Meeting Title: Omni Project Handoff and Training Date: 2026-05-07 Meeting participants: Scratchpad Notetaker, Greg Stoutenburg, Demilade Agboola, Advait Nandakumar Menon, Caitlyn Vaughn, Uttam Kumaran, Nandika Jhunjhunwala
WEBVTT
1 00:01:11.720 ⇒ 00:01:12.720 Advait Nandakumar Menon: Hey, guys.
2 00:01:14.020 ⇒ 00:01:14.980 Greg Stoutenburg: Hey, how’s it going?
3 00:01:16.100 ⇒ 00:01:17.780 Advait Nandakumar Menon: Oh, I’m good, how about you?
4 00:01:18.250 ⇒ 00:01:19.269 Greg Stoutenburg: Very good.
5 00:01:37.620 ⇒ 00:01:38.850 Caitlyn Vaughn: Hello!
6 00:01:39.600 ⇒ 00:01:40.670 Greg Stoutenburg: How’s it going?
7 00:01:40.670 ⇒ 00:01:41.999 Caitlyn Vaughn: How are you?
8 00:01:42.320 ⇒ 00:01:44.270 Greg Stoutenburg: Very, very good. How are you?
9 00:01:44.770 ⇒ 00:01:46.730 Caitlyn Vaughn: Wow, that’s a lot of fairies.
10 00:01:46.730 ⇒ 00:01:49.330 Greg Stoutenburg: Two varies, yeah, well, it’s sunny out, that’s it, it’s all taken.
11 00:01:49.330 ⇒ 00:01:52.450 Caitlyn Vaughn: I know, it’s not here! You keep stealing all of our sunshine.
12 00:01:52.450 ⇒ 00:01:55.769 Greg Stoutenburg: Yeah, I know, that’s right, that’s what they say, sunny Pennsylvania.
13 00:01:56.160 ⇒ 00:01:59.610 Caitlyn Vaughn: I forgot you’re in Pennsylvania, that’s so random.
14 00:01:59.610 ⇒ 00:02:00.220 Greg Stoutenburg: Yeah.
15 00:02:00.660 ⇒ 00:02:02.669 Greg Stoutenburg: Well, Uten went to college here.
16 00:02:03.250 ⇒ 00:02:06.010 Greg Stoutenburg: Really? And, he went to Bucknell.
17 00:02:06.010 ⇒ 00:02:06.970 Caitlyn Vaughn: Whaaat?
18 00:02:06.970 ⇒ 00:02:09.720 Greg Stoutenburg: Yeah, so somehow we have a Pennsylvania contingent.
19 00:02:09.880 ⇒ 00:02:18.099 Caitlyn Vaughn: Huh. Yeah, I guess that… I guess that makes sense, but it feels so random. I feel like the only thing I know in Pennsylvania is, like, Hershey’s.
20 00:02:18.830 ⇒ 00:02:22.919 Greg Stoutenburg: Yeah, yeah, that’s, that’s about 45 minutes from here.
21 00:02:23.160 ⇒ 00:02:24.220 Caitlyn Vaughn: No way.
22 00:02:24.220 ⇒ 00:02:26.309 Greg Stoutenburg: Yeah, yeah. Been to Hershey Park.
23 00:02:26.540 ⇒ 00:02:28.190 Caitlyn Vaughn: That was my first job.
24 00:02:28.870 ⇒ 00:02:31.330 Greg Stoutenburg: You worked at a Hershey’s… at Hershey Park?
25 00:02:31.330 ⇒ 00:02:32.840 Caitlyn Vaughn: At the urgent company.
26 00:02:32.840 ⇒ 00:02:34.600 Greg Stoutenburg: Oh, no kidding. Yeah.
27 00:02:34.600 ⇒ 00:02:36.890 Caitlyn Vaughn: It was, very corporate.
28 00:02:37.110 ⇒ 00:02:42.370 Greg Stoutenburg: Yeah, I’m… That… that seems right. It… it… it feels corporate.
29 00:02:42.370 ⇒ 00:02:44.720 Caitlyn Vaughn: Yeah, it was a pass, for sure.
30 00:02:44.720 ⇒ 00:02:55.509 Greg Stoutenburg: Yeah, yeah. I, I mean, I come to hoping my oldest, I was talking to him about, like, summer jobs he might pick up when he’s a little bit older, and he could, like, be a ride operator or something like that, or serve
31 00:02:56.530 ⇒ 00:02:57.759 Greg Stoutenburg: Might be a fun summer job.
32 00:02:57.760 ⇒ 00:02:58.559 Caitlyn Vaughn: Not too far away.
33 00:02:58.870 ⇒ 00:03:00.909 Caitlyn Vaughn: Yeah, I think about, like…
34 00:03:01.040 ⇒ 00:03:09.940 Caitlyn Vaughn: once a week, when I’m in a mini spiral, I’m like, should I quit my job and open, like, a bouquet shop where I just make bouquets of flowers?
35 00:03:10.140 ⇒ 00:03:16.959 Uttam Kumaran: That’s amazing! I… I don’t even… like, yeah, I go buy flowers from Central Market, I see them all day, just tying stuff up, and…
36 00:03:17.720 ⇒ 00:03:29.309 Uttam Kumaran: You’re not… you’re not… you’re typically… I mean, there are some stories you’re buying flowers, and it’s, like, a bad… bad news, but, like, it’s usually good news, people are excited, going to a party, Valentine’s, like, it’s good, good clients.
37 00:03:29.310 ⇒ 00:03:40.539 Caitlyn Vaughn: Yeah, it’s a good vibe, and I’m like, why am I trying to, like, get caught up in the next machine learning technique, or, like, get better at engineering when I could make bouquets of flowers, you know?
38 00:03:41.020 ⇒ 00:03:41.599 Greg Stoutenburg: It’s gonna say a lot.
39 00:03:41.600 ⇒ 00:03:41.990 Uttam Kumaran: Great.
40 00:03:41.990 ⇒ 00:03:43.610 Greg Stoutenburg: Before AI is making bouquets of flowers.
41 00:03:44.990 ⇒ 00:03:49.140 Uttam Kumaran: Yeah, yeah, yeah. And everything smells great around you, you can always have flowers.
42 00:03:49.140 ⇒ 00:03:51.339 Caitlyn Vaughn: Yeah, that’s my backup plan.
43 00:03:51.630 ⇒ 00:03:52.420 Greg Stoutenburg: Yeah.
44 00:03:52.950 ⇒ 00:03:55.799 Greg Stoutenburg: Yeah, so as long as you can take probably a really, really big pay cut, then,
45 00:03:55.800 ⇒ 00:04:01.839 Caitlyn Vaughn: Yeah, yeah. Yeah, exactly. All you need to do is make significantly less money.
46 00:04:01.840 ⇒ 00:04:05.909 Greg Stoutenburg: Yeah, that is… downside. Yeah,
47 00:04:06.040 ⇒ 00:04:17.570 Greg Stoutenburg: Cool, alright, well, alright, cool. Nanic is on, awesome. I was just gonna do a quick roll call. And then that will be everyone who said they were coming, so we can just jump in. So,
48 00:04:18.470 ⇒ 00:04:19.880 Greg Stoutenburg: Hey Nanaka, how’s it going today?
49 00:04:19.880 ⇒ 00:04:21.719 Nandika Jhunjhunwala: Hi, sorry for the delay.
50 00:04:21.720 ⇒ 00:04:27.459 Greg Stoutenburg: No, no problem. We’re just talking about alternative career paths, like, florist, or…
51 00:04:27.570 ⇒ 00:04:30.450 Greg Stoutenburg: hot dog server, or AI engineer.
52 00:04:30.640 ⇒ 00:04:47.080 Greg Stoutenburg: Yeah, okay. I… I kept this deck really short, so we’re just focused very narrowly on the handoff, and want to show you what’s in the works, and and then we’ll talk about it. So, of 5 slides, this is one of them.
53 00:04:47.640 ⇒ 00:04:48.350 Caitlyn Vaughn: Cool.
54 00:04:48.350 ⇒ 00:04:50.290 Greg Stoutenburg: So that’s… that’s how short we’re talking here.
55 00:04:50.670 ⇒ 00:04:55.550 Greg Stoutenburg: Why will… why will it not advance? Okay. Deliverables.
56 00:04:56.060 ⇒ 00:05:01.039 Greg Stoutenburg: Create a clean wiki of all the documentation, from ingestion to dashboards, and make sure that you have that.
57 00:05:01.930 ⇒ 00:05:03.819 Greg Stoutenburg: Move the pre-aggregated tables.
58 00:05:04.130 ⇒ 00:05:06.710 Greg Stoutenburg: Is, interviews and topics.
59 00:05:07.580 ⇒ 00:05:12.399 Greg Stoutenburg: Enable you to build with Omni, of course, generally, and add joins to global topics.
60 00:05:12.400 ⇒ 00:05:16.649 Caitlyn Vaughn: Wait, really quick question here. So, it says move pre-aggregated tables.
61 00:05:16.770 ⇒ 00:05:21.880 Caitlyn Vaughn: Does this mean, like, the tables that were created before they were aggregated?
62 00:05:22.330 ⇒ 00:05:32.450 Greg Stoutenburg: Yeah, so ensure that those things exist in Omni as views and tables. That that data that was sort of, you know, pre-modeled and then connected to Omni just lives in Omni.
63 00:05:32.640 ⇒ 00:05:33.320 Nandika Jhunjhunwala: Got it.
64 00:05:33.600 ⇒ 00:05:40.000 Nandika Jhunjhunwala: So, I guess what they’re saying… you’re saying is you would do those aggregations in Omni versus DBT?
65 00:05:40.350 ⇒ 00:05:40.960 Greg Stoutenburg: Yes.
66 00:05:41.260 ⇒ 00:05:43.010 Caitlyn Vaughn: Okay, amazing. We’re aligned.
67 00:05:43.200 ⇒ 00:05:44.360 Greg Stoutenburg: Yep. Cool.
68 00:05:44.640 ⇒ 00:05:53.159 Greg Stoutenburg: Tasks. So, what we’ll do, and then where the handoff is for you. So this is… this top one is what we were just talking about.
69 00:05:53.660 ⇒ 00:05:57.099 Greg Stoutenburg: Put those joins at the topic level, avoiding pre-aggregation.
70 00:05:58.100 ⇒ 00:06:03.139 Greg Stoutenburg: mature table conformity. Most of these, Caitlin, are revised copy and paste from what you shared the other day.
71 00:06:03.140 ⇒ 00:06:04.310 Caitlyn Vaughn: Amazing. Yeah.
72 00:06:04.310 ⇒ 00:06:09.450 Greg Stoutenburg: Build tables to lowest useful grain, define metrics in the semantic layer.
73 00:06:09.550 ⇒ 00:06:15.329 Greg Stoutenburg: And then that will appear as well in default tasks, so this is sort of a shared project to a degree, especially
74 00:06:15.720 ⇒ 00:06:17.460 Greg Stoutenburg: You pick up the builds going forward.
75 00:06:18.000 ⇒ 00:06:26.520 Greg Stoutenburg: And Advait will do a training on building Omni using AI. So, you know, I mentioned the other day, like, using the CLI.
76 00:06:26.520 ⇒ 00:06:27.010 Caitlyn Vaughn: On your food.
77 00:06:27.010 ⇒ 00:06:31.119 Greg Stoutenburg: To do things like create topics, add semantic content, things like that.
78 00:06:31.790 ⇒ 00:06:32.590 Caitlyn Vaughn: Okay, cool.
79 00:06:32.590 ⇒ 00:06:44.989 Greg Stoutenburg: On your side, similarly, continuing with that work, building rich human-readable semantic layer, when… when Ovate’s provided the enablement, you’ll be able to, you’ll be able to pick that up.
80 00:06:45.200 ⇒ 00:06:52.440 Greg Stoutenburg: Similarly, defining the metrics there, and then resuming dashboarding, building to whatever, you know, degree you need to going forward.
81 00:06:55.330 ⇒ 00:06:56.140 Greg Stoutenburg: Okay.
82 00:06:56.270 ⇒ 00:07:02.029 Greg Stoutenburg: So these… these 5 milestones I had shared, I think.
83 00:07:02.310 ⇒ 00:07:18.619 Greg Stoutenburg: maybe 2 weeks ago, or a little under? And so, I’m just marking progress against them here. So, trainings have been delivered, that was last week. New model and topic builds, have made a lot of progress, expect to be completed by end of week, and new topics can then be built on them immediately.
84 00:07:19.740 ⇒ 00:07:20.370 Caitlyn Vaughn: Okay, awesome.
85 00:07:20.370 ⇒ 00:07:25.669 Greg Stoutenburg: That is just, it’s documentation and training, and making sure that you feel good to just pick it up and run with it from there.
86 00:07:29.260 ⇒ 00:07:30.269 Greg Stoutenburg: That’s all 5.
87 00:07:30.380 ⇒ 00:07:36.629 Greg Stoutenburg: Now, I said… I would show you the tables… where did I put the link to the tables?
88 00:07:39.220 ⇒ 00:07:40.370 Greg Stoutenburg: One sec.
89 00:07:40.700 ⇒ 00:07:45.509 Greg Stoutenburg: I said in the chat that I would show you the tables that the team has been putting together.
90 00:07:46.180 ⇒ 00:07:50.700 Greg Stoutenburg: And… Maybe. I’ll need some,
91 00:07:52.430 ⇒ 00:07:54.950 Greg Stoutenburg: I’ll need to call in help here.
92 00:07:55.260 ⇒ 00:07:56.910 Greg Stoutenburg: To walk through these.
93 00:07:57.230 ⇒ 00:07:58.750 Greg Stoutenburg: Someone could jump in.
94 00:08:03.570 ⇒ 00:08:06.039 Greg Stoutenburg: Maybe, Demi, could you drive this update here?
95 00:08:10.300 ⇒ 00:08:21.049 Demilade Agboola: Oh, still muted. Yeah, so basically, we’re basically trying to split the tables into, like, dimensions and facts that we can then use to enrich each other.
96 00:08:21.070 ⇒ 00:08:32.909 Demilade Agboola: And this will form the basis of a lot of topics within Omni. So, instead of having, like, the aggregations that you have seen, in the reporting tables, you would have, for instance, like, a fact opportunity.
97 00:08:33.240 ⇒ 00:08:47.310 Demilade Agboola: table, where, like, all the opportunities are there. And then you can have a fact-opportunity Timeline Table, where we’re able to put all the opportunities and the timeline at which they occur together, which gives you the flexibility to join into, say.
98 00:08:47.350 ⇒ 00:08:58.410 Demilade Agboola: the contacts to get information about the contacts and the opportunities. You can join into Dean users, which is the internal default users, but you can start to see things around, like.
99 00:08:58.460 ⇒ 00:09:04.979 Demilade Agboola: Who the account owners were, and you can start to see opportunities tied to account owners.
100 00:09:05.070 ⇒ 00:09:08.520 Demilade Agboola: to redeem users. So that’s basically the…
101 00:09:09.120 ⇒ 00:09:12.009 Demilade Agboola: concept of what’s going on here.
102 00:09:12.240 ⇒ 00:09:18.520 Demilade Agboola: this is my personal worksheet, and I’m just, like, kind of, like, putting the stages, so most of this is done.
103 00:09:20.090 ⇒ 00:09:35.909 Demilade Agboola: And then, once we’re able to, like, merge that into production, we will create the topics. So again, the plan is end of week. We can have all of this available, and then we would kind of… so part of why
104 00:09:36.400 ⇒ 00:09:38.550 Demilade Agboola: Adam, my process to this is…
105 00:09:38.680 ⇒ 00:09:41.850 Demilade Agboola: documenting what exactly is the HFACT topic.
106 00:09:42.140 ⇒ 00:09:53.980 Demilade Agboola: documenting the join, so what keys join to what tables. So, hey, these are the contact ID, it joins to this table’s ID, main ID, or primary key. This is… this table’s
107 00:09:54.310 ⇒ 00:10:00.060 Demilade Agboola: main key and joins to that table, so that’s kind of the meticulous process I’m going through with all of this, so you can know
108 00:10:00.490 ⇒ 00:10:13.250 Demilade Agboola: What table exists, what joins exist, what information contains in… what information is contained in each table, what different columns stand for and represent, and so that can give you the flexibility you need to be able to
109 00:10:13.250 ⇒ 00:10:21.410 Demilade Agboola: Put different things together, and kind of make whatever, like, visualizations and analysis you care to find.
110 00:10:24.270 ⇒ 00:10:26.700 Nandika Jhunjhunwala: Thank you for sharing. That looks great.
111 00:10:26.820 ⇒ 00:10:30.420 Nandika Jhunjhunwala: I don’t know if we have time now, or later.
112 00:10:30.560 ⇒ 00:10:40.949 Nandika Jhunjhunwala: I would love, like, a walkthrough of all the dim tables and… and stuff. Like, like, what’s jumping out to me is, like, the dim date table. Does that join to, like.
113 00:10:41.240 ⇒ 00:10:43.370 Nandika Jhunjhunwala: Other dimension tables?
114 00:10:43.530 ⇒ 00:10:46.689 Nandika Jhunjhunwala: And so on and so forth. Does that…
115 00:10:47.270 ⇒ 00:10:54.899 Demilade Agboola: Yeah, I mean, so the concept of dimensions is that you use that to enrich, Whatever, like.
116 00:10:55.550 ⇒ 00:10:58.549 Demilade Agboola: Whatever fact tables exist, or whatever analysis you’re trying to do.
117 00:10:58.550 ⇒ 00:10:59.160 Nandika Jhunjhunwala: Yo.
118 00:10:59.160 ⇒ 00:11:10.869 Demilade Agboola: So for dim dates in this case, it’s just basically a calendar spine. So it’s just every day running from 2023 all… 2023 all the way to 2030.
119 00:11:11.190 ⇒ 00:11:24.560 Demilade Agboola: 2031. And the basic idea is you already have all the information, so for every single date in there, you have information of what quarter it belongs to, you have information of what day of the week it was, you have information about all that kind of stuff.
120 00:11:24.560 ⇒ 00:11:31.800 Demilade Agboola: So now, if you join it, if you have dates available in any table, and you join into that, you can quickly start to do analysis of, hey.
121 00:11:31.860 ⇒ 00:11:38.580 Demilade Agboola: do we sell, like, do people do engage more on Mondays versus Fridays? Because, again, DMDAT already has, like, all that.
122 00:11:38.580 ⇒ 00:11:38.910 Nandika Jhunjhunwala: Yeah.
123 00:11:38.910 ⇒ 00:11:48.489 Demilade Agboola: about what each date represents in terms of, like, the day of the week, the week of the year, quarter of the year, so that’s kind of the information in there.
124 00:11:48.520 ⇒ 00:12:00.960 Demilade Agboola: But you can use it to enrich whatever analysis you want to do. If you want to do weekly analysis, if you want to do quarterly analysis, it’s just there for you to join to, you know, the dates that already exist in whatever table, and you can use it there.
125 00:12:02.620 ⇒ 00:12:07.589 Nandika Jhunjhunwala: Cool, and does that table have, like, foreign keys stored? Is that how you join them, or…
126 00:12:07.920 ⇒ 00:12:11.680 Demilade Agboola: So the name date would be joined by the date, so that would.
127 00:12:11.680 ⇒ 00:12:13.530 Nandika Jhunjhunwala: Oh, okay, got it, got it.
128 00:12:13.940 ⇒ 00:12:27.820 Demilade Agboola: each date, we’ve already put out all the information for each date, and, like, like I said, so, like, it will say, hey, today is the 7th of May 2026, and that’s the Thursday, it’s in Q2, it’s, like, it has all that
129 00:12:28.520 ⇒ 00:12:34.920 Demilade Agboola: So when you join into another, like, table that has the dates, you can start to quickly get information on, like, is it a Thursday, is it a Friday?
130 00:12:35.340 ⇒ 00:12:37.579 Demilade Agboola: And you can start to use that for your analysis if you want to say.
131 00:12:37.580 ⇒ 00:12:38.350 Nandika Jhunjhunwala: Got it.
132 00:12:38.350 ⇒ 00:12:44.590 Demilade Agboola: what day of the week do people, like, do this? What quarters do we seem to do well in? Like, you don’t have to now start to, like.
133 00:12:44.690 ⇒ 00:12:47.679 Demilade Agboola: apply any SQL logic to get all that information.
134 00:12:47.900 ⇒ 00:12:51.019 Nandika Jhunjhunwala: Oh, that’s great. That sounds great. Thank you.
135 00:12:51.430 ⇒ 00:12:57.589 Nandika Jhunjhunwala: Can you also talk about the Salesforce campaign table? Super interested, like, what data that has.
136 00:12:57.990 ⇒ 00:13:07.609 Demilade Agboola: Yeah, so it’s basically, the campaign table. So, as you can see, it’s in progress, so that’s kind of what I’m working on right now. But basically, it’s the… it will join to…
137 00:13:07.770 ⇒ 00:13:12.460 Demilade Agboola: The opportunity table, and the opportunity table has a column called Campaign ID.
138 00:13:13.160 ⇒ 00:13:13.780 Nandika Jhunjhunwala: So…
139 00:13:13.780 ⇒ 00:13:14.950 Demilade Agboola: campaign ID,
140 00:13:15.100 ⇒ 00:13:23.669 Demilade Agboola: will tie it to, the Salesforce campaign, and you can then use that to enrich it to get an idea of what campaigns led to what opportunities.
141 00:13:24.000 ⇒ 00:13:28.860 Demilade Agboola: We’ve been, Salesforce.
142 00:13:28.860 ⇒ 00:13:33.819 Nandika Jhunjhunwala: Not on that, we actually don’t really use that field a ton.
143 00:13:34.010 ⇒ 00:13:37.229 Nandika Jhunjhunwala: Yeah. Like, the Salesforce campaign.
144 00:13:37.410 ⇒ 00:13:47.229 Nandika Jhunjhunwala: Type to the opportunity. I just… just want to flag that. I don’t know if you’re free later today, we can talk through all the Salesforce tables.
145 00:13:47.430 ⇒ 00:14:04.720 Nandika Jhunjhunwala: And maybe, before you progress on this further, we can align on, like, what Salesforce objects and tables you would want, like, a DIMM table or a fact table out of. Does that… does that sound feasible? Because I’m not sure if we need this campaign table.
146 00:14:04.880 ⇒ 00:14:07.510 Nandika Jhunjhunwala: But I do see, like, Salesforce tasks, that’s great.
147 00:14:07.920 ⇒ 00:14:11.319 Nandika Jhunjhunwala: And, like, Salesforce Opportunity, and all of that.
148 00:14:11.640 ⇒ 00:14:13.160 Nandika Jhunjhunwala: So, yeah.
149 00:14:14.470 ⇒ 00:14:25.839 Demilade Agboola: Yeah, that’s the… yeah, sure, we can always, like, sync. I’ll try and toss some time on your calendar today. I mean, part of why, like, given the opportunity, the campaign IDs are not null.
150 00:14:25.970 ⇒ 00:14:29.269 Demilade Agboola: Most of them have, like, a campaign ID associated with it.
151 00:14:29.270 ⇒ 00:14:29.840 Nandika Jhunjhunwala: Yay!
152 00:14:29.840 ⇒ 00:14:34.419 Demilade Agboola: So potentially, like, creating it just means you have the…
153 00:14:34.420 ⇒ 00:14:35.120 Nandika Jhunjhunwala: the data.
154 00:14:35.120 ⇒ 00:14:37.890 Demilade Agboola: But you have the data, yeah, and you can also, like…
155 00:14:37.900 ⇒ 00:14:50.900 Demilade Agboola: Decide, like, further down the line if you want to enrich it with whatever, like, information you care for, because now the opportunity… like, the tables are there, and then these are the building blocks for whatever analysis you might decide to do.
156 00:14:50.900 ⇒ 00:15:09.569 Demilade Agboola: Rather down the line. So I think part of what we’re just trying, like, in a lot of this is just building out the foundations for you to be able to do whatever you care to do, for those online, and try to put whatever guardrails we can to ensure that, like, you don’t run into any, like, weirdness or any,
157 00:15:09.670 ⇒ 00:15:12.940 Demilade Agboola: Bad data joints, or bad logic.
158 00:15:13.500 ⇒ 00:15:16.029 Demilade Agboola: We’re just trying to ensure that we put those guardrails in place.
159 00:15:16.310 ⇒ 00:15:16.729 Greg Stoutenburg: And we really.
160 00:15:16.730 ⇒ 00:15:17.520 Nandika Jhunjhunwala: Yeah, so…
161 00:15:17.520 ⇒ 00:15:18.420 Greg Stoutenburg: request,
162 00:15:18.420 ⇒ 00:15:18.910 Nandika Jhunjhunwala: Totally.
163 00:15:18.910 ⇒ 00:15:28.040 Greg Stoutenburg: weeks ago. Oh, sorry. Yeah, we heard you on the spirit of the request a couple weeks ago, just to make sure that, you know, you didn’t feel like you were locked into, you know, for example.
164 00:15:28.040 ⇒ 00:15:44.070 Greg Stoutenburg: a 7-day measure being, you know, sort of a hard limit, for example. So we try to give you the smallest building blocks possible, and that’s really what these represent, so that you can then, you know, do whatever joints you want to do, aggregate however you want to aggregate, and that’s totally in your control.
165 00:15:44.950 ⇒ 00:15:46.599 Nandika Jhunjhunwala: No, I think this, this model, like.
166 00:15:46.900 ⇒ 00:15:47.680 Caitlyn Vaughn: Looks way better.
167 00:15:47.680 ⇒ 00:15:49.199 Nandika Jhunjhunwala: I think it’s great, yeah.
168 00:15:49.200 ⇒ 00:16:14.159 Caitlyn Vaughn: Yeah, I have a question, this is just, like, me not knowing, so would love to maybe use this to educate myself. So, the fact tables here have, like, Salesforce in them still. When we talked previously, I think we talked about, like, not assigning tables to a specific, like, raw source. It looks like none of the DIMM tables have a raw source, unless it’s, like, Salesforce campaign, which is obviously
169 00:16:14.160 ⇒ 00:16:16.090 Caitlyn Vaughn: only coming from Salesforce.
170 00:16:16.090 ⇒ 00:16:24.439 Caitlyn Vaughn: So for these fact tables, is there a reason why we’re doing, like, Salesforce-specific tables for these?
171 00:16:25.660 ⇒ 00:16:30.199 Demilade Agboola: This is because the… what they represent are Salesforce objects.
172 00:16:30.350 ⇒ 00:16:33.480 Demilade Agboola: So, like, opportunities are only found within Salesforce?
173 00:16:33.480 ⇒ 00:16:33.835 Nandika Jhunjhunwala: J.
174 00:16:34.190 ⇒ 00:16:37.099 Demilade Agboola: So we’re just trying to say, like, this is the Salesforce…
175 00:16:37.360 ⇒ 00:16:46.600 Demilade Agboola: opportunity table, where we can see all the information about Salesforce opportunities. Some of these… some of these tables, again, so for instance, deem users.
176 00:16:46.620 ⇒ 00:17:02.800 Demilade Agboola: right now, in terms of, like, the tables we have, because again, Dimi is an internal facing thing, so in there, you’ll see stuff around, like, Yo, Nadica, you’ll see stuff about Laura, Ryan, and so that’s, like, how you can… and Deanna, so you can start to use that to tie to
177 00:17:02.920 ⇒ 00:17:09.570 Demilade Agboola: other things as to, like, who was the account owner, because there’s the user IDs, so you can type other things there.
178 00:17:09.720 ⇒ 00:17:25.930 Demilade Agboola: who was, like, the BDR on this, and all that stuff. So in that case, it is still largely Salesforce right now, but if you get a new source down the line where you feel like we need to enrich it with
179 00:17:26.060 ⇒ 00:17:26.900 Demilade Agboola: you know.
180 00:17:27.099 ⇒ 00:17:42.740 Demilade Agboola: pipeline ID, user ID, you could also do that. So that’s why I didn’t necessarily name it, Salesforce users, because I… there is room for flexibility for that. But in terms of things that are coming from ASOS,
181 00:17:42.860 ⇒ 00:17:58.439 Demilade Agboola: that, like, opportunities are a Salesforce concept, so they’re, like, kind of hard-locked to that. And so further down the line, if you want to… or, for instance, the fact Torch Day Daily, which is going to be the model that shows, the interaction within Salesforce.
182 00:17:58.720 ⇒ 00:18:03.089 Demilade Agboola: on an object. Again, it’s kind of like a Salesforce concept, so that.
183 00:18:03.090 ⇒ 00:18:03.480 Nandika Jhunjhunwala: It’s kind of.
184 00:18:03.480 ⇒ 00:18:05.930 Demilade Agboola: what naming in Salesforce. Totally.
185 00:18:05.930 ⇒ 00:18:21.089 Greg Stoutenburg: And Demi, just, you know, just to clarify, right, like, these additional things that are being mentioned, these sort of down-the-line tables that you’re mentioning, those would be tables that would be composed of smaller tables, like, that are here, that are sort of more fundamental, right?
186 00:18:21.180 ⇒ 00:18:32.340 Demilade Agboola: Yeah, and also, like, down the line, I kind of mean in the sense of, 6 months from now, a year from now, if you decide to, like.
187 00:18:32.570 ⇒ 00:18:37.070 Demilade Agboola: binge some things in your architecture. You know, you want to…
188 00:18:38.500 ⇒ 00:18:43.259 Demilade Agboola: also add more and reach the data that exists. You have the opportunity to
189 00:18:43.570 ⇒ 00:18:46.510 Demilade Agboola: And also, like, if you feel like
190 00:18:47.510 ⇒ 00:18:53.390 Demilade Agboola: These tables exist, and you want to be able to add, say, hyperlang data into, like, the user table.
191 00:18:53.390 ⇒ 00:18:53.829 Caitlyn Vaughn: We could do it.
192 00:18:53.830 ⇒ 00:18:54.450 Demilade Agboola: that.
193 00:18:54.450 ⇒ 00:18:55.420 Caitlyn Vaughn: Hmm…
194 00:18:55.420 ⇒ 00:19:00.040 Demilade Agboola: But in terms of, like, fact sales opportunities, like, again, that’s a very, like, silly.
195 00:19:00.040 ⇒ 00:19:00.570 Nandika Jhunjhunwala: Absolutely.
196 00:19:00.880 ⇒ 00:19:01.290 Caitlyn Vaughn: Yeah.
197 00:19:01.290 ⇒ 00:19:11.260 Demilade Agboola: And if you do switch, you probably will just have a new table called, you know, fact, whatever CRM matrix is, if that is a thing. And then you can now try a union.
198 00:19:11.440 ⇒ 00:19:18.810 Demilade Agboola: But you still have two distinct, like, blocks where you can be like, hey, this is everything that related to Salesforce and opportunities.
199 00:19:19.360 ⇒ 00:19:29.410 Demilade Agboola: concept, and this is everything that relates to our new CRM, another concept, and you can kind of merge it, or potentially, if they have different schemas, you can just have them, you know.
200 00:19:29.810 ⇒ 00:19:30.320 Demilade Agboola: Okay.
201 00:19:30.590 ⇒ 00:19:31.900 Nandika Jhunjhunwala: Yeah, totally.
202 00:19:31.900 ⇒ 00:19:38.639 Caitlyn Vaughn: That makes sense. Also, second question. So, I see that there’s the dim date table.
203 00:19:38.840 ⇒ 00:19:44.919 Caitlyn Vaughn: Is there a reason why we would have the date in a dimension table versus as, like, a measure?
204 00:19:46.670 ⇒ 00:19:54.570 Demilade Agboola: So, a… The dimensional table, will provide
205 00:19:54.970 ⇒ 00:19:57.679 Demilade Agboola: The information we need to enrich it, so…
206 00:19:58.560 ⇒ 00:20:04.469 Demilade Agboola: I’ll aim, like, if you have your DIN, and you then add it to whatever you need.
207 00:20:04.620 ⇒ 00:20:11.190 Demilade Agboola: That starts to give us, like, more context, so… Think of it like… I want to know…
208 00:20:12.640 ⇒ 00:20:27.420 Demilade Agboola: So think of, like, think of dimensions as categories of some sort, and measured as, like, numerical values, so the run… your ARR will be a measure, because your… that value is a running value, it’s a… you can start to do, like.
209 00:20:27.840 ⇒ 00:20:35.349 Demilade Agboola: sums and, like, actual, like, aggregations on it. Dimensions are not meant… necessarily meant for…
210 00:20:35.750 ⇒ 00:20:38.460 Demilade Agboola: That sort of thing, dimensions are, again.
211 00:20:38.650 ⇒ 00:20:51.289 Demilade Agboola: like, categories. So, for instance, we can start to say what categories of users do this, and so if you go to DIM users, you can start to see, hey, this is an admin, this is a regular user, this is the.
212 00:20:51.290 ⇒ 00:20:52.240 Caitlyn Vaughn: Hmm…
213 00:20:52.240 ⇒ 00:20:53.050 Demilade Agboola: And so.
214 00:20:53.050 ⇒ 00:20:53.870 Uttam Kumaran: They’re like.
215 00:20:53.870 ⇒ 00:20:55.160 Demilade Agboola: In the categories of that.
216 00:20:55.160 ⇒ 00:21:01.799 Uttam Kumaran: Yeah, in, like, Dim Date, for example, Caitlin, you’re gonna have, like, the date, but you’ll have, like, what is the week of the year, what is the…
217 00:21:01.800 ⇒ 00:21:02.140 Caitlyn Vaughn: Hmm.
218 00:21:02.140 ⇒ 00:21:17.439 Uttam Kumaran: Day name, like, all of the attributes about that date, so that instead of going into, like, into each table and saying, cool, we’re gonna add day of month and day of year, like, for example, let’s say you’re doing some, like, what is the same day last year?
219 00:21:17.440 ⇒ 00:21:17.950 Caitlyn Vaughn: You want to say?
220 00:21:17.950 ⇒ 00:21:31.109 Uttam Kumaran: cool, this is the 170th day this year. You can actually just join to dim date whenever you need that, versus, like, populating that column across any time there’s a date. So it’s kind of like a description table of, like.
221 00:21:31.340 ⇒ 00:21:43.840 Uttam Kumaran: every way we want to represent that date lives in dim date. And so, the other thing is, like, we may have measures where there is no activity on a given day. And so, if you were to just select
222 00:21:43.840 ⇒ 00:21:56.219 Uttam Kumaran: like, from that table, you would have days where there wouldn’t be a date record, because let’s say, like, no opportunities were logged that day. But what would happen is downstream, you would actually not get zeros, you’re gonna get null.
223 00:21:56.240 ⇒ 00:22:04.649 Uttam Kumaran: And so, part of it is that if you start from a date-dim date, and there is a value for every date, whatever you join into that.
224 00:22:04.950 ⇒ 00:22:11.820 Uttam Kumaran: regardless if it’s there or not, there’s going to be a record for every date. So these are just, like, some, like… just, like, gotchas from doing, like, date modes.
225 00:22:11.820 ⇒ 00:22:12.280 Caitlyn Vaughn: Yeah.
226 00:22:12.280 ⇒ 00:22:13.510 Uttam Kumaran: Basically. Right.
227 00:22:13.510 ⇒ 00:22:28.079 Caitlyn Vaughn: Okay, yeah, that totally makes sense, that’s super interesting. And then my last question, so there’s two sheets at the bottom, Demi and Awash. Is that two different projects going on into the same, or is this…
228 00:22:28.320 ⇒ 00:22:32.200 Greg Stoutenburg: Yeah, Devin, can you speak to how these relate to each other? The way you guys split it up?
229 00:22:32.550 ⇒ 00:22:45.230 Demilade Agboola: Yeah, so in terms of just, like, relationships, it’s basically the entire, like, fact and dimensional modeling splits into two. Again, ultimately, we want to get that out by end of week.
230 00:22:45.640 ⇒ 00:22:46.070 Caitlyn Vaughn: Amazing.
231 00:22:46.070 ⇒ 00:23:02.310 Demilade Agboola: The goal here is not just to get it out, but get it out in good quality state. So, for us to be able to get that out, instead of me handling every single thing, and having to QA every single thing, which obviously can leave room for, like, lapses in, like.
232 00:23:02.460 ⇒ 00:23:06.609 Demilade Agboola: Quality assess… quality, quality control.
233 00:23:06.660 ⇒ 00:23:24.759 Demilade Agboola: which are basically spacing across two people, so that, you know, I can focus and actually do, like, again, like I said, with all this that’s going on, I’m actually, like, putting the name of every column, what it represents, what the joins are, and just that level of thoroughness can come much easier when there are less models to focus on.
234 00:23:25.570 ⇒ 00:23:29.550 Demilade Agboola: just split it into two, so he’s handling that part of it, I’m handling this part of it.
235 00:23:29.790 ⇒ 00:23:30.580 Caitlyn Vaughn: Okay, cool.
236 00:23:30.580 ⇒ 00:23:32.830 Demilade Agboola: It’s the same project, same concept.
237 00:23:33.130 ⇒ 00:23:35.850 Caitlyn Vaughn: Okay, are you able to share this with us so we can…
238 00:23:36.070 ⇒ 00:23:39.529 Caitlyn Vaughn: Just, like, see what this is all the time.
239 00:23:39.770 ⇒ 00:23:41.429 Demilade Agboola: Yeah, sure.
240 00:23:41.430 ⇒ 00:23:44.970 Greg Stoutenburg: Is this helpful, just so you can see what, like, boxes they’re attached?
241 00:23:44.970 ⇒ 00:23:45.500 Caitlyn Vaughn: Yeah, this was…
242 00:23:45.500 ⇒ 00:23:50.160 Greg Stoutenburg: the, sure, is it okay, Demi, if I just send it to the team?
243 00:23:50.410 ⇒ 00:23:58.160 Demilade Agboola: Yeah, sure, like I said, this is, like, my live worksheet, so, like, I’m kind of… I’m working on stuff, and just, like, moving things around.
244 00:23:58.160 ⇒ 00:23:59.800 Greg Stoutenburg: Should I just move this to the default folder?
245 00:24:01.060 ⇒ 00:24:03.380 Nandika Jhunjhunwala: I have a few more questions, if that’s okay?
246 00:24:04.330 ⇒ 00:24:04.949 Demilade Agboola: Sure.
247 00:24:05.380 ⇒ 00:24:07.999 Greg Stoutenburg: Looks like you’ll have to do it, Demi. It looks like I can’t move it.
248 00:24:08.650 ⇒ 00:24:11.510 Demilade Agboola: Gotcha. I’ll look at it right now.
249 00:24:12.580 ⇒ 00:24:16.269 Greg Stoutenburg: Keep going. Well, I can just share manually, I guess, that’s fine.
250 00:24:17.640 ⇒ 00:24:19.379 Greg Stoutenburg: I don’t want to slow it down. Good.
251 00:24:20.420 ⇒ 00:24:38.039 Nandika Jhunjhunwala: I was wondering, like, again, this is me educating myself as well, why, like, for example, like, there’s a fact Salesforce task, why that’s a fact table, and why DIM Salesforce Campaign is, like, a DIM table. I’m assuming you thought, like, campaign is, like, its own entity, sort of, like, category?
252 00:24:38.170 ⇒ 00:24:46.129 Nandika Jhunjhunwala: And then task is not, perhaps, by the data modeling, process. Is that, like, what… what that is?
253 00:24:46.510 ⇒ 00:24:48.469 Demilade Agboola: So it’s a function of…
254 00:24:49.670 ⇒ 00:24:52.410 Demilade Agboola: what is used to enrich what. So in.
255 00:24:52.410 ⇒ 00:24:52.979 Nandika Jhunjhunwala: In terms of how.
256 00:24:52.980 ⇒ 00:24:54.879 Demilade Agboola: Salesforce has the data.
257 00:24:55.040 ⇒ 00:25:00.590 Demilade Agboola: Opportunities are the events that occur. Opportunities are what happens.
258 00:25:00.590 ⇒ 00:25:01.800 Nandika Jhunjhunwala: But, yeah.
259 00:25:01.800 ⇒ 00:25:09.580 Demilade Agboola: Because when a client comes into Salesforce. And so how do we know what campaign brought that, opportunity.
260 00:25:09.580 ⇒ 00:25:10.529 Nandika Jhunjhunwala: It’s the.
261 00:25:10.530 ⇒ 00:25:17.439 Demilade Agboola: dimension of campaign. So we can then say, okay, so these are a list of campaigns that we have. We can use it to
262 00:25:17.920 ⇒ 00:25:18.940 Demilade Agboola: opportunity.
263 00:25:19.200 ⇒ 00:25:20.480 Demilade Agboola: In fact, it comes our way.
264 00:25:20.480 ⇒ 00:25:21.469 Nandika Jhunjhunwala: Got it.
265 00:25:21.830 ⇒ 00:25:25.649 Nandika Jhunjhunwala: No, that totally makes sense. Just want to reiterate that
266 00:25:26.200 ⇒ 00:25:30.240 Nandika Jhunjhunwala: Not all opportunities are tied to campaigns.
267 00:25:30.370 ⇒ 00:25:38.160 Nandika Jhunjhunwala: Some campaigns are, like, organically… some opportunities organically occur in terms of inbound meetings.
268 00:25:38.270 ⇒ 00:25:41.810 Nandika Jhunjhunwala: So just want to make that clarification.
269 00:25:41.960 ⇒ 00:25:51.429 Nandika Jhunjhunwala: The other question I had was, are we gonna have, like, DIM customer, or, like, DIM company, DIM account sort of tables?
270 00:25:52.280 ⇒ 00:25:54.910 Nandika Jhunjhunwala: You see the…
271 00:25:54.910 ⇒ 00:26:02.609 Demilade Agboola: So that’s part of what Oasis will be working on, or is working on. It’s the DIM customer table, where we can have,
272 00:26:03.470 ⇒ 00:26:10.659 Demilade Agboola: A complete view of a customer across the different, like, Entities within the.
273 00:26:10.660 ⇒ 00:26:11.020 Nandika Jhunjhunwala: before.
274 00:26:11.020 ⇒ 00:26:16.179 Demilade Agboola: space. So, for instance, we’ll get the customer, we’ll get the Salesforce ID, we’ll get the.
275 00:26:16.180 ⇒ 00:26:16.700 Nandika Jhunjhunwala: Mmm.
276 00:26:16.700 ⇒ 00:26:22.150 Demilade Agboola: one ID, and we’ll get, like, whatever other IDs that they have across multiple… Software.
277 00:26:23.330 ⇒ 00:26:28.370 Nandika Jhunjhunwala: So I’m assuming there’d be, like, one customer table, and one, like, maybe DIM account, and account could be just, like.
278 00:26:28.470 ⇒ 00:26:32.630 Nandika Jhunjhunwala: Salesforce… company records? Is that…
279 00:26:32.970 ⇒ 00:26:39.760 Demilade Agboola: Yeah, so it would have DIM customer, and what we can… what we can do is have a flag for is.
280 00:26:40.150 ⇒ 00:26:41.400 Demilade Agboola: active customer.
281 00:26:41.800 ⇒ 00:26:42.370 Nandika Jhunjhunwala: Mmm.
282 00:26:42.370 ⇒ 00:26:45.799 Demilade Agboola: So, instead of having, like, DM… like, it’s just gonna be one… Yeah.
283 00:26:45.800 ⇒ 00:26:46.400 Nandika Jhunjhunwala: one.
284 00:26:46.400 ⇒ 00:26:49.039 Demilade Agboola: one spot where all that exists, and so…
285 00:26:49.240 ⇒ 00:26:56.229 Demilade Agboola: you can have the flag for his active customer based off of, you know, their current subscription status, or, like, their current Salesforce status.
286 00:26:56.510 ⇒ 00:27:02.169 Demilade Agboola: And then you can use that to, like, further downstream, do whatever calculations you want to do.
287 00:27:03.210 ⇒ 00:27:04.529 Nandika Jhunjhunwala: Cool, that sounds great.
288 00:27:04.760 ⇒ 00:27:12.419 Nandika Jhunjhunwala: The other question I had, again, going back to Salesforce data, I just could have spent so much time there,
289 00:27:12.770 ⇒ 00:27:31.229 Nandika Jhunjhunwala: I was wondering, I think, like, I’m sure maybe you guys fixed it, but, like, previously when we were, running queries on Bobby, and it was pulling from, like, one-time raw export Salesforce data, obviously the numbers were off. So I was wondering, like, just wanted to double confirm that the numbers are reflecting what’s in Salesforce now, if you guys have run a check.
290 00:27:31.300 ⇒ 00:27:34.890 Nandika Jhunjhunwala: Are able to confirm that that data is, like, synced and aligned.
291 00:27:35.610 ⇒ 00:27:52.549 Demilade Agboola: So that will be part of, like, the omni topic, and omni topics that we will, like, the roles and AI context will add around that. Couple of things. One is, yeah, we will ensure that it goes to the live Salesforce data, so that any, like, opportunities or any questions around Salesforce
292 00:27:52.550 ⇒ 00:27:57.240 Demilade Agboola: are figured out from the live running data, which, again.
293 00:27:57.240 ⇒ 00:28:02.360 Demilade Agboola: Updates every morning, so that we can trust the resentio of that.
294 00:28:03.580 ⇒ 00:28:07.050 Demilade Agboola: In terms of some of the logic,
295 00:28:07.520 ⇒ 00:28:13.909 Demilade Agboola: There are still some bits that are tied to Like, old, like, exports?
296 00:28:14.120 ⇒ 00:28:20.020 Demilade Agboola: So that would be things around, like, the ID of setting, like, meetings. So, like.
297 00:28:20.130 ⇒ 00:28:23.960 Demilade Agboola: In terms of, like, Salesforce,
298 00:28:25.030 ⇒ 00:28:28.249 Demilade Agboola: Salesforce events, which are calendar bookings in Salesforce.
299 00:28:28.500 ⇒ 00:28:43.819 Demilade Agboola: or factorial meetings, actually. It’s enriched with some of the export meeting information, so that makes it… gives it a fuller view of what that meeting represented. But things like that, because it’s…
300 00:28:44.020 ⇒ 00:28:46.820 Demilade Agboola: Out of, like, the old data.
301 00:28:47.250 ⇒ 00:28:55.140 Demilade Agboola: It needs to, like, once you’re able to get the live data in and you can replace the export data with the live data, that would,
302 00:28:55.320 ⇒ 00:29:00.520 Demilade Agboola: give you the view you need consistently. So the meetings will be updated, so the factual
303 00:29:00.650 ⇒ 00:29:18.029 Demilade Agboola: models are based off Salesforce data. In terms of enriching that information, some of the enrichment comes from export data. Over time, that enriched part of it will not be up to date, because again, it’s exported data, but the actual base model will keep
304 00:29:18.200 ⇒ 00:29:29.790 Demilade Agboola: Populating, just not the, you know, details, like, oh, this was the name of the meeting, or this was what happened in that meeting, or the duration of that meeting, which happens to be product data instead.
305 00:29:31.750 ⇒ 00:29:33.420 Nandika Jhunjhunwala: Right,
306 00:29:33.630 ⇒ 00:29:52.099 Nandika Jhunjhunwala: That makes sense. I guess more so, like, we were just doing, like, general aggregations, like, total number of opportunities, total number of accounts, like, that baseline data should match. But I totally understand that, like, some of the modeled graphics or, like, queries might be a little out of sync, because we’re currently in the process of modeling.
307 00:29:52.570 ⇒ 00:29:53.120 Demilade Agboola: Yeah.
308 00:29:53.120 ⇒ 00:29:54.290 Nandika Jhunjhunwala: But, yeah.
309 00:29:54.770 ⇒ 00:29:55.480 Demilade Agboola: I could lock control.
310 00:29:55.480 ⇒ 00:29:56.690 Nandika Jhunjhunwala: information on that.
311 00:29:57.410 ⇒ 00:29:59.769 Demilade Agboola: Sure, so we’ll do a couple, like, queries.
312 00:29:59.770 ⇒ 00:30:00.540 Nandika Jhunjhunwala: Yeah.
313 00:30:00.540 ⇒ 00:30:15.160 Demilade Agboola: prior to, like, handing over, like, full… the topics to you. But, like, again, the major concept is, like… so, for instance, the meetings that we see in Salesforce exist, we can put them there. We can say, hey, this meeting ties to this meeting in the product data, right?
314 00:30:15.160 ⇒ 00:30:24.259 Demilade Agboola: So, from the product data, we can say this was the name of the meeting, for instance, and this was the duration of the meeting. But obviously, as the product data is still
315 00:30:24.590 ⇒ 00:30:28.130 Demilade Agboola: those… Rich… like, that enriches.
316 00:30:28.130 ⇒ 00:30:29.110 Nandika Jhunjhunwala: That’s a roommate.
317 00:30:29.110 ⇒ 00:30:29.770 Demilade Agboola: Exactly.
318 00:30:29.770 ⇒ 00:30:30.160 Nandika Jhunjhunwala: Yes.
319 00:30:30.160 ⇒ 00:30:32.820 Demilade Agboola: But we can still see the meetings that occur, so if you were still going to do.
320 00:30:32.820 ⇒ 00:30:33.310 Nandika Jhunjhunwala: Yeah.
321 00:30:33.310 ⇒ 00:30:39.610 Demilade Agboola: it will be fine. It will just not have the information that you would… Totally. Or, like, or you would care for, as well.
322 00:30:39.610 ⇒ 00:30:40.179 Nandika Jhunjhunwala: That makes a ton.
323 00:30:40.180 ⇒ 00:30:40.659 Demilade Agboola: Hold on a sec.
324 00:30:40.660 ⇒ 00:30:41.220 Nandika Jhunjhunwala: Yep.
325 00:30:45.380 ⇒ 00:30:53.150 Nandika Jhunjhunwala: Cool, thank you for answering all my questions. No, that sounds great. I’ll dig into it more with Caitlin, but thank you, that was great.
326 00:30:53.370 ⇒ 00:31:01.300 Demilade Agboola: Alright, sounds good. We definitely look forward to, like, hand this over to you by tomorrow, like, in terms of, like, the DBT aspect of it. In terms of Omni.
327 00:31:01.610 ⇒ 00:31:08.869 Demilade Agboola: creating the broad topics, because again, the idea of topics in Omni is I’m creating, like, the…
328 00:31:09.260 ⇒ 00:31:16.570 Demilade Agboola: because you know how in dbt, I’m like, this is… this joins to this. Omni starts to put that together in terms of, like,
329 00:31:16.890 ⇒ 00:31:20.619 Demilade Agboola: Data sets, so we can say, hey, This is the fact.
330 00:31:21.550 ⇒ 00:31:32.610 Demilade Agboola: this is the fact opportunity, let’s join it to the DN campaign, and then we have, like, those broader data sets that you can use for, like, fast, easy analysis.
331 00:31:36.540 ⇒ 00:31:37.320 Demilade Agboola: Yep.
332 00:31:37.340 ⇒ 00:31:49.449 Greg Stoutenburg: Cool. Well, glad we, glad we were able to take some time and sort of talk through the strategy here. We just really want to make sure that you’re able to see, plainly, that the building blocks that you’ll need to have handed over so that you can do whatever follow-up
333 00:31:49.450 ⇒ 00:31:57.709 Greg Stoutenburg: join, joins you want to do is going to be possible. The team’s made really good progress on this, and so, you know, you’re able to see there.
334 00:31:57.730 ⇒ 00:32:10.550 Greg Stoutenburg: where we are now, nearly everything that’s not completed is in PR, with just a few exceptions, and like Demi said, you know, end of the week is the goal for getting all of these done, and then we turn to topic creation.
335 00:32:11.430 ⇒ 00:32:14.960 Caitlyn Vaughn: Cool. And end of week being tomorrow, or end of week being Sunday?
336 00:32:15.140 ⇒ 00:32:16.440 Caitlyn Vaughn: Just to clarify.
337 00:32:16.950 ⇒ 00:32:19.649 Greg Stoutenburg: I don’t know, Demi, do you work Sunday, or do you mean tomorrow?
338 00:32:20.060 ⇒ 00:32:23.909 Demilade Agboola: Yeah, it’s tomorrow the deal, if it’s Friday. There we go.
339 00:32:23.910 ⇒ 00:32:25.510 Caitlyn Vaughn: That’s great.
340 00:32:25.510 ⇒ 00:32:34.209 Greg Stoutenburg: I was gonna say, I would have meant tomorrow, but okay, cool. Cool. All right, anything else that’s sort of top of mind on…
341 00:32:34.330 ⇒ 00:32:39.770 Greg Stoutenburg: What we’re building here, the direction that we’re going, or, or even, you know, further down, like.
342 00:32:39.990 ⇒ 00:32:50.350 Greg Stoutenburg: Topics that you are super sure you want to make sure exist in a certain way, that go beyond, sort of, ones we’ve scoped out previously?
343 00:32:51.650 ⇒ 00:33:00.820 Caitlyn Vaughn: I think, the ones that exist and are in scope are the ones that I’m, like, most worried about right now. Everything else can happen later,
344 00:33:01.180 ⇒ 00:33:07.080 Caitlyn Vaughn: I think the other dbt training would be great to do the,
345 00:33:07.360 ⇒ 00:33:12.280 Caitlyn Vaughn: like, building with… are we doing it with Claude Code or something?
346 00:33:12.860 ⇒ 00:33:18.069 Caitlyn Vaughn: That would be helpful to see how we can, like, do this non-manually.
347 00:33:18.720 ⇒ 00:33:26.009 Greg Stoutenburg: Okay, so you’d like to get… so, similar to what we’ve talked about for Omni, you’d like to get a kind of AI building with, building for DBT walkthrough?
348 00:33:26.240 ⇒ 00:33:27.510 Caitlyn Vaughn: Yeah, that’d be great.
349 00:33:27.510 ⇒ 00:33:28.690 Greg Stoutenburg: Yeah, we can set that up.
350 00:33:29.190 ⇒ 00:33:30.100 Greg Stoutenburg: For sure.
351 00:33:30.460 ⇒ 00:33:33.100 Greg Stoutenburg: Yep. You and Nanaka, same, same group?
352 00:33:33.210 ⇒ 00:33:34.090 Greg Stoutenburg: Cool.
353 00:33:34.090 ⇒ 00:33:34.930 Caitlyn Vaughn: pod.
354 00:33:34.930 ⇒ 00:33:36.860 Greg Stoutenburg: Yeah, we’ll do that, yeah, I know, it’s pretty good.
355 00:33:37.660 ⇒ 00:33:46.360 Demilade Agboola: Just… just a question on, like, the training so far. Have they been useful? Have you been able to find, like, some comfort using dbt and Omni?
356 00:33:46.870 ⇒ 00:33:47.679 Demilade Agboola: What was that?
357 00:33:47.980 ⇒ 00:33:54.210 Caitlyn Vaughn: I think they’ve been super helpful. I think it’s been helpful to, like, get some outside opinions and just, like.
358 00:33:54.860 ⇒ 00:34:04.430 Caitlyn Vaughn: get a good sense of what we actually do want, you know, so that we can, like, tell you what we want, because before we were like, just do this thing, and then that’s not very clear, so…
359 00:34:04.610 ⇒ 00:34:16.439 Caitlyn Vaughn: You know, not only being able to, like, have confidence in what we actually want being built, but, like, know now that we are on track feels good, and then have a bit more confidence that once this is passed off, we’ll be in a better place.
360 00:34:16.699 ⇒ 00:34:17.269 Greg Stoutenburg: Yep.
361 00:34:18.219 ⇒ 00:34:19.299 Greg Stoutenburg: Yep, good.
362 00:34:21.879 ⇒ 00:34:30.579 Greg Stoutenburg: Cool. Okay. Well, we’ll make sure to set that up, and keep you updated on when, the new tables are all finished.
363 00:34:30.579 ⇒ 00:34:42.679 Greg Stoutenburg: We’ll make sure that we run by you, like, what the topic list is that we’re gonna put together, so that you can, you know, again, just, you know, feel confident that you know what is where, and can work with it.
364 00:34:43.090 ⇒ 00:34:49.170 Caitlyn Vaughn: Cool. Thank you guys so much. I feel like you’re really, like, on track and pulling it together, and I so appreciate it.
365 00:34:49.179 ⇒ 00:35:06.419 Greg Stoutenburg: Yeah, yeah, yeah, thanks. Yeah, we, you know, we really want to… wanted to leave you in a good position where you, where you have control over the things that you are going to be working with, so… Amazing. Yeah, heard you on that. So, cool. Alright, well, we’re… we’re at, 1.35, so I think we can just wrap up, unless there’s anything else.
366 00:35:06.950 ⇒ 00:35:08.180 Caitlyn Vaughn: No, I think we’re good.
367 00:35:08.180 ⇒ 00:35:08.620 Nandika Jhunjhunwala: Dutch.
368 00:35:08.620 ⇒ 00:35:11.130 Greg Stoutenburg: Awesome. Alright. Have a good day, everybody. Thanks.
369 00:35:11.130 ⇒ 00:35:12.090 Caitlyn Vaughn: Guys, bye.
370 00:35:12.090 ⇒ 00:35:12.960 Demilade Agboola: Bye.
371 00:35:12.960 ⇒ 00:35:13.500 Greg Stoutenburg: I…