Meeting Title: Brainforge x Urban Stems Inventory Sync Date: 2025-07-10 Meeting participants: Perry’s Fellow Note Taker, ianbiles, pk.arthur, Amber Lin, Demilade Agboola, Emily Giant, felipefaria, Stephanie Plaza
WEBVTT
1 00:01:55.610 ⇒ 00:01:57.040 Amber Lin: Hi, everyone!
2 00:01:57.750 ⇒ 00:02:02.003 Amber Lin: We’ll wait a little bit for everyone to trickle in. And
3 00:02:02.780 ⇒ 00:02:10.339 Amber Lin: today we’ll just talk about the current status of the inventory Mart, and how that will impact everybody.
4 00:02:14.690 ⇒ 00:02:15.930 pk.arthur: Sounds good. Thank you.
5 00:02:32.160 ⇒ 00:02:36.150 Emily Giant: I will ping the others that are not on the call yet.
6 00:02:37.960 ⇒ 00:02:45.790 Amber Lin: Okay, usually in the 1st 5 min before it, just probably still meeting, still trickling in.
7 00:02:45.790 ⇒ 00:02:57.879 Emily Giant: Thursdays are always like I’ve had. I’ve been on a meeting since 9 Am. And I know Thursdays are like just like this for people. Always a very administrative day.
8 00:03:26.470 ⇒ 00:03:31.530 Emily Giant: I would say. As long as either Stephanie or Perry joins, then
9 00:03:31.870 ⇒ 00:03:46.489 Emily Giant: we can begin. Jesse and Ian are on the same team. So if one of them is present, it’s usually like, okay, they can information share and same with
10 00:03:47.170 ⇒ 00:03:50.209 Emily Giant: Steph and Perry. I’ll ping both of them.
11 00:04:15.440 ⇒ 00:04:20.470 felipefaria: I know that they are in the office today. That my
12 00:04:20.610 ⇒ 00:04:26.679 felipefaria: have something to do it, because I know that they might get caught up in some conversations or stuff like that. So.
13 00:04:28.250 ⇒ 00:04:31.990 Emily Giant: The office is the ultimate derailing of a schedule.
14 00:04:33.620 ⇒ 00:04:38.110 felipefaria: Yeah, I see everybody that’s here is working from home right.
15 00:04:38.750 ⇒ 00:04:39.320 felipefaria: Yeah.
16 00:04:43.431 ⇒ 00:04:48.550 Emily Giant: I think we’re okay to get started.
17 00:04:49.470 ⇒ 00:04:51.730 Emily Giant: I don’t want to lose too much more time.
18 00:04:53.220 ⇒ 00:04:53.790 Amber Lin: Okay.
19 00:04:54.068 ⇒ 00:04:57.129 ianbiles: Note. Take his recording, too, so she can just look back.
20 00:04:57.320 ⇒ 00:04:58.160 Emily Giant: It’s true.
21 00:05:00.570 ⇒ 00:05:02.559 Amber Lin: Don’t let it take it away.
22 00:05:10.210 ⇒ 00:05:12.686 Demilade Agboola: I realize I was speaking while muted.
23 00:05:13.500 ⇒ 00:05:16.809 Demilade Agboola: Hi! Everyone. So to those who know me
24 00:05:17.030 ⇒ 00:05:20.180 Demilade Agboola: no introduction is required, but generally my name is.
25 00:05:20.460 ⇒ 00:05:25.119 Demilade Agboola: I’ve been working with the brain for team for a couple of months. Now my name is Damn Lade. I am the
26 00:05:25.370 ⇒ 00:05:29.039 Demilade Agboola: analytics engineer over at Brainforge.
27 00:05:29.380 ⇒ 00:05:34.350 Demilade Agboola: and I figured like we’ve been working on stuff for the past couple of months.
28 00:05:35.050 ⇒ 00:05:42.540 Demilade Agboola: And it’s not like, ultimately clear what’s been happening, what like, what what’s the end product in some cases.
29 00:05:44.520 ⇒ 00:05:49.159 Demilade Agboola: So here’s just like a quick meeting to like run an update on what’s been going on.
30 00:05:52.300 ⇒ 00:06:01.269 Demilade Agboola: yeah. So we’re just talking about progress on inventory. So you just have that currently exists. And what we’ve done to help with that.
31 00:06:01.410 ⇒ 00:06:08.120 Demilade Agboola: And data is coming to you shortly. So things to like be excited for So
32 00:06:08.790 ⇒ 00:06:16.239 Demilade Agboola: in terms of how we’ve been working with the urban sense team, we can divide it into the Pre mother’s Day post mother’s day, and like
33 00:06:16.810 ⇒ 00:06:18.749 Demilade Agboola: the next steps we’re going to take.
34 00:06:19.190 ⇒ 00:06:22.110 Demilade Agboola: So before mother’s day, we had to like
35 00:06:22.380 ⇒ 00:06:26.650 Demilade Agboola: reconstruct things, we had set up polytomic. Polytomic is an ingestion tool.
36 00:06:26.770 ⇒ 00:06:29.849 Demilade Agboola: So we put in Netsu netsuite data.
37 00:06:30.538 ⇒ 00:06:38.209 Demilade Agboola: We’re able to like nail down the business logic through a lot of calls with Alex, Emily and Perry and also Felipe.
38 00:06:38.520 ⇒ 00:06:43.840 Demilade Agboola: and then we’re able to build out the inventory models and structure. And these were
39 00:06:44.190 ⇒ 00:06:46.380 Demilade Agboola: like the numbers that we used
40 00:06:46.640 ⇒ 00:06:48.999 Demilade Agboola: in our dashboard for Mother’s Day.
41 00:06:50.600 ⇒ 00:06:56.000 Demilade Agboola: And we set it up in such a way that it runs every like 30 min, so you could get as
42 00:06:56.120 ⇒ 00:07:00.039 Demilade Agboola: fresh data as possible. Make your mother’s day decisions
43 00:07:01.580 ⇒ 00:07:03.330 Demilade Agboola: They were distracted for mother’s day.
44 00:07:03.530 ⇒ 00:07:09.740 Demilade Agboola: But the problem, you know, is all the we realize when we’re done. There were some things we needed to work on.
45 00:07:09.980 ⇒ 00:07:17.949 Demilade Agboola: and so post Mother’s Day, part of what we’ve been debugging was like, we discovered there were some negative available for sale lots.
46 00:07:19.008 ⇒ 00:07:21.340 Demilade Agboola: In some cases those were true.
47 00:07:21.540 ⇒ 00:07:28.400 Demilade Agboola: but we also had issues in some of our calculations and part of what we’ve been debugging, just getting things aligned on that.
48 00:07:29.504 ⇒ 00:07:41.549 Demilade Agboola: We said, set up tests to be able to calculate or figure out when these were occurring, and setting up snapshots to be able to see what’s historically going on. That’s making these values go negative.
49 00:07:42.310 ⇒ 00:07:51.340 Demilade Agboola: And then, right now, we’re kind of in this phase where we’re focusing on like the adjustment type modeling. So for every lot.
50 00:07:52.086 ⇒ 00:07:58.619 Demilade Agboola: What is the value of the shrinkage? What is the value of redelivery subscriptions?
51 00:07:58.850 ⇒ 00:08:06.890 Demilade Agboola: And we’re able to also quantify that by how many of those like, how many of those values of, say, shrinkage for delivery.
52 00:08:07.150 ⇒ 00:08:20.189 Demilade Agboola: where I’m not sure, like redelivery or subscription, or committed or uncommitted like how many of them actually belong to the lot? So you can also get visibility into things like forced upgrades, and all of that.
53 00:08:20.460 ⇒ 00:08:30.870 Demilade Agboola: And so the idea is, we’re in this phase now, and we’re going to start like slowly rolling that into the dashboard. So you can have that sort of visibility into what’s going on behind the numbers as well.
54 00:08:31.988 ⇒ 00:08:36.269 Demilade Agboola: Going forward as well. What we’re trying to do is
55 00:08:36.630 ⇒ 00:08:47.070 Demilade Agboola: also incor incorporate data platform documentation. So we will start to create more documentation about these tables, what what they contain, what the columns represent.
56 00:08:47.210 ⇒ 00:08:53.610 Demilade Agboola: what assumptions were made into the different like le levels of our logic.
57 00:08:54.440 ⇒ 00:09:04.680 Demilade Agboola: And so that allows you to Qa more effectively, give feedback. And just, you know, provide solutions to us as well. And so the next step and what we’re looking at is
58 00:09:04.830 ⇒ 00:09:11.459 Demilade Agboola: a plane solutions for the non floral and hard goods. But you can have like full inventory scope.
59 00:09:12.500 ⇒ 00:09:18.110 Demilade Agboola: Also, we need to Qa. And as I’m sure most of you know, there are a lot of edge cases with
60 00:09:18.210 ⇒ 00:09:32.039 Demilade Agboola: open stems data, so we’ll obviously need to handle those edge cases. And you know, people give us feedback and let us know. Hey, things are going great. But we have issues when this occurs, and then we’ll figure out how to solve that here.
61 00:09:32.270 ⇒ 00:09:40.130 Demilade Agboola: and then we’ll put even more dbt test because we want to flag and ensure that whatever data you guys are using of the highest quality.
62 00:09:40.940 ⇒ 00:09:52.080 Demilade Agboola: And then we’ll incorporate observability tools. So we’re thinking like a Meta plane where we can be able to find out what’s going on. And if things are going wrong with the dashboards.
63 00:09:52.410 ⇒ 00:09:55.790 Demilade Agboola: even before the business stakeholders realize that.
64 00:09:56.470 ⇒ 00:10:01.089 Demilade Agboola: And obviously all of this, we’re trying to just like roll out all these numbers into our dashboards.
65 00:10:01.870 ⇒ 00:10:09.120 Demilade Agboola: And so for in terms of architecture, we’ve just set up like a flow from netsuite into polytomic.
66 00:10:09.290 ⇒ 00:10:11.670 Demilade Agboola: The regular things still remain the same.
67 00:10:11.820 ⇒ 00:10:18.240 Demilade Agboola: and we’ve created like Dbt within our dbt, we’ve created some architecture which will fit looker
68 00:10:18.900 ⇒ 00:10:22.680 Demilade Agboola: and high level. These are the new tables we’ve created.
69 00:10:23.222 ⇒ 00:10:25.820 Demilade Agboola: The basic idea of this is
70 00:10:26.040 ⇒ 00:10:30.339 Demilade Agboola: we are creating what we call Martin. Dbt, so, Marta, what are supposed to feed you.
71 00:10:31.053 ⇒ 00:10:36.890 Demilade Agboola: But before we have the March model before we have the march models that feed the analytics
72 00:10:36.990 ⇒ 00:10:46.839 Demilade Agboola: that feed looker, we have to have intermediate models that build logic in here. So the idea of this is, think of it like legal, where you have like building blocks that things come on top of.
73 00:10:46.990 ⇒ 00:10:48.620 Demilade Agboola: and the idea is
74 00:10:49.330 ⇒ 00:10:54.189 Demilade Agboola: in this layer. It allows us to be able to like troubleshoot and figure out what’s wrong.
75 00:10:54.410 ⇒ 00:11:13.789 Demilade Agboola: So if you know, hey, the quantity received looks weird, you know where to go for the quantity received calculations and where all of that takes place, and you can kind of debug that way that allows us to be able to build, because one of the problems we discovered was it was really hard to debug previously. And that’s kind of part of what we’re solving for right now.
76 00:11:14.770 ⇒ 00:11:20.490 Demilade Agboola: So I’m not gonna read necessarily read through. I could always share the slides, attach it to this meeting.
77 00:11:20.890 ⇒ 00:11:24.260 Demilade Agboola: But the basic idea is these are the different models that exist.
78 00:11:25.790 ⇒ 00:11:29.620 Demilade Agboola: And we’re able to calculate things for different values by lot.
79 00:11:29.800 ⇒ 00:11:33.079 Demilade Agboola: So and in some cases by sub order.
80 00:11:34.930 ⇒ 00:11:45.300 Demilade Agboola: So yeah, we have this as well. We’ve done things for fulfillment centers, reconciliations, presale committed purchase orders, redeliveries adjusted
81 00:11:45.470 ⇒ 00:11:50.429 Demilade Agboola: as well as things for sub orders that have lots and suborders that don’t have lots.
82 00:11:50.840 ⇒ 00:12:01.820 Demilade Agboola: and then we’re able to start rolling everything up into what we call marks. So now we’re able to see by lot and product information the total values that make up
83 00:12:02.010 ⇒ 00:12:08.650 Demilade Agboola: on the like. What’s happened on the lot, though we see things like the subscription, the sales, the redelivery
84 00:12:09.186 ⇒ 00:12:18.460 Demilade Agboola: and also we have tables that provide, like the fiscal dates, the product info the receiving quantities on order on hand and available for sales quantities.
85 00:12:18.770 ⇒ 00:12:28.769 Demilade Agboola: And, like I said, some of these numbers have already been exposed, and account kind of what you already use, but we’re even getting more numbers, and we will use them even more
86 00:12:28.900 ⇒ 00:12:33.770 Demilade Agboola: in more dashboards coming forward based on me.
87 00:12:35.060 ⇒ 00:12:40.829 Demilade Agboola: What exactly have we been doing? Well, we’ve been assessing our pipelines to ensure that you get data
88 00:12:40.950 ⇒ 00:12:42.470 Demilade Agboola: frequent as possible.
89 00:12:43.220 ⇒ 00:12:48.659 Demilade Agboola: so that during work hours you don’t have more than a 30 min delay between what’s going on
90 00:12:48.800 ⇒ 00:12:53.980 Demilade Agboola: in real time as well, and what you’re able to see and make decisions of.
91 00:12:54.130 ⇒ 00:13:04.629 Demilade Agboola: So that’s very important, so that you can always, you know, be on top of things. If things go negative, you can always figure out like, why do we have a negative available for sale? What’s going on? How do we action that?
92 00:13:05.536 ⇒ 00:13:16.220 Demilade Agboola: We’re also setting up cycles to be able to ensure that, like the transformations that, applying the logic to the data also don’t take longer and sync up with that.
93 00:13:16.957 ⇒ 00:13:21.440 Demilade Agboola: So all of this enables like up to date inventory visibility.
94 00:13:21.770 ⇒ 00:13:24.799 Demilade Agboola: We want you to be able to make faster decision processes.
95 00:13:26.200 ⇒ 00:13:31.449 Demilade Agboola: And then we also want you to have, like better operational efficiency, like being able to discover.
96 00:13:31.580 ⇒ 00:13:38.860 Demilade Agboola: One of the things we have discovered, for instance, is, we actually had instances where we had negative available for sale, which is a problem.
97 00:13:39.378 ⇒ 00:13:51.230 Demilade Agboola: Being able to see that, get that level of visibility to make those decisions, being able to understand why you had shrinkage, being able to understand where you know there were. There were buffers that
98 00:13:51.530 ⇒ 00:13:59.229 Demilade Agboola: were not supposed to be there, but they were there like those sort of numbers we’re able to, you know. Make them available to you so you can provide
99 00:14:00.440 ⇒ 00:14:03.260 Demilade Agboola: that’s the impute to the business.
100 00:14:03.910 ⇒ 00:14:15.729 Demilade Agboola: And so now for us, next steps are we need to roll this out to more dashboards over the next week or so Emily will make these these numbers available to you in Looker
101 00:14:16.120 ⇒ 00:14:20.630 Demilade Agboola: until 30 days. We would want you to incorporate and test them as much as possible
102 00:14:20.830 ⇒ 00:14:25.479 Demilade Agboola: because we want to hear feedback. We understand that like urban stems is very unique.
103 00:14:26.250 ⇒ 00:14:27.840 Demilade Agboola: Things expire.
104 00:14:27.980 ⇒ 00:14:41.350 Demilade Agboola: There’s redelivery things change. And so we want to help these edge cases like it might work for 90% of every single case. But like, if there’s a 5% or 10% where we’re just off to let us know, so we can be able to.
105 00:14:41.570 ⇒ 00:14:45.670 Demilade Agboola: you know, pick the logic to accommodate that like Edge case.
106 00:14:45.840 ⇒ 00:14:58.849 Demilade Agboola: Also, the next step is want to incorporate like, we want to work on the non floral and hard goods. So we are able to give you the numbers on those as well. Again. All things being equal, they should also come out next week as well.
107 00:14:59.517 ⇒ 00:15:04.590 Demilade Agboola: So you can also start to see what’s going on with your hard, good yep.
108 00:15:05.260 ⇒ 00:15:15.739 Demilade Agboola: And then we also like, I said, the the whole thing about queuing and solving for more edge cases. We’re looking forward to feedback from you, and also the current run takes about 7 min to run.
109 00:15:16.246 ⇒ 00:15:26.680 Demilade Agboola: I believe we can get to under 5 min by building more efficient codes and just being able to like, speak certain things. But that’s on on the technical side.
110 00:15:26.890 ⇒ 00:15:37.359 Demilade Agboola: Also, we want to have more data quality tests, because as much as we want to give you data as quickly as possible. We want to ensure that whatever data you’re using is of the highest quality possible.
111 00:15:38.050 ⇒ 00:15:45.920 Demilade Agboola: And if there are any issues. We want to be able to tell you before you recognize that there are issues that hey? There is an issue with our data.
112 00:15:46.180 ⇒ 00:15:48.399 Demilade Agboola: We are working on it, and we’ll let you know.
113 00:15:48.850 ⇒ 00:15:54.649 Demilade Agboola: and also like special requests from you know the team and what the users of entry.
114 00:15:55.099 ⇒ 00:16:04.089 Demilade Agboola: I know we’ve been talking to Felipe this week, and he’s been very useful, like letting us know certain things will be useful to him on a daily
115 00:16:04.749 ⇒ 00:16:08.100 Demilade Agboola: and just being able to get that sort of feedback like, Hey.
116 00:16:08.250 ⇒ 00:16:11.889 Demilade Agboola: these numbers have been helpful. These numbers have helped quite a bit.
117 00:16:12.040 ⇒ 00:16:16.870 Demilade Agboola: However, we would, you know, appreciate having numbers about this above, that
118 00:16:17.050 ⇒ 00:16:21.200 Demilade Agboola: that would allow us to be able to meet you at your point of needs on a daily.
119 00:16:21.410 ⇒ 00:16:24.538 Demilade Agboola: and ensure that not only are your numbers.
120 00:16:25.120 ⇒ 00:16:30.790 Demilade Agboola: they’re. They’re also useful to you because there’s no point having numbers that really don’t help you on a daily
121 00:16:32.133 ⇒ 00:16:38.719 Demilade Agboola: so yes, that is the slide, and just let let you know what’s going on.
122 00:16:39.580 ⇒ 00:16:46.130 Demilade Agboola: and also letting you know what’s to come. So if you have any questions, if you have any feedback. Do let me know.
123 00:16:48.420 ⇒ 00:16:57.450 felipefaria: Thank you and and thank you guys for working on this. We’re finally in a better position than what we were before. So
124 00:16:57.580 ⇒ 00:17:09.180 felipefaria: really appreciate it. I just have a question, though, on the on the known floral and hard goods it seems like that’s is positioned. There is kind of like a next phase thing or but
125 00:17:09.470 ⇒ 00:17:16.530 felipefaria: there has been some improvement done already on the base sales data, right? And I’m seeing the numbers kind of like
126 00:17:17.109 ⇒ 00:17:30.159 felipefaria: basically aligned. And there, there are some variances. And I know that I will use like some specific examples. But is there some structural changes that you guys are planning to do? Or is it just more refinement of the current data.
127 00:17:31.420 ⇒ 00:17:36.470 Demilade Agboola: So a bit a bit of both. So we’re trying to.
128 00:17:36.680 ⇒ 00:17:44.979 Demilade Agboola: because we don’t want to cause any disruption. We created a parallel, like infrastructure to what currently exists in terms of the Dbt models.
129 00:17:45.558 ⇒ 00:17:48.490 Demilade Agboola: So right now, we’re currently building out
130 00:17:48.860 ⇒ 00:17:56.840 Demilade Agboola: the non floral hard goods portion of that now we do have some of those numbers, but the numbers, especially when it comes to non lotter
131 00:17:58.230 ⇒ 00:17:59.230 Demilade Agboola: are good.
132 00:17:59.490 ⇒ 00:18:07.429 Demilade Agboola: It it starts to get really Wonky there. But right now we’re trying to like build that out and flesh that out such that it accommodates both lotted and not lotted.
133 00:18:07.580 ⇒ 00:18:12.070 Demilade Agboola: And so the numbers become way more robust than what you currently have right now.
134 00:18:12.510 ⇒ 00:18:28.119 Demilade Agboola: So that’s kind of the next phase and part of, I say next phase. But we’re literally working on as we speak. But the idea is we want to be able to roll it out to you by like next week, so you can start to test Qa the numbers. And just let us know. Hey, these numbers don’t work as well as they should
135 00:18:28.532 ⇒ 00:18:33.330 Demilade Agboola: or there seems to be an edge case where they don’t work. It’s the way we think it should.
136 00:18:34.520 ⇒ 00:18:46.880 felipefaria: Got it. Thank you. Yeah. And on that, I think the one note that I mentioned to you guys earlier this week is right now on the basis data can only look at the data by delivery date.
137 00:18:47.316 ⇒ 00:18:53.610 felipefaria: Ideally, it would be by fulfillment date. It just should track along with kind of like how we look at the
138 00:18:54.100 ⇒ 00:18:58.769 felipefaria: at the floral site, and it’s a little bit easier to validate
139 00:18:59.320 ⇒ 00:19:13.080 felipefaria: whether the numbers are correct compared to dash. Because if you’re just looking at delivery date, it gets a little bit tricky with having to check what shipped on Sunday from certain Fcs that are being delivered on Monday.
140 00:19:13.650 ⇒ 00:19:22.359 felipefaria: No floral have a longer lead time. So it’s like no 2 days. So like all this sort of stuff. So it’s just a note on that. But
141 00:19:22.963 ⇒ 00:19:36.010 Emily Giant: Is that in base sales data, that there’s no fulfillment date, or it’s the inventory mark. I I had this as a note and was like, I. I know we have prep date, which is technically the fulfillment date and base sales data.
142 00:19:36.500 ⇒ 00:19:45.260 felipefaria: Yeah, I haven’t seen the the mart one yet. Yeah, it might be a. It might be a case of just the
143 00:19:45.450 ⇒ 00:19:57.819 felipefaria: the measure being named differently. I’m gonna check like on the prep date one cause I I was honestly looking by fulfillment date, because that’s how it’s labeled on the
144 00:19:58.500 ⇒ 00:20:02.020 felipefaria: on the floral looks that I have so.
145 00:20:02.310 ⇒ 00:20:07.680 Emily Giant: Try prep date and based sales data. And then it shouldn’t.
146 00:20:07.830 ⇒ 00:20:31.600 Emily Giant: I need to check that? I actually have like, added that layer in Looker. That’s on my. So I’m doing the hard, good mark today, and then tomorrow, by end of day, you should have all of the fields that we’ve built available. Not that you want to like do that on a Friday night, but on Monday. So all of the things that like there’s
147 00:20:32.290 ⇒ 00:20:48.879 Emily Giant: I like this meeting because we can like discuss what you’re seeing versus what I’m seeing, because oftentimes I don’t realize that you can’t see what I see in my local instance. So I think that that’s happening. But yeah, I can definitely check in on that the fulfillment date thing because
148 00:20:49.490 ⇒ 00:20:50.650 Emily Giant: you need that.
149 00:20:51.050 ⇒ 00:20:54.889 felipefaria: And is is the goal for eventually base sales data to
150 00:20:55.687 ⇒ 00:21:00.340 felipefaria: be phased phased out? Or are we gonna keep.
151 00:21:01.189 ⇒ 00:21:03.710 felipefaria: Because and what I’m trying to get at is just like
152 00:21:03.830 ⇒ 00:21:12.929 felipefaria: ideally, we would have an alignment on kind of how these things are called in different databases. If we are, if we do have it, I think it’s always been an issue in terms of
153 00:21:13.040 ⇒ 00:21:30.482 felipefaria: in the past, right? Like things being like trying to find exactly what you want, because it’s not named exactly how you expect it to be named. So it’s just a call out. If we’re using kind of different data sets and stuff like that should try to align how things are called
154 00:21:30.960 ⇒ 00:21:34.090 felipefaria: in each of those it would avoid some confusion.
155 00:21:34.440 ⇒ 00:21:41.710 Emily Giant: Do you want to speak to that at all? Or amber? I know that that’s like part of the project, but I don’t know when.
156 00:21:43.222 ⇒ 00:21:48.297 Demilade Agboola: I think as we’re rolling things out, we will need to be able to.
157 00:21:50.210 ⇒ 00:21:55.119 Demilade Agboola: I think part of rolling things out is a bit of a stress test. In sense of.
158 00:21:55.370 ⇒ 00:21:58.809 Demilade Agboola: we need to be able to see how you’re able to use
159 00:21:59.880 ⇒ 00:22:07.650 Demilade Agboola: the data that’s available. And if there any issues with that potentially as much as possible. We will keep it as close to
160 00:22:08.193 ⇒ 00:22:22.869 Demilade Agboola: how things are named. And even in the process of building out these numbers. Emily has been of great help to be able to say, Hey, we don’t try not to call it unlotted or lotted use on committed instead, because it could cause confusion further down the line.
161 00:22:23.266 ⇒ 00:22:31.010 Demilade Agboola: So things like that we are trying to be mindful of the fact that there are downstream flows already, and we don’t want to be disruptive of that.
162 00:22:31.524 ⇒ 00:22:38.350 Demilade Agboola: But yes, if there are numbers that just look funny, or just don’t seem to fit into like current definitions of how things are.
163 00:22:38.790 ⇒ 00:22:41.750 Demilade Agboola: Please feel free to reach out. We will look into it.
164 00:22:42.620 ⇒ 00:22:47.859 Demilade Agboola: and but yeah, I know we do have things like fulfillment in our data as well. But, like again.
165 00:22:48.250 ⇒ 00:22:49.670 Demilade Agboola: these things really
166 00:22:49.860 ⇒ 00:23:07.389 Demilade Agboola: change, depending on how people view things. And it might just be like, we’re using a table where this is what hopefully, mandate is but the format. That is another table with a different definition of mandate or different way, recognize hopefully, mandate. So yeah, we’ll definitely just try and like, keep an eye on that. But yeah.
167 00:23:07.390 ⇒ 00:23:25.119 felipefaria: Yeah, yeah, if if they have different definitions, it makes sense to have different names. But if it’s exact, exactly the same thing, right? Like. If if you’re looking at, I I assume that prep date and fulfillment date would be the same thing, right? So it should ideally be called just one thing, it’s the same thing with like distribution, point and fulfillment center.
168 00:23:26.540 ⇒ 00:23:28.310 felipefaria: Sorry, Emily, you’re on mute.
169 00:23:29.780 ⇒ 00:23:30.340 Emily Giant: Yeah.
170 00:23:30.340 ⇒ 00:23:46.790 Emily Giant: I couldn’t agree more that, like, there is so much duplication and confusion like, especially with product type, that to me is like one of the worst, like, there’s 6 or 7 different product type filters that all do allegedly the same thing.
171 00:23:48.270 ⇒ 00:24:14.320 Emily Giant: so that’s definitely on my radar. And a lot of those are going to fall away in what Kyo is doing. So I know. Last week we reviewed dashboards that are going to get deprecated in Looker. He’s been working on that throughout the week. It will cost some disruption. Nothing’s getting like hard deleted. So if you’re missing a dashboard like just shout it out to me, and we can like revive it. But
172 00:24:14.320 ⇒ 00:24:19.459 Emily Giant: a lot of those views that the fields are coming from that are duplicates or named Weird.
173 00:24:19.500 ⇒ 00:24:31.930 Emily Giant: Those are. Gonna go away as a side effect of what he’s doing. There’s just so much extra garbage in the back end of Looker that that’s where we’re seeing all of that duplication.
174 00:24:32.570 ⇒ 00:24:34.609 Emily Giant: But it is easy to like
175 00:24:35.120 ⇒ 00:24:41.250 Emily Giant: swap in the field. We’re gonna keep for all of the ones that are
176 00:24:41.610 ⇒ 00:24:47.620 Emily Giant: like the same thing, but with a different name. So I think it’s deciding together what we think the name should be.
177 00:24:47.760 ⇒ 00:24:50.230 Emily Giant: so that it makes the most sense.
178 00:24:51.680 ⇒ 00:25:08.740 Demilade Agboola: Yes, I agree. Also, I did have a question to the team, but I’m not sure. I know, like we’re all users of data. But I’m not sure who actually builds out the different like looks, views, and like dashboards. And I’m wondering if we could
179 00:25:09.360 ⇒ 00:25:12.430 Demilade Agboola: one figure out ways in which we could standardize things.
180 00:25:12.600 ⇒ 00:25:16.850 Demilade Agboola: so that, like, we don’t have a situation where we just like delete the bloats right now.
181 00:25:16.980 ⇒ 00:25:21.570 Demilade Agboola: and 6 months one year down the line. We’re like back at a similar spot.
182 00:25:22.158 ⇒ 00:25:26.340 Demilade Agboola: So number one, like figuring out standardization processes will be very helpful.
183 00:25:26.897 ⇒ 00:25:33.139 Demilade Agboola: And just to, if there’s anything we could also do to help with that process of like.
184 00:25:33.990 ⇒ 00:25:41.240 Demilade Agboola: how like, what data do you need? How do you need to access it? Granularity, things like that like, How do you
185 00:25:41.370 ⇒ 00:25:47.070 Demilade Agboola: want that available to you? So we can keep that in mind while we’re like building out all these models.
186 00:25:49.400 ⇒ 00:25:52.324 felipefaria: Yeah, and I’ll keep that in mind. But
187 00:25:53.940 ⇒ 00:25:57.833 felipefaria: ideally, like, once we have everything built
188 00:25:59.090 ⇒ 00:26:11.901 felipefaria: essentially like like a a map right of like, just with descriptions of how things are. And because I think in the company there’s a lot of people that build looks themselves
189 00:26:12.580 ⇒ 00:26:25.339 felipefaria: dashboards might be a little bit more specific. But I know that there’s a lot of people that do dashboards as well, especially people that are presenting things or wanna provide visibility for their teams on certain key data points.
190 00:26:25.680 ⇒ 00:26:34.440 felipefaria: But yeah, yeah, essentially, essentially, that.
191 00:26:37.600 ⇒ 00:26:42.113 Demilade Agboola: Okay, it does seem like it might be hard to keep everything
192 00:26:44.990 ⇒ 00:26:51.179 Demilade Agboola: clean, because if everyone can like, or if a lot of people, not everyone. But if a lot of people can create like dashboards and like looks.
193 00:26:51.593 ⇒ 00:26:59.249 Demilade Agboola: potentially, things can explode after time. And we can have the same issue, where we have so many things that no one has used in like months.
194 00:27:01.150 ⇒ 00:27:02.060 Demilade Agboola: Okay.
195 00:27:02.480 ⇒ 00:27:15.449 Demilade Agboola: that’s that’s good to know. At least, he helps us be able to think of how to come up with the data governance strategy that just to be able to think of like what’s the best practice? How often should we, you know, do cleanups and stuff so that things don’t get out of hand.
196 00:27:15.450 ⇒ 00:27:16.180 Emily Giant: Yeah.
197 00:27:16.774 ⇒ 00:27:25.989 Emily Giant: I think it’s like some of the things that like as we carry forward these meetings and like, have dashboards to demo. It’s doing some
198 00:27:26.160 ⇒ 00:27:28.210 Emily Giant: training on how
199 00:27:28.590 ⇒ 00:27:46.609 Emily Giant: you don’t have to save every look that like you can save a URL, and it’s a quick snapshot, and it doesn’t have to clutter up. It’s just kind of things that I’ve learned in the last year about how to not clutter, looker and like what should be saved versus what’s like. Share this for a second, and then it’s gone.
200 00:27:47.080 ⇒ 00:27:49.500 Stephanie Plaza: And then, oh, sorry!
201 00:27:49.760 ⇒ 00:27:50.789 Emily Giant: Go for it, Steph.
202 00:27:50.790 ⇒ 00:27:57.659 Stephanie Plaza: No, I also think that like when I share things with Dean Capel, for example, like he has different permissions than I do.
203 00:27:58.050 ⇒ 00:28:06.140 Stephanie Plaza: So there’s some like very quick things that he wants to be able to see that I then do have to save as a look to show, and I’m never going to use it again. It’s a 1 time. Thing.
204 00:28:06.599 ⇒ 00:28:12.830 Stephanie Plaza: So yeah, I think maybe either, like rules of like, Okay, go through your own stuff and delete what you don’t need versus like.
205 00:28:13.390 ⇒ 00:28:17.269 Stephanie Plaza: How many people should have, what permissions to make things cleaner.
206 00:28:18.240 ⇒ 00:28:27.081 Emily Giant: Yeah, and a lot of the work that’s being done, that like the messiness that you have to deal with in the field picker.
207 00:28:27.670 ⇒ 00:28:41.640 Emily Giant: I think that that’s 1 of the messiest things about Looker, and like having all of the extraneous views go away, is going to be such a substantial difference for new users that, like a lot of what we’re saying now, won’t even
208 00:28:41.970 ⇒ 00:28:58.600 Emily Giant: be an issue down the line. And to Felipe’s point I’m trying to add in descriptions. There’s a way in lookml to add, like the definition of the field, like directly. So when you hover over it, it can tell the user what it is. And we’re we’re trying to add that like, add.
209 00:28:58.600 ⇒ 00:28:59.180 felipefaria: Yeah.
210 00:28:59.180 ⇒ 00:29:04.789 Emily Giant: This up. That’s part of it. And then, like providing documentation is so important.
211 00:29:05.190 ⇒ 00:29:12.119 felipefaria: Yeah, Steven had something like that. I don’t know. If you remember, I think it was like a 1 star, or like it was a website.
212 00:29:12.120 ⇒ 00:29:12.680 Emily Giant: Yeah.
213 00:29:12.680 ⇒ 00:29:30.499 felipefaria: Yeah, and that he just put the descriptions of everything. But if we had kind of like different tables, right like before, we have inventory transactions except and then inventory except and based sales data. It’s just like a description of kind of like what each of those are, and
214 00:29:30.680 ⇒ 00:29:48.370 felipefaria: because then people would be able to build whatever they need. I think it’s pretty straightforward to build kind of looks in in Looker. It’s just a matter of like knowing where to find the information that you that you’re looking for, because there’s a lot in there right there’s a lot of information. So.
215 00:29:48.770 ⇒ 00:30:11.589 Emily Giant: Well, if everyone here does what I do, I just always go to base sales data because it has the most fields, and that’s not like necessarily best practice. Nor is it easier. But it’s like my default behavior, because I’m like, well, tableau items. Xf has 300 columns. So it’s going to be in there somewhere, something that I need but it won’t be like that anymore. It will like what
216 00:30:12.040 ⇒ 00:30:20.809 Emily Giant: the the end goal of this is like that. The marts are marts. Are the explorers in looker. So when you’re going to like start building.
217 00:30:21.060 ⇒ 00:30:43.349 Emily Giant: it’s not going to be hard for Pk to be like, I’m going to the marketing mart, because this is custom made for the questions that I answer on a daily basis. And like I could restrict him from ever going to the inventory mart, and he would never know, because it’s just not what he needs. So we’re trying to like really, tailor these explores in a way that
218 00:30:43.670 ⇒ 00:30:45.460 Emily Giant: you only need yours.
219 00:30:47.270 ⇒ 00:30:54.070 Emily Giant: and if it does cross reference, a different one that will be available to you. You don’t have to go to that different one. But like.
220 00:30:54.420 ⇒ 00:31:00.000 Emily Giant: that’s what explorers are meant to be. And it just exploded over time. There were
221 00:31:00.250 ⇒ 00:31:14.849 Emily Giant: too many different systems that we integrated with without cross referencing them on the back end. And it just got messy. And that’s why it’s so impossible. But that’s definitely part of the end goal is like having like 5 explores.
222 00:31:15.060 ⇒ 00:31:17.810 Emily Giant: And you go to yours.
223 00:31:18.010 ⇒ 00:31:26.989 Emily Giant: Is that a good way to describe it, Demo Lotte, like it’s hard to like. Know what it’s like on the other side of 55 explorers. Every time you start to build a look or look.
224 00:31:27.670 ⇒ 00:31:40.310 Demilade Agboola: Yeah, definitely. That’s part of, I meant, like the data governance strategy and just like figuring out how to be able to manage all these things, because part of the idea of March is even within Dbts, we can start to create different, like
225 00:31:41.070 ⇒ 00:31:49.790 Demilade Agboola: older structures for different things. So inventory goes into one place wherever and you can go to another place. Marketing can go to another place, and that allows us to create like
226 00:31:50.100 ⇒ 00:31:54.649 Demilade Agboola: similar models that we can then expose in looker.
227 00:31:54.920 ⇒ 00:32:11.600 Demilade Agboola: and have, like a clarity of like. This, is supposed to be for inventory. This is supposed to be for marketing, and that can allow people to be able to just go to what they need rather than having to like one table that has 300 different columns, and have to like, let them figure out what they need.
228 00:32:12.073 ⇒ 00:32:17.779 Demilade Agboola: So yeah, we’re just trying to make things cleaner for use across the team.
229 00:32:19.010 ⇒ 00:32:19.530 Emily Giant: Yeah.
230 00:32:21.230 ⇒ 00:32:26.070 Demilade Agboola: So that’s it from me and from the team. I don’t know if anyone had any other thing they wanted to say.
231 00:32:28.790 ⇒ 00:32:38.960 Demilade Agboola: Okay, in that case, I guess we could call it today. Been great talking to everyone in next 2 weeks. By then we should have like data like at your fingertips. So I’m excited about that.
232 00:32:39.540 ⇒ 00:32:41.080 Emily Giant: Sounds good. Thank you. Guys.
233 00:32:41.080 ⇒ 00:32:42.140 Emily Giant: See your faces.
234 00:32:42.140 ⇒ 00:32:42.670 felipefaria: Bye, bye.
235 00:32:42.670 ⇒ 00:32:43.199 Stephanie Plaza: Thanks guys.
236 00:32:43.200 ⇒ 00:32:43.910 ianbiles: Guys.
237 00:32:43.910 ⇒ 00:32:44.260 Emily Giant: Bye.