Meeting Title: Omni Data Platform Weekly Review Date: 2026-03-19 Meeting participants: Greg Stoutenburg, Uttam Kumaran, Demilade Agboola, Nandika Jhunjhunwala, Mustafa Raja, Lev Katreczko, Caitlyn Vaughn
WEBVTT
1 00:00:07.460 ⇒ 00:00:10.759 Greg Stoutenburg: Every… can’t do…
2 00:00:10.910 ⇒ 00:00:12.890 Uttam Kumaran: I know, you gotta edit it, dude.
3 00:00:12.890 ⇒ 00:00:18.400 Greg Stoutenburg: One thing Cursor can’t do is create an event and not put a video link in it.
4 00:00:18.630 ⇒ 00:00:22.560 Uttam Kumaran: Wait, no, it… oh yeah, it cannot, right now.
5 00:00:22.560 ⇒ 00:00:24.559 Greg Stoutenburg: It cannot. It cannot do that.
6 00:00:24.560 ⇒ 00:00:25.609 Uttam Kumaran: You’re right, you’re right.
7 00:00:50.430 ⇒ 00:00:51.160 Greg Stoutenburg: Okay.
8 00:01:02.330 ⇒ 00:01:09.929 Uttam Kumaran: Demi, I’m just gonna share… I’m just gonna toss… I have the CSC for the MART, I’m just gonna toss it into the platform sheet, and I’ll just share.
9 00:01:10.220 ⇒ 00:01:13.910 Uttam Kumaran: I… we don’t… like, I think this is something interesting, like.
10 00:01:14.150 ⇒ 00:01:19.179 Uttam Kumaran: No, no, it’s sometimes tough to share how much work is going on the modeling side without just pulling up GitHub.
11 00:01:19.310 ⇒ 00:01:22.440 Uttam Kumaran: So, I’m trying to think of some better ways to…
12 00:01:23.200 ⇒ 00:01:25.509 Uttam Kumaran: to display that, you know? But,
13 00:01:26.240 ⇒ 00:01:28.840 Uttam Kumaran: I’ll share it in the spreadsheet, that’s okay.
14 00:01:29.150 ⇒ 00:01:30.270 Demilade Agboola: Okay, sounds good.
15 00:01:32.680 ⇒ 00:01:33.560 Nandika Jhunjhunwala: Hello.
16 00:01:33.730 ⇒ 00:01:34.630 Uttam Kumaran: Hello!
17 00:01:34.630 ⇒ 00:01:35.490 Greg Stoutenburg: Bo.
18 00:01:38.770 ⇒ 00:01:39.850 Nandika Jhunjhunwala: How’s it going?
19 00:01:40.470 ⇒ 00:01:41.370 Uttam Kumaran: Good.
20 00:01:41.370 ⇒ 00:01:42.519 Greg Stoutenburg: Great, how are you?
21 00:01:43.510 ⇒ 00:01:49.940 Nandika Jhunjhunwala: Good, sorry my camera’s off, I’m not feeling super well today, so I don’t look really good. All good.
22 00:01:49.940 ⇒ 00:01:50.660 Greg Stoutenburg: to that.
23 00:02:01.180 ⇒ 00:02:06.530 Uttam Kumaran: I think we have some good updates on to go today, so I think you should have a lot to play around with.
24 00:02:08.169 ⇒ 00:02:08.839 Nandika Jhunjhunwala: Nice.
25 00:02:20.140 ⇒ 00:02:25.450 Greg Stoutenburg: I know that Caitlin was marked as attending… Yep.
26 00:02:25.880 ⇒ 00:02:28.769 Nandika Jhunjhunwala: Yeah, I think she’s, she’s joining in a few minutes.
27 00:02:28.770 ⇒ 00:02:29.710 Greg Stoutenburg: Okay, no problem.
28 00:02:29.870 ⇒ 00:02:31.029 Nandika Jhunjhunwala: Sorry about that.
29 00:02:34.190 ⇒ 00:02:34.900 Nandika Jhunjhunwala: Right.
30 00:02:46.240 ⇒ 00:02:46.899 Caitlyn Vaughn: Hello! Hello!
31 00:02:47.560 ⇒ 00:02:48.600 Uttam Kumaran: No…
32 00:02:48.950 ⇒ 00:02:49.380 Caitlyn Vaughn: Family.
33 00:02:49.380 ⇒ 00:02:50.120 Greg Stoutenburg: Hello, everyone.
34 00:02:53.260 ⇒ 00:02:54.060 Lev Katreczko: Oops.
35 00:02:56.840 ⇒ 00:02:57.470 Caitlyn Vaughn: Okay.
36 00:02:57.770 ⇒ 00:02:58.710 Uttam Kumaran: Yes.
37 00:02:58.710 ⇒ 00:02:59.930 Caitlyn Vaughn: Okay, good.
38 00:03:00.650 ⇒ 00:03:02.009 Uttam Kumaran: Greg, let’s go for it.
39 00:03:03.160 ⇒ 00:03:05.269 Greg Stoutenburg: Let’s go, let’s kick it off.
40 00:03:08.580 ⇒ 00:03:09.740 Greg Stoutenburg: Share button.
41 00:03:10.390 ⇒ 00:03:13.899 Greg Stoutenburg: Here we go. Okay, weekly review.
42 00:03:14.800 ⇒ 00:03:25.249 Greg Stoutenburg: Discussion points, summary, key wins on data platform and analytics, our progress on dashboarding, timelines coming up, key wins on product analytics, and what we’re working on now.
43 00:03:26.220 ⇒ 00:03:29.239 Greg Stoutenburg: For the weekly overview, we’re,
44 00:03:29.290 ⇒ 00:03:39.560 Greg Stoutenburg: We’re excited about the dashboarding work that we’ve managed to get done in the last bit here. I’ll turn it over to you, Tom, in a second to discuss that, but, we’ve got…
45 00:03:39.560 ⇒ 00:03:50.890 Greg Stoutenburg: We’ve got data matching where we want it to be, we’ve got financial summary dashboard and internal review, and we’re QAing the customer reporting and enablement dashboards, and we’ll be able to share them soon.
46 00:03:50.890 ⇒ 00:04:09.169 Greg Stoutenburg: On the product analytics side, the posthog building is in full swing. Thanks, Nautica, for the review, both of the tables engagement dashboard that I put together, and a whole bunch of new charts in that dashboard that we’ll, that we’ll go through next week. So, nice work on that. Really like seeing what’s coming out from there.
47 00:04:09.170 ⇒ 00:04:15.839 Greg Stoutenburg: On the data platform and analytics side, we’ll roll out the financial summary and customer reporting and enablement dashboards.
48 00:04:16.170 ⇒ 00:04:18.260 Greg Stoutenburg: Hey, Tom, you wanna take it from here?
49 00:04:18.269 ⇒ 00:04:31.859 Uttam Kumaran: Yes, so I think I’ll walk through this, and then I’ll just probably share a couple things. So, yeah, we’ve… I think we’ve made some great progress on dashboards, in particular on the modeling side, Caitlin. I think we actually found that, like, equals was,
50 00:04:32.389 ⇒ 00:04:39.279 Uttam Kumaran: understate… was… overstating? Demi? I forgot. In one way, it was… in one way, it was… it was off.
51 00:04:39.280 ⇒ 00:04:40.330 Demilade Agboola: I was overstating.
52 00:04:40.560 ⇒ 00:04:44.040 Uttam Kumaran: Overstating. And so, a couple things we found is, like.
53 00:04:45.040 ⇒ 00:04:56.239 Uttam Kumaran: they’re… in equals, the way it works is they’re also writing SQL queries on top of opportunities stuff, but it doesn’t seem like there was any… anyone on their team that ever asked, like.
54 00:04:56.470 ⇒ 00:05:11.799 Uttam Kumaran: hey, is this the way, like, a contraction should be modeled, or a downgrade, or an upgrade? And, luckily we… that’s… we asked those questions, and so we basically found that, like, in… in aligning our data with theirs, we were like, hey, this isn’t…
55 00:05:11.970 ⇒ 00:05:21.520 Uttam Kumaran: like, right? And I think that’s what kicked off those threads with Lauren Ryan, which is great. Like, totally expected. I think at this point, we feel like…
56 00:05:21.970 ⇒ 00:05:27.679 Uttam Kumaran: The stuff we build is actually more accurate, but actually we can identify the decisions made in the…
57 00:05:28.130 ⇒ 00:05:43.919 Uttam Kumaran: to get to those metrics, so feeling, like, really good there. I think also Laura and Ryan are sort of now, in the loop, like, looking at those things. And of course, like, there’s a ton more functionality in Omni to drill down into pieces.
58 00:05:43.920 ⇒ 00:05:51.730 Uttam Kumaran: And then the… so… so that’s one piece, and I’ll pull up the dashboards right after this. We have,
59 00:05:51.870 ⇒ 00:06:01.180 Uttam Kumaran: the first versions of dashboards for, finance and for, like, the ARR, like, dashboard. I think finance
60 00:06:01.180 ⇒ 00:06:16.349 Uttam Kumaran: like Laura mentioned, she just wants to have access to that, so we’re… we just have it in Omni, but it’s provisioned to her. And then we also started working on the dashboard for customer reporting and enablement, but all of that is actually modeled, and so ready for
61 00:06:16.380 ⇒ 00:06:18.500 Uttam Kumaran: Us to query and do more…
62 00:06:18.900 ⇒ 00:06:26.030 Uttam Kumaran: you know, build the dashboards on top of. So if I can share, Greg, I just wanted to show a couple things, but I guess any thoughts
63 00:06:26.450 ⇒ 00:06:27.440 Uttam Kumaran: So far.
64 00:06:29.200 ⇒ 00:06:39.159 Caitlyn Vaughn: No, this is looking good. I love, I love that you guys found some errors in, like, our current numbers, because that’s super compelling for our team.
65 00:06:39.320 ⇒ 00:06:41.930 Caitlyn Vaughn: Definitely very, like, confidence-building.
66 00:06:42.100 ⇒ 00:06:42.680 Uttam Kumaran: Cool.
67 00:06:43.400 ⇒ 00:06:44.110 Greg Stoutenburg: Good, good.
68 00:06:44.760 ⇒ 00:06:46.109 Uttam Kumaran: You’re talking about?
69 00:06:46.650 ⇒ 00:06:47.059 Uttam Kumaran: We’ll take it.
70 00:06:47.060 ⇒ 00:06:48.240 Greg Stoutenburg: orders before.
71 00:06:48.240 ⇒ 00:06:51.550 Uttam Kumaran: I can take it over, I just wanted to share some of the dashboards.
72 00:06:52.140 ⇒ 00:06:58.299 Uttam Kumaran: So, yeah, on the ARR side, I mean, one, I think…
73 00:06:58.500 ⇒ 00:07:16.270 Uttam Kumaran: it looks great, so I feel like we’ve fixed a lot of the labels. I think, like, what we’re kind of hoping to show here is, like, it is a build of what was in equals, but I think it’s, like, super, super crisp, like, all of the different pieces of ARR, the components.
74 00:07:16.270 ⇒ 00:07:19.960 Uttam Kumaran: High-level summary, of, like.
75 00:07:19.960 ⇒ 00:07:30.269 Uttam Kumaran: of where things are. We can add drill downs into here, like, if you’re like, hey, I want to see, like, all the pieces that are part of this, we can… we can configure a lot of that.
76 00:07:30.670 ⇒ 00:07:35.830 Uttam Kumaran: Scrolling down, you also can see, like, who’s starting, who’s restarting, who’s churned.
77 00:07:37.920 ⇒ 00:07:56.140 Uttam Kumaran: stuff is going up and to the right, and then new customer changes over the last 30 days. I think the only piece I’ll say here is, like, we weren’t involved as much in, like, the dashboard design, so part of this exercise was literally just matching apples-apples on equals, but now that we are, I think
78 00:07:56.330 ⇒ 00:08:00.689 Uttam Kumaran: we’re totally open to making these changes. I think the…
79 00:08:00.800 ⇒ 00:08:06.179 Uttam Kumaran: The default team can also totally make any You know, changes to this.
80 00:08:06.310 ⇒ 00:08:09.659 Uttam Kumaran: But that’s this dashboard, like, any questions?
81 00:08:10.350 ⇒ 00:08:11.749 Uttam Kumaran: on this piece…
82 00:08:12.350 ⇒ 00:08:15.419 Caitlyn Vaughn: You’re talking about the, like, the layout and the design of this.
83 00:08:15.420 ⇒ 00:08:17.859 Uttam Kumaran: Yeah, layout, design, any new charts.
84 00:08:18.460 ⇒ 00:08:19.020 Uttam Kumaran: like…
85 00:08:19.170 ⇒ 00:08:28.320 Uttam Kumaran: Yeah, and this is… I think Laura was the primary in terms of requirements for the equals dashboard, so we can create another version of this. This has…
86 00:08:28.750 ⇒ 00:08:33.889 Uttam Kumaran: I think this has, like, the baseline. I have some recommendations on… Some more things to add.
87 00:08:34.409 ⇒ 00:08:37.179 Uttam Kumaran: So… Yeah.
88 00:08:37.620 ⇒ 00:08:44.800 Caitlyn Vaughn: Cool, yeah. I would definitely love for this to look better, but I love that everything is there, that’s, like, the most important part.
89 00:08:44.950 ⇒ 00:08:48.970 Caitlyn Vaughn: And Laura was working with you on this, right? On the airport dashboard?
90 00:08:48.970 ⇒ 00:08:49.820 Uttam Kumaran: Yes.
91 00:08:49.820 ⇒ 00:08:52.610 Caitlyn Vaughn: Okay. Yeah, there’s probably, like, a good…
92 00:08:52.760 ⇒ 00:09:03.619 Caitlyn Vaughn: Maybe this is a good opportunity for us to create more of, like, a templatized dashboard, so at least when people are, like, toggling through the different dashboards, and it’s a ton of new information, they can expect a certain, like.
93 00:09:03.990 ⇒ 00:09:04.580 Uttam Kumaran: Yeah.
94 00:09:04.740 ⇒ 00:09:09.019 Caitlyn Vaughn: Something probably simple to begin would be great.
95 00:09:09.250 ⇒ 00:09:15.889 Uttam Kumaran: Yeah, so I can… I can make some of those recommendations, like, for example, we’ll have, like, a little, like, how to use this dashboard, like, text thing to talk.
96 00:09:16.160 ⇒ 00:09:16.910 Caitlyn Vaughn: Yeah. With, like… Yeah.
97 00:09:16.910 ⇒ 00:09:19.790 Uttam Kumaran: Definitions, examples, like, call-outs.
98 00:09:19.790 ⇒ 00:09:20.140 Caitlyn Vaughn: Yeah.
99 00:09:20.140 ⇒ 00:09:29.869 Uttam Kumaran: And then we can have sections, you know, like, again, I think it’s helpful to look at, like, this month at the top, and then look at things that are trending. Yeah.
100 00:09:30.020 ⇒ 00:09:40.259 Uttam Kumaran: this is really around customers, this is all around ARR, there’s a few other sections that I’d suggest, so maybe we can take a pass internally, like.
101 00:09:40.580 ⇒ 00:09:47.420 Uttam Kumaran: like, proposed set of changes, and then run it by, you know, you and Laura, and then we can make those.
102 00:09:47.730 ⇒ 00:09:48.370 Caitlyn Vaughn: That sounds great.
103 00:09:48.370 ⇒ 00:09:48.810 Demilade Agboola: Alt.
104 00:09:48.810 ⇒ 00:09:57.770 Caitlyn Vaughn: So, we’ve gone ahead on the ARR dashboard, the financial dashboard is done, right? Yes. Okay, cool. And where are we going next?
105 00:09:57.770 ⇒ 00:10:00.399 Uttam Kumaran: Yeah, Demi, do you want to go? Sorry, I don’t know if you had anything else.
106 00:10:00.400 ⇒ 00:10:17.589 Demilade Agboola: I just wanted to say that I also think what I find particularly cool about this dash is you can select any customer or any selection of customers, and you can kind of see the story, like, the entire dashboard will change to reflect the story. Say, for instance, you go there and type in, like, Cherry, for instance.
107 00:10:18.000 ⇒ 00:10:20.660 Demilade Agboola: We can kind of just see…
108 00:10:21.400 ⇒ 00:10:26.769 Demilade Agboola: what Cherry’s story is, and what it has been since they were… on the team.
109 00:10:27.070 ⇒ 00:10:37.689 Demilade Agboola: So we can see what their total ERR is, we can see that they had, like, an expansion, in September of last year, and we can kind of just see all of that.
110 00:10:38.520 ⇒ 00:10:46.680 Uttam Kumaran: Yeah, so one thing I think could be nice is, like, as we decide on these filters, we can either have a section where you can just filter to one customer and see their story.
111 00:10:46.960 ⇒ 00:11:06.560 Uttam Kumaran: Or filters all customers. That’s sort of why I was asking about also the segmentation of customers. I forgot who on the default team was working on that, but that’s something that we can layer in, so we can start to break this up, and you can filter down. I think also these, like, customer-level filters will be a lot of, like, what we show on the, like, sales dashboards as well, so…
112 00:11:06.690 ⇒ 00:11:15.279 Caitlyn Vaughn: Yeah, that’s super interesting. I mean, we have the breakdown of, like, Tier 1, Tier 2, Tier 3 customers, and I think… I want to say Sid is working on, like.
113 00:11:15.280 ⇒ 00:11:16.750 Uttam Kumaran: Yeah, you mentioned Sid.
114 00:11:16.750 ⇒ 00:11:19.549 Caitlyn Vaughn: new definition for Phoenix or whatever,
115 00:11:19.850 ⇒ 00:11:21.969 Caitlyn Vaughn: But it would be interesting to see, like.
116 00:11:22.480 ⇒ 00:11:25.059 Uttam Kumaran: Can we just go ahead and do the Tier 1, Tier 2, Tier 3?
117 00:11:25.530 ⇒ 00:11:27.790 Caitlyn Vaughn: What do you mean? Like, should you guys just do it?
118 00:11:28.060 ⇒ 00:11:30.840 Uttam Kumaran: Yeah, if it’s, like, an existing definition, then we can…
119 00:11:30.970 ⇒ 00:11:34.439 Uttam Kumaran: put it in, and then when… if Sid modifies it, we’ll just change the definitions.
120 00:11:34.440 ⇒ 00:11:52.439 Caitlyn Vaughn: Yeah, that’s kind of what I’m saying, like, we have, like, one definition for vanilla and one for Phoenix. Because, like, our top-tier customers for vanilla, like, let’s say… or let’s say our mid-tier customer’s paying us, like, 40K. Like, we hope that our mid-tier customer on Phoenix is paying us, like, half a million dollars.
121 00:11:52.440 ⇒ 00:11:52.770 Uttam Kumaran: Yeah.
122 00:11:53.380 ⇒ 00:11:56.609 Caitlyn Vaughn: So, I guess kind of different definitions, but…
123 00:11:56.610 ⇒ 00:12:03.470 Uttam Kumaran: do you have those, like, top of mind? Like, what the definitions are? Because I would… I’d rather just do it if it’s understood now.
124 00:12:03.990 ⇒ 00:12:04.500 Uttam Kumaran: Ben.
125 00:12:04.500 ⇒ 00:12:04.889 Caitlyn Vaughn: wicked hands.
126 00:12:04.890 ⇒ 00:12:07.080 Uttam Kumaran: to sit and be like, you have a view of, like.
127 00:12:07.330 ⇒ 00:12:24.959 Uttam Kumaran: the existing situation, so that when you… when you update it, we’re not, like, both… like, we don’t… it’s not just, one, a lot of work on our side, but, like, he can… people are already gonna be seeing these tiers in the dashboards, so it’ll be easy for him to, like, explain, hey, here’s how the tiers are changing.
128 00:12:26.100 ⇒ 00:12:34.340 Caitlyn Vaughn: Yeah, that would be really… I mean, even if we just did what we have today, because all of our customers are currently vanilla… Yeah. Yeah, let me find that for you.
129 00:12:34.340 ⇒ 00:12:34.950 Uttam Kumaran: Okay.
130 00:12:38.880 ⇒ 00:12:45.090 Uttam Kumaran: Yeah, because that’ll be just another cut we put into this. So you’ll be able to filter by the customer type.
131 00:12:45.720 ⇒ 00:12:46.400 Uttam Kumaran: and…
132 00:12:46.740 ⇒ 00:12:47.250 Caitlyn Vaughn: Good year.
133 00:12:47.250 ⇒ 00:12:48.500 Uttam Kumaran: That’d be great, yeah.
134 00:12:48.500 ⇒ 00:12:50.919 Caitlyn Vaughn: Hmm. Yeah, that is really interesting.
135 00:12:51.540 ⇒ 00:12:54.230 Uttam Kumaran: So you’ll see, like, what is the ARR, things like…
136 00:12:54.760 ⇒ 00:13:02.310 Uttam Kumaran: when you have customer segments, you can say, what is the ARR contribution by customer segment? Like, which segment is driving more ARR over time?
137 00:13:05.290 ⇒ 00:13:08.649 Caitlyn Vaughn: Yeah, it’s, like, a little bit vague, but I can send you.
138 00:13:09.640 ⇒ 00:13:12.499 Caitlyn Vaughn: We’ll send it to you in Slack. Okay. Even as Vegas.
139 00:13:12.500 ⇒ 00:13:19.550 Uttam Kumaran: At least we’ll… will… will force us to make a decision, and then if it’s gonna change, at least it’s… we have something in there.
140 00:13:19.550 ⇒ 00:13:20.600 Caitlyn Vaughn: Okay, cool.
141 00:13:21.080 ⇒ 00:13:38.909 Uttam Kumaran: And then we started working on customer reporting enabled. This is just, like, we’re in progress today, so we have meeting duration, users, things modeled, but really the… it’s hard for us to show you the work that’s going on in, like, the data modeling side, because it’s not always visual, so one thing that I outlined here, and I think
142 00:13:38.910 ⇒ 00:13:46.130 Uttam Kumaran: Nandika, this may be helpful for you as well, is, like, what are the models we are creating, by mart?
143 00:13:46.130 ⇒ 00:13:59.360 Uttam Kumaran: And MART is a… is a way that we describe, sort of, an area of, you know, data, like, an area of the business. So if I was to just filter to, like, GTM, and filter out the other one…
144 00:13:59.780 ⇒ 00:14:02.939 Uttam Kumaran: You can see that we’ve created these tables.
145 00:14:03.160 ⇒ 00:14:14.729 Uttam Kumaran: related to everything go-to-market ARR. So, monthly, daily by customer, ARR changes, and so this is what we then go build,
146 00:14:14.910 ⇒ 00:14:25.630 Uttam Kumaran: the dashboards on top of, but these are now available to query. So I think, Nandika, we were meeting, and I think you kind of saw the difficulty in, like, going direct to raw to do some of these.
147 00:14:25.970 ⇒ 00:14:38.689 Uttam Kumaran: now these are all available, running daily, for us to build more stuff on top of. And then we’re gonna take, of course, the first pass at every dashboard, but, like, this is, like, I would say.
148 00:14:38.920 ⇒ 00:14:46.729 Uttam Kumaran: 50-60% of the work actually goes into this, and then the last mile is, like, getting everything visualized in the dashboard.
149 00:14:48.240 ⇒ 00:14:58.189 Uttam Kumaran: That way, you’re not looking at this and being like, oh my god, we’re, like, nowhere. We actually, like, have everything… we actually have a lot done for, for, customer,
150 00:14:58.340 ⇒ 00:15:06.100 Uttam Kumaran: Customer dimensions, like, we have a customer 360, and then we also have things around, customer enablement, support tickets.
151 00:15:06.270 ⇒ 00:15:08.640 Uttam Kumaran: Activity, things like that.
152 00:15:09.940 ⇒ 00:15:14.509 Nandika Jhunjhunwala: Is it possible for you to share that with us, so we can look into it further?
153 00:15:14.700 ⇒ 00:15:15.880 Uttam Kumaran: This sheet?
154 00:15:15.880 ⇒ 00:15:16.680 Nandika Jhunjhunwala: Yeah.
155 00:15:16.680 ⇒ 00:15:21.450 Uttam Kumaran: Yeah, you should have access to the sheet, actually, but I will, okay. Yeah, I’ll share it out.
156 00:15:22.000 ⇒ 00:15:24.319 Uttam Kumaran: Yeah, I’ll just, pop it in here.
157 00:15:25.520 ⇒ 00:15:28.620 Caitlyn Vaughn: Nautica, you know the sheet with, like, everything in it? Like, all the…
158 00:15:28.620 ⇒ 00:15:30.080 Nandika Jhunjhunwala: Oh, okay.
159 00:15:30.080 ⇒ 00:15:31.869 Caitlyn Vaughn: Keep adding tabs to it.
160 00:15:31.870 ⇒ 00:15:34.270 Greg Stoutenburg: Everything sheet. You ask a question, we add a tab.
161 00:15:36.540 ⇒ 00:15:47.709 Uttam Kumaran: Yeah, this is the everything, because otherwise it’s going to become 100 spreadsheets, and it’s not often easy, like, I… it’s not easy for you to go, like, for everybody to go into, like, Mother Duck, or even, like.
162 00:15:47.710 ⇒ 00:15:48.340 Caitlyn Vaughn: Yeah.
163 00:15:48.340 ⇒ 00:15:51.740 Uttam Kumaran: So I’m like, let’s just… these are all the marts, and we’ll keep this up to date.
164 00:15:52.960 ⇒ 00:16:00.330 Uttam Kumaran: This is, like, really the core logic tables that are being… being… that are powering Omni right now.
165 00:16:00.330 ⇒ 00:16:02.119 Caitlyn Vaughn: And these are in Mother Duck?
166 00:16:02.120 ⇒ 00:16:04.660 Uttam Kumaran: These are all in Mother Duck, and the code is in GitHub.
167 00:16:04.840 ⇒ 00:16:05.450 Caitlyn Vaughn: Okay.
168 00:16:05.450 ⇒ 00:16:05.840 Nandika Jhunjhunwala: And…
169 00:16:05.840 ⇒ 00:16:07.620 Caitlyn Vaughn: Yeah, go ahead, Nanika.
170 00:16:07.620 ⇒ 00:16:14.099 Nandika Jhunjhunwala: Sorry, I was wondering, like, if we see, like, within a certain mart, like.
171 00:16:14.200 ⇒ 00:16:18.769 Nandika Jhunjhunwala: data that we would want modeled further, is that possible for us to propose?
172 00:16:18.770 ⇒ 00:16:36.059 Uttam Kumaran: Yeah, 100%. So if you were like, hey, there, like, a good example… like, a really great example is, like, this tier situation, right? So if you go into, like, the, like, one of the customer tables, and you’re like, hey, we want to propose a new, dimension called tier, and here’s a definition.
173 00:16:36.110 ⇒ 00:16:45.450 Uttam Kumaran: yeah, I mean, we would just add that in. If you’re also like, hey, we think, like, this logic is defined differently, we can add that in. Or if you’re like, hey, I actually want a new model.
174 00:16:45.520 ⇒ 00:16:51.120 Uttam Kumaran: I think the way I would suggest proposing a new model is actually with what the ask is.
175 00:16:51.480 ⇒ 00:17:01.749 Uttam Kumaran: Because then we can collaborate on, like, the structure of the model, because if you’re saying, hey, I just want a table with, like, these 10 columns, there actually may be a better way for us to join some of these to get there.
176 00:17:01.830 ⇒ 00:17:14.609 Uttam Kumaran: So what’s helpful is, like, I want to create this chart to tell this story. I’m thinking about using these models. What do you think? And, like, that’s a good, like, package for us to be like, okay, can our existing model solve this, or should we model anything new?
177 00:17:14.720 ⇒ 00:17:17.440 Uttam Kumaran: Ideally, we want to avoid
178 00:17:17.569 ⇒ 00:17:24.329 Uttam Kumaran: single report models, right? Like, some of these, we have 5 models that will go power, like, tons and tons of
179 00:17:24.450 ⇒ 00:17:28.419 Uttam Kumaran: Views and reports, so that’s probably my only thing there.
180 00:17:29.650 ⇒ 00:17:32.450 Uttam Kumaran: But if you have any thoughts already, like, we can note them down.
181 00:17:33.790 ⇒ 00:17:43.380 Nandika Jhunjhunwala: Yeah, thank you, I will look into this further, so I appreciate you sharing. Yeah. And can you model across two different marts, or is it…
182 00:17:43.380 ⇒ 00:17:49.199 Uttam Kumaran: Yes, so some of these actually are that way, meaning we are gonna pull
183 00:17:49.300 ⇒ 00:18:05.090 Uttam Kumaran: like, meaning we don’t model a mart just on, like, Salesforce, right? It’s actually modeled more towards a business set of questions, like, more of a topic. So, product support may use things from Salesforce, from Hyperline, and from, you know, anything else.
184 00:18:05.090 ⇒ 00:18:17.740 Uttam Kumaran: From post hoc or whatever. So it’s modeled across multiple sources, and yes, there will be marts that sit on top of these. You could think about one that’s, like, let’s say it’s an executive view, like.
185 00:18:17.740 ⇒ 00:18:24.360 Uttam Kumaran: show me all the core KPIs daily. That may… we may model that, and they may pull from all of these.
186 00:18:24.430 ⇒ 00:18:31.119 Uttam Kumaran: Right? And that’s a good example of, like, a mart that may be cross-model, cross-mart, yeah.
187 00:18:31.360 ⇒ 00:18:36.039 Nandika Jhunjhunwala: Sorry, I guess I meant more so, like, can you make dashboards off of, like.
188 00:18:36.270 ⇒ 00:18:47.680 Nandika Jhunjhunwala: two different marts, like, if I had a finance and PL mart and a go-to-market mart, and I wanted to create dashboards, or even, like, one inside, pulling data from, like, both the marts, would.
189 00:18:47.680 ⇒ 00:18:48.040 Uttam Kumaran: Yeah.
190 00:18:48.040 ⇒ 00:18:52.959 Nandika Jhunjhunwala: Create a different join, or would we just, like, query data from two different words?
191 00:18:52.960 ⇒ 00:19:01.400 Uttam Kumaran: Yeah, so actually, like, if you were to, like, edit this dashboard, for example, and go to workbook, you can… you can actually pull in,
192 00:19:01.800 ⇒ 00:19:12.579 Uttam Kumaran: you can pull in things from multiple topics into the same dashboard, so you don’t need to… it doesn’t need to be the same omni topic, that’s not a requirement. One…
193 00:19:12.700 ⇒ 00:19:16.890 Uttam Kumaran: Tile will be one topic, but you can have things for multiple here.
194 00:19:17.040 ⇒ 00:19:17.880 Uttam Kumaran: Totally.
195 00:19:18.160 ⇒ 00:19:19.490 Nandika Jhunjhunwala: Cool. Thank you.
196 00:19:20.930 ⇒ 00:19:23.939 Uttam Kumaran: And so, just to, like, kind of, I think, put a bow on this…
197 00:19:24.050 ⇒ 00:19:29.689 Uttam Kumaran: this one, so if I kind of, like, look at where we were,
198 00:19:30.570 ⇒ 00:19:43.700 Uttam Kumaran: like, compared to our… let me just show, like, where we were in terms of the baseline. We actually moved some stuff backwards, which is good. I think we took a little bit to get started on,
199 00:19:44.160 ⇒ 00:19:49.050 Uttam Kumaran: Data modeling, but now we’ve actually finished modeling for…
200 00:19:49.260 ⇒ 00:19:52.990 Uttam Kumaran: the Laura… Laura’s, like, finance, and,
201 00:19:53.150 ⇒ 00:20:05.459 Uttam Kumaran: the ARR dashboard, we finished the first set of models for customer reporting, and so we’re basically in modeling for that, and then we’re also modeling, everything for,
202 00:20:05.660 ⇒ 00:20:13.250 Uttam Kumaran: Salesforce right now. So, I’m expecting that, like, once we close around off Salesforce, Hyperline,
203 00:20:13.550 ⇒ 00:20:21.750 Uttam Kumaran: and the customer-related product analytics, these three are sort of done. I pretty feel pretty confident that
204 00:20:22.340 ⇒ 00:20:28.359 Uttam Kumaran: If we can get Laura to sign off on that ARR dashboard, and if we feel good about the finance dashboard.
205 00:20:28.540 ⇒ 00:20:33.530 Uttam Kumaran: And by next… by next… by probably end of tomorrow, we should have a view of, like, the…
206 00:20:33.660 ⇒ 00:20:35.330 Uttam Kumaran: Product analytics stuff.
207 00:20:35.540 ⇒ 00:20:41.070 Uttam Kumaran: I have no… I’m pretty confident we can close these three out by next week. Oh, wow.
208 00:20:41.220 ⇒ 00:20:43.560 Uttam Kumaran: Yeah, I… I feel… yeah, go ahead, Lev.
209 00:20:44.910 ⇒ 00:21:03.689 Lev Katreczko: Hey, yeah, sorry to jump in, I didn’t mean to cut you off. I, I dumped a couple, like, additional go-to-market dashboards in the doc recently. I didn’t realize that we were sort of reaching the final stages of go-to-market data modeling, but a lot of those, those,
210 00:21:04.080 ⇒ 00:21:23.539 Lev Katreczko: dashboards or metrics, as we know, are very much, like, predicated on tasks, rather than some of the other stuff we’ve been chatting about here. So, I’d love to hear a little bit, you know, obviously, Utam, you and I went back and forth in Slack about, like, some of those potentially being out of scope. Yeah.
211 00:21:23.540 ⇒ 00:21:36.529 Lev Katreczko: I don’t know if this is the right forum to talk about it, or if we should tag up at another point, but I think that, like, the outbound dashboards are, given the complexity of the data, probably, like.
212 00:21:36.530 ⇒ 00:21:44.760 Lev Katreczko: some of the highest leverage go-to-market dashboards that we could be cooking up, aside from, like, you know, sort of key finance stuff.
213 00:21:44.790 ⇒ 00:21:52.119 Lev Katreczko: So yeah, that’s definitely, like, very high priority from, folks like me and Lauda, as far as, you know, internal conversations go.
214 00:21:52.460 ⇒ 00:22:11.439 Uttam Kumaran: Okay, great. And so that is, that is, this sheet for everybody that, yeah, me and Lev are going back and forth on. So yeah, I sent this to the team this week. We’re just trying to close out this, like, customer products, but I… I feel… I feel like we should hit this before moving on to marketing stuff.
215 00:22:11.440 ⇒ 00:22:17.119 Uttam Kumaran: I think we just have to communicate that to Stan if, like, his stuff is gonna start to get pushed, but…
216 00:22:17.160 ⇒ 00:22:23.739 Uttam Kumaran: I also agree that I want to try to do the V1 of as many of these while we’re on the subject as possible.
217 00:22:23.810 ⇒ 00:22:37.760 Uttam Kumaran: And so I was sort of going back and forth on, like, okay, is there a priority? Actually, I think for the most part, we should be able to do a lot of these, and yeah, maybe we can grab… maybe we should grab time and just… me and Demi can talk through and just make sure.
218 00:22:40.100 ⇒ 00:22:46.270 Uttam Kumaran: So, Debbie, I don’t know if you want to take a look, when you have time, just at this, and leave some commentary?
219 00:22:47.280 ⇒ 00:22:56.639 Lev Katreczko: And, like, not all of this needs to be as elaborate as it might be in this version. I think there’s… there is, for sure, some prioritization that could be done.
220 00:22:56.890 ⇒ 00:23:04.680 Lev Katreczko: I’m also, like, more than happy to step in and help reconfigure the actual CRM, if it makes any of this easier with respect to, like.
221 00:23:04.830 ⇒ 00:23:18.610 Lev Katreczko: you know, pushing data into fields that might make it a little easier for you guys to model on top of this stuff, but yeah, I think, you know, let me know if you want to chat about that stuff, because I’m more than happy to help.
222 00:23:18.610 ⇒ 00:23:34.899 Uttam Kumaran: Yeah, I think what I want to do, Lev, is get you all of this modeled, and then get, like, create just a simple dashboard, because you’re more than capable of, like, editing the dashboard from there, so then we can basically walk you through, like, how to create views in Omni, and then you’ll be running. So…
223 00:23:34.900 ⇒ 00:23:36.050 Lev Katreczko: I mean, that’d be sick.
224 00:23:36.050 ⇒ 00:23:51.900 Uttam Kumaran: Yeah, the first piece is, like, I want you to see, like, if some of these values are unfilled in Salesforce, or, like, don’t have joins, then the data will show that. So there may be just a few days where we QA, and then… and then you can take it from there.
225 00:23:52.490 ⇒ 00:23:54.160 Lev Katreczko: That would be perfect. Thank you.
226 00:23:54.160 ⇒ 00:23:54.720 Uttam Kumaran: Okay.
227 00:23:56.760 ⇒ 00:23:57.280 Uttam Kumaran: Great.
228 00:23:57.610 ⇒ 00:24:10.119 Demilade Agboola: Also, like, it would be helpful in the… in the sheet, or in the doc, actually, if you can just put, like, any filters as well that you would want to be able to look at the data by. So maybe you’ll need to look by, you know.
229 00:24:10.320 ⇒ 00:24:16.499 Demilade Agboola: Account owner, or rep, or, you know, those type of things, that would be very helpful for the modeling piece as well.
230 00:24:22.670 ⇒ 00:24:29.580 Uttam Kumaran: Okay, I think that is… what I had to share on data modeling side.
231 00:24:29.710 ⇒ 00:24:37.089 Uttam Kumaran: So I’ll go ahead and update… I think we’ll go ahead and update some of the Gantt, with… with Lev stuff, but…
232 00:24:37.240 ⇒ 00:24:39.639 Uttam Kumaran: Yeah, overall, I feel like we’ve… we’re, like.
233 00:24:40.510 ⇒ 00:24:43.779 Uttam Kumaran: shipping these models pretty fast, so I’m feeling a lot better.
234 00:24:44.730 ⇒ 00:24:57.150 Uttam Kumaran: Yeah. And then I think, Caitlin, maybe you tell me, like, is it… would it be good to do, like, a more focused call where we just, like, look at some of the metric definitions? I know this call, it’s more about, like, show and tell, so…
235 00:24:57.260 ⇒ 00:25:04.930 Uttam Kumaran: We can do a call, like, with me, you, and Laura and Ryan, looking at the dashboard in detail, versus, like, going back forth in Slack.
236 00:25:04.930 ⇒ 00:25:12.740 Caitlyn Vaughn: Yeah, it would probably be good to just do, like, a 15-minute call and go through each dashboard before we sign off on them.
237 00:25:13.620 ⇒ 00:25:15.960 Caitlyn Vaughn: What’s most helpful for me is just, like.
238 00:25:16.250 ⇒ 00:25:27.950 Caitlyn Vaughn: being a fly on the wall when you’re going through these calls. So, like, call with Laura was helpful, when you do… I’m looking at the Instagant. When you do your next call, maybe do one with Deanna, and I can jump.
239 00:25:27.950 ⇒ 00:25:28.290 Uttam Kumaran: Yeah.
240 00:25:28.290 ⇒ 00:25:33.589 Caitlyn Vaughn: And then another one with Laura for go-to-market metrics, right? Maybe Brian included in that, too.
241 00:25:33.590 ⇒ 00:25:35.449 Uttam Kumaran: Yeah, yeah, Ryan’s on that, yeah.
242 00:25:36.500 ⇒ 00:25:38.899 Caitlyn Vaughn: Just so I can grok what’s going on.
243 00:25:38.900 ⇒ 00:25:39.400 Uttam Kumaran: Okay.
244 00:25:39.780 ⇒ 00:25:42.140 Caitlyn Vaughn: But yeah, that sounds great. Things are moving.
245 00:25:43.090 ⇒ 00:25:43.840 Uttam Kumaran: Cool.
246 00:25:44.740 ⇒ 00:25:46.360 Uttam Kumaran: Alright, Greg, back to you.
247 00:25:46.360 ⇒ 00:25:47.570 Greg Stoutenburg: Look back to me.
248 00:25:47.720 ⇒ 00:25:51.430 Greg Stoutenburg: Find the… Spot.
249 00:25:52.440 ⇒ 00:25:53.730 Greg Stoutenburg: Have the spot.
250 00:25:55.820 ⇒ 00:26:20.690 Greg Stoutenburg: All right, Cubans, sort of alluded to this already, we got the thumbs up from you all on the tables engagement chart, that I put together to just kind of show how someone was interacting with a feature, broadly speaking. The main thing being, well, of course, we need a tables engagement chart, but also, to sort of serve as a template for what it looks like when we’re measuring that someone is using some part of the product. So, that looks good, and we can use that as a template.
251 00:26:20.690 ⇒ 00:26:40.619 Greg Stoutenburg: And then the next step is to review, sort of in more detail, the… the more comprehensive dashboard that Nandika put together. Nandika, thanks for finding the time in my calendar next week, and we can… we can go through that, figure out what we want to, keep as is, what we want to tweak, how we want to organize it, and move forward there.
252 00:26:43.900 ⇒ 00:26:53.159 Greg Stoutenburg: Sort of just talked about these things, they’re in progress already. We’ve got pretty clear steps to get to the finish line, having reviewed the Gantt and made those changes that we just discussed?
253 00:26:54.420 ⇒ 00:26:57.539 Greg Stoutenburg: And… there we go. I think I did mean…
254 00:26:57.670 ⇒ 00:27:04.060 Greg Stoutenburg: to revise this, say, ta-da, you know, maybe get, like, a rocket or something like that. Maybe next week.
255 00:27:04.250 ⇒ 00:27:07.319 Greg Stoutenburg: I’ll open… I’ll add a column to the spreadsheet to say.
256 00:27:07.960 ⇒ 00:27:10.599 Greg Stoutenburg: Changes to the deck, that are forthcoming, so…
257 00:27:10.990 ⇒ 00:27:12.320 Caitlyn Vaughn: Amazing.
258 00:27:12.420 ⇒ 00:27:15.870 Greg Stoutenburg: Thanks, Utah. Thanks. Yeah, okay. Yeah.
259 00:27:15.870 ⇒ 00:27:31.160 Uttam Kumaran: What else on the product analytics side, Caitlin? Like, I sort of mentioned to the team that, like, okay, I want to really push the data modeling side out, but I know that, like, I feel like we got… we have more dashboards. I mean, one thing we could do is review
260 00:27:31.430 ⇒ 00:27:36.610 Uttam Kumaran: I kind of want to think about this meeting, and then maybe starting to look at some of this data together.
261 00:27:36.720 ⇒ 00:27:40.709 Uttam Kumaran: Versus just, like, hey, we pushed out this dashboard.
262 00:27:40.980 ⇒ 00:27:49.100 Uttam Kumaran: like, what do you think about that? Like, I feel like we’re gonna continue to push these out, but I want to start to talk about some of the things we’re seeing, actually.
263 00:27:50.030 ⇒ 00:27:53.589 Caitlyn Vaughn: Yeah, and when you say, talk about some of the things that you’re seeing, do you mean, like.
264 00:27:54.310 ⇒ 00:27:56.089 Caitlyn Vaughn: You’re finding interesting things in our.
265 00:27:56.090 ⇒ 00:27:56.680 Uttam Kumaran: Yes.
266 00:27:56.680 ⇒ 00:27:59.219 Caitlyn Vaughn: So you’re finding discrepancies, or, like, what do you mean?
267 00:27:59.220 ⇒ 00:28:01.470 Uttam Kumaran: I think the discrepancy…
268 00:28:02.220 ⇒ 00:28:12.649 Uttam Kumaran: the discrepancies, we’re gonna handle, I think, how we usually handle, which is, like, hey, we notice this is off. That’s more QA. This is more of, like, hey, we put it together, like, a new view, or… and, like.
269 00:28:12.970 ⇒ 00:28:16.199 Uttam Kumaran: Would love to have, like, a, like, a more strategy conversation about, like…
270 00:28:16.200 ⇒ 00:28:16.570 Caitlyn Vaughn: Yeah.
271 00:28:16.570 ⇒ 00:28:17.640 Uttam Kumaran: What something is.
272 00:28:18.080 ⇒ 00:28:22.630 Uttam Kumaran: yeah, I just want to try to carve time out for that.
273 00:28:22.920 ⇒ 00:28:28.449 Caitlyn Vaughn: Yeah, let’s absolutely do that, that’s a great idea. Do you guys have capacity to do that?
274 00:28:29.250 ⇒ 00:28:40.859 Uttam Kumaran: Yeah, I mean, I think some of the stuff… I don’t know, Greg, on the product analytics side, like, I don’t know what you’re seeing already, or if there’s anything there that you can also already look at. Certainly on some of this metrics I’m already seeing.
275 00:28:41.120 ⇒ 00:28:43.989 Uttam Kumaran: I think there’s some stuff that I’d love to talk about.
276 00:28:44.210 ⇒ 00:28:50.050 Caitlyn Vaughn: Oh yeah, do we have a product analytics dashboard? We have a customer product activity dashboard.
277 00:28:50.170 ⇒ 00:28:52.129 Caitlyn Vaughn: Which is, I guess, kind of the same.
278 00:28:52.130 ⇒ 00:28:54.160 Uttam Kumaran: In posthog, or in, Omni?
279 00:28:54.160 ⇒ 00:28:56.959 Caitlyn Vaughn: Yeah, I’m looking at the, Instagram, like, the plan.
280 00:28:56.960 ⇒ 00:28:58.250 Uttam Kumaran: Okay, yeah.
281 00:28:58.700 ⇒ 00:29:03.160 Caitlyn Vaughn: Yeah, I guess we don’t have, like, a product-specific Gantt, but…
282 00:29:03.740 ⇒ 00:29:10.910 Caitlyn Vaughn: I’m assuming that Lev, this Customer Product Activity Dashboard, will have a lot of the things that.
283 00:29:10.910 ⇒ 00:29:11.490 Uttam Kumaran: Yes.
284 00:29:11.490 ⇒ 00:29:12.880 Caitlyn Vaughn: for PLG stuff.
285 00:29:12.880 ⇒ 00:29:13.370 Uttam Kumaran: Yeah.
286 00:29:14.000 ⇒ 00:29:17.410 Uttam Kumaran: Meetings booked, like, all… a lot of, like, sign-ins, things like that.
287 00:29:17.410 ⇒ 00:29:21.460 Caitlyn Vaughn: Hmm, yeah. But on the product side, it would be interesting to, like.
288 00:29:21.670 ⇒ 00:29:33.759 Caitlyn Vaughn: be able to point to, you know, Sid and I are, like, dying on hills for features, and once we actually release them, like, seeing if people are really using them or not. That’ll probably be a good way for us to, like.
289 00:29:34.180 ⇒ 00:29:49.330 Caitlyn Vaughn: be more analytical about product going forward. So yeah, I would love to do that. We’re also about to push a bunch of PRs, and merge them into the main branch this week. So on the event tracking side, there should be a lot more work in the next week.
290 00:29:49.700 ⇒ 00:29:50.620 Uttam Kumaran: Sounds good. Nice.
291 00:29:50.620 ⇒ 00:29:51.140 Greg Stoutenburg: you know.
292 00:29:52.860 ⇒ 00:29:53.880 Greg Stoutenburg: Fantastic.
293 00:29:54.930 ⇒ 00:30:13.170 Lev Katreczko: Yeah, I think that as far as, you know, product analytics and sales activity go hand-in-hand, there… when the time comes, will be really interesting insights to derive from both the combination of, like, sales outreach and, you know, customer user behavior, and
294 00:30:13.200 ⇒ 00:30:27.099 Lev Katreczko: Mapping out, like, different dynamics at play, and what’s successful, what’s unsuccessful, what are, like, weird edge cases that we should tumble down on. So, just, you know, another thing that, I’m looking forward to.
295 00:30:28.520 ⇒ 00:30:29.260 Caitlyn Vaughn: Team.
296 00:30:30.950 ⇒ 00:30:31.600 Uttam Kumaran: Okay.
297 00:30:31.740 ⇒ 00:30:37.820 Uttam Kumaran: So I think, Greg, yeah, for next week, let’s just plan on that, like, let’s just do a section just talking a little bit about what we’re seeing in the data.
298 00:30:37.960 ⇒ 00:30:41.160 Uttam Kumaran: And yeah, I’m excited. I think by the time we talk next week, we’ll have
299 00:30:41.430 ⇒ 00:30:46.480 Uttam Kumaran: we’ll have all three of these out and be modeling, I think, kind of a lot of lab stuff.
300 00:30:47.630 ⇒ 00:30:56.239 Caitlyn Vaughn: Oh, but also for the product data, A, did you… no, I don’t think we got sorted on the, like, vanilla product data or the Phoenix product data, right?
301 00:30:56.240 ⇒ 00:30:58.900 Uttam Kumaran: We have some answers, except one.
302 00:30:59.620 ⇒ 00:31:01.840 Uttam Kumaran: Like, you mean the thread of questions?
303 00:31:01.840 ⇒ 00:31:02.440 Demilade Agboola: Worst.
304 00:31:02.850 ⇒ 00:31:04.159 Caitlyn Vaughn: No, I mean, like, on…
305 00:31:04.160 ⇒ 00:31:04.950 Demilade Agboola: the data.
306 00:31:05.620 ⇒ 00:31:06.609 Caitlyn Vaughn: Do I?
307 00:31:06.610 ⇒ 00:31:08.400 Demilade Agboola: Do you mean, like, access to the data?
308 00:31:08.400 ⇒ 00:31:09.089 Caitlyn Vaughn: We took a half.
309 00:31:09.090 ⇒ 00:31:11.290 Demilade Agboola: Yeah, Victor has still been a blocker, so…
310 00:31:11.290 ⇒ 00:31:11.690 Caitlyn Vaughn: Yeah.
311 00:31:11.690 ⇒ 00:31:15.449 Demilade Agboola: The meantime, just so we have something to show proof of concept.
312 00:31:15.890 ⇒ 00:31:28.499 Demilade Agboola: the one-time export to be able to, visualize the dashboard, and once the product is then… once we get access to the product data, we will then kind of swap that out, so we will have, like, live…
313 00:31:28.790 ⇒ 00:31:35.340 Demilade Agboola: data, and that would allow us to have, like, you know, insights every, like, 4 hours, because that’s how often dbt runs.
314 00:31:35.540 ⇒ 00:31:36.590 Caitlyn Vaughn: Okay, cool.
315 00:31:36.590 ⇒ 00:31:42.800 Uttam Kumaran: Caitlin, where we left off is, like, I think you wanted to connect me with… Picture…
316 00:31:44.020 ⇒ 00:31:45.419 Uttam Kumaran: To talk about Superbase.
317 00:31:46.110 ⇒ 00:31:46.430 Caitlyn Vaughn: Yeah.
318 00:31:46.430 ⇒ 00:31:47.650 Uttam Kumaran: Trade-only stuff?
319 00:31:47.650 ⇒ 00:31:52.130 Caitlyn Vaughn: Yeah, I did send him a message last Friday and say.
320 00:31:52.330 ⇒ 00:31:55.560 Caitlyn Vaughn: Let’s get something together, and he hasn’t responded yet, so…
321 00:31:56.360 ⇒ 00:31:59.679 Caitlyn Vaughn: I will ping him again. I’m gonna call him. I’m just gonna call him.
322 00:31:59.910 ⇒ 00:32:05.739 Uttam Kumaran: Okay, okay, give him a call, and then, yeah, I mean, let me know, I’ll patch in and talk about Superbase read-only export.
323 00:32:05.740 ⇒ 00:32:11.689 Caitlyn Vaughn: Okay. Yeah, yeah, yeah. I think we were, we were, like, blocked on… we’re supposed to, like, upgrade our,
324 00:32:11.690 ⇒ 00:32:12.440 Uttam Kumaran: Yes.
325 00:32:12.770 ⇒ 00:32:18.750 Caitlyn Vaughn: our Super Supabase instance to, like, be able to have read replicas.
326 00:32:18.870 ⇒ 00:32:28.790 Caitlyn Vaughn: But I think that didn’t end up happening, I don’t know. It’s like, I think this is, like, a smaller priority for them, and then upstream is, like, a bunch of other things, but, like…
327 00:32:28.970 ⇒ 00:32:31.680 Caitlyn Vaughn: I need this to happen, and we need this to happen.
328 00:32:31.680 ⇒ 00:32:37.699 Uttam Kumaran: Yeah. I mean, at least by next week, you… you can at least show them the dashboard that’s not… that’s not loading new data.
329 00:32:37.700 ⇒ 00:32:42.030 Caitlyn Vaughn: Yeah. Would it be helpful for Thomas to send new data, right?
330 00:32:42.030 ⇒ 00:32:43.080 Uttam Kumaran: Yes, yeah.
331 00:32:43.080 ⇒ 00:32:44.430 Caitlyn Vaughn: Let’s do that.
332 00:32:44.430 ⇒ 00:32:48.100 Uttam Kumaran: Whatever you sent last time, you could just send us another export of that. Yeah.
333 00:32:48.100 ⇒ 00:32:51.080 Caitlyn Vaughn: Okay, cool. Nandika, have you talked to Thomas?
334 00:32:51.560 ⇒ 00:32:59.759 Nandika Jhunjhunwala: I mentioned it to him briefly, but I haven’t, like, fully asked him, because I wasn’t sure what exactly he needs to share. Is it, like, vanilla or Phoenix data?
335 00:33:00.310 ⇒ 00:33:03.580 Caitlyn Vaughn: It’s, vanilla data. I’ll just ping it really quick.
336 00:33:12.090 ⇒ 00:33:13.060 Caitlyn Vaughn: Okay, cool.
337 00:33:13.060 ⇒ 00:33:13.660 Uttam Kumaran: Okay.
338 00:33:14.480 ⇒ 00:33:17.979 Uttam Kumaran: Great, so I guess I will… I’m gonna suggest,
339 00:33:18.240 ⇒ 00:33:21.860 Uttam Kumaran: time, like, 15 minutes to meet with Miyu.
340 00:33:22.200 ⇒ 00:33:24.970 Uttam Kumaran: Laura and Ryan, and maybe Monday.
341 00:33:25.040 ⇒ 00:33:26.170 Caitlyn Vaughn: Yeah, that sounds great.
342 00:33:26.170 ⇒ 00:33:27.710 Uttam Kumaran: We’ll slam through as many as we can.
343 00:33:27.960 ⇒ 00:33:28.710 Caitlyn Vaughn: Cool.
344 00:33:29.090 ⇒ 00:33:31.220 Caitlyn Vaughn: Yay! Thanks so much, you guys!
345 00:33:31.490 ⇒ 00:33:46.220 Uttam Kumaran: Yeah, thank you. And then, yeah, also, sorry, one more thing. I want to start looking at, like, like, blobby usage, and I think starting to look at that weekly would be great, too. Like, seeing how many people are looking at dashboards, how many people are using the AI. Yeah. So one thing when I… when I…
346 00:33:46.350 ⇒ 00:33:50.900 Uttam Kumaran: present to Laura and Ryan, I want to show the AI thing so that
347 00:33:51.470 ⇒ 00:33:55.659 Uttam Kumaran: they, like, they’re busy, I want them to try to use that to, like, ask questions.
348 00:33:55.660 ⇒ 00:34:03.170 Caitlyn Vaughn: Yeah, that’s actually a good idea. I think right now we have a part… a user of one of Blobby at this org, and it’s Nandica.
349 00:34:03.170 ⇒ 00:34:06.959 Uttam Kumaran: I know, we need more blobby lovers.
350 00:34:06.960 ⇒ 00:34:07.440 Caitlyn Vaughn: Yeah.
351 00:34:07.440 ⇒ 00:34:08.590 Uttam Kumaran: Oh, yes.
352 00:34:09.580 ⇒ 00:34:13.819 Caitlyn Vaughn: You know, you’re gonna keep pushing it. I need… I need to be continuously reminded, so…
353 00:34:13.820 ⇒ 00:34:24.609 Uttam Kumaran: then once there, like, if Ryan and Laura are, like, there, and I know Lev, as soon as your stuff is out, you’ll be there, then they’ll be telling other people on their team, like, where did you get that? Oh, go, you guys should go, you start using this now, so…
354 00:34:24.610 ⇒ 00:34:25.219 Caitlyn Vaughn: Yeah.
355 00:34:25.449 ⇒ 00:34:28.459 Uttam Kumaran: I think I just want to show them that that’s… there’s a new way for us.
356 00:34:29.169 ⇒ 00:34:30.619 Uttam Kumaran: Value from that data, so…
357 00:34:30.620 ⇒ 00:34:32.789 Caitlyn Vaughn: Yeah, now that it’s all cleaned out, too.
358 00:34:32.790 ⇒ 00:34:33.370 Uttam Kumaran: Yeah.
359 00:34:34.000 ⇒ 00:34:42.130 Caitlyn Vaughn: Cool, I also wonder if this would be interesting for us to connect in, Nico built, like, an agent, like a chat agent?
360 00:34:42.139 ⇒ 00:34:43.689 Uttam Kumaran: Oh, yeah, is it internal?
361 00:34:43.690 ⇒ 00:34:57.020 Caitlyn Vaughn: Yeah, it’s, like, pulling data right now, and it’s supposed to be eventually, like, part of our product, but right now we built an internal one, and it’s giving very wrong data, but it’s, like, pulling data correctly, and the data’s wrong, right? So…
362 00:34:57.020 ⇒ 00:35:09.139 Uttam Kumaran: So, Omni has an MCP, they don’t have a native Slack connection yet, but, we just, we, like, just built… we can build a simple one on top of that. Okay. So you basically can ask questions in Slack.
363 00:35:09.140 ⇒ 00:35:12.530 Caitlyn Vaughn: Do we have the MPC on our plan, though, or no?
364 00:35:12.530 ⇒ 00:35:13.480 Uttam Kumaran: Yeah, yeah, yeah.
365 00:35:15.480 ⇒ 00:35:17.750 Caitlyn Vaughn: You’ve been struggling to connect the MPC.
366 00:35:17.750 ⇒ 00:35:20.169 Uttam Kumaran: So maybe it’s something that we’ll… we can look at, but…
367 00:35:20.170 ⇒ 00:35:27.759 Nandika Jhunjhunwala: Yeah, maybe I don’t have the permissions, but I tried to, like, generate a personal access token to connect the MCP, it didn’t work for me.
368 00:35:28.060 ⇒ 00:35:28.400 Caitlyn Vaughn: Hmm.
369 00:35:28.400 ⇒ 00:35:36.810 Nandika Jhunjhunwala: Then I was also reading through, like, the tooling available via the MCP, and I’m not sure if it’s… I think it’s mostly, like, read-only stuff.
370 00:35:36.810 ⇒ 00:35:41.759 Uttam Kumaran: Yeah, it’s, like, we… we have some, like, they’re… they’re gonna, they’re releasing more, like, right?
371 00:35:42.010 ⇒ 00:35:44.539 Uttam Kumaran: Features, like, we’re using some of them.
372 00:35:44.650 ⇒ 00:35:48.460 Uttam Kumaran: But it’s, like, all in… it’s all, like, beta that… I don’t think it’s all, like, a lot of it’s GA.
373 00:35:49.250 ⇒ 00:35:57.140 Uttam Kumaran: So… but really, I think for reading data and for querying, Blobby is, like, the… First thing, you know?
374 00:35:57.340 ⇒ 00:35:58.210 Nandika Jhunjhunwala: For sure, yeah.
375 00:35:58.210 ⇒ 00:35:58.540 Uttam Kumaran: Yeah, thank you.
376 00:35:59.210 ⇒ 00:36:03.079 Nandika Jhunjhunwala: I can connect, the Omni to Slack.
377 00:36:03.440 ⇒ 00:36:11.159 Nandika Jhunjhunwala: As well, if needed. Would the use case then be, like, being notified if there’s, like, a certain spike in a graph, or is it, like…
378 00:36:11.750 ⇒ 00:36:14.690 Uttam Kumaran: Yeah, I think the first use case is just, like, ask a ques… like.
379 00:36:14.690 ⇒ 00:36:15.220 Nandika Jhunjhunwala: Okay.
380 00:36:15.220 ⇒ 00:36:17.500 Uttam Kumaran: Don’t have to go into Omni to ask questions.
381 00:36:17.870 ⇒ 00:36:23.490 Uttam Kumaran: And then I think we can think about, like, yeah, alerting. You can already do scheduled reports.
382 00:36:23.920 ⇒ 00:36:28.860 Uttam Kumaran: But I think, yeah, just being able to be, like, in a thread and then just hit the thing for a question…
383 00:36:29.390 ⇒ 00:36:30.310 Uttam Kumaran: Would be really nice.
384 00:36:33.070 ⇒ 00:36:35.930 Uttam Kumaran: Because then, again, people would see that, so… Yeah. Yeah.
385 00:36:36.460 ⇒ 00:36:38.920 Caitlyn Vaughn: Okay, yeah, let’s definitely do that.
386 00:36:39.430 ⇒ 00:36:43.310 Caitlyn Vaughn: And I’ll let Nico know about the MPC server, because that’s great news.
387 00:36:46.410 ⇒ 00:36:46.760 Uttam Kumaran: Okay.
388 00:36:46.760 ⇒ 00:36:47.450 Caitlyn Vaughn: Pitbull.
389 00:36:47.450 ⇒ 00:36:48.080 Greg Stoutenburg: Bye.
390 00:36:48.450 ⇒ 00:36:49.349 Greg Stoutenburg: Sounds good.
391 00:36:49.350 ⇒ 00:36:50.000 Uttam Kumaran: Alright.
392 00:36:50.420 ⇒ 00:36:51.799 Uttam Kumaran: Thank you, everyone. Appreciate it.
393 00:36:51.810 ⇒ 00:36:54.470 Caitlyn Vaughn: Bye guys, thank you. Bye-bye.