Meeting Title: US x BF | Standup Date: 2025-10-16 Meeting participants: Awaish Kumar, Demilade Agboola, Emily Giant, Amber Lin
WEBVTT
1 00:04:11.640 ⇒ 00:04:12.659 Demilade Agboola: Alright, everyone.
2 00:04:13.040 ⇒ 00:04:14.150 Emily Giant: Hello.
3 00:04:14.450 ⇒ 00:04:15.300 Awaish Kumar: removed.
4 00:04:18.029 ⇒ 00:04:19.469 Emily Giant: How’s it going?
5 00:04:19.930 ⇒ 00:04:27.909 Demilade Agboola: Pretty good. So Amber is back, but I’m not sure if she’s joining in, so we could start, and if she joins in time, sure.
6 00:04:28.190 ⇒ 00:04:29.360 Demilade Agboola: If not…
7 00:04:32.790 ⇒ 00:04:35.009 Emily Giant: If not, if not, then she does not.
8 00:04:35.340 ⇒ 00:04:39.719 Demilade Agboola: Exactly. So, give me one second, I’ll share my screen.
9 00:04:40.220 ⇒ 00:04:40.770 Emily Giant: Bye.
10 00:04:44.200 ⇒ 00:04:46.830 Demilade Agboola: share… next stop.
11 00:04:50.360 ⇒ 00:04:57.109 Demilade Agboola: Alright, so we can start with Oisha. Oh, Amber’s here!
12 00:04:57.680 ⇒ 00:04:59.640 Awaish Kumar: Oh, hi! Welcome back!
13 00:04:59.640 ⇒ 00:05:00.950 Amber Lin: Hello!
14 00:05:03.510 ⇒ 00:05:04.710 Emily Giant: How was your time off?
15 00:05:05.780 ⇒ 00:05:07.350 Amber Lin: Not enough.
16 00:05:08.010 ⇒ 00:05:09.110 Demilade Agboola: It was very…
17 00:05:09.110 ⇒ 00:05:16.889 Amber Lin: It was very nice. My family’s leaving, midnight today, so I’ll still get some time with them after I finish work today.
18 00:05:17.240 ⇒ 00:05:18.250 Emily Giant: That’s good.
19 00:05:19.010 ⇒ 00:05:19.710 Amber Lin: Yeah.
20 00:05:23.600 ⇒ 00:05:28.070 Demilade Agboola: Okay, so I was just borrowing through tickets with… the team.
21 00:05:30.520 ⇒ 00:05:33.280 Demilade Agboola: Alright, Always, do we have any…
22 00:05:33.470 ⇒ 00:05:35.129 Demilade Agboola: Feedback on any of the tasks?
23 00:05:38.570 ⇒ 00:05:42.359 Awaish Kumar: Yes, actually, for the…
24 00:05:47.630 ⇒ 00:05:56.730 Awaish Kumar: Okay, for North PIM order attribution tasks, I have been, so for the Shopify part.
25 00:05:57.090 ⇒ 00:06:01.889 Awaish Kumar: is running, as I said. But Nazvita, I have it working on getting the exports
26 00:06:02.040 ⇒ 00:06:05.489 Awaish Kumar: And I’ve explored ways how to, like,
27 00:06:05.910 ⇒ 00:06:11.760 Awaish Kumar: make that pipeline. But the problem is, when I’m trying to export, Excellent.
28 00:06:11.970 ⇒ 00:06:18.420 Awaish Kumar: Sometimes, Northstream generates a CSV file, And sometimes it doesn’t…
29 00:06:19.170 ⇒ 00:06:21.549 Awaish Kumar: So, I’m not sure what’s happening.
30 00:06:22.220 ⇒ 00:06:27.020 Awaish Kumar: In Northstream, you have to go in… to the dashboard, travel…
31 00:06:28.090 ⇒ 00:06:32.310 Awaish Kumar: Create, export, and then they have a…
32 00:06:32.880 ⇒ 00:06:39.369 Awaish Kumar: a documents page, right? You have to go there to find out your export.
33 00:06:39.580 ⇒ 00:06:48.579 Awaish Kumar: And the problem is, till… since yesterday, I’ve tried, like, multiple exports, and I only got… got one.
34 00:06:49.870 ⇒ 00:06:56.210 Awaish Kumar: And that’s why I’m able to put everything. And also, when we are…
35 00:06:56.430 ⇒ 00:06:59.179 Awaish Kumar: If you export all the orders.
36 00:06:59.470 ⇒ 00:07:08.289 Awaish Kumar: The thing is that if the order is data, there’s no way to identify which ad platform they come from.
37 00:07:08.910 ⇒ 00:07:15.819 Awaish Kumar: The only way… To do that is to get multiple exports and, like, filter first.
38 00:07:16.020 ⇒ 00:07:21.600 Awaish Kumar: by an ad platform, which is a top-level filter, like Facebook AdWords.
39 00:07:22.130 ⇒ 00:07:29.430 Awaish Kumar: TikTok, and then you download the export, and then we can put it in a table, that way we know what
40 00:07:29.840 ⇒ 00:07:32.479 Awaish Kumar: a duty of source or a graphical site.
41 00:07:32.660 ⇒ 00:07:34.790 Awaish Kumar: Orion platform, it is coming from.
42 00:07:35.330 ⇒ 00:07:41.889 Awaish Kumar: And that’s the issue. When I am filtering by different end platforms and trying to get multiple exports.
43 00:07:42.090 ⇒ 00:07:47.270 Awaish Kumar: I’m not able to get… Like, it doesn’t show any further documents.
44 00:07:49.130 ⇒ 00:07:51.470 Awaish Kumar: And they’re, like, a list of experts.
45 00:07:51.870 ⇒ 00:08:00.839 Awaish Kumar: I’m not sure what to do here. Maybe, like, 2 or 3 export, like, not stream support, sorry, to ask them, like, why…
46 00:08:01.120 ⇒ 00:08:04.580 Awaish Kumar: We are not able to generate multiple exports here.
47 00:08:06.600 ⇒ 00:08:11.209 Demilade Agboola: Yeah, I think… is it possible for us to reach out to Northbeam and find out why?
48 00:08:12.520 ⇒ 00:08:15.170 Awaish Kumar: Yeah, yeah, that’s what I’m saying, like, there’s the only way because…
49 00:08:15.590 ⇒ 00:08:19.620 Awaish Kumar: on their platform, there’s nothing much I can do. I can just go…
50 00:08:19.780 ⇒ 00:08:22.559 Awaish Kumar: Like, the only way I can find
51 00:08:22.680 ⇒ 00:08:30.779 Awaish Kumar: referral site for, like, ad platform for each order is to first filter by ad platform, and then get export.
52 00:08:31.030 ⇒ 00:08:37.320 Awaish Kumar: And, to do that, I have to get multiple experts to cover all the orders.
53 00:08:37.510 ⇒ 00:08:40.729 Awaish Kumar: Hence, I’m not getting it in the exports list.
54 00:08:40.980 ⇒ 00:08:47.079 Awaish Kumar: of multiple CSVs, which they share on their, documents, homepage.
55 00:08:47.670 ⇒ 00:08:51.370 Awaish Kumar: So, the only way to find out is that I will write
56 00:08:51.630 ⇒ 00:09:05.280 Awaish Kumar: to Northwave support, but the issue is, also is that they are very slow. I have tried contacting them for, like, one issue for another client, and
57 00:09:05.690 ⇒ 00:09:08.560 Awaish Kumar: They haven’t replied me yet for, like, 2 days.
58 00:09:10.470 ⇒ 00:09:11.359 Demilade Agboola: Oh, okay.
59 00:09:16.520 ⇒ 00:09:17.300 Amber Lin: Okay.
60 00:09:18.280 ⇒ 00:09:24.980 Amber Lin: I think on this test, we probably should also let Zach know on what’s going on. Emily, do you have…
61 00:09:24.980 ⇒ 00:09:25.800 Emily Giant: Yeah, he’s…
62 00:09:25.800 ⇒ 00:09:26.550 Amber Lin: 6 here.
63 00:09:26.960 ⇒ 00:09:32.710 Emily Giant: Yeah, he’s out on, Thursday, today, and tomorrow.
64 00:09:34.530 ⇒ 00:09:37.699 Emily Giant: But yeah, I would definitely ping him, he’ll still see it.
65 00:09:37.900 ⇒ 00:09:41.150 Demilade Agboola: That way, he’ll see it right when he comes back.
66 00:09:41.880 ⇒ 00:09:42.270 Demilade Agboola: Yeah.
67 00:09:42.270 ⇒ 00:09:44.059 Amber Lin: So we’ll let Zach know.
68 00:09:44.890 ⇒ 00:09:47.480 Demilade Agboola: Yeah, so this is also an issue that we…
69 00:09:48.240 ⇒ 00:09:51.419 Demilade Agboola: I’m trying to get ahead of, because, you know,
70 00:09:53.950 ⇒ 00:09:59.349 Demilade Agboola: it was… it’s not necessarily, like, it’s a… it was a request made by PK yesterday.
71 00:09:59.540 ⇒ 00:10:07.300 Demilade Agboola: For the performance reports. It’s not, like, high prime right now, but obviously we want to get ahead of any issues with NPIM.
72 00:10:08.580 ⇒ 00:10:15.899 Awaish Kumar: Okay, yeah. And the other thing about GSS, I’m looking at, I’ve been looking at this
73 00:10:16.150 ⇒ 00:10:19.729 Awaish Kumar: view the… in the looker.
74 00:10:19.880 ⇒ 00:10:26.890 Awaish Kumar: And I see the difference she… she was talking about, like, 33K versus… 14K.
75 00:10:27.040 ⇒ 00:10:35.390 Awaish Kumar: But that… both the data is coming from GF4, And I suspect, that…
76 00:10:35.700 ⇒ 00:10:44.730 Awaish Kumar: The audience behavior table might have duplicates or something, because…
77 00:10:45.580 ⇒ 00:10:51.620 Awaish Kumar: In both the… like, both these tables are coming from same connector, Salesforce…
78 00:10:52.170 ⇒ 00:10:54.669 Awaish Kumar: So, there should not be any…
79 00:10:55.340 ⇒ 00:10:58.179 Awaish Kumar: Differences, because we are not applying any
80 00:11:00.160 ⇒ 00:11:03.510 Awaish Kumar: Transformations, and we are not getting it from multiple
81 00:11:03.840 ⇒ 00:11:07.499 Awaish Kumar: Different ways, so we… maybe somewhere…
82 00:11:07.870 ⇒ 00:11:25.589 Awaish Kumar: somewhere we are, like, mismatching it, but the thing is that there’s a stretch… there’s a connector in the stretch which gets two tables from GF4. One is called the audience behavior. Another one is a bigger name for sessions, I don’t remember the full name.
83 00:11:25.610 ⇒ 00:11:28.880 Awaish Kumar: And when we calculate it for using both the different
84 00:11:29.030 ⇒ 00:11:32.259 Awaish Kumar: tables, the total count is different.
85 00:11:34.520 ⇒ 00:11:43.390 Awaish Kumar: And I suspect, though, Suspect that the other table… the table looker table is kind of… Maybe Hannah.
86 00:11:43.940 ⇒ 00:11:48.150 Awaish Kumar: the… how the GF4 is categorizing it is maybe…
87 00:11:48.340 ⇒ 00:11:50.540 Awaish Kumar: It’s been giving us some duplicates.
88 00:11:52.360 ⇒ 00:11:56.470 Demilade Agboola: Yeah, I think what we could just do is also maybe just look at the…
89 00:11:56.940 ⇒ 00:12:04.810 Demilade Agboola: export, like, what was ingested. If the raw numbers are matching GA4 raw numbers, then…
90 00:12:04.810 ⇒ 00:12:06.950 Awaish Kumar: It’s not doing any exporting, right?
91 00:12:08.360 ⇒ 00:12:10.379 Demilade Agboola: No, I mean, like, the ingestion, not the export.
92 00:12:10.380 ⇒ 00:12:14.480 Awaish Kumar: So, yeah, that’s what I mentioned, that, okay, so, like.
93 00:12:17.650 ⇒ 00:12:23.009 Awaish Kumar: the… like, I just looked at the query, like, in the looker.
94 00:12:23.200 ⇒ 00:12:27.220 Awaish Kumar: view, there’s the SQL query being used
95 00:12:27.530 ⇒ 00:12:35.869 Awaish Kumar: I just looked at that. It is actually just carrying data from g4.audiencebehavior table.
96 00:12:36.670 ⇒ 00:12:47.040 Awaish Kumar: GF4 data, right? And the other table where the table I ingested is coming from is going to is also the same data set, GF4.
97 00:12:47.420 ⇒ 00:12:49.450 Awaish Kumar: something sessions.
98 00:12:50.060 ⇒ 00:12:55.620 Awaish Kumar: And, and it is exactly just counting sessions?
99 00:12:55.730 ⇒ 00:13:01.150 Awaish Kumar: From both, and it’s just giving the… numbers.
100 00:13:01.260 ⇒ 00:13:06.010 Awaish Kumar: So, like, it’s just raw data, like, there’s no transformation on it.
101 00:13:06.560 ⇒ 00:13:07.270 Demilade Agboola: Yeah.
102 00:13:07.930 ⇒ 00:13:13.839 Demilade Agboola: Okay, I think we might need to just spike and just understand if we should be using a different table.
103 00:13:13.920 ⇒ 00:13:19.899 Awaish Kumar: Or, like, what exactly is going on, if the definition of the numbers are not exactly the same? Maybe sessions…
104 00:13:19.900 ⇒ 00:13:28.140 Demilade Agboola: in GA4 is maybe distinct, versus the sessions here might not be… like, there should be some disparity.
105 00:13:29.590 ⇒ 00:13:34.579 Awaish Kumar: The thing is, both are from GF4. Like, do we have access to GF4 platform itself?
106 00:13:34.720 ⇒ 00:13:37.859 Awaish Kumar: I’ve been looking at that, but I couldn’t find that.
107 00:13:38.260 ⇒ 00:13:38.990 Demilade Agboola: No, we don’t.
108 00:13:38.990 ⇒ 00:13:39.950 Emily Giant: Kay has it.
109 00:13:39.950 ⇒ 00:13:44.419 Demilade Agboola: the PK does, but we can make a request if we really need it from, Alex.
110 00:13:44.760 ⇒ 00:13:47.730 Demilade Agboola: Alexa or Zach, and they’ll get… they’ll get us access.
111 00:13:47.730 ⇒ 00:13:53.420 Awaish Kumar: So I might get into GF4 itself and find out which… which is… which one is correct value.
112 00:13:54.150 ⇒ 00:13:55.759 Demilade Agboola: Okay, sounds good.
113 00:13:57.600 ⇒ 00:14:01.080 Demilade Agboola: Alright, for my ticket, right now.
114 00:14:01.250 ⇒ 00:14:16.479 Demilade Agboola: highest priority has been the test with the revenue, so we’ve done that. At this point, I think we can actually kind of close it. But what I’m doing now, and I don’t have a ticket for that, but what I’m doing now is building out the
115 00:14:16.930 ⇒ 00:14:22.480 Demilade Agboola: The final revenue models, basically, and that’s because
116 00:14:23.210 ⇒ 00:14:27.109 Demilade Agboola: Emily has been able to push the subscription, so now I’m building out the entire…
117 00:14:27.430 ⇒ 00:14:35.050 Demilade Agboola: Revenue for all the orders, so both subscription orders and non-orders, and we will have that by tomorrow, basically.
118 00:14:36.940 ⇒ 00:14:39.799 Demilade Agboola: So there’s that.
119 00:14:40.690 ⇒ 00:14:44.750 Demilade Agboola: This is… Oh, faulty, it’s…
120 00:14:44.940 ⇒ 00:14:51.809 Demilade Agboola: not necessarily high prize, because it’s… the reason why it was needed was for the strikethrough in…
121 00:14:52.630 ⇒ 00:15:00.459 Demilade Agboola: The revenue, but we’ve been able to do that without necessarily building this, so it’s not necessarily… it’s not needed right now.
122 00:15:01.150 ⇒ 00:15:05.310 Demilade Agboola: I think we could just put it in the backlog, potentially.
123 00:15:05.620 ⇒ 00:15:07.260 Demilade Agboola: And then…
124 00:15:08.490 ⇒ 00:15:16.340 Demilade Agboola: For unscheduling, this hasn’t been done, not high priority. This, I think, can be canceled, because we don’t need it.
125 00:15:16.930 ⇒ 00:15:22.189 Demilade Agboola: We’re not doing a factor borders, we’re doing a factor line items, and that already has tests.
126 00:15:26.340 ⇒ 00:15:33.450 Demilade Agboola: Yeah, so these ones are in the back burner because, again, they’re low pride, they’re not… these are things for PK that are not necessarily…
127 00:15:33.970 ⇒ 00:15:35.790 Demilade Agboola: The highest priority right now.
128 00:15:39.420 ⇒ 00:15:41.550 Amber Lin: I’ll add the priorities for them.
129 00:15:42.360 ⇒ 00:15:44.230 Demilade Agboola: Okay, sounds good.
130 00:15:46.360 ⇒ 00:15:49.549 Demilade Agboola: I think I’m working on, like, the… so the daily revenue
131 00:15:49.720 ⇒ 00:15:52.629 Demilade Agboola: models, and that’s what I’m working on right now, basically.
132 00:15:52.630 ⇒ 00:15:53.320 Amber Lin: Huh.
133 00:15:53.400 ⇒ 00:15:54.589 Demilade Agboola: All those data.
134 00:15:54.590 ⇒ 00:16:03.869 Amber Lin: doing the QA with Perry? Like, the SOMP QA with Perry? She’s out after this week, right?
135 00:16:04.230 ⇒ 00:16:04.770 Demilade Agboola: No.
136 00:16:04.770 ⇒ 00:16:06.459 Emily Giant: She’s here till the 22nd.
137 00:16:06.710 ⇒ 00:16:07.659 Amber Lin: Oh, okay.
138 00:16:08.330 ⇒ 00:16:16.480 Demilade Agboola: Alright, so we can always do another one with her next week, but we did do it with her. We were able to get clarification on some things. It’s in a…
139 00:16:16.980 ⇒ 00:16:27.669 Demilade Agboola: she gave feedback on some things, and right now the numbers are in a, like, really good spot. We just need to be able to tie everything together, and then we’ll probably do another QA with her early next week, just so we’re…
140 00:16:27.920 ⇒ 00:16:29.810 Demilade Agboola: We’re all on the same page with everything.
141 00:16:35.860 ⇒ 00:16:37.180 Demilade Agboola: And then…
142 00:16:39.260 ⇒ 00:16:51.189 Amber Lin: Okay, okay, that ticket also has acceptance criteria. Oh, if you said we don’t need the fact suborders, I assume Emily also don’t need to do the Looker, like, LookML Explorer for fact suborders, right?
143 00:16:51.190 ⇒ 00:16:54.089 Emily Giant: I think we do need that, because that’s the delivery…
144 00:16:54.840 ⇒ 00:16:55.440 Amber Lin: Huh.
145 00:16:55.440 ⇒ 00:17:00.259 Emily Giant: the delivery information, right? Or is that coming through somewhere else?
146 00:17:00.830 ⇒ 00:17:09.379 Demilade Agboola: So the idea is we can always use the… we can think about it, but, like, the idea is you can always use the fact-order line items, and then rejuvenate some of them.
147 00:17:09.630 ⇒ 00:17:10.970 Demilade Agboola: information, so…
148 00:17:12.119 ⇒ 00:17:16.349 Emily Giant: I would say that it’s not the highest priority to get fact-up suborders done.
149 00:17:18.159 ⇒ 00:17:22.819 Amber Lin: Are we ever getting it done? I thought we canceled it. I can update the ticket name.
150 00:17:24.589 ⇒ 00:17:28.989 Demilade Agboola: So that was actually a test for suborders, not actually even building our suborders, so…
151 00:17:30.799 ⇒ 00:17:37.219 Demilade Agboola: But, yeah, we could always, like, look at the… like, if it’s delivery information, delivery information can always be added.
152 00:17:37.429 ⇒ 00:17:41.709 Demilade Agboola: That’s not… that’s not necessarily a hard thing to do.
153 00:17:42.140 ⇒ 00:17:42.760 Amber Lin: Okay.
154 00:17:43.220 ⇒ 00:17:44.490 Emily Giant: Yeah, true.
155 00:17:44.490 ⇒ 00:17:45.580 Amber Lin: Sounds good.
156 00:17:50.350 ⇒ 00:17:51.200 Amber Lin: Right.
157 00:17:51.200 ⇒ 00:17:55.610 Demilade Agboola: Then, for Emily, do you have any updates on the tickets you’re working on?
158 00:17:56.070 ⇒ 00:18:12.889 Emily Giant: No updates outside of we pushed the first, subscription for loop and historical. Right now, I’m working on, the last part of the historical subscriptions data, which is the, like, old, old, old subscriptions from, like, 2017 through 2020.
159 00:18:12.890 ⇒ 00:18:21.899 Emily Giant: But I wanted to get the, like, current and most recent historical ones out there so the Demolata could use it for the revenue work, but that is…
160 00:18:21.900 ⇒ 00:18:26.539 Emily Giant: updated in Looker and is, deployed in dbt.
161 00:18:27.350 ⇒ 00:18:29.379 Amber Lin: Is that a ticket anywhere?
162 00:18:29.910 ⇒ 00:18:34.890 Emily Giant: Yeah, it’s historical aligned legacy subscriptions to updated MART models, sticky.
163 00:18:34.890 ⇒ 00:18:35.540 Amber Lin: internal.
164 00:18:35.540 ⇒ 00:18:38.180 Emily Giant: So I’m on to the internal part.
165 00:18:38.820 ⇒ 00:18:39.590 Amber Lin: Awesome.
166 00:18:41.110 ⇒ 00:18:41.870 Amber Lin: Okay.
167 00:18:43.610 ⇒ 00:18:52.139 Amber Lin: And then… Any updates with PK? Is he doing any of the tickets we wanted him to do?
168 00:18:54.110 ⇒ 00:19:12.110 Emily Giant: Yeah, he’s building that seed file for the old promo codes, he’s working on that currently. And, as far as the Google Analytics, I think that he’s reviewing, some documentation I sent over with GA4 to find, like,
169 00:19:12.240 ⇒ 00:19:20.709 Emily Giant: If we’re not replicating certain custom fields that are available, because he would know that best, but we did meet with him yesterday and work on some of those things.
170 00:19:21.470 ⇒ 00:19:26.220 Amber Lin: Okay. Is there a ticket about the seed files, or is it just a category?
171 00:19:26.220 ⇒ 00:19:28.599 Emily Giant: I think it’s the promo code categories one.
172 00:19:28.600 ⇒ 00:19:29.210 Amber Lin: Oh, okay.
173 00:19:29.210 ⇒ 00:19:33.270 Emily Giant: part of that. That’s, like, where he’s at in that step, but it is in progress.
174 00:19:33.270 ⇒ 00:19:35.009 Amber Lin: Sounds good.
175 00:19:37.500 ⇒ 00:19:38.270 Amber Lin: Alright.
176 00:19:44.090 ⇒ 00:19:44.860 Amber Lin: Okay.
177 00:19:46.510 ⇒ 00:19:52.570 Amber Lin: Looking at this week…
178 00:19:54.950 ⇒ 00:20:01.319 Amber Lin: Can I push out any of these things? I mean, I just added it to this cycle because I wasn’t sure if we’re doing it.
179 00:20:01.510 ⇒ 00:20:05.219 Amber Lin: this week, but I assume the meta playing…
180 00:20:06.900 ⇒ 00:20:09.980 Amber Lin: Channels, and this are for next week.
181 00:20:13.110 ⇒ 00:20:15.150 Demilade Agboola: Yeah, all those gym saw as well.
182 00:20:15.790 ⇒ 00:20:16.410 Amber Lin: Okay.
183 00:20:19.150 ⇒ 00:20:24.930 Amber Lin: So we’ll just mostly do the… this… Are we doinging this ticket?
184 00:20:25.410 ⇒ 00:20:27.670 Amber Lin: The OMS refunds?
185 00:20:31.040 ⇒ 00:20:31.970 Amber Lin: by anything?
186 00:20:33.190 ⇒ 00:20:38.890 Demilade Agboola: I don’t think it’s blocked by anything, to be honest. I…
187 00:20:39.050 ⇒ 00:20:43.050 Demilade Agboola: Emery, what would be the use case for OMH refunds as a table?
188 00:20:44.480 ⇒ 00:20:47.150 Emily Giant: I don’t know if it’s just historical.
189 00:20:47.280 ⇒ 00:20:49.780 Emily Giant: I’m guessing it’s just historical data.
190 00:20:50.110 ⇒ 00:20:53.720 Emily Giant: to, like, append to the new Shopify data for refunds.
191 00:20:55.140 ⇒ 00:20:57.720 Demilade Agboola: Yeah, but we already have refunds in Shopify data, though.
192 00:20:58.370 ⇒ 00:21:01.589 Emily Giant: All of them, even from prior to the migration.
193 00:21:02.340 ⇒ 00:21:05.159 Demilade Agboola: No, but in that case, we will just have…
194 00:21:05.320 ⇒ 00:21:12.109 Demilade Agboola: the table that, like, we can have all that data from, like, an OMS table. I’m asking specifically about an OMS refunds table.
195 00:21:12.540 ⇒ 00:21:14.730 Demilade Agboola: So, like, if we have an OMS, like.
196 00:21:14.850 ⇒ 00:21:19.469 Demilade Agboola: Table where we have all the orders and refunds in one table.
197 00:21:20.000 ⇒ 00:21:22.820 Demilade Agboola: Versus just the refund-specific table.
198 00:21:24.000 ⇒ 00:21:26.649 Emily Giant: No, we don’t need a refund-specific table.
199 00:21:26.860 ⇒ 00:21:30.799 Demilade Agboola: Yeah, that’s what I’m saying. So that’s why I’m like, why do we need that OMS refund table?
200 00:21:31.480 ⇒ 00:21:39.560 Emily Giant: The only thing that I would say of this, would be that we need to, have a model that
201 00:21:39.850 ⇒ 00:21:48.290 Emily Giant: connects the reasons for the refunds to… which is in OMS, to the refunds in, Shopify.
202 00:21:49.110 ⇒ 00:21:49.999 Demilade Agboola: Okay, fair enough.
203 00:21:50.000 ⇒ 00:21:51.789 Emily Giant: And that’s in the… it’s called, like.
204 00:21:52.180 ⇒ 00:21:55.459 Emily Giant: CareTags is the current existing model.
205 00:21:58.380 ⇒ 00:21:59.080 Demilade Agboola: Okay.
206 00:22:06.630 ⇒ 00:22:07.300 Amber Lin: Okay.
207 00:22:08.000 ⇒ 00:22:13.290 Amber Lin: Sounds good. So, it seems that it’s, like, medium or low priority for now.
208 00:22:15.860 ⇒ 00:22:17.360 Emily Giant: I would say medium, not low.
209 00:22:17.360 ⇒ 00:22:20.020 Amber Lin: Okay, okay, I’ll put it in medium.
210 00:22:23.250 ⇒ 00:22:29.240 Amber Lin: Oish, do you think you’ll have time to look at it this week, or will it be next week, if you have.
211 00:22:29.240 ⇒ 00:22:34.520 Awaish Kumar: Yeah, these two and the North Beam.
212 00:22:37.550 ⇒ 00:22:38.980 Awaish Kumar: Are these duplicates?
213 00:22:40.580 ⇒ 00:22:41.130 Amber Lin: Okay.
214 00:22:41.130 ⇒ 00:22:44.880 Awaish Kumar: One is auto-attribution, and the one is just ingestion.
215 00:22:45.290 ⇒ 00:22:46.790 Amber Lin: Oh, Okay.
216 00:22:48.960 ⇒ 00:22:51.530 Awaish Kumar: Yeah, this one we can do in this next cycle.
217 00:22:52.530 ⇒ 00:22:53.200 Amber Lin: Okay.
218 00:22:56.260 ⇒ 00:22:57.040 Amber Lin: Alright.
219 00:22:57.440 ⇒ 00:22:59.060 Amber Lin: That’s all.
220 00:22:59.440 ⇒ 00:23:02.530 Amber Lin: These don’t have assignees, but it’s okay.
221 00:23:03.600 ⇒ 00:23:09.660 Amber Lin: Alrighty, thanks. So, I think Uta might want to have the stand-up
222 00:23:09.850 ⇒ 00:23:25.489 Amber Lin: in the bunch of Batch 2 stand-ups we have tomorrow, so I’m gonna see how we want it, because Emily, we still want to meet with you at least a few times a week. But I know you also have your working session, so it might save us some meeting time, so…
223 00:23:25.780 ⇒ 00:23:26.150 Emily Giant: We’ll see.
224 00:23:26.150 ⇒ 00:23:34.289 Amber Lin: how it ends up, and maybe you can update, say, Temlade or ways in your working sessions, and then think that way we can have less meetings.
225 00:23:34.700 ⇒ 00:23:43.250 Emily Giant: Yeah, that sounds good. Awish, I’d love to meet with you at some point to just see if I can help move forward the… the GA4 stuff.
226 00:23:44.770 ⇒ 00:23:58.970 Awaish Kumar: Yeah, sure. I have already requested for access, so if I can get access to GF4, I can actually… can verify which table is giving us the correct data, and then I can figure out, like, which…
227 00:23:59.040 ⇒ 00:24:09.939 Awaish Kumar: what is missing, or what is the difference between table, two tables. Right now, these two tables are, like, kind of source of truth for me, because that’s what coming directly from connector.
228 00:24:10.030 ⇒ 00:24:14.219 Awaish Kumar: And I don’t know what’s happening. Until I see something which is…
229 00:24:14.220 ⇒ 00:24:14.810 Amber Lin: Huh.
230 00:24:14.810 ⇒ 00:24:18.099 Awaish Kumar: It changes my source of truth, definition of source of truth.
231 00:24:19.390 ⇒ 00:24:25.559 Emily Giant: Okay, I am gonna submit a ticket… to Alex for this,
232 00:24:27.280 ⇒ 00:24:30.519 Emily Giant: In our… in our other, our company,
233 00:24:31.600 ⇒ 00:24:33.580 Emily Giant: board, so I’m doing that right now.
234 00:24:34.020 ⇒ 00:24:34.420 Awaish Kumar: Okay.
235 00:24:34.420 ⇒ 00:24:35.290 Amber Lin: Gotcha.
236 00:24:44.810 ⇒ 00:24:45.560 Emily Giant: Okay, cool.
237 00:24:47.810 ⇒ 00:24:51.010 Amber Lin: Great. Okay Thanks, everyone!
238 00:24:51.010 ⇒ 00:24:52.150 Awaish Kumar: Right?
239 00:24:52.500 ⇒ 00:24:53.080 Demilade Agboola: Thank you.
240 00:24:53.080 ⇒ 00:24:54.470 Emily Giant: Bye, have a good one.