Meeting Title: Brainforge x Omni Data Modeling Training Date: 2026-05-01 Meeting participants: Scratchpad Notetaker, Greg Stoutenburg, Uttam Kumaran, Nandika Jhunjhunwala, Caitlyn Vaughn, Rebecca Bruggman, Omni Notetaker
WEBVTT
1 00:00:48.140 ⇒ 00:00:49.270 Uttam Kumaran: Hello?
2 00:00:49.820 ⇒ 00:00:50.630 Greg Stoutenburg: Hey, Tom.
3 00:00:51.780 ⇒ 00:00:54.449 Greg Stoutenburg: I’ll have to drop, half past the hour of my time.
4 00:00:54.450 ⇒ 00:00:55.040 Uttam Kumaran: Okay.
5 00:01:06.510 ⇒ 00:01:07.420 Greg Stoutenburg: Hey, Nadica.
6 00:01:08.500 ⇒ 00:01:09.340 Uttam Kumaran: Hello.
7 00:01:09.530 ⇒ 00:01:10.380 Nandika Jhunjhunwala: Aye!
8 00:01:14.750 ⇒ 00:01:16.090 Greg Stoutenburg: Back for more.
9 00:01:16.620 ⇒ 00:01:17.280 Nandika Jhunjhunwala: Yeah.
10 00:01:18.050 ⇒ 00:01:27.329 Nandika Jhunjhunwala: Excited, just waiting on Caitlin. I know she’s gonna be here soon, too. Yeah. Our previous meeting just ended, so I think she’s running a little bit late.
11 00:01:27.550 ⇒ 00:01:28.750 Greg Stoutenburg: Yep, no problem.
12 00:01:46.830 ⇒ 00:01:52.139 Greg Stoutenburg: While we wait, did you have any questions on DBT after Brian’s presentation yesterday?
13 00:01:53.260 ⇒ 00:01:59.089 Nandika Jhunjhunwala: Not DBT particular questions, but more so questions on…
14 00:01:59.590 ⇒ 00:02:04.690 Nandika Jhunjhunwala: DPT choices we’ve made, based on that lesson, yeah.
15 00:02:05.470 ⇒ 00:02:10.960 Nandika Jhunjhunwala: Yeah, I would love, like, a walkthrough of that later, but for now, we can pivot into Omni.
16 00:02:11.210 ⇒ 00:02:13.569 Greg Stoutenburg: Yeah, sure. Yup. Just wanted to check.
17 00:02:14.660 ⇒ 00:02:16.300 Greg Stoutenburg: Hello! Hey, Caitlin, happy Friday.
18 00:02:16.300 ⇒ 00:02:17.260 Caitlyn Vaughn: Happy Friday!
19 00:02:17.260 ⇒ 00:02:18.180 Uttam Kumaran: Blue.
20 00:02:22.270 ⇒ 00:02:25.200 Uttam Kumaran: We’re waiting for… Becca, Greg?
21 00:02:25.200 ⇒ 00:02:26.459 Greg Stoutenburg: Yeah, waiting for Becca.
22 00:02:26.740 ⇒ 00:02:27.420 Uttam Kumaran: Okay.
23 00:02:31.570 ⇒ 00:02:32.170 Greg Stoutenburg: I’ll just…
24 00:02:32.170 ⇒ 00:02:35.570 Caitlyn Vaughn: It’s boring right here, and it’s just making me so sleepy.
25 00:02:36.270 ⇒ 00:02:36.970 Greg Stoutenburg: Let’s go.
26 00:02:36.970 ⇒ 00:02:38.220 Uttam Kumaran: Weird weather.
27 00:02:38.220 ⇒ 00:02:39.360 Nandika Jhunjhunwala: CB, too.
28 00:02:39.660 ⇒ 00:02:43.739 Uttam Kumaran: It’s kind of sad, I kind of was looking forward to being, like, sunny and, like…
29 00:02:44.330 ⇒ 00:02:46.120 Caitlyn Vaughn: I know, same.
30 00:02:46.310 ⇒ 00:02:52.480 Caitlyn Vaughn: And now, pickleball is canceled, and horseback riding is canceled, and all of the things.
31 00:02:52.920 ⇒ 00:02:54.420 Uttam Kumaran: What about the indoor spot?
32 00:02:54.920 ⇒ 00:03:01.610 Caitlyn Vaughn: Oh, yeah, yeah, we’re, we have some construction going on to reroute the water, because it’s flooded.
33 00:03:01.610 ⇒ 00:03:02.390 Uttam Kumaran: Okay, okay.
34 00:03:02.990 ⇒ 00:03:04.769 Greg Stoutenburg: Sounds fun, that’s cool. Ruined.
35 00:03:06.100 ⇒ 00:03:09.900 Rebecca Bruggman: What? What? Am I going into a flooding conversation? This does not sound fun.
36 00:03:10.250 ⇒ 00:03:12.440 Caitlyn Vaughn: Yeah, it’s kind of wet outside.
37 00:03:12.440 ⇒ 00:03:17.369 Uttam Kumaran: Flooding and… it’s basically, like, flooding in Austin. This never… I’ve never seen it rain this much.
38 00:03:17.370 ⇒ 00:03:18.100 Caitlyn Vaughn: Thanks so much.
39 00:03:18.100 ⇒ 00:03:18.840 Uttam Kumaran: Tropical.
40 00:03:19.810 ⇒ 00:03:21.580 Rebecca Bruggman: 3… oh man.
41 00:03:21.820 ⇒ 00:03:23.030 Caitlyn Vaughn: It’s been, like, months.
42 00:03:23.190 ⇒ 00:03:35.950 Rebecca Bruggman: Oh, good luck, y’all. Rain is so hard. Like, you have this moment, I live in San Francisco, and in January, we’ll often get, like, a week or so of it raining a lot, and you’re just like, alright, we’re gonna see how sealed this house is today.
43 00:03:35.950 ⇒ 00:03:44.710 Caitlyn Vaughn: Yeah, that’s so real. You’re like, oh god. And there’s, like, nothing you could really do in the moment, you know? You just have to, like, patch it and deal with it.
44 00:03:44.870 ⇒ 00:03:51.959 Rebecca Bruggman: Yeah, we had our neighbors, poor things, we literally one day just saw that they had this, like, massive tarp over the back of their…
45 00:03:52.140 ⇒ 00:03:53.700 Rebecca Bruggman: Oh my god.
46 00:03:53.700 ⇒ 00:03:54.300 Uttam Kumaran: Gosh.
47 00:03:54.300 ⇒ 00:03:55.930 Rebecca Bruggman: Be like, are you guys so.
48 00:03:55.930 ⇒ 00:03:57.170 Uttam Kumaran: Whatever it takes.
49 00:03:57.170 ⇒ 00:04:00.110 Rebecca Bruggman: I don’t know, they’re just, like, houses in Sealed, we’ve discovered.
50 00:04:00.710 ⇒ 00:04:01.450 Rebecca Bruggman: Poor phone.
51 00:04:01.450 ⇒ 00:04:08.369 Uttam Kumaran: Oh, no. It does give you an appreciation that, like, no rain gets in, like, how is that possible?
52 00:04:09.440 ⇒ 00:04:16.950 Rebecca Bruggman: Yeah, 100%. Well, good luck, y’all. I know it’s a lot. And also, then you can go outside, no one likes that.
53 00:04:16.950 ⇒ 00:04:18.480 Caitlyn Vaughn: Yeah, exactly.
54 00:04:18.480 ⇒ 00:04:25.029 Greg Stoutenburg: I went for a motorcycle ride this morning, and I’ll be doing the same, like, basically after this call is over, so I just wanted to rub that in.
55 00:04:26.270 ⇒ 00:04:28.509 Rebecca Bruggman: Where are you located? Away from the rain.
56 00:04:28.510 ⇒ 00:04:30.440 Greg Stoutenburg: I’m in South Central Pennsylvania.
57 00:04:30.440 ⇒ 00:04:30.990 Rebecca Bruggman: Huh.
58 00:04:31.530 ⇒ 00:04:34.399 Greg Stoutenburg: Baltimore, yeah. And it is sunny and gorgeous out.
59 00:04:34.880 ⇒ 00:04:36.499 Greg Stoutenburg: I even just threw on a weekend shirt.
60 00:04:36.680 ⇒ 00:04:37.600 Uttam Kumaran: Like, every once.
61 00:04:37.600 ⇒ 00:04:38.490 Greg Stoutenburg: between calls.
62 00:04:38.600 ⇒ 00:04:39.560 Uttam Kumaran: I’ll just…
63 00:04:39.560 ⇒ 00:04:40.400 Caitlyn Vaughn: Life.
64 00:04:40.400 ⇒ 00:04:42.450 Greg Stoutenburg: I’ll stop. I’m going too hard.
65 00:04:46.780 ⇒ 00:04:48.670 Rebecca Bruggman: Weird.
66 00:04:48.950 ⇒ 00:05:07.820 Rebecca Bruggman: Well, I know we come together today, on this Friday, to talk about topics. I’m Becca, Managing Architect here over on the Professional Services team at Omni. But just, since I’m getting kind of, like, jumping in here, midstream with y’all, just maybe we could do some intros, and then I’d also just love to hear, like, where you’d like to focus the time today.
67 00:05:09.110 ⇒ 00:05:10.320 Rebecca Bruggman: Caitlin, do you want to kick us off?
68 00:05:10.320 ⇒ 00:05:26.829 Caitlyn Vaughn: Yeah, I guess you probably know the Brainforge team. I’m Caitlin, I re… I lead our product growth team, so I kind of straddle product and growth intuitively, and right now, Nandika and I are tackling data engineering, so…
69 00:05:26.990 ⇒ 00:05:38.220 Caitlyn Vaughn: We’re going to be hopefully taking over this project here shortly, and, like, building out our own, systems, and kind of take over the work that Brainforge has done over the last 6 or so months. And Nantica.
70 00:05:39.120 ⇒ 00:05:43.549 Nandika Jhunjhunwala: Yeah, hi. Sorry, I’m just yawning, I don’t mean to, but…
71 00:05:43.700 ⇒ 00:05:51.279 Nandika Jhunjhunwala: It’s that day. I work… I’m on the growth team, my role is growth engineering, but…
72 00:05:51.460 ⇒ 00:06:02.839 Nandika Jhunjhunwala: I do a bunch of random stuff at the company as well. I don’t know if there are buckets for me as of yet, but yeah, like I said, like, Caitlin said, currently just working on…
73 00:06:03.180 ⇒ 00:06:11.969 Nandika Jhunjhunwala: Data stuff, and… ramping up on the offboarding process with Brainport, so… really appreciate having you here. Yeah.
74 00:06:14.940 ⇒ 00:06:24.309 Rebecca Bruggman: Cool. Alright. Well, awesome, that’s super helpful. And then, I’d love to hear just, like, what are… what are you hoping we’ll cover today, just to make sure I focus the time?
75 00:06:26.070 ⇒ 00:06:29.229 Caitlyn Vaughn: Yeah, I can start. So…
76 00:06:29.410 ⇒ 00:06:43.039 Caitlyn Vaughn: I guess for, like, a bit of context on our tech stack, we are using Polytomic to pipe all of our data sources into Mother Duck, and then Omni lays on top of Mother Duck, so… I think…
77 00:06:43.170 ⇒ 00:06:53.069 Caitlyn Vaughn: What would be helpful would potentially be, on my end, to better understand how we can put together,
78 00:06:54.230 ⇒ 00:07:02.440 Caitlyn Vaughn: like, charts and workbooks. I think right now, we’re having some issues with, like, using Blobby to generate
79 00:07:03.120 ⇒ 00:07:06.520 Caitlyn Vaughn: Charts and workbooks and queries,
80 00:07:06.780 ⇒ 00:07:19.639 Caitlyn Vaughn: partially from some of the data modeling choices we made, but partially maybe because I don’t yet know how to, like, really interact with Blobby, or, like, the best ways to, like, phrase my queries,
81 00:07:19.740 ⇒ 00:07:26.919 Caitlyn Vaughn: to get, like, the best results out of it, so maybe we could patch that, and Nandik, I don’t know if you have anything on your side.
82 00:07:28.700 ⇒ 00:07:32.910 Nandika Jhunjhunwala: Yeah, I guess to add on to that, how we can…
83 00:07:33.420 ⇒ 00:07:47.829 Nandika Jhunjhunwala: is there, like, what… what should… what data modeling choices should be informed from, like, the way we can configure Omni, especially in terms of Blobby with, like, a baseline view, joins relationships? Like, we’re looking at the, like, configuration file from Bobby yesterday.
84 00:07:47.830 ⇒ 00:08:01.679 Nandika Jhunjhunwala: So if you have guidance on, like, how those should be configured, and how, like, some of the best teams using, like, most use… making most use out of Omni are, like, using it, how we should surface the data.
85 00:08:01.740 ⇒ 00:08:04.310 Nandika Jhunjhunwala: from our warehouse into Omni, and, like.
86 00:08:04.530 ⇒ 00:08:10.670 Nandika Jhunjhunwala: decisions for, like, technical and non-technical user, that can make it, like, easy for them to…
87 00:08:10.890 ⇒ 00:08:13.120 Nandika Jhunjhunwala: Get their hands on data as well.
88 00:08:14.590 ⇒ 00:08:33.609 Caitlyn Vaughn: We’ve also been talking about, like, where to hold the semantics of our data modeling, if we should keep it in dbt or put it in Omni, so I don’t know if you have, like, context on, you know, benefits or drawbacks to each of them, or, like, if you could advise one of the two options, but that might be interesting as well.
89 00:08:35.570 ⇒ 00:08:46.100 Rebecca Bruggman: We can cover all those things. Okay, I’m just pulling up a couple of things for y’all. Just give me one tick while I got some docs rockin’ and rollin’.
90 00:08:46.600 ⇒ 00:08:54.640 Rebecca Bruggman: Let’s see… Models…
91 00:08:55.500 ⇒ 00:09:03.070 Rebecca Bruggman: We have exact documentation for where to put what, where. I’ll send that over to you,
92 00:09:03.200 ⇒ 00:09:15.430 Rebecca Bruggman: But then we can go through it live as well. Cool. Okay, this all sounds great. Let’s rock and roll. Okay, I’m in your instance, so we can kind of do it from there.
93 00:09:15.710 ⇒ 00:09:20.340 Rebecca Bruggman: So it sounds like, if I were to kind of summarize what y’all were just saying, it’s like.
94 00:09:20.620 ⇒ 00:09:31.580 Rebecca Bruggman: building topics effectively, and how to make them as, like, useful as possible for your end users, as well as how to make them, like, really useful for Blobby. Where to put…
95 00:09:31.960 ⇒ 00:09:44.550 Rebecca Bruggman: context. Is it in Omni? Is it in DBT? How to kind of think about all those things? And then just, like, general data modeling, and then, like, utilization of Lobby. Does that cover everything?
96 00:09:46.040 ⇒ 00:09:50.229 Rebecca Bruggman: Alright, sweet. Let’s poke around your instance a little bit. Okay.
97 00:09:50.760 ⇒ 00:09:55.729 Rebecca Bruggman: We have Mother Doc… Cool, we have a couple of top- we have a lot of topics set up.
98 00:09:56.120 ⇒ 00:10:03.859 Rebecca Bruggman: How do you feel about the num- like, the topics that are set up currently? Does this cover everything you need, or do you feel like there are…
99 00:10:03.960 ⇒ 00:10:07.230 Rebecca Bruggman: Others that you need, or do you need to get oriented? I don’t know what they are.
100 00:10:08.120 ⇒ 00:10:12.179 Caitlyn Vaughn: I feel okay about topics, I think.
101 00:10:12.680 ⇒ 00:10:17.480 Caitlyn Vaughn: what is… I mean, I tried to yesterday generate,
102 00:10:18.030 ⇒ 00:10:22.040 Caitlyn Vaughn: what was the query that we’re trying to do? We were trying to compare,
103 00:10:22.340 ⇒ 00:10:27.359 Caitlyn Vaughn: We were trying to generate the revenue of every closed one company.
104 00:10:28.560 ⇒ 00:10:33.980 Caitlyn Vaughn: So this is a good example of, like, even look at this topics list.
105 00:10:33.980 ⇒ 00:10:34.560 Rebecca Bruggman: Huh.
106 00:10:34.560 ⇒ 00:10:45.410 Caitlyn Vaughn: As we were going through, it’s hard to figure out, like, where that data lives within these topics. Like, for me, I would intuitively go to, like, go to market.
107 00:10:46.100 ⇒ 00:10:51.859 Caitlyn Vaughn: And then, you know, look through that, and that doesn’t really look like it, so then I would maybe go to executive, and then there’s…
108 00:10:52.050 ⇒ 00:10:54.609 Caitlyn Vaughn: 6 different ARR topics.
109 00:10:55.280 ⇒ 00:11:00.409 Caitlyn Vaughn: So, how do we figure out, like, Where that information is.
110 00:11:01.270 ⇒ 00:11:12.540 Rebecca Bruggman: Greenforge, do you have a perspective on this? Because I think you’ve done a lot of the sort of, like, building out here of, like, I don’t know, for these sorts of questions, like, how the team should be thinking about, like, where to go and how to direct folks?
111 00:11:12.540 ⇒ 00:11:26.079 Uttam Kumaran: Yeah, I mean, so the original architecture that we chose, again, was, like, we’re gonna build, sort of, dbt is gonna hold most of the, you know, logic on core metrics, and the topics are primarily for joins, and then the semantic layer.
112 00:11:26.180 ⇒ 00:11:40.160 Uttam Kumaran: I think right now, I kind of agree in that we should probably consolidate to a few topics where we have, like, the core joins made, but I think even just, like, an overview for Caitlin and Nandik on, like, topic design.
113 00:11:40.180 ⇒ 00:11:48.700 Uttam Kumaran: would be helpful, and just, like, a baseline on, like, okay, how should topics be used, and the difference between modeling in dbt versus modeling
114 00:11:48.800 ⇒ 00:11:58.940 Uttam Kumaran: the semantic layer in Omni. I think even just, like, the fundamentals there, I think, will help sort of ladder up into, like, okay, how can we consolidate these if that’s the direction, like, we want to go? Does that make sense?
115 00:11:59.630 ⇒ 00:12:06.829 Rebecca Bruggman: Yeah. And then, how did you all come to, like, these being the core set of topics that got built out? I’m just curious about that background.
116 00:12:07.470 ⇒ 00:12:23.689 Uttam Kumaran: Yeah, I mean, it’s sort of based on the, like, kind of the dashboard requirements that we had. So we… we worked with the team on, like, okay, these are the dashboards, these are the metrics we want to show, and then we work kind of backwards, what sources, what core marts tables that we’re building, and then back up to the topics.
117 00:12:24.330 ⇒ 00:12:37.009 Rebecca Bruggman: Gotcha. Okay. That’s all… that’s helpful context, just as I’m getting sort of, like, oriented on… on the… the history here. Cool. Something I will say, Caitlin, to kind of answer your question is,
118 00:12:37.250 ⇒ 00:12:43.179 Rebecca Bruggman: For those… do you feel like the question you’re trying to ask is, like, a core question you feel like a lot of people will be asking?
119 00:12:44.150 ⇒ 00:12:52.340 Caitlyn Vaughn: Yeah, I think the end user for us is our completely non-technical folks.
120 00:12:52.760 ⇒ 00:12:53.770 Caitlyn Vaughn: Though…
121 00:12:54.760 ⇒ 00:13:07.430 Caitlyn Vaughn: I guess what I… what I want to understand is probably, like, where this is today, and, like, how we can maybe consolidate it, but also just, like, what is the framework for how people should be…
122 00:13:07.780 ⇒ 00:13:09.379 Caitlyn Vaughn: Selecting a topic.
123 00:13:10.060 ⇒ 00:13:20.979 Rebecca Bruggman: Brainforge team, just asking back, for, like, this question, do you have a topic you would expect this to be pulling from currently?
124 00:13:21.340 ⇒ 00:13:23.400 Uttam Kumaran: Yeah, I mean, I would say, like.
125 00:13:23.540 ⇒ 00:13:28.400 Uttam Kumaran: Like, a good example is, like, we… anything customer should come from, like, customer health overview.
126 00:13:28.490 ⇒ 00:13:46.789 Uttam Kumaran: And then there are a few go-to-market topics that, you know, have a few, like, dashboard-specific metrics, but we’re… we’re open to, like, we could… we could consolidate, but I guess, like, I just want to focus on, like, what are the standards, like, that we can share with default on, like.
127 00:13:46.970 ⇒ 00:13:53.489 Uttam Kumaran: How to build topics, and, like, what a topic should contain, what are the areas in where you should have separate topics for…
128 00:13:53.750 ⇒ 00:13:57.460 Uttam Kumaran: You know, given business domain, or what are the areas and where you should consolidate?
129 00:13:58.950 ⇒ 00:14:21.339 Rebecca Bruggman: Okay, cool. I’m just asking, because I’m more trying to see, like, where, like, in an ideal path, like, what should Blobby know to go to currently to, like, answer that question? Where does this data live? So, thank you for the context, because, Caitlin, one thing, I’d be curious to see… can you repeat the question? Because I’m curious to see just, like, what happens today if Blobby tries to go and answer this question for you.
130 00:14:21.340 ⇒ 00:14:24.569 Caitlyn Vaughn: Yeah, the question I was trying to ask yesterday was…
131 00:14:24.720 ⇒ 00:14:31.740 Caitlyn Vaughn: Can we see a list of every… Customer’s ARR.
132 00:14:32.300 ⇒ 00:14:33.070 Rebecca Bruggman: Error.
133 00:14:34.080 ⇒ 00:14:34.880 Rebecca Bruggman: Cool.
134 00:14:35.730 ⇒ 00:14:38.990 Rebecca Bruggman: Because what should happen, excuse me, is…
135 00:14:39.320 ⇒ 00:14:43.989 Rebecca Bruggman: Do you see this auto-select topic, this will basically go through everything that you’ll have.
136 00:14:44.360 ⇒ 00:14:59.450 Rebecca Bruggman: And then based on what’s in there, both from the joins, the AI context, like, everything that’s sort of within the AI context window broadly, Blobby will go and try to infer, like, this is the topic I should be pulling.
137 00:14:59.450 ⇒ 00:15:09.400 Rebecca Bruggman: To get this information. So, does this look like what you would want, or I’m just curious how… how does this response?
138 00:15:10.120 ⇒ 00:15:14.720 Caitlyn Vaughn: Yeah… It pulled 236 customers.
139 00:15:15.430 ⇒ 00:15:16.630 Rebecca Bruggman: Yep, that’s what it looks like.
140 00:15:16.920 ⇒ 00:15:17.840 Caitlyn Vaughn: Okay.
141 00:15:18.730 ⇒ 00:15:23.869 Rebecca Bruggman: Now, I will say, this defaults to is last month, so these filters are on here.
142 00:15:23.870 ⇒ 00:15:24.379 Caitlyn Vaughn: But you can…
143 00:15:24.380 ⇒ 00:15:32.100 Rebecca Bruggman: So, for example, if you didn’t want… if you wanted metric month to be, like, all-time, you could just ask, like, hey, can you actually remove that filter, or something like that.
144 00:15:32.100 ⇒ 00:15:46.189 Caitlyn Vaughn: Mmm, okay. Yeah, I mean, it looks, like, directionally correct. I think yesterday, when we had asked… I guess, let me look at, like, the exact, framing of the question that I had asked.
145 00:15:46.760 ⇒ 00:15:47.920 Caitlyn Vaughn: Gents…
146 00:15:54.550 ⇒ 00:15:58.960 Caitlyn Vaughn: Okay, I said, list out the annual ARR of each opportunity.
147 00:15:59.760 ⇒ 00:16:13.890 Rebecca Bruggman: Okay, let’s try that one. Okay, cool. We’ll just do a quick refresh. Sweet. List out the annual error of each opportunity.
148 00:16:13.990 ⇒ 00:16:16.800 Rebecca Bruggman: Cool.
149 00:16:17.970 ⇒ 00:16:21.520 Rebecca Bruggman: Typing is not my forte today.
150 00:16:21.520 ⇒ 00:16:22.650 Caitlyn Vaughn: In front of other people.
151 00:16:22.650 ⇒ 00:16:23.250 Rebecca Bruggman: I know.
152 00:16:24.920 ⇒ 00:16:26.760 Rebecca Bruggman: Live demos are always fun.
153 00:16:26.760 ⇒ 00:16:27.430 Caitlyn Vaughn: Yeah.
154 00:16:27.430 ⇒ 00:16:31.499 Rebecca Bruggman: And thanks for stepping through all of this, I’m just trying to sort of, like, orient on, like, where y’all are at.
155 00:16:31.500 ⇒ 00:16:32.480 Caitlyn Vaughn: Yeah!
156 00:16:32.480 ⇒ 00:16:42.369 Rebecca Bruggman: Stuff like that, so I really appreciate it. Okay, cool. So, I don’t see a dedicated opportunities topic, and so I think that’s where Blobby is maybe getting stuck here.
157 00:16:42.740 ⇒ 00:16:48.179 Rebecca Bruggman: Of, like, knowing where to go, around opportunities. Yeah.
158 00:16:48.180 ⇒ 00:17:11.549 Rebecca Bruggman: So, let’s look over here. Do you have, like, if we were coming back here, because what I’m trying to also step through is, like, something in terms of how I think about approaching, is, like, if you know some of your core questions, or core types of questions, it can be helpful to almost, like, list them out and have, like, a punch list to say, like, do all… sort of what we’re doing now, like, do all of our topics answer these types of core questions or not?
159 00:17:11.550 ⇒ 00:17:20.000 Rebecca Bruggman: And then just almost, like, iterate from there. That’s sort of how I think about, like, framing, topics and sort of, like, how to,
160 00:17:20.230 ⇒ 00:17:44.030 Rebecca Bruggman: containerize them, I guess I’ll use that word if it’s a real word. It’s basically, like, what set of questions is this topic meant to answer? I think, like, an older paradigm is sort of, like, building, you know, like, a topic for every dashboard or something like that, but I think especially in, like, an AI world where you’re looking to do self-serve and, you know, doing a lot of this AI stuff, it’s like, what are the questions that this is, like, really looking to answer?
161 00:17:44.030 ⇒ 00:17:46.960 Rebecca Bruggman: Is, like, a good… can be a helpful framing.
162 00:17:46.960 ⇒ 00:17:49.479 Rebecca Bruggman: Is there…
163 00:17:49.480 ⇒ 00:17:56.560 Rebecca Bruggman: Like, if we’re going into your schemas, is there, like, a, table you’d expect this to be pulling from?
164 00:17:57.460 ⇒ 00:18:03.060 Caitlyn Vaughn: I imagine this to be from, like, probably a DIM customer.
165 00:18:03.060 ⇒ 00:18:03.660 Rebecca Bruggman: Okay.
166 00:18:07.510 ⇒ 00:18:09.159 Rebecca Bruggman: So maybe Salesforce?
167 00:18:09.420 ⇒ 00:18:10.170 Uttam Kumaran: Yeah.
168 00:18:10.540 ⇒ 00:18:11.420 Caitlyn Vaughn: Yeah.
169 00:18:11.420 ⇒ 00:18:18.370 Rebecca Bruggman: Okay, cool. And then let’s see if that exists… Salesforce…
170 00:18:18.670 ⇒ 00:18:21.730 Rebecca Bruggman: Because then this searches within,
171 00:18:22.590 ⇒ 00:18:27.289 Rebecca Bruggman: files… so it doesn’t look… well, let’s see… metadata…
172 00:18:27.880 ⇒ 00:18:35.420 Rebecca Bruggman: field, sorry, I’m just scrolling through as I’m getting oriented. Okay, so it does look like… in here…
173 00:18:37.510 ⇒ 00:18:40.010 Rebecca Bruggman: This is a rep scorecard.
174 00:18:44.160 ⇒ 00:18:51.229 Rebecca Bruggman: Let’s see, customer health, meetings booked… I’m just kind of looking at the topics that y’all have right now, and kind of what’s in there.
175 00:18:51.820 ⇒ 00:18:55.510 Rebecca Bruggman: Customer enablement… Team user growth.
176 00:18:56.520 ⇒ 00:19:20.039 Rebecca Bruggman: So yeah, I think the gap, what it feels like right now, and this is actually really helpful, because, like, this is sort of where you’re, like, kicking the tires of, like, do the topics actually answer all the questions we have, in addition to, like, you know, making sure they have the right information for dashboards we’re building, is, like, you probably need an opportunity topic, is probably the, like, the… one of the gaps here in terms of, like, making sure there’s, like, a topic that Blobby can clearly go to.
177 00:19:20.040 ⇒ 00:19:24.579 Rebecca Bruggman: Do you know how to build out a topic? Would that be helpful to step through?
178 00:19:24.580 ⇒ 00:19:25.980 Caitlyn Vaughn: That would be really helpful, yeah.
179 00:19:26.700 ⇒ 00:19:28.130 Rebecca Bruggman: Let’s rock and roll, let’s do it.
180 00:19:28.550 ⇒ 00:19:37.140 Rebecca Bruggman: Okay, so, a couple of ways to get there. You can either, if you’re in the shared model, you can click Explore here. I’ll also show you how to do this through New.
181 00:19:37.850 ⇒ 00:20:01.750 Rebecca Bruggman: And then going through the workbook, this would take you to the same path. I’m just kind of showing you two different options. We’ll start in Browse All Views. Now, you can do this within… directly within the shared model. I personally like starting in the workbook, and then you can kind of build within here. There’s a lot of UI features, and then you can potentially promote up to the shared model once you’re ready, and then it could be available to everyone. So that’s… that’s just usually how I like to operate.
182 00:20:01.850 ⇒ 00:20:10.640 Rebecca Bruggman: I’ll put us into a branch. We’ll do a new branch, Becca… Adding a topic.
183 00:20:11.070 ⇒ 00:20:26.549 Rebecca Bruggman: I’m just gonna say ops, because opportunities I’m not gonna spell right. And I’ll note, I use this Becca slash to create a foldering system, which can be really nice, because otherwise any branch just gets saved at the root, so it just makes it a little bit easier to navigate them back to.
184 00:20:27.030 ⇒ 00:20:38.729 Rebecca Bruggman: Oh, and I’ll just put it back on Omni, so it’s a little more clear for you. Okay, sweet. Okay, so we probably want… do you want the DIM… we’re saying DIM customers, right? Just pulling that back.
185 00:20:39.400 ⇒ 00:20:40.350 Caitlyn Vaughn: Yes.
186 00:20:41.180 ⇒ 00:20:45.929 Rebecca Bruggman: Okay, let’s see, so we want… do you want this one, or Salesforce, probably, yeah?
187 00:20:46.310 ⇒ 00:20:50.810 Caitlyn Vaughn: I’m actually not, can you open Dim Customer 360 so we can see what’s inside?
188 00:20:50.810 ⇒ 00:20:51.740 Rebecca Bruggman: Yeah, yeah, for sure.
189 00:20:52.050 ⇒ 00:20:52.850 Caitlyn Vaughn: Okay.
190 00:20:53.970 ⇒ 00:20:59.130 Caitlyn Vaughn: So, it looks like 360 is more Hyperline data, and then will you scroll down?
191 00:20:59.410 ⇒ 00:21:00.070 Rebecca Bruggman: Yeah, of course.
192 00:21:00.810 ⇒ 00:21:09.110 Caitlyn Vaughn: And then it looks like Salesforce is… Account… CS.
193 00:21:11.150 ⇒ 00:21:14.690 Caitlyn Vaughn: Yeah, I guess if we’re… are we building out an opportunity topic?
194 00:21:15.160 ⇒ 00:21:18.750 Rebecca Bruggman: That’s what I’m thinking, yeah, to sort of answer these sorts of opportunity questions.
195 00:21:18.750 ⇒ 00:21:20.990 Caitlyn Vaughn: Okay, so I guess just, like.
196 00:21:22.110 ⇒ 00:21:30.200 Caitlyn Vaughn: bigger picture backing up so that I can answer your questions. Are you thinking that we would add some opportunity data to one of these, like, DIM customer.
197 00:21:31.420 ⇒ 00:21:33.470 Rebecca Bruggman: Yep, so we’d have a topic.
198 00:21:33.740 ⇒ 00:21:48.240 Rebecca Bruggman: We could, how I’m thinking about the topic, and you keep me honest in terms of what you think is the right scope for, like, the data within this, but it would basically be for any questions around, like, opportunities or, like, sort of answering, like, this… these sorts of questions of, like, listing.
199 00:21:48.720 ⇒ 00:21:56.050 Rebecca Bruggman: they are for each opportunity. Yeah. What tables would we need within that topic for it to be able to answer those sorts of questions?
200 00:21:56.190 ⇒ 00:22:00.550 Uttam Kumaran: Yeah, there should be, like, a dim… GTM account.
201 00:22:00.550 ⇒ 00:22:01.210 Rebecca Bruggman: This one?
202 00:22:01.210 ⇒ 00:22:03.660 Uttam Kumaran: There should also be, like, a fact.
203 00:22:04.250 ⇒ 00:22:04.880 Rebecca Bruggman: Pipeline.
204 00:22:04.880 ⇒ 00:22:07.120 Uttam Kumaran: or Fact Influence Pipeline.
205 00:22:07.540 ⇒ 00:22:12.519 Uttam Kumaran: You should see some fields in there, I think if you search for fields, for opportunity.
206 00:22:13.280 ⇒ 00:22:17.590 Rebecca Bruggman: Or… Yeah.
207 00:22:17.930 ⇒ 00:22:18.850 Rebecca Bruggman: So, like, some of the…
208 00:22:18.850 ⇒ 00:22:20.389 Uttam Kumaran: Influence pipeline, yeah.
209 00:22:22.730 ⇒ 00:22:24.339 Caitlyn Vaughn: What is Influence Pipeline?
210 00:22:25.050 ⇒ 00:22:29.460 Uttam Kumaran: This was, like, where we were looking… yeah… sorry, go ahead, Nandica.
211 00:22:30.310 ⇒ 00:22:32.880 Nandika Jhunjhunwala: Oh, no, sorry, I was just looking at the fields, like…
212 00:22:33.160 ⇒ 00:22:36.810 Nandika Jhunjhunwala: Closed one opportunities… I’m not sure what influence is.
213 00:22:36.810 ⇒ 00:22:41.330 Uttam Kumaran: Yeah, we were looking at, like, it’s, like, the day, the rep, and, like, what pipelines that they…
214 00:22:41.330 ⇒ 00:22:42.280 Nandika Jhunjhunwala: Hmm…
215 00:22:42.280 ⇒ 00:22:46.949 Uttam Kumaran: Basically, there’s a field in Salesforce, like, Influence Pipeline, Influence Opportunities, yeah.
216 00:22:49.920 ⇒ 00:22:52.940 Caitlyn Vaughn: Okay, so maybe that is where we put this?
217 00:22:53.340 ⇒ 00:22:57.600 Nandika Jhunjhunwala: I don’t think so. I think this is, like, more rep-specific. Like.
218 00:22:57.920 ⇒ 00:22:59.849 Nandika Jhunjhunwala: rep influence back, and I’d be like.
219 00:22:59.850 ⇒ 00:23:00.580 Caitlyn Vaughn: the blue, the.
220 00:23:00.580 ⇒ 00:23:03.280 Nandika Jhunjhunwala: But then influenced by, like, outbound, too.
221 00:23:03.280 ⇒ 00:23:03.650 Caitlyn Vaughn: Okay.
222 00:23:03.650 ⇒ 00:23:05.690 Nandika Jhunjhunwala: Yeah.
223 00:23:05.690 ⇒ 00:23:12.880 Caitlyn Vaughn: Okay, I guess my question now is, like, is the data already here for… Pipeline.
224 00:23:13.090 ⇒ 00:23:14.160 Caitlyn Vaughn: Revenue.
225 00:23:14.830 ⇒ 00:23:16.940 Rebecca Bruggman: Let’s see… pipeline…
226 00:23:18.870 ⇒ 00:23:24.159 Rebecca Bruggman: Oh, let’s see… I’m looking at this Fact Influence Pipeline snapshot. Is that probably the right table?
227 00:23:25.560 ⇒ 00:23:27.119 Nandika Jhunjhunwala: I would say no.
228 00:23:27.120 ⇒ 00:23:27.870 Caitlyn Vaughn: Hmm, let’s see…
229 00:23:27.870 ⇒ 00:23:30.529 Nandika Jhunjhunwala: It’s… it’s, again, rep-specific, and…
230 00:23:31.160 ⇒ 00:23:34.309 Nandika Jhunjhunwala: more for VDR analysis.
231 00:23:34.490 ⇒ 00:23:36.720 Nandika Jhunjhunwala: But maybe that would be more of…
232 00:23:36.720 ⇒ 00:23:42.739 Uttam Kumaran: Yeah, but I think we’re building it all… I think we’re building it all from this, because everything is associated with a rep.
233 00:23:43.420 ⇒ 00:23:48.119 Uttam Kumaran: But you can still do, like, date, and… Total amount of pipeline.
234 00:23:48.120 ⇒ 00:23:53.439 Nandika Jhunjhunwala: not all opportunities are associated with the rep. Oh, okay. So if you were joining on…
235 00:23:53.710 ⇒ 00:23:57.210 Nandika Jhunjhunwala: Influenced by plan table, there would be gaps in that data.
236 00:23:57.990 ⇒ 00:24:00.649 Caitlyn Vaughn: Probably want to join on a count, right?
237 00:24:01.030 ⇒ 00:24:01.690 Nandika Jhunjhunwala: Yes.
238 00:24:02.070 ⇒ 00:24:02.600 Caitlyn Vaughn: Okay.
239 00:24:04.170 ⇒ 00:24:12.629 Rebecca Bruggman: So would our base then be Customer Salesforce, or are you thinking Customer 360? Like, what would be the base in terms of, like, capturing all those accounts?
240 00:24:13.700 ⇒ 00:24:21.899 Caitlyn Vaughn: Probably Customer Salesforce, but I think we’ll probably, like, merge these three, dim tables and…
241 00:24:21.900 ⇒ 00:24:22.940 Nandika Jhunjhunwala: Book sales.
242 00:24:23.220 ⇒ 00:24:24.209 Caitlyn Vaughn: Yeah, so it’ll just.
243 00:24:24.210 ⇒ 00:24:27.620 Nandika Jhunjhunwala: I think, hopefully, in the future, we can have one customer gym table.
244 00:24:27.620 ⇒ 00:24:33.270 Caitlyn Vaughn: Yeah. So it’s fine, where we put it, we’ll merge it in. So maybe do Salesforce.
245 00:24:33.700 ⇒ 00:24:41.690 Nandika Jhunjhunwala: I think for now, so sorry, 360 has an estimated Hyperline ARR, so we could get the ARR field from there.
246 00:24:42.310 ⇒ 00:24:47.139 Caitlyn Vaughn: Hmm, but we’re not pulling the ARR from Hyperline, I thought we were pulling it from Salesforce.
247 00:24:47.310 ⇒ 00:24:51.690 Nandika Jhunjhunwala: the other table doesn’t have an ER field, the Salesforce one.
248 00:24:51.690 ⇒ 00:24:58.880 Uttam Kumaran: Yeah, I guess, we can also go backwards. If there’s… if there’s a dashboard that has the value, we can back into the…
249 00:24:59.100 ⇒ 00:25:08.069 Nandika Jhunjhunwala: I don’t think we’ve currently made any of those joins on, like, Opportunity and ARR. Okay. So, I’m assuming that doesn’t exist. Oh, but…
250 00:25:08.400 ⇒ 00:25:15.500 Nandika Jhunjhunwala: I don’t know what cohort baseline ARR is. I saw this, like, field come up yesterday, too.
251 00:25:17.900 ⇒ 00:25:35.400 Uttam Kumaran: Yeah, I think part of the challenge here is, like, I don’t know if we have, like, I don’t know if we’ve gotten to the point where we can see… like, we’ve really just nailed the rep-based, like, BDR analysis. I don’t know if we have a clean view of just all Salesforce opportunities. Like, it’s in our intermediate model, but I don’t think we have a fact.
252 00:25:35.740 ⇒ 00:25:36.280 Nandika Jhunjhunwala: Table for that.
253 00:25:36.640 ⇒ 00:25:37.210 Caitlyn Vaughn: Hmm.
254 00:25:37.210 ⇒ 00:25:39.629 Uttam Kumaran: So, like, because I… in the… in dbt, there’s…
255 00:25:39.760 ⇒ 00:25:48.829 Uttam Kumaran: we have a thing called Salesforce Opportunities, but I don’t think we’ve had to create a dashboard just on that outside of, like, associating with a rep. So maybe it’s best…
256 00:25:49.120 ⇒ 00:25:54.099 Uttam Kumaran: For this call, we just, like, used the rep-based one, and just walked through, like, creating
257 00:25:54.290 ⇒ 00:26:00.780 Uttam Kumaran: A topic just on that, and then on our team, we can create the… Just a sole opportunities view.
258 00:26:04.530 ⇒ 00:26:05.150 Rebecca Bruggman: Okay.
259 00:26:05.610 ⇒ 00:26:15.869 Rebecca Bruggman: Is there any other topics you think might be helpful? I’m just thinking of, like, something that might be more, like, useful in the medium to long term, since I know there’s some data reworking y’all have to do.
260 00:26:17.220 ⇒ 00:26:26.529 Caitlyn Vaughn: Yeah, I think the, like, the struggle here is that I’m not really sure, like, there’s so many tables that I’m not really sure, like, what…
261 00:26:28.900 ⇒ 00:26:35.259 Caitlyn Vaughn: lore of each one is, like, what the point is, so I’m not really sure, like, where we would put what data.
262 00:26:36.100 ⇒ 00:26:37.629 Rebecca Bruggman: Mmm, gotcha. Okay.
263 00:26:37.630 ⇒ 00:26:43.910 Caitlyn Vaughn: So, I think opportunities would be a good place for us to start, because that is more urgent need.
264 00:26:44.130 ⇒ 00:26:49.710 Rebecca Bruggman: Okay, sweet, let’s do it. Okay, so should we start with… we’re thinking the 360?
265 00:26:50.750 ⇒ 00:26:54.969 Rebecca Bruggman: Okay, sweet, let’s do it. Okay, we’ll come to the triple dot, so we’re in all views and fields.
266 00:26:56.010 ⇒ 00:27:08.660 Caitlyn Vaughn: And actually, I have another question on this. Would we… when we’re editing a topic, or when we want to, like, add data like this, do we have to edit a current topic and, like, add it in, or could we create, like, a separate table for it?
267 00:27:10.800 ⇒ 00:27:14.170 Caitlyn Vaughn: Like, would we want a separate opportunities table?
268 00:27:15.600 ⇒ 00:27:29.450 Rebecca Bruggman: I think you only need a separate opportunities table just because the data as you want it doesn’t exist currently, like, as comprehensively. But if the data already existed, you would just use that table to put it into a topic and then do your joins from there.
269 00:27:29.750 ⇒ 00:27:37.159 Caitlyn Vaughn: Okay, and when you say the data exists, do you mean it’s, like, been modeled and it is already in our database kind of thing?
270 00:27:37.530 ⇒ 00:27:43.419 Rebecca Bruggman: It sounds like, based on what the Brainforge team is saying, is that it’s in, like, an intermediate layer, but isn’t kind of in a place.
271 00:27:43.420 ⇒ 00:27:44.630 Nandika Jhunjhunwala: Modern, yeah.
272 00:27:44.630 ⇒ 00:27:46.610 Rebecca Bruggman: it’s not… Omni isn’t ingesting it yet.
273 00:27:46.610 ⇒ 00:27:47.480 Caitlyn Vaughn: Yeah.
274 00:27:47.730 ⇒ 00:27:49.100 Caitlyn Vaughn: Okay, I’m following.
275 00:27:50.350 ⇒ 00:27:53.190 Uttam Kumaran: We created the view just for the BDR rep.
276 00:27:53.550 ⇒ 00:27:55.270 Uttam Kumaran: dashboard.
277 00:27:55.440 ⇒ 00:28:00.930 Uttam Kumaran: I mean, I don’t think we created any views that is just, like, purely on opportunities, that’s in marts.
278 00:28:01.160 ⇒ 00:28:04.259 Uttam Kumaran: So that’s, like, a change we’ll have to do, and just bring that in.
279 00:28:05.210 ⇒ 00:28:05.770 Rebecca Bruggman: Cool.
280 00:28:06.360 ⇒ 00:28:06.740 Nandika Jhunjhunwala: Sorry.
281 00:28:07.070 ⇒ 00:28:07.930 Nandika Jhunjhunwala: firm.
282 00:28:07.930 ⇒ 00:28:08.470 Rebecca Bruggman: Okay.
283 00:28:10.380 ⇒ 00:28:10.980 Rebecca Bruggman: Very good.
284 00:28:10.980 ⇒ 00:28:16.920 Nandika Jhunjhunwala: I know we removed the raw data to, like, not confuse Blobby, but
285 00:28:17.240 ⇒ 00:28:22.950 Nandika Jhunjhunwala: besides, like, minimal cleanup, the opportunity table should just be as is from Salesforce.
286 00:28:24.290 ⇒ 00:28:34.349 Nandika Jhunjhunwala: So, are you saying… I was confused by you saying that’s in a mart, like, is it part of, like, an account mart, like a customer mart or something that you’re trying to bring in here?
287 00:28:34.560 ⇒ 00:28:36.530 Nandika Jhunjhunwala: Or am I misunderstanding?
288 00:28:36.530 ⇒ 00:28:42.479 Uttam Kumaran: Yeah, there’s two things. So one is, like, it’s definitely not minimal cleanup between raw and Martz.
289 00:28:42.670 ⇒ 00:28:47.450 Uttam Kumaran: Like, there’s quite a lot of joining. There’s almost, like, 15 different opportunities.
290 00:28:47.450 ⇒ 00:28:47.810 Nandika Jhunjhunwala: the table.
291 00:28:47.810 ⇒ 00:28:51.369 Uttam Kumaran: that we’re, like, joining and cleaning up to create, like, one concise
292 00:28:51.570 ⇒ 00:29:09.079 Uttam Kumaran: view of opportunities. And I think the second piece, and maybe, Becca, we can even talk about this, is, like, I think the default team was asking, like, okay, why not just expose all of the tables to Blobby? And what the challenge we were having is when we exposed it, it was basically going to
293 00:29:09.250 ⇒ 00:29:13.720 Uttam Kumaran: anywhere in dbt, like Marts, intermediate, RAW, right? Instead of just…
294 00:29:13.940 ⇒ 00:29:21.240 Uttam Kumaran: the final, sort of, production layer. Maybe we could… we could even spend a moment on that, on, like, how you guys recommend
295 00:29:21.960 ⇒ 00:29:25.579 Uttam Kumaran: you know, exposing dbt within Omni.
296 00:29:27.080 ⇒ 00:29:52.070 Rebecca Bruggman: Yeah, I’ll answer your first question around the sort of, like, exposing old tables. I think it goes back to almost, like, Caitlin, what you’re sort of, seeing now, like, as, like, a person trying to use this of, like, what table do I go to? Like, what’s the right thing for me to do? Like, that’s basically what will happen to Blobby if you’re sort of, like, you know, unlocking all of these tables versus, like, having the topics where there’s a lot more, like, AI context and clarity driven off of, like.
297 00:29:52.070 ⇒ 00:29:55.660 Rebecca Bruggman: Like, these are the… this is where you go to answer these sorts of questions.
298 00:29:55.660 ⇒ 00:30:10.619 Rebecca Bruggman: Because otherwise, I mean, you’re even seeing this in, like, the data you have today. Like, there are a lot of things that are named very similarly, or, like, they have some additional sort of, like, context behind them of, like, why to use one versus the other. So that’s where building the topics, that then that’s what.