Meeting Title: Google Calendar Meeting (not synced) Date: 2025-06-12 Meeting participants: Perry’s Fellow Note Taker, pk.arthur, Amber Lin, ianbiles, felipefaria, Jesse, Emily Giant, Santiago Posso, Stephanie Plaza, perry
WEBVTT
1 00:01:32.910 ⇒ 00:01:34.460 Amber Lin: Hi! Everyone.
2 00:01:37.110 ⇒ 00:01:37.860 pk.arthur: Hello!
3 00:01:38.430 ⇒ 00:01:40.840 Amber Lin: Hi! Nice to meet you all.
4 00:01:42.020 ⇒ 00:01:43.710 pk.arthur: Nice to meet you, too. How are you doing.
5 00:01:43.840 ⇒ 00:01:53.030 Amber Lin: I’m good I’m amber, as my name Tag says. I’m working with Rainforge, and we’re we’re helping you guys.
6 00:01:53.495 ⇒ 00:02:20.489 Amber Lin: Rebuild everything related to data. And I wanted to have this meeting to meet you guys because this is ultimately why we’re even building is so to make your lives easier. Because I talked to Emily, and I heard that there was a lot of struggles and a lot of daily pains and a lot of things breaking and so I want this session for us to get to know each other and for me to update you on our current progress.
7 00:02:21.010 ⇒ 00:02:28.580 Amber Lin: and especially hear from you what are the most painful things, and how we can best help you.
8 00:02:32.130 ⇒ 00:02:33.300 Amber Lin: Hi, Emily!
9 00:02:34.630 ⇒ 00:02:39.580 Emily Giant: Hi, everybody! It’s good to see everyone in one room very rarely happens.
10 00:02:40.050 ⇒ 00:02:46.630 Amber Lin: I’m glad I make it happen. Are we missing anybody? Is this everyone.
11 00:02:47.090 ⇒ 00:02:51.639 Emily Giant: Perry, her note taker is here, but she is not here.
12 00:02:51.640 ⇒ 00:02:54.464 Emily Giant: Hmm, Santi just got here.
13 00:02:55.920 ⇒ 00:03:00.420 Emily Giant: I feel like we’re still missing wilder finance.
14 00:03:00.620 ⇒ 00:03:01.890 Amber Lin: Hmm, okay.
15 00:03:05.240 ⇒ 00:03:06.230 Emily Giant: Staff.
16 00:03:07.060 ⇒ 00:03:09.050 Emily Giant: Let me check the list again.
17 00:03:21.820 ⇒ 00:03:23.670 Amber Lin: Is everybody working from home?
18 00:03:25.480 ⇒ 00:03:26.330 felipefaria: Yes.
19 00:03:26.330 ⇒ 00:03:30.220 Amber Lin: Oh, that’s very nice, and are you all in the same time? Zone.
20 00:03:30.760 ⇒ 00:03:31.510 Emily Giant: Oh no!
21 00:03:31.680 ⇒ 00:03:33.240 Amber Lin: Oh, okay.
22 00:03:33.240 ⇒ 00:03:36.039 Emily Giant: Jesse’s in the Philippines.
23 00:03:36.040 ⇒ 00:03:36.640 Amber Lin: Hmm.
24 00:03:37.840 ⇒ 00:03:40.359 Emily Giant: Santi’s in Colombia, in Bogota.
25 00:03:41.100 ⇒ 00:03:42.350 Amber Lin: Wow!
26 00:03:42.350 ⇒ 00:03:45.000 Emily Giant: I don’t. Pk! Where are you? You’re in Baltimore, aren’t you?
27 00:03:45.140 ⇒ 00:03:47.979 Emily Giant: I’m Maryland. Yeah, I’m College Park.
28 00:03:48.500 ⇒ 00:03:50.260 Emily Giant: Ian’s in La.
29 00:03:51.100 ⇒ 00:03:52.819 Amber Lin: Oh, really. Okay.
30 00:03:52.820 ⇒ 00:03:53.940 Emily Giant: Amber’s in La.
31 00:03:53.940 ⇒ 00:03:54.880 Amber Lin: I’m in la.
32 00:03:54.940 ⇒ 00:03:57.840 ianbiles: Oh, Amber, hey? West Coast!
33 00:03:59.040 ⇒ 00:04:02.889 ianbiles: You look like you were from La, not from here.
34 00:04:02.890 ⇒ 00:04:04.829 ianbiles: New York. I’m from New York.
35 00:04:04.830 ⇒ 00:04:05.450 Amber Lin: Okay.
36 00:04:05.700 ⇒ 00:04:13.760 Amber Lin: I see, yeah, a lot of my teams are also in the Philippines, and then some of them are in Europe.
37 00:04:13.960 ⇒ 00:04:19.609 Amber Lin: So my time zone is very messed up because my meetings with my team’s really early.
38 00:04:21.930 ⇒ 00:04:28.250 Amber Lin: Okay, let’s just get started. This meeting is recorded. So if they want to watch it. They have a recording.
39 00:04:28.930 ⇒ 00:04:29.900 Amber Lin: Hi, Perry.
40 00:04:30.160 ⇒ 00:04:39.520 Amber Lin: and let me share my screen. I made a teeny, tiny presentation. With the 30 min I had between my stand up and this meeting.
41 00:04:39.840 ⇒ 00:04:42.139 Amber Lin: Let me share, screen.
42 00:04:43.190 ⇒ 00:04:44.840 Amber Lin: And
43 00:04:45.870 ⇒ 00:04:51.399 Amber Lin: oh, before do we do that? Do we want to do a round of intros? Or is it just me that doesn’t know everyone.
44 00:04:51.872 ⇒ 00:04:56.229 Emily Giant: For you, though amber like this is not gonna be a short process of us working.
45 00:04:56.230 ⇒ 00:04:57.599 Amber Lin: Okay. Okay. Okay.
46 00:04:57.600 ⇒ 00:05:06.670 Emily Giant: Probably worth going through and meeting everyone so that you can put their voices and faces to the descriptions I’ve given of them.
47 00:05:06.670 ⇒ 00:05:25.339 Amber Lin: Okay. Sounds good. I imagine you all know every know each other. I’ll introduce myself. So my name is Amber. I’m working for Brainforge as a project manager, and I joined Boyforge around like 3 3 Ish months ago.
48 00:05:25.530 ⇒ 00:05:41.340 Amber Lin: and I joined urban stems around a month ago. And so what we do is we help different companies fix their data problems very simply put more about me. I’m a
49 00:05:41.620 ⇒ 00:05:44.169 Amber Lin: photographer outside of work.
50 00:05:44.693 ⇒ 00:05:47.450 Amber Lin: This is also my dried flower section.
51 00:05:49.715 ⇒ 00:06:01.620 Amber Lin: and I’m based in La. But I grew up in China, and then I spent a few years each in Canada, Hong Kong, and Milan and Italy. So
52 00:06:01.740 ⇒ 00:06:05.880 Amber Lin: I’m a little bit all over the place, and now I am back in La.
53 00:06:07.780 ⇒ 00:06:09.429 Amber Lin: That’s a little bit about me.
54 00:06:09.890 ⇒ 00:06:18.159 Amber Lin: I would love to hear intros from everyone, so I just don’t know if we have time, but I think I’ll be individually working with every single one of you, so
55 00:06:19.040 ⇒ 00:06:22.009 Amber Lin: we’ll have time to get know. Get to know each other.
56 00:06:23.080 ⇒ 00:06:23.760 ianbiles: Oh no!
57 00:06:25.480 ⇒ 00:06:26.090 felipefaria: Yeah.
58 00:06:27.880 ⇒ 00:06:30.689 Amber Lin: I’m gonna share my, can. Everyone see my screen.
59 00:06:31.960 ⇒ 00:06:32.470 felipefaria: Yep.
60 00:06:32.820 ⇒ 00:06:36.880 Amber Lin: Awesome. Okay? So quick, little slideshow.
61 00:06:38.270 ⇒ 00:06:43.430 Amber Lin: So essentially, what we’re dealing with, what we’re doing is this whole.
62 00:06:43.960 ⇒ 00:06:59.290 Amber Lin: this, this diagram puts it very nicely. We have nice designers. So they made this. And so you guys probably all know how things work and how that impacts your life. Most of you probably look work within the just with looker.
63 00:06:59.480 ⇒ 00:07:12.590 Amber Lin: That’s probably your daily life. But whenever something happens you probably had to go back to the sources into your data marts, or even all the way back to
64 00:07:12.710 ⇒ 00:07:25.250 Amber Lin: the original source, is to look at. Okay, what’s going wrong? What’s happening? And what is what? What was causing the issues. And so essentially, what we’re helping you guys do is to
65 00:07:25.980 ⇒ 00:07:34.249 Amber Lin: refracture parts that’s causing issues and to make your lives better. So to.
66 00:07:34.600 ⇒ 00:07:38.379 Amber Lin: I want to talk about it in 3 parts. So we’re going to start with Looker.
67 00:07:38.640 ⇒ 00:07:46.230 Amber Lin: And now I’m going to talk about the DVD. Mars, and then the ingestion flow from all these different sources.
68 00:07:46.450 ⇒ 00:07:53.469 Amber Lin: and after we did a quick audit of looker, it turns out there was almost 600 tables.
69 00:07:53.640 ⇒ 00:08:19.599 Amber Lin: 600 dashboards, and only 18 of them was used, and I don’t know how you guys usually navigate the dashboard, but it’s probably pretty hard to even search up the ones you want and maybe sometimes you use one. You forgot about it. And we created another one. And they that doesn’t have all the data. And so I bet that was pretty frustrating that dashboards also break down.
70 00:08:19.940 ⇒ 00:08:32.780 Amber Lin: And so that’s the 1st thing that we have in mind is looker is what you guys use, and we will put in a lot of energy into making sure that it doesn’t cause too much trouble for you.
71 00:08:34.480 ⇒ 00:08:50.240 Amber Lin: And so the next part on this is when we look at the ingestion source ingestion tables and the data marts. So we’ll note after we did a quick audit. We found that there was
72 00:08:50.550 ⇒ 00:08:54.029 Amber Lin: a hundred, a thousand, 500 ingestion tables.
73 00:08:54.400 ⇒ 00:08:59.519 Amber Lin: and a lot of them were not used, or they were unnecessary.
74 00:08:59.880 ⇒ 00:09:23.030 Amber Lin: and also for the data marts. They were not really organized up to industry standard. They were organized by source. And so there was a lot of times where there’s duplicates or things are inaccurate. And probably that was the reason why your dashboards were crashing down is because they weren’t really built on solid foundations.
75 00:09:24.360 ⇒ 00:09:36.710 Amber Lin: And so what we’ll try what we’re currently doing for you guys is we’re working very, very closely with Emily and with Alex and Zach. And we’re looking to
76 00:09:36.910 ⇒ 00:09:38.590 Amber Lin: target 3 areas.
77 00:09:39.020 ⇒ 00:09:46.109 Amber Lin: So first, st to deprecate the unused dashboards and to with you guys together rebuild the inaccurate ones.
78 00:09:46.490 ⇒ 00:09:59.250 Amber Lin: We’re going to deprecate the unused ingestion tables, so that whenever something does happen, those who have to go into the sources to figure out what’s happening can have an easier time actually finding what they need.
79 00:09:59.770 ⇒ 00:10:08.479 Amber Lin: And then, similarly, for the Dbt models, the Dbt marts to refractor based on business functions
80 00:10:08.750 ⇒ 00:10:13.430 Amber Lin: and to match the industry best practices.
81 00:10:14.970 ⇒ 00:10:18.980 Amber Lin: I’m gonna pause here, if everything, if we have any questions.
82 00:10:23.240 ⇒ 00:10:26.967 Amber Lin: Okay, I hear silence. I hear no questions. Very good.
83 00:10:28.490 ⇒ 00:10:46.720 Amber Lin: So this is what we’re at with looker and ingestion cleanup. So we already finished our initial audit, and we have deprecated the very obviously unused ones. And next up we’ll be looking at the dashboards and ingestion tables that are in use.
84 00:10:47.210 ⇒ 00:11:05.030 Amber Lin: and we’ll need to evaluate the accuracy and also to work very closely with you guys to rebuild or consolidate them. And this is where we’ll be working very closely together. And we’ll need a lot of input from you guys on what they should look like.
85 00:11:08.490 ⇒ 00:11:12.370 Amber Lin: And this is, I think this is the
86 00:11:12.520 ⇒ 00:11:15.830 Amber Lin: foundational piece of it of making sure that
87 00:11:16.120 ⇒ 00:11:25.239 Amber Lin: data that supports all these dashboards are organized up to the industry standard and therefore doesn’t.
88 00:11:25.750 ⇒ 00:11:38.249 Amber Lin: Prevents a lot of issues from the start. And so we’re approaching it from business functions. We’re starting from inventory, which we have been doing for the past past few months.
89 00:11:38.580 ⇒ 00:11:58.502 Amber Lin: and we’re just wrapping it up in the current sprint. And Felipe probably has already seen some of the benefits from the improvements, as Emily have told me. So. I’m very happy to hear that, and when we wrap that up, a lot of the things will come together and hopefully, you guys can envy Felipe’s easy life.
90 00:11:59.410 ⇒ 00:12:00.209 Amber Lin: I will never.
91 00:12:00.210 ⇒ 00:12:01.872 Amber Lin: It will never be that easy.
92 00:12:02.570 ⇒ 00:12:06.452 Emily Giant: Maybe maybe by the end of next week his life will be forever changed.
93 00:12:07.530 ⇒ 00:12:09.469 Emily Giant: Dealt with a lot, but he’s definitely.
94 00:12:09.470 ⇒ 00:12:10.040 Amber Lin: Yeah.
95 00:12:10.040 ⇒ 00:12:13.650 Emily Giant: Like getting the impacts at the front end of the work.
96 00:12:15.700 ⇒ 00:12:37.730 Amber Lin: Awesome, and next up we’ll be doing revenue, because revenue will be the one that impacts the most people. And we want to just make your lives as easy as possible as quick as possible, and that will include rebuilding, the shopify or refunds and discounts loyalty, date data, historical revenue.
97 00:12:37.730 ⇒ 00:12:46.539 Amber Lin: And we’re currently doing an audit of what needs to be built. And in that process we’ll also seek your opinions and your feedback on
98 00:12:47.190 ⇒ 00:12:53.239 Amber Lin: what has just been really, really painful, and what just keeps breaking and does not work.
99 00:12:54.390 ⇒ 00:13:14.669 Amber Lin: that will take a bit of time I would take, I would say, a few months. I’m estimating between 2 and 3 months, because that’s somewhat how the inventory mark took. But when we have more capacity we’ll start on the customer care, Mark, because that’s what I heard that this will also impact a few of you in the room.
100 00:13:15.343 ⇒ 00:13:20.779 Amber Lin: In terms of refund allocations, complaint, tagging, and delivery issues.
101 00:13:21.120 ⇒ 00:13:26.264 Amber Lin: and we want to gather your feedback on them as soon as possible. So we know what we’re
102 00:13:26.740 ⇒ 00:13:28.590 Amber Lin: what we’re rebuilding.
103 00:13:31.010 ⇒ 00:13:45.010 Amber Lin: I put down a list of what this means to you from my quick session with Emily. Based on what you guys do on a day to day, and how rebuilding this data is going to help you.
104 00:13:45.580 ⇒ 00:13:56.629 Amber Lin: And so, starting off, Olivia has already seen the inventory fixes which will really help with any real time supply chain decisions.
105 00:13:57.360 ⇒ 00:14:03.680 Amber Lin: and especially when we start with revenue.
106 00:14:03.800 ⇒ 00:14:06.420 Amber Lin: Perry will see the benefits
107 00:14:07.108 ⇒ 00:14:15.180 Amber Lin: in terms of having granular level unit or Kit level forecasting and demand planning abilities
108 00:14:16.220 ⇒ 00:14:20.400 Amber Lin: and going down the line.
109 00:14:21.060 ⇒ 00:14:28.269 Amber Lin: Think I think Bk and Stephanie will also see the benefits from the revenue mart. Especially
110 00:14:28.680 ⇒ 00:14:44.770 Amber Lin: access to the marketing marketing data merge data. So that will probably be very different from what you guys are currently using. But I think we can promise that it will be a lot more reliable, and will make your lives easier
111 00:14:48.110 ⇒ 00:14:51.280 Amber Lin: and great.
112 00:14:54.480 ⇒ 00:15:01.419 Amber Lin: so I think I want to pause here for us. Just have a more free discussion on any questions you have
113 00:15:01.909 ⇒ 00:15:11.619 Amber Lin: based on what we’re doing. Maybe Emily can also help you and help you understand what we’re trying to do and how it would impact you. So
114 00:15:13.150 ⇒ 00:15:15.770 Amber Lin: opening the grounds for any questions.
115 00:15:19.394 ⇒ 00:15:30.020 Emily Giant: One of the things that I wanted to note was, I know that, like a lot of the people on this call are experiencing glitchiness with, like the shopify
116 00:15:30.170 ⇒ 00:15:32.230 Emily Giant: product data table
117 00:15:32.750 ⇒ 00:15:58.109 Emily Giant: these that is independent of this larger scope work. So just back up all of this like foundational work that we’re doing with. If there are bugs or things like what’s happening with shopify product data, we’re still slotting that in for an interim fix. So like Stephanie and Perry, like that work is happening
118 00:15:58.120 ⇒ 00:16:02.879 Emily Giant: in tandem with this like, it will still get rebuilt. But we’re not gonna like
119 00:16:02.910 ⇒ 00:16:06.729 Emily Giant: allowed deprecated tables to exist for 3 months.
120 00:16:07.180 ⇒ 00:16:16.440 Emily Giant: Just wanted to add that context, because I’m sure hearing like this will happen in 3 months, was scary. But it will be reliable, like
121 00:16:17.250 ⇒ 00:16:24.100 Emily Giant: going forward in a much more consistent way that we don’t have to do. These duct tape fixes.
122 00:16:33.760 ⇒ 00:16:46.020 Amber Lin: And I guess if if I don’t hear any questions, can I? Can. I just hear from everybody the biggest pains you have been experiencing, so I can write them down and relay them to the team.
123 00:16:50.570 ⇒ 00:16:52.319 Emily Giant: Perry, you want to go first.st
124 00:16:54.940 ⇒ 00:16:55.520 perry: Well.
125 00:16:56.566 ⇒ 00:17:25.790 perry: no, I think the revenue one is the big one for me, I think. Really just there wasn’t a part of our data that Steph or I wasn’t touching prior to shopify. So I think just getting that confidence back in that. Because I know you guys have been working so hard and everything. But I think things are slipping out from under us occasionally, just because the customers always do something that we didn’t think that they were gonna do, and that always loop in the data. So I think that the skew level stuff is really big. But the top line for me is really just based. So we can understand
126 00:17:25.790 ⇒ 00:17:26.420 perry: kind of
127 00:17:26.589 ⇒ 00:17:33.670 perry: how people are moving and shaking in the business, and what they’re doing will just free up a lot of kind of like back of mind concern for Steph, and I.
128 00:17:35.220 ⇒ 00:17:44.490 Amber Lin: so what I hear is that visible just overall visibility is super important, and then skew comes after. After that.
129 00:17:46.930 ⇒ 00:17:48.770 perry: Thank you for me. You know what I mean. I don’t know.
130 00:17:49.270 ⇒ 00:17:50.090 perry: Don’t know about that, but.
131 00:17:51.490 ⇒ 00:17:52.470 Amber Lin: Okay.
132 00:17:53.600 ⇒ 00:18:00.749 Amber Lin: Stephanie, do you have the same sentiment? I think you guys were probably probably work very closely together. Yeah.
133 00:18:00.750 ⇒ 00:18:03.640 Amber Lin: I I think it’s just like an overall confidence.
134 00:18:04.280 ⇒ 00:18:10.089 Stephanie Plaza: Product level data, financial level data. Sorry for the background. But yeah.
135 00:18:10.410 ⇒ 00:18:11.440 Amber Lin: Hmm, okay.
136 00:18:11.570 ⇒ 00:18:17.609 Emily Giant: Yeah. And thank you things with their team. That’s also been like, really inconsistent over time is.
137 00:18:17.610 ⇒ 00:18:17.970 Amber Lin: Pro.
138 00:18:17.970 ⇒ 00:18:42.010 Emily Giant: And loyalty points and credits and top line and it’s easy to like overlook that piece, but, like the more time goes on, the weirder. Our like discounting logic is. So I think that that’s gonna be a big value. Add to it’s just like knowing that we have historical and forward, looking like reliable accounting of discounts and promos.
139 00:18:43.020 ⇒ 00:18:43.570 perry: Yeah.
140 00:18:45.690 ⇒ 00:18:50.730 perry: I guess I’ll pass that one off to Pk, cause I’m not really involved in that anymore. So I’ll let Pk take.
141 00:18:51.195 ⇒ 00:18:51.660 Amber Lin: Okay.
142 00:18:51.660 ⇒ 00:18:53.260 perry: Take my hand out of that pot.
143 00:18:53.677 ⇒ 00:18:57.019 pk.arthur: Yeah, thank you. I’ll say for me
144 00:18:57.400 ⇒ 00:19:02.549 pk.arthur: the biggest. So for some context, I’m relatively new to urban stems is like my 1st month.
145 00:19:02.850 ⇒ 00:19:04.870 Amber Lin: And with urban stem, so.
146 00:19:04.970 ⇒ 00:19:24.879 pk.arthur: So the biggest pain point for me is just accuracy. There’s like looking between looker from what I see on looker and Ga, 4 and shopify. There’s been several times I go to Emily just to confirm the accuracy of the data I’m seeing from, for example, Ga. 4 or looker. So that is the biggest pain point. Right now I’ll say for me.
147 00:19:25.650 ⇒ 00:19:26.190 Amber Lin: Yeah.
148 00:19:26.190 ⇒ 00:19:31.063 Emily Giant: Well, I always say pk, I say no, it’s not, and then I have no follow ups.
149 00:19:32.960 ⇒ 00:19:33.720 pk.arthur: Yeah.
150 00:19:36.220 ⇒ 00:19:39.760 Amber Lin: Well, I mean it is. It is an answer. It is correct.
151 00:19:41.680 ⇒ 00:19:51.647 pk.arthur: Yeah. So that’s what I’ll say. The biggest one for me is also the promo, because I’m doing some promo analysis right now, too, so it would definitely be helpful to see
152 00:19:52.370 ⇒ 00:20:00.220 pk.arthur: when exactly your promo was used, who was used by that amount, just having all that, and Looker would definitely help save. Some time.
153 00:20:00.220 ⇒ 00:20:00.800 Amber Lin: Hmm.
154 00:20:00.800 ⇒ 00:20:01.910 pk.arthur: This is for sure.
155 00:20:04.650 ⇒ 00:20:09.260 Amber Lin: Okay. Okay, okay, I’ll make. I’ll make you pick the next person.
156 00:20:09.440 ⇒ 00:20:10.080 pk.arthur: Oh!
157 00:20:10.080 ⇒ 00:20:10.840 Amber Lin: Remixed them.
158 00:20:10.940 ⇒ 00:20:16.989 pk.arthur: Who we got I’ll go with Ian because you’re next on my on my list.
159 00:20:18.290 ⇒ 00:20:19.543 ianbiles: Hey, guys?
160 00:20:20.430 ⇒ 00:20:36.385 ianbiles: as crazy as it sounds. I feel like also, like all of the above, for me and Jesse are kind of combined for care. And then a lot of credit and refund data, just making sure that those are accurate. It feels like a lot of times. It feels really accurate. And then we start to dig a little bit, and it’s like, Oh, something’s a little funky there.
161 00:20:36.780 ⇒ 00:20:52.679 ianbiles: no matter what and so I feel like just making sure that that’s accurate. And then kind of like literally everything else everybody said kind of somehow ties into what me and Jesse are doing so. I’ll speak for Jesse, unless Jesse has anything else to say. But I feel like our our roles are kind of like this, so
162 00:20:54.940 ⇒ 00:20:59.970 ianbiles: am I passing it along. Who’s left Felipe?
163 00:21:03.020 ⇒ 00:21:16.300 felipefaria: Hey, guys? Yeah. Well, so knock on wood. I think that we have fixed the sales redelivery subscription issues. I’m in the process of kinda just retroactively
164 00:21:16.560 ⇒ 00:21:25.899 felipefaria: completing the weekly recaps and reconciling the the numbers. And I’ll let Emily. And and just know if there’s any sort of issues that I find during the Qa.
165 00:21:26.030 ⇒ 00:21:30.410 felipefaria: And I sent actually, prior to this meeting, just a note to Emily on.
166 00:21:30.520 ⇒ 00:21:46.099 felipefaria: on kind of like the main things that I’m looking at to be fixed or just introduced into looker. And one of them is just like the base, the base sales data which is the the table that we use to look at hard, good sales.
167 00:21:47.380 ⇒ 00:21:57.165 felipefaria: For some reason it’s not working properly. And it’s kinda understating sales of of products. We also have some issues with,
168 00:21:58.280 ⇒ 00:22:10.820 felipefaria: properly showing the sales of vases that are exclusively paired with a bouquet. So, aside from that, I think one of the requests that we also did is
169 00:22:12.190 ⇒ 00:22:17.260 felipefaria: having visibility into forced upgrades on top of we do degrees
170 00:22:18.014 ⇒ 00:22:32.180 felipefaria: and then the 3rd one is is a report that we used to have in the previous tables, which was like the inventory, except for inventory transactions, except which is just the the
171 00:22:32.450 ⇒ 00:22:38.120 felipefaria: suborders. By map, type and showing the component
172 00:22:38.846 ⇒ 00:22:45.080 felipefaria: in the parents queue this table is, it hasn’t really been working properly since we transition to
173 00:22:45.440 ⇒ 00:22:50.749 felipefaria: to shopify, and is an important piece of kind of like what I work with, and.
174 00:22:51.080 ⇒ 00:23:02.119 felipefaria: For Santiago as well. I know the Santiago would use that for some validation of of packaging utilization and all this sort of stuff. So those are kind of like the main things that are on my radar right now.
175 00:23:06.460 ⇒ 00:23:08.542 felipefaria: I’ll toss it over to Santiago.
176 00:23:09.120 ⇒ 00:23:22.880 Santiago Posso: Well, I’m I’m with you along with you on Ian as well. I think the main issues that we are facing is like the connection between the data and the reporting. We have been seeing, like
177 00:23:22.940 ⇒ 00:23:39.480 Santiago Posso: some issues with the information coming from Zendesk, because sometimes we have in the report. But we have to go deep, to check in another sources to find the real reason. So I think those disconnections are the one that are affecting on my side.
178 00:23:41.290 ⇒ 00:23:45.030 Amber Lin: Okay, I took notes on what everybody said.
179 00:23:45.240 ⇒ 00:23:57.999 Amber Lin: and we we will. I’m gonna record them and translate it to the team. We’re gonna work with Emily to talk about how these are. Gonna get realized. What does the timeline look like? What you guys should expect?
180 00:23:58.170 ⇒ 00:24:04.280 Amber Lin: And let me just see if there’s other slides. Oh, there’s 1 last slide I have.
181 00:24:06.460 ⇒ 00:24:07.500 Amber Lin: And
182 00:24:08.330 ⇒ 00:24:35.980 Amber Lin: so, after this, of what we would like to see from you is a bit of patience on a quick fixes, it will definitely get fixed. We’re we’re allowing for around 30% of our the Brainforge team’s time as well to dedicate to these quick fixes. And of course Emily will be helping with these with these as well. We will also
183 00:24:36.220 ⇒ 00:24:55.640 Amber Lin: love for your input, just like what we did right now for the future state of what you want it to look like, what’s not going well, what is going well, and to have the open feedback loops to. Just. I want you guys to feel comfortable to just tell us, hey? That was not helpful, or Hey, that was really good. Can we do more of that?
184 00:24:56.070 ⇒ 00:25:16.609 Amber Lin: And so that’s kind of what we want to see from this group, because we’ll be working together quite a bit from now to build these items together, and I was wondering if this time works for everyone on the Sync. Or perhaps I think at 1 point we’ll just having. We’ll just start to have
185 00:25:17.970 ⇒ 00:25:25.269 Amber Lin: perhaps one on one or smaller sinks as well. I want to hear from you guys. What do you guys think.
186 00:25:29.800 ⇒ 00:25:31.299 Stephanie Plaza: I’m good with this time.
187 00:25:32.260 ⇒ 00:25:33.710 felipefaria: Yeah. It works for me, too.
188 00:25:33.710 ⇒ 00:25:34.550 perry: Yeah, same.
189 00:25:34.770 ⇒ 00:25:36.360 pk.arthur: Okay, you, too, for me.
190 00:25:36.780 ⇒ 00:25:45.569 Amber Lin: Sounds good. I’m planning to use this as a as a time for us to update you on what happened.
191 00:25:45.720 ⇒ 00:26:06.639 Amber Lin: And if there’s anything we want to get aligned on. If there’s any specific issue that we haven’t brought up throughout the week, we can bring them up here and depending on how it goes. We perhaps will do this either weekly, probably weekly or bi-weekly, and then, if any individual sessions is needed, I’ll book it with you guys as well.
192 00:26:08.425 ⇒ 00:26:14.209 perry: Really quickly, Emily, how does this Thursday meeting align to the beginning of new sprints
193 00:26:14.400 ⇒ 00:26:18.829 perry: like? Is this meeting before a new sprint, or is it after the start of a new sprint.
194 00:26:18.830 ⇒ 00:26:24.270 Emily Giant: Like smack in the middle like we start them on Tuesdays, and the
195 00:26:24.730 ⇒ 00:26:27.529 Emily Giant: sprints are offset from the urban stem sprints.
196 00:26:27.530 ⇒ 00:26:28.280 perry: Got it.
197 00:26:28.730 ⇒ 00:26:36.729 Emily Giant: So, yeah, so we’re kind of like always starting a sprint if that makes sense, because either urban stems or the.
198 00:26:36.870 ⇒ 00:26:47.009 perry: Yeah, cause I just figure it’s good for us to align that way if we want to. If, like, we all have to change something that there’s a sprint that we can like instead of like being 2 weeks from now, we can action it.
199 00:26:47.320 ⇒ 00:26:54.299 Emily Giant: Like when you have, like the revenue blowing up or products Xf table stopped being supported by shopify like that’s immediately getting worked on.
200 00:26:54.300 ⇒ 00:26:55.130 perry: Yeah, yeah.
201 00:26:55.130 ⇒ 00:27:01.669 Emily Giant: Immediately. But the other things. I I hear you like working on prioritization. There.
202 00:27:01.820 ⇒ 00:27:17.490 perry: Well cause. I just think that I think that all being aligned cross function, what those prioritizations. And if something is popping up that we all think needs to get fixed. We can. We’re all on the same page about what that pushes to another sprint, and we can all kind of understand that, because I think that’s we don’t all touch each other’s things so that can get kind of tricky to understand.
203 00:27:17.640 ⇒ 00:27:18.350 Emily Giant: Yeah.
204 00:27:19.000 ⇒ 00:27:20.420 Amber Lin: That’s a great point.
205 00:27:20.950 ⇒ 00:27:33.849 Amber Lin: Okay, awesome. I will. I will send out another invite, for I think either next week or 2 weeks from now. And then I’ll see you guys. Then, if you don’t have any more questions.
206 00:27:35.360 ⇒ 00:27:36.030 perry: Cool.
207 00:27:36.030 ⇒ 00:27:36.870 felipefaria: That’s good. Thank you.
208 00:27:37.450 ⇒ 00:27:38.030 perry: Guys.
209 00:27:38.330 ⇒ 00:27:39.640 Amber Lin: Lovely meeting. You guys.
210 00:27:39.850 ⇒ 00:27:40.819 Stephanie Plaza: Nice to see you again.
211 00:27:41.300 ⇒ 00:27:42.910 ianbiles: You, too. Bye, guys.
212 00:27:43.390 ⇒ 00:27:44.480 Amber Lin: Bye.