Meeting Title: Default | Brainforge: Omni Training Date: 2025-12-11 Meeting participants: read.ai meeting notes, Scratchpad Notetaker, Thomas Pilger, Mustafa Raja, Daanveer Hehr, Demilade Agboola, Uttam Kumaran, Lev Katreczko, laurakrivec, Ryan DeForest, Amber Lin, Caitlyn Vaughn, Stan Rymkiewicz
WEBVTT
1 00:01:12.350 ⇒ 00:01:13.470 Uttam Kumaran: Hello!
2 00:01:15.620 ⇒ 00:01:17.389 Daanveer Hehr: Hey, Tom. It’s nice to meet you.
3 00:01:17.390 ⇒ 00:01:19.439 Uttam Kumaran: Hey, nice to meet you too, how are you?
4 00:01:19.880 ⇒ 00:01:20.820 Daanveer Hehr: Doing well.
5 00:01:21.140 ⇒ 00:01:22.239 Daanveer Hehr: Doing pretty good.
6 00:01:27.940 ⇒ 00:01:29.530 Uttam Kumaran: Hey, Lev, long time.
7 00:01:31.310 ⇒ 00:01:32.120 Lev Katreczko: Alas?
8 00:01:32.820 ⇒ 00:01:33.980 Lev Katreczko: Good to see ya.
9 00:01:34.540 ⇒ 00:01:41.300 Uttam Kumaran: Dude, I really liked your last LinkedIn post. I don’t know, was it last week? I was like… I sent it to a bunch of people, actually.
10 00:01:42.240 ⇒ 00:01:43.480 Lev Katreczko: Oh, no way.
11 00:01:43.900 ⇒ 00:01:44.850 Lev Katreczko: Thanks, man.
12 00:01:45.150 ⇒ 00:01:47.000 Uttam Kumaran: That’s, like, too much alpha you’re giving out.
13 00:01:47.430 ⇒ 00:01:56.380 Lev Katreczko: For free. Thanks, man, yeah, maybe I’m happy it, it only got a couple likes, but I appreciate the kind words.
14 00:01:56.380 ⇒ 00:01:59.159 Uttam Kumaran: No, I liked it, let me check. I was, like, I was like.
15 00:01:59.160 ⇒ 00:01:59.910 Lev Katreczko: I think you did.
16 00:01:59.910 ⇒ 00:02:04.839 Uttam Kumaran: It’s actually, like, really complicated. I don’t know if everybody reading this, like, understands what…
17 00:02:04.990 ⇒ 00:02:07.790 Uttam Kumaran: you’re talking about, but I thought it was great.
18 00:02:08.800 ⇒ 00:02:16.149 Lev Katreczko: 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.
19 00:02:16.420 ⇒ 00:02:20.460 Lev Katreczko: And a lot of ideas that I, like, really strongly believe in.
20 00:02:20.610 ⇒ 00:02:25.169 Lev Katreczko: So, yeah, that was, that was a little journal entry.
21 00:02:25.640 ⇒ 00:02:36.949 Uttam Kumaran: Nice. You should keep doing it. I feel like that’s, like… I try to do that on LinkedIn, too, but the problem with LinkedIn is it’s a big, like… there’s a lot of lurkers. I don’t feel like a lot of people write, and so…
22 00:02:37.460 ⇒ 00:02:42.539 Uttam Kumaran: I’m sure a lot of people, you know, read it, but yeah, I sent it to some people internally.
23 00:02:43.420 ⇒ 00:02:50.520 Lev Katreczko: Thanks. Yeah, I was actually just writing one before this call, so I’m trying to kind of keep my foot on the gas there.
24 00:02:50.720 ⇒ 00:02:52.130 Uttam Kumaran: Nice.
25 00:02:57.270 ⇒ 00:02:59.419 Uttam Kumaran: We’ll wait for Caitlin. Hey, Ryan.
26 00:03:01.060 ⇒ 00:03:02.209 Ryan DeForest: Good morning, good morning.
27 00:03:02.830 ⇒ 00:03:03.920 Uttam Kumaran: Good morning.
28 00:03:21.140 ⇒ 00:03:22.620 Uttam Kumaran: How’s the new office?
29 00:03:26.320 ⇒ 00:03:27.220 Uttam Kumaran: Looks.
30 00:03:27.220 ⇒ 00:03:27.810 Lev Katreczko: upgrade.
31 00:03:28.740 ⇒ 00:03:33.589 Uttam Kumaran: The roof… the roof is, like, very high up. I can tell it’s a big space.
32 00:03:34.670 ⇒ 00:03:37.660 Daanveer Hehr: It’s like, what, 3 times bigger than our last place?
33 00:03:39.450 ⇒ 00:03:45.999 Daanveer Hehr: Yeah, about 3 times per year. It’s still coming together. We’re slowly adding pieces to it, making it more livable.
34 00:03:46.550 ⇒ 00:03:47.250 Uttam Kumaran: Nice.
35 00:03:47.860 ⇒ 00:03:49.640 Uttam Kumaran: Yeah, livable.
36 00:03:49.640 ⇒ 00:03:52.090 Daanveer Hehr: It’s getting to a point where we don’t want to leave from here.
37 00:03:52.090 ⇒ 00:03:55.979 Uttam Kumaran: Yeah, that’s good. Where is there to go in the arc anyways? You know, you’re gonna go home?
38 00:03:55.980 ⇒ 00:03:58.690 Lev Katreczko: Dan and I need it to be lovable, that’s for sure.
39 00:03:58.690 ⇒ 00:03:59.230 Daanveer Hehr: Yeah.
40 00:04:00.660 ⇒ 00:04:15.029 Uttam Kumaran: Yeah, I… when I was working, at WeWork in New York, the office, like, yeah, it was just, like, it’s so much nicer, but it’s, like, a super nice office, but… and everything is, like, amazing furniture, and you’re like, I’m just gonna stay here.
41 00:04:15.130 ⇒ 00:04:20.840 Daanveer Hehr: as long as I can, go right home, sleep, and come right back.
42 00:04:21.440 ⇒ 00:04:23.380 Daanveer Hehr: Yeah, that was the idea in our build.
43 00:04:23.780 ⇒ 00:04:24.410 Uttam Kumaran: God.
44 00:04:24.410 ⇒ 00:04:26.629 Lev Katreczko: Don’t let them know, but that’s the point.
45 00:04:27.670 ⇒ 00:04:36.579 Uttam Kumaran: I’m sure they’re not, I’m sure they’re, I’m sure they’re, they’re okay with that. You guys spending more time in the office.
46 00:04:37.080 ⇒ 00:04:41.080 Uttam Kumaran: Cool. Caleb, are we waiting on, anyone else?
47 00:04:41.440 ⇒ 00:04:51.050 Caitlyn Vaughn: 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.
48 00:04:51.050 ⇒ 00:04:59.830 Uttam Kumaran: 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.
49 00:04:59.830 ⇒ 00:05:15.220 Uttam Kumaran: So yeah, we’re the Brainforge team. My name is Utam, I, lead Brainforge. On the call here, you have a few of us, some, all of who, Mustafa, Amber, Demi, we all support the default team, sort of in different areas.
50 00:05:15.320 ⇒ 00:05:32.030 Uttam Kumaran: And today, we’re just going to be going through our goal for, you know, establishing, you know, reporting at default, reporting on product analytics, customers, revenue, and sort of, you know, how we’re helping Default get there. This is…
51 00:05:32.210 ⇒ 00:05:50.369 Uttam Kumaran: a very collaborative meeting, so I really don’t want to talk for a whole time, and I know Demi’s gonna do a little demo after, and he doesn’t want to talk the whole time, so, yeah, I mean, we love the product. I’ve known Caitlin for a while. We use the product as well, so we’re familiar with
52 00:05:50.370 ⇒ 00:05:52.580 Uttam Kumaran: Workflows, and…
53 00:05:52.580 ⇒ 00:06:04.570 Uttam Kumaran: forms, and, you know, we have a couple people on our sales team that are leveraging it. So, today we’re gonna be going through how we’ve established, you know, reporting so far, and how to use the Omni tool.
54 00:06:04.570 ⇒ 00:06:17.040 Uttam Kumaran: 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 gonna walk through.
55 00:06:17.040 ⇒ 00:06:18.210 Uttam Kumaran: End-to-end, how to…
56 00:06:18.210 ⇒ 00:06:37.070 Uttam Kumaran: 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.
57 00:06:37.070 ⇒ 00:06:44.500 Uttam Kumaran: 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 gonna go through
58 00:06:44.500 ⇒ 00:06:49.170 Uttam Kumaran: Couple of slides that’s just gonna give… You know,
59 00:06:49.270 ⇒ 00:07:06.520 Uttam Kumaran: it’s just gonna 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’s gonna be explaining. So let me just pull this up on my side, and we will go through this.
60 00:07:06.710 ⇒ 00:07:16.720 Caitlyn Vaughn: Also, really quickly, before we begin, I’m looking at Omni right now. I’m gonna invite anybody who is not… doesn’t currently have a seat, so that you guys can actually, like, play around with it. Great.
61 00:07:16.720 ⇒ 00:07:33.950 Uttam Kumaran: And 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 is working through the dashboard, feel free to, you know, do the… the exact same. So let me get this, going.
62 00:07:39.380 ⇒ 00:07:40.180 Uttam Kumaran: Okay…
63 00:07:47.730 ⇒ 00:07:52.209 Lev Katreczko: A quick call-out. I don’t know if this is on the docket for today, but just…
64 00:07:52.330 ⇒ 00:08:00.519 Lev Katreczko: to, satisfy my own personal curiosities. I’d love at some point to hear, like, at a super high level.
65 00:08:00.870 ⇒ 00:08:09.689 Lev Katreczko: 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
66 00:08:09.810 ⇒ 00:08:17.240 Lev Katreczko: this world a little bit more, CDP stuff, and, like, just kind of curious on what you guys cooked up.
67 00:08:18.130 ⇒ 00:08:19.850 Uttam Kumaran: Yeah, totally, I’m happy… yeah, go ahead.
68 00:08:19.850 ⇒ 00:08:28.549 Caitlyn Vaughn: Yeah, love, you, me, and Nico, I’ll set up some time for us, maybe today or tomorrow, to go through, like, the infrastructure stuff. I know
69 00:08:28.550 ⇒ 00:08:42.120 Caitlyn Vaughn: 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…
70 00:08:42.130 ⇒ 00:08:44.830 Caitlyn Vaughn: Yeah, yeah, yeah. The right data is, like, ported in for you.
71 00:08:44.830 ⇒ 00:08:46.560 Lev Katreczko: Alright, I won’t derail us.
72 00:08:48.430 ⇒ 00:08:50.490 Uttam Kumaran: Great. Okay.
73 00:08:50.770 ⇒ 00:08:54.230 Uttam Kumaran: So… Let’s go ahead…
74 00:08:57.410 ⇒ 00:08:59.640 Uttam Kumaran: Cool, so,
75 00:08:59.760 ⇒ 00:09:14.720 Uttam Kumaran: this is the high level of, you know, the Omni platform. So, Omni is a BI tool. I’m happy to, you know, my career and the folks on the Brainforce team, all we do is establish data infrastructure and build reporting platforms.
76 00:09:14.720 ⇒ 00:09:20.689 Uttam Kumaran: In business intelligence, you may have heard of Tableau, Looker, Mode, Sigma.
77 00:09:21.220 ⇒ 00:09:32.680 Uttam Kumaran: 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
78 00:09:32.760 ⇒ 00:09:46.609 Uttam Kumaran: 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.
79 00:09:46.610 ⇒ 00:09:57.020 Uttam Kumaran: And, you know, that’s our goal here. Omni is a new tool that’s sort of competing in business intelligence. They’ve been around maybe, like, 4 or 5 years. But really, really awesome team.
80 00:09:57.020 ⇒ 00:10:18.530 Uttam Kumaran: 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 favorites, and so, you know, sort of excited to bring it here to default. What is Omni? So, Omni, is basically…
81 00:10:18.530 ⇒ 00:10:34.039 Uttam Kumaran: 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… 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.
82 00:10:34.040 ⇒ 00:10:42.550 Uttam Kumaran: 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.
83 00:10:42.650 ⇒ 00:10:48.490 Uttam Kumaran: you know, that’s… that’s all it is. So this is where you’re creating visualizations, creating analysis,
84 00:10:48.710 ⇒ 00:10:53.419 Uttam Kumaran: using AI to ask questions over that analysis. You have what’s called a shared model.
85 00:10:53.860 ⇒ 00:11:07.190 Uttam Kumaran: 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.
86 00:11:07.390 ⇒ 00:11:14.749 Uttam Kumaran: instead of just having to remember the SQL to write that join, and having to, you know, copy and paste a SQL query every time.
87 00:11:14.980 ⇒ 00:11:33.139 Uttam Kumaran: 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.
88 00:11:33.140 ⇒ 00:11:40.109 Uttam Kumaran: constructs. And so, this is a little bit more about, like, you know, kind of how it works.
89 00:11:40.110 ⇒ 00:11:49.859 Uttam Kumaran: And I think, Caitlin, 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
90 00:11:49.860 ⇒ 00:12:10.360 Uttam Kumaran: 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 dashboard where we just put everything in. More than likely, this will start to get bifurcated into dashboards that focus on different areas.
91 00:12:10.360 ⇒ 00:12:13.569 Uttam Kumaran: Whether it’s sales, whether it’s product.
92 00:12:13.570 ⇒ 00:12:26.310 Uttam Kumaran: 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.
93 00:12:26.310 ⇒ 00:12:36.799 Uttam Kumaran: 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.
94 00:12:36.890 ⇒ 00:12:56.809 Uttam Kumaran: And so, you know, you can kind of see the variety of things that is possible here. This is, you know, us working directly with Caitlin to produce this, and so our hope is that we work with each of you or your teams, to sort of also support you in creating dashboards that, you know, work to, you know, assist in your
95 00:12:56.810 ⇒ 00:13:00.089 Uttam Kumaran: And your day-to-day for decisions, but lots of…
96 00:13:00.320 ⇒ 00:13:17.540 Uttam Kumaran: 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?
97 00:13:17.740 ⇒ 00:13:29.990 Uttam Kumaran: Who’s booking meetings? Like, all of 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.
98 00:13:30.190 ⇒ 00:13:31.780 Uttam Kumaran: Any questions?
99 00:13:32.320 ⇒ 00:13:33.530 Uttam Kumaran: So far.
100 00:13:34.740 ⇒ 00:13:41.160 Daanveer Hehr: I have a quick one, and maybe you answered this and I missed it. Are questions asked at a high level on the
101 00:13:41.270 ⇒ 00:13:45.150 Daanveer Hehr: At the highest level of the platform, is it on a per-table basis?
102 00:13:46.120 ⇒ 00:13:58.309 Uttam Kumaran: 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.
103 00:13:58.310 ⇒ 00:14:12.450 Uttam Kumaran: 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,
104 00:14:12.470 ⇒ 00:14:23.630 Uttam Kumaran: And then, for example, but the sales topic may include stuff from Salesforce, stuff from emails, stuff from ads. So that’s sort of how the collections are… are sort of made in Omni.
105 00:14:25.320 ⇒ 00:14:40.169 laurakrivec: And I had a question also. In terms of financial information, like company level, so revenue, you know, whatever we want, burn, whatever we want, can you do that as well? And do you, in this case, do you integrate with…
106 00:14:40.440 ⇒ 00:14:46.259 laurakrivec: like, what? Quickbooks, Stripe, can you integrate with all that?
107 00:14:46.910 ⇒ 00:14:54.410 Uttam Kumaran: Great question. Yeah, so, at the moment, yes, we do have revenue per client there. Right now, we’re bringing everything from Hyperline.
108 00:14:54.410 ⇒ 00:15:11.830 Uttam Kumaran: 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
109 00:15:11.830 ⇒ 00:15:14.259 Uttam Kumaran: you know, ARR, MRR by team.
110 00:15:14.260 ⇒ 00:15:22.410 Uttam Kumaran: In a… side-by-side with their usage. So one of the things that we worked, you know, recently to produce for Caitlin is, okay, we want to look at
111 00:15:22.410 ⇒ 00:15:33.480 Uttam Kumaran: 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.
112 00:15:33.480 ⇒ 00:15:44.640 Uttam Kumaran: 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 reported use case for it.
113 00:15:45.090 ⇒ 00:15:45.620 laurakrivec: Right, but…
114 00:15:45.620 ⇒ 00:15:46.070 Uttam Kumaran: The net.
115 00:15:46.070 ⇒ 00:15:56.349 laurakrivec: We can do it… my question is, 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 online?
116 00:15:56.350 ⇒ 00:16:09.079 Uttam Kumaran: Totally, yes, you can actually query it, like, on an entirety level, yeah. So it’s not just at the individual customer, you can basically run the query across all, you know, default customers, users, teams, yeah.
117 00:16:09.350 ⇒ 00:16:09.890 laurakrivec: Great.
118 00:16:10.750 ⇒ 00:16:28.959 Caitlyn Vaughn: 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.
119 00:16:29.110 ⇒ 00:16:54.040 Caitlyn Vaughn: 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, we’re starting with our product data, and with Hyperlane data. So, part of the reason why I wanted 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
120 00:16:54.040 ⇒ 00:17:00.319 Caitlyn Vaughn: need data-wise. So, Laura, if you’re like, oh, it would be really helpful if we could have QuickBooks and visualize financial data.
121 00:17:00.320 ⇒ 00:17:22.930 Caitlyn Vaughn: we can absolutely do that, and, like, hook that up for you, and we’re not gonna 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.
122 00:17:24.520 ⇒ 00:17:35.040 Caitlyn Vaughn: What database? We are doing, S3 as our data lake, and Mother Duck as our database, for, like, data modeling, and then Omni on top of that.
123 00:17:35.860 ⇒ 00:17:45.919 Ryan DeForest: Yeah, I mean, just to… just to make sure, so, like, we’re… all these questions are kind of stemming from, I think, like, Laura and me were talking about data, Laura and Nico were talking about data, Lev and Nico, me and Lev, like.
124 00:17:46.130 ⇒ 00:17:56.059 Ryan DeForest: this is, like, something that’s top of mind, so I apologize for, like, the questions coming out of left field. Like, me and Caitlin have a meeting after this to talk about it a little bit more, too. We’re just all kind of, like.
125 00:17:56.170 ⇒ 00:18:07.290 Ryan DeForest: 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.
126 00:18:07.800 ⇒ 00:18:08.679 Uttam Kumaran: Yeah, no problem.
127 00:18:10.620 ⇒ 00:18:25.110 Uttam Kumaran: Cool, and then just continuing on, so, again, I just want to give you guys the verbiage, we’ll go through the actual demo in just a sec. So, there… in Omni, just like most products, like, you create content, right? You’re creating dashboards, and so there is a folder system.
128 00:18:25.110 ⇒ 00:18:33.439 Uttam Kumaran: When you get to the home page, really quickly, if you want to just jump right in, you can just hit New Analysis, and they’ll take you right to the page in which you can create
129 00:18:33.440 ⇒ 00:18:39.869 Uttam Kumaran: you know, analyses directly on topics. Once you’re in a dashboard,
130 00:18:39.960 ⇒ 00:18:45.009 Uttam Kumaran: There are filters at the top. You can click on individual items to drill down into them.
131 00:18:46.390 ⇒ 00:19:05.189 Uttam Kumaran: 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,
132 00:19:05.190 ⇒ 00:19:22.099 Uttam Kumaran: you know, I just think it’s very, very flexible. Additionally, you can download things as Excel, PDFs, and of course, you can schedule these directly to your email, or to Slack, or to external webhooks, or SFTPs, you know, if you need to send these to other integrations.
133 00:19:23.620 ⇒ 00:19:39.939 Uttam Kumaran: Going into workbooks. So, workbooks are really, you know, the area to do all of your analysis. A 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. And…
134 00:19:40.000 ⇒ 00:19:47.989 Uttam Kumaran: the workbook is a dashboard in itself. It’s a collection of each of these different tiles. And so…
135 00:19:48.260 ⇒ 00:19:58.939 Uttam Kumaran: 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’ve facilitated the joins between
136 00:19:58.940 ⇒ 00:20:21.629 Uttam Kumaran: 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, you know, for the folks interested, we’ll sort of bring you into the deeper layers, which is, like, creating views and fields, and ultimately, you know, I think there will be folks that are also just running, you know, SQL queries, potentially, directly onto, the data warehouse.
137 00:20:21.630 ⇒ 00:20:40.779 Uttam Kumaran: 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… and the way we partner is, you know, as you have questions, we can direct you to the right topics, and as you’re missing fields or missing logic.
138 00:20:40.780 ⇒ 00:20:45.689 Uttam Kumaran: Those are the things in where we’ll assist to help create them, and just make more of those available, but…
139 00:20:45.710 ⇒ 00:20:51.640 Uttam Kumaran: This team is free to create dashboards and create workbooks, and start to answer, you know, questions.
140 00:20:51.740 ⇒ 00:20:53.899 Uttam Kumaran: I think that’s…
141 00:20:55.610 ⇒ 00:21:09.789 Uttam Kumaran: 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 could start writing SQL directly. Again, I think most folks will start directly from the topics themselves.
142 00:21:09.790 ⇒ 00:21:20.419 Uttam Kumaran: Once you’re in a workbook, and let’s say in this example, they’re doing events or users by states, right, some fictional company, you’ll see that you have fields here on the left side.
143 00:21:20.520 ⇒ 00:21:38.129 Uttam Kumaran: this is the web event tracking topic. As I mentioned, topics are a collection of views. View is basically a table joint. So for… 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.
144 00:21:38.480 ⇒ 00:21:39.789 Uttam Kumaran: Yeah, go ahead, Caitlin.
145 00:21:40.360 ⇒ 00:21:51.039 Caitlyn Vaughn: 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.
146 00:21:51.040 ⇒ 00:21:51.400 Uttam Kumaran: Okay.
147 00:21:51.400 ⇒ 00:21:55.270 Caitlyn Vaughn: about, or, like, what is a SQL query, those kinds of things.
148 00:21:55.270 ⇒ 00:22:12.839 Uttam Kumaran: 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.
149 00:22:13.030 ⇒ 00:22:29.580 Uttam Kumaran: So, and yeah, and of course, like, all of us on the team are more than happy to explain. All of us, you know, all we do is write SQL and do data work, so happy to go as deep as we need, you know, into, like, the inner workings of, like, how this product works and how our tables work.
150 00:22:30.750 ⇒ 00:22:36.920 Caitlyn Vaughn: And then, Ryan has a question. Any particular reason we went from… with Omni instead of equals?
151 00:22:37.200 ⇒ 00:22:50.979 Uttam Kumaran: Yeah, a couple of reasons. One, Equals is really focused, you know, their product roots are really in, like, financial reporting, and second, it’s just a lot of flexibility.
152 00:22:50.980 ⇒ 00:23:02.600 Uttam Kumaran: The Equals product is pretty good for some use cases, we have some clients that started on Equals, but a lot of them, as they start to get disparate data sources and want to start to build their own data warehouse for analytics.
153 00:23:02.600 ⇒ 00:23:08.980 Uttam Kumaran: They tend to move towards, a business intelligence tool like Omni, or Looker or Tableau.
154 00:23:10.570 ⇒ 00:23:15.880 Uttam Kumaran: I didn’t know if the… if the team originally… I don’t know, Caitlin, you might have mentioned that the team was…
155 00:23:16.230 ⇒ 00:23:18.839 Uttam Kumaran: Using equals at some point.
156 00:23:18.840 ⇒ 00:23:26.229 Caitlyn Vaughn: I think we have, like, Nico and maybe Laura using equals, just, I think, mainly to visualize, like, revenue and, like, financial.
157 00:23:26.230 ⇒ 00:23:41.819 Uttam Kumaran: It’s very focused on revenue go-to-market, not so much on, like, anything. You know, which is great as, like, when you’re first starting, just, like, plug everything in and just, like, run through. But, you know, for anyone that has used these, like.
158 00:23:41.870 ⇒ 00:23:51.849 Uttam Kumaran: business line-focused BI tools, quickly as you try to plug other things in, it… they may not facilitate, or they may just say, like, we’re not… this is not the tool for that.
159 00:23:51.920 ⇒ 00:23:53.900 Uttam Kumaran: I don’t know if that’s been your experience, Laura.
160 00:23:54.300 ⇒ 00:24:03.270 laurakrivec: 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?
161 00:24:03.270 ⇒ 00:24:04.080 Uttam Kumaran: That’s correct.
162 00:24:04.580 ⇒ 00:24:05.569 laurakrivec: Yeah, great.
163 00:24:06.260 ⇒ 00:24:23.560 Uttam Kumaran: 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, you know, but everything that you can do in that tool, you can accomplish
164 00:24:23.800 ⇒ 00:24:24.830 Uttam Kumaran: accomplish here.
165 00:24:26.850 ⇒ 00:24:31.540 Stan Rymkiewicz: So, I have a quick question. Maybe I missed it in the beginning, but…
166 00:24:31.870 ⇒ 00:24:37.049 Stan Rymkiewicz: what are… what are the data sources in Omni that I can use to view the data?
167 00:24:38.260 ⇒ 00:24:49.349 Uttam Kumaran: Yeah, so right now, we have data coming in from Hyperline, and we have data coming in from the product, you know, so product usage. So, customers, workflows, forms.
168 00:24:49.400 ⇒ 00:25:00.320 Uttam Kumaran: several different fields, and Demolati will outline what’s available today in the topic. We are driving towards Salesforce, and we are also driving towards Amplitude for product events.
169 00:25:00.720 ⇒ 00:25:05.469 Uttam Kumaran: kind of probably in that order. And then, you know, as we…
170 00:25:05.640 ⇒ 00:25:22.389 Uttam Kumaran: sort of expand if there’s need for, you know, 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 their, you know, as there’s reporting requirements.
171 00:25:22.390 ⇒ 00:25:26.749 Uttam Kumaran: You know, and so the way that works is we take
172 00:25:26.750 ⇒ 00:25:35.119 Uttam Kumaran: that data from that source system, we land it into the data warehouse, which, as I mentioned, is using Mother Duck. Omni sits on top of that.
173 00:25:35.580 ⇒ 00:25:44.799 Uttam Kumaran: 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.
174 00:25:44.800 ⇒ 00:25:56.799 Uttam Kumaran: you will 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?
175 00:25:56.800 ⇒ 00:26:00.930 Uttam Kumaran: ex-customer. And so that’s sort of the flow.
176 00:26:02.490 ⇒ 00:26:07.309 Uttam Kumaran: Depending on the difficulty of the data source, it’s, like, anywhere from, like.
177 00:26:07.490 ⇒ 00:26:10.810 Uttam Kumaran: One week to a few weeks to sort of do that end-to-end, typically.
178 00:26:15.150 ⇒ 00:26:29.830 Uttam Kumaran: And then once you’re in a workbook, you can click right here at the top to go to Vids. So, you know, we’ll see that you have, like, web events by state here. Once you click on VIDs, you can actually… you’ll go to this chart, this area, where you can actually start to
179 00:26:29.870 ⇒ 00:26:47.210 Uttam Kumaran: do a lot of your charting. Omni has a great amount of visualization options, which, you know, for a lot of the analytics work that we do is really, really helpful, but, you know, of course, just the basic bar charts and, things like that would be,
180 00:26:47.210 ⇒ 00:27:04.820 Uttam Kumaran: you know, available, and I think that’s… that should be, you know, 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.
181 00:27:04.820 ⇒ 00:27:10.219 Uttam Kumaran: The date, the week, the month, the year, so you can begin to aggregate on those measures.
182 00:27:10.310 ⇒ 00:27:13.260 Uttam Kumaran: And then…
183 00:27:13.720 ⇒ 00:27:28.179 Uttam Kumaran: 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, like, select, an individual metric or dimension.
184 00:27:28.180 ⇒ 00:27:47.740 Uttam Kumaran: 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?
185 00:27:47.740 ⇒ 00:27:53.220 Uttam Kumaran: And so, this is typically a count of user IDs, count being
186 00:27:53.220 ⇒ 00:28:06.989 Uttam Kumaran: 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
187 00:28:06.990 ⇒ 00:28:26.080 Uttam Kumaran: If you’re in an Excel sheet, you just may think about these as columns, but the… depending on the type of… whether it’s a dimension and measure, you can do certain things. For example, you may want to run a sum of all, you know, money made. You may want to run a count of all users, where those two are measures.
188 00:28:26.080 ⇒ 00:28:28.190 Uttam Kumaran: But if you bring in something like state.
189 00:28:28.190 ⇒ 00:28:34.400 Uttam Kumaran: Or the traffic source, or the zip code, those are all describing those measures.
190 00:28:35.520 ⇒ 00:28:53.119 Uttam Kumaran: Another way of explaining it 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, right? And so you should see that kind of going up over time.
191 00:28:54.480 ⇒ 00:29:09.099 Uttam Kumaran: there’s a… depending on who you are, you may be more of a visual learner, you may be more of someone that just needs to go in and play around with this, you… you may be… you may have been really great at school and can go through slides and do this. I’m more of the, I just need to jump in and play around, but…
192 00:29:09.100 ⇒ 00:29:28.110 Uttam Kumaran: Hopefully this gives you a little bit of insight into, like, the terminology. There’s a… this, you know, 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 to sort of help companies establish this, so I’m more than happy to
193 00:29:28.110 ⇒ 00:29:44.340 Uttam Kumaran: spend time individually or in another session sort of going through the terminology. I did want to leave time, and maybe, Demolata, 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
194 00:29:44.340 ⇒ 00:29:50.010 Uttam Kumaran: you know, Q&A, and then, you know, talk about next steps. So, Demi, I can, I can pass it to you.
195 00:29:52.300 ⇒ 00:30:01.049 Demilade Agboola: Thank you. Hi everyone, my name is Dimladeh. I… I’ve been working… Can you see my screen?
196 00:30:01.700 ⇒ 00:30:02.330 Uttam Kumaran: Yes.
197 00:30:02.750 ⇒ 00:30:07.099 Demilade Agboola: Great. Alright, so I have been working in Omni for a bit.
198 00:30:08.970 ⇒ 00:30:15.149 Demilade Agboola: the… so let’s start off with model doc. So this is kind of where the data lives.
199 00:30:15.360 ⇒ 00:30:21.590 Demilade Agboola: Right now. So there’s just a bunch of raw data that we’ve gotten from different, like, sources.
200 00:30:22.070 ⇒ 00:30:24.340 Demilade Agboola: And we have them living in here.
201 00:30:24.670 ⇒ 00:30:33.539 Demilade Agboola: And we have Mother Doc connected to Omni, so it’s built on top of Omni. And so this is the homepage, this is what you will see when you
202 00:30:33.750 ⇒ 00:30:35.599 Demilade Agboola: Load up your ARMI screen.
203 00:30:35.940 ⇒ 00:30:40.430 Demilade Agboola: And the way we’ll start is we’ll click on New, Right here?
204 00:30:40.800 ⇒ 00:30:47.330 Demilade Agboola: And so, this would take us to, like, our starting points, right? So there are a couple of ways we could start.
205 00:30:48.040 ⇒ 00:30:53.279 Demilade Agboola: We could directly query the database, so, like, we’ve seen…
206 00:30:57.040 ⇒ 00:30:59.179 Demilade Agboola: Wait a second for it to respond, alright.
207 00:30:59.350 ⇒ 00:31:03.820 Demilade Agboola: So we saw the data in there, and we can actually just directly query it here.
208 00:31:04.470 ⇒ 00:31:10.939 Demilade Agboola: But… So, seeing raw events… Let’s limit it to,
209 00:31:12.970 ⇒ 00:31:19.750 Demilade Agboola: And so what this just means is select everything from raw events, and just pick 10 rows.
210 00:31:20.190 ⇒ 00:31:23.799 Demilade Agboola: I’m not exactly sure why it’s taking a bit…
211 00:31:24.080 ⇒ 00:31:31.359 Demilade Agboola: But yeah, so it’s giving us the raw data as is, and we can kind of just see what the events were and what was going on there.
212 00:31:32.320 ⇒ 00:31:36.950 Demilade Agboola: So let’s go back, because that’s not necessarily what we want to use it for.
213 00:31:37.220 ⇒ 00:31:41.699 Demilade Agboola: So, if we go back here, we can start to explore something called topics.
214 00:31:42.480 ⇒ 00:31:54.800 Stan Rymkiewicz: So topics are… Sir, I have a… sorry, to cut you off. I just want a quick question about the, the database. I noticed that the schema doesn’t match our current, like, product.
215 00:31:55.090 ⇒ 00:31:58.080 Stan Rymkiewicz: SuperBaseDB.
216 00:31:58.320 ⇒ 00:32:11.970 Stan Rymkiewicz: how… and I’m sort of not only used to, but I know how to work around parameters of that schema. Is this a different schema? If I were to write the same SQL query that I usually do.
217 00:32:11.970 ⇒ 00:32:19.880 Stan Rymkiewicz: Following our schema naming conventions, would this still work, or that’s a different schema? Because it’s coming from the data warehouse?
218 00:32:20.380 ⇒ 00:32:39.209 Uttam Kumaran: 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, like, 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 Mother Duck.
219 00:32:39.210 ⇒ 00:32:57.370 Uttam Kumaran: 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 in Omni. So, Stan, if you want to share that in the channel, we can kind of help you do that.
220 00:32:58.060 ⇒ 00:33:03.789 Stan Rymkiewicz: Cool, cool, thank you. I was just more in, like, it often comes very much…
221 00:33:04.410 ⇒ 00:33:19.790 Stan Rymkiewicz: ad hoc. So, for example, on Monday, I did a query to understand how many meetings were booked per each team, from a scheduling lens versus a workflow, right? I would assume I would have to probably go into the meetings, the raw meetings, and hopefully
222 00:33:19.860 ⇒ 00:33:30.119 Stan Rymkiewicz: they are… they have the same sort of value as in source, and do I then have to, like, join it with a member table, and the… the…
223 00:33:30.220 ⇒ 00:33:32.599 Stan Rymkiewicz: The team table to get the team names.
224 00:33:33.170 ⇒ 00:33:47.079 Uttam Kumaran: 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, like, is it the right ID to join? Is this in a JSON field or not? Those are the things that our team already has cleaned up.
225 00:33:47.080 ⇒ 00:33:53.959 Uttam Kumaran: and just made simply available within one of the topics. And so that hopefully saved you the time of, like.
226 00:33:54.070 ⇒ 00:34:03.760 Uttam Kumaran: Remembering how to join or how to extract things, and instead, you’re… you can now ask that question and immediately ask the second, third, fourth follow-up question, create the dashboard, ship it.
227 00:34:03.760 ⇒ 00:34:16.250 Uttam Kumaran: 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, you know, how we would have produced that answer directly in Omni, too.
228 00:34:16.850 ⇒ 00:34:21.350 Stan Rymkiewicz: Yeah, okay, sure, I can send you… and then… 30 seconds.
229 00:34:22.540 ⇒ 00:34:38.769 Demilade Agboola: Okay, so we have topics, so I kind of showed the SQL part, or the SQL 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
230 00:34:38.770 ⇒ 00:34:53.770 Demilade Agboola: 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.
231 00:34:53.850 ⇒ 00:35:01.920 Demilade Agboola: join together, we will then analyze it and be able to have quick insights based on meeting analysis all the time. So…
232 00:35:02.430 ⇒ 00:35:08.660 Demilade Agboola: for those who might not necessarily know what joins are, basically in tables in
233 00:35:08.880 ⇒ 00:35:11.920 Demilade Agboola: Our database are not just built
234 00:35:12.150 ⇒ 00:35:21.480 Demilade Agboola: 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.
235 00:35:22.110 ⇒ 00:35:41.059 Demilade Agboola: 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.
236 00:35:41.230 ⇒ 00:35:57.629 Demilade Agboola: that will be in a separate table. So what that join… when we do that kind of join, 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.
237 00:35:57.630 ⇒ 00:36:06.040 Demilade Agboola: on what day. So that’s kind of what joiners are doing in the background. We’re just trying to get more complete information from the different tables that exist.
238 00:36:06.260 ⇒ 00:36:13.029 Demilade Agboola: And so if we open up topics, so let’s look at the integration analysis topic that has been created.
239 00:36:15.050 ⇒ 00:36:18.359 Demilade Agboola: We can see that… so let’s go to the model layers.
240 00:36:20.980 ⇒ 00:36:23.329 Demilade Agboola: Sorry, I’m trying to open up…
241 00:36:23.610 ⇒ 00:36:26.049 Demilade Agboola: So we can see that in this topic.
242 00:36:30.330 ⇒ 00:36:39.000 Demilade Agboola: what we’re doing here is we have a table called the Integration Team Daily Completion, and what that is, it’s… is a…
243 00:36:39.390 ⇒ 00:36:45.730 Demilade Agboola: The integration from the workflows of different forms have been extracted out, and we’re able to now see
244 00:36:46.280 ⇒ 00:36:49.649 Demilade Agboola: How many times in a day were those
245 00:36:49.900 ⇒ 00:36:54.499 Demilade Agboola: What forms containing those integrations completed or not completed?
246 00:36:55.030 ⇒ 00:37:08.099 Demilade Agboola: 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 into the Teams data, so we can start to see, okay, so what teams
247 00:37:08.180 ⇒ 00:37:22.629 Demilade Agboola: contain having, like, what teams used 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
248 00:37:22.820 ⇒ 00:37:39.039 Demilade Agboola: 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 IDs, are using, like, what’s… like, what… how many teams, like, what teams have…
249 00:37:39.290 ⇒ 00:37:45.299 Demilade Agboola: the number of integration IDs. So, for instance, we can see default is using 30 integrations,
250 00:37:45.750 ⇒ 00:37:51.920 Demilade Agboola: Cherry is using 20 integrations, we can kind of start to see how many integrations each team is using.
251 00:37:52.340 ⇒ 00:38:06.940 Demilade Agboola: 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
252 00:38:07.730 ⇒ 00:38:16.569 Demilade Agboola: View, so this view represents another table in the backend, in Mother Doc. Joined to this table.
253 00:38:16.680 ⇒ 00:38:18.469 Demilade Agboola: I make it a left join.
254 00:38:19.020 ⇒ 00:38:28.800 Demilade Agboola: where we’re saying the team ID is equal to the team ID, so when you’re making a join, there has to be a key. So the key makes you know that
255 00:38:28.930 ⇒ 00:38:38.120 Demilade Agboola: this value in this table corresponds to that value in that table, and that’s what’s going on here. And when I say many to one, that means in forms.
256 00:38:38.760 ⇒ 00:38:42.979 Demilade Agboola: There, like, one team can have multi… like, one team can have multiple forms.
257 00:38:43.360 ⇒ 00:38:49.109 Demilade Agboola: And so, there are many… for one form, there can be multiple teams associated there.
258 00:38:49.310 ⇒ 00:38:50.160 Demilade Agboola: But…
259 00:38:51.200 ⇒ 00:39:03.229 Demilade Agboola: there’s only going to be one team in the team list. So you’re just saying, okay, this is the relationship. And that’s kind of what’s just going on here. They’re just a bunch of joins, and they’re all represented here.
260 00:39:03.400 ⇒ 00:39:15.709 Demilade Agboola: 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 can just take the topic and use it as you deem fits.
261 00:39:16.080 ⇒ 00:39:24.379 Demilade Agboola: 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.
262 00:39:25.970 ⇒ 00:39:27.739 Demilade Agboola: I will view a workbook.
263 00:39:30.440 ⇒ 00:39:32.209 Demilade Agboola: So, once this loads up.
264 00:39:32.390 ⇒ 00:39:37.339 Demilade Agboola: it gives us a couple of options. One is we can ask a question about the data.
265 00:39:37.940 ⇒ 00:39:47.519 Demilade Agboola: Right? So this is using AI. You can ask a question, and it will give us an answer. But let’s start off with actually building something for ourselves.
266 00:39:48.630 ⇒ 00:40:02.789 Demilade Agboola: 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 when the submission… when integrations were used. So for every week, group by week.
267 00:40:02.900 ⇒ 00:40:05.380 Demilade Agboola: Let’s see the submissions total.
268 00:40:05.970 ⇒ 00:40:09.010 Demilade Agboola: So now we can see the total submissions over time.
269 00:40:09.700 ⇒ 00:40:12.040 Demilade Agboola: And we can create a chart for this.
270 00:40:12.340 ⇒ 00:40:18.949 Demilade Agboola: So now we can quickly see that, hey, submissions Obviously, grow over time.
271 00:40:19.130 ⇒ 00:40:34.659 Demilade Agboola: 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.
272 00:40:34.660 ⇒ 00:40:44.310 Uttam Kumaran: Yeah, maybe I just pause there, one second. Is everyone with us so far? So what you’re seeing here are the form submissions
273 00:40:44.460 ⇒ 00:40:49.669 Uttam Kumaran: Over time, both the completed ones and incompleted ones.
274 00:40:50.180 ⇒ 00:40:54.450 Uttam Kumaran: So this is just the, kind of, the example that we’re gonna walk through. Is everyone sort of, like.
275 00:40:56.160 ⇒ 00:41:03.210 Uttam Kumaran: I mean, I assume everybody knows forms and submissions, but I guess more of, like, is everyone still with us? Like, any questions so far?
276 00:41:03.610 ⇒ 00:41:05.350 Caitlyn Vaughn: I actually have a question.
277 00:41:05.350 ⇒ 00:41:05.890 Uttam Kumaran: Please.
278 00:41:05.890 ⇒ 00:41:14.779 Caitlyn Vaughn: how can a form be submitted if it’s not completed? You mean, like, they didn’t fill out all of the, like, fields?
279 00:41:15.170 ⇒ 00:41:22.730 Demilade Agboola: So, there is… so, the way the submissions data works is that every single time people, like.
280 00:41:22.980 ⇒ 00:41:24.320 Demilade Agboola: Engage the form.
281 00:41:24.520 ⇒ 00:41:25.909 Demilade Agboola: It tracks it.
282 00:41:26.230 ⇒ 00:41:28.440 Demilade Agboola: But then, when it’s completed.
283 00:41:28.580 ⇒ 00:41:42.250 Demilade Agboola: But it tracks it, and then there’s a flag. 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.
284 00:41:42.950 ⇒ 00:41:47.029 Demilade Agboola: But it will track, the progress that was made.
285 00:41:47.820 ⇒ 00:41:59.389 Uttam Kumaran: This is sort of, Caitlin, based on just the backend. The backend database, you know, the superbase database, has submissions. That’s just the name of it, and then there is a completed true-false.
286 00:42:00.200 ⇒ 00:42:02.190 Caitlyn Vaughn: Okay, so it’s, like, starting…
287 00:42:02.190 ⇒ 00:42:04.249 Uttam Kumaran: I think that submission can imply…
288 00:42:04.250 ⇒ 00:42:04.610 Caitlyn Vaughn: Yeah, I get it.
289 00:42:04.610 ⇒ 00:42:07.010 Uttam Kumaran: A submission can imply that you submitted.
290 00:42:07.260 ⇒ 00:42:07.770 Caitlyn Vaughn: Yeah.
291 00:42:07.770 ⇒ 00:42:10.419 Uttam Kumaran: Yeah, I guess maybe not the best name, but…
292 00:42:10.760 ⇒ 00:42:14.860 Uttam Kumaran: Yes, it’s, like, started versus completed. Correct.
293 00:42:15.050 ⇒ 00:42:19.590 Caitlyn Vaughn: Okay, cool. We’re having to, like, unweb all of our backend engineering, like, title.
294 00:42:19.590 ⇒ 00:42:25.529 Uttam Kumaran: Yeah, because I mean, I would assume you never thought people would start and, like, stop, and yeah, I mean, this is…
295 00:42:26.150 ⇒ 00:42:27.040 Caitlyn Vaughn: Okay, I’m following.
296 00:42:27.420 ⇒ 00:42:28.020 Caitlyn Vaughn: Okay.
297 00:42:28.020 ⇒ 00:42:28.490 Demilade Agboola: Yep.
298 00:42:28.490 ⇒ 00:42:42.980 Uttam Kumaran: And the other thing, Demolade, one thing we don’t have, we’re gonna start to add descriptions everywhere, so you’ll be able to start to see and, like, for the most common questions, like, what does a submission mean? We will add that as a description here, so, great.
299 00:42:45.510 ⇒ 00:42:51.270 Demilade Agboola: So, we can… one thing we can do is also we can rename this, so we can say, hey, submissions…
300 00:42:51.540 ⇒ 00:42:58.620 Demilade Agboola: Or legacy from… progress over time.
301 00:42:58.980 ⇒ 00:43:03.279 Demilade Agboola: of cis… Or… one progress by week.
302 00:43:04.190 ⇒ 00:43:05.949 Demilade Agboola: Alright, so we can save this.
303 00:43:06.390 ⇒ 00:43:07.310 Demilade Agboola: No.
304 00:43:09.600 ⇒ 00:43:14.080 Demilade Agboola: We can click here, so what this does here is it creates a dashboard.
305 00:43:14.610 ⇒ 00:43:16.279 Demilade Agboola: And so when we click here.
306 00:43:17.540 ⇒ 00:43:27.110 Demilade Agboola: We’re asked to name it, so we can say, hey, let’s call this our… Integration… Dashboard…
307 00:43:27.830 ⇒ 00:43:32.819 Demilade Agboola: So, it gives us a couple of options on where to save. So, my document is personal.
308 00:43:33.180 ⇒ 00:43:43.109 Demilade Agboola: So if you’re working on something yourself, and you don’t necessarily need other people to see it, or you only want a select group of people to see it, you can add it to my document.
309 00:43:43.280 ⇒ 00:43:49.010 Demilade Agboola: The hub is where everything lives, so that’s where everyone across the teams can see.
310 00:43:49.170 ⇒ 00:43:55.330 Demilade Agboola: So let’s say we’re just creating a prototype so far, we can say, hey, let’s let this live in my documents.
311 00:43:56.260 ⇒ 00:43:57.250 Demilade Agboola: So…
312 00:43:57.560 ⇒ 00:44:07.049 Demilade Agboola: 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.
313 00:44:08.190 ⇒ 00:44:14.410 Demilade Agboola: And so let’s say we want to have more, information, like.
314 00:44:14.690 ⇒ 00:44:19.400 Demilade Agboola: there’s definitely more stuff we can glean from this. So let’s create a new…
315 00:44:21.240 ⇒ 00:44:26.679 Demilade Agboola: So it’s called a query, but it just opens a new tab. We go back to our topic.
316 00:44:26.970 ⇒ 00:44:29.399 Demilade Agboola: And so now we can start to say, okay.
317 00:44:29.870 ⇒ 00:44:32.300 Demilade Agboola: If we look at the teams.
318 00:44:36.970 ⇒ 00:44:38.789 Demilade Agboola: How many complete?
319 00:44:40.810 ⇒ 00:44:42.849 Demilade Agboola: Submissions that they have.
320 00:44:43.020 ⇒ 00:44:50.799 Demilade Agboola: And… So, a couple of things. Remember, like I said, we’re creating a complete data sets, so it’s…
321 00:44:51.060 ⇒ 00:44:58.240 Demilade Agboola: this integration completion is joined to the team information. So, like I said.
322 00:44:58.370 ⇒ 00:45:07.110 Demilade Agboola: 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 the Enrich Teams, we have the name.
323 00:45:07.410 ⇒ 00:45:11.629 Demilade Agboola: So, once we click on the name here, It will add it.
324 00:45:15.120 ⇒ 00:45:21.190 Demilade Agboola: So now we can see Cherry has the most complete, submissions.
325 00:45:21.510 ⇒ 00:45:26.619 Demilade Agboola: and then call talk, and then Ample Market, and then Open Phone, and then…
326 00:45:26.940 ⇒ 00:45:38.549 Demilade Agboola: pre-apply. 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 a limit. So we can say, hey, I only care for the top 15.
327 00:45:39.040 ⇒ 00:45:46.680 Demilade Agboola: Right? So, once that’s done, And to be fair, like, Team ID,
328 00:45:47.280 ⇒ 00:45:54.059 Demilade Agboola: again, people don’t work with Team ID, we work with names, generally speaking, so what we can do is we can remove Team ID,
329 00:45:54.820 ⇒ 00:46:00.840 Demilade Agboola: And now we can create a chart. And so now, We have a chart.
330 00:46:01.470 ⇒ 00:46:05.790 Demilade Agboola: For the top 15, teams, based off complete.
331 00:46:05.920 ⇒ 00:46:14.140 Demilade Agboola: submissions, right? So, what do we do? We can rename our chat, we can see top…
332 00:46:14.990 ⇒ 00:46:19.909 Demilade Agboola: 15 teams by complete TED.
333 00:46:20.320 ⇒ 00:46:21.690 Demilade Agboola: submissions.
334 00:46:23.490 ⇒ 00:46:24.870 Demilade Agboola: And so we can save that.
335 00:46:25.310 ⇒ 00:46:29.140 Demilade Agboola: Now, if we go back to our dashboard, you can see it.
336 00:46:29.990 ⇒ 00:46:40.299 Demilade Agboola: 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.
337 00:46:40.490 ⇒ 00:46:42.550 Demilade Agboola: Alright, so let’s go back to our workbook.
338 00:46:42.830 ⇒ 00:46:46.889 Demilade Agboola: Do we have any questions? Are there any things that we’re thinking of?
339 00:46:47.250 ⇒ 00:46:51.210 Demilade Agboola: Is this… Helpful?
340 00:46:51.960 ⇒ 00:46:53.200 Caitlyn Vaughn: This is awesome, yeah.
341 00:46:54.870 ⇒ 00:46:59.650 Demilade Agboola: So let’s also think of another thing that we potentially could care for.
342 00:47:00.190 ⇒ 00:47:04.130 Demilade Agboola: We could say, hey, how many integration IDs
343 00:47:04.310 ⇒ 00:47:06.649 Demilade Agboola: Our different team is using, right?
344 00:47:07.540 ⇒ 00:47:13.899 Demilade Agboola: 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.
345 00:47:14.030 ⇒ 00:47:18.720 Demilade Agboola: So let’s see… and I get a list.
346 00:47:18.850 ⇒ 00:47:20.760 Demilade Agboola: And I get a chart.
347 00:47:22.130 ⇒ 00:47:23.770 Demilade Agboola: For the top.
348 00:47:24.270 ⇒ 00:47:27.430 Demilade Agboola: 15… Teams?
349 00:47:28.060 ⇒ 00:47:37.240 Demilade Agboola: by number… of integration… used.
350 00:47:38.490 ⇒ 00:47:45.990 Demilade Agboola: Ignore any… balls in CNN.
351 00:47:47.770 ⇒ 00:47:49.319 Demilade Agboola: Right, so let’s see.
352 00:47:49.900 ⇒ 00:47:55.529 Demilade Agboola: So the AI assistant is trying to think of a query and think of what’s going to happen, so it’s plotting…
353 00:47:58.330 ⇒ 00:48:06.590 Demilade Agboola: Alright, so it’s been able to say, okay, so these are the team names with integration IDs,
354 00:48:07.230 ⇒ 00:48:12.570 Demilade Agboola: And it’s… let’s see… I’m gonna try it with a simple approach.
355 00:48:13.690 ⇒ 00:48:21.570 Demilade Agboola: Okay, let’s try… I need the count of integrations used.
356 00:48:37.240 ⇒ 00:48:38.010 Demilade Agboola: Alright.
357 00:48:38.850 ⇒ 00:48:54.589 Demilade Agboola: Britt, so now we’ve been able to see these are the number of teams that are using the different… so clearly there’s some stuff about testing, which I guess is not… that’s not a team, so we can actually say, hey, so we filtered out any null teams.
358 00:48:54.650 ⇒ 00:48:58.999 Demilade Agboola: So you can see default uses about 37 integrations.
359 00:49:00.650 ⇒ 00:49:06.770 Demilade Agboola: This is probably, like, a dummy name. This is also testing. Cursor uses 20.
360 00:49:06.880 ⇒ 00:49:12.120 Demilade Agboola: spot I use is 20. So let’s say we want to get rid of the…
361 00:49:12.910 ⇒ 00:49:14.960 Demilade Agboola: The things that were like, okay.
362 00:49:17.160 ⇒ 00:49:19.760 Demilade Agboola: we don’t want to see testing, right? So let’s see…
363 00:49:20.650 ⇒ 00:49:27.649 Demilade Agboola: Let’s get rid of things that… contain tests… Leave it to end test.
364 00:49:31.650 ⇒ 00:49:32.400 Demilade Agboola: Alright.
365 00:49:33.800 ⇒ 00:49:35.300 Demilade Agboola: company.
366 00:49:39.090 ⇒ 00:49:42.860 Demilade Agboola: Alright, so now we’re seeing actual company names.
367 00:49:43.420 ⇒ 00:49:58.099 Demilade Agboola: And we’re saying that, okay, for instance, Cherry only 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.
368 00:49:58.580 ⇒ 00:50:02.349 Demilade Agboola: We don’t really want to see default on what we’re doing as well.
369 00:50:02.600 ⇒ 00:50:04.750 Demilade Agboola: So, we can also filter out default.
370 00:50:05.480 ⇒ 00:50:13.029 Demilade Agboola: And so now… We have a list of everyone who’s using default.
371 00:50:13.140 ⇒ 00:50:15.569 Demilade Agboola: Or who’s using our integrations?
372 00:50:15.920 ⇒ 00:50:17.869 Demilade Agboola: We can see top.
373 00:50:18.530 ⇒ 00:50:19.760 Demilade Agboola: 15 minutes.
374 00:50:20.690 ⇒ 00:50:24.130 Demilade Agboola: Teams by integrations used.
375 00:50:28.150 ⇒ 00:50:30.949 Demilade Agboola: Alright, so now on our dashboard, we have
376 00:50:31.410 ⇒ 00:50:49.519 Demilade Agboola: information, depending on what you’re trying to do, what you’re trying to see, and what matters to you. So if you care about 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 the couple, maybe only one or two, we can also do the inverse of this, by the way.
377 00:50:49.520 ⇒ 00:50:55.810 Demilade Agboola: 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
378 00:50:55.810 ⇒ 00:51:01.710 Demilade Agboola: one or two integrations, maybe you might want them to use more. I see Caitlin has her hand up.
379 00:51:02.560 ⇒ 00:51:19.359 Caitlyn Vaughn: Yeah, so I’ll, like, kind of connect the dots back for, like, why this is so interesting. So this integration dataset 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.
380 00:51:19.360 ⇒ 00:51:25.169 Caitlyn Vaughn: what integration should we build into the new product, and which integrations from Vanilla
381 00:51:25.170 ⇒ 00:51:27.359 Caitlyn Vaughn: Should we include, or are worth, like.
382 00:51:27.480 ⇒ 00:51:47.650 Caitlyn Vaughn: 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 dataset, 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.
383 00:51:47.850 ⇒ 00:51:56.849 Caitlyn Vaughn: Objectively, from a product lens, what integrations should we spend time porting over, because a ton of people are using them, versus, like.
384 00:51:57.210 ⇒ 00:52:09.869 Caitlyn Vaughn: maybe nobody is using loops, or, like, maybe nobody is using audio, right? So, it might not be worth our time to, like, build that in the new product versus, like, prioritizing new functionality.
385 00:52:11.280 ⇒ 00:52:11.930 Demilade Agboola: Yeah.
386 00:52:12.100 ⇒ 00:52:15.780 Demilade Agboola: So… Like, honestly, there’s a lot more we can do.
387 00:52:16.120 ⇒ 00:52:33.839 Demilade Agboola: Right? We have about 8 minutes left. But, like, honestly, we have, like, the integration IDs, so we know each integration. We can see by integration how many completed submissions we got, how many incomplete submissions we got. So that, you can create charts of that and kind of compare and see, hey.
388 00:52:33.840 ⇒ 00:52:41.949 Demilade Agboola: This tends to get a lot of completions, this tends to have a lot of incompletions, so, like, maybe this might not be worth our engineering time.
389 00:52:43.770 ⇒ 00:52:50.940 Demilade Agboola: Which is 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.
390 00:52:51.770 ⇒ 00:53:01.439 Demilade Agboola: So we can say, hey, let’s publish this. So now, in Dimlady’s documents, there exists a dashboard that says all of this, right?
391 00:53:01.680 ⇒ 00:53:18.239 Demilade Agboola: But at some point, if we’ve gotten to 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 document, 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.
392 00:53:19.100 ⇒ 00:53:20.729 Demilade Agboola: And now you go to Hub.
393 00:53:20.950 ⇒ 00:53:26.169 Demilade Agboola: Now, a couple of things, you can create a folder and put it there. You can say, you know,
394 00:53:30.380 ⇒ 00:53:33.530 Demilade Agboola: Integration… analysis.
395 00:53:34.290 ⇒ 00:53:35.530 Demilade Agboola: And you can save it.
396 00:53:35.770 ⇒ 00:53:39.460 Demilade Agboola: And now… In the Integration Analysis folder.
397 00:53:39.750 ⇒ 00:53:42.699 Demilade Agboola: You can go in there and see.
398 00:53:43.130 ⇒ 00:53:44.700 Demilade Agboola: And then you move it there.
399 00:53:44.800 ⇒ 00:53:53.609 Demilade Agboola: And so now, every other person on the team can come into integration analysis and see your dashboard on Integration Analysis, right?
400 00:53:53.820 ⇒ 00:53:56.720 Demilade Agboola: So I’ve been doing some stuff earlier.
401 00:53:56.850 ⇒ 00:54:00.800 Demilade Agboola: You can kind of see… that here.
402 00:54:01.530 ⇒ 00:54:07.780 Demilade Agboola: Oh… Yeah, so I’ve been doing things like, you know, worse integrations by incomplete submissions.
403 00:54:07.920 ⇒ 00:54:17.879 Demilade Agboola: So how many… integrations struggle to get… and get… struggle to get, like, complete submissions.
404 00:54:18.010 ⇒ 00:54:24.380 Demilade Agboola: top integrations by complete submission, so if else gets a lot of completed submissions,
405 00:54:24.510 ⇒ 00:54:27.049 Demilade Agboola: Top teams by complete submissions.
406 00:54:27.320 ⇒ 00:54:34.250 Demilade Agboola: 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
407 00:54:34.420 ⇒ 00:54:44.360 Demilade Agboola: in incomplete 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.
408 00:54:44.730 ⇒ 00:54:51.839 Demilade Agboola: Yeah, so basically, these are the kind of things you can play around with, get an idea of what you’re doing as a…
409 00:54:51.950 ⇒ 00:55:00.750 Demilade Agboola: 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.
410 00:55:01.240 ⇒ 00:55:06.059 Demilade Agboola: Does anyone have any questions, or anything that, you know, you would like us to talk about?
411 00:55:06.490 ⇒ 00:55:08.450 Demilade Agboola: If you’re a bit confused about anything.
412 00:55:13.070 ⇒ 00:55:15.240 Daanveer Hehr: I have a quick question, so…
413 00:55:15.620 ⇒ 00:55:19.560 Daanveer Hehr: Okay, Lynn, you mentioned that Thomas is uploading this information
414 00:55:20.040 ⇒ 00:55:24.960 Daanveer Hehr: On a month-to-month basis, so it’s not, like, live data that we’re able to pull, right?
415 00:55:26.390 ⇒ 00:55:27.170 Daanveer Hehr: Okay.
416 00:55:27.310 ⇒ 00:55:35.209 Caitlyn Vaughn: Yeah, we’re, kind of blocked right now on the engineering front, so I think we’re gonna pivot
417 00:55:35.470 ⇒ 00:55:50.680 Caitlyn Vaughn: 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 day.
418 00:55:51.720 ⇒ 00:56:00.609 Daanveer Hehr: Okay, yeah, I mean, a weekly… weekly upload would be pretty beneficial for, like, the CS… CS slash implementations.
419 00:56:00.610 ⇒ 00:56:02.840 Caitlyn Vaughn: As far as, like, you know.
420 00:56:02.890 ⇒ 00:56:07.000 Daanveer Hehr: Are they… are they using the product over a week or two-week basis?
421 00:56:07.000 ⇒ 00:56:07.700 Caitlyn Vaughn: So…
422 00:56:08.130 ⇒ 00:56:17.010 Daanveer Hehr: Or if it’s, like, a 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 a week, that would be great, too.
423 00:56:17.010 ⇒ 00:56:17.820 Caitlyn Vaughn: Cool.
424 00:56:17.970 ⇒ 00:56:28.699 Caitlyn Vaughn: 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?
425 00:56:29.250 ⇒ 00:56:30.150 Demilade Agboola: Gotcha.
426 00:56:30.150 ⇒ 00:56:33.670 Caitlyn Vaughn: So the product that I was well was used to build this dashboard.
427 00:56:33.670 ⇒ 00:56:53.329 Demilade Agboola: 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…
428 00:56:54.360 ⇒ 00:56:58.010 Demilade Agboola: bar charts, you can create…
429 00:56:59.580 ⇒ 00:57:06.039 Demilade Agboola: basically all of this stuff, pie charts. You can create, like, Geographic maps as well.
430 00:57:06.980 ⇒ 00:57:13.150 Demilade Agboola: And so all of that was done the same way we just did it, but the difference was, you know, changing,
431 00:57:13.650 ⇒ 00:57:20.900 Demilade Agboola: Changing the chart selection, like, if you look up the top, you might notice, or you may have noticed, that
432 00:57:23.800 ⇒ 00:57:30.070 Demilade Agboola: You may have noticed that in the chart, Sorry, my… per month.
433 00:57:30.780 ⇒ 00:57:36.900 Demilade Agboola: Alright, you may have noticed in the chart that you have the… oh, my bad. You have the option to
434 00:57:37.930 ⇒ 00:57:42.670 Demilade Agboola: Pick a different chart. So you can pick different charts from here based on what you need.
435 00:57:42.990 ⇒ 00:57:53.130 Demilade Agboola: 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 would just change that in here. Now, the…
436 00:57:53.500 ⇒ 00:57:55.800 Demilade Agboola: This product is…
437 00:57:55.930 ⇒ 00:58:07.739 Demilade Agboola: a different topic. So, the topic we used 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.
438 00:58:07.830 ⇒ 00:58:20.210 Demilade Agboola: Right? So, from the farms, to the teams, to the members, we’re just trying to join as many things as possible to have, like, a large table where you can kind of do different analysis across board.
439 00:58:21.300 ⇒ 00:58:31.060 Demilade Agboola: Ideally, we will want to have smaller topics for 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, all…
440 00:58:31.070 ⇒ 00:58:41.690 Demilade Agboola: If what you need is literally, like, financial analysis, we will create those, like, topics for you, and then you can just go into your topics, create revenue charts, create,
441 00:58:41.760 ⇒ 00:58:52.889 Demilade Agboola: whatever charts, like, usage versus 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
442 00:58:52.890 ⇒ 00:59:06.069 Demilade Agboola: 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, we have things about the teams, we have things about members, about meetings.
443 00:59:06.140 ⇒ 00:59:17.950 Demilade Agboola: 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?
444 00:59:18.180 ⇒ 00:59:38.019 Demilade Agboola: Is it an early business, or a small, like, is it 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 teams, to be honest, are using.
445 00:59:38.030 ⇒ 00:59:43.300 Demilade Agboola: default. We have that information available as well, and so that’s in the product topic.
446 00:59:45.930 ⇒ 00:59:55.610 Caitlyn Vaughn: 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,
447 00:59:57.160 ⇒ 01:00:13.249 Caitlyn Vaughn: 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 datasets and, like.
448 01:00:13.250 ⇒ 01:00:20.969 Caitlyn Vaughn: Be able to leverage this, for your own work streams, but any final questions or anything that we didn’t cover?
449 01:00:21.690 ⇒ 01:00:41.440 Uttam Kumaran: Yeah, I think one thing, Caitlin, and I mentioned this this morning, is if it would be helpful for maybe, like, the next week or two to just have, like, 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
450 01:00:41.540 ⇒ 01:00:58.110 Uttam Kumaran: 10 to 20% of time, like, getting into this tool can be overwhelming, so I was just gonna propose that we just put a standing meeting, you know, like, on Tuesdays, that way Demi can, you know, 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
451 01:00:58.320 ⇒ 01:01:05.780 Uttam Kumaran: some were 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’ll just…
452 01:01:06.070 ⇒ 01:01:14.299 Uttam Kumaran: I know we’re getting into Crystal, maybe just book that for 2 weeks, and we’ll kind of see how valuable it is, with just everybody that’s on this… this call.
453 01:01:17.090 ⇒ 01:01:17.630 Caitlyn Vaughn: Well…
454 01:01:18.040 ⇒ 01:01:19.510 Demilade Agboola: Yeah, cool.
455 01:01:20.030 ⇒ 01:01:28.440 Demilade Agboola: Look forward to that, and looking 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.
456 01:01:29.540 ⇒ 01:01:36.149 Caitlyn Vaughn: Cool, awesome. Like I said, we don’t have Brainforge Forever, so if you have some use cases in mind, like.
457 01:01:36.150 ⇒ 01:01:50.100 Caitlyn Vaughn: 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
458 01:01:50.580 ⇒ 01:01:53.430 Caitlyn Vaughn: channel with Brainforged. Dan, you have one?
459 01:01:53.600 ⇒ 01:02:02.220 Daanveer Hehr: Yeah, is there any, like, correlation to Salesforce fields that we can use, looking up, like, teams?
460 01:02:02.470 ⇒ 01:02:11.569 Daanveer Hehr: Because we have accounts in, like, implementation stage, so I would want to target, like, filter based off those, exactly.
461 01:02:12.770 ⇒ 01:02:16.580 Caitlyn Vaughn: You mean being able to, like, pull in Salesforce team ID?
462 01:02:17.160 ⇒ 01:02:34.150 Daanveer Hehr: Well, like, so, like, each… each one of our team IDs is, like, attached to, like, a domain. I would 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.
463 01:02:34.550 ⇒ 01:02:37.810 Uttam Kumaran: So as soon as we land the Salesforce data, Caitlin.
464 01:02:37.810 ⇒ 01:02:40.070 Caitlyn Vaughn: It will be… it will be possible. Yeah.
465 01:02:40.210 ⇒ 01:02:43.050 Uttam Kumaran: So we have one, we have one stakeholder that’s asking.
466 01:02:43.050 ⇒ 01:02:43.880 Caitlyn Vaughn: For it now.
467 01:02:47.030 ⇒ 01:02:52.159 Uttam Kumaran: Yes, but definitely possible. As soon as we land it, it’s probably, like, a few days out from enabling that.
468 01:02:52.460 ⇒ 01:02:53.340 Daanveer Hehr: Okay, cool.
469 01:02:53.940 ⇒ 01:03:04.839 Caitlyn Vaughn: Okay, I’ll be in the office section next week, then. Okay, great. Stan or Laura, do you guys have any requests for data that you want to be included in this?
470 01:03:06.990 ⇒ 01:03:11.019 Stan Rymkiewicz: I have to log in once you allow me in there.
471 01:03:11.020 ⇒ 01:03:12.310 Caitlyn Vaughn: You’re allowed.
472 01:03:12.760 ⇒ 01:03:21.469 Stan Rymkiewicz: 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 used, equals for, and…
473 01:03:22.440 ⇒ 01:03:23.949 Caitlyn Vaughn: Fine, if any.
474 01:03:24.070 ⇒ 01:03:29.969 Stan Rymkiewicz: Yeah, discrepancy side, it’s fine. I’ll let you. I’ll let Steam. Brainforesting, no.
475 01:03:31.230 ⇒ 01:03:32.390 Caitlyn Vaughn: Sounds great. Thank you.
476 01:03:32.620 ⇒ 01:03:33.680 Demilade Agboola: Thank you, okay.
477 01:03:33.680 ⇒ 01:03:35.030 laurakrivec: Same.
478 01:03:36.080 ⇒ 01:03:37.219 Caitlyn Vaughn: What’d you say, Laura?
479 01:03:37.370 ⇒ 01:03:42.009 laurakrivec: No, I’ll do the same. I want to check, you know, what’s there now, and then go from there.
480 01:03:42.250 ⇒ 01:03:43.399 Caitlyn Vaughn: Okay, amazing.
481 01:03:43.750 ⇒ 01:03:52.160 Caitlyn Vaughn: Awesome, well thanks for everyone’s time. You guys know where to find Brain Forge in our Slack channel, and let us know if you have any questions.
482 01:03:54.260 ⇒ 01:03:54.780 Daanveer Hehr: Thank you.
483 01:03:55.380 ⇒ 01:03:55.960 Demilade Agboola: Thank you.
484 01:03:55.960 ⇒ 01:03:56.640 Uttam Kumaran: Bye.
485 01:03:56.640 ⇒ 01:03:57.100 Demilade Agboola: Bye.