're missing. Everybody loves to tell us what to work on next. But I would say 60% a month is the number we love to brag about.
Scott Townsend: Well, congratulations for that. That's a very large number and something to be proud of. So I just want to give a quick heads up to the audience. This would be a great time, if you do have questions for Mike, this would be a great time to tap them into the Q&A. We've got about nine minutes left, and we'll take as many questions as we can.
Mike Makas: While you're pulling up the questions, the last thing I would kind of share that we didn't totally hit is – Hopefully, these tips work for all of you, and you become the victim of your own success of getting things out there, being successful. What we've done that's really taken it to the whole new level is we operate like a software team, and we have a full roadmap of projects. They're alineated by department. We sit with those departments every few months and say, here's what we're working on next. And we're now into a place where if the marketing and CX team has 10 projects, they negotiate between each other what's the next project. So it really helps us all. So I hope some of these tips are helpful for everyone, and then you can be equally as successful.
Scott Townsend: Thanks, Mike. So a question came in about audit trails and what do you do for changes made to your data by a team member?
Mike Makas: So we have all that data. Let me start with the basics. So Dome will provide you what's called an activity log. And so you can dig into that as detailed as you want or need to. You can look at things down to the views or changes. Fortunate for us, we've not ever had, in a year and a half, not ever had to have a security-like audit log. And I think it's for a few reasons. uh one we limit the admins to people that are um i want to say highly trained but like deep in our data teams where it's just their job and they do it all the time and then we only give out an editor permission to um those employees that raise their hand and they go through our process and then per chart per page or per dashboard, we lock down permissions where you'd have to be an admin to modify it. So we don't run into this type of thing. I won't say it's never happened. I've experienced where I've logged in like this isn't the same as it used to be. You can then go look at the log of who changed it and when. And then for us, we log our work in Jira tickets so you can kind of go back and re-associate it. But I would answer the two ways. For us, it hasn't been a problem. it the one or two minor instances that you could replicate out to a bigger one we've been able to trace it back through the logs but overall we just haven't really experienced it because of the process and practices we put up i think it's i think this is more of a process question than it is a domo one but domo does facilitate the answer if you need it got it thank you another question came in just asking how big is the team that developed the initial 80 or so dashboards for launch So there were 80 or so charts on about three to five dashboards to clarify at our launch that we did in the first 40-ish days. And at that time, we have a partner team. So we're in our, we work in our, my team is in the operations function of the business. We're kind of centralized. We have a hybrid analyst approach where we don't have embedded analysts in the teams. We have them centrally located so they can matrix work for teams. And then we have a partner, data engineering. And our different responsibilities is they set up, data engineering sets up the pipelines, the connections, the APIs to get the data into the warehouse. where we bring in a lot of data pre-domo. And then our team does all the research, charting, dashboard work. So I think those are probably traditional for you. But the answer to the question is for the launch, when we rolled out the 80 or so charts across three to five dashboards, our team was, I have a picture somewhere, I think in my head, there's like six or seven of us. And that was, a lot of that work was to cut over a tool that we, weren't successful with prior to Domo and that we really needed to get out. But hopefully that answers six or seven then. Today, we have, the company has 543 employees, whatever I showed or didn't show. And the data engineering team is four. And then there's five analysts on my team. So we're about 10 people covering both data engineering and analytics. Got it.
Scott Townsend: And those six people, how long did it take for you to get that launch ready? How long was that project?
Mike Makas: Oh, like I said, like the 42 days. So I think I swear it's 42, but right around that many days and weeks, 42 days. And most of that work, as most people on the call probably know, is not creating a chart or adding the colors or labels. It's the upstream pipelines and connections and then the data validation. So at that moment in our history, it was a much bigger challenge. to make sure the data was right because we had a lot to lose. Like they were at this point of like the 2000 plus charts we have, you know, if we made a mistake on one, we don't want to do it, but we have a year and a half of trust built up where we could go fix it. So that back then, most of the work was on data validation that what showed in the chart matched what production did versus time and Domo. Domo makes that part easy.
Scott Townsend: Got it. Thank you for that. So another question is revolving around your origin story. When you were coming up with the use cases to work on, how did you go about sourcing those projects or deciding what those use cases would be?
Mike Makas: When we started at this... a year and a half, two years ago, it was to replace manual processes. Like some people were pulling Excel exports of data and multiple people, if not tens, were building pivot tables to get these answers on like usage. So that was a lot in the beginning was to knock out manual work and engineering requests. Today, it's quite different. Today, our work kind of goes into three buckets. One, we're almost done pulling through all of our surfaces and features and offerings for our product to just study and analyze anything. So we're probably 70% of the way done with that now in two years. So we got the last chapter there. The second is... different business app connections like so for example our company's switching over to zendesk right so the data is not pulled in where you used to use salesforce so we didn't have to migrate all that work so second bucket of work for us is just to keep the lights on if businesses change apps like help pull that new pipeline replace charts and then the third where we're really trying to pivot our my department is We don't want to be all in the business of just counting things from a database, but we want to get more into forecasting, projections, correlation causations, and like deeper research so we can be like an advisory service to the executives in the company. And so I'd say those are like the three buckets we get into. And Domo is already helping us with some predictive models and charts for things like support tickets or users. You'd probably to hire next year, if they do it by tickets or users, everybody's kind of guessing. But now we use the linear regression models in Domo to kind of make that projection so they feel more confident in it.
Scott Townsend: That's great to hear. That's great to hear. Well, thank you so much, Mike. Unfortunately, we don't have time to answer every question, but... We will do our best to answer questions after this webinar over email. So if your question wasn't answered, please check your inbox for some responses from the Domo team over the next day or so. Mike, thank you so much for your time today. We're so inspired by what you're doing and how you're using Domo to impact growth and foster data-driven culture at Degree. Thank you so much for your time today. Thank you, everyone who attended. Be sure to check out the valuable resources linked right under the Q&A window. And don't forget to register for our next Get Leverage event. We're going to take a little break for the holidays and then come back in 2021. So thank you all for joining us, and we'll see you next time.
