Meeting Title: US x BF | Standup Date: 2025-06-20 Meeting participants: Amber Lin, Caio Velasco, Demilade Agboola
WEBVTT
1 00:09:23.970 ⇒ 00:09:25.149 Caio Velasco: Hi amber.
2 00:09:29.100 ⇒ 00:09:30.100 Amber Lin: Hello!
3 00:09:32.380 ⇒ 00:09:33.840 Amber Lin: How are you feeling?
4 00:09:34.750 ⇒ 00:09:35.843 Caio Velasco: Hello, sorry.
5 00:09:37.820 ⇒ 00:09:38.860 Caio Velasco: Okay.
6 00:09:39.520 ⇒ 00:09:41.940 Caio Velasco: Sorry I was out. My.
7 00:09:42.290 ⇒ 00:09:47.540 Caio Velasco: so I was actually confused because I had a 1 on one with you. Tom.
8 00:09:47.650 ⇒ 00:09:48.580 Amber Lin: But.
9 00:09:48.700 ⇒ 00:09:53.490 Caio Velasco: I was not sure if people were work working today or not, because he
10 00:09:53.620 ⇒ 00:09:58.840 Caio Velasco: just replied me a bit after saying that he was behind or something, so I was a bit confused.
11 00:09:58.840 ⇒ 00:10:04.239 Amber Lin: I see it was yesterday yesterday in the Us. It was June 19, th
12 00:10:04.430 ⇒ 00:10:07.509 Amber Lin: so it was a. It was a holiday.
13 00:10:08.130 ⇒ 00:10:10.040 Amber Lin: Oh, okay, yeah.
14 00:10:11.240 ⇒ 00:10:13.488 Caio Velasco: Okay, so sorry that I’m super late.
15 00:10:13.810 ⇒ 00:10:19.080 Amber Lin: No, all good. At least I caught you. I was just about to log off. So this is good.
16 00:10:19.694 ⇒ 00:10:32.790 Amber Lin: I saw that you put another a comment on one of the tickets about dashboards. I tried. I tried to read it. I don’t think I completely understand what it means. Can you help me understand it?
17 00:10:34.240 ⇒ 00:10:36.579 Caio Velasco: Which which number is that one?
18 00:10:36.943 ⇒ 00:10:46.399 Amber Lin: I think it’s 1 1 that’s done the one that Emily marked it marked the call in with Yes and no. I don’t remember the number.
19 00:10:46.810 ⇒ 00:10:53.050 Caio Velasco: Yeah, it’s 1, 2, 4. Let me read again.
20 00:11:00.768 ⇒ 00:11:02.909 Caio Velasco: Okay, that’s the thing. So
21 00:11:03.450 ⇒ 00:11:19.079 Caio Velasco: even though I I’m still updating things regarding the mapping between dashboards and Dbt models. That was done. But still I’m improving, because it’s not. Everything’s not there. But it’s like, let’s say that more than half was done.
22 00:11:19.744 ⇒ 00:11:27.985 Caio Velasco: and and then I stopped a bit. That work and I wanted to move a bit into modeling, just to finally start.
23 00:11:29.123 ⇒ 00:11:36.920 Caio Velasco: So I was re-watching the video with Emily that she was going over a lot of things
24 00:11:37.585 ⇒ 00:11:48.540 Caio Velasco: then I was also like Chat Gpt and the the demo in the Brain Forge website for that meeting, trying to get information out of it.
25 00:11:48.820 ⇒ 00:11:58.540 Caio Velasco: And I started to do some organization of how I would learn about the revenue model, or or inventory, or those things.
26 00:11:58.690 ⇒ 00:12:00.617 Caio Velasco: and I understood that
27 00:12:01.880 ⇒ 00:12:05.479 Caio Velasco: It will be also super difficult for her to
28 00:12:05.670 ⇒ 00:12:25.810 Caio Velasco: teach us everything. She will do what she can. We can do stuff in the working session, but I feel that and a good way to start was so I understood the big picture. But then I would have to go into the most important models and look at how is the logic there. What are the fields? What is happening
29 00:12:26.776 ⇒ 00:12:42.989 Caio Velasco: so maybe a good way to do that is to start from dashboard. So let’s say that the most important one is the daily performance. Send, and then that model has that dashboard has, you know, these tables.
30 00:12:43.170 ⇒ 00:12:49.839 Caio Velasco: So I would look into those tables and and start to understand the logic over there, so
31 00:12:51.290 ⇒ 00:13:00.029 Caio Velasco: given that she already marked lot of dashboards as deprecated or not the one that are not deprecated. It would be good to have
32 00:13:00.130 ⇒ 00:13:05.740 Caio Velasco: an understanding of. What are the most important ones? That’s basically what I’m trying to say.
33 00:13:06.590 ⇒ 00:13:08.850 Amber Lin: I see, I see when
34 00:13:09.351 ⇒ 00:13:27.450 Amber Lin: I get I get that learning. The revenue models is pretty tough, and when I me and a lot of, I mean, when we were planning for the next cycle. Oh, I don’t think you were there, I see. That’s why. So we for next cycle. Let me just share my screen.
35 00:13:28.125 ⇒ 00:13:41.759 Amber Lin: We’re gonna start auditing the different areas of the revenue mark, which is somewhat similar to what you’re saying. And let me display it by project.
36 00:13:43.630 ⇒ 00:13:52.329 Amber Lin: So for revenue, we have these areas and we split it between you and de Mulade. So
37 00:13:52.470 ⇒ 00:13:56.600 Amber Lin: like you will have time to learn these areas. And then
38 00:13:57.126 ⇒ 00:14:03.660 Amber Lin: I’m asking to still flesh out these ticket. To, I think, to audit means to no
39 00:14:04.020 ⇒ 00:14:15.459 Amber Lin: where. Every everything related to refunds is how they relate together and come up with a plan of how we can combine them into
40 00:14:15.720 ⇒ 00:14:17.020 Amber Lin: a mart.
41 00:14:17.820 ⇒ 00:14:33.059 Caio Velasco: Okay, okay, that’s good. That’s good. I have one question that I would have is when how was this areas defined? Because I already see that there’s a refund one, a discount, one, and subscriptions one. Because that’s also important. I would like to see
42 00:14:33.220 ⇒ 00:14:38.550 Caio Velasco: all the areas that we have and then break down just to also bigger picture.
43 00:14:39.520 ⇒ 00:14:49.780 Amber Lin: Okay, I see so we want a general exploration ticket before we do that.
44 00:14:50.220 ⇒ 00:14:51.660 Caio Velasco: Would be nice. Yes.
45 00:14:51.660 ⇒ 00:14:52.650 Amber Lin: I see. Okay.
46 00:14:52.650 ⇒ 00:14:56.080 Caio Velasco: For example, if you open the
47 00:14:56.440 ⇒ 00:14:59.730 Caio Velasco: do, you have the spreadsheet over there, or should I send to you.
48 00:15:00.094 ⇒ 00:15:04.469 Amber Lin: I think I have it. Just tell me what tab it is.
49 00:15:04.470 ⇒ 00:15:06.020 Caio Velasco: It’s the.
50 00:15:06.470 ⇒ 00:15:10.680 Caio Velasco: For example, I created one call modeling at the end.
51 00:15:22.320 ⇒ 00:15:28.570 Caio Velasco: All the way to that bit before before to the left.
52 00:15:29.980 ⇒ 00:15:32.909 Caio Velasco: Yes, modeling and glossary.
53 00:15:33.820 ⇒ 00:15:38.463 Caio Velasco: So this was me trying to like, understand the bigger picture like,
54 00:15:39.470 ⇒ 00:15:45.619 Caio Velasco: Well, I was assuming that we have some data march, or we wanna have some data march. For example.
55 00:15:46.230 ⇒ 00:15:55.749 Caio Velasco: Those one that you just showed me about all the maybe they would be inside revenue or inside inventory, and then they would be.
56 00:15:55.910 ⇒ 00:15:59.920 Caio Velasco: for example, also connected to an idea of a business unit.
57 00:16:00.030 ⇒ 00:16:03.150 Caio Velasco: So I mean, I wanna see this
58 00:16:04.450 ⇒ 00:16:07.890 Caio Velasco: droop, I would say, like, this thing’s broken down by
59 00:16:07.990 ⇒ 00:16:18.249 Caio Velasco: smaller topics, so that I see like, what is the bigger picture in in their side? And then how are we getting to specifically those ones that you put in the tickets.
60 00:16:19.740 ⇒ 00:16:29.859 Amber Lin: I see, those in the tickets are from demalade, I think he’s still doing, you can see my screen over here, this is his current cycle.
61 00:16:30.010 ⇒ 00:16:38.560 Amber Lin: He is still doing the revenue rebuild plan, and as a deliverable of that
62 00:16:38.660 ⇒ 00:16:41.079 Amber Lin: ticket I’m asking him to flesh out
63 00:16:41.220 ⇒ 00:16:46.039 Amber Lin: the tickets in here, because this is just our assumptions right now.
64 00:16:46.610 ⇒ 00:16:52.839 Amber Lin: And honestly, I think this ticket might be a
65 00:16:53.350 ⇒ 00:17:10.459 Amber Lin: might be a good place for you to start, because I think when you talk about dashboards, it’s and looking at their lineage lineage. It’s, I think, doing. This ticket allows you to look at the current like everything in the current revenue model and identify
66 00:17:10.976 ⇒ 00:17:25.070 Amber Lin: the legacy ones which and the ones that’s not not actually having data streams going into them which will leave you with the ones that’s important and used.
67 00:17:26.170 ⇒ 00:17:29.820 Caio Velasco: Okay? And my 1st question here would actually be.
68 00:17:29.930 ⇒ 00:17:37.870 Caio Velasco: where are the revenue models? And what are they? Because I think that’s what we are trying to find given that they are very disorganized.
69 00:17:40.907 ⇒ 00:17:45.880 Amber Lin: I think that will be a question for Demo Lade.
70 00:17:48.860 ⇒ 00:17:49.430 Amber Lin: Yeah.
71 00:17:52.000 ⇒ 00:18:01.850 Amber Lin: sounds good. Yeah. I think this would be a good ticket to do this cycle so that you get an understanding of revenue.
72 00:18:03.703 ⇒ 00:18:05.790 Amber Lin: Looking at.
73 00:18:06.860 ⇒ 00:18:11.580 Amber Lin: Yeah, I wanted to check with everybody on the status of their tickets.
74 00:18:11.860 ⇒ 00:18:15.000 Amber Lin: Think I’m going to check in with Dom Lade about his rebuilds
75 00:18:15.240 ⇒ 00:18:18.629 Amber Lin: because they are due soon. But they’re not in progress yet.
76 00:18:20.820 ⇒ 00:18:28.290 Caio Velasco: So for mine. Remind me of what is on the table. What will be next? What is blocked? Because I’m also a bit confused.
77 00:18:28.460 ⇒ 00:18:31.100 Amber Lin: Yeah, all good. So this one
78 00:18:31.200 ⇒ 00:18:36.340 Amber Lin: for user dashboards, I think Emily is has has?
79 00:18:37.790 ⇒ 00:18:39.379 Amber Lin: Oh, I see.
80 00:18:44.000 ⇒ 00:18:46.530 Amber Lin: Do you know, if she’s done with this.
81 00:18:48.206 ⇒ 00:18:51.360 Caio Velasco: She hasn’t said anything, or Disney.
82 00:18:51.940 ⇒ 00:18:59.020 Amber Lin: Okay, okay? So that’s why I said, this was blocked
83 00:18:59.240 ⇒ 00:19:08.239 Amber Lin: cause we need her accuracy rating to do this. So that’ll probably be next Monday, because she says she’ll finish it this Friday, so that’ll be Monday.
84 00:19:08.630 ⇒ 00:19:16.429 Amber Lin: And then, looking at this one, Are we sorry I didn’t.
85 00:19:17.780 ⇒ 00:19:20.530 Caio Velasco: Yeah, I made some some new changes. Yeah.
86 00:19:24.590 ⇒ 00:19:30.059 Amber Lin: Oh, I see. And you just had a call with Utam, are we? What’s the plan? On deprecating.
87 00:19:30.970 ⇒ 00:19:57.179 Caio Velasco: So I think now, at least, I got like a nice picture from what was happening, because yesterday I kind of like started everything from 0, but using everything I already done. Obviously. So then I was like, Oh, we actually have more objects than only tables. Tables is just one object, so that we also have views, and even another one called whatever late binding view that I didn’t even know what it is. So.
88 00:19:57.180 ⇒ 00:20:02.589 Amber Lin: Explore when I was yeah, it’s like something weird in the redshift.
89 00:20:03.073 ⇒ 00:20:29.639 Caio Velasco: So then, at least like tables and views are important in a list, so that you know, when we are deprecating things, we have to see what are tables, what are views, and what are, what are the important things happening? And then now we have the ones that are used by Dbt, the ones that are not used by Dbt. Both tables and views. So the next step, since you thumb also know that this is quite a complicated work.
90 00:20:30.186 ⇒ 00:20:35.420 Caio Velasco: He wanted to understand what would be the rule to deprecate them.
91 00:20:35.800 ⇒ 00:20:45.510 Caio Velasco: So I mean, the next step would be actually to deprecate. Yes, but then we we need the rule to understand if everything, if what we have already in that
92 00:20:45.660 ⇒ 00:20:52.780 Caio Velasco: spreadsheet, it’s sufficient or not, because I was able to go to the manifest Dot Json that is written there.
93 00:20:53.489 ⇒ 00:20:57.879 Caio Velasco: I was able to confirm the references in analytics dot analytics
94 00:20:58.294 ⇒ 00:21:03.840 Caio Velasco: so kind of we might have everything we need, but then I think you then has to look at it.
95 00:21:05.430 ⇒ 00:21:13.110 Amber Lin: So I, I see that’s great. So this ticket, we’re, are we waiting for utam to to review.
96 00:21:13.740 ⇒ 00:21:15.850 Caio Velasco: So are we confirming what I.
97 00:21:15.850 ⇒ 00:21:18.690 Amber Lin: Emily or Rubenstem’s people.
98 00:21:19.100 ⇒ 00:21:27.789 Caio Velasco: So we actually the next thing to do would be to have the call on Monday. We do, Tom, which I already sent him a proposal for
99 00:21:28.420 ⇒ 00:21:33.049 Caio Velasco: or Monday morning his morning? Not sure if he confirmed or not.
100 00:21:33.430 ⇒ 00:21:36.399 Caio Velasco: and then I would go over this with him.
101 00:21:41.360 ⇒ 00:21:42.180 Amber Lin: Sounds good.
102 00:21:42.750 ⇒ 00:21:44.110 Caio Velasco: So.
103 00:21:44.640 ⇒ 00:21:50.710 Amber Lin: I would say that, and me.
104 00:21:51.500 ⇒ 00:21:53.210 Caio Velasco: If you also want, you can
105 00:21:53.440 ⇒ 00:21:57.405 Caio Velasco: change the 2 points or 5 points because it was definitely 5 points.
106 00:21:58.750 ⇒ 00:22:06.419 Amber Lin: Yeah, let me let me do that. Okay, I just needed to log this as long as it’s logged that will be good
107 00:22:06.620 ⇒ 00:22:12.240 Amber Lin: and no, I’ll just say it’s in progress.
108 00:22:12.550 ⇒ 00:22:18.210 Amber Lin: You know what. I’ll put it there so.
109 00:22:20.150 ⇒ 00:22:23.089 Caio Velasco: And then the boss has to. Yeah, go ahead.
110 00:22:23.090 ⇒ 00:22:26.669 Amber Lin: I think today we can do this, the cost estimate.
111 00:22:26.890 ⇒ 00:22:33.209 Amber Lin: I don’t think we turned. We turned an initial part off already, right in in.
112 00:22:33.870 ⇒ 00:22:36.370 Caio Velasco: Yeah. We turned a few a few off. Yes.
113 00:22:36.370 ⇒ 00:22:47.060 Amber Lin: I see, I see, I think after we turn off more we can do a quick S quick, like cost estimate like this is, this is, gonna be more of.
114 00:22:47.460 ⇒ 00:22:54.230 Amber Lin: I don’t know if it’s qualitative or for quantitative calculation of cost. But I think we just need to
115 00:22:54.540 ⇒ 00:22:56.450 Amber Lin: come up with some numbers.
116 00:22:57.460 ⇒ 00:23:02.120 Caio Velasco: Okay, yeah. So at least. So since we also have the the.
117 00:23:02.590 ⇒ 00:23:10.060 Caio Velasco: there is another internal. Yeah. Well, the the that one, the 1, 1 4 he has to be after that one.
118 00:23:11.500 ⇒ 00:23:17.979 Amber Lin: Yeah, yeah, I agree, this is due next Monday. So
119 00:23:18.270 ⇒ 00:23:20.509 Amber Lin: I think today, we can do this one.
120 00:23:21.320 ⇒ 00:23:39.559 Amber Lin: So you can. You can just spend time learning about the revenue models. And honestly, I think when I when I have, when I give spike tickets with learning tickets, I always just assign somewhat of an output. I I hope that this is a good output to come out of a like a learning spike.
121 00:23:40.730 ⇒ 00:23:50.597 Caio Velasco: Yeah, again, that one I would need, at least to tell me the what is relevant. Where are they? I have already started some very simple things
122 00:23:51.280 ⇒ 00:23:56.510 Caio Velasco: Going over some videos and and whatnot. But yeah, definitely, I don’t even know where to demo.
123 00:23:56.510 ⇒ 00:24:01.640 Amber Lin: Do you? Do you want to book like a 30 min? Call with them, Laudy.
124 00:24:01.950 ⇒ 00:24:06.619 Amber Lin: Just grab something on his calendar. I think that will really help speed things up.
125 00:24:07.190 ⇒ 00:24:07.719 Caio Velasco: For sure.
126 00:24:08.410 ⇒ 00:24:12.369 Amber Lin: Can you do that on his calendar now and then.
127 00:24:12.820 ⇒ 00:24:16.209 Amber Lin: because I don’t want it to be blocked.
128 00:24:16.930 ⇒ 00:24:19.130 Caio Velasco: Yeah, let me check on. That will have to be.
129 00:24:19.130 ⇒ 00:24:21.609 Amber Lin: Yeah, especially. I know your day is ending now.
130 00:24:23.370 ⇒ 00:24:30.539 Caio Velasco: Let me check, let me search for.
131 00:24:31.710 ⇒ 00:24:34.940 Amber Lin: I mean he should be free for the like.
132 00:24:36.560 ⇒ 00:24:40.268 Amber Lin: I mean, if he’s working my hours here. Hello!
133 00:24:40.680 ⇒ 00:24:41.530 Amber Lin: Oh, hi!
134 00:24:41.530 ⇒ 00:24:42.260 Caio Velasco: Perfect.
135 00:24:43.910 ⇒ 00:24:49.470 Amber Lin: I was. I was talking to Kyle about his tickets. So
136 00:24:50.340 ⇒ 00:25:05.409 Amber Lin: I think this one is a good start for Kyle to understand what’s going on in the revenue models. But Kyle asks, where are the revenue models? And what are they? So because urban system is so completely disorganized? We’re going to need that from you.
137 00:25:06.661 ⇒ 00:25:16.420 Demilade Agboola: Sure, I don’t have a full scope of all the revenue models, because my focus. And at least when I came in, the focus was on.
138 00:25:17.620 ⇒ 00:25:23.489 Demilade Agboola: Called inventory. So inventory is more of the scope in which I’m more comfortable with.
139 00:25:24.290 ⇒ 00:25:30.060 Demilade Agboola: But obviously I have played around with some of the models so I can send you some of them. But I’m not sure it’ll be the full scope.
140 00:25:32.060 ⇒ 00:25:37.070 Amber Lin: Is all of the models where, like what tool is all of the models in.
141 00:25:38.600 ⇒ 00:25:40.249 Demilade Agboola: Just like dvt like.
142 00:25:40.250 ⇒ 00:25:52.009 Amber Lin: Oh, okay, I mean, would their name kind of be related to revenue? So if Kyle gets a complete list of the names, then maybe AI can suggest which one might be revenue.
143 00:25:53.210 ⇒ 00:25:54.380 Demilade Agboola: I mean.
144 00:25:54.930 ⇒ 00:26:06.109 Demilade Agboola: there are like setting models out that feed revenue. So, for instance, the the model that feeds like the revenue dashboard is something called Tableau’s items. Xf.
145 00:26:06.270 ⇒ 00:26:07.610 Amber Lin: Oh no!
146 00:26:09.440 ⇒ 00:26:13.529 Demilade Agboola: It’s it’s 1 of those models where, like, literally.
147 00:26:13.880 ⇒ 00:26:15.699 Demilade Agboola: there’s so much going on, there.
148 00:26:15.700 ⇒ 00:26:21.880 Amber Lin: I see I see it feeds everything, cause it’s by like tool source.
149 00:26:24.020 ⇒ 00:26:26.860 Demilade Agboola: Oh, no, actually, no, that’s just the name. So I think.
150 00:26:26.860 ⇒ 00:26:27.770 Amber Lin: Oh, okay.
151 00:26:28.370 ⇒ 00:26:41.049 Demilade Agboola: The Xf is like cross function. I can’t remember how they they name their things, but it’s just it’s a weird name. But yeah, tableau license is where that is. It’s a mark model. It’s
152 00:26:42.910 ⇒ 00:26:43.979 Demilade Agboola: it’s like 300.
153 00:26:43.980 ⇒ 00:26:44.830 Caio Velasco: Let me
154 00:26:45.100 ⇒ 00:26:58.880 Caio Velasco: let me ask you like a few questions, just to see if I understand. For example, when we say revenue model, are we assuming one exists on on their side? Or are we trying to build one from whatever they have
155 00:26:59.150 ⇒ 00:27:24.680 Caio Velasco: in terms of like? If at some in the one of the 800 dashboards. They are calculating price, then that should be related to a potential revenue model. I mean that that’s what I need to understand. I really need to understand, like from end to end, what is happening, so that if I go to their models I can try to find stuff, because I don’t think they have like a revenue model or inventory model, right? Like a marked.
156 00:27:25.280 ⇒ 00:27:31.219 Demilade Agboola: No, they don’t have a mark for inventory or revenue. They basically have
157 00:27:32.201 ⇒ 00:27:37.180 Demilade Agboola: a model, which is which is what I just mentioned, like tableau’s item to excel.
158 00:27:37.820 ⇒ 00:27:49.759 Demilade Agboola: And it’s basically at that point where they’ve put together all the aggregations that they have been doing previously into like the final form quote unquote that they then use for their dashboards.
159 00:27:50.750 ⇒ 00:27:55.950 Demilade Agboola: So if the idea would be, you would like
160 00:27:56.570 ⇒ 00:28:02.620 Demilade Agboola: one, it might be helpful to hop on a call with Emily. But just even beyond that, like.
161 00:28:02.840 ⇒ 00:28:12.890 Demilade Agboola: once you get the tableaus items, except you will need to start like going upstream and figuring out like what? What tables feed into this model
162 00:28:13.320 ⇒ 00:28:18.009 Demilade Agboola: in terms of revenue so like, if doesn’t really have anything to revenue that won’t be helpful.
163 00:28:19.230 ⇒ 00:28:20.120 Demilade Agboola: But
164 00:28:20.640 ⇒ 00:28:46.730 Demilade Agboola: what else like, where where’s this coming from? What are the sources? And like it might help to go through? I don’t expect you to truly understand everything at once, but like, if you go through, have some ideas of what’s going on, it helps you ask like better questions to Emily when you hop on a call with her because you’re able to say, Okay, so I don’t understand this part of it. Or I understood this part versus, like, you know, just coming in.
165 00:28:47.510 ⇒ 00:28:50.730 Demilade Agboola: It’s a huge answer like, that’s what I have to say. It’s not.
166 00:28:50.730 ⇒ 00:29:19.430 Caio Velasco: No, no problem. No, I I get it. So I think that. Okay, I understand that. So let’s let me put like this. So let’s assume that we are building both a revenue and an inventory model, and then, on their side, they have to tell us what are the most important tables for each one. You just said that the double Xf it’s 1 of them, and then we have to assume that’s true, and go from there and try to like reverse engineer upstream.
167 00:29:19.680 ⇒ 00:29:23.669 Caio Velasco: What? What are the the sources, and whatever like the business logic?
168 00:29:23.920 ⇒ 00:29:24.809 Caio Velasco: Does that make sense.
169 00:29:25.450 ⇒ 00:29:43.180 Demilade Agboola: Exactly. So. We’re trying to like rebuild revenue so that there’s literally a revenue flow. And anybody who’s trying to troubleshoot in the future can understand. Hey, this is everything that concerns revenue in one place, and they can understand, and then they can be able to like. It’s right now. It’s a huge mess.
170 00:29:43.520 ⇒ 00:29:55.289 Demilade Agboola: Sometimes things have been migrated from one system to another system, and that’s affected revenue calculation. And you know they’ve been doing patchwork here and there. And because of all these patches.
171 00:29:55.709 ⇒ 00:30:05.609 Demilade Agboola: literally, the the reason why, like the revenue, might jump by like a hundred KA week is literally because of a fix here. So like just being able to like, okay.
172 00:30:05.610 ⇒ 00:30:29.920 Demilade Agboola: this is pre pre migration. This is like how revenue is calculated. Create a model for that this is after migration. This is how revenue is calculated. Create a model for that. This is a union of everything. Just start like breaking things down properly into a way that, like anybody that’s trying to troubleshoot can go. Okay. Things have broken in how revenue is being calculated right now.
173 00:30:30.090 ⇒ 00:30:37.219 Demilade Agboola: So you know where to go to versus like you’re just jumping across multiple models. Things are just confusing that sort of thing.
174 00:30:38.080 ⇒ 00:30:43.220 Caio Velasco: Okay, okay, perfect. So brilliant.
175 00:30:43.220 ⇒ 00:30:59.539 Amber Lin: And then I have a question. Yes, I wanna ask, is this the best ticket for Kyle to understand like the 1st to get a sense of the revenue models, because I know you’re also doing this.
176 00:31:00.495 ⇒ 00:31:04.310 Amber Lin: The revenue Mart rebuild plan. And then.
177 00:31:05.200 ⇒ 00:31:17.300 Amber Lin: Kyle’s looking at the we had a ticket for him to look at the legacy models that with that, with inactive streams like, what do you think is the best way for Kyle to do this initial step.
178 00:31:25.280 ⇒ 00:31:28.750 Demilade Agboola: I mean to be fair like Kyle still have to come up to speed with
179 00:31:29.400 ⇒ 00:31:31.939 Demilade Agboola: all of what’s going on in terms of revenue.
180 00:31:32.710 ⇒ 00:31:37.799 Demilade Agboola: So let me see.
181 00:31:39.930 ⇒ 00:31:45.279 Caio Velasco: For example. Maybe this will help Amber if you can open the that modeling tab again
182 00:31:45.570 ⇒ 00:31:53.970 Caio Velasco: so that they may take a look. Maybe that’s something that we could improve, and then would be related to this ticket or to any other ticket.
183 00:32:01.494 ⇒ 00:32:11.239 Demilade Agboola: Yeah, I I think for this in terms of like what this ticket entails. It would just be like trying to figure out what the where the revenue is coming from
184 00:32:11.630 ⇒ 00:32:14.149 Demilade Agboola: in terms of like the models that fit in it.
185 00:32:14.530 ⇒ 00:32:18.759 Demilade Agboola: Like I said, they’ve had like migrations and stuff. So it’s like figuring out
186 00:32:19.100 ⇒ 00:32:22.610 Demilade Agboola: where like if there any like migrated tables.
187 00:32:23.567 ⇒ 00:32:26.769 Demilade Agboola: like before the migration. So pre-migration tables
188 00:32:27.290 ⇒ 00:32:34.339 Demilade Agboola: that they’ve been, are they still active? If they’re still active. Obviously, we need to like deactivate the ingestion of them.
189 00:32:34.660 ⇒ 00:32:48.430 Demilade Agboola: So that’s 1. But 2 would also be like in terms of modeling. I would prefer us build out like legacy models that like, okay, so this is from 2023 to 2024, you know.
190 00:32:48.560 ⇒ 00:32:54.220 Demilade Agboola: before the like. This is how revenue is calculated. So we have a table that literally just has that
191 00:32:54.880 ⇒ 00:33:08.540 Demilade Agboola: that’s the legacy model. So if anybody wants output from that, they can go there and get that like data from there. But right now, what what’s happening is, everything is in one like, usually like big
192 00:33:08.680 ⇒ 00:33:20.115 Demilade Agboola: model. So it’s like, just case when statements that people are making and it it makes it messy. Sometimes it’s hard to figure out what’s going on in there.
193 00:33:21.020 ⇒ 00:33:29.710 Demilade Agboola: and so like, if we realize that there’s an issue, sometimes it’s hard to just go back and just handle things individually, like, like
194 00:33:30.110 ⇒ 00:33:32.080 Demilade Agboola: different chunks of the data.
195 00:33:32.520 ⇒ 00:34:01.329 Demilade Agboola: So the idea is like, Okay, how do we like clearly demarcate everything? So before 2024, this is how it was coming in. This is how we calculated revenue. And this is all your historical revenue up until the migration post migration. This is how we’re calculating your revenue. These are. This is the logic. This is what we’re doing. And this is how it works like just that, so that things can be clear. And so, if anything can easily go. Oh, it’s the post migration data that we need to look into.
196 00:34:01.330 ⇒ 00:34:05.829 Amber Lin: Oh, I see so legacy means pre-migration.
197 00:34:06.560 ⇒ 00:34:16.279 Demilade Agboola: Sort of, yeah, like, largely, yeah, just basically inactive. Like, they’re not necessarily actively calculating, like new data with that logic.
198 00:34:23.810 ⇒ 00:34:24.420 Amber Lin: hmm.
199 00:34:24.429 ⇒ 00:34:30.759 Caio Velasco: So basically, what you’re saying is to identify a table before and after migration.
200 00:34:32.139 ⇒ 00:34:36.129 Demilade Agboola: It doesn’t exist like. But the idea is we’re going to have to create them. Yes.
201 00:34:36.549 ⇒ 00:34:39.619 Demilade Agboola: So it’s not like you identify anything you’re just trying to like, create them
202 00:34:41.909 ⇒ 00:34:58.669 Demilade Agboola: so like in terms of sources. Yes, they do exist like. There’s some sources that from before migration some sources are after migration, but in terms of like the data models that we’re creating for them, they don’t have like a before after migration. Everything’s just like lumps together, and it gets really very messy.
203 00:35:00.760 ⇒ 00:35:01.160 Caio Velasco: So.
204 00:35:01.160 ⇒ 00:35:08.019 Amber Lin: I see. So I guess the 1st 1st step is to identify what sources are pre and post migration.
205 00:35:08.150 ⇒ 00:35:09.560 Amber Lin: and then
206 00:35:09.870 ⇒ 00:35:18.180 Amber Lin: like come up with what we need to build for it, because, I think, identify and create the legacy model should be 2 steps
207 00:35:21.290 ⇒ 00:35:22.730 Amber Lin: kind of were saying.
208 00:35:24.410 ⇒ 00:35:28.150 Caio Velasco: Yeah, I’m still trying to understand, like the the whole picture.
209 00:35:28.410 ⇒ 00:35:29.180 Caio Velasco: Hmm.
210 00:35:30.220 ⇒ 00:35:37.609 Caio Velasco: cause there’s a lot of open, ended ideas. For example, we don’t have. They don’t have revenue models
211 00:35:37.770 ⇒ 00:35:38.840 Caio Velasco: or start.
212 00:35:39.070 ⇒ 00:35:41.470 Caio Velasco: Then we want to build one
213 00:35:41.650 ⇒ 00:35:46.439 Caio Velasco: that that didn’t exist and another one that doesn’t exist.
214 00:35:46.610 ⇒ 00:35:52.830 Caio Velasco: That’s more or less what we are doing right. We are trying to reverse engineering stuff before migration.
215 00:35:53.070 ⇒ 00:35:57.970 Caio Velasco: maybe to understand how the logic was. And then we wanna also do something new.
216 00:35:58.300 ⇒ 00:36:01.509 Caio Velasco: because that’s the one that has to be correct?
217 00:36:01.964 ⇒ 00:36:06.180 Caio Velasco: Is, are we going? Is it? Is it making sense? Am I understanding.
218 00:36:07.370 ⇒ 00:36:21.569 Demilade Agboola: I mean, they have revenue models in the sense of. They have models that calculate revenue. They don’t have explicit revenue models in the sense of this is our revenue model. They have white tables where they put a lot of things in there.
219 00:36:21.810 ⇒ 00:36:24.819 Demilade Agboola: right? So because of that.
220 00:36:25.950 ⇒ 00:36:37.410 Demilade Agboola: it’s hard to figure out like a lot of things that are going on there like till this very day. There are things around order total that are problematic.
221 00:36:38.000 ⇒ 00:36:39.020 Demilade Agboola: right?
222 00:36:40.260 ⇒ 00:36:55.359 Demilade Agboola: things like that. It’s like just being able to be like, okay, so order total before this day was calculated this way. And you have a model that addresses that auto total after this days, was done this way, and we calculate that this is how like
223 00:36:58.020 ⇒ 00:36:59.220 Demilade Agboola: Discounts work
224 00:36:59.530 ⇒ 00:37:15.280 Demilade Agboola: before migration. You have that more like just clearly, like just us being able to clearly put the logic there, such that, like anybody who is trying to hop in it can understand the flow, understand? What’s going on, can understand, like
225 00:37:16.150 ⇒ 00:37:17.360 Demilade Agboola: what is.
226 00:37:18.550 ⇒ 00:37:37.139 Demilade Agboola: how do we fix these things right like, because troubleshooting is really hard with like, I promise you. Sometimes like I hop on calls with Emily, and for, like the 1st 15 min I’m confused. Right like, it’s just like a long ass. 800 line code where I’m trying to figure out like, where could it be breaking? It’s really hard to like troubleshoot that way.
227 00:37:37.140 ⇒ 00:37:48.789 Demilade Agboola: But if you have like bits of logic where it’s like, this is the pre-migration code. This is how things work pre-migration. This is the post-migration code, and this is where we did our union on them, or you know we did it. Whatever you’re trying to do.
228 00:37:48.790 ⇒ 00:38:17.799 Demilade Agboola: and this is how we tie it back to products. And this is how we tie it back to customers. And so we know how much each customer is bringing us in terms of revenue. We know how much each customer is bringing us, how much each product is giving us in terms of revenue, things like that, like being able to just ensure that we’re able to build like these chunks that are like, easy to read, very understandable and follow, like Dbt’s best practices in terms of how data should flow in a system.
229 00:38:18.580 ⇒ 00:38:20.439 Demilade Agboola: We don’t necessarily have that.
230 00:38:22.090 ⇒ 00:38:30.000 Caio Velasco: Okay, Amber, if you have a a second, can you open the that modeling tab
231 00:38:30.120 ⇒ 00:38:33.320 Caio Velasco: just to see if it’s making sense? What I’m doing here.
232 00:38:38.123 ⇒ 00:38:39.939 Amber Lin: No sorry phone ticket here.
233 00:38:39.940 ⇒ 00:38:41.660 Caio Velasco: From this? Yeah, from this spreadsheet.
234 00:38:42.720 ⇒ 00:38:43.440 Amber Lin: Oh!
235 00:38:45.545 ⇒ 00:38:47.490 Amber Lin: Here!
236 00:38:49.450 ⇒ 00:38:51.609 Demilade Agboola: I need to hop to an eaten call.
237 00:38:53.060 ⇒ 00:38:53.600 Amber Lin: Hmm.
238 00:38:55.860 ⇒ 00:39:06.329 Caio Velasco: Okay? So then I can, I can do this with with on Monday. Do you work month? same hours as I do on Monday, for example, morning.
239 00:39:06.570 ⇒ 00:39:08.220 Demilade Agboola: I’m general.
240 00:39:09.974 ⇒ 00:39:19.349 Demilade Agboola: I’m generally available around the clock. I generally work ast, but like if you text me or you block my calendar early in the day. I’m fine like that that works for me.
241 00:39:19.750 ⇒ 00:39:23.070 Caio Velasco: Okay. I’ll do this for Monday, then, and then we go over this.
242 00:39:23.880 ⇒ 00:39:25.550 Demilade Agboola: Andrew sounds good.
243 00:39:25.550 ⇒ 00:39:27.980 Amber Lin: We have enough time. If we do a Monday
244 00:39:31.250 ⇒ 00:39:33.180 Amber Lin: we might have to push this. Then.
245 00:39:33.840 ⇒ 00:39:36.069 Caio Velasco: Oh, yeah, this is a huge one, for sure.
246 00:39:36.470 ⇒ 00:39:41.910 Amber Lin: I see. Okay, one quick. 5 seconds. Are these on track?
247 00:39:42.220 ⇒ 00:39:47.730 Amber Lin: Sorry the sorry the inventory ones are your tickets on track?
248 00:39:52.400 ⇒ 00:40:03.250 Demilade Agboola: one to 7 is 1. 0, 5 isn’t, or it even has been quite something this week. So that has kind of some of my focus away from.
249 00:40:03.260 ⇒ 00:40:04.280 Demilade Agboola: but I
250 00:40:04.280 ⇒ 00:40:09.550 Demilade Agboola: like catch up over the weekend and just try and get some work done, especially on, like one or 5.
251 00:40:11.130 ⇒ 00:40:16.080 Amber Lin: Okay, sounds good. And you guys will sync on this. Thank you. All.
252 00:40:17.090 ⇒ 00:40:18.390 Caio Velasco: Thank you. Thank you. I appreciate.
253 00:40:18.980 ⇒ 00:40:20.170 Amber Lin: Alrighty! Bye-bye.
254 00:40:20.444 ⇒ 00:40:20.719 Demilade Agboola: Right.