Meeting Title: US x BF | Grooming Date: 2025-08-26 Meeting participants: Demilade Agboola, Emily Giant, Emily’s Fellow Note Taker, Amber Lin, Uttam Kumaran
WEBVTT
1 00:00:25.840 ⇒ 00:00:30.769 Emily Giant: Why is my fellow note-taker in every meeting? This is so obnoxious.
2 00:02:34.670 ⇒ 00:02:35.860 Amber Lin: Hello!
3 00:02:36.940 ⇒ 00:02:38.010 Emily Giant: Hi.
4 00:02:39.200 ⇒ 00:02:39.960 Demilade Agboola: Hello.
5 00:02:41.820 ⇒ 00:02:43.849 Amber Lin: Welcome back to everybody.
6 00:02:44.600 ⇒ 00:02:46.690 Demilade Agboola: Thank you.
7 00:02:50.520 ⇒ 00:02:52.239 Demilade Agboola: Sorry, did you just hear something?
8 00:02:52.660 ⇒ 00:02:54.590 Amber Lin: Oh, I said, were you on vacation?
9 00:02:54.830 ⇒ 00:02:57.249 Demilade Agboola: Oh, no, no, I was… I wasn’t feeling good yesterday.
10 00:02:57.610 ⇒ 00:03:00.279 Amber Lin: Oh no, I see, I don’t feel better now?
11 00:03:00.610 ⇒ 00:03:05.019 Demilade Agboola: Yeah, definitely much better. Yesterday, I had, like, a fever and some nausea.
12 00:03:06.830 ⇒ 00:03:07.360 Emily Giant: Whoa!
13 00:03:07.360 ⇒ 00:03:09.289 Demilade Agboola: It’s… it’s better now.
14 00:03:10.030 ⇒ 00:03:12.080 Amber Lin: Okay, I’m glad to hear that.
15 00:03:12.330 ⇒ 00:03:12.920 Demilade Agboola: Hmm.
16 00:03:12.920 ⇒ 00:03:15.830 Amber Lin: … Well…
17 00:03:16.180 ⇒ 00:03:26.470 Amber Lin: I’m waiting for Utam to join, because he wanted the grooming session to be today, instead of Wednesday. Let me message him.
18 00:03:28.810 ⇒ 00:03:29.729 Uttam Kumaran: Gosh.
19 00:03:29.730 ⇒ 00:03:30.440 Emily Giant: here.
20 00:03:30.440 ⇒ 00:03:31.770 Amber Lin: Oh, hi!
21 00:03:31.910 ⇒ 00:03:32.560 Uttam Kumaran: H.
22 00:03:33.030 ⇒ 00:03:33.980 Amber Lin: Hello.
23 00:03:34.840 ⇒ 00:03:44.199 Amber Lin: So I wanted to do grooming today, and then at the end, we can think about what the message we want to send to the CEO.
24 00:03:44.370 ⇒ 00:03:48.730 Amber Lin: So… let me pull up…
25 00:03:58.470 ⇒ 00:04:05.920 Amber Lin: Oh… So, this cycle… Still has about a week.
26 00:04:07.820 ⇒ 00:04:22.199 Amber Lin: Do you think we have a chance of getting these done? I just don’t know if we can get all of them done. Is there some things that we’re going to move to the next cycle?
27 00:04:23.650 ⇒ 00:04:29.129 Uttam Kumaran: Yeah, a lot of my time this week has been spent on, like, some refactoring work, …
28 00:04:29.300 ⇒ 00:04:35.450 Uttam Kumaran: I have to… Fixed some of the jobs that were failing, and then today….
29 00:04:43.160 ⇒ 00:04:45.640 Emily Giant: Did he cut off for everyone, or just me?
30 00:04:45.640 ⇒ 00:04:47.599 Amber Lin: Yeah, for me as well.
31 00:04:49.250 ⇒ 00:04:51.430 Emily Giant: But Tom, we can’t hear you all of a sudden.
32 00:04:55.620 ⇒ 00:04:56.380 Amber Lin: Huh.
33 00:04:57.270 ⇒ 00:04:58.030 Amber Lin: Okay.
34 00:04:58.030 ⇒ 00:05:16.989 Uttam Kumaran: I think I may have got cut off. Yeah, I basically, I have some deprecation work to do, and I met with Emily today on some organization stuff, so I need to finish that up today. So these are, like, ad hoc things that came up, and then I’m gonna… I’m still working on Northbeam. Emily and I messaged
35 00:05:17.270 ⇒ 00:05:19.149 Uttam Kumaran: Someone for some help there.
36 00:05:19.850 ⇒ 00:05:23.360 Uttam Kumaran: And then I’m gonna continue on to the revenue models.
37 00:05:23.720 ⇒ 00:05:28.340 Uttam Kumaran: So… but these are, like, the jobs were failing, so I kind of had to just go in and….
38 00:05:28.890 ⇒ 00:05:29.290 Amber Lin: Excellent.
39 00:05:29.290 ⇒ 00:05:31.299 Uttam Kumaran: So they’re kind of a sprint interrupt.
40 00:05:31.990 ⇒ 00:05:34.359 Amber Lin: Yeah, I hear that. …
41 00:05:35.140 ⇒ 00:05:40.280 Amber Lin: Is there anything here that we’re gonna move to the next cycle? We have about a week left.
42 00:05:42.630 ⇒ 00:05:44.860 Uttam Kumaran: Probably the dbt tests.
43 00:05:45.730 ⇒ 00:05:46.360 Amber Lin: Oh.
44 00:05:47.870 ⇒ 00:05:50.329 Uttam Kumaran: Yeah, like, all the dbt tests we can move.
45 00:05:58.980 ⇒ 00:05:59.850 Amber Lin: …
46 00:06:01.460 ⇒ 00:06:11.690 Amber Lin: Between the transactions and the subscriptions and refunds, which ones are we keeping, and which ones are we moving, or are we keeping all of them?
47 00:06:13.180 ⇒ 00:06:16.359 Uttam Kumaran: I’m gonna keep all of them, and try to…
48 00:06:17.230 ⇒ 00:06:19.059 Uttam Kumaran: get it done. I don’t have a clear…
49 00:06:19.570 ⇒ 00:06:26.579 Uttam Kumaran: I don’t know yet what the, like, what comes after what, but I’m gonna try to get as much of this done as I can this week.
50 00:06:26.870 ⇒ 00:06:27.550 Amber Lin: Okay.
51 00:06:27.940 ⇒ 00:06:31.059 Uttam Kumaran: Probably subscriptions can move to next week.
52 00:06:33.030 ⇒ 00:06:36.080 Uttam Kumaran: Because… yeah, I haven’t confirmed the loop data yet.
53 00:06:37.210 ⇒ 00:06:37.870 Amber Lin: Okay.
54 00:06:38.040 ⇒ 00:06:39.260 Uttam Kumaran: So….
55 00:06:39.410 ⇒ 00:06:44.969 Amber Lin: I can add a ticket to confirm loop data, and I’ll move these to the next cycle.
56 00:06:54.650 ⇒ 00:06:55.370 Amber Lin: Okay.
57 00:06:57.530 ⇒ 00:06:58.390 Amber Lin: Okay.
58 00:07:02.070 ⇒ 00:07:06.849 Amber Lin: Sounds good. Emily, any updates on… on these?
59 00:07:08.110 ⇒ 00:07:15.440 Emily Giant: I… I chatted with, them a lot about them this morning. I…
60 00:07:16.260 ⇒ 00:07:23.030 Emily Giant: Yeah, they’re gonna be done this sprint. They’re like, They’re, like, This close.
61 00:07:23.220 ⇒ 00:07:30.680 Emily Giant: … Except for the 331 and 332. Those are gonna have to kick to the next one.
62 00:07:31.120 ⇒ 00:07:31.760 Amber Lin: Okay.
63 00:07:32.050 ⇒ 00:07:33.080 Amber Lin: Sounds good.
64 00:07:33.080 ⇒ 00:07:34.989 Emily Giant: But the rest were… the rest are fine.
65 00:07:35.320 ⇒ 00:07:36.050 Amber Lin: Bye.
66 00:07:36.470 ⇒ 00:07:45.460 Emily Giant: I’ve gotta add some, like, window functions to… like, the hard good table is still being a little bit weird, but I think we have a fix now, so…
67 00:07:46.100 ⇒ 00:07:46.800 Emily Giant: Should be okay.
68 00:07:48.400 ⇒ 00:07:54.940 Amber Lin: Serverless input here. Do you think, you can get these done?
69 00:07:56.090 ⇒ 00:07:58.899 Amber Lin: … Do we have to kick anything out?
70 00:07:59.860 ⇒ 00:08:08.259 Demilade Agboola: So, for the first… Let me see some fract orders… …
71 00:08:09.390 ⇒ 00:08:13.700 Demilade Agboola: I think we can prob- we can get fact orders done, …
72 00:08:14.300 ⇒ 00:08:22.520 Demilade Agboola: I think that should be fine. Facts of orders… so fact orders and the fact orders test, like, so 271 and 309.
73 00:08:22.730 ⇒ 00:08:24.030 Demilade Agboola: Should be done.
74 00:08:24.820 ⇒ 00:08:28.119 Amber Lin: Oh. 272 is….
75 00:08:28.120 ⇒ 00:08:32.370 Demilade Agboola: sketchy. It might be… can’t promise it, but potentially, yeah.
76 00:08:32.370 ⇒ 00:08:33.610 Amber Lin: Oh, okay.
77 00:08:35.950 ⇒ 00:08:39.249 Amber Lin: So I’ll keep it as these one to do and these ones.
78 00:08:40.179 ⇒ 00:08:40.939 Demilade Agboola: Yeah.
79 00:08:40.940 ⇒ 00:08:42.530 Amber Lin: Maybe. Okay.
80 00:08:43.690 ⇒ 00:08:49.000 Amber Lin: Sounds good. So for next cycle, I’m gonna group these by project.
81 00:08:49.820 ⇒ 00:08:52.179 Amber Lin: No, no project.
82 00:08:54.960 ⇒ 00:08:56.940 Amber Lin: Yeah, so we have…
83 00:09:01.980 ⇒ 00:09:12.630 Amber Lin: From the revenue part, we have these summary tables, subscriptions, And then, historical… was…
84 00:09:12.730 ⇒ 00:09:15.340 Amber Lin: And then let’s go here…
85 00:09:15.570 ⇒ 00:09:21.299 Amber Lin: There’s no… is there anything from inventory that we want to… want to do next cycle?
86 00:09:26.890 ⇒ 00:09:27.480 Demilade Agboola: Farm.
87 00:09:27.480 ⇒ 00:09:36.520 Emily Giant: I would say, like, adding the snapshots to Looker. I still haven’t, like, added or created any dashboards to do with the snapshots.
88 00:09:38.090 ⇒ 00:09:40.929 Amber Lin: Is it this one, or is it something different?
89 00:09:44.680 ⇒ 00:09:46.040 Emily Giant: This one what? Which one?
90 00:09:46.730 ⇒ 00:09:53.139 Amber Lin: Create a looker report for uncommitted orders. Is it the same, or is it create Looker Snapshot?
91 00:09:53.140 ⇒ 00:09:53.970 Emily Giant: same.
92 00:09:54.310 ⇒ 00:09:54.770 Amber Lin: I see.
93 00:09:54.770 ⇒ 00:10:11.990 Emily Giant: I think that we can probably, like, just delete the uncommitted orders one. Like, the analysts can do that. That will take 2 seconds, so, … but the… adding, a view… I would have to add, like, an entire view and add it to the model for the snapshot data.
94 00:10:15.390 ⇒ 00:10:21.430 Emily Giant: And that might get done this sprint. Denalade and I were gonna chat through it tomorrow on the working session.
95 00:10:23.000 ⇒ 00:10:29.069 Emily Giant: And with the… subscription stuff moved out of this sprint, …
96 00:10:29.180 ⇒ 00:10:35.040 Emily Giant: we can move that in. But then, no, I don’t think there are any inventory anything for next sprint.
97 00:10:35.350 ⇒ 00:10:39.829 Amber Lin: Okay, sounds good. I’m gonna say this is for…
98 00:10:42.240 ⇒ 00:10:45.210 Amber Lin: Cycle. How many points would this one be?
99 00:10:48.150 ⇒ 00:10:49.120 Emily Giant: Probably 3?
100 00:10:49.630 ⇒ 00:10:50.220 Amber Lin: Okay.
101 00:10:51.000 ⇒ 00:10:52.420 Amber Lin: Two, three points.
102 00:10:53.980 ⇒ 00:11:01.810 Amber Lin: Is that… Like, are these… Still… Things you want to do.
103 00:11:06.120 ⇒ 00:11:07.760 Emily Giant: Yes, absolutely.
104 00:11:08.280 ⇒ 00:11:14.569 Emily Giant: Although creating the snapshot data in Looker is connected to 165. That’s, like, why.
105 00:11:14.930 ⇒ 00:11:18.919 Emily Giant: I need to get it into Looker so that I can create that report.
106 00:11:19.870 ⇒ 00:11:21.420 Amber Lin: Mmm, I see.
107 00:11:22.210 ⇒ 00:11:30.180 Amber Lin: I’m just… I can just move this one in, or should I keep this separate?
108 00:11:32.140 ⇒ 00:11:36.019 Emily Giant: You can… Move it in, but it’s definitely at risk already.
109 00:11:37.110 ⇒ 00:11:38.450 Amber Lin: I see. Okay.
110 00:11:39.130 ⇒ 00:11:44.030 Emily Giant: Is it only Tuesday? Yeah, because next week’s a short week with Labor Day.
111 00:11:44.370 ⇒ 00:11:49.370 Amber Lin: Mmm, I see, that makes sense. Okay, so I’ll just… we’ll just keep that one in.
112 00:11:49.660 ⇒ 00:11:57.229 Amber Lin: This one… How many points is this one, and do we want it?
113 00:11:58.850 ⇒ 00:12:00.620 Amber Lin: It’s from 5 weeks ago.
114 00:12:01.900 ⇒ 00:12:06.109 Emily Giant: Yeah… number of hours that the purchase was made prior to cutoff.
115 00:12:06.920 ⇒ 00:12:08.250 Emily Giant: Someone needed it.
116 00:12:08.360 ⇒ 00:12:10.420 Emily Giant: You can backlog it, though.
117 00:12:10.420 ⇒ 00:12:12.540 Demilade Agboola: I think this was a Felipe request.
118 00:12:12.930 ⇒ 00:12:14.829 Emily Giant: Yeah, probably.
119 00:12:23.530 ⇒ 00:12:24.580 Amber Lin: Alright.
120 00:12:25.050 ⇒ 00:12:34.410 Amber Lin: So, looking at revenue… So, we have… the DBT tests…
121 00:12:34.890 ⇒ 00:12:42.029 Amber Lin: Should I move all of these tests, I guess except for the ones in progress, into the next cycle?
122 00:12:42.150 ⇒ 00:12:44.990 Amber Lin: As well as these additional data models.
123 00:12:49.190 ⇒ 00:12:52.179 Demilade Agboola: Yeah, I believe so.
124 00:12:55.050 ⇒ 00:12:57.880 Amber Lin: … What’s this?
125 00:12:58.960 ⇒ 00:12:59.800 Amber Lin: Whoa.
126 00:13:02.110 ⇒ 00:13:07.640 Amber Lin: Is… is this needed? I made this, I don’t know if this is needed, because we have…
127 00:13:08.260 ⇒ 00:13:15.350 Amber Lin: revenue validation with finance after the summary tables, but I don’t… is this something that we’re doing after…
128 00:13:16.180 ⇒ 00:13:19.219 Amber Lin: Like, are these duplicates, do you think?
129 00:13:23.700 ⇒ 00:13:25.540 Uttam Kumaran: Yeah, I think so.
130 00:13:25.740 ⇒ 00:13:27.000 Uttam Kumaran: I don’t know what….
131 00:13:27.000 ⇒ 00:13:27.640 Demilade Agboola: Nuh-
132 00:13:27.640 ⇒ 00:13:28.980 Uttam Kumaran: 315 is.
133 00:13:28.980 ⇒ 00:13:37.459 Demilade Agboola: I’m not sure what 315 is, but I think 316 is about, like, just once we have the models that we’re trying to build out now.
134 00:13:37.840 ⇒ 00:13:47.450 Amber Lin: Yeah, interesting, just meet… to meet with the… the stakeholders is what I meant there. Tests… Okay.
135 00:13:48.880 ⇒ 00:13:57.630 Amber Lin: Move due date… And then… Test… to us.
136 00:13:58.220 ⇒ 00:13:58.940 Amber Lin: Okay.
137 00:13:59.710 ⇒ 00:14:00.570 Amber Lin: Okay.
138 00:14:01.580 ⇒ 00:14:08.540 Amber Lin: Looks good. And then… Additional data models, I have all of them.
139 00:14:10.240 ⇒ 00:14:15.120 Amber Lin: Okay, subscriptions… All of that for the next cycle.
140 00:14:15.720 ⇒ 00:14:22.169 Amber Lin: Is there anything related to Looker that we’re gonna add to next cycle?
141 00:14:23.510 ⇒ 00:14:26.370 Amber Lin: Or, I mean, we could… we could point these.
142 00:14:31.930 ⇒ 00:14:33.509 Amber Lin: Okay, let me….
143 00:14:33.510 ⇒ 00:14:38.830 Uttam Kumaran: Yeah, I guess I’m curious about, like, how the transition for getting inventory and stuff into Looker
144 00:14:39.050 ⇒ 00:14:43.259 Uttam Kumaran: Or, like, the new revenue models, like, is that something we….
145 00:14:43.270 ⇒ 00:14:47.040 Amber Lin: We just hand off to them, or, like, what’s the process?
146 00:14:47.450 ⇒ 00:14:58.830 Emily Giant: I usually put it into Looker, but, inventory, because that was, that’s the only thing that we’ve really put in at this point, I’d love just some guidance with, like.
147 00:14:59.850 ⇒ 00:15:08.500 Emily Giant: how to… like, best practices here, but I can do it. It’s not… … It’s not hard.
148 00:15:11.070 ⇒ 00:15:13.650 Demilade Agboola: It’s turnkey, but time-consuming.
149 00:15:13.980 ⇒ 00:15:16.869 Demilade Agboola: What sort of… what sort of guidance would you need?
150 00:15:17.390 ⇒ 00:15:19.869 Emily Giant: Whether I should, like, rebuild
151 00:15:20.230 ⇒ 00:15:27.859 Emily Giant: whether I should build a new model entirely, or whether I should, like, Build on to top-line sales.
152 00:15:30.580 ⇒ 00:15:32.190 Emily Giant: And if so, like….
153 00:15:32.370 ⇒ 00:15:37.070 Demilade Agboola: What do you mean, build a new model? Are you talking about, like, model in dbt, or are you talking about an explore?
154 00:15:37.070 ⇒ 00:15:37.750 Uttam Kumaran: In Looker.
155 00:15:37.750 ⇒ 00:15:45.169 Emily Giant: In Looker. So, there’s a bunch of Explorers, and they’ll all live in the same model, but right now, we have, like, a bunch of junky models.
156 00:15:45.500 ⇒ 00:15:50.920 Emily Giant: And, my gut feeling is that we should completely overhaul
157 00:15:51.420 ⇒ 00:15:56.289 Emily Giant: the top-line sales model and the KPI model, which are, like, the most used, …
158 00:15:56.840 ⇒ 00:16:08.280 Emily Giant: So, those all, like, have relationships between the explorers, whether it’s, like, one-to-one or, like, one-to-many, and that’s kind of where I’m looking for the guidance, is making sure that, like.
159 00:16:08.510 ⇒ 00:16:16.200 Emily Giant: like, with inventory, how it’s got that bizarre aggregation, like, that, we’re setting it up so that, like.
160 00:16:16.670 ⇒ 00:16:23.790 Emily Giant: certain models don’t commingle, or parts of it do, or just, like, making sure that, like.
161 00:16:24.350 ⇒ 00:16:32.959 Emily Giant: the UX we’re building with this new stuff is clean and not just, like, junked in with all the existing stuff.
162 00:16:34.690 ⇒ 00:16:42.800 Demilade Agboola: I think what we might need to do is create, like, a deadline slash announcement that, hey, this is the new model.
163 00:16:42.990 ⇒ 00:16:45.649 Demilade Agboola: Explores need to be built off of this.
164 00:16:47.190 ⇒ 00:16:53.330 Demilade Agboola: We’ll put… will potentially, like, deprecate the current existing infrastructure.
165 00:16:55.400 ⇒ 00:16:57.400 Demilade Agboola: whatever date. I think.
166 00:16:57.400 ⇒ 00:16:58.110 Emily Giant: Yeah.
167 00:16:58.250 ⇒ 00:17:18.140 Demilade Agboola: For two reasons. One, it gives people a heads up, forces them to use the new data, also force them to QA it as well, and then also potentially can also maybe let us know things that the current model answers that maybe our current, like, the model we’ve built doesn’t necessarily answer.
168 00:17:18.839 ⇒ 00:17:19.509 Emily Giant: -
169 00:17:22.779 ⇒ 00:17:24.529 Emily Giant: Yeah, I agree.
170 00:17:28.099 ⇒ 00:17:38.629 Amber Lin: So is this a natural transition to take us to go to these Looker sessions with the team after we complete the summary tables?
171 00:17:40.779 ⇒ 00:17:48.449 Amber Lin: Or do we still have to make things, incremental? Because I know we have one ticket here about that.
172 00:17:51.460 ⇒ 00:17:58.899 Demilade Agboola: So the incremental part is just for dbt models, that’s the, dbt jobs thing, trying to speed up setting processes.
173 00:17:59.070 ⇒ 00:17:59.630 Amber Lin: Oh.
174 00:18:03.030 ⇒ 00:18:07.660 Demilade Agboola: B… Sorry, I didn’t get the first part of the question.
175 00:18:09.450 ⇒ 00:18:17.519 Amber Lin: Is this a natural progression that we’re doing these Looker sessions after we finish the daily, monthly revenue tables?
176 00:18:17.520 ⇒ 00:18:36.749 Demilade Agboola: Yeah, once we have the numbers for revenue, it’s much easier to show them to the different teams, and then they can either give feedback of if the numbers meet their current aggregations, if we’re applying the logic correctly, or if we have selected some of the logic.
177 00:18:37.170 ⇒ 00:18:43.539 Amber Lin: Okay, how big are these tickets as a rough estimate?
178 00:18:44.190 ⇒ 00:18:46.839 Amber Lin: Based on what we did for inventorying, do you think?
179 00:18:48.670 ⇒ 00:18:53.430 Demilade Agboola: I mean… Potentially, I think, like, two…
180 00:18:53.850 ⇒ 00:18:56.680 Demilade Agboola: 30-45 minute session should be fine.
181 00:18:57.090 ⇒ 00:18:57.660 Demilade Agboola: ….
182 00:18:57.660 ⇒ 00:18:58.200 Amber Lin: Huh?
183 00:18:58.200 ⇒ 00:19:03.689 Demilade Agboola: To let them get conversant with the data, and another one to get feedback for…
184 00:19:04.170 ⇒ 00:19:05.810 Demilade Agboola: All, like, on the data.
185 00:19:06.370 ⇒ 00:19:08.960 Amber Lin: So I can do one point for each?
186 00:19:10.120 ⇒ 00:19:10.980 Demilade Agboola: Sure.
187 00:19:12.620 ⇒ 00:19:18.770 Amber Lin: And the… Updating the look MLs Explorers.
188 00:19:20.580 ⇒ 00:19:23.189 Amber Lin: How much would each of these be?
189 00:19:27.010 ⇒ 00:19:40.330 Demilade Agboola: So I think it depends. I don’t know if necessarily you’ll want to do explores for each of these fact tables, but, … I know Emily does the explores, or the models.
190 00:19:40.880 ⇒ 00:19:45.269 Demilade Agboola: So, I’m not exactly sure how long that takes her. One point? Two points?
191 00:19:45.910 ⇒ 00:19:50.950 Emily Giant: I mean, it doesn’t take long. It’s one point to build out and explore, but…
192 00:19:51.260 ⇒ 00:19:58.690 Emily Giant: I think it does depend on, like, whether we’re building out the entire new, like, instance in the process.
193 00:19:58.690 ⇒ 00:20:00.920 Amber Lin: Because that is a little different.
194 00:20:01.330 ⇒ 00:20:03.700 Emily Giant: That’s, like… Yeah.
195 00:20:04.180 ⇒ 00:20:08.020 Amber Lin: I guess it’s… you can just put it as one point, and I’m just not gonna build up.
196 00:20:08.660 ⇒ 00:20:11.619 Emily Giant: Much, until we have it all, like.
197 00:20:12.160 ⇒ 00:20:16.540 Emily Giant: done, and then I can start swapping stuff out as a different task.
198 00:20:16.850 ⇒ 00:20:17.570 Amber Lin: Okay.
199 00:20:17.790 ⇒ 00:20:28.070 Amber Lin: Then looking at… The upcoming one… …
200 00:20:31.190 ⇒ 00:20:39.070 Amber Lin: Alright… Subscriptions model… Projects revenue…
201 00:21:02.330 ⇒ 00:21:03.180 Amber Lin: Okay.
202 00:21:04.290 ⇒ 00:21:06.040 Amber Lin: So…
203 00:21:06.420 ⇒ 00:21:11.589 Amber Lin: I think I’m only gonna assign these to you, and then we can update them as we have more info.
204 00:21:12.030 ⇒ 00:21:24.350 Amber Lin: … And then… What about these daily, weekly, monthly summary tables? Who would be doing those?
205 00:21:26.030 ⇒ 00:21:27.570 Demilade Agboola: Oh, most likely me.
206 00:21:27.960 ⇒ 00:21:28.750 Amber Lin: Okay.
207 00:21:40.020 ⇒ 00:21:47.070 Amber Lin: And what about revenue… marketing revenue summary and BOMEOM subscription snapshots?
208 00:21:52.840 ⇒ 00:21:58.400 Demilade Agboola: Markets revenue, again, it will most likely be me, but… Can’t be straight up.
209 00:22:00.500 ⇒ 00:22:03.590 Demilade Agboola: Snapshot is… it’s gonna be me.
210 00:22:06.000 ⇒ 00:22:13.009 Amber Lin: Let’s see. Utum is building the subscription model, though, are you handling the subscription snapshots?
211 00:22:13.570 ⇒ 00:22:20.239 Demilade Agboola: No. Well, so I’ll probably handle the snapshots, but, like, what’s really not the subhistron models, yes.
212 00:22:20.240 ⇒ 00:22:21.369 Amber Lin: I see.
213 00:22:28.500 ⇒ 00:22:34.640 Amber Lin: … These looker sessions with the team.
214 00:22:43.420 ⇒ 00:22:46.549 Amber Lin: all the different sessions with the stakeholders.
215 00:22:47.660 ⇒ 00:22:57.360 Demilade Agboola: Hmm… I mean, potentially would just, like, be myself, and maybe Emily, or myself, and…
216 00:22:58.110 ⇒ 00:22:59.899 Demilade Agboola: Where we just walked through…
217 00:23:00.010 ⇒ 00:23:08.349 Demilade Agboola: I mean, yeah, Emily… Emily’s the one who will be showing the people the stuff in the dashboard, so I think myself and Emily will be…
218 00:23:08.490 ⇒ 00:23:09.590 Demilade Agboola: Trispy.
219 00:23:10.570 ⇒ 00:23:11.600 Amber Lin: Okay.
220 00:23:13.200 ⇒ 00:23:14.400 Amber Lin: So…
221 00:23:25.720 ⇒ 00:23:31.600 Amber Lin: Okay, … Most of these have… Estimates…
222 00:23:36.820 ⇒ 00:23:46.029 Amber Lin: Burn… how big is the fact subscriptions model, and what point estimate should I give it?
223 00:23:46.210 ⇒ 00:23:47.669 Demilade Agboola: I’m dropped from the call.
224 00:23:48.010 ⇒ 00:23:53.839 Amber Lin: Oh, I see. What… what estimate should I give a subscriptions model?
225 00:23:54.270 ⇒ 00:23:57.020 Emily Giant: That’s a big N. I would say more than 5.
226 00:23:59.390 ⇒ 00:24:05.530 Emily Giant: It’s all new, too, but 5 would be my lowest estimate for that. What do you think, Demolade?
227 00:24:05.920 ⇒ 00:24:08.210 Demilade Agboola: Yeah, I would say 5 thereafter.
228 00:24:08.870 ⇒ 00:24:09.620 Amber Lin: Okay.
229 00:24:16.000 ⇒ 00:24:16.860 Amber Lin: Hello.
230 00:24:17.980 ⇒ 00:24:23.000 Amber Lin: And we will have some things carrying over from this sprint.
231 00:24:23.340 ⇒ 00:24:30.940 Amber Lin: Is there… could we close out these, or do you have any… anyone helping you review the models?
232 00:24:31.390 ⇒ 00:24:35.500 Amber Lin: Yeah, I’m just reviewing them for now, so… Great.
233 00:24:38.490 ⇒ 00:24:42.089 Amber Lin: And then, Emily, you have someone reviewing these too, right?
234 00:24:43.900 ⇒ 00:24:44.630 Emily Giant: Yes.
235 00:24:44.790 ⇒ 00:24:45.540 Amber Lin: Awesome.
236 00:24:46.020 ⇒ 00:24:52.049 Amber Lin: Okay, … We have 5 minutes left. Can we…
237 00:24:52.180 ⇒ 00:24:55.130 Amber Lin: How can we write an update for…
238 00:24:55.270 ⇒ 00:25:01.189 Amber Lin: The CEO. How should we phrase it? So, what do we do post-Mother’s Day?
239 00:25:04.130 ⇒ 00:25:06.769 Demilade Agboola: Oh, post-Mother’s Day, ….
240 00:25:08.080 ⇒ 00:25:11.640 Demilade Agboola: So, so post-Mother’s Day, we have been…
241 00:25:11.810 ⇒ 00:25:21.859 Demilade Agboola: building out the models to flesh out inventory. So now we have inventory adjustments per…
242 00:25:22.990 ⇒ 00:25:25.790 Demilade Agboola: For each inventory, like, for each lot.
243 00:25:28.620 ⇒ 00:25:30.480 Demilade Agboola: So we haven’t know each other a lot.
244 00:25:30.800 ⇒ 00:25:38.080 Demilade Agboola: So this includes… Like, spoilage… spoilage, shrinkage… ….
245 00:25:41.530 ⇒ 00:25:53.529 Emily Giant: Redelivery, subscription, so any reconciliation and any sale type adjustment, as well as whether or not the order was successfully committed to an inventory lot in the system.
246 00:25:53.650 ⇒ 00:25:56.859 Emily Giant: So this will give us, like, better insight into,
247 00:25:58.150 ⇒ 00:26:07.650 Emily Giant: Like, whether or not our spoilage numbers are accurate, how often the system is failing to claim inventory.
248 00:26:07.770 ⇒ 00:26:09.439 Amber Lin: More allotted items.
249 00:26:19.570 ⇒ 00:26:21.570 Amber Lin: The system is failing on….
250 00:26:23.390 ⇒ 00:26:26.910 Emily Giant: Claiming inventory for lots of items.
251 00:26:32.430 ⇒ 00:26:37.920 Emily Giant: We also have, suborder level granularity.
252 00:26:41.490 ⇒ 00:26:48.110 Emily Giant: So, any… Adjustments on a lot can be tied back to the item and the suborder ID.
253 00:26:54.160 ⇒ 00:26:57.800 Emily Giant: Which will make it, possible to…
254 00:26:58.110 ⇒ 00:27:02.400 Emily Giant: Cross-reference all revenue and sales data in the future.
255 00:27:08.950 ⇒ 00:27:18.499 Emily Giant: Also completely refractored all of the historical inventory data to align with the new structure, so that, there’s no break between
256 00:27:18.870 ⇒ 00:27:22.960 Emily Giant: like… The past and now.
257 00:27:23.580 ⇒ 00:27:33.729 Emily Giant: And, … We corrected for, … the… Lack of suborder level granularity.
258 00:27:34.290 ⇒ 00:27:36.850 Emily Giant: In inventory views in the past.
259 00:27:39.090 ⇒ 00:27:43.160 Emily Giant: And we’ve already connected that, or actually, it’s this PR that I’m about to push.
260 00:27:43.310 ⇒ 00:27:48.529 Emily Giant: Corrected that where it was missing from, component-level data.
261 00:27:52.390 ⇒ 00:27:55.340 Emily Giant: We also replaced all of our product
262 00:27:56.100 ⇒ 00:27:59.670 Emily Giant: tables with, native Shopify data.
263 00:28:09.440 ⇒ 00:28:13.509 Demilade Agboola: And right now, the PR that we’re working on is 4H.
264 00:28:13.780 ⇒ 00:28:21.710 Demilade Agboola: … For each order, we are being able to… we’re trying to aggregate, like, the taxes, discounts.
265 00:28:22.010 ⇒ 00:28:25.199 Demilade Agboola: Through the Shopify data.
266 00:28:26.110 ⇒ 00:28:30.109 Demilade Agboola: So that we can much easily calculate revenue in the future.
267 00:28:39.590 ⇒ 00:28:42.979 Amber Lin: Okay. Awesome, that’s the inventory side. Anything else?
268 00:28:43.530 ⇒ 00:28:51.090 Emily Giant: Yes, well, not inventory, but, UTAM built out an aggregate customer
269 00:28:51.400 ⇒ 00:28:57.390 Emily Giant: Table, so that, no matter what system, like, past or present, we’re referencing.
270 00:28:58.120 ⇒ 00:29:03.860 Emily Giant: Each customer has, like, a streamlined profile in Looker.
271 00:29:03.990 ⇒ 00:29:10.529 Emily Giant: That will connect their… customer ID to any system we’re referencing.
272 00:29:24.100 ⇒ 00:29:36.990 Amber Lin: Awesome. And I imagine on the revenue side, it’s mostly just models, nothing… have we had any impact yet, or are we just still in the building phase?
273 00:29:37.850 ⇒ 00:29:40.310 Demilade Agboola: They’re still building phase for revenue.
274 00:29:41.270 ⇒ 00:29:46.080 Emily Giant: I would also add that, like, we have the net new integration that we’re working through.
275 00:29:46.520 ⇒ 00:29:49.300 Emily Giant: for Loop and North Beam.
276 00:29:50.910 ⇒ 00:29:55.670 Demilade Agboola: So now we can start having visibility into subscription data.
277 00:29:57.990 ⇒ 00:30:07.179 Emily Giant: Yeah, forecasting… … Will be possible again, and that had deprecated.
278 00:30:07.180 ⇒ 00:30:07.860 Amber Lin: No.
279 00:30:08.780 ⇒ 00:30:09.930 Emily Giant: Entirely.
280 00:30:12.860 ⇒ 00:30:13.480 Amber Lin: Okay.
281 00:30:14.490 ⇒ 00:30:23.080 Amber Lin: Awesome. I’ll copy that to the channel. I’ll edit this a bit, and then copy that to the channel. Thanks both. This was great.
282 00:30:23.970 ⇒ 00:30:25.240 Emily Giant: Alright, awesome, thank you.
283 00:30:25.690 ⇒ 00:30:26.350 Amber Lin: Bye!
284 00:30:26.350 ⇒ 00:30:28.160 Emily Giant: Bye. Talk soon. Bye.