default_omni_demo

Meeting Title: Default | Brainforge: Omni Training Date: Dec 11 Meeting participants: Caitlyn, Amber Lin, Mustafa Raja, Laura, Deanna, Dhehr, Lev, Stan, Grey, Ryan, Demilade Agboola, Tpilger, Mengfei

Transcript:

Me: Hello.
Them: Anytime. Nice to meet you.
Me: Hey, nice to meet you, too. How are you?
Them: Doing well. Don’t forget.
Me: Hey, man, long time.
Them: To see.
Me: Dude, I really like your last LinkedIn post. I know it was last week. I sent it to a bunch of people, actually.
Them: Oh, no way.
Me: Yeah.
Them: Thanks, man.
Me: That’s, like, too much Alpha you’re giving out. For free.
Them: Thanks, man. Yeah, maybe I’m happy it only got a couple likes, but I appreciate the kind words. Yeah.
Me: I have. Dang.
Them: I think you did.
Me: Really complicated. I don’t know if everybody reading this understands. What you’re talking about, but I thought it was great.
Them: Thank you. Thank you. I really enjoyed writing it. It was like a big brain dump from a lot of the stuff that we’ve been thinking about lately. And a lot of ideas that I really strongly believe in. So, yeah, it was a little journal entry.
Me: Nice. You should keep doing it. I feel like that’s, like. I try to do that on LinkedIn too, but the throttle on LinkedIn is a lot of lurkers. I don’t feel like a lot of people. Right. And so I’m sure a lot of people are Reddit. But, yeah, I sent it to some people internally.
Them: Thanks. Yeah, I was actually just writing one before this call, so I’m trying to kind of keep my foot on the gas.
Me: Nice.
Them: There.
Me: Well, wait for Caitlyn. Hey, ryan.
Them: Good morning. Good morning.
Me: Good morning. How’s the new office? Looks big. The roof is, like, very high up. I could tell it’s a big space.
Them: Bigger than or less place. Yeah, about three times quicker. It’s still coming in together, slowly adding pieces to it. Making it more livable.
Me: Nice. Yeah, livable.
Them: It’s getting to a point it’s getting to a point where we don’t want to leave from here.
Me: Yeah, that’s good. Where is there the going arc anyways? You’re going to go home. What does that mean?
Them: Yeah. Dan, and I need it to be lovable. That’s for sure.
Me: Yeah, when I was working. We work in new york. The office, like, yeah, it was just like. It’s so much nicer than. It’s like, a super nice office, but. And everything is, like, amazing furniture. Like, I’m just going to stay here. As long as I can go right home, sleep, and come right back.
Them: Yeah, that. That was the idea in our build. Don’t let them know. But that’s the point.
Me: I’m sure they’re not. I’m sure they’re. I’m sure they’re okay with that. Are you guys spending more time in the office? Cool. Cait. Lyn. Are we waiting on anyone else?
Them: I would say, let’s just roll. And then if people join in later, they can. I know there’s a few people that couldn’t make it last minute, but they asked for the recording.
Me: Okay? Perfect. Cool. Yeah. So maybe I’ll just do a quick round of interest. I feel like I know some folks. But there will be a bunch of folks listening. So. Yeah, the Brainforge team. My name is Utam. I lead Brainforge on the call here. You have a few of us. All. Who? Mustafa, Amber, Demi. We all support the Default team, sort of in different areas. And today we’re just going to be going through our goal for establishing reporting at Default reporting on product analytics, customers revenue. And sort of, you know how we’re. We’re helping Default get there. This is a very collaborative meeting, so I really don’t want to talk for a whole time. And I know Demi is going to do a little demo after, and he doesn’t want to talk the whole time, so, yeah, I mean. We, we, we love the product. I’ve known Caitlyn for a while. We use the product as well. So we’re familiar with workflows and forums. And, you know, we have a couple people on our sales team that are leveraging it. So today we’re going to be going through how we’ve established, you know, reporting so far and how to use the Omni Tool. It is a complicated tool. It is not as easy to use as Default, I would say. So there are, you know, reports, dashboards, queries to run. We’re going to walk through end to end, how to, you know, query your ask your first question query and build your first dashboard. But this is, you know, something that we want. We’re going to be available in Slack to kind of help each of you start to get into data and ask questions. And as you get deeper and deeper, we’ll show you how to build the entire sausage you know yourself. But, of course, this is what we do every day, so you know more than happy to assist in any one of those areas. So I’m just going to go through a couple of slides that’s just going to give, you know, it’s just going to give us a little bit of context on, like, the verbiage and, like, when we think about, you know, business intelligence, you know. And omni, you know, what are the words and some of the things that Demi is going to be explaining. So let me just pull this up on my side, and we will go through this.
Them: Also, really quickly, before we begin, I’m looking at Omni right now. I’m going to invite anybody who doesn’t currently have a seat so that you guys can actually, like, play around with it.
Me: Please. Feel free as we’re walking through this. Like, I know everybody here is great at multitasking, so if you could pull it up and jump in there and poke around. And while Demi’s working through his dashboard, feel free to, you know, do the. The exact same. So let me get this going. Okay?
Them: Hey, quick call out. I don’t know if this is on docket for today, but just to satisfy my own personal curiosities. I’d love at some point to hear, like, at a super high level. What the kind of, like, infrastructure is before Omni. Like, what you guys have been working on recently. I’ve been kind of out of the loop, but I’m starting to think about this world a little bit more. CDP stuff and, like, just. Just kind of curious on. On what you guys cooked.
Me: Yeah, totally. Yeah, go ahead, Caitlyn.
Them: Hey. Yeah. Levi and Nico all set up some time for us maybe today or tomorrow to go through, like, the infrastructure stuff. I know Nico was talking yesterday about, like, wanting to use this for, like, some sales stuff. Which is great. Super on board with it. But let’s talk about the, like, infrastructure stuff later. For now, I want you to, like, learn Omni, feel really comfortable with it, and then I’ll make sure that the right data is, like, ported in for you. All right. I won’t derail us.
Me: Great. Okay? So let’s go ahead. Cool, so. This is the. The high level of, you know, the Omni platform. So AMI is a bi tool. I’m happy to. You know, my career and the folks on the Brainforge team, all we do is establish data infrastructure and build reporting platforms in business intelligence. You may have heard of Tableau Looker. Mode, Sigma, Power, Bi. These are all business intelligence tools. Business intelligence is basically helping you run queries on data that helps you make decisions. And so ultimately, our goal is to help Default make more decisions and more accurate decisions. I know a lot of folks are doing manual reporting. They’re pulling data out of source systems or maybe there’s no way for you to get data, you know, currently, and so this should solve, you know, all of those. And, you know, that’s our goal here. Omni is a new tool that sort of competing in business intelligence. They’ve been around maybe, like four or five years, but really, really awesome team. And, you know, after, when we go to a lot of our clients, all we do is implement tools, and so we try to implement the ones that help us, you know, deliver. Better and, you know, help our clients. And so this is, like, one of our. Our favorites. And so, you know, sort of excited to bring it here to Default. What is Omni? So Omni is basically a tool that allows you to run queries onto a data store. And we’ll be going through what’s what we’re using for a data store. And, you know, a little bit into that when Demi, you know, does his demo. But basically, here are the core concepts. So you have what’s called a workbook. Workbook is a dashboard. It’s basically, if you think of it like an Excel workbook, where you have tabs and you can link things back, you know, that’s. That’s all it is. So this is where you’re creating visualizations, creating analysis. Using AI to ask questions. Over that analysis, you have what’s called a shared model. In the shared model is where you’re actually joining tables together. For example, if you want to join workflows to the team that owns the workflow, there’s a team id, Right? And so you need to join that together. Instead of just having to remember the sequel to write that join and having to, you know, copy paste a SQL query every time. A tool like Omni allows you to pre configure those joins. And so when you go through and just click on the team ID and click on the workflow, that join automatically happens. So it just makes it a lot easier for stakeholders to ask questions fast instead of having to remember these relationship, you know, constructs. And so this is a little bit more about, like, you know, kind of how it works. And I think, Caitlyn, when we kind of go into broader infrastructure, we can talk about, like, the world of data at Default. For this conversation, again, we’re mainly focusing on just getting reporting out of Omni. And so today you know, to date, we actually have this dashboard set up that, you know, maybe some of you have already seen. This is sort of like a catch all voucher where we just put everything in. More than likely, this will start to get bifurcated into dashboards that focus on different areas, whether it’s sales, whether it’s product, whether it’s account management, whether it’s an individual dashboard for an enterprise customer. But, you know, I think this is a great place to start to look at the variety of things you can do in Omni. Both on the visualization side, on the. Like when you hover, when you use AI to ask a question. So this is a. This is where we sort of put everything to date. And so, you know, you can kind of see the variety of things that is, is possible here. This is, you know, us working directly with Caitlyn to produce this. And so our hope is that we work with each of you, your teams, to sort of also support you in creating dashboards that, you know, work to you. Know, assist in your in your day to day for decisions. But lots of juicy stuff in here. Anything you see in here is already modeled and ready to ask questions. So for example, ARR by customer who’s running the most workflows? Like where are our customers? How much have they raised? Who’s booking meetings. Like, all these questions are possible today. And so hopefully that makes everybody excited to kind of get into Omni and start, you know, asking some of those. Any questions? So far.
Them: I have a quick one and maybe you answered this and I missed it. Are questions asked at a high level. On the. At the highest level of the platform. Is it on a per table basis?
Me: No, it is actually on what is called a topic, and a topic is a collection of tables. I will be sort of going through a little bit of, like, the process of topics, tables, workbooks, but you actually will start by asking a question. On a series of like Basically, a topic is like for example, the product topic may include customers, may include workflow runs, may include usage. And then, for example, the sales topic may include stuff from Salesforce. Stuff from email, stuff from ads. So that’s kind of how the collections are sort of made in Omni.
Them: And I had a question also in terms of financial information, like company level, so revenue, you know, whatever we want, burn, whatever. Can you do that as well? And do you in for in this case do integrate with like what? QuickBooks trip. Can you integrate with all that?
Me: Great question. Yeah. So at the moment, yes, we do have revenue per client there. Right now, we’re bringing everything from hyperline into the platform. So not only, like, how much we’re billing, but we’ve broken down. Like, are these people seat based? Are they on something custom? Are they on a flat fee? I know, a lot of that is sort of getting standardized, which is amazing. But our team has worked to sort of clean that up. So that is available today. Looking at, you know, ARR by team. In a side by side with their usage. So one of the things that we, we worked, you know, recently to produce for Caitlyn is, okay, we want to look at who’s paying us the most, but also relate that to who’s using us the most. And so are there, are there changes we can make? To pricing. Are there product usage things we can glean, but. So that’s all coming from Hyperline. And then what if we end up migrating to Stripe or whatever sources we end up using? Operationally, it can totally end up here if there’s a reporting use case for it. That’s like the Net.
Them: Right. But we can do it. My question is, I. I understand. We can do it by customer and all that. I’m asking more on a higher level. So you know how financial situation of Default. Can we see it in Omni? Yes. Okay.
Me: Totally. Yes. You can actually query it, like, on an entirety level. Yeah. So it’s not just that the individual customer. You can basically run the query across all, you know, Default customers, users, teams.
Them: Great. Okay? Great. Yeah. And to clarify here, Laura and Lev, you kind of asked similar questions. So basically, Omni is just our tool for, like, analytics. Right. We can see the data and interact with it and ask questions and, like, create charts to help us visualize the questions that we have. But essentially just like High Level without diving in too far. We basically have a database in the backend that we’re syncing all of our sources to. So we’re starting pretty simply with Salesforce. We’re starting with amplitude or starting with our product data and with hyperline data. So part of the reason why I want to do this is especially for, like, the people in this room. You guys have some kind of, you know, skin in the game on something that you need, data wise. So, Laura, if you’re like, oh, it’d be really helpful if we could have QuickBooks and Visualize. Financial data. We can absolutely do that and, like, hook that up for you. And we’re not going to have the Brainforge team forever. So it’s really great to have them here now so they can, like, hook everything up that we want in the product. And by us, like, learning how to actually use it, then we can, like, not have to, you know, go to Brainforge every single time we have a question. We can just. We have the data in place. We can ask the right questions and figure out how to, like, create charts. What database we are doing S3 of their data. Lake and Mother Duck as our database for like data modeling and then Omni on top of that. I mean. Just. Just to make sure. So, like, we’re. All these questions are kind of stemi from, I think, like, Laura and me, we’re talking about data. Laura and Nico, we’re talking about the Lev and Nico, me and Lev. Like, this is, like, something that’s top of mind. So I apologize for, like, the questions coming out of left field. Like, me and Caitlyn have a meeting after this to talk about it a little bit more, too. We’re just all kind of like, this is all super top of mind. We don’t know how to get from A to B. So we’re kind of excited to see this in general. To see the possibilities. So we’ll all get on the same page and we’ll go from there. But I appreciate it.
Me: Yeah, no problem. Cool. And then just continuing on. So, again, I just want to give you guys a verbiage. We’ll go through the actual demo in just a sec. So in. In Omni, just like most products, like, you create content, right? You’re creating dashboards, and so there is a folder system. When you get to the home page really quickly, if you want to just jump right in, you can just hit new analysis, and I’ll take you right to the page in which you can create, you know, analyses directly on topics. Once you’re in a dashboard. There are filters at the top. You can click on individual items to drill down into them. The filters are very, very sophisticated. This is a data product, and filtering is, like, a huge component. So a lot of the features you’ll see in Omni, the reason we chose the tools, they’re just very extensive. So if you’re doing seasonality analysis, You know, certain years versus certain years. You know, I just think it’s, it’s very, very flexible. Additionally, you can download Things as Excel PDFs, and of course you can schedule these directed your email or to Slack or to external webhooks or SFTP’s. You know, if you need to send these to other integrations. Going into workbooks. So workbooks are really, you know, the area to do all of your analysis. So workbook. You can think of it like an Excel workbook. You have several different tabs of individual analyses that all come together. In a dashboard. The workbook is a dashboard in itself. It’s a collection of each of these different tiles. And so in a workbook mode, you have a couple different modes, you have topics. So these are topics that right now the Brainforge team is creating, where we facilitated the joins between Hyperline, the product data, other data. And so we allow you to sort of, in a safe way, just start to bring in metrics. Additionally, for the folks interested, we’ll sort of bring you into the deeper layers. Which is like creating views and fields. And then ultimately, I think there will be folks that are also just running SQL queries, potentially directly onto the data warehouse. But for the most part, we expect that most people will be leveraging topics. And where our job really gets tough is knowing the questions in your head. And so one thing that we’re in the way we partner is as you have questions, we can direct you to the right topics. And as you’re missing fields or missing logic, those are the things in where we’ll assist to help create them and just make more of those available. But this team is free to create Dashboards and create workbooks and start to answer questions. I think that. Maybe the one thing I’ll go into is and this will take some time as you get into Omni is Once you go into a workbook, you can check out all the views or you can start writing SQL directly again. I think most folks will start directly from the topics themselves. Once you’re in a workbook. And let’s say in this example, they’re doing events or users by states, some fictional company. You’ll see that you have fields here on the left side. This is the web event tracking topic. As I mentioned, topics are collection of views. View is basically a table join. So for Default, right now I think we only have one or two topics, but as we start to bring in more data into availability, there will be more topics. Go ahead, Caitlyn.
Them: Yeah. Also, a lot of people on this team are probably not familiar at all with any kind of terminology around, like data engineering. So things like joins, maybe we could talk about. Or like, what is a SQL query, those kinds of things.
Me: Okay? Sure. Yeah. I think maybe Demi, I think that’s good feedback. I think when we go through the demo, let’s start really simply with even outlining what tables we have access to. And maybe even. I think even this morning, you showed me kind of the query that we’re replicating. I think that would be great. Of course, all of us on the team are more than happy to explain. All of us. All we do is write SQL and do data work. So, happy to go as deep as we need into the inner workings of how this product works and how our tables work.
Them: And then really quickly, Ryan has a question. Any particular reason we went from with omni instead of equals?
Me: Yeah. A couple of reasons. One, equals is really focused. Their product roots are really in financial reporting. And second, it’s just a lot of flexibility. The Equals product is pretty good for some use case. And we have some clients that started on Equals, but a lot of them, they start to get disparate data sources and want to sort of build their own data warehouse for analytics. They tend to move towards a business intelligence tool like Omni or Looker Tableau. I didn’t know if the team originally. I don’t know, Caitlyn, you might have mentioned that the team was using equals at some point.
Them: Yeah, I think we have, like, Nico and maybe Laura using equals, just, I think, mainly to visualize, like, revenue and, like, financial data.
Me: Revenue go to market, not so much on anything. Which is great is when you’re first starting, just plug everything in and just run through. But for anyone that has used these business line focused BI tools quickly as you try to plug other things in, they may not facilitate or they may just say like this is not the tool for that.
Them: Yeah. Y. Eah.
Me: I don’t know if that’s been your experience, Laura.
Them: Yeah, I think so. So if I understand correctly, we can basically do pretty much everything that we do in equals. Also in omni, right? Okay? Yeah, greg.
Me: And so the advantage of equals is that because they are using a couple of fixed data sources, They can come out of the box with a lot of the dashboards in this situation, we will be building some of that. But everything that you can do in that tool, you can accomplish here.
Them: So I had a quick question. Maybe I missed it in the beginning. But what are. What are the data sources and omni that I can use to view the data?
Me: Yeah. So right now we have data coming in from Hyperline, and we have data coming in from the product, so product usage. So customers workflows forms several different fields, and Demilade will outline what’s available today in the topic. We are driving towards Salesforce and we are also driving towards amplitude for product events. Kind of probably in that order. And then as we sort of expand, if there is need for reconciled financial data from QuickBooks, if there’s other data in ad platforms or on the marketing side like Klaviyo or other things, we can start to bring that in. As there’s reporting requirements, the way that works is we take that data from that source system, we land it into the data warehouse which, as mentioned is using is mother duck Omni sits on top of that. And so that is sort of the flow of data from those systems into here and then in Omni is where we’re combining. So let’s say we were to bring in Klaviyo data. You’ll be emailing your customers. We will be joining on customer email between your product data that has the Default customers in Klaviyo, which has some customers to see something like, how many emails did we send X customer? So that’s sort of the flow. Depending on the difficulty of the data source. It’s like anywhere from one week to a few weeks to sort of do that at 10, typically. And then once you’re in a workbook, you can click right here at the top to go to Viv. So we’ll see that you have, like, web events by state here. Once you click on vids. You can actually. You’ll go to discharge this area where you can actually start to do a lot of your charting. Omni has a great amount of visualization options, which, for a lot of the analytics work that we do, is really helpful. But of course, just the basic bar charts. And things like that available. And I think that should be the majority of things. A lot of the data that we look at for Default is time series based, right? So users over time, meetings booked, overtime, revenue over time. So all of the views that we produce will have the availability for a time series, the date, the week, the month, the year, so you can begin to aggregate on those measures. And then. Yeah, we kind of went through filters. The other thing is, let’s say you’re in a workbook and you want to filter. You may want to go in and actually just select an individual metric or dimension and measure. And so one thing that I think we glossed over a little bit is just like, what? Is a dimension and measure, and maybe I can talk a little bit about that. So the way I like to explain it is dimensions describe measures. So a measure can be, in this example, the number of users. Right. And so this is typically a count of user IDs, count being literally count every single user ID that is in California. Right. So where California is the description of those users. And so it’s helpful that in the data world, this is how we describe if you’re in an Excel sheet, you just may think about these as columns, but depending on the type of whether it’s a dimension of measure, you can do certain things. For example, you may want to run a sum of all money made you may want to run a count of all users where those two are measures, but if you bring in something like State or the traffic source or the zip code, those are all describing those measures. Another way of explaining is that dimensions are typically the x axis. And some in measures are typically the y axis. So a dimension could be user creation date. And the measure will be the number of users. And so you should see that kind of going up over time. Depending on who you are, you may be more of a visual learner. You may be more of someone that needs to go in and play around with this. You may have been really great at school and can go through slides into this. I’m more of the I just need to jump in and play around, but hopefully this gives you a little bit of insight into the terminology. If this is your first time doing reporting and looking at business intelligence, it will take much more than just this conversation to learn. But this is all we do is this sort of help companies establish this. So I’m more than happy to spend time individually or in another session, sort of going through the terminology. I did want to leave time and maybe Demilade. I can pass it to you to sort of go through the demo. And then that way, I’m sure that’ll lead to a lot more questions, and then we can save some time at the end for q and a and then talk about next step. So, Demi, I can pass it to you.
Them: Thank you. Hi, everyone. My name is Demilade I have been working. Can you see my screen?
Me: Yes.
Them: Great. All right. So I have been working in army for a bit. So let’s start off with model doc. So this is kind of where the data lives right now. So there’s just a bunch of our data that we’ve gotten from different, like, sources. And we have them living in here. And we have method dot connected to Omni. So it’s built on top of Omni. And so this is the home page. This is what you will see when you load up your omni screen. And the way we’ll start is we’ll click on new. Right here. And so this would take us to, like, our starting point, right? So there are a couple of ways we could start. We could directly query the database so, like, we’ve seen. For the first one. All right. So we saw the data in there, and we can actually just directly query it here. But. So see raw events. Let’s limit it to counter host. And so what does this just means is select everything from raw events and just pick 10 rows. I’m not exactly sure why it’s taking a bit. But yeah, so it’s given us the raw data as is, and we can kind of just see what the events were and what’s going on there. So let’s go back, because that’s not necessary. What we want to use it for. So if we go back here, we can start to explore something called topics. So topics are. I have a. Sorry to catch you off. I just want a quick question about the database. I noticed the schema doesn’t match our current product. Superbase db. How. And I’m sort of not only used to, but I know how to work around parameters of that schema. Is there a different schema? If I were to write the same SQL query that I usually do, Following our schema naming conventions. Would this still work, or. That’s a different schema because it’s coming from the data warehouse.
Me: It will be slightly different. But ultimately our goal is that you can actually move away from having to write and kind of keep these blurbs of SQL, and you can actually move towards creating this directly in Omni. Under the hood. Omni is writing a query on top of motherduck. But naturally, because we are moving this and combining this with other data, Ideally, you should be able to move away from that. If you have your queries, we can totally show you how to replicate that exact result set directly at Omni. So, Stan, if you want to share that in the channel, We can kind of help you do that.
Them: Cool. Cool. Thank. I was just more in like it’s often comes very much. Ad hoc. So, for example, on Monday I did a query on to understand how many meetings were booked per each team from a scheduling link versus a workflow. Right. I would assume I would have to probably go into the meetings, the raw meetings, and hopefully they have the same sort of value as in source.
Me: Yeah.
Them: And do I then have to, like, join into a member table and the team table to get the team name?
Me: So exactly what you’re describing are the things that I know. Even everybody at the company, I’m sure, has a similar question. But remembering is it the right idea to join? Is this an adjacent field or not? Those are the things that our team already has cleaned up and just made simply available within one of the topics. And so that hopefully saved you the time of remembering how to join or how to extract things. And instead you can now ask that question and then Immediately ask the 2nd, 3rd, 4th follow up question, create the dashboard, ship it. And so I think, Stan, one thing we could do is if you want to share that query, even the one you did on Monday. We could show you. We can walk with you on how to produce that. How we would have produced that answer directly on me, too.
Them: Okay? Y. Eah, okay, sure. I can send you.
Me: Perfect.
Them: 30 seconds. Okay, so we have topics. So I kind of showed the SQL part or the CO part, so that we can kind of see that we’re directly connected to the database. And then one layer above that is what we call topics. So topics are when we start to put concepts together. And so, like, Stan just mentioned, like, you know, the meetings we can have a topic for meeting analysis, for instance, where we will have all the joins important to analyze the meetings, like all the tables. Join together. We’ll then analyze it and be able to have quick insights based on meeting analysis. All the time. So. For those who might not necessarily know what joins are basically in tables in our database. And not just built across every single thing all at once, they’re broken into chunks. And when you have them in those sort of chunks, if you’re going to make analysis, That, you know, that takes everything together. You will need to join different tables. So, for instance, in one table we can have an event. In the events table. You can have the team ID and the name of the meeting that was held. Right, but we don’t know the name of the team. That will be in a separate table. So what that join. When we do that kind of join? What that. What happens there is we’re saying, hey, I need to get the name of the team that had this meeting. So now when you join those two tables together, you can get a complete view of what team had what. Meeting on what? Day. So that’s kind of what joins are doing in the background. We’re just trying to get more complete information from the different tables that exist. And so if we open up topic. So let’s look at the integration analysis. Topic that has been created. We can see that. So let’s go to the model layers. Sorry. I’m trying to open up so we can see that in this topic. What we’re doing here is we have a table called the Integration Team Daily Completion. And what that is? It’s is the integration from the workflows of different forms have been extracted out and we’re able to now see. How many times in a day? Where those what forms containing those integrations, completed or not completed? So we’ve been able to get that information so we can see the different integrations within that form. And then now what we’re doing is we’re joining to the teams data so we can start to see, okay, so what teams contain having like what teams use what forms that contain what integrations and. What was the completion rate of that? So this is just an integration analysis topic, and that allows us to be able to quickly hop in here, and if we want to quickly view what teams are doing what and how well integrations are doing across teams, we can start to do that. So, for instance, we can start to see like what teams have, how many integration ideas. Are using. Like what’s how many teams? Like what teams have the numbers. So for instance, we can see Default is using 30 integrations. Cherry issues in 20 integrations, we can kind of start to see how many integrations each team is using. And so we have that. And so what we define here is the relationship. So this is where the joins are happening. And basically, just to say we’re saying, hey, from the raw forms. Join to this. View. So this view represents another table in the backend in mother doc joints at this table. And make it a left. Join. Where we’re seeing the team ID is equals to the team id. So when you’re making a join, there has to be a key. So the key makes you know that this value in this table corresponds to that value in that table, and that’s what’s going on here. And we’re seeing. Many to one. That means in forms. That, like, one team can have mult like, one team can have multiple forms. And so there are many for one form that can be multiple teams associated. There. But. There’s only going to be one team in the team list. So we’re just saying, okay, this is the relationship. And that’s kind of what’s just going on here. There’s just a bunch of joins and the represent, and they are represented here. So the beauty of creating topics is once someone does this for you, or once it’s created, you don’t have to keep joining every single time. You could just take the topic. And use it as uim fit. So what does this look like when we’re trying to now say, okay, let’s look at the integration analysis so when we’ll go into the workbook, I will view workbook. So once this loads up, it gives us a couple of options. One is we can ask a question about the data. Right. So this is using AI. You can ask a question and it would give us an answer. But let’s start off with actually building something for ourselves. So let’s just say I want to see how, like, submissions have come over time. I can say hey, for every week. So this is the usage date 1 integrations were used. So for every week, group by week. Let’s see the submissions total. So now we can see the total submissions over time. And we can create a chart for this. So now we can quickly see that. Hey, submissions. Obviously grow over time. We’ve had spikes here and there. And this is what it looks like as at October 13th. So that’s. We can kind of see the submissions over time. And if we’re like, okay, cool, let’s. Let’s have this as an overall dashboard.
Me: Yeah, maybe I just pause there one second. Is everyone with us so far? So what you’re seeing here are the form submissions. Over time. Both the completed ones and incompleted ones. So this is just a kind of example that we’re going to walk through. Is everyone sort of like, I assume everybody knows forms and submissions, but I guess more of like, is everyone still with us? Any questions so far?
Them: I actually have a question.
Me: Please.
Them: How can a form be submitted if it’s not completed? You mean, like, they didn’t fill out all of the like fields? So there is. So the way the submissions data works is that every single time people like engage the form, it tracks it, but then when it’s completed, For a tracks it, and then there’s a flag. If. If it’s not completed, it will be false, but once it’s completed, it’s true, and that’s when the workflow is triggered and all of that happens. So it’s possible for a form to be started. Not completed. But it will track the progress that was made.
Me: So this is sort of alien based on just the back end. The back end database, the Supabase database, has submissions.
Them: I see.
Me: That’s just the name of it. And then there is a completed true, false.
Them: Okay, so it’s, like, starting to respond to a form.
Me: I get? That. Can. Yeah, I get that. Submission can imply that you submitted.
Them: Yeah. Yeah.
Me: Yeah, I guess. Maybe not the best name, but yes, it started versus completed, correct?
Them: Okay, cool. We’re having to, like, unweb all of our back end engineering, like, titles for everything also.
Me: Thought people would start and stop and. Yeah, I mean, this is very common.
Them: Yeah. Okay, I’m following.
Me: Okay?
Them: Okay?
Me: The other thing, Demilade. One thing we don’t have is we’re going to start to add descriptions everywhere. So you’ll be able to start to see and like, for the most common questions, like what is a submission mean? We will add that as a description here. So great.
Them: So we can. One thing we can do is also we can rename this so we can say, hey, submissions. Allegedly form. Progress. Over time. Or on progress by week. All right? So we can save this. Now. We can click here. So what this does here is it creates a dashboard. And so when we click here, We’re asked to name it so we can say, hey, let’s call this our integration. Dashboard. So it gives us a couple of options on where to save. So my document is personal. So if you’re working on something yourself, and you don’t necessarily need other people’s states, or you only want a select group of people to see it, you can add it to my documents. The hub is where everything lives, so that that’s where everyone across the teams can see. So let’s say we’re just creating a prototype. So far, we can say, hey, let’s let this sleep in my documents. So great. So we already have our first thing in here. But obviously this is still sparse, so let’s try and add a little bit more information about stuff so we can go back to the workbook. And so let’s say we want to have more information. Like, there’s definitely more stuff we can glean from this. So let’s create a new. So it’s called query, but it just opens a new tab. We go back to our topic. And so now we can start to say okay. If we look at the teams. How many complete. Submissions do they have? And. So a couple of things. Remember, like I said, we’re creating complete data sets. So it’s this integration. Completion is joined to the team information. So, like I said, this is giving us team id, but we don’t necessarily know what team ID means. Like, what team is this? So in here, in game, which teams, we have the name. So once we click on the name here, It will add it. So now we can see Cherry has the most complete submissions. And then cal talk and then ample market and then open phone and then reply. Now, there are 295 rows, which is a lot, and we might not necessarily want to visualize that. So what we can do here is we can add the limit so we can say, hey, I only care for the top 15. Right. So once that’s done. And to be fair, Like team id. Again, people don’t work with team id. We work with names, generally speaking. So what we can do, if we can remove team id, And now we can create a chart. And so now. We have a chart. For the top 15 teams. Based off complete. Submissions, right? So what do we do? We can rename our chart. Now we can say top. 15 teams. By completed. Submissions. That’s what we can save. That. Now, if we go back to our dashboard, We can see it. So if we wanted to keep track of things over time, we can kind of see the form progress by week. We can also see the top 15 teams by completed submissions. All right, so let’s go back to our workbook. Do we have any questions? Are there any things that we’re thinking of? Is this? Helpful. This is awesome. Yeah. All right. So let’s also think of another thing that we potentially could care for. We could say, hey, how many integration IDs are different teams using, right? So let’s try the AI Part of it. So this is where instead of dragging and doing all that that we’ve been doing, let’s try something cool. So let’s say. And I get a list and I get a chart. For the top. 15. Teams. By number. Of integration. Used. In team n. Right, so let’s see. So the AI assistance is trying to think of a query and think of what’s going to happen. So it’s plotting. All right. So it’s been able to say. Okay, so these are the team names within scription IDs. And it’s, let’s see. What the sample approach. Okay, let’s try. Count of integrations used. All right. Where I want here from. And there’ll be a little back and forth, but not much. And the sky in LA that I am dating, testing. He’s in la, I’m in Nashville, and he’s busy, I’m busy. So I can see if Default uses about 37 relationship integration. Text. Once in a blue moon. This is also testing what he will do is uses 24, uses 20. So let’s say we want to get rid of it. Just depends. It’s every few days that we’re like, okay, a week where I won’t hear from him. And by the way, I’m not reaching. We don’t want to see testing. Right. So let’s see. And I think we. Let’s get rid of things. Something going on? Leave it and test. All right. And company. All right. So now we’re seeing actual company names. We’re seeing that. Okay. For instance, Cherry, when he has 16 integrations, Delve uses 16 as well. And so now we can kind of see how many integrations different teams are using, so we can see which teams are a bit curious about stuff. Also we can say, hey. We don’t really want to see Default and what we’re doing as well. So we can also filter out Default. And so now have you sacrificed more? We have a list of and everyone which is in Default. Or who’s using our integration. He was seven. We can see Top. Will you join me in thanking Mr. Barth for his service to our country? Sure. I’m grateful for every single person that has served our country and follows our laws. Can you please tell Mr. Park why you deported him? Every one of them needs. Right? So now and I have dashboard, we have struggle with PT information. What we’re trying to do, what you’re trying to see, I want matters to you. So if you care about complete completions and you want to see what teams are getting completions, you can see this. If you care about integrations like, how many people are adopting or just using decouple. Maybe only one or two. You can also do the inverse of this, by the way, so we can see, hey, let’s see the lowest number of integrations used by teams. So you might see, hey, a team is only using one or two integrations. Maybe you might want them to use more. I think Caitlyn has her handle. Yeah. So I’ll, like, kind of connect the dots back for why this is so interesting. So this integration data set that we just pulled in, when I was working on workflows. Right. Like, we’re building it for Phoenix, we were trying to think through, like, what integration should we build into the new product and. Which integrations from vanilla. Should we include or are worth, like, spending the engineering time to build? And there were actually a couple that we thought maybe wouldn’t even be worth porting over. So I had asked the Brainforge team to, like, scrub this data set, and they pulled in all of the integrations and users and joined the two. Tables, users, and number of integrations. And now we’re able to see, like, objectively from a product lens. What integrations should we spend time porting over? Because a ton of people are using them versus like, maybe nobody is using Loops or like, maybe nobody is using Adio. Right? So it might not be worth our time to, like, build that in the new product versus, like, prioritizing new functionality. Yeah. So, like, honestly, there’s. There’s a lot more we can do. Right where the have about eight minutes left. But like, honestly, we have like the integration ID so we know each integrations. We can see by integration how many compute set submissions we got, how many incomplete submissions we got. So that you can create charts of that and kind of compare and see. Hey, this tends to get a lot of completions, this tends to have a lot of incompletion. So, like, maybe this might not be worth our engineering time. Just actually so much we can do. But, like, in terms of this dashboard, At this point, what we can do is we can kind of publish it. So we can say, hey, let’s publish this. So now in Demade’s documents, there exists the dashboard that says all of this. Right. But at some point, if. If we got into the point where we feel like, okay, this is great, we need stakeholders to see this. Remember, like I said, if it’s in my documents, only you and whoever you give specific access to can see it. But if you want the general team to see it, you need to move. It. And now you go to Hub. Now, a couple of things. You can create a folder and put it there. You can see, you know. Integration analysis. And you can see it. And now in the integration analysis folder, You can go in there. And see. And then you move it there. And so now every other person on the team can come into input analysis and see your dashboard. On integration analysis, right? So I’ve been doing some stuff earlier. You can kind of see. That here. Yeah. So I’ve been doing things, like, you know what? Integrations by incomplete submissions. So how many integrations struggle to get in, complain, get, struggle to get, like, complete submissions? Talking to questions by complete submission. So if else gets a lot of completed submissions, top teams by complete submissions. So Cherry has a lot of computer submissions. We can kind of see submissions by month, and we can see that there was a spike in incompet submissions in the month of August. So you can dig deeper into that and kind of figure out what was causing that spike or what integrations were responsible for that spike. Yeah. So, basically, these are the kind of things you can play around with, get an idea of what you’re doing as a hypothesis in your document, and then you can push it to the hub, where everybody else on the team can also get a view of what you’ve been working on. Does anyone have any questions or anything that, you know, you would like us to talk about? If you’re a bit confused about anything. I have a quick question. So, Kylan, you mentioned that Thomas is uploading this information. On a month to month basis, so it’s not like live data that we’re able to pull, right? Yeah. We’re kind of blocked right now on the engineering front, so I think we’re gonna pivot, at least for the duration of vanilla. We’ll pivot to, like, probably once a week. We can push this data so it’s a little bit more up to date, but it won’t be, probably. Until Phoenix for us to set up, like, live data. And by live data, I mean like, probably once a. Once a day. Okay. Yeah. I mean, a weekly, weekly upload would be pretty beneficial for, like, the CS implementations front as far as, like, you know, are they, are they using the product over the. Over a week or two replaces. Or if it’s like a certain. Certain amount of data sets that I’ll be able to pinpoint. And then if we can just do that for, like, a week or week, that would be great. Too cool. Yep, for sure. If no one else has questions. Demi, really quickly, do you want to just show the product data that we do have available? So that people can look through it. Gotcha. So the product that I was will was used to build this dashboard. As you might have noticed, we didn’t do anything too crazy with the, with the dashboards we just created now because the focus was more of, like, understanding the access to it. But you can see that you can actually do, like, a lot. You can create tables, you can create, like, single number, like, values. You can also create. Bar charts. You can create. Basically all of this stuff, pie charts. You can create, like, Geographic maps as well. And so all of that was done the same way. We just did it, but the difference was, you know, changing. The charts selection. Like, if you look up the top, you might notice or you may have noticed that. You may have noticed that in the chart. Sorry. My temperature for a month. All right. You may have noticed in the chart that you have the. Oh, my God. You have the option. To pick a different chart. So you can pick a different charts from here based on what you need. And that is kind of how you can change the chart. So if you’re like, oh, I don’t want a line graph. I want a bar chart, you just change that in here now. This product is a different topic. So the topic we use to demo was the integration analysis topic, but the product topic is basically a combination of almost every single thing across board in one place. Right. So from the forms to the teams that the members were just trying to join, as many things as possible to have, like, a large table where you can kind of do different analysis on crossport. Ideally, we would want to have smaller topics, a lot of things, so it’s kind of easier for different people to have different access to those topics. And it’s easier for, like, data governance. So it’s like, oh, if what you need is literally, like, financial analysis, We will create those, like, topics for you. And then you can just go to your topics, create revenue charts, create whatever charts like users revenue charts. And you can have that same thing. If you’re, like, focused on just, like, integration and, like, trying to define if that’s what, like, the product direction of the product, we can create topics for that. But, yeah, the product basically has a lot of things. So we have things about submission, you have things about the teams, we have things about members, about meetings. And then in things like enriched teams, we have the team information, but we also have some other things, like the industry, is it a mid market team? Is it an early business or a small? Like it’s a small business. We have like the locality, we have the industry, we have like the employee count and things from Perplexity AI that we’ve used to enrich, like the team information. So stuff about Cherry, stuff about like whatever it tends to be, honestly, using Default. We have that information available as well. And so that’s in the product topic. Okay. Amazing. Yeah, there’s a, like, all of our main SKUs are here in the product data, so you guys are, of course, welcome to, like, use this, create charts. I think what I wanted everyone to take away from this is if you want a certain source in here that we don’t have, You have some, like, use cases in mind? Definitely ping, and we can make sure to get those into Omni so that you can, you know, build your own data sets and, like, build a. Leverage this for your own work streams. But any final questions or anything that we didn’t cover?
Me: Yeah, I think one thing, Caitlyn, I mentioned this morning is if it would be helpful for maybe like the next week or two to just have a standing office hours where people can come in and ask questions. I don’t know if we need to sort of keep it, like, super long term, but at least as people are getting into Omni, I know the first 10 to 20% of time, like, getting into this tool can be overwhelming. So I was just going to propose that we just put a standing meeting. You know, like on Tuesdays. That way Demi can answer questions for folks. And then if there are follow ups or there’s things we need to build, we can do that. But it just gets people somewhere standing. So we don’t need to book time with, like every single person. And as people pair with us, you know, everybody learns. So maybe I know we’re getting into Chris film. He was booked that for two weeks, and we’ll kind of see how valuable it is. Which is everybody that’s on this, this call.
Them: Yeah.
Me: Cool.
Them: So look forward to that and look forward to how we can also help you build out topics and like, help you get the information you need as like, when you need it. Cool. Awesome. Like I said, we don’t have Brainforge forever. So if you have some use cases in mind, like lean on them now and get things in product sooner rather than later. Otherwise poor Thomas is gonna have to try to figure it out himself. And he’s a busy guy, so if you guys have any. Questions. We also have a channel with brainforged. Dan, you have one. Yeah. Is there any correlation to Salesforce fields that we can use looking up like teams? Because we have accounts in, like, implementation stage, so I would want to target, like, filter based off those. Exactly. You mean being able to, like, pull in Salesforce team id?
Me: Nice.
Them: Well, like, so, like each. Each one of our team IDs is like, attached to, like, a domain. On match the domain to an account and then look up the account stage. And then I need the. I need just information on, like, all where stage equals implementation.
Me: So as soon as we land this, the salesforce data, Caitlyn.
Them: So, like, is that possible?
Me: It will be. It will be possible.
Them: Then we can do that. Yeah.
Me: So we have one stakeholder that’s asking.
Them: Dope. Perfect.
Me: For it now. Yes, but. But definitely possible. As soon as we land it. It’s probably, like a few days out from. From enabling that.
Them: Okay, cool. Okay. I’ll be in the office section next week, then.
Me: Okay? Great.
Them: Amazing. Stan or Laura. Do you guys have any requests for data that you want to be included in this? I have to log in. Once you allow me in there, you’re allowed. Oh, that’s perfect. So I’m gonna log in and see what data is there. I’m gonna take a look at what I found or what I use equals for and find, if any. Yeah, discrepancy side is fine. I’ll let you Brainforge team now. Thank you.
Me: Okay?
Them: Thank you. I’ll do the same.
Me: Cool.
Them: What’d you say, Laura? I’ll do the same. I want to check, you know, what’s there now and then go from there. Okay. Amazing. Awesome. Well, thanks for everyone’s time. You guys know where to find Brainforge and our Slack Channel and let us know if you have any questions.
Me: Thank you.
Them: Thank you.
Me: Bye.
Them: Bye.