Meeting Title: US x BF | Grooming Date: 2025-06-04 Meeting participants: Emily Giant, Amber Lin, Demilade Agboola
WEBVTT
1 00:00:14.260 ⇒ 00:00:14.920 Amber Lin: Okay.
2 00:00:15.650 ⇒ 00:00:16.820 Amber Lin: Hi, Emily.
3 00:00:17.130 ⇒ 00:00:18.160 Emily Giant: Hello!
4 00:00:19.320 ⇒ 00:00:25.579 Amber Lin: Let’s wait for a few other people to join. You probably have some stuff you need to do. So. Yeah, just wait for them to come.
5 00:00:25.920 ⇒ 00:00:27.110 Emily Giant: Sounds good.
6 00:00:28.450 ⇒ 00:00:40.230 Emily Giant: Yeah. Everything like imploded this week with, it’s like organization. Knew that brain forge would be kicking off. And so they were like, okay, we’re gonna be really transparent about it.
7 00:00:40.850 ⇒ 00:00:42.929 Emily Giant: Every data table has broken all at once.
8 00:00:43.180 ⇒ 00:00:44.420 Amber Lin: Oh, gosh!
9 00:00:44.420 ⇒ 00:00:49.530 Emily Giant: So I’m just like going back and forth between combing the Internet for like.
10 00:00:49.530 ⇒ 00:00:50.070 Amber Lin: Hmm.
11 00:00:50.070 ⇒ 00:00:56.640 Emily Giant: Shopify templates, things that people have used. But our use case is so specific.
12 00:00:57.320 ⇒ 00:01:04.930 Emily Giant: Like it. It’s good for some things, but for like not a lot. It knows like the way that we
13 00:01:05.760 ⇒ 00:01:18.285 Emily Giant: just the whole business logic of flowers is so specific because they die at such a rapid speed, and like quantifying inventory.
14 00:01:19.240 ⇒ 00:01:32.370 Emily Giant: with the subscriptions being a certain price for like this part of the year. And then for another part, like we went from never having promotions for subscriptions to like without really telling
15 00:01:32.640 ⇒ 00:01:36.099 Emily Giant: Bi to like having tons of sales.
16 00:01:36.100 ⇒ 00:01:36.430 Amber Lin: First.st
17 00:01:36.430 ⇒ 00:01:55.690 Emily Giant: We used to be able to quantify revenue on those really easily. And then suddenly, it was like, what is $1,500 charge, and it was like a subscription not tagged as a subscription not amortized as prepaid. And it’s like, what are we doing here like? At least let me know, so that when it looks like something broke.
18 00:01:56.110 ⇒ 00:01:59.580 Emily Giant: I know if it actually did. And like yesterday.
19 00:01:59.980 ⇒ 00:02:27.169 Emily Giant: someone wrote into me about like a weird thing they were seeing. And now there are like custom kits that are coming through in the data. I don’t know what they are. I didn’t know about them, but like our data does not know how to process that in its logic, for, like, it’s just such a mess, it’s such a mess. So I’m just like freaking out more than being productive, trying to like, understand what’s going on. So I can do something. But
20 00:02:27.410 ⇒ 00:02:32.520 Emily Giant: yeah, that’s where I’m at. It’s just always something
21 00:02:34.240 ⇒ 00:02:37.359 Emily Giant: I can’t wait to like get it in a better spot, where.
22 00:02:38.140 ⇒ 00:02:42.420 Emily Giant: like, I know where to even start with troubleshooting.
23 00:02:44.820 ⇒ 00:02:45.590 Amber Lin: Mean.
24 00:02:45.720 ⇒ 00:02:50.319 Amber Lin: I’m I’m glad we’re here to help. Somebody has already seen the shit show.
25 00:02:50.320 ⇒ 00:02:50.900 Emily Giant: Yeah.
26 00:02:51.360 ⇒ 00:02:54.120 Amber Lin: Kyle is starting to learn. Yeah.
27 00:02:54.120 ⇒ 00:02:54.890 Emily Giant: Yeah.
28 00:02:55.780 ⇒ 00:02:58.760 Emily Giant: Oh, it is a shit show indeed.
29 00:02:59.050 ⇒ 00:03:14.140 Amber Lin: Okay, they’re already setting away for 5 min. Let me grab Kyle. If you if you want to grab Zach, that will also be helpful. And then let me just look at our linear, and I’ll bring up anything that we need to discuss, and then be ready for a girl.
30 00:03:14.280 ⇒ 00:03:15.040 Emily Giant: Go on.
31 00:04:02.190 ⇒ 00:04:13.309 Emily Giant: It looks like Zack had to take an interview, since they’re trying to backfill that position. He had to take an interview over this slot so he can’t come, but if we record it, send the notes.
32 00:04:13.700 ⇒ 00:04:14.740 Amber Lin: Sounds good.
33 00:04:14.920 ⇒ 00:04:17.430 Amber Lin: I’ll just wait for Demala to come back.
34 00:04:17.740 ⇒ 00:04:18.550 Emily Giant: Okay.
35 00:04:18.550 ⇒ 00:04:19.680 Amber Lin: Go, grab, file.
36 00:05:31.390 ⇒ 00:05:32.650 Demilade Agboola: Okay. So I’m back.
37 00:05:35.020 ⇒ 00:05:35.980 Amber Lin: Hello!
38 00:05:36.640 ⇒ 00:05:42.070 Amber Lin: I talked to Kyle. I haven’t heard back, but he’ll he’ll join me, he can.
39 00:05:43.717 ⇒ 00:05:46.710 Amber Lin: Looking at. Let me share my screen.
40 00:05:53.050 ⇒ 00:06:03.879 Amber Lin: So I 1st looked at all the issues here. Everything is tagged per project. So I think we’re good to go into the project view. And then
41 00:06:05.300 ⇒ 00:06:08.860 Amber Lin: just look at what we need to do next.
42 00:06:09.460 ⇒ 00:06:14.430 Amber Lin: So if we go here, if we start with oh.
43 00:06:15.990 ⇒ 00:06:25.490 Amber Lin: alright! Let’s start with Looker, because I think we pretty much know what’s gonna what’s what needs to happen next, I think for next cycle we’re going to be looking at
44 00:06:26.760 ⇒ 00:06:43.389 Amber Lin: because we’ll hopefully finish up verifying the usage and accuracy or usage we already have. And then for next cycle we’re going to look at. Let’s flag them based on used, unused, accurate and accurate. We’ll present the findings, and then we will.
45 00:06:43.880 ⇒ 00:06:53.670 Amber Lin: I think we can deprecate the unused and inaccurate ones and start to
46 00:06:53.830 ⇒ 00:07:03.369 Amber Lin: say, Meet with all the analyst owners, and start to think about how we’re going to build a replacement for those that’s used and inaccurate.
47 00:07:03.780 ⇒ 00:07:06.939 Amber Lin: So how we’re going to approach these 2 mainly
48 00:07:10.015 ⇒ 00:07:16.980 Amber Lin: I guess, Demo Emily, anything that comes up to you for the for looker is this.
49 00:07:17.780 ⇒ 00:07:23.620 Amber Lin: is this like a good process that we should? We can take.
50 00:07:30.523 ⇒ 00:07:34.649 Demilade Agboola: Yes, I think it is. I think just being able to
51 00:07:36.220 ⇒ 00:07:49.279 Demilade Agboola: quickly split our like. Look at the board. They’re just Dbt models, and all of that into like this set of is it being used? Is it accurate that list that allows us to move quickly.
52 00:07:49.390 ⇒ 00:08:00.839 Demilade Agboola: and we can know what’s important to like, actually spend a lot of time on in terms of refactoring or like just making it more useful for the entire
53 00:08:01.338 ⇒ 00:08:02.880 Demilade Agboola: I went to Sam’s team.
54 00:08:03.330 ⇒ 00:08:04.025 Amber Lin: Awesome.
55 00:08:07.360 ⇒ 00:08:20.309 Amber Lin: Oh, gosh! Oh, I am also sneezing. Let’s skip estimates to all of these. Then that will help us a lot when we actually do the planning and assign us to assign this throughout the cycle. So
56 00:08:20.510 ⇒ 00:08:24.099 Amber Lin: let’s start with this. This tool doesn’t have estimate yet. So
57 00:08:24.700 ⇒ 00:08:33.029 Amber Lin: how long do you think this would take? I know demolan has a creative table, and Emily Emily Emily has to rate them, and we really don’t know yet.
58 00:08:33.770 ⇒ 00:08:35.242 Amber Lin: But we love to
59 00:08:35.610 ⇒ 00:08:37.450 Emily Giant: It’ll probably be like a 1 or a 2.
60 00:08:38.059 ⇒ 00:08:38.749 Amber Lin: Okay.
61 00:08:38.750 ⇒ 00:08:40.970 Emily Giant: One’s probably enough, but.
62 00:08:41.250 ⇒ 00:08:42.490 Amber Lin: I’ll give it a 2. Yeah.
63 00:08:42.490 ⇒ 00:08:43.870 Emily Giant: Yeah, just in case.
64 00:08:43.870 ⇒ 00:08:44.610 Amber Lin: Okay.
65 00:08:45.520 ⇒ 00:08:46.880 Amber Lin: Sounds good.
66 00:08:47.060 ⇒ 00:08:49.299 Amber Lin: And going back here.
67 00:08:50.580 ⇒ 00:08:58.490 Amber Lin: think flagging wouldn’t take that. Not long is my gut feeling. But don’t worry. What do you think.
68 00:09:03.320 ⇒ 00:09:11.299 Demilade Agboola: I mean, there are a lot of dashboards. But yeah, I think it will. It would largely just be a function of, I think use on the use should be easy, because.
69 00:09:11.300 ⇒ 00:09:11.620 Amber Lin: Yeah.
70 00:09:11.620 ⇒ 00:09:16.929 Demilade Agboola: We can query it and figure it out. I think accurate and accurate might be the trickier part.
71 00:09:17.480 ⇒ 00:09:24.900 Demilade Agboola: But that’s part of why I want us to be able to know what models are useful and reliable. So once we know that we can kind of see.
72 00:09:25.040 ⇒ 00:09:34.759 Demilade Agboola: What does the power and the things that are downstream, of those models that are reliable or accurate or inaccurate. We can then just use that blanket statement to like knock them out.
73 00:09:35.070 ⇒ 00:09:35.830 Amber Lin: Hmm.
74 00:09:36.080 ⇒ 00:09:37.030 Demilade Agboola: Let’s see.
75 00:09:37.380 ⇒ 00:09:38.850 Amber Lin: What does
76 00:09:47.460 ⇒ 00:09:48.540 Amber Lin: there is some?
77 00:09:50.910 ⇒ 00:09:54.340 Amber Lin: All right, so alright.
78 00:09:54.590 ⇒ 00:09:55.400 Amber Lin: But
79 00:09:56.380 ⇒ 00:10:06.690 Amber Lin: I still don’t think this would take a whole like whole day right? I still think we’re looking towards this range. What do you think.
80 00:10:08.188 ⇒ 00:10:13.960 Demilade Agboola: Yeah, I think like 1.2 points, especially if the first, st like the Dbt models, part has been done.
81 00:10:14.810 ⇒ 00:10:15.970 Amber Lin: Sounds good.
82 00:10:16.550 ⇒ 00:10:18.420 Amber Lin: That’s 2 points.
83 00:10:19.370 ⇒ 00:10:22.739 Amber Lin: And it’s look at.
84 00:10:22.740 ⇒ 00:10:26.849 Demilade Agboola: Heads up that I’m I’m about to start eating, so if it takes.
85 00:10:26.850 ⇒ 00:10:27.910 Emily Giant: No worries.
86 00:10:27.910 ⇒ 00:10:30.110 Demilade Agboola: Maybe take a couple of seconds for me to respond.
87 00:10:31.460 ⇒ 00:10:33.339 Demilade Agboola: I’m trying to. I’m trying to finish up what I’m eating.
88 00:10:33.340 ⇒ 00:10:35.070 Amber Lin: Okay, that’s all. Good.
89 00:10:35.070 ⇒ 00:10:36.610 Emily Giant: Please eat. Yes.
90 00:10:37.740 ⇒ 00:10:40.670 Amber Lin: And this findings I think we need to.
91 00:10:40.790 ⇒ 00:10:54.290 Amber Lin: I think we flash them and present and and oh, my, or tab.
92 00:10:55.170 ⇒ 00:11:01.590 Amber Lin: So this is essentially, we categorize them into used.
93 00:11:01.960 ⇒ 00:11:06.429 Amber Lin: Oh, well, it’s I think this would take like a meeting.
94 00:11:06.550 ⇒ 00:11:08.270 Amber Lin: Probably.
95 00:11:09.190 ⇒ 00:11:09.930 Amber Lin: Yeah.
96 00:11:16.824 ⇒ 00:11:20.909 Demilade Agboola: So this is the like presenting the findings.
97 00:11:20.910 ⇒ 00:11:21.960 Amber Lin: Flagged it.
98 00:11:22.290 ⇒ 00:11:27.589 Demilade Agboola: Yeah, that’s that’s just basically a meeting and saying, Hey, this is what we’ve noticed. This. These are what we’re gonna do based off of.
99 00:11:27.590 ⇒ 00:11:33.290 Amber Lin: Okay, sounds good. I’m gonna put it here. 1.1 meeting will do.
100 00:11:34.350 ⇒ 00:11:44.139 Amber Lin: And then, I think this part.
101 00:11:44.290 ⇒ 00:11:50.400 Amber Lin: we’ll probably need a process of like
102 00:11:53.620 ⇒ 00:11:56.899 Amber Lin: like, maybe we mark them and then wait for a week
103 00:11:57.470 ⇒ 00:12:01.799 Amber Lin: to just make sure, and then just take them. Take them out.
104 00:12:01.970 ⇒ 00:12:07.009 Amber Lin: Emily, what do you think is the process for this? For the ones that’s not using is inaccurate.
105 00:12:08.153 ⇒ 00:12:10.119 Emily Giant: Probably a 1 or 2.
106 00:12:10.550 ⇒ 00:12:11.210 Amber Lin: Yeah.
107 00:12:11.710 ⇒ 00:12:13.500 Emily Giant: There’s so many.
108 00:12:13.700 ⇒ 00:12:19.550 Amber Lin: It’s I’m thinking about the process of how this can be done right? Because we have.
109 00:12:19.910 ⇒ 00:12:32.210 Amber Lin: 1st of all, we have a list of these unused plus in that dashboards.
110 00:12:32.210 ⇒ 00:12:37.269 Emily Giant: Yeah. Now, honestly, probably a 2. Cause, I’m thinking through the process. And it’s like.
111 00:12:37.270 ⇒ 00:12:37.760 Amber Lin: Yeah.
112 00:12:37.760 ⇒ 00:12:40.929 Emily Giant: Be removed from multiple models. They’re going to have to have.
113 00:12:40.930 ⇒ 00:12:41.390 Amber Lin: Oh!
114 00:12:41.390 ⇒ 00:12:53.170 Emily Giant: The fields replaced with something else. If one of the fields is used in a dashboard, so yeah.
115 00:12:53.740 ⇒ 00:12:54.745 Amber Lin: Yeah.
116 00:12:56.320 ⇒ 00:13:09.520 Amber Lin: I feel like, even if it’s unused and inaccurate, there will be a scale of this. Because, remember, for accuracy, we have a scale of one to 10, so we should start from start from the least.
117 00:13:10.290 ⇒ 00:13:17.319 Amber Lin: Probably should use a combination score of, say the least, use, and the most inaccurate.
118 00:13:17.320 ⇒ 00:13:18.850 Emily Giant: Yeah, that makes sense.
119 00:13:18.850 ⇒ 00:13:27.950 Amber Lin: Least used at most is that spirits, and then.
120 00:13:28.100 ⇒ 00:13:33.120 Amber Lin: once we flag them, should we wait a little bit to remove them, or should we
121 00:13:33.724 ⇒ 00:13:36.150 Amber Lin: should we just remove them immediately?
122 00:13:36.390 ⇒ 00:13:38.969 Emily Giant: I would probably run it by the stakeholders.
123 00:13:38.970 ⇒ 00:13:39.320 Amber Lin: Okay.
124 00:13:39.320 ⇒ 00:13:44.150 Emily Giant: If you’re not used then no, just get rid of them. Nobody will notice but
125 00:13:44.510 ⇒ 00:13:50.260 Emily Giant: for ones that are used, or these are unused. No, just get rid of them. We can just.
126 00:13:50.260 ⇒ 00:13:53.620 Amber Lin: We don’t need to wait a week to just verify. Nobody uses it.
127 00:13:55.580 ⇒ 00:13:59.959 Emily Giant: I can run a report to see the last time that any of the dashboards were used.
128 00:14:00.200 ⇒ 00:14:07.310 Emily Giant: So if it’s been more than a year. Then I think we can get rid of it. If it’s been less than a year, I can reach out to the stakeholder.
129 00:14:07.950 ⇒ 00:14:17.950 Amber Lin: Will and move if you say used for minimum here.
130 00:14:19.120 ⇒ 00:14:23.739 Demilade Agboola: Are there any like seasonal dashboards that can only be used like once a year?
131 00:14:27.080 ⇒ 00:14:28.280 Emily Giant: Not really.
132 00:14:29.220 ⇒ 00:14:33.320 Emily Giant: I can pay attention to that, though. I’ll make sure to like audit for
133 00:14:34.000 ⇒ 00:14:38.700 Emily Giant: any kind of like annually reference dashboard. But I don’t really think we have those.
134 00:14:38.990 ⇒ 00:14:39.560 Amber Lin: Hmm!
135 00:14:41.280 ⇒ 00:14:48.050 Amber Lin: Sounds good, I think, when we define, I think we should define the usage a little bit more. Which
136 00:14:48.625 ⇒ 00:14:53.800 Amber Lin: I want to ask Kyle about, because he already got the usage stats from Looker.
137 00:14:53.800 ⇒ 00:14:54.189 Emily Giant: And I want.
138 00:14:54.190 ⇒ 00:14:56.530 Amber Lin: Under how he’s going to classify or rank.
139 00:14:57.676 ⇒ 00:14:59.170 Amber Lin: These usage.
140 00:15:00.970 ⇒ 00:15:01.390 Emily Giant: -
141 00:15:01.390 ⇒ 00:15:06.099 Amber Lin: From that we’ll be able to say, Okay, what is the least the least used?
142 00:15:06.740 ⇒ 00:15:16.470 Amber Lin: Let me go check real quick dashboards, that’s what I’ll say, cool dashboards. Audit.
143 00:15:16.790 ⇒ 00:15:27.499 Amber Lin: Oh, okay, great. Let me also just let me share here this one.
144 00:15:28.210 ⇒ 00:15:32.850 Amber Lin: So this is the dashboard audit that came from
145 00:15:36.177 ⇒ 00:15:40.950 Amber Lin: that Kyle did. So we’ll have the last access date.
146 00:15:44.910 ⇒ 00:15:50.100 Amber Lin: Oh, gosh! This is so much stuff, poor Kyle, for everybody.
147 00:15:50.340 ⇒ 00:15:54.330 Amber Lin: Yeah, I guess we we can filter. By this, at least.
148 00:15:54.330 ⇒ 00:15:54.830 Emily Giant: Yeah, that.
149 00:15:54.830 ⇒ 00:16:01.509 Amber Lin: And if they’re last access by, say, 2023, then we can just directly get rid of them, especially if
150 00:16:01.740 ⇒ 00:16:03.479 Amber Lin: the view count is low.
151 00:16:06.810 ⇒ 00:16:08.830 Amber Lin: But then we need to consider.
152 00:16:09.030 ⇒ 00:16:16.820 Amber Lin: we need to also consider the accuracy, okay and
153 00:16:35.540 ⇒ 00:16:48.249 Amber Lin: and good ones. Yeah, give me one based on that, then, is blog by usage.
154 00:16:48.910 ⇒ 00:16:51.890 Amber Lin: Okay. Sounds good present findings.
155 00:16:52.990 ⇒ 00:16:53.829 Amber Lin: This one.
156 00:16:55.820 ⇒ 00:17:02.960 Amber Lin: this one we probably would ha would end up having a ticket for each one, each one of them.
157 00:17:03.160 ⇒ 00:17:04.650 Amber Lin: It’s my assumption.
158 00:17:07.500 ⇒ 00:17:12.680 Amber Lin: But I guess the 1st step we could less
159 00:17:12.849 ⇒ 00:17:20.580 Amber Lin: list all that needs to be rebuilt, ranked by priority.
160 00:17:25.230 ⇒ 00:17:26.790 Amber Lin: And then
161 00:17:30.750 ⇒ 00:17:42.860 Amber Lin: great tickets for 3 builds or
162 00:17:54.430 ⇒ 00:17:55.200 Amber Lin: -oh.
163 00:18:09.650 ⇒ 00:18:13.969 Amber Lin: okay, this is, gonna be a project.
164 00:18:14.620 ⇒ 00:18:22.479 Amber Lin: Now become the project with inch pre build.
165 00:18:23.520 ⇒ 00:18:24.150 Emily Giant: Yeah.
166 00:18:24.950 ⇒ 00:18:32.010 Amber Lin: Okay, that’s good. That is very big needs to be broken down.
167 00:18:34.470 ⇒ 00:18:35.800 Amber Lin: And then.
168 00:18:37.260 ⇒ 00:18:43.460 Amber Lin: okay, I think any of these that we want to move. And I think that’s enough for next cycle for looker
169 00:18:45.230 ⇒ 00:18:46.959 Amber Lin: looking at these.
170 00:18:51.548 ⇒ 00:18:55.859 Amber Lin: yeah, these are mostly like freshness tiles full.
171 00:18:56.280 ⇒ 00:18:59.919 Amber Lin: and those are part of the rebuild, or after the rebuild.
172 00:19:00.370 ⇒ 00:19:01.390 Amber Lin: So let’s.
173 00:19:01.390 ⇒ 00:19:01.960 Emily Giant: I agree.
174 00:19:01.960 ⇒ 00:19:04.419 Amber Lin: Back to the different projects.
175 00:19:05.240 ⇒ 00:19:07.870 Amber Lin: Redshift issues.
176 00:19:09.160 ⇒ 00:19:14.130 Amber Lin: Okay, very. For last usage review.
177 00:19:14.240 ⇒ 00:19:17.769 Amber Lin: Oh, we don’t have points on these.
178 00:19:22.150 ⇒ 00:19:29.909 Amber Lin: I mean, you guys would still need to both you and which one would need to pass through these.
179 00:19:30.080 ⇒ 00:19:30.670 Emily Giant: Yeah.
180 00:19:30.670 ⇒ 00:19:36.320 Amber Lin: So say, like 2 or 3, maybe.
181 00:19:54.130 ⇒ 00:19:59.170 Amber Lin: Oh, okay. So next week we should
182 00:19:59.530 ⇒ 00:20:02.330 Amber Lin: next cycle. I mean there should be.
183 00:20:03.740 ⇒ 00:20:04.530 Amber Lin: I’m good evening.
184 00:20:05.950 ⇒ 00:20:19.090 Amber Lin: We have a week. I don’t know if we can get the flagging done in this cycle, so maybe
185 00:20:19.320 ⇒ 00:20:24.190 Amber Lin: you can move the flag for decisions to this ticket.
186 00:20:27.520 ⇒ 00:20:28.310 Emily Giant: Yeah.
187 00:20:29.620 ⇒ 00:20:32.570 Amber Lin: Okay, that’s quite interesting.
188 00:20:32.990 ⇒ 00:20:34.280 Amber Lin: And
189 00:20:37.970 ⇒ 00:20:48.770 Amber Lin: hmm, oh, unused tables!
190 00:20:51.270 ⇒ 00:20:52.000 Amber Lin: Awesome.
191 00:20:52.360 ⇒ 00:20:53.030 Amber Lin: Wow!
192 00:20:53.620 ⇒ 00:20:57.750 Amber Lin: Should we wait a week or 2 weeks before we deprecate them?
193 00:21:00.620 ⇒ 00:21:05.210 Emily Giant: The redshift models. I don’t know. We I don’t think so. I think we could do it right away.
194 00:21:05.780 ⇒ 00:21:06.380 Amber Lin: Oh!
195 00:21:06.380 ⇒ 00:21:13.250 Emily Giant: That I mean, that’s just that’s me being the cowboy that I am. But we know we’re not using them.
196 00:21:13.530 ⇒ 00:21:14.100 Amber Lin: Okay.
197 00:21:14.100 ⇒ 00:21:17.140 Emily Giant: And no like.
198 00:21:17.610 ⇒ 00:21:18.760 Emily Giant: I’m not
199 00:21:18.970 ⇒ 00:21:25.350 Emily Giant: totally sure what the risk is like. Them. A lot of be the risk there with turning off ones we know aren’t being used.
200 00:21:26.540 ⇒ 00:21:29.800 Demilade Agboola: Well, I I think it might depend on how we define usage.
201 00:21:30.010 ⇒ 00:21:30.780 Emily Giant: Hmm, okay.
202 00:21:30.780 ⇒ 00:21:43.911 Demilade Agboola: Because, again, if we define it improperly, it turn. It might turn out that like again. It might be used every quarter or every whatever period, but, like we just were not aware of it. We looked at so
203 00:21:44.970 ⇒ 00:21:45.600 Demilade Agboola: we willing.
204 00:21:46.190 ⇒ 00:21:50.749 Emily Giant: Something, cause there’s some that I’m like. We do not use this, and I like know it.
205 00:21:51.080 ⇒ 00:21:51.810 Amber Lin: No.
206 00:21:51.810 ⇒ 00:21:56.150 Emily Giant: With certainty. But there are others that I hear what you’re saying there, with the risk.
207 00:21:58.940 ⇒ 00:22:03.729 Demilade Agboola: Yeah, as long as like, we know ones that are for sure that we’re not using. Yeah, we can obviously.
208 00:22:03.730 ⇒ 00:22:04.220 Amber Lin: Look.
209 00:22:04.220 ⇒ 00:22:06.829 Demilade Agboola: I do about redshift and get rid of them.
210 00:22:07.940 ⇒ 00:22:22.830 Amber Lin: Yeah, so I guess here we categorize by like, there’s level of is sure that.com
211 00:22:23.970 ⇒ 00:22:25.560 Amber Lin: wants that.
212 00:22:26.330 ⇒ 00:22:29.780 Amber Lin: Okay? Sure. Okay? And then we have
213 00:22:34.310 ⇒ 00:22:41.820 Amber Lin: and there’s ones that the numbers show that we don’t use them. And then there’s also ones that might be at risk.
214 00:22:46.150 ⇒ 00:22:48.069 Amber Lin: And then there’s
215 00:22:52.890 ⇒ 00:22:54.170 Amber Lin: usage.
216 00:22:55.180 ⇒ 00:23:02.030 Amber Lin: And then, based on that, we can turn off, turn them off, wait few weeks, and that
217 00:23:02.250 ⇒ 00:23:04.369 Amber Lin: maybe wait and then deprecate them.
218 00:23:06.021 ⇒ 00:23:09.639 Amber Lin: I don’t know if this is still something we need.
219 00:23:10.780 ⇒ 00:23:15.280 Amber Lin: But right now we say we have a long list of
220 00:23:16.559 ⇒ 00:23:21.470 Amber Lin: stuff we’ve got from the different stuff we got from Redshift. But.
221 00:23:23.250 ⇒ 00:23:34.909 Amber Lin: I guess I think what Kyle wanted this for is in order to deprecate. We kind of need to know what they’re used for. But if we’re just basing it off the numbers that we don’t need this, what do you guys think.
222 00:23:36.600 ⇒ 00:23:39.090 Emily Giant: I think that makes sense to me.
223 00:23:40.400 ⇒ 00:23:40.990 Amber Lin: Hmm.
224 00:23:40.990 ⇒ 00:23:42.309 Demilade Agboola: Yeah, I agree with that.
225 00:23:44.270 ⇒ 00:23:44.980 Amber Lin: I don’t know.
226 00:23:45.290 ⇒ 00:23:50.610 Emily Giant: It would be hard, for, like I can see if if I do the turn off the unused tables, etc.
227 00:23:50.780 ⇒ 00:23:57.560 Emily Giant: then it wouldn’t be as necessary for Kyle to understand, like the business need in order to
228 00:23:58.590 ⇒ 00:23:59.640 Emily Giant: turn it off.
229 00:24:03.274 ⇒ 00:24:08.230 Amber Lin: so sorry, it’s a conclusion that we don’t need this do we need this.
230 00:24:08.440 ⇒ 00:24:10.469 Emily Giant: I don’t think so, but.
231 00:24:10.470 ⇒ 00:24:11.120 Amber Lin: Okay.
232 00:24:11.120 ⇒ 00:24:20.349 Emily Giant: Maybe that would be, but from from my end I’m the least at risk getting rid of that, because I know what function is. So what do you think?
233 00:24:21.500 ⇒ 00:24:23.302 Emily Giant: Sorry. I know you’re trying to eat.
234 00:24:24.570 ⇒ 00:24:25.729 Demilade Agboola: No, I.
235 00:24:25.730 ⇒ 00:24:26.549 Emily Giant: Thank you.
236 00:24:27.370 ⇒ 00:24:29.654 Demilade Agboola: Yeah, I agree with that. I feel like
237 00:24:32.360 ⇒ 00:24:34.520 Demilade Agboola: Once we are able to.
238 00:24:35.350 ⇒ 00:24:36.939 Demilade Agboola: I was able to figure out, like
239 00:24:37.490 ⇒ 00:24:43.229 Demilade Agboola: the other metrics, like, based on usage and activity and and accuracy.
240 00:24:43.230 ⇒ 00:24:43.630 Emily Giant: Hmm.
241 00:24:43.630 ⇒ 00:24:47.920 Demilade Agboola: Guess like, it’s less important. This is less important.
242 00:24:54.340 ⇒ 00:25:01.360 Amber Lin: okay, I’m gonna cancel this one, yeah, less stuff to do
243 00:25:02.147 ⇒ 00:25:12.260 Amber Lin: so i guess we also need to check was touched by Ddt, so we are
244 00:25:12.910 ⇒ 00:25:20.140 Amber Lin: sorry. What do we call this in redshift? Do you call them tables? Do we call them ingestion flows? What are we trying to deprecate? I just.
245 00:25:20.580 ⇒ 00:25:28.390 Emily Giant: I always call them table. But I’m academically trained person in data science. So
246 00:25:28.810 ⇒ 00:25:30.780 Emily Giant: I don’t know if we need to call them that.
247 00:25:31.320 ⇒ 00:25:32.869 Amber Lin: Do we call them tables?
248 00:25:33.950 ⇒ 00:25:37.342 Demilade Agboola: For the for ease. We could just see tables.
249 00:25:38.070 ⇒ 00:25:41.800 Demilade Agboola: so like I think tables works fine for for we’re trying to communicate.
250 00:25:41.800 ⇒ 00:25:42.270 Amber Lin: Oh no!
251 00:25:42.820 ⇒ 00:25:45.879 Emily Giant: Your entire schemas that we don’t need.
252 00:25:46.470 ⇒ 00:25:46.930 Emily Giant: 0.
253 00:25:46.930 ⇒ 00:25:47.980 Demilade Agboola: Yeah, so.
254 00:25:47.980 ⇒ 00:25:48.899 Emily Giant: I couldn’t. Yeah.
255 00:25:48.900 ⇒ 00:25:55.203 Demilade Agboola: I I think it’s just important to just go like, hey? What tables are like are being
256 00:25:56.200 ⇒ 00:26:00.999 Demilade Agboola: I mean, at outputs of dbt, or like in pits of Dbt, and I like.
257 00:26:01.560 ⇒ 00:26:03.730 Demilade Agboola: and if it’s not within dbt.
258 00:26:04.180 ⇒ 00:26:08.489 Demilade Agboola: is it being used? Are we using it like, how do we?
259 00:26:08.720 ⇒ 00:26:10.899 Demilade Agboola: What’s the. Is there a plan to use it
260 00:26:11.060 ⇒ 00:26:13.290 Demilade Agboola: like, just kind of get an idea.
261 00:26:13.600 ⇒ 00:26:14.820 Demilade Agboola: And then.
262 00:26:15.100 ⇒ 00:26:27.650 Demilade Agboola: once we’re done with that, once we’re done with that, we can start thinking of how to deprecate on use schemas on use tables, or just basically try to like manage it.
263 00:26:28.387 ⇒ 00:26:31.840 Demilade Agboola: Because there’s there’s a lot of data in in the redshift, and
264 00:26:32.710 ⇒ 00:26:35.180 Demilade Agboola: we don’t use. We don’t use a ton of it to be honest.
265 00:26:35.180 ⇒ 00:26:44.160 Amber Lin: I see. So don’t. Is this is this ticket 87? Is it related to 86? Because they’re both about usage?
266 00:26:45.740 ⇒ 00:26:52.230 Demilade Agboola: Yeah, so this is about like, 86 is just about like last usage, like, who is.
267 00:26:53.300 ⇒ 00:26:54.650 Demilade Agboola: Actively querying it.
268 00:26:55.170 ⇒ 00:27:00.649 Demilade Agboola: 87 is more of like Dbt outpute or impute, like.
269 00:27:00.650 ⇒ 00:27:01.220 Amber Lin: Bye.
270 00:27:01.220 ⇒ 00:27:12.619 Demilade Agboola: Interacting or interfacing with it. But it’s still possible to have tables that in a schema, in a database that Dbt is not interacting with. But someone else is using it, you know. So.
271 00:27:12.620 ⇒ 00:27:14.620 Amber Lin: Oh, okay.
272 00:27:15.390 ⇒ 00:27:26.139 Amber Lin: And for those, what would we? What would be? What would that be? Because if they’re not in Dbt, but this also use. We want them to be in Dbt. Right?
273 00:27:27.090 ⇒ 00:27:40.320 Demilade Agboola: Oh, yeah, ideally. Yeah, that’s I mean, ideally, we’ll figure out why it’s not in DVD, there might be, you know, reasons why it’s not DVD, but obviously, like once we figure that out, it informs part of our like audits like roadmap, and we can say.
274 00:27:40.320 ⇒ 00:27:41.100 Amber Lin: I see.
275 00:27:41.100 ⇒ 00:27:50.839 Demilade Agboola: If it’s not, if it’s not in Dvt. Because of these reasons how we consider this, is there a better way to go about it is it being like, properly
276 00:27:50.980 ⇒ 00:28:03.619 Demilade Agboola: utilize, and even if we discover, even if we discover all of that, and we keep it the same way, it is at least we can properly document what those tables are. And even if that’s all the events team gets out of it.
277 00:28:04.020 ⇒ 00:28:05.230 Demilade Agboola: That is being known.
278 00:28:05.570 ⇒ 00:28:11.860 Demilade Agboola: Like they get a 360 view of all their tables, and like what is currently happening and processes.
279 00:28:12.220 ⇒ 00:28:15.980 Amber Lin: Awesome. So it seems like this should be
280 00:28:16.200 ⇒ 00:28:22.669 Amber Lin: before Amy works sort of concurrently with this one
281 00:28:23.660 ⇒ 00:28:29.749 Amber Lin: cause do we need to? Is just last users usage enough to
282 00:28:30.260 ⇒ 00:28:36.009 Amber Lin: help Emily and Uta make the decision? Or do we need the Dvt you.
283 00:28:36.340 ⇒ 00:28:40.289 Amber Lin: if it’s touched by Dvt for them to make this decision.
284 00:28:41.940 ⇒ 00:28:48.000 Demilade Agboola: And I think it’s helpful to have that as well. Just so we have the full scope of what’s happening across the entire database.
285 00:28:49.065 ⇒ 00:28:59.520 Amber Lin: Then the question now is, do we move this in cycle, or do we move 88 out of cycle? After this, after the Dvt.
286 00:29:03.150 ⇒ 00:29:04.939 Demilade Agboola: How long would this take?
287 00:29:06.820 ⇒ 00:29:12.710 Demilade Agboola: I think if we have all tables we can kind of quickly do a quick match to all the Dvt models.
288 00:29:13.040 ⇒ 00:29:18.230 Demilade Agboola: and then we can figure out it shouldn’t take too long to be honest.
289 00:29:18.380 ⇒ 00:29:22.180 Amber Lin: Would you say that it would take like.
290 00:29:24.690 ⇒ 00:29:25.940 Demilade Agboola: Walnuts, planes.
291 00:29:25.940 ⇒ 00:29:30.979 Amber Lin: 2 points. Okay, so it sounds like we have to move this into cycle.
292 00:29:32.620 ⇒ 00:29:33.410 Amber Lin: Yeah.
293 00:29:34.485 ⇒ 00:29:35.010 Demilade Agboola: Yes.
294 00:29:35.250 ⇒ 00:29:36.640 Amber Lin: Okay, sounds good.
295 00:29:37.410 ⇒ 00:29:43.397 Amber Lin: We’ll have to move that into cycle after query usage.
296 00:29:45.870 ⇒ 00:29:50.909 Amber Lin: And then I guess we send list to Emily.
297 00:29:51.070 ⇒ 00:29:54.119 Amber Lin: We’ll just say we sent it, and then
298 00:29:54.310 ⇒ 00:30:00.359 Amber Lin: we will do the flagging like everything to flag will be for next cycle. How’s that?
299 00:30:01.750 ⇒ 00:30:02.380 Emily Giant: Sounds good.
300 00:30:03.125 ⇒ 00:30:03.870 Amber Lin: Okay.
301 00:30:04.130 ⇒ 00:30:05.410 Demilade Agboola: Yeah, sounds good.
302 00:30:05.980 ⇒ 00:30:10.560 Amber Lin: Okay, let me say 5, 4 decisions.
303 00:30:14.210 ⇒ 00:30:16.910 Amber Lin: And then I don’t forget to follow that.
304 00:30:18.370 ⇒ 00:30:20.779 Amber Lin: So that’s going to take 1 point.
305 00:30:24.110 ⇒ 00:30:24.860 Amber Lin: Yeah.
306 00:30:30.980 ⇒ 00:30:36.080 Amber Lin: Okay, so seems like those 2 will take a bit for
307 00:30:37.190 ⇒ 00:30:42.290 Amber Lin: the next cycle. So sounds like, we’re actually doing those decisions.
308 00:30:42.590 ⇒ 00:30:45.749 Amber Lin: Once next cycle starts which I’m okay with.
309 00:30:46.080 ⇒ 00:30:48.200 Amber Lin: say, these will take a while.
310 00:30:48.570 ⇒ 00:30:49.230 Emily Giant: Yeah.
311 00:30:52.620 ⇒ 00:31:04.000 Amber Lin: So I’ll have to flush those tickets out a little bit. More decisions, let me say, assigned to
312 00:31:04.960 ⇒ 00:31:11.769 Amber Lin: finally, and then and estimate this, as
313 00:31:13.450 ⇒ 00:31:17.800 Amber Lin: 2 points are a number is what we said, and then this.
314 00:31:18.440 ⇒ 00:31:20.770 Amber Lin: I don’t know how long this would take.
315 00:31:22.590 ⇒ 00:31:25.290 Amber Lin: because we have different categories of stuff.
316 00:31:29.266 ⇒ 00:31:29.813 Emily Giant: Yeah.
317 00:31:34.350 ⇒ 00:31:42.360 Demilade Agboola: I think once we have identified what the tables are, it shouldn’t the actual execution. Isn’t that long.
318 00:31:42.844 ⇒ 00:31:43.330 Amber Lin: Okay.
319 00:31:43.330 ⇒ 00:31:48.270 Demilade Agboola: It’s just basically yeah, it’s just basically deleting quote, unquote, stable.
320 00:31:48.270 ⇒ 00:31:49.020 Amber Lin: Sounds good.
321 00:31:49.020 ⇒ 00:31:51.549 Emily Giant: The one that can’t all be done in one sitting.
322 00:31:52.615 ⇒ 00:31:54.340 Amber Lin: Definitely, so.
323 00:31:54.340 ⇒ 00:32:00.080 Demilade Agboola: Yeah, I mean you. You can. Once you have a script, you can just drop all the you can drop all tables at once.
324 00:32:00.330 ⇒ 00:32:01.730 Amber Lin: Oh, what? Okay.
325 00:32:03.380 ⇒ 00:32:04.030 Emily Giant: Oh!
326 00:32:04.820 ⇒ 00:32:17.050 Demilade Agboola: I mean, obviously, I wouldn’t. We? We would have to do batch by batch, because I feel that I feel that is safer. But yeah, you couldn’t technically drop all the tables you want to do at once.
327 00:32:17.050 ⇒ 00:32:17.400 Emily Giant: Okay.
328 00:32:18.750 ⇒ 00:32:25.190 Amber Lin: Okay, so this wouldn’t take that long. I think. Max, 2 points that’s, my estimate
329 00:32:25.570 ⇒ 00:32:31.580 Amber Lin: okay, that’s for redshift, I think I feel good about about this.
330 00:32:35.390 ⇒ 00:32:36.110 Amber Lin: Let’s see.
331 00:32:36.780 ⇒ 00:32:39.420 Amber Lin: Go back to
332 00:32:40.347 ⇒ 00:32:46.700 Amber Lin: think this is the main, the main part of it, because next cycle we’re also introducing
333 00:32:47.550 ⇒ 00:32:50.630 Amber Lin: some a little bit of revenue stuff.
334 00:32:50.870 ⇒ 00:32:52.100 Amber Lin: Yeah, renew them.
335 00:32:52.790 ⇒ 00:32:56.100 Amber Lin: Audit that so?
336 00:33:02.200 ⇒ 00:33:09.169 Amber Lin: And looking at the inventory part, how much time do we have left?
337 00:33:12.830 ⇒ 00:33:15.400 Amber Lin: We have okay.
338 00:33:15.400 ⇒ 00:33:16.909 Emily Giant: Revenue is gonna be worse.
339 00:33:17.500 ⇒ 00:33:21.030 Emily Giant: It’s gonna take. It’s gonna take twice as long.
340 00:33:21.030 ⇒ 00:33:21.720 Amber Lin: Here.
341 00:33:21.720 ⇒ 00:33:27.729 Emily Giant: Yeah, demo lot. And I built the inventory mart recently. So there just aren’t as many like.
342 00:33:28.150 ⇒ 00:33:29.030 Amber Lin: Issues.
343 00:33:29.030 ⇒ 00:33:29.710 Emily Giant: Yeah.
344 00:33:29.950 ⇒ 00:33:30.540 Amber Lin: Fun.
345 00:33:31.150 ⇒ 00:33:32.000 Amber Lin: Okay?
346 00:33:34.940 ⇒ 00:33:41.629 Amber Lin: So for next cycle, this is something that we’re believe we’re finishing up this cycle
347 00:33:41.820 ⇒ 00:33:46.340 Amber Lin: to remodel it and design how we’re gonna refractor the rest.
348 00:33:47.904 ⇒ 00:33:50.739 Amber Lin: Devil! And Emily, you guys told me what
349 00:33:50.910 ⇒ 00:33:53.819 Amber Lin: we will be doing next cycle.
350 00:33:58.708 ⇒ 00:34:02.379 Demilade Agboola: So next cycle, I think, is more of the
351 00:34:03.960 ⇒ 00:34:06.240 Demilade Agboola: I’m trying to see if we can get the include.
352 00:34:07.110 ⇒ 00:34:10.600 Demilade Agboola: 96. Can we get. Try and see if we can get 96 into this cycle.
353 00:34:11.020 ⇒ 00:34:11.659 Amber Lin: Hmm.
354 00:34:13.639 ⇒ 00:34:14.550 Demilade Agboola: So.
355 00:34:15.060 ⇒ 00:34:19.020 Amber Lin: That’s okay is that it’s I can probably Tracy.
356 00:34:19.020 ⇒ 00:34:22.380 Emily Giant: I can probably like, make the models for him to audit, and.
357 00:34:22.380 ⇒ 00:34:22.920 Amber Lin: Hmm.
358 00:34:22.929 ⇒ 00:34:24.899 Emily Giant: Clean up next week.
359 00:34:26.300 ⇒ 00:34:30.940 Amber Lin: Hmm, yeah. Cause I mean, technically, next week is still part of the cycle. Right? Yeah, yes.
360 00:34:31.449 ⇒ 00:34:33.339 Demilade Agboola: Yeah. So that’s that’s why I said the cycle.
361 00:34:33.340 ⇒ 00:34:34.250 Emily Giant: Totally do it.
362 00:34:39.940 ⇒ 00:34:40.659 Amber Lin: Hey? Court?
363 00:34:41.370 ⇒ 00:34:42.820 Amber Lin: Maybe those are secret.
364 00:34:43.480 ⇒ 00:34:44.179 Amber Lin: Finish.
365 00:34:45.440 ⇒ 00:34:46.679 Amber Lin: June 11.th
366 00:34:47.969 ⇒ 00:34:50.800 Amber Lin: This is Paul.
367 00:34:51.429 ⇒ 00:34:54.839 Amber Lin: Oh, June 11th to June 23.rd Okay.
368 00:34:58.880 ⇒ 00:35:04.120 Amber Lin: okay, we’ll squeeze that in, should we?
369 00:35:04.630 ⇒ 00:35:11.049 Amber Lin: Okay, I’ll keep that deadline. So this, we would say, is end of the cycle.
370 00:35:13.660 ⇒ 00:35:15.680 Amber Lin: This 1, 96.
371 00:35:17.430 ⇒ 00:35:21.160 Demilade Agboola: Yeah, we’ll we’ll definitely try and see if we can hit it by inner circle.
372 00:35:25.940 ⇒ 00:35:30.269 Amber Lin: So who should I assign it to if we have moved it in cycle?
373 00:35:31.050 ⇒ 00:35:33.820 Amber Lin: So I need to model them 1st and didn’t.
374 00:35:34.179 ⇒ 00:35:39.570 Emily Giant: His audit inventory, logic and design refractor would come. 95 would come after that.
375 00:35:39.570 ⇒ 00:35:40.759 Amber Lin: Oh, okay. Okay.
376 00:35:41.080 ⇒ 00:35:42.639 Emily Giant: That audit.
377 00:35:42.940 ⇒ 00:35:50.080 Amber Lin: Sounds good, let me say Mark, as blocked, blocked by.
378 00:35:50.630 ⇒ 00:35:53.979 Emily Giant: Yeah, you can’t do it till the stuff’s done, so that makes sense.
379 00:35:53.980 ⇒ 00:35:55.220 Amber Lin: I see, I see.
380 00:35:56.290 ⇒ 00:35:59.662 Emily Giant: And then down the lobby. There’s that whole issue with
381 00:36:00.500 ⇒ 00:36:06.269 Emily Giant: receiving that I had. Did I loop you into that yesterday? How like the receiving logic is not
382 00:36:06.550 ⇒ 00:36:08.569 Emily Giant: accounting for Qa
383 00:36:08.890 ⇒ 00:36:15.369 Emily Giant: units, and it’s throwing off revenue. It might be in like the jira board, or something, but that should also be.
384 00:36:15.370 ⇒ 00:36:16.000 Demilade Agboola: Did.
385 00:36:16.000 ⇒ 00:36:16.650 Emily Giant: Yes.
386 00:36:17.000 ⇒ 00:36:19.419 Demilade Agboola: Is is that the Felipe? That.
387 00:36:19.420 ⇒ 00:36:19.880 Emily Giant: Yeah.
388 00:36:19.880 ⇒ 00:36:21.050 Demilade Agboola: Filipino, product.
389 00:36:21.610 ⇒ 00:36:22.210 Emily Giant: Yeah.
390 00:36:23.010 ⇒ 00:36:26.409 Demilade Agboola: Yeah, I was also going to look at that. Yeah, it should also be here.
391 00:36:28.970 ⇒ 00:36:31.429 Demilade Agboola: So we let me try and.
392 00:36:32.270 ⇒ 00:36:32.950 Emily Giant: Okay.
393 00:36:33.640 ⇒ 00:36:34.710 Emily Giant: Yeah. Yeah.
394 00:36:35.050 ⇒ 00:36:35.760 Emily Giant: Fine.
395 00:36:37.483 ⇒ 00:36:40.340 Amber Lin: Is that somewhere here, year or.
396 00:36:40.840 ⇒ 00:36:45.650 Demilade Agboola: Oh, no, it’s not here, it’s on their board, but not necessarily on ours.
397 00:36:45.650 ⇒ 00:36:46.630 Amber Lin: Yeah. Oh.
398 00:36:46.916 ⇒ 00:36:53.999 Demilade Agboola: Give me one second, but it’s not like it’s a slack message, but wasn’t on the
399 00:36:55.340 ⇒ 00:36:57.279 Demilade Agboola: main slack ball.
400 00:36:58.360 ⇒ 00:37:00.389 Demilade Agboola: Give me one second.
401 00:37:06.590 ⇒ 00:37:07.340 Amber Lin: And
402 00:37:14.020 ⇒ 00:37:20.200 Amber Lin: And oh, I feel like maybe we should split.
403 00:37:20.200 ⇒ 00:37:21.799 Demilade Agboola: I just sent it to your slack.
404 00:37:21.990 ⇒ 00:37:24.000 Amber Lin: Oh, okay, let me go. Look at that.
405 00:37:24.510 ⇒ 00:37:33.299 Demilade Agboola: But, like, I will send you the actual message. But effectively, there is. There’s some data issues with some of their.
406 00:37:34.243 ⇒ 00:37:36.650 Demilade Agboola: Some of the viz. The eventually, viz.
407 00:37:37.210 ⇒ 00:37:48.450 Demilade Agboola: And so I’m trying to look into that it’s high priority. So
408 00:37:48.740 ⇒ 00:37:50.890 Demilade Agboola: that kind of takes away from some of the other.
409 00:37:51.278 ⇒ 00:37:54.769 Amber Lin: Yeah, let me go check your message. Should we
410 00:37:55.620 ⇒ 00:38:01.079 Amber Lin: put it in? Do you mind? Just put it in our linear as well?
411 00:38:02.500 ⇒ 00:38:05.049 Amber Lin: I don’t know if that’s how things should work.
412 00:38:06.040 ⇒ 00:38:08.069 Amber Lin: Let me check that account.
413 00:38:09.560 ⇒ 00:38:11.449 Demilade Agboola: Yeah, I sent it to you, because, like.
414 00:38:12.530 ⇒ 00:38:16.180 Demilade Agboola: Just creates. That I don’t like. Linear is not necessarily my strongest suit.
415 00:38:16.560 ⇒ 00:38:18.139 Demilade Agboola: I can make things happen.
416 00:38:18.810 ⇒ 00:38:19.140 Emily Giant: Yeah.
417 00:38:19.140 ⇒ 00:38:19.820 Demilade Agboola: You know what’s.
418 00:38:22.940 ⇒ 00:38:23.490 Emily Giant: I can make.
419 00:38:23.490 ⇒ 00:38:23.830 Amber Lin: Yeah.
420 00:38:23.830 ⇒ 00:38:25.690 Emily Giant: Happens. I like that.
421 00:38:25.690 ⇒ 00:38:32.169 Amber Lin: So this is A is sorry I was trying to understand what this is.
422 00:38:35.730 ⇒ 00:38:38.899 Amber Lin: I can just copy that. I don’t know.
423 00:38:39.600 ⇒ 00:38:40.999 Demilade Agboola: So effectively.
424 00:38:42.584 ⇒ 00:38:48.970 Demilade Agboola: That is a message from Felipe is
425 00:38:49.317 ⇒ 00:38:52.100 Demilade Agboola: their team. And he noticed some data issues.
426 00:38:52.781 ⇒ 00:38:58.798 Demilade Agboola: Based off that. Emily sent that to me as well as
427 00:38:59.630 ⇒ 00:39:02.970 Demilade Agboola: Kyle tagged us there, and she’s kind of explained what was going on.
428 00:39:03.710 ⇒ 00:39:06.200 Demilade Agboola: That would require investigation into it.
429 00:39:06.680 ⇒ 00:39:07.400 Demilade Agboola: So.
430 00:39:07.750 ⇒ 00:39:11.960 Amber Lin: So this is an issue with the inventory mark that needs investigations.
431 00:39:11.960 ⇒ 00:39:12.280 Emily Giant: Yep.
432 00:39:12.280 ⇒ 00:39:12.910 Demilade Agboola: Exactly.
433 00:39:13.330 ⇒ 00:39:21.040 Emily Giant: New one like nothing. We haven’t seen it before, which means it probably just hasn’t happened before. So we didn’t have a way to test it. But
434 00:39:21.670 ⇒ 00:39:22.300 Emily Giant: yeah.
435 00:39:23.270 ⇒ 00:39:25.619 Amber Lin: So what should we do?
436 00:39:26.790 ⇒ 00:39:34.279 Amber Lin: So seems like we need to look into what it is and create a test test for it. Moving forward. Okay.
437 00:39:34.280 ⇒ 00:39:45.050 Emily Giant: I’m pretty sure that it’s a logic problem. With how? Because available for sale and sell through our calculations. And
438 00:39:46.250 ⇒ 00:39:50.079 Emily Giant: it’s almost certainly a logic issue with
439 00:39:51.070 ⇒ 00:40:05.240 Emily Giant: receiving amounts, because currently, when items are received as bad or unsellable, that number isn’t showing up, and the the logic to calculate those other things.
440 00:40:05.390 ⇒ 00:40:05.910 Emily Giant: is.
441 00:40:05.910 ⇒ 00:40:06.630 Amber Lin: Hmm.
442 00:40:06.980 ⇒ 00:40:12.569 Emily Giant: Based on the amount received, or that number being larger than 0. So
443 00:40:13.220 ⇒ 00:40:22.150 Emily Giant: demo latte, I can definitely tweak the receiving part of the logic. But it’s those calculations that I feel like I need a second set of eyes.
444 00:40:22.881 ⇒ 00:40:26.500 Emily Giant: To make sure that, like all of the things are
445 00:40:26.700 ⇒ 00:40:33.350 Emily Giant: being accounted for. There’s just so many variables going on with, like.
446 00:40:34.750 ⇒ 00:40:42.290 Emily Giant: I don’t know. I just. The logic is wrong, and I want to make sure that I’m not making it in a vacuum.
447 00:40:43.690 ⇒ 00:40:44.690 Demilade Agboola: Okay. Sounds good.
448 00:40:45.096 ⇒ 00:40:49.969 Amber Lin: Okay, so I would assign this to Emily, and then or to
449 00:40:51.169 ⇒ 00:40:58.360 Amber Lin: so we, I guess what I hear is that emily will provide some basic investigations, and Demalado will help verify.
450 00:40:58.530 ⇒ 00:40:59.170 Emily Giant: Yeah.
451 00:40:59.170 ⇒ 00:40:59.645 Amber Lin: Okay.
452 00:41:00.230 ⇒ 00:41:06.140 Amber Lin: So I’m gonna put this into cycle. And then who should I assign it to?
453 00:41:07.310 ⇒ 00:41:10.230 Emily Giant: You can assign it to me, but I’m hoping to like.
454 00:41:11.140 ⇒ 00:41:17.830 Emily Giant: Take the receiving logic portion of it, and then pass it off to Demo A, since he’s touched these models before.
455 00:41:17.830 ⇒ 00:41:18.180 Amber Lin: So.
456 00:41:18.180 ⇒ 00:41:23.699 Emily Giant: To test the calculations and like what needs to be changed there. But
457 00:41:24.650 ⇒ 00:41:29.220 Emily Giant: I will see. I mean, I don’t know if that works for you. Demalade. I know you also have a lot on your plate.
458 00:41:30.570 ⇒ 00:41:31.590 Demilade Agboola: Well, that’s fine!
459 00:41:31.590 ⇒ 00:41:32.240 Emily Giant: Okay.
460 00:41:33.370 ⇒ 00:41:36.179 Amber Lin: Sounds good. I put that into
461 00:41:36.750 ⇒ 00:41:45.420 Amber Lin: this to do cycle, I think. Then, what? Let’s break this ticket down into 2 separate things. We’ll just do it audit
462 00:41:45.840 ⇒ 00:41:50.660 Amber Lin: and then design later. I don’t know you. Also, I remember you said this has to be done like
463 00:41:50.780 ⇒ 00:41:52.040 Amber Lin: together.
464 00:41:52.550 ⇒ 00:41:56.910 Demilade Agboola: Or not, but like cause, as as I’m figuring things out, I would.
465 00:41:57.300 ⇒ 00:42:01.929 Demilade Agboola: You know, kind of want to like, be putting down my thoughts and like how to design it properly.
466 00:42:03.940 ⇒ 00:42:06.880 Amber Lin: Yeah, I just don’t know if you would have
467 00:42:07.090 ⇒ 00:42:16.559 Amber Lin: well, I guess this is next week, right this is this is something emily gives you you this one next week, and then you do this
468 00:42:16.750 ⇒ 00:42:20.089 Amber Lin: next week, and then this is somewhat for this week.
469 00:42:21.075 ⇒ 00:42:23.680 Demilade Agboola: Think what we need to do is prioritize.
470 00:42:23.920 ⇒ 00:42:32.760 Demilade Agboola: So like which ones must be done by the end of cycle. I think that’s more the important thing. So like data issues, I think, sounds like one of the number one things that.
471 00:42:32.760 ⇒ 00:42:33.710 Amber Lin: Yeah.
472 00:42:34.300 ⇒ 00:42:34.720 Emily Giant: Yep.
473 00:42:35.140 ⇒ 00:42:35.890 Amber Lin: Okay?
474 00:42:37.370 ⇒ 00:42:43.250 Amber Lin: And I would say, this is after, because a lot of this is dependent on 96.
475 00:42:43.590 ⇒ 00:42:48.269 Amber Lin: So can I put a due date on this one. What would that be?
476 00:42:51.310 ⇒ 00:42:53.969 Emily Giant: In 90 nineties. Which one are you putting the date on.
477 00:42:53.970 ⇒ 00:42:55.500 Amber Lin: The data issues.
478 00:42:56.814 ⇒ 00:42:58.870 Emily Giant: I would say Friday.
479 00:43:01.200 ⇒ 00:43:03.190 Amber Lin: Okay, sounds, good.
480 00:43:03.920 ⇒ 00:43:05.850 Emily Giant: Wait. Today’s Tuesday or Wednesday.
481 00:43:05.850 ⇒ 00:43:07.290 Amber Lin: Today’s Wednesday.
482 00:43:07.950 ⇒ 00:43:09.270 Emily Giant: Yeah. Friday’s fine.
483 00:43:09.270 ⇒ 00:43:09.950 Amber Lin: Okay.
484 00:43:11.130 ⇒ 00:43:22.949 Amber Lin: and then remodeling. I don’t know. If, say, Emily, you said, you’ll do this next week, and next week we’ll only have Monday and Tuesday in cycle. Do you think
485 00:43:23.070 ⇒ 00:43:29.220 Amber Lin: we should just move boot this to next cycle because I don’t want to force you to like.
486 00:43:29.400 ⇒ 00:43:33.720 Amber Lin: because Monday mornings are also busy like you probably need. I don’t know how long this would take.
487 00:43:33.720 ⇒ 00:43:38.519 Emily Giant: Fine. I just have so many meetings this week that I’m like, where am I actually going to do that?
488 00:43:39.400 ⇒ 00:43:40.430 Emily Giant: It’s to me.
489 00:43:40.560 ⇒ 00:43:44.980 Amber Lin: Okay. So then it sounds like this is gonna get moved outside.
490 00:43:49.110 ⇒ 00:43:49.900 Demilade Agboola: Sounds good.
491 00:43:50.050 ⇒ 00:43:56.196 Amber Lin: Okay, I’ve gotten instructions to not change due dates. But
492 00:43:57.370 ⇒ 00:44:04.530 Amber Lin: it is what it? Yeah, I’ll say, move to next cycle to a call to date.
493 00:44:04.870 ⇒ 00:44:06.980 Amber Lin: Urgent issue.
494 00:44:17.930 ⇒ 00:44:23.410 Amber Lin: Okay, that will inform this.
495 00:44:24.600 ⇒ 00:44:28.899 Amber Lin: Oh, I guess. Next cycle we’ll also have to do some stuff
496 00:44:29.870 ⇒ 00:44:37.320 Amber Lin: as we build. I think we don’t have time for this. Let me move alright.
497 00:44:38.110 ⇒ 00:44:41.629 Amber Lin: This 2 status 2 for development.
498 00:44:42.480 ⇒ 00:44:46.580 Amber Lin: This one will need be requirements started. Okay? Great.
499 00:44:46.940 ⇒ 00:44:52.870 Amber Lin: 10 min left 15 min left. Let’s go to revenue.
500 00:44:55.140 ⇒ 00:45:01.640 Amber Lin: There’s nothing really in revenue. I we will have to build out these tickets. But where should we start.
501 00:45:05.450 ⇒ 00:45:10.670 Emily Giant: I guess. Audit revenue models and snapshots makes the most sense to me.
502 00:45:11.944 ⇒ 00:45:15.050 Amber Lin: What are we trying to do with this auditing.
503 00:45:16.478 ⇒ 00:45:17.672 Emily Giant: I would say.
504 00:45:18.070 ⇒ 00:45:28.450 Demilade Agboola: Try and figure out like what? Like what the models look like. Figure out the disparity between what they should look like and where they currently are. Now.
505 00:45:29.094 ⇒ 00:45:34.339 Demilade Agboola: also, just get a feel of like whatever data issue dish issues that exist.
506 00:45:34.660 ⇒ 00:45:35.290 Amber Lin: You know.
507 00:45:35.740 ⇒ 00:45:38.889 Demilade Agboola: Yeah. So I think that’s just like
508 00:45:39.270 ⇒ 00:45:51.419 Demilade Agboola: figuring out like what legacy systems were. Just like, basically trying to understand kind of how we do with inventory. And then we’re gonna have to figure out how we want to rebuild it, so that you know things look much better.
509 00:45:51.420 ⇒ 00:45:51.980 Amber Lin: Hmm.
510 00:45:56.700 ⇒ 00:46:02.149 Emily Giant: I would add, like legacy tables like identifying
511 00:46:02.430 ⇒ 00:46:14.009 Emily Giant: where inactive data streams are still tied to a model and don’t need to be.
512 00:46:15.200 ⇒ 00:46:18.868 Amber Lin: Okay, that seems like some of the stuff we’re doing with red Shift and
513 00:46:19.780 ⇒ 00:46:22.930 Amber Lin: looker will be really helpful because we’re also looking at Dvt.
514 00:46:23.230 ⇒ 00:46:25.070 Amber Lin: Streams.
515 00:46:25.480 ⇒ 00:46:31.789 Amber Lin: 2 tables. Do we need to talk to any stakeholders for this? Or is this just an internal investigation.
516 00:46:31.790 ⇒ 00:46:33.380 Emily Giant: Just us, yep. Okay.
517 00:46:34.700 ⇒ 00:46:36.133 Amber Lin: Sounds good.
518 00:46:37.050 ⇒ 00:46:39.130 Amber Lin: How long do we estimate this will take.
519 00:46:43.140 ⇒ 00:46:45.160 Emily Giant: A minute, probably 2 points.
520 00:46:45.880 ⇒ 00:46:52.399 Emily Giant: There are so many, so many issues and so many different models to handle revenue.
521 00:46:53.420 ⇒ 00:47:00.980 Amber Lin: I think 1 point is like an hour or less. 2 points is like 2 to 3 h, 3 points, probably half a day.
522 00:47:01.340 ⇒ 00:47:05.749 Amber Lin: To like 5 to 6 h like 5 points is
523 00:47:05.930 ⇒ 00:47:10.239 Amber Lin: a day or 2, and 8 points is very big. Needs to be broken down.
524 00:47:10.410 ⇒ 00:47:12.520 Emily Giant: Okay. Then it’s 3.
525 00:47:12.520 ⇒ 00:47:17.560 Amber Lin: Okay, I I agree. Should I assign this to Emily or Don Lati?
526 00:47:18.430 ⇒ 00:47:22.180 Emily Giant: I think it would have to be me only in that like.
527 00:47:22.520 ⇒ 00:47:24.230 Emily Giant: It’s such a mess that.
528 00:47:24.230 ⇒ 00:47:24.800 Amber Lin: Hmm.
529 00:47:25.610 ⇒ 00:47:30.250 Emily Giant: It would either be like paired work or just me doing it.
530 00:47:30.250 ⇒ 00:47:31.950 Amber Lin: Hmm, okay.
531 00:47:32.565 ⇒ 00:47:45.190 Amber Lin: let us know how we can support do we have any documentations? Maybe that the one we use for inventory on how we’re gonna document all of this, because there’s a lot of stuff we need to write down.
532 00:47:48.400 ⇒ 00:47:49.260 Demilade Agboola: We can do it.
533 00:47:49.808 ⇒ 00:47:51.999 Emily Giant: In that same spreadsheet.
534 00:47:55.800 ⇒ 00:48:03.219 Amber Lin: So sounds like we need to do. We need to list everything that’s currently in the revenue model.
535 00:48:03.571 ⇒ 00:48:04.979 Emily Giant: That would be good.
536 00:48:04.980 ⇒ 00:48:10.869 Amber Lin: Okay, let’s list all the new models.
537 00:48:11.520 ⇒ 00:48:15.550 Amber Lin: And what is it?
538 00:48:15.830 ⇒ 00:48:17.140 Amber Lin: Spreadsheets?
539 00:48:23.640 ⇒ 00:48:27.860 Amber Lin: And okay.
540 00:48:30.950 ⇒ 00:48:32.589 Amber Lin: think they will.
541 00:48:32.930 ⇒ 00:48:36.210 Amber Lin: One of the asparities.
542 00:48:36.530 ⇒ 00:48:41.180 Amber Lin: We already know what the ideal state is right, or do we not know that yet.
543 00:48:41.790 ⇒ 00:48:42.652 Emily Giant: We do not.
544 00:48:43.210 ⇒ 00:48:45.079 Amber Lin: Okay, so we need to define.
545 00:48:45.280 ⇒ 00:48:54.332 Emily Giant: Yeah, my feeling is that we’re gonna probably have to switch entirely over to shopify tables instead of what we’re using now.
546 00:48:55.100 ⇒ 00:48:56.849 Emily Giant: there’s a lot of unknowns.
547 00:48:57.120 ⇒ 00:48:57.590 Amber Lin: Which.
548 00:48:57.590 ⇒ 00:48:58.840 Emily Giant: Spotify Tables.
549 00:49:00.500 ⇒ 00:49:07.890 Amber Lin: Okay, let me make this into a new ticket. Then, because this is just auditing and then we need to define the
550 00:49:09.910 ⇒ 00:49:19.170 Amber Lin: define ideal state and identify disparities.
551 00:49:19.900 ⇒ 00:49:28.959 Amber Lin: That’s a that’s after it ordered trying what we want.
552 00:49:31.310 ⇒ 00:49:33.329 Amber Lin: And I guess we also need a
553 00:49:33.850 ⇒ 00:49:37.560 Amber Lin: like a rebuild plan like how we’re gonna rebuild this.
554 00:49:43.660 ⇒ 00:49:49.469 Amber Lin: I don’t know how that’s gonna take I don’t think either snow yet.
555 00:49:50.030 ⇒ 00:49:58.709 Demilade Agboola: Yeah, I I think the kind of things that once you get closer, it’s easier to to scope because you know what the full workload entails.
556 00:49:59.601 ⇒ 00:50:08.870 Demilade Agboola: But now it will just be guesswork. Because you know, how many models are we talking about like, how do we define certain metrics. How do we ensure
557 00:50:09.442 ⇒ 00:50:14.649 Demilade Agboola: like, there’s a lot of like logic that goes into how urbanists calculates revenue.
558 00:50:14.900 ⇒ 00:50:18.490 Amber Lin: We need to like properly, spell that out things like that. So
559 00:50:19.860 ⇒ 00:50:25.389 Amber Lin: be a different ticket to just identify like we need to. That sounds like we need to meet with stakeholders.
560 00:50:25.390 ⇒ 00:50:26.100 Emily Giant: Yes.
561 00:50:26.100 ⇒ 00:50:34.959 Amber Lin: Okay, so meet so define revenue logic.
562 00:50:36.130 ⇒ 00:50:41.540 Amber Lin: go ahead and stick hold. There’s and
563 00:50:45.300 ⇒ 00:50:51.930 Amber Lin: should this happen as we’re auditing it, or should it happen when we’re defining the ideal state? How.
564 00:50:51.940 ⇒ 00:50:57.900 Emily Giant: The ideal state would be a better step. The audit will be messy.
565 00:51:00.550 ⇒ 00:51:01.360 Amber Lin: Okay,
566 00:51:06.300 ⇒ 00:51:13.270 Amber Lin: So we define. I guess we define is this, oh, exist.
567 00:51:14.440 ⇒ 00:51:19.850 Amber Lin: Better order like we define meet with the stakeholders, and then we define the ideal state.
568 00:51:24.530 ⇒ 00:51:29.030 Demilade Agboola: I, I mean, I think it’s 1 of those things where, like, I, feel, we can
569 00:51:29.210 ⇒ 00:51:30.640 Demilade Agboola: walk and chew Gum
570 00:51:32.300 ⇒ 00:51:39.559 Demilade Agboola: they could kind of be in parallel in the sense. Okay, we will be auditing. And as we’re auditing.
571 00:51:39.790 ⇒ 00:51:46.629 Demilade Agboola: we will hear what the stakeholders stakeholders have to say issues they’ve had in the past, or what they’re currently, you know, struggling with.
572 00:51:46.920 ⇒ 00:51:53.000 Demilade Agboola: And then that also helps us define, like the ideal state of like what should B
573 00:51:53.570 ⇒ 00:51:55.509 Demilade Agboola: in the, in the proper state.
574 00:51:55.790 ⇒ 00:51:56.360 Amber Lin: Hmm.
575 00:52:04.070 ⇒ 00:52:04.770 Amber Lin: okay.
576 00:52:05.350 ⇒ 00:52:12.130 Amber Lin: So these things probably just happens kind of in parallel. Next cycle.
577 00:52:13.030 ⇒ 00:52:18.320 Amber Lin: My hunch is that this will take up at least like a week and a half.
578 00:52:18.320 ⇒ 00:52:20.869 Emily Giant: Yeah, it’s gonna be almost an entire cycle.
579 00:52:20.870 ⇒ 00:52:25.290 Amber Lin: Yeah. So I think that’s pretty good. And I think we once we have the rebuild plan.
580 00:52:25.890 ⇒ 00:52:28.450 Amber Lin: this will be good. This will get fleshed out.
581 00:52:28.560 ⇒ 00:52:37.830 Amber Lin: and then we’ll we’ll consider these as well. I think that’s good, cool, all right, quick overview.
582 00:52:37.970 ⇒ 00:52:50.710 Amber Lin: So for next cycle seems like it’s these inventory look or stuff, or it’s just revenue.
583 00:52:52.810 ⇒ 00:52:53.720 Amber Lin: Okay?
584 00:52:55.710 ⇒ 00:52:56.710 Amber Lin: Awesome.
585 00:52:58.210 ⇒ 00:53:01.409 Amber Lin: Any any other things that pops through your mind.
586 00:53:05.370 ⇒ 00:53:06.919 Emily Giant: Not for me. Huh!
587 00:53:07.330 ⇒ 00:53:08.920 Demilade Agboola: I’m not there right now.
588 00:53:09.200 ⇒ 00:53:15.709 Amber Lin: Okay, sounds good. We meet pretty often. Things will pop up alrighty.
589 00:53:15.710 ⇒ 00:53:16.300 Emily Giant: Well, thank you.
590 00:53:16.300 ⇒ 00:53:21.410 Amber Lin: Thank you. I mean, I hope you’re. I hope you get better with your sneezes.
591 00:53:21.730 ⇒ 00:53:24.320 Emily Giant: No hope so too.
592 00:53:24.320 ⇒ 00:53:27.850 Amber Lin: Alright! I would love to see the cats next time.
593 00:53:27.850 ⇒ 00:53:32.089 Emily Giant: So they will make their appearance when I least want them to. So.
594 00:53:32.710 ⇒ 00:53:33.110 Amber Lin: Okay.
595 00:53:33.110 ⇒ 00:53:36.190 Demilade Agboola: Oh, trust trust me about that. They they make an appearance.
596 00:53:36.520 ⇒ 00:53:37.130 Amber Lin: Yeah.
597 00:53:38.660 ⇒ 00:53:40.089 Emily Giant: If you ever see me go.
598 00:53:40.350 ⇒ 00:53:42.620 Emily Giant: I have a window next to my desk, and.
599 00:53:42.620 ⇒ 00:53:42.970 Amber Lin: Cool.
600 00:53:42.970 ⇒ 00:53:52.499 Emily Giant: Actual cat will jump on it when she wants to like hang from the screen. So I have to pause the meeting and like open the window, and like Hurry her in from the window. It happens
601 00:53:52.670 ⇒ 00:53:55.050 Emily Giant: them a lot I can attest every day.
602 00:53:55.820 ⇒ 00:53:56.960 Amber Lin: Oh, wow!
603 00:53:57.450 ⇒ 00:54:02.089 Emily Giant: Okay, I I don’t want the others to hear that.
604 00:54:02.090 ⇒ 00:54:03.060 Amber Lin: Oh!
605 00:54:03.280 ⇒ 00:54:05.190 Emily Giant: I’ll bring the kitten down at some point, too.
606 00:54:05.190 ⇒ 00:54:06.730 Amber Lin: Okay. I’m excited.
607 00:54:07.880 ⇒ 00:54:10.309 Emily Giant: Alright. Well, I’ll talk to you.
608 00:54:11.360 ⇒ 00:54:11.760 Amber Lin: Hi! All!
609 00:54:11.760 ⇒ 00:54:12.439 Demilade Agboola: How are you?