Meeting Title: Omni Dashboards Project Check-in Date: 2026-02-26 Meeting participants: Demilade Agboola, Scratchpad Notetaker, Nandika Jhunjhunwala, Mustafa Raja, Greg Stoutenburg, Caitlyn Vaughn
WEBVTT
1 00:00:26.790 ⇒ 00:00:27.940 Nandika Jhunjhunwala: Hi!
2 00:00:29.200 ⇒ 00:00:30.690 Nandika Jhunjhunwala: Hi, Nadica, how are you?
3 00:00:31.960 ⇒ 00:00:33.059 Nandika Jhunjhunwala: Good, how are you?
4 00:00:33.340 ⇒ 00:00:34.390 Demilade Agboola: I’m doing very well.
5 00:00:35.020 ⇒ 00:00:36.200 Demilade Agboola: Actually…
6 00:00:39.890 ⇒ 00:00:41.000 Demilade Agboola: How’s your week been?
7 00:00:43.030 ⇒ 00:00:45.809 Nandika Jhunjhunwala: It’s been good. A little busy. What about you?
8 00:00:47.390 ⇒ 00:00:49.609 Demilade Agboola: Busy as well, but good as well.
9 00:00:51.100 ⇒ 00:00:55.230 Nandika Jhunjhunwala: So, like, out of curiosity, like, how many clients do you manage?
10 00:00:56.600 ⇒ 00:00:59.069 Demilade Agboola: Like, me personally, or, like, the, like, the company?
11 00:00:59.360 ⇒ 00:01:00.559 Nandika Jhunjhunwala: Like, you personally.
12 00:01:01.590 ⇒ 00:01:05.680 Demilade Agboola: So, personally, I’m on, three, but in different
13 00:01:06.830 ⇒ 00:01:12.340 Demilade Agboola: Some, like, default, I play an active role, some I’m in a more supporting role, but yeah.
14 00:01:14.350 ⇒ 00:01:14.989 Nandika Jhunjhunwala: Got it.
15 00:01:18.330 ⇒ 00:01:19.760 Greg Stoutenburg: Hey guys, Hanonda.
16 00:01:20.360 ⇒ 00:01:21.090 Nandika Jhunjhunwala: Bye!
17 00:01:23.210 ⇒ 00:01:26.619 Nandika Jhunjhunwala: Sorry, I’m just finishing up lunch, that’s why my camera’s off.
18 00:01:26.940 ⇒ 00:01:28.190 Greg Stoutenburg: Cool. What’s for lunch?
19 00:01:28.850 ⇒ 00:01:29.440 Nandika Jhunjhunwala: That’s gone.
20 00:01:30.230 ⇒ 00:01:36.849 Nandika Jhunjhunwala: I went to this Thai place, so just, like, some… classic, like, Thai food.
21 00:01:36.850 ⇒ 00:01:37.220 Greg Stoutenburg: Nice.
22 00:01:39.270 ⇒ 00:01:41.929 Greg Stoutenburg: I had to see you last night. Yeah, go ahead and tell me.
23 00:01:41.990 ⇒ 00:01:48.000 Demilade Agboola: I was going to say, I’m really into, like, Vietnamese. I know it’s not that, it’s not that, but, like, I started getting to, like, Vietnamese food recently.
24 00:01:48.120 ⇒ 00:01:51.010 Nandika Jhunjhunwala: I’m so much in love with it.
25 00:01:51.240 ⇒ 00:01:52.610 Nandika Jhunjhunwala: Yeah, it’s so good.
26 00:01:54.340 ⇒ 00:01:55.100 Greg Stoutenburg: Hey, Kalen.
27 00:01:55.100 ⇒ 00:01:58.210 Caitlyn Vaughn: How’s it going? Got a lot of haircuts going on!
28 00:01:58.610 ⇒ 00:02:00.689 Greg Stoutenburg: Well, haircut week.
29 00:02:00.690 ⇒ 00:02:03.020 Caitlyn Vaughn: A lot less hair in the groups today.
30 00:02:03.640 ⇒ 00:02:11.369 Demilade Agboola: I, like I said, I just woke up one day, I was like, I want a new haircut. And I’m like, that’s it. It was just…
31 00:02:11.370 ⇒ 00:02:12.130 Caitlyn Vaughn: Whoa.
32 00:02:12.130 ⇒ 00:02:14.289 Greg Stoutenburg: That’s it?
33 00:02:14.290 ⇒ 00:02:22.520 Demilade Agboola: Like, it really was… it was literally that’s it. And the funny thing is, like, there’s a barbershop, like, two streets away, so it’s, like, really close.
34 00:02:23.000 ⇒ 00:02:25.540 Demilade Agboola: So I was, like, I went to get coffee.
35 00:02:25.650 ⇒ 00:02:28.930 Demilade Agboola: I saw the shop, I walked in, and I’m like, hey.
36 00:02:29.570 ⇒ 00:02:31.039 Demilade Agboola: Do you want to call her? And he’s like.
37 00:02:31.270 ⇒ 00:02:36.609 Demilade Agboola: pure, and I sat down, and he’s like, oh, because of, like, because he was, like, crocheted
38 00:02:36.740 ⇒ 00:02:42.950 Demilade Agboola: locked. He’s like, oh, it’s so low, like, it’s really on your scalp, so if you don’t cut it really low.
39 00:02:43.270 ⇒ 00:02:48.849 Demilade Agboola: you’re gonna have patches, and it’s not gonna look good. And I’m like, okay, sure, cut you low, and just… and that was it.
40 00:02:49.010 ⇒ 00:02:52.340 Demilade Agboola: That was it.
41 00:02:52.340 ⇒ 00:02:55.399 Greg Stoutenburg: Some people impulse by tech.
42 00:02:55.780 ⇒ 00:02:56.350 Greg Stoutenburg: Yeah, yeah.
43 00:02:56.350 ⇒ 00:03:00.469 Caitlyn Vaughn: I was just saying, yeah, some people, you know, impulse buy a new phone.
44 00:03:00.470 ⇒ 00:03:07.080 Greg Stoutenburg: Or, you know, a new book. Demi’s like, I’m gonna impulse buy a totally different look.
45 00:03:07.080 ⇒ 00:03:08.440 Demilade Agboola: Exactly, exactly.
46 00:03:08.440 ⇒ 00:03:10.940 Caitlyn Vaughn: fun. New year, new you.
47 00:03:10.940 ⇒ 00:03:11.390 Greg Stoutenburg: Yeah.
48 00:03:11.390 ⇒ 00:03:14.650 Demilade Agboola: I mean, a bit late for New Year and New Me, but…
49 00:03:14.650 ⇒ 00:03:17.589 Caitlyn Vaughn: Never too late, Demi. Yeah.
50 00:03:17.590 ⇒ 00:03:19.539 Demilade Agboola: This fiscal calendar’s a little different.
51 00:03:19.540 ⇒ 00:03:21.900 Caitlyn Vaughn: Oh, gosh.
52 00:03:22.780 ⇒ 00:03:24.390 Caitlyn Vaughn: Well, how’s it going?
53 00:03:25.220 ⇒ 00:03:33.450 Demilade Agboola: Pretty good, pretty good, have some stuff to, like, share, and that’s… We’ll be talking about.
54 00:03:37.670 ⇒ 00:03:43.519 Demilade Agboola: Okay, so this week, in terms of what we’ve been able to accomplish.
55 00:03:44.150 ⇒ 00:03:49.570 Demilade Agboola: So we’ve been able to build dbt models and, across the ingested data sources.
56 00:03:50.290 ⇒ 00:03:51.230 Demilade Agboola: I…
57 00:03:51.800 ⇒ 00:03:59.129 Demilade Agboola: We’ve also started building out, like, the V1 arming dashboards, based off the metrics we started talking to the team about.
58 00:03:59.430 ⇒ 00:04:04.700 Demilade Agboola: I personally have a call with Laura after, like, well, later on today,
59 00:04:04.920 ⇒ 00:04:08.480 Demilade Agboola: the idea is we want… I want to be able to sync with her on…
60 00:04:08.690 ⇒ 00:04:21.040 Demilade Agboola: the metrics in terms of what data sources should we use, because again, we can get some metrics from Salesforce and some from QuickBooks. I just want us to be on the same page with where the sources are coming from, and…
61 00:04:21.500 ⇒ 00:04:24.439 Demilade Agboola: So the last part of, like, what I want to sync with her about.
62 00:04:24.920 ⇒ 00:04:30.260 Demilade Agboola: And also, on the product analytics side, events are being tracked in posthog, and then we’ve started
63 00:04:31.400 ⇒ 00:04:36.980 Demilade Agboola: The chat pages are being stood up, and the user implementation… property implementation is underway.
64 00:04:37.520 ⇒ 00:04:40.320 Demilade Agboola: And so, the next step for that will be
65 00:04:42.030 ⇒ 00:04:46.189 Demilade Agboola: For the product analytics, we’ll need to sign off on the analytics audit and planning phase.
66 00:04:46.680 ⇒ 00:04:49.999 Demilade Agboola: And then we’ll need to continue the user property tracking.
67 00:04:50.240 ⇒ 00:04:52.700 Demilade Agboola: And the event tracking implementation.
68 00:04:53.290 ⇒ 00:04:54.250 Demilade Agboola: on the…
69 00:04:54.610 ⇒ 00:05:07.639 Demilade Agboola: data, platforms, and analytics sides will just deliver the V1 on your dashboard, get feedback. The idea of that is the numbers aren’t always gonna, like, match or be as accurate as we want, but the idea is we’ll get feedback.
70 00:05:07.760 ⇒ 00:05:13.810 Demilade Agboola: understand what… We might be miscalculating or misrepresenting in terms of the business logic.
71 00:05:13.950 ⇒ 00:05:18.990 Demilade Agboola: And use that to then build a V2 that is much more closer to…
72 00:05:19.140 ⇒ 00:05:23.720 Demilade Agboola: What the stakeholders expect to see in their dashboards that they use on a day-to-day.
73 00:05:24.450 ⇒ 00:05:32.310 Demilade Agboola: And then also along with that, part of the delivering and getting feedback is we would have to continue modeling on the…
74 00:05:32.460 ⇒ 00:05:35.040 Demilade Agboola: Customer, like, productivity and product…
75 00:05:35.230 ⇒ 00:05:42.039 Demilade Agboola: activity dashboard, as well as the GTM dashboard, because again, the idea is, once we get feedback off of that, we will know
76 00:05:42.760 ⇒ 00:05:51.479 Demilade Agboola: Oh, okay, the way we assumed, implementation start date is wrong, let’s change that so that it looks more appropriate to what, you know…
77 00:05:51.680 ⇒ 00:05:53.350 Demilade Agboola: The stakeholder needs to see.
78 00:05:53.940 ⇒ 00:06:03.869 Demilade Agboola: And the other thing we need to do is we need to instrument dbt scheduled runs, so therefore, that way we can have dbt running consistently to transform the data to match what we need to see.
79 00:06:06.180 ⇒ 00:06:08.390 Nandika Jhunjhunwala: I have a few questions here. Okay.
80 00:06:08.390 ⇒ 00:06:10.469 Demilade Agboola: Olga, should I go back, or do you wanna…
81 00:06:10.840 ⇒ 00:06:22.340 Nandika Jhunjhunwala: So yeah, if possible, could you go back? Yeah. So, just wanted to clarify, like, the V1 Omni dashboards, does that include the stuff you’re doing for Laura, or is that more comprehensive?
82 00:06:22.530 ⇒ 00:06:25.350 Nandika Jhunjhunwala: Including, like, other departments as well.
83 00:06:25.720 ⇒ 00:06:29.909 Demilade Agboola: So, V1 is for, like, what I shared for the metrics
84 00:06:30.200 ⇒ 00:06:38.040 Demilade Agboola: Document, so that would include both, like, the customer, customer enablement, Dashboard, as well as the…
85 00:06:38.890 ⇒ 00:06:40.350 Demilade Agboola: Financial Summary Dashboard.
86 00:06:41.210 ⇒ 00:06:41.730 Demilade Agboola: Fuller.
87 00:06:41.730 ⇒ 00:06:42.370 Nandika Jhunjhunwala: Meh.
88 00:06:42.370 ⇒ 00:06:42.920 Demilade Agboola: Yeah.
89 00:06:42.920 ⇒ 00:06:53.300 Nandika Jhunjhunwala: And… and is the customer product activity dashboard the one that Lev has made the ask for? Is that… is that the one, or…
90 00:06:53.300 ⇒ 00:06:58.879 Demilade Agboola: No, so this is not for Lev yet, this will be for… this is for, Lauren. Lauren D.
91 00:06:58.880 ⇒ 00:07:02.069 Nandika Jhunjhunwala: Okay. Got it. Okay, makes sense.
92 00:07:02.970 ⇒ 00:07:03.920 Nandika Jhunjhunwala: Thank you.
93 00:07:04.320 ⇒ 00:07:05.060 Demilade Agboola: Okay.
94 00:07:08.130 ⇒ 00:07:16.439 Demilade Agboola: So, key wins, we’ve been able to build out the dashboards completely. I’m sorry, the models completely.
95 00:07:16.580 ⇒ 00:07:22.899 Demilade Agboola: That being said, I think the word complete should be in quotes, because it, again, is an iterative process.
96 00:07:23.340 ⇒ 00:07:33.320 Demilade Agboola: We will get feedback based off of what we’ve done, and how we need to, like, modify the models to fit what the business assumptions and business context of the individual users are.
97 00:07:34.320 ⇒ 00:07:40.719 Demilade Agboola: In terms of, like, V1 dashboards, we started building that out. We’ve been able to start to put some metrics together.
98 00:07:41.970 ⇒ 00:07:44.500 Demilade Agboola: And in fact, I…
99 00:07:46.040 ⇒ 00:08:02.300 Demilade Agboola: have been able to… so, like, some are really based off of what, like, I know. We’ve been able to put things like the invoiced amount by month, the paid amount by month, the analyzed ARR. I think these are part of what I would have to go over Laura about, because how I calculated it
100 00:08:02.520 ⇒ 00:08:06.929 Demilade Agboola: I’m sure she might have, like, questions around that, and being able to figure that out.
101 00:08:07.120 ⇒ 00:08:09.770 Demilade Agboola: So new customers this month.
102 00:08:10.030 ⇒ 00:08:18.660 Demilade Agboola: And all of that, so I’m building out the dashboard based off of Give me one second… This?
103 00:08:19.510 ⇒ 00:08:23.659 Demilade Agboola: And so this is based off of Omni. I’m sorry, this is based off of Salesforce.
104 00:08:24.100 ⇒ 00:08:37.050 Demilade Agboola: And the metrics I’m using are based off of QuickBooks. I want to be sure if she needs specifically Salesforce or QuickBooks, if she has any issues, like, using Salesforce or QuickBooks, which ones would she prefer to use Salesforce for versus QuickBooks for?
105 00:08:37.150 ⇒ 00:08:52.990 Demilade Agboola: And then use that, all of that in total to be able to build the dashboard that she needs. So that’s part of, like, the conversations, I mean, and the kind of feedback I will be getting to be able to determine what exactly needs to be what in the dashboard.
106 00:08:55.180 ⇒ 00:08:58.240 Demilade Agboola: Okay.
107 00:09:01.130 ⇒ 00:09:05.590 Demilade Agboola: And in terms of the workflow, we’re currently here,
108 00:09:06.170 ⇒ 00:09:13.899 Demilade Agboola: we’re looking at, like, we’re doing the dashboarding work, as well as some of the modeling for the customer reporting and enablement for Laura and Dean. And Dean, sorry.
109 00:09:14.150 ⇒ 00:09:19.470 Demilade Agboola: And the idea is we want to kind of finish that up, get some more context.
110 00:09:19.880 ⇒ 00:09:24.710 Demilade Agboola: For what is needed, and then we can fully have our dashboards put out.
111 00:09:24.960 ⇒ 00:09:26.960 Demilade Agboola: For everybody across the team.
112 00:09:28.460 ⇒ 00:09:34.070 Caitlyn Vaughn: Cool. Also, I met with Polytomic, like, a week or two ago, and we signed a contract with them.
113 00:09:34.180 ⇒ 00:09:39.989 Caitlyn Vaughn: But we only signed a contract for ETL and not reverse ETL.
114 00:09:39.990 ⇒ 00:09:54.809 Caitlyn Vaughn: Okay. They were wanting to charge, like, 5 or 6K, which is what we signed for, like, all of the data sources to be piped into S3, and then I think the reverse ETL, he was wanting to charge us, like, 12K plus annually, which is quite a bit. I don’t know.
115 00:09:54.810 ⇒ 00:09:55.330 Demilade Agboola: Yeah.
116 00:09:55.530 ⇒ 00:10:01.859 Caitlyn Vaughn: If there’s, like, another option for us to find a tool that’s, like, more cost-effective.
117 00:10:02.090 ⇒ 00:10:12.640 Caitlyn Vaughn: then maybe that’s the route we go with. If it’s super necessary, we can, like, cross that bridge, but just pointing it out, because we don’t have a reverse ETL tool right now.
118 00:10:13.290 ⇒ 00:10:22.540 Demilade Agboola: Alright, so that’s very helpful information for two reasons. One is we’ll be able to, like, do some research and get back to you on, cost-effective…
119 00:10:22.860 ⇒ 00:10:28.479 Demilade Agboola: or at least more cost-effective ETL tools, if we can find any on the market, so the idea is…
120 00:10:30.200 ⇒ 00:10:33.189 Demilade Agboola: Under 12K, obviously, will be the target.
121 00:10:33.430 ⇒ 00:10:40.680 Demilade Agboola: And two is because I know you’re now under contract with them, I can start to push for custom connectors for things around, like,
122 00:10:40.950 ⇒ 00:10:45.869 Demilade Agboola: hyperlion and factors.ai, because those connectors currently don’t exist.
123 00:10:45.990 ⇒ 00:10:51.599 Demilade Agboola: And they were holding back off building that until you were, you know, you had a contract with them, so…
124 00:10:52.190 ⇒ 00:10:54.640 Demilade Agboola: We’ve been able to unblock with that.
125 00:10:55.050 ⇒ 00:11:00.210 Caitlyn Vaughn: Cool. Is his name Glib? Is that how you say his name? He… Galib?
126 00:11:00.520 ⇒ 00:11:01.390 Demilade Agboola: Yes.
127 00:11:01.390 ⇒ 00:11:08.820 Caitlyn Vaughn: Galib messaged me and said that they’re gonna sand a pipeline, like, a week ago, so should be any day now.
128 00:11:09.160 ⇒ 00:11:14.530 Demilade Agboola: Okay, alright, so I will just follow up on that, in terms of just, like, knowing what’s going on there.
129 00:11:15.250 ⇒ 00:11:20.340 Demilade Agboola: So yes, once we have those connectors, Hyperline and Factors.ai will be able to ingest that data as well.
130 00:11:20.720 ⇒ 00:11:21.580 Caitlyn Vaughn: Perfect.
131 00:11:21.750 ⇒ 00:11:22.490 Demilade Agboola: Okay.
132 00:11:25.180 ⇒ 00:11:26.480 Demilade Agboola: Alright, so this is…
133 00:11:26.480 ⇒ 00:11:27.140 Greg Stoutenburg: Cool.
134 00:11:27.510 ⇒ 00:11:52.379 Greg Stoutenburg: Yep, alright, yep, what we’re working on now is, is setting up user properties, so I put a message in the Slack channel yesterday. I trimmed the tracking plan that we’ve got in place a little bit, and tried to focus in on the user properties that I think are going to be most relevant to Phoenix. So the next step then would be to begin instrumenting those as we go, and then finally, you know, just making sure that Caitlin, you, and Annika both feel good about the
135 00:11:52.380 ⇒ 00:11:54.970 Greg Stoutenburg: Event Tracking Plan in the shape that it’s in now.
136 00:11:54.970 ⇒ 00:12:12.439 Greg Stoutenburg: You know, we mapped out the workflows that we’re able to see already, and Nautica set up a lot of, created a lot of events in PostHog based off of resources that are available in the backend, and a couple of auto-capture things. So, we just want to get a thumbs up on that, and we’ll move forward with the user properties.
137 00:12:13.710 ⇒ 00:12:18.400 Caitlyn Vaughn: Cool, that sounds good. On the product lens, we ended up…
138 00:12:18.730 ⇒ 00:12:31.330 Caitlyn Vaughn: like, adjusting our timeline for release, we want to make sure that there’s, like, a certain threshold of what is going to be available on, like, onboard ready. So our new timeline is probably, like.
139 00:12:31.800 ⇒ 00:12:49.250 Caitlyn Vaughn: sometime in April for the product actually being, like, fully baked for its first version, at least, so… Okay. I was just saying this to Nanica the other day. I know you guys have done a ton of instrumentation, that’s great. It will probably continue, like, new things will be continuously rolled out for the next month or so.
140 00:12:49.250 ⇒ 00:13:04.800 Greg Stoutenburg: Yeah, great, yep, thanks for the update. That sounds good, and we’ll just keep operating. I think… I think our cadence has been good. I think that this approach of just being responsive to what’s coming out and tracking workflows as they are available in staging is the way to go, so we can just keep doing that right until we go live.
141 00:13:05.420 ⇒ 00:13:18.290 Caitlyn Vaughn: Cool. I also have for you, I found all of the, like, table schemas in our, product data, and then one of our engineers actually went and, like, built you guys out a…
142 00:13:18.500 ⇒ 00:13:27.580 Caitlyn Vaughn: like, a diagram, it’s still a little bit chaotic, if I’m gonna be honest. I don’t think it quite… he was like, this is gonna really help, and I opened it, and I was like, wow, that’s…
143 00:13:27.890 ⇒ 00:13:29.620 Greg Stoutenburg: Like, this would really help an engineer.
144 00:13:29.620 ⇒ 00:13:37.999 Caitlyn Vaughn: Yeah, I was like, wow, that’s a lot. But this is… this is what he’s built out for all of the, like, table schemas.
145 00:13:38.490 ⇒ 00:13:41.410 Caitlyn Vaughn: So at least you can see, like, what is in each object.
146 00:13:41.540 ⇒ 00:13:42.190 Greg Stoutenburg: Okay.
147 00:13:42.650 ⇒ 00:13:43.870 Caitlyn Vaughn: I can share this with you.
148 00:13:43.870 ⇒ 00:13:46.139 Greg Stoutenburg: Yeah, that’d be great. I’ll study that.
149 00:13:46.340 ⇒ 00:13:47.470 Caitlyn Vaughn: Cool, me too.
150 00:13:50.070 ⇒ 00:13:54.269 Greg Stoutenburg: Great, yep, that’s, we can move forward, Demi, that’s it for here.
151 00:13:55.350 ⇒ 00:13:58.529 Demilade Agboola: Okay, sounds good. So…
152 00:14:01.500 ⇒ 00:14:08.910 Demilade Agboola: In terms of risks and, like, potential, like, things we need to mitigate, risk will still be around Postgres access, so…
153 00:14:08.910 ⇒ 00:14:09.920 Greg Stoutenburg: Both sides.
154 00:14:11.190 ⇒ 00:14:14.389 Greg Stoutenburg: Oh, I’m sorry, I forgot, I didn’t realize. I was talking.
155 00:14:15.340 ⇒ 00:14:20.610 Demilade Agboola: Okay, so Postgres Access, is still pending.
156 00:14:22.530 ⇒ 00:14:23.580 Demilade Agboola: That’s very cute.
157 00:14:25.170 ⇒ 00:14:28.300 Demilade Agboola: So Postgres Arcus is still pending,
158 00:14:28.410 ⇒ 00:14:32.159 Demilade Agboola: I know Victor has had a lot on his plate the last couple weeks.
159 00:14:32.280 ⇒ 00:14:40.059 Demilade Agboola: But it’s just something to just highlight as to, like, why certain metrics will be hard or not possible to calculate right now.
160 00:14:41.120 ⇒ 00:14:46.210 Demilade Agboola: The plan will be to follow up with Victor, and just kind of see
161 00:14:46.420 ⇒ 00:14:48.759 Demilade Agboola: What the resolution looks like.
162 00:14:49.190 ⇒ 00:14:56.700 Demilade Agboola: And then another risk that we would like to mitigate will just be the timeline around, like, Phoenix?
163 00:14:57.160 ⇒ 00:15:04.089 Demilade Agboola: So, because we’re, like, because it’s shipping in stages, and it constrains how much can be fully implemented.
164 00:15:04.470 ⇒ 00:15:14.050 Demilade Agboola: And so, right now, what we’re just gonna keep doing is just instrumenting per workflow as each piece ships, and we’ll be reacting to each piece as it comes out.
165 00:15:14.410 ⇒ 00:15:21.509 Demilade Agboola: So that’s just… those are the kind of things we’re just worth thinking about in terms of risks and potentially things that might cause hiccups here and there.
166 00:15:21.740 ⇒ 00:15:24.020 Demilade Agboola: Any thoughts on that?
167 00:15:27.870 ⇒ 00:15:30.540 Caitlyn Vaughn: Sorry, on which part?
168 00:15:30.890 ⇒ 00:15:35.400 Demilade Agboola: On just, like, the risks and, like, how, like, you propose we might be able to…
169 00:15:36.350 ⇒ 00:15:46.829 Demilade Agboola: handle it better? Like, for instance, Victor, can we be able to sync on a call, or would that be something we should just wait for next week, for instance, where he might have a bit more bandwidth?
170 00:15:47.440 ⇒ 00:16:00.800 Caitlyn Vaughn: Yeah, so we just had our board meeting yesterday, and, like, the last week was kind of a shitshow, but it seems calmer now, in theory. So I’m writing him a message right now. We definitely need to just get that hooked up so we can be done with it, and then…
171 00:16:01.260 ⇒ 00:16:07.169 Caitlyn Vaughn: I updated you guys with the Phoenix delivery timeline. Any questions on that?
172 00:16:10.010 ⇒ 00:16:12.229 Greg Stoutenburg: No, it was just in the slide before you said that, so…
173 00:16:12.230 ⇒ 00:16:12.970 Demilade Agboola: Oh my god.
174 00:16:12.970 ⇒ 00:16:16.020 Greg Stoutenburg: Thanks for anticipating.
175 00:16:16.020 ⇒ 00:16:17.940 Caitlyn Vaughn: Okay? Cool.
176 00:16:19.270 ⇒ 00:16:22.330 Demilade Agboola: Okay, I think that’s it from us this week.
177 00:16:22.450 ⇒ 00:16:30.649 Demilade Agboola: I know from the messages in Slack that Nautica had some questions. I don’t know if there are any other questions that we’ll, like…
178 00:16:30.650 ⇒ 00:16:31.520 Nandika Jhunjhunwala: Yo!
179 00:16:31.970 ⇒ 00:16:48.029 Nandika Jhunjhunwala: I was wondering, like, if you have time today to give me, like, a really short Omnihash course, or, like, on the data schema, like, in Mother Dog, that I can reference to, like, build some really quick dashboards, even with the dev.
180 00:16:48.230 ⇒ 00:16:51.889 Nandika Jhunjhunwala: dev schema, for the growth team, I think…
181 00:16:52.500 ⇒ 00:16:59.200 Nandika Jhunjhunwala: for them, it’s like, they just want… it’s, like, really simple stuff that they want to track, so I think for the time being, like.
182 00:16:59.470 ⇒ 00:17:09.180 Nandika Jhunjhunwala: I don’t expect that to change with the data modeling, like, in prod as well, so, I would love to, like, just enable myself and do it,
183 00:17:09.470 ⇒ 00:17:14.219 Nandika Jhunjhunwala: So if I could ask for, like, a really quick dashboard, like, a crash course, or, like.
184 00:17:14.530 ⇒ 00:17:18.839 Nandika Jhunjhunwala: information on the schema in my doc, like, that would be much appreciated.
185 00:17:19.380 ⇒ 00:17:20.479 Demilade Agboola: Okay, sounds good.
186 00:17:20.480 ⇒ 00:17:37.520 Nandika Jhunjhunwala: Also, I just wanted to, like, know what kind of metrics are you trying to look at, so… Yeah, so I can elaborate on that. So, there’s, like, it’s… this is basically for the growth team, so this is an overview of, like, business development, and basically.
187 00:17:37.640 ⇒ 00:17:50.050 Nandika Jhunjhunwala: we want to track, like, BDR activity, like, sales activity, so they want to track, like, number of calls made in a month, number of emails sent in a month, and then group that by, like, person.
188 00:17:50.710 ⇒ 00:18:02.110 Nandika Jhunjhunwala: And, like, number of opportunities created, by, like, via, like, sales activity, and, like, again, group that by, like, time and, like, person, properties, and…
189 00:18:02.840 ⇒ 00:18:10.350 Nandika Jhunjhunwala: then translate that into, like, pipeline generated off of those opportunities created and stuff like that.
190 00:18:10.860 ⇒ 00:18:18.580 Nandika Jhunjhunwala: So, those are, like, the preliminary dashboards to start off with, and I think that data is…
191 00:18:19.020 ⇒ 00:18:33.320 Nandika Jhunjhunwala: in Omni from Salesforce, I just don’t know how exactly it’s modeled, or, like, under what fields they are, and, like, if those fields have, like, further, like, subtypes that I need to refer to. So just to give you context on, like, sales activity, like.
192 00:18:33.960 ⇒ 00:18:37.159 Nandika Jhunjhunwala: Calls and emails are logged as tasks.
193 00:18:37.290 ⇒ 00:18:38.560 Nandika Jhunjhunwala: on an account?
194 00:18:38.820 ⇒ 00:18:43.099 Nandika Jhunjhunwala: And I saw a task field in Salesforce, but then those tasks
195 00:18:43.250 ⇒ 00:18:50.759 Nandika Jhunjhunwala: in Salesforce have further subtypes, so I wasn’t sure if you were, like, syncing that, or, like, how I could filter by that.
196 00:18:50.900 ⇒ 00:18:54.769 Nandika Jhunjhunwala: That was, like, more of, like, a direct question,
197 00:18:55.090 ⇒ 00:18:58.580 Nandika Jhunjhunwala: If you can… if you have answers… if you have an answer to that, yeah.
198 00:18:59.440 ⇒ 00:19:05.259 Demilade Agboola: Yeah, so the answer to that is, and that’s part of why I said, like, we have a dev schema where, like.
199 00:19:06.200 ⇒ 00:19:07.120 Demilade Agboola: Pretty quickly.
200 00:19:07.310 ⇒ 00:19:12.839 Demilade Agboola: So we’re going by… especially so that we save cost in terms of, like, first of ingestion.
201 00:19:13.100 ⇒ 00:19:17.179 Demilade Agboola: We’re ingesting tables that we feel we need immediately.
202 00:19:17.400 ⇒ 00:19:29.269 Demilade Agboola: And so if there are, for instance, metrics that we see that maybe we can’t… part of the table… like, tables we’re ingesting cannot answer, we would have to go find the appropriate tables, ingest that as well. So that’s one thing, saying, if you…
203 00:19:29.450 ⇒ 00:19:33.940 Demilade Agboola: How metrics that you might notice that we can’t… we don’t seem to have right now.
204 00:19:34.760 ⇒ 00:19:39.389 Demilade Agboola: probably, like, what we’ll need to do, so that’s one. Two is also…
205 00:19:40.060 ⇒ 00:19:46.520 Demilade Agboola: In terms of modeling, we’re currently modeling for metrics right now, because there’s just a lot of data across multiple
206 00:19:46.740 ⇒ 00:19:50.190 Demilade Agboola: Sources, so we have amplitude, plane.
207 00:19:50.190 ⇒ 00:19:50.690 Nandika Jhunjhunwala: Yeah.
208 00:19:50.690 ⇒ 00:19:51.840 Demilade Agboola: And all of that, so…
209 00:19:52.030 ⇒ 00:20:00.550 Demilade Agboola: Obviously, we’re just kind of modeling two or three metrics, so some of the metrics you might want might not just be modeled out right now.
210 00:20:00.660 ⇒ 00:20:05.960 Demilade Agboola: So the first step with that would be, hey, this is how we would need to model it.
211 00:20:06.210 ⇒ 00:20:08.859 Demilade Agboola: So it will be taking maybe the Salesforce data.
212 00:20:09.590 ⇒ 00:20:12.609 Demilade Agboola: It could be something as simple as the tax,
213 00:20:12.790 ⇒ 00:20:23.440 Demilade Agboola: Column, extracting it, and maybe standardizing it, and then just using it with the customer data, and just saying, hey, for each customer, this is the number of times they did this.
214 00:20:23.670 ⇒ 00:20:27.999 Demilade Agboola: Per day, and so we can start to use that to now create a dashboard.
215 00:20:28.130 ⇒ 00:20:35.199 Demilade Agboola: So, where we can see the number of times they did X activities, emails or meetings per month, for instance.
216 00:20:35.440 ⇒ 00:20:41.299 Demilade Agboola: So the idea is… Right now, a lot of things are, like, moving really quickly.
217 00:20:41.820 ⇒ 00:20:42.220 Nandika Jhunjhunwala: Wow.
218 00:20:42.220 ⇒ 00:20:56.520 Demilade Agboola: And there’s… there’s not a lot of, like, settled data, where it’s just like, hey, this is the data, go use it, build out your dashboard. We would either… most likely, especially if it’s not in the current flow of what we’re working towards right now.
219 00:20:56.630 ⇒ 00:21:02.229 Demilade Agboola: We’ll either need to ingest it, or potentially need to model it, or in some cases, both.
220 00:21:03.820 ⇒ 00:21:06.189 Nandika Jhunjhunwala: That makes a ton of sense. I just, like…
221 00:21:06.350 ⇒ 00:21:22.820 Nandika Jhunjhunwala: I… I’m just doing some initial exploration, so I don’t know if that data is in there or not, so that’s what would be really helpful for me to have, like, a schema or something I can refer to, for stuff that is currently there, like, being ingested or has been modeled.
222 00:21:23.040 ⇒ 00:21:25.759 Nandika Jhunjhunwala: That would be super helpful to me.
223 00:21:26.530 ⇒ 00:21:29.449 Demilade Agboola: Alright, so, off the top, off the…
224 00:21:29.800 ⇒ 00:21:35.139 Demilade Agboola: there are two main schemas where, like, the, ETL data kind of flows through.
225 00:21:35.310 ⇒ 00:21:39.810 Demilade Agboola: One is, like, right now, so one is the raw export.
226 00:21:39.990 ⇒ 00:21:42.559 Demilade Agboola: Or, sorry, raw data schema.
227 00:21:43.540 ⇒ 00:21:48.169 Demilade Agboola: So in raw data, it’s organized by each. Do you have access to MotherDoc?
228 00:21:48.450 ⇒ 00:21:49.000 Demilade Agboola: By any chance.
229 00:21:49.000 ⇒ 00:21:50.210 Nandika Jhunjhunwala: I don’t.
230 00:21:50.210 ⇒ 00:21:50.940 Demilade Agboola: Okay.
231 00:21:51.590 ⇒ 00:21:56.770 Demilade Agboola: Alright, I need to look at that and give you… Access to it.
232 00:21:58.400 ⇒ 00:22:01.060 Demilade Agboola: I’m just gonna share my screen so you have an idea of what.
233 00:22:01.690 ⇒ 00:22:03.199 Demilade Agboola: How things are structured right now.
234 00:22:04.970 ⇒ 00:22:06.690 Demilade Agboola: So…
235 00:22:06.930 ⇒ 00:22:11.530 Demilade Agboola: a model doc, it gives you my DB right off the… right off the bat, so that’s why this exists.
236 00:22:12.420 ⇒ 00:22:17.020 Demilade Agboola: So we have the raw export. Raw export was what was used
237 00:22:17.300 ⇒ 00:22:21.740 Demilade Agboola: When we were, like, manually doing one-time exports of things.
238 00:22:21.940 ⇒ 00:22:22.580 Nandika Jhunjhunwala: Yeah.
239 00:22:22.580 ⇒ 00:22:23.850 Demilade Agboola: That’s what’s there.
240 00:22:24.110 ⇒ 00:22:30.070 Demilade Agboola: We have raw data. Raw data is literally the exports…
241 00:22:30.070 ⇒ 00:22:30.520 Nandika Jhunjhunwala: Got it.
242 00:22:30.520 ⇒ 00:22:31.630 Demilade Agboola: A little polyatomic.
243 00:22:31.740 ⇒ 00:22:35.080 Demilade Agboola: So that’s why we have amplitudes, so these are all the amplitude tables.
244 00:22:36.420 ⇒ 00:22:39.069 Demilade Agboola: We have plain, all the plain tables.
245 00:22:40.880 ⇒ 00:22:44.690 Demilade Agboola: We have QuickBooks, Salesforce, Stripe, and these are all the tables in there.
246 00:22:44.840 ⇒ 00:22:49.570 Demilade Agboola: Containing all that information. So, that’s the first thing to note.
247 00:22:50.130 ⇒ 00:22:58.950 Demilade Agboola: Now, obviously, because they love sources, and they’re all doing, like, different things, being able to put them together is what we’re trying to do, and get them going.
248 00:22:59.210 ⇒ 00:23:05.780 Demilade Agboola: So because of that, we have a dev, so that’s currently where we’re trying to, like, model all of this data together.
249 00:23:06.040 ⇒ 00:23:10.280 Demilade Agboola: So we have the raw table, which… or the raw tables, which are, like, basically, like.
250 00:23:10.490 ⇒ 00:23:15.820 Demilade Agboola: Reflections of the initial, like, world models.
251 00:23:15.970 ⇒ 00:23:20.760 Demilade Agboola: There’s some data cleaning here and there, also standardizing of some data types.
252 00:23:21.230 ⇒ 00:23:29.189 Demilade Agboola: And then we have the intermediate model, where we’re trying to put the concepts together, so, like, oh, what are the companies playing, what’s going on there.
253 00:23:29.300 ⇒ 00:23:33.719 Demilade Agboola: But the idea is, the final thing is we have the mat, so we’re trying to have, like.
254 00:23:33.850 ⇒ 00:23:38.140 Demilade Agboola: the customer data for, like, QuickBooks, for instance.
255 00:23:39.050 ⇒ 00:23:44.689 Demilade Agboola: And so now you can kind of just see the major information on QuickBook customers.
256 00:23:45.470 ⇒ 00:23:47.090 Demilade Agboola: Sorry. Second.
257 00:23:48.900 ⇒ 00:23:52.579 Demilade Agboola: Yeah, so we’re seeing QuickBook Customize, we’re seeing the name.
258 00:23:53.090 ⇒ 00:23:59.130 Demilade Agboola: And we’ll kind of get to some of the details around that, so how much was their lifetime paid amount, and what’s going on there.
259 00:24:00.260 ⇒ 00:24:02.729 Demilade Agboola: things with Salesforce as well.
260 00:24:02.930 ⇒ 00:24:06.320 Demilade Agboola: And so, yeah, the idea is just basically, some of these are, like.
261 00:24:06.810 ⇒ 00:24:13.630 Demilade Agboola: Will exist, some of it might not exist, and it will be up to us to just kind of define some of these metrics and put it in there, in dev.
262 00:24:13.950 ⇒ 00:24:17.740 Demilade Agboola: And so you can then access it and use it for the dashboard you need.
263 00:24:18.860 ⇒ 00:24:20.970 Nandika Jhunjhunwala: Yeah, no, that makes a ton of sense, like…
264 00:24:21.090 ⇒ 00:24:31.030 Nandika Jhunjhunwala: Could I ask for, like, a particular metric? Would you know if that exists in Mother Doc yet, or will that be something that we will have to model out further? So…
265 00:24:31.170 ⇒ 00:24:41.970 Nandika Jhunjhunwala: that’s, like, my main question right now. So like I mentioned, like, on the account level in Salesforce, there is a task object attached to the account.
266 00:24:42.360 ⇒ 00:24:52.829 Nandika Jhunjhunwala: And the task object is sort of, like, nested data, if that makes sense. The task op… yeah, I’m just, like, curious if you’re syncing that in, and if you’re not, I think…
267 00:24:53.210 ⇒ 00:25:00.639 Nandika Jhunjhunwala: let’s have that conversation of, like, starting to ingest that and model that, because that, I think, is important for the growth team to track, like, activity.
268 00:25:01.250 ⇒ 00:25:05.840 Demilade Agboola: Okay, is that… sorry, what table is that in?
269 00:25:06.670 ⇒ 00:25:11.959 Nandika Jhunjhunwala: Yeah, so… I was in Omni,
270 00:25:12.470 ⇒ 00:25:22.149 Nandika Jhunjhunwala: And I was going through, like, the raw dash… like, the raw data sources, and I think under Salesforce, under Account, there’s a field called Task.
271 00:25:22.420 ⇒ 00:25:26.570 Demilade Agboola: Alright, so therefore, it’s in there. So we’ll probably just need to model it.
272 00:25:26.570 ⇒ 00:25:30.849 Nandika Jhunjhunwala: Yeah, I’m just not sure, like, to what granularity is it in there.
273 00:25:31.170 ⇒ 00:25:31.550 Demilade Agboola: Okay.
274 00:25:31.550 ⇒ 00:25:32.050 Nandika Jhunjhunwala: Oh.
275 00:25:32.250 ⇒ 00:25:36.679 Demilade Agboola: that’s what we need to explore, and we’ll be able to get back to you on that.
276 00:25:37.500 ⇒ 00:25:38.290 Demilade Agboola: So, tab…
277 00:25:38.290 ⇒ 00:25:43.329 Nandika Jhunjhunwala: And do you… I can share what it looks like on Salesforce, too, if that helps.
278 00:25:49.120 ⇒ 00:25:55.600 Nandika Jhunjhunwala: Or I can attach a screenshot or something. Sorry, let me just log in, and I’ll show what you… what it… show you what it looks like.
279 00:25:56.030 ⇒ 00:25:57.420 Demilade Agboola: Okay, appreciate that.
280 00:25:58.780 ⇒ 00:26:01.189 Demilade Agboola: So you said tasks in Salesforce.
281 00:26:01.590 ⇒ 00:26:02.380 Demilade Agboola: count.
282 00:26:03.190 ⇒ 00:26:05.329 Nandika Jhunjhunwala: Yes, the account object.
283 00:26:06.350 ⇒ 00:26:07.150 Demilade Agboola: Interesting.
284 00:26:08.070 ⇒ 00:26:09.080 Demilade Agboola: Because I’m…
285 00:26:09.080 ⇒ 00:26:10.480 Nandika Jhunjhunwala: Oh, okay.
286 00:26:10.480 ⇒ 00:26:11.420 Demilade Agboola: That’s fine.
287 00:26:12.580 ⇒ 00:26:19.570 Nandika Jhunjhunwala: Okay, I might be totally wrong, but… so let me… or it could be… So if I…
288 00:26:20.220 ⇒ 00:26:27.359 Nandika Jhunjhunwala: click on, like, an account. Let me see which one would be a good example.
289 00:26:30.400 ⇒ 00:26:32.420 Nandika Jhunjhunwala: So, under activity.
290 00:26:32.650 ⇒ 00:26:46.270 Nandika Jhunjhunwala: there’s, like, all these, like, tasks, and what this basically is, is, like, the BDR is, like, calling them or, like, emailing them, and these get automatically sync from the software they’re using for email and calls.
291 00:26:46.440 ⇒ 00:26:54.870 Nandika Jhunjhunwala: So this is, like, sort of a nested object under account, so, like, you can see that this is the account, and this is the activity.
292 00:26:55.250 ⇒ 00:26:59.030 Nandika Jhunjhunwala: And you can click into the activity for the…
293 00:26:59.360 ⇒ 00:27:01.039 Nandika Jhunjhunwala: And there’s a lot more data.
294 00:27:02.490 ⇒ 00:27:07.570 Demilade Agboola: Gotcha. So what do you need to see for this? You need to see what task was done, basically.
295 00:27:08.000 ⇒ 00:27:19.889 Nandika Jhunjhunwala: Yes, like, the task type. So basically, task has two subtypes, as far as I know, like, call or email, and then I’m sure, like, any text details that might be there could also be, like, a field.
296 00:27:20.220 ⇒ 00:27:34.810 Nandika Jhunjhunwala: And, like, who… who logged the… like, who the task was assigned to, and who did they, like, reach out to? So in this case, like, this task was assigned to this person called Jack, and then they reached out to this person called Max.
297 00:27:34.990 ⇒ 00:27:39.740 Nandika Jhunjhunwala: So I think that would be great to know. Yeah.
298 00:27:40.840 ⇒ 00:27:44.850 Nandika Jhunjhunwala: So, like, I think all that data exists, like, in this form here.
299 00:27:44.850 ⇒ 00:27:49.719 Demilade Agboola: Alright, so we’ll have to look into the raw data. I don’t… off the top of my head, I don’t think we…
300 00:27:50.370 ⇒ 00:27:51.159 Demilade Agboola: reading that?
301 00:27:51.530 ⇒ 00:27:52.210 Nandika Jhunjhunwala: Okay.
302 00:27:52.210 ⇒ 00:27:57.919 Demilade Agboola: But would have to find the appropriate… like, Salesforce has, like, over, what, 300 tables or some ridiculous amount.
303 00:27:57.920 ⇒ 00:27:58.960 Nandika Jhunjhunwala: For sure.
304 00:27:58.960 ⇒ 00:28:00.330 Demilade Agboola: Yeah.
305 00:28:00.330 ⇒ 00:28:04.570 Nandika Jhunjhunwala: Yeah, I did see it, so let me try and find it for you.
306 00:28:04.570 ⇒ 00:28:05.030 Demilade Agboola: Oh, okay.
307 00:28:05.030 ⇒ 00:28:09.179 Nandika Jhunjhunwala: I saw, like, I didn’t see what form it was in.
308 00:28:09.330 ⇒ 00:28:14.509 Nandika Jhunjhunwala: But I definitely saw, like, in the raw export, in the raw data.
309 00:28:14.840 ⇒ 00:28:17.979 Nandika Jhunjhunwala: From Salesforce, sorry, let me pull it up.
310 00:28:21.940 ⇒ 00:28:24.449 Nandika Jhunjhunwala: Yes. Can I share my screen?
311 00:28:24.780 ⇒ 00:28:25.340 Demilade Agboola: Sure.
312 00:28:25.680 ⇒ 00:28:26.360 Nandika Jhunjhunwala: Okay.
313 00:28:27.480 ⇒ 00:28:31.879 Nandika Jhunjhunwala: So… Just an Omni, so this is, like…
314 00:28:32.490 ⇒ 00:28:35.940 Nandika Jhunjhunwala: under your account, like, there’s a field called Salesforce Ask.
315 00:28:36.860 ⇒ 00:28:37.650 Demilade Agboola: Gotcha.
316 00:28:37.850 ⇒ 00:28:38.470 Nandika Jhunjhunwala: Yep.
317 00:28:39.090 ⇒ 00:28:42.339 Nandika Jhunjhunwala: So I’m not sure what this actually contains, but…
318 00:28:42.530 ⇒ 00:28:48.169 Nandika Jhunjhunwala: I haven’t had time to, like, fully explore, but I’m sure, like, once I read some SQL queries, like, I would know, like, more.
319 00:28:48.530 ⇒ 00:28:51.689 Demilade Agboola: Alright, so that’s the one-time export, so I’ll just look into.
320 00:28:51.690 ⇒ 00:28:53.190 Nandika Jhunjhunwala: Oh, okay.
321 00:28:53.190 ⇒ 00:28:57.810 Demilade Agboola: Yeah, so raw data is what contains, like, the automated export.
322 00:28:58.080 ⇒ 00:29:00.330 Demilade Agboola: So if there’s anything you notice in.
323 00:29:00.330 ⇒ 00:29:03.640 Nandika Jhunjhunwala: That was in the automated export, so it’s right here.
324 00:29:04.060 ⇒ 00:29:07.529 Nandika Jhunjhunwala: I think. Or am I looking at the wrong space.
325 00:29:07.530 ⇒ 00:29:09.600 Demilade Agboola: Yeah, so I don’t think it’s in here, because I just.
326 00:29:09.600 ⇒ 00:29:11.650 Nandika Jhunjhunwala: Oh, okay, got it.
327 00:29:12.060 ⇒ 00:29:15.240 Demilade Agboola: So I think Salesforce Task is in…
328 00:29:16.230 ⇒ 00:29:19.439 Demilade Agboola: it’s in the… it’s the one-time export, so I’ll have to find that one.
329 00:29:20.020 ⇒ 00:29:28.960 Demilade Agboola: like, table, and then import it, and then import it through Polyatomic, so that data will keep flowing, and then we can do all the modeling based off of that, so that won’t be helpful.
330 00:29:28.960 ⇒ 00:29:31.419 Nandika Jhunjhunwala: Got it. Okay, that would be great, yeah.
331 00:29:31.420 ⇒ 00:29:37.309 Demilade Agboola: Right. Also, like, while we’re, like, in the thread that we’re on, you can just also collect the metrics you want, so I can help you…
332 00:29:37.310 ⇒ 00:29:37.770 Nandika Jhunjhunwala: Hmm.
333 00:29:38.080 ⇒ 00:29:42.720 Demilade Agboola: If there are other things I need to ingest as well, it will be much easier to do that all at once.
334 00:29:43.210 ⇒ 00:29:47.020 Nandika Jhunjhunwala: Yeah, for sure. I will… what I will do is, like, I’ll explore what’s…
335 00:29:47.150 ⇒ 00:29:49.870 Nandika Jhunjhunwala: Ingested under, like, the raw data.
336 00:29:50.030 ⇒ 00:29:50.360 Demilade Agboola: Okay.
337 00:29:50.360 ⇒ 00:29:51.090 Nandika Jhunjhunwala: Mmm.
338 00:29:51.740 ⇒ 00:29:56.559 Nandika Jhunjhunwala: I’ll explore that today, and yeah, if I see something missing, I’ll ping you. That sounds good.
339 00:29:56.790 ⇒ 00:29:57.859 Demilade Agboola: Alright, appreciate that.
340 00:30:00.080 ⇒ 00:30:00.850 Demilade Agboola: Okay.
341 00:30:01.160 ⇒ 00:30:02.150 Nandika Jhunjhunwala: Thank you.
342 00:30:02.150 ⇒ 00:30:03.820 Demilade Agboola: Questions, or…
343 00:30:05.510 ⇒ 00:30:13.289 Caitlyn Vaughn: Only other thing is, it looks like we have the metrics up for two of the dashboards that we want built.
344 00:30:13.360 ⇒ 00:30:27.919 Caitlyn Vaughn: Is there any way we can just fast-forward and get metrics up for all of them? Because I feel like this was helpful for our team to see, like, what was going to be included, and then, like, have some opinions on what they actually want to see, so we can just get that out of the way, probably makes sense.
345 00:30:28.330 ⇒ 00:30:33.950 Demilade Agboola: Okay, yeah, so for the other dashboards, I will work with Mustafa, and we’ll send over the…
346 00:30:34.670 ⇒ 00:30:36.229 Demilade Agboola: Like, full list of metrics.
347 00:30:36.800 ⇒ 00:30:46.519 Demilade Agboola: And then, so all the different stakeholders can go through. I know there’s also, like, a bit of a chat, not chat, actually, a document where, like, some of these metrics have been listed.
348 00:30:47.090 ⇒ 00:30:50.810 Demilade Agboola: And I think that’ll just allow us to be able to put it together, put the.
349 00:30:50.810 ⇒ 00:30:51.210 Caitlyn Vaughn: Hmm.
350 00:30:51.610 ⇒ 00:30:54.760 Demilade Agboola: As well as, like, how we want it defined.
351 00:30:55.730 ⇒ 00:31:07.819 Caitlyn Vaughn: Yeah, perfect. Yeah, I think that’s, like, a very helpful list, the metrics doc that you have right now. Also, I pinged Victor, and I said, hey, do we have anything, any updates for, like.
352 00:31:07.820 ⇒ 00:31:17.119 Caitlyn Vaughn: post… syncing in Postgres, basically, and he said, I’m looking at some vanilla infrastructure database stuff tonight, so I’ll let you know if that might be relevant. And then I said.
353 00:31:17.320 ⇒ 00:31:23.550 Caitlyn Vaughn: Great. I know the last time we were talking about, like, duplicating our product data and, like, hooking it up to S3, so…
354 00:31:24.080 ⇒ 00:31:28.969 Caitlyn Vaughn: Let me know. So hopefully we’ll get something back tomorrow, but he’s responded, which is exciting.
355 00:31:29.150 ⇒ 00:31:31.480 Demilade Agboola: Alright, sounds good, look forward to… to that.
356 00:31:32.910 ⇒ 00:31:33.470 Caitlyn Vaughn: Boom.
357 00:31:39.360 ⇒ 00:31:40.130 Demilade Agboola: Okay.
358 00:31:40.610 ⇒ 00:31:44.119 Nandika Jhunjhunwala: Yeah. I have some more updates, yeah.
359 00:31:44.230 ⇒ 00:32:02.110 Nandika Jhunjhunwala: So, I instrumented a bunch of stuff, last Friday, and then I passed it over to the engineers for review, and they had a few opinions on it. So I basically… we… they basically refactored the code, to create, like, what they call an analytics bus.
360 00:32:02.250 ⇒ 00:32:12.340 Nandika Jhunjhunwala: So, sort of, like, abstracting away post-hoc calls into, like, one file, and they created their own, like, events emitter, so replacing…
361 00:32:12.440 ⇒ 00:32:27.219 Nandika Jhunjhunwala: all post hoc calls it, like, a custom, like, function, and then calling posthog in one file. So, sort of, like, refactoring the code. So, some of those events have been merged into, like, like, production
362 00:32:27.430 ⇒ 00:32:34.860 Nandika Jhunjhunwala: As of now, but I have been told to, like, instrument piece by piece, because that branch is in active development.
363 00:32:34.920 ⇒ 00:32:41.269 Nandika Jhunjhunwala: So, what I’m doing is adding that code to, like, the main branch, like, one by one.
364 00:32:41.320 ⇒ 00:32:56.359 Nandika Jhunjhunwala: And that’s a little bit of a slower process, because that requires engineers to review the branch and, like, approve it, and then that gets merged into main. But the good news is, like, once that’s merged into main, it just, like, means when the product goes live, like, that’s gonna already be there.
365 00:32:56.500 ⇒ 00:32:59.259 Nandika Jhunjhunwala: So, it’s gonna be more of, like.
366 00:33:00.200 ⇒ 00:33:04.179 Nandika Jhunjhunwala: A process that’s gonna be, like… it’s gonna have more supervision, so it’s gonna be, like.
367 00:33:04.370 ⇒ 00:33:13.069 Nandika Jhunjhunwala: it’s gonna take a little bit more time to, like, instrument events, because I’m waiting on, like, the engineers to review my code before, like, it being into production.
368 00:33:13.750 ⇒ 00:33:15.800 Nandika Jhunjhunwala: It’s just sad update on my end.
369 00:33:18.460 ⇒ 00:33:24.710 Greg Stoutenburg: Yeah, cool, thanks for the update. Yeah, that sounds good, I think that’s fine. The main thing is just making sure that.
370 00:33:24.710 ⇒ 00:33:25.090 Nandika Jhunjhunwala: somewhere.
371 00:33:25.090 ⇒ 00:33:30.070 Greg Stoutenburg: instrumenting tracks to workflows that users would actually use, so I think that’s fine.
372 00:33:30.070 ⇒ 00:33:31.099 Nandika Jhunjhunwala: Sure. Yeah, yeah, yeah.
373 00:33:31.100 ⇒ 00:33:43.109 Greg Stoutenburg: Yeah, yeah, so can you… in the tracking plan doc, where you put up the tabs for things that have been instrumented already, is that up-to-date? Does that reflect what the engineers…
374 00:33:43.110 ⇒ 00:33:43.800 Nandika Jhunjhunwala: So…
375 00:33:43.800 ⇒ 00:33:44.770 Greg Stoutenburg: Pushed out.
376 00:33:44.770 ⇒ 00:33:49.010 Nandika Jhunjhunwala: No, so… Half of that is currently, like.
377 00:33:49.110 ⇒ 00:34:01.190 Nandika Jhunjhunwala: it’s technically been instrumented, but it hasn’t been merged into main. Okay. So, it just means I have to refactor it. I could technically refactor all of it in, like, just one… one push, but…
378 00:34:01.260 ⇒ 00:34:10.919 Nandika Jhunjhunwala: because it’s in development, like, they can’t all merge that together, because it causes, like, merge conflicts, because that code kind of changes on a daily basis. Yeah.
379 00:34:11.190 ⇒ 00:34:16.780 Nandika Jhunjhunwala: So what that means is it’s just gonna take a little bit of time for it to, like, be final.
380 00:34:16.900 ⇒ 00:34:19.999 Nandika Jhunjhunwala: Like, that event, like, those events, but…
381 00:34:20.280 ⇒ 00:34:22.910 Nandika Jhunjhunwala: Yeah, I think, like, once I’m caught up in…
382 00:34:23.150 ⇒ 00:34:28.509 Nandika Jhunjhunwala: Once, they, like, they’re gonna, like, consistently, like, review my code and, like.
383 00:34:28.630 ⇒ 00:34:32.450 Nandika Jhunjhunwala: merge it in domains so that those events are live.
384 00:34:33.320 ⇒ 00:34:38.040 Nandika Jhunjhunwala: So that was one distinction I wanted to make, and then the other distinction was
385 00:34:38.750 ⇒ 00:34:47.260 Nandika Jhunjhunwala: a lot of, like, new updates that you might see on your Vercel instance are not necessarily live in main, because those are still under review.
386 00:34:47.300 ⇒ 00:35:01.160 Nandika Jhunjhunwala: So I think, like you said, it would be great for us to, like, get together and plan what events are, like, important to instrument, and go off of that. So then I’m spending time only instrumenting the important ones, and we can then make workflows off of that.
387 00:35:01.160 ⇒ 00:35:05.399 Greg Stoutenburg: Yep, not to mention we, not to mention we don’t give you an astronomical post hoc bill.
388 00:35:05.690 ⇒ 00:35:07.440 Nandika Jhunjhunwala: So that sounds good.
389 00:35:07.610 ⇒ 00:35:14.209 Greg Stoutenburg: Yeah, that sounds great. So, yeah, and I saw that you put 4 for this afternoon, Sounds great. Yep.
390 00:35:14.210 ⇒ 00:35:14.939 Nandika Jhunjhunwala: Sounds good.
391 00:35:14.940 ⇒ 00:35:16.110 Greg Stoutenburg: Yeah, we’ll talk then.
392 00:35:16.980 ⇒ 00:35:17.760 Nandika Jhunjhunwala: Thank you.
393 00:35:17.760 ⇒ 00:35:18.470 Greg Stoutenburg: Okay.
394 00:35:18.960 ⇒ 00:35:21.659 Demilade Agboola: Alright, thank you. Alright, thanks all.
395 00:35:21.660 ⇒ 00:35:22.960 Greg Stoutenburg: Kalen, you wanna hop on the other one now?
396 00:35:22.960 ⇒ 00:35:24.649 Caitlyn Vaughn: Yeah, let’s jump with the other one.
397 00:35:24.650 ⇒ 00:35:25.919 Greg Stoutenburg: Let’s go. Alright, see y’all. Thanks.
398 00:35:25.920 ⇒ 00:35:26.469 Caitlyn Vaughn: Okay, bye.
399 00:35:26.470 ⇒ 00:35:27.060 Nandika Jhunjhunwala: Bye.