Meeting Title: LMNT x Brainforge Alignment Date: 2026-05-04 Meeting participants: Robert Tseng, Shivani Amar, Uttam Kumaran
WEBVTT
1 00:01:37.660 ⇒ 00:01:38.860 Shivani Amar: Hello!
2 00:01:40.520 ⇒ 00:01:41.340 Robert Tseng: Ayy!
3 00:01:42.490 ⇒ 00:01:43.510 Shivani Amar: How you doing?
4 00:01:44.180 ⇒ 00:01:48.199 Robert Tseng: Good, how are you? How was your, weekend? Or long weekend?
5 00:01:48.200 ⇒ 00:01:52.909 Shivani Amar: Yeah, it was really nice. Like, a wonderful weekend. How about you?
6 00:01:52.910 ⇒ 00:01:55.350 Robert Tseng: Good. Great to hear.
7 00:01:55.530 ⇒ 00:02:01.620 Robert Tseng: I was… yeah, I was… I was at conferences last week, so it was good, restful, just to come back, recover.
8 00:02:01.620 ⇒ 00:02:05.860 Shivani Amar: Has the weather been consistently nice here? I guess you were also not in town. I’m like.
9 00:02:05.860 ⇒ 00:02:07.160 Robert Tseng: I was in Boston.
10 00:02:07.370 ⇒ 00:02:10.809 Robert Tseng: Yeah, today’s beautiful. Boston was very cold, so… Tomorrow’s gonna be good.
11 00:02:10.810 ⇒ 00:02:13.200 Shivani Amar: Beautiful, too. Okay. Yeah.
12 00:02:13.650 ⇒ 00:02:15.890 Shivani Amar: Wow. And then it’ll be rainy.
13 00:02:16.600 ⇒ 00:02:19.290 Shivani Amar: Gotta get all the rain out at the beginning of May.
14 00:02:19.290 ⇒ 00:02:22.180 Uttam Kumaran: I’m going… I’m going to, like, Scottsdale area next.
15 00:02:22.340 ⇒ 00:02:24.550 Uttam Kumaran: Next weekend, not this weekend.
16 00:02:24.550 ⇒ 00:02:25.360 Shivani Amar: for party?
17 00:02:25.360 ⇒ 00:02:26.819 Uttam Kumaran: Yeah, bachelor party.
18 00:02:26.970 ⇒ 00:02:35.300 Shivani Amar: Man, I… Sedona is, like, a magical place. Have you been there before?
19 00:02:35.710 ⇒ 00:02:36.400 Uttam Kumaran: -
20 00:02:37.390 ⇒ 00:02:39.540 Shivani Amar: Sedona’s so beautiful.
21 00:02:40.300 ⇒ 00:02:42.550 Robert Tseng: You do the hot air balloons? Is that what they’re known for?
22 00:02:43.860 ⇒ 00:02:47.560 Uttam Kumaran: Yeah, I think so, right? Yeah. Or this… well, I know Sedona Speedway.
23 00:02:47.920 ⇒ 00:02:52.829 Uttam Kumaran: Like, they have a really… Right. Yeah, big NASCAR speedway. Yeah.
24 00:02:54.900 ⇒ 00:03:02.380 Shivani Amar: Okay, so let’s do a little check-in. What do you, what do you guys want to cover?
25 00:03:04.830 ⇒ 00:03:18.659 Uttam Kumaran: Yeah, I mean, I wanted to share… we spent some time last week kind of on the ingestion and modeling side, basically working on a plan for observability, and alerting. I think one of the things that I want to get ahead of
26 00:03:18.730 ⇒ 00:03:37.799 Uttam Kumaran: especially because we’re not gonna go, like, super live with everything until fall, is making sure that issues like Emerson data being stale, metrics not being accurate, like, we start to push there. I feel pretty good with how far we are on the, like, modeling side.
27 00:03:38.020 ⇒ 00:03:51.800 Uttam Kumaran: So, I want to, like, nail, like, okay, what does, like, alerting look like? Previously, we would have just recommended, like, a vendor, but I think we took some time internally, like, what is our… how are we gonna do this across, like.
28 00:03:52.320 ⇒ 00:04:06.280 Uttam Kumaran: Previously, we would just use a tool called, like, Metaplane. There’s a couple of vendor tools. They’re expensive, and they’re actually not that great, and so there’s actually a way for us to build this that I think will be really effective, to catch things like, hey, data is stale, and you get an alert in Slack.
29 00:04:06.630 ⇒ 00:04:15.160 Uttam Kumaran: Or hey, this metric is now off, based on some… we pushed some new code, and a past metric has changed. So some of those changes.
30 00:04:15.180 ⇒ 00:04:33.720 Uttam Kumaran: That’s, like, gonna be our side of the QA, because otherwise, it’s gonna be hard for us to say, okay, like, the system is fine and working, and then for us to take feedback once we’ve locked down QA for a given dashboard, like, these are the metrics that we’re expecting. Yeah. So, we’ll present that to tech this week.
31 00:04:33.780 ⇒ 00:04:35.180 Uttam Kumaran: Okay. On Wednesday.
32 00:04:35.550 ⇒ 00:04:36.340 Shivani Amar: Cool.
33 00:04:38.940 ⇒ 00:04:45.989 Shivani Amar: Okay, that’s… that’s good. What are the other topics? Let’s name the topics for this call and frame it before we dive in.
34 00:04:47.130 ⇒ 00:04:49.779 Robert Tseng: Yeah, I mean, I’ll address just kind of, like, the…
35 00:04:49.780 ⇒ 00:05:09.319 Robert Tseng: supply chain kind of data documentation sources that you were calling out. So I know that there’s… I mean, Garrett is… Garrett is off, Greg’s back, and what does that mean? Like, where do we actually leave off on what you were expecting to see in the data sources and core metrics kind of tab for our data platform docs? I can… I can address that.
36 00:05:09.930 ⇒ 00:05:13.800 Shivani Amar: Okay, cool. And then, I think just some space to give feedback, also.
37 00:05:13.800 ⇒ 00:05:14.160 Robert Tseng: Yes.
38 00:05:14.160 ⇒ 00:05:19.939 Shivani Amar: general, like, just general feedback, and I think we should do this, like, once every couple weeks, because, like, now we’re just…
39 00:05:20.380 ⇒ 00:05:33.589 Shivani Amar: so much, and I just feel like we need to be feeling like we’re in such a groove, and I’m not sure I completely feel that yet, right? And so, I think the three of us doing this at some regular…
40 00:05:33.930 ⇒ 00:05:35.830 Shivani Amar: Cadence is not a bad idea.
41 00:05:36.020 ⇒ 00:05:46.890 Shivani Amar: Okay? Yeah. Like, yes, we have the, like, weekly with the full team to be like, okay, how’s the project progressing? But, like, sometimes I also need space to, like, freely, like, give feedback about the team.
42 00:05:47.120 ⇒ 00:05:55.709 Shivani Amar: Right? And my question, if we had done this prior to you telling me about Garrett, would be like, what is Garrett adding value? Like, how is he adding value?
43 00:05:55.850 ⇒ 00:06:02.619 Shivani Amar: And so, hopefully these calls… also, I don’t know if you’re recording this call, but hopefully these calls are not, like, going into.
44 00:06:02.620 ⇒ 00:06:03.540 Robert Tseng: I am. Is that okay?
45 00:06:03.540 ⇒ 00:06:04.280 Shivani Amar: Brain…
46 00:06:05.070 ⇒ 00:06:11.069 Shivani Amar: Well, it’s like, if Garrett is going to read the notes and then see that I’m like, where is he adding value? I don’t want to hurt anybody’s feelings.
47 00:06:11.070 ⇒ 00:06:16.149 Uttam Kumaran: No, it’s a… yeah, we’re… it’s only coming to, like, the people on the call, so…
48 00:06:16.150 ⇒ 00:06:24.269 Shivani Amar: Yeah, okay, that’s fine, because I just am like, if it goes into your notion as a call that we had, then it could just upset somebody, so that’s where I’m…
49 00:06:24.390 ⇒ 00:06:27.419 Shivani Amar: Wanting to understand. Okay, so,
50 00:06:27.880 ⇒ 00:06:33.659 Shivani Amar: on the supply chain piece, Robert, why don’t you kick off with, like, kind of, like, where you are, where you didn’t…
51 00:06:33.970 ⇒ 00:06:38.700 Shivani Amar: Land the plane, what the next steps are, and then,
52 00:06:41.080 ⇒ 00:06:53.909 Shivani Amar: And then maybe I can shift into some feedback. I’m trying to… there’s also, like, this, like, open ERP question that I want to, like, share some context around about, like, but… but yeah, I think if we stay on supply chain, all of that will come out naturally.
53 00:06:54.410 ⇒ 00:07:00.659 Robert Tseng: Okay, yeah, so I think there’s, like, there was a disconnect, I’ll just share my screen. Okay, so there was, like, obviously this is your supply chain dock.
54 00:07:00.730 ⇒ 00:07:16.139 Robert Tseng: you know, this was the exact summary you put together, you and Desire were going back and forth here, so she kind of thought this was, like, the package that you wanted to be able to send to BP, so we didn’t go back and edit our previous doc. I know that you put this together, this is our translation.
55 00:07:16.140 ⇒ 00:07:16.790 Shivani Amar: Yeah.
56 00:07:16.790 ⇒ 00:07:29.259 Robert Tseng: this is our translation of, like, you know, we didn’t go and pick out every command as a source, but, like, kind of where we are in terms of, like, what sources actually need to be… what do we consider to be data sources, how are we prioritizing it?
57 00:07:29.310 ⇒ 00:07:43.520 Robert Tseng: what I wanted to have done since last week was to put a timeline on it. We didn’t… we didn’t get the, the estimated timeline yet. Team asked for until Wednesday. So, so I think that’s probably where we dropped in terms of not getting as far as we wanted to.
58 00:07:43.990 ⇒ 00:07:49.360 Robert Tseng: I think the explanation is really just, I think, from the data discovery.
59 00:07:49.550 ⇒ 00:07:59.589 Robert Tseng: There’s a lot of reports here. I know that, you, you and, Jasmine are on… were on, were on these calls with Lauren and Julie. Yeah, I think even just…
60 00:07:59.890 ⇒ 00:08:14.760 Robert Tseng: Trying to identify which, you know, when we pull up a random, you know, like, report like this, and it’s, like, figuring out, like, what… what is… what does… what is the core… what are the set of core metrics that would, if we were able to calculate it with the source data.
61 00:08:14.760 ⇒ 00:08:19.339 Robert Tseng: be… be considered good. I think it was more straightforward with some of our other sources, but
62 00:08:19.340 ⇒ 00:08:37.889 Robert Tseng: I think this exercise is just taking longer on the supply chain side. So, we need to… we need to go through all of those, actually write out what those metrics are here, so that we can accurately put, like, what a timeline would be. So, I think that’s… that’s what’s… that’s what’s missing, and I’m driving the team towards, by Wednesday.
63 00:08:40.159 ⇒ 00:08:51.849 Shivani Amar: Okay, cool. So let’s go into some feedback from this, like, or like, what are… expectations, almost. So…
64 00:08:52.039 ⇒ 00:08:55.529 Shivani Amar: I’m trying to, like, create almost,
65 00:08:56.039 ⇒ 00:08:58.919 Shivani Amar: Like, a playbook, if you think about it as…
66 00:08:59.769 ⇒ 00:09:11.459 Shivani Amar: what should any teammate at Element who’s engaging with Brain Forge and me, like, expect in terms of flow? And Brain… like, Element is super into the idea of
67 00:09:11.859 ⇒ 00:09:21.489 Shivani Amar: how do you playbook something? Is it called the learning zone, or is it, like, something that’s now been playbooked? And Brianna is very into that language around, like.
68 00:09:22.149 ⇒ 00:09:29.529 Shivani Amar: at what point has this become a playbooked, like, workflow? And so, the discovery that we did with
69 00:09:30.029 ⇒ 00:09:35.699 Shivani Amar: revenue sides of the business, I think were kind of, like, lightweight, like, they weren’t, like.
70 00:09:35.889 ⇒ 00:09:44.949 Shivani Amar: formulaic. Like, we were just going… we were talking to Carlos, and I think the supply chain, we tried to be a little bit more structured. We still haven’t, like, landed the plane here completely.
71 00:09:45.059 ⇒ 00:09:53.999 Shivani Amar: But what I’m trying to understand is if… Pull this up.
72 00:09:56.049 ⇒ 00:09:58.569 Shivani Amar: If the process should be like this.
73 00:09:59.149 ⇒ 00:10:09.949 Shivani Amar: I do a little short call with a VP to say, hey, we’re doing discovery with your team. And granted, there might not be that many more teams we’re doing this with, but we might circle back to some that we did discovery with before, okay?
74 00:10:10.059 ⇒ 00:10:14.119 Shivani Amar: I did a little call with the VP to say, hey, we’re gonna start doing discovery.
75 00:10:14.289 ⇒ 00:10:19.999 Shivani Amar: We have some kind of, like, standard discovery questions that we do with…
76 00:10:20.649 ⇒ 00:10:24.049 Shivani Amar: Let me show you something. Okay, so…
77 00:10:26.899 ⇒ 00:10:35.029 Shivani Amar: Okay. Like, this to me is called the divisional audit. I do a kickoff, then we do these discovery calls, where this discovery call is just, like.
78 00:10:35.550 ⇒ 00:10:40.859 Shivani Amar: We’re priming the teammate we’re doing the discovery call with, and we’re kind of just going through, like.
79 00:10:40.940 ⇒ 00:10:59.289 Shivani Amar: what’s your role, your function, where it fits with an element? What decisions are you accountable for? What metrics matter most? Where does your data live? How do you currently pull it? What’s working? What’s manual? Where does reporting fall short? I know our supply chain questions were a bit more, like, specific, and all of these will be tailored based off the team that we’re doing this with, but, like.
80 00:10:59.290 ⇒ 00:11:06.550 Shivani Amar: this is where I’m trying to get to, that, like, anybody in the business kind of understands, like, what’s… what do I have to be prepped for before having this conversation?
81 00:11:07.070 ⇒ 00:11:18.709 Shivani Amar: You do these discovery calls, and you ask the team to do their map of current reporting. Do we think that this is a valuable step just for supply chain, or do you think eventually we would have tabs for retail and for e-commerce?
82 00:11:18.710 ⇒ 00:11:30.199 Shivani Amar: Where they’re showing you similar things with them, where they’re like, oh, this is what I’m pulling from Target… Target’s portal itself, and you can be like, oh man, that has zip code data, but what we’re getting from Emerson doesn’t have zip code data.
83 00:11:30.200 ⇒ 00:11:30.770 Uttam Kumaran: Yeah.
84 00:11:30.770 ⇒ 00:11:46.119 Shivani Amar: Right? So, like, I’m just trying to make sure that, like, is this… is this report set up properly? Is this adding value? Yes, we’re doing the discovery on the Jasmine side of things, but with them, is this something that you’re referencing as well, as you’re getting acquainted with the supply chain part of the business?
85 00:11:47.140 ⇒ 00:11:50.339 Shivani Amar: That’s an open question, that was a question. Yeah. Is it?
86 00:11:50.510 ⇒ 00:11:54.270 Robert Tseng: Yeah, the team’s already been looking at that, and it’s restored, you know, yeah, we already.
87 00:11:54.270 ⇒ 00:11:54.800 Uttam Kumaran: Yes.
88 00:11:54.800 ⇒ 00:11:55.969 Robert Tseng: on Friday, yeah.
89 00:11:55.970 ⇒ 00:12:10.340 Shivani Amar: Okay, cool. So then I’m like, maybe that’s, like, maybe we revisit retail and e-commerce and wholesale, and we’re like, yo, like, give us sample reports that you pull, even if we’re now ingesting Shopify. It can give us a sense of, like, what they’re pulling regularly regardless.
90 00:12:11.290 ⇒ 00:12:16.540 Shivani Amar: Related to their things, right? Just to make sure that we’re being comprehensive about all the data that we’ve brought in.
91 00:12:16.750 ⇒ 00:12:20.829 Shivani Amar: Does that resonate with you? That this will be a standard step in the divisional audit?
92 00:12:21.540 ⇒ 00:12:22.190 Uttam Kumaran: Yeah?
93 00:12:22.190 ⇒ 00:12:25.469 Shivani Amar: Okay. Does it resonate with you that we would want to revisit?
94 00:12:26.390 ⇒ 00:12:28.080 Uttam Kumaran: For the… yes.
95 00:12:28.080 ⇒ 00:12:30.670 Shivani Amar: Okay. Then…
96 00:12:31.380 ⇒ 00:12:41.299 Shivani Amar: what we did with Jasmine was we then go back to Julie and Lauren and the people that we talked to, and we say, like, okay, you’ve now put this data in here.
97 00:12:42.270 ⇒ 00:12:48.270 Shivani Amar: Talk us through it. Anything… and then we just, like, take some other notes, and like, hey, like, are there,
98 00:12:48.540 ⇒ 00:12:52.460 Shivani Amar: Are there, like, other things around,
99 00:12:52.570 ⇒ 00:13:02.859 Shivani Amar: you know, like, opportunity for error, like, Jasmine was just taking other notes as we were talking to them, okay? To make sure that we, like, kind of understood, like, why they’re pulling this report and internalizing that.
100 00:13:03.030 ⇒ 00:13:08.200 Shivani Amar: So, does that feel like a good step in the process? That we, like, actually revisit this document?
101 00:13:08.200 ⇒ 00:13:08.920 Uttam Kumaran: Yes.
102 00:13:08.920 ⇒ 00:13:09.630 Shivani Amar: Okay.
103 00:13:09.770 ⇒ 00:13:14.390 Shivani Amar: And is it that just me and Jasmine, or Utham, would that be useful for you to join that as well?
104 00:13:14.390 ⇒ 00:13:18.059 Uttam Kumaran: Yeah, I think it depends on whoever’s gonna be, like, the primary, like.
105 00:13:18.230 ⇒ 00:13:22.160 Uttam Kumaran: analysts on that domain, Robert, but I… for me, on the…
106 00:13:22.330 ⇒ 00:13:30.159 Uttam Kumaran: for me, it’s like, from what I’m covering on the ingestion side, I just really need to know what are all the metrics and, like, the core sources, right?
107 00:13:30.720 ⇒ 00:13:31.330 Robert Tseng: Yeah.
108 00:13:32.590 ⇒ 00:13:35.329 Shivani Amar: And do you think Jasmine will do a good job of translating that?
109 00:13:35.800 ⇒ 00:13:36.760 Shivani Amar: to you.
110 00:13:38.370 ⇒ 00:13:54.430 Uttam Kumaran: Yeah, I think that’s where we have to kind of partner, because on my side, I have to kind of hold understanding of, like, what’s the source, when are we going to get that data, and then she’s kind of holding, like, okay, what is the business questions we’re trying to ask? So I think one more of these where we kind of review would be great. I don’t think I need to be on, like.
111 00:13:54.590 ⇒ 00:13:59.579 Uttam Kumaran: some of the earlier discoveries, but this is where a handoff comes to ingestion and modeling.
112 00:13:59.850 ⇒ 00:14:03.149 Uttam Kumaran: So, like, I think at this point, this is a good place.
113 00:14:03.300 ⇒ 00:14:09.620 Shivani Amar: Okay. So, Brain Forge, this is, like, Jasmine and Robert.
114 00:14:09.800 ⇒ 00:14:12.139 Shivani Amar: And then this is Jasmine and Utam.
115 00:14:13.560 ⇒ 00:14:14.550 Shivani Amar: Okay.
116 00:14:15.510 ⇒ 00:14:17.239 Shivani Amar: That makes sense to me.
117 00:14:17.240 ⇒ 00:14:17.890 Uttam Kumaran: Yeah.
118 00:14:17.890 ⇒ 00:14:18.860 Shivani Amar: Okay?
119 00:14:19.550 ⇒ 00:14:35.850 Shivani Amar: Then, there’s a readout, and I, like… like, I’m trying to, like, make this process really crisp, and I don’t think I’ve landed the plane on the readout for… for Brian for supply chain, because we also even haven’t done… like, we haven’t done all of these follow-up calls with everybody.
120 00:14:35.850 ⇒ 00:14:36.170 Uttam Kumaran: Yeah.
121 00:14:36.170 ⇒ 00:14:37.829 Shivani Amar: I’ve only done it with a couple people.
122 00:14:38.170 ⇒ 00:14:40.260 Shivani Amar: So, I’m like.
123 00:14:40.840 ⇒ 00:14:50.429 Shivani Amar: I was, like, trying to do an exact summary for him, because I was like, oh, we did a sprint, I want him to know, like, at least an interim update of, like, what I found, and so,
124 00:14:50.730 ⇒ 00:14:57.769 Shivani Amar: my sense is when I asked for that, let’s look at docs for a second,
125 00:14:58.560 ⇒ 00:15:03.900 Shivani Amar: This is owned by anyone? Maybe owned by me? Let’s see…
126 00:15:08.920 ⇒ 00:15:09.750 Shivani Amar: like…
127 00:15:11.220 ⇒ 00:15:28.099 Shivani Amar: There’s no single source of truth for inventory, manual processes limit scalability and auditability. Like, I was just trying to give them, like, a high-level thing of what we learned, and that’s kind of what I’d asked for, but then what you guys gave… I don’t… who wrote the original thing that you guys sent me, Robert?
128 00:15:29.680 ⇒ 00:15:32.750 Robert Tseng: I mean, I… I signed off on it, so I wrote it.
129 00:15:32.750 ⇒ 00:15:36.850 Shivani Amar: Okay, thank you. And so…
130 00:15:36.900 ⇒ 00:15:55.359 Shivani Amar: like, when I was looking at this, I was like, this is really long, and then we’re, like, talking about every meeting, and, like, is that needed, versus just, like, if this theoretically were to be… like, I now understand what Phil said when he’s, like, I’m drowning in Brainforge’s memos, because I’m like, it’s like, wait, like, I don’t even know…
131 00:15:55.580 ⇒ 00:16:01.820 Shivani Amar: how to read this. So many pages. Like, that’s just my high-level takeaway. It’s like 12 pages, and I’m like.
132 00:16:01.820 ⇒ 00:16:20.209 Shivani Amar: give me a synthesis of, like, where we are, and then it’s suddenly, like, a 12-page thing. And there’s one that says TLDR, so is that all I need to read? And it’s, like, it’s not… this is… I can’t… like, I’m gonna be, like, straight up that I can’t even, like, look at this and digest it properly. So, like, added some notes, but really, like, this doesn’t work for me.
133 00:16:20.230 ⇒ 00:16:21.110 Shivani Amar: in…
134 00:16:21.110 ⇒ 00:16:29.289 Robert Tseng: Yeah, I mean, in my original Slack message, I said the first two pages were for you, the rest was… I mean, it’s like a… there were different chunks for different people on the team, so…
135 00:16:29.290 ⇒ 00:16:32.050 Uttam Kumaran: Yeah, like, I need some of this depth.
136 00:16:32.600 ⇒ 00:16:41.490 Uttam Kumaran: For our engineering side, so I think we should just agree, like, because I… I would like to be able to read all this, so we can go make sure we’re getting all the data.
137 00:16:41.490 ⇒ 00:16:42.170 Shivani Amar: Okay.
138 00:16:42.170 ⇒ 00:16:42.930 Uttam Kumaran: So…
139 00:16:43.310 ⇒ 00:16:50.110 Uttam Kumaran: like, so, but then I agree, like, I think we need to have something that is that BP handout that’s definitely not this. This is, like.
140 00:16:50.340 ⇒ 00:16:52.230 Uttam Kumaran: Feels more internal on our team.
141 00:16:52.230 ⇒ 00:16:52.889 Shivani Amar: Yeah, so…
142 00:16:52.890 ⇒ 00:16:56.509 Uttam Kumaran: Or, like, internal on, like, this, you know, our Brainforge element team.
143 00:16:56.510 ⇒ 00:16:57.280 Shivani Amar: Yeah.
144 00:16:57.700 ⇒ 00:16:59.010 Shivani Amar: So…
145 00:16:59.360 ⇒ 00:17:15.949 Uttam Kumaran: Because I don’t want to… because if on our side, right, as engineering builds the ingestion and models, I’m like, we’re just going to refer to this as the requirements for all of our modeling, right? Yeah. And then that way, I don’t have to… I’m not going to go back to Jasmine or back to Element stakeholders, like, I’m building off of this.
146 00:17:15.950 ⇒ 00:17:31.019 Shivani Amar: But, like, the thing is, it’s like, let’s say this was your draft for the VP of Supply Chain, like, next step saying review notes with Jasmine, Garrett, and with Vid, he doesn’t know who any of those people are. That’s not a next step that he cares about in any way, shape, or form.
147 00:17:31.300 ⇒ 00:17:33.180 Shivani Amar: At all, right?
148 00:17:33.420 ⇒ 00:17:50.260 Shivani Amar: Like, at all. So, like, this feels so internal to me, and it misses the mark on, like, like, do you see the difference between what I’m trying to do here, which is, like, look, we found that there’s no single source of truth of inventory. Maybe that’s too obvious and he already knows that, but it’s like.
149 00:17:50.300 ⇒ 00:18:04.689 Shivani Amar: Or we just say, like, multiple inventory views exist at the co-man, whatever, often with conflicting values and timing. Manual, like, I can edit this myself, but, like, you see how the flavor of these documents are, like, completely different? It’s like, what did we actually learn?
150 00:18:04.860 ⇒ 00:18:07.259 Shivani Amar: Versus, like, very tactically…
151 00:18:08.130 ⇒ 00:18:14.199 Shivani Amar: where did the other one go? Very… I don’t even know where, I might have just closed it, I don’t know. Here. Like…
152 00:18:15.170 ⇒ 00:18:18.750 Shivani Amar: Approve PM phases and tickets, Garrett, and the
153 00:18:19.300 ⇒ 00:18:23.480 Shivani Amar: Like, that is not for anybody outside of you guys.
154 00:18:25.220 ⇒ 00:18:27.030 Shivani Amar: That’s not even for me.
155 00:18:31.450 ⇒ 00:18:37.849 Shivani Amar: So, like, I’m still stuck on, like, I need to be such a filter for the communication, but this is actually, like.
156 00:18:38.000 ⇒ 00:18:49.270 Shivani Amar: This feels like it’s, like, wastes my brain space a little bit. It’s like, I’m like, I don’t… what am I supposed to glean from this that’s useful to me? And that’s not a good client relationship to have.
157 00:18:55.110 ⇒ 00:18:58.249 Shivani Amar: Confirm pre-scope work and convert it into tickets.
158 00:18:58.420 ⇒ 00:19:01.839 Shivani Amar: I’m like, How is that useful for my brain?
159 00:19:02.340 ⇒ 00:19:05.249 Shivani Amar: Versus, what did we learn from the calls that we had?
160 00:19:11.270 ⇒ 00:19:11.790 Robert Tseng: sec.
161 00:19:11.790 ⇒ 00:19:20.019 Shivani Amar: We did discovery calls, yes, fine. We did some discovery calls. What did we learn? What are we concerned about? What do we think next steps could be?
162 00:19:25.180 ⇒ 00:19:26.110 Uttam Kumaran: Yeah.
163 00:19:26.280 ⇒ 00:19:30.239 Shivani Amar: I fundamentally need this to change, and so I’m like, this is, like.
164 00:19:30.390 ⇒ 00:19:41.959 Shivani Amar: Big, big-time feedback. Because, like, this document really doesn’t cut it for me. Even if we say, oh, it was only the first two pages for you, the first two pages don’t work for me. The first page doesn’t work for me.
165 00:19:44.780 ⇒ 00:19:51.000 Uttam Kumaran: But I think we should just… in that… in that sort of flow doc, Shivani, that you have, we should just outline the outputs.
166 00:19:51.610 ⇒ 00:19:56.899 Uttam Kumaran: And… because I do need something that’s, like, of this depth for the ingestion side.
167 00:19:56.900 ⇒ 00:19:58.500 Shivani Amar: If it’s what you guys need.
168 00:19:58.500 ⇒ 00:20:00.040 Uttam Kumaran: I totally hear you on…
169 00:20:00.230 ⇒ 00:20:05.299 Uttam Kumaran: On your side, and something that you can shape and send outward, we should split this.
170 00:20:05.550 ⇒ 00:20:09.859 Shivani Amar: And, like, this is, like, okay, like, this is helpful for me to get into your brain of, like.
171 00:20:10.800 ⇒ 00:20:18.190 Shivani Amar: some of the questions you have that are coming out of the discovery. Now we’re getting into something useful that, like, I would actually want to discuss this with you guys.
172 00:20:18.320 ⇒ 00:20:31.260 Shivani Amar: Like, being like, okay, like, Shivani, we have, like, 5 open questions, like, this is how we’re thinking about it, do these align with your open questions? That’s the way to, like, synthesize good discovery, and make sure that we’re, like, all aligned on the next steps.
173 00:20:32.140 ⇒ 00:20:33.230 Shivani Amar: And so.
174 00:20:33.230 ⇒ 00:20:39.340 Robert Tseng: Yeah, I think… I think that’s why this was in the first two pages. I think you sent over what the executive summary draft should be. We didn’t go and build another deck.
175 00:20:39.340 ⇒ 00:21:03.310 Shivani Amar: I actually think it’s a very defensive way of framing it. Like, I actually… like, I don’t actually hear you accepting the feedback fully on, like, that first page being… I hear Utham doing it, Robert, but I don’t hear you saying, like, that first page was garbage for you, I’m sorry. Like, like, this is, like, a starting point, and I’m glad it’s on the second page, but, like, if I’m thinking about what it means to have a good landing.
176 00:21:03.310 ⇒ 00:21:11.459 Shivani Amar: after doing discovery, let’s talk about what that looks like, which is like, hey, a week after doing some of these discovery calls, we should say, what are our open questions?
177 00:21:11.590 ⇒ 00:21:13.769 Shivani Amar: Let’s codify the process on it.
178 00:21:16.240 ⇒ 00:21:24.399 Shivani Amar: Like, just saying, yeah, that’s why it’s on the second page, to me doesn’t, like, mean that you’re, like, listening to the feedback that the first page was not good enough.
179 00:21:26.150 ⇒ 00:21:26.900 Robert Tseng: Okay.
180 00:21:29.500 ⇒ 00:21:46.350 Shivani Amar: guys, I’m, like, I’m still in the zone of, like, questioning $92,000 a month, if this is the type of output. I’m being very honest. And I’m not trying to just create stress or, like, a bad vibe, but I’m like, this just… I need to have the honest discussion that I’m like, I don’t… the comms aren’t fully working for me.
181 00:21:48.190 ⇒ 00:21:50.570 Uttam Kumaran: Yeah, so I think we have to just rethink this
182 00:21:50.820 ⇒ 00:22:00.389 Uttam Kumaran: as part of that entire process, what is the element-facing output doc, and then what is the… what is our internal docs that we need to do? Because we still need to document
183 00:22:00.620 ⇒ 00:22:04.679 Uttam Kumaran: But we need another version of the… of the first two pages that…
184 00:22:04.980 ⇒ 00:22:09.480 Uttam Kumaran: Basically, are close enough to being able to circulate internally.
185 00:22:20.520 ⇒ 00:22:32.200 Shivani Amar: like, these are good questions, and this makes sense, that we’re like, hey, which supply chain objects move from current systems into NetSuite first versus Snowflake first? That’s, like, a big question, right? And so I’m like.
186 00:22:32.530 ⇒ 00:22:35.960 Shivani Amar: This feels like a helpful output, but then…
187 00:22:36.090 ⇒ 00:22:49.829 Shivani Amar: I’m like, who is the audience for this? Is it, hey, Shivani, we need to talk about this? Is this, hey, these are the questions we want Brian, the VP, to have visibility into, that we’re trying to figure this out? Like, we need to think about the audience.
188 00:22:50.710 ⇒ 00:22:53.679 Shivani Amar: And to me, my understanding is that, like.
189 00:22:53.840 ⇒ 00:23:08.200 Shivani Amar: I need to rework this a little bit, it’s not done, but, like, maybe I need to write this, because I don’t trust the… like, I… which is fine, maybe, but I’m just, like, I didn’t get… I didn’t get any version of this, and it’s like…
190 00:23:08.930 ⇒ 00:23:12.319 Shivani Amar: I don’t even know if my next steps were right here. Like, we’re gonna stand…
191 00:23:12.320 ⇒ 00:23:20.960 Uttam Kumaran: I think we should provide you with, like, our takeaway, and then I think you should write, like, okay, based on that, here’s where we are.
192 00:23:20.960 ⇒ 00:23:21.650 Shivani Amar: Yeah.
193 00:23:23.160 ⇒ 00:23:27.549 Uttam Kumaran: Based on who the audience is or the business, but we should write one that goes just to you.
194 00:23:46.290 ⇒ 00:23:48.290 Shivani Amar: And then, yes, then there’s all of this.
195 00:23:48.290 ⇒ 00:23:49.699 Uttam Kumaran: Then there’s the assist, yeah.
196 00:23:49.700 ⇒ 00:23:54.829 Shivani Amar: Because I, like, didn’t under… I was like, I don’t really understand what part of this is even for Brian.
197 00:23:55.410 ⇒ 00:24:09.760 Shivani Amar: And so, like, the deliverable on a VP-level synthesis of the discovery calls, I felt like that didn’t even, like, come to fruition. I typed something in, and Jasmine’s kind of, like, adding comments, but, like, it’s like, okay, we didn’t land the plane here, but my question is.
198 00:24:09.930 ⇒ 00:24:11.940 Shivani Amar: Before we land the plane on that.
199 00:24:12.180 ⇒ 00:24:21.040 Shivani Amar: is that even the right order of operations? At what point should I send the VP an update, saying, hey, hey, I’ve been having conversations with all of your teammates, here are some lightweight notes that I have.
200 00:24:21.460 ⇒ 00:24:27.749 Shivani Amar: Or is it like, no, don’t send them anything until we’ve, like, gone back to every stakeholder that’s given us
201 00:24:27.980 ⇒ 00:24:29.230 Shivani Amar: the report.
202 00:24:29.360 ⇒ 00:24:35.899 Shivani Amar: Really synthesized all of the open questions and next steps, and then we give them, like, something really well produced.
203 00:24:36.010 ⇒ 00:24:43.820 Shivani Amar: Or do we feel like an interim update? And, like, this is also an element culture question, I don’t know if Brian’s even thinking about it. I don’t know if Brian is seeking an update.
204 00:24:43.930 ⇒ 00:24:49.480 Shivani Amar: But if I were a VP, and I had somebody talking to my teammates, I’d be kind of curious, like, what did you learn talking to my teammates?
205 00:24:49.480 ⇒ 00:24:50.720 Uttam Kumaran: It’s a good paper trail, also.
206 00:24:50.720 ⇒ 00:25:03.609 Shivani Amar: Yeah, so I’m like, maybe it’s a short thing that’s like, hey, these are some themes that we’re doing, these are some next steps, we’re trying to, like, we’re gonna go back to the rest of your team and try to understand all the manual reports that they, like, are pulling and why.
207 00:25:04.110 ⇒ 00:25:05.830 Shivani Amar: We’ve done that with Lauren.
208 00:25:06.010 ⇒ 00:25:12.709 Shivani Amar: and Julie, but we haven’t done it with the sparkling side of the business, or the 3PL side of the business.
209 00:25:12.970 ⇒ 00:25:15.409 Shivani Amar: And then,
210 00:25:15.690 ⇒ 00:25:27.459 Shivani Amar: by the way, like, we’re also trying to figure out, like, how… the best ingestion pathway for all of this data, and if it will be… what of the data will flow through NetSuite first versus Snowflake first.
211 00:25:27.770 ⇒ 00:25:28.300 Uttam Kumaran: Yeah.
212 00:25:28.300 ⇒ 00:25:30.609 Shivani Amar: Right? And so, like, that’s like a…
213 00:25:30.820 ⇒ 00:25:39.120 Shivani Amar: couple paragraphs that maybe I could send, or, like, some bullet points, and, like, I can draft that and run it by you guys today, but I just want to educate you that that’s kind of the ask.
214 00:25:39.520 ⇒ 00:25:40.550 Shivani Amar: like…
215 00:25:40.680 ⇒ 00:25:56.989 Shivani Amar: I’m gonna draft it and try to role model it, but my hope is that you actually internalize what I send and say, like, the next time we’re doing VP comms, let me learn from what this draft was, so that, like, what I… what I draft in the future for Shivani is more aligned with, like, what are… what are we learning?
216 00:25:57.580 ⇒ 00:25:58.319 Uttam Kumaran: Yeah, I think there’s.
217 00:25:58.320 ⇒ 00:26:05.169 Shivani Amar: High-level steps, not build the tickets, Garrett’s gonna build the tickets, but, like, we’re trying to figure out how to ingest this data next.
218 00:26:05.360 ⇒ 00:26:13.310 Uttam Kumaran: Yeah, I think there’s, like, 3 docs. There’s something that’s, like, ongoing that’s, like, the build dock. It’s kind of like the product requirements doc for, like.
219 00:26:13.430 ⇒ 00:26:28.530 Uttam Kumaran: this whole domain. That is, like, what ingestion and modeling team need as, like, an output from this group. There’s also the, like, Brainforge, like, business-facing synthesis of, like, what we generally, like, what are the themes that we found?
220 00:26:28.690 ⇒ 00:26:45.029 Uttam Kumaran: that, I think, Shivani, if you see that and you’re like, okay, some of these we should just dig deeper, or… okay, generally, like, we’re kind of, like, further enough, then I think that is what turns into the VP facing. Like, you take that and either, like, synthesize that one layer up.
221 00:26:45.030 ⇒ 00:26:45.490 Shivani Amar: Yeah.
222 00:26:45.490 ⇒ 00:26:48.659 Uttam Kumaran: And then if we read that, and we’re like, okay, there’s still gaps.
223 00:26:48.800 ⇒ 00:26:57.260 Uttam Kumaran: Right? Like, on e-commerce, when, like, we started going through, or, like, let’s say wholesale, by the time we did a few of those calls, we’re like, we generally have, like, a full picture.
224 00:26:57.260 ⇒ 00:26:57.900 Shivani Amar: Yeah.
225 00:26:58.330 ⇒ 00:27:13.330 Uttam Kumaran: then I think it’s… you’re able to go one step ahead, so I think that is kind of, like, what we should have done in this moment, which is, like, put together our synthesis, and then it’s like, okay, we’re ready to share, or we probably need a couple more turns of the wheel. Both of those basically become living docs.
226 00:27:13.630 ⇒ 00:27:16.919 Shivani Amar: And this is where, like, I’m in these discovery calls as well.
227 00:27:17.080 ⇒ 00:27:27.020 Shivani Amar: But I want to know what you guys are learning from this. Yeah. Because you guys have done this before, and you guys have ingested supply chain data, probably, and you guys have, yeah.
228 00:27:27.570 ⇒ 00:27:29.530 Shivani Amar: Dealt with manual reports.
229 00:27:29.720 ⇒ 00:27:40.159 Shivani Amar: and either you think there’s an automated way of doing this, like, I… like, all these manual reports, I’m still sitting here with this question, like, what do you guys typically do with companies that get all these manual reports from…
230 00:27:40.450 ⇒ 00:27:41.920 Uttam Kumaran: Yeah. Yeah.
231 00:27:41.920 ⇒ 00:27:48.059 Shivani Amar: You get… do you just… have a… A script that puts the…
232 00:27:48.220 ⇒ 00:27:51.370 Uttam Kumaran: So I guess, but my question for that is, like, that reads more of, like.
233 00:27:52.050 ⇒ 00:27:59.400 Uttam Kumaran: the theme there would be, like, heavy and manual reports. Like, we need to consider a technical solution there, but.
234 00:27:59.400 ⇒ 00:27:59.720 Shivani Amar: Yeah.
235 00:27:59.890 ⇒ 00:28:02.629 Uttam Kumaran: comfortable with that. Like, this isn’t out of the realm of this.
236 00:28:02.630 ⇒ 00:28:09.480 Shivani Amar: But I don’t even know what you’re comfortable with. So it’s like, I feel like what I’ve been hungry… I’m not even talking about the VP summary anymore.
237 00:28:09.480 ⇒ 00:28:10.150 Uttam Kumaran: Yeah, yeah.
238 00:28:10.150 ⇒ 00:28:23.710 Shivani Amar: I’ve been hungry for a conversation around, like, so what’d you guys feel about the supply chain discovery calls? And, like, what, like, what… what are the things that we need to do to get this data in? And, like, tactically, like, what’s our path?
239 00:28:23.710 ⇒ 00:28:24.690 Uttam Kumaran: Right? Yeah.
240 00:28:24.690 ⇒ 00:28:26.009 Shivani Amar: And.
241 00:28:30.190 ⇒ 00:28:35.060 Uttam Kumaran: It’s, it’s, it’s buried in that large document, but there needs to be a high level of, like.
242 00:28:35.390 ⇒ 00:28:44.720 Uttam Kumaran: On the ingestion side, on the modeling side, and generally, this lines up with what we’ve seen. Or actually, this is very, very bespoke and custom, and that here are some risks.
243 00:28:44.720 ⇒ 00:28:45.370 Shivani Amar: Yeah.
244 00:28:45.520 ⇒ 00:28:48.159 Uttam Kumaran: If you want to drill down, we drill down.
245 00:28:48.160 ⇒ 00:28:48.530 Shivani Amar: Yeah.
246 00:28:48.530 ⇒ 00:28:51.139 Uttam Kumaran: And, like, that’s, I think, more of, like, yeah.
247 00:28:51.390 ⇒ 00:28:58.519 Shivani Amar: And at what point should you and I have that touchpoint with them? Like, because it sounds like it’s an ingestion. How do we ingest? It’s not a Jasmine question.
248 00:28:58.520 ⇒ 00:28:59.429 Uttam Kumaran: Sure, sure.
249 00:28:59.430 ⇒ 00:29:02.669 Shivani Amar: It’s a you question. Yeah. And, and like…
250 00:29:03.080 ⇒ 00:29:16.499 Shivani Amar: the how do we ingest it? It’s like, well, the first question being, should it go to NetSuite before it goes to whatever? But, like, the way that they’re thinking, the finance team is thinking about that, is that supply chain teammates are going to type stuff into NetSuite. That’s not the ideal.
251 00:29:16.500 ⇒ 00:29:17.239 Uttam Kumaran: Yeah, yeah.
252 00:29:17.240 ⇒ 00:29:22.119 Shivani Amar: So if you have a way to be like, no, we can get that data into Snowflake and pump it back into NetSuite.
253 00:29:22.120 ⇒ 00:29:22.580 Uttam Kumaran: Yes.
254 00:29:22.580 ⇒ 00:29:26.760 Shivani Amar: That’s way better than people typing shit into NetSuite.
255 00:29:26.760 ⇒ 00:29:27.870 Uttam Kumaran: Yeah, yeah.
256 00:29:27.870 ⇒ 00:29:35.269 Shivani Amar: So, I’m like… like, that’s, like, I think the topic that you have around, like, data, whatever, like.
257 00:29:35.270 ⇒ 00:29:35.710 Uttam Kumaran: Yes.
258 00:29:35.710 ⇒ 00:29:39.370 Shivani Amar: is fine for Wednesday, but I think the main.
259 00:29:39.370 ⇒ 00:29:39.880 Uttam Kumaran: I hear you.
260 00:29:39.880 ⇒ 00:29:41.779 Shivani Amar: that I want to go through is…
261 00:29:42.480 ⇒ 00:29:51.139 Shivani Amar: Is, how are we investing supply chain data the most effectively for, like, the 3-year version of what this business looks like, versus, like, just trying to get it done?
262 00:29:51.140 ⇒ 00:29:51.640 Uttam Kumaran: AI.
263 00:29:51.640 ⇒ 00:29:54.100 Shivani Amar: Like, what do we want to automate? And so…
264 00:29:54.100 ⇒ 00:29:54.680 Uttam Kumaran: Yeah.
265 00:29:55.200 ⇒ 00:30:10.650 Shivani Amar: Or… or it’s the next tech session, I don’t know, because we’re going to be talking to Atomic this week, and they’re going to be doing supply forecasting for us, and they claim to have, like, a 3PL, or sorry, not a 3PL, they claim to have, like, an ETL, right, that, like, can, like, get warehouse data easily.
266 00:30:10.650 ⇒ 00:30:25.310 Shivani Amar: So I’m like, maybe instead of doing Polyatomic and us modeling it in dbt, Atomic’s gonna do it, and then we’ll take their data and put it into Snowflake, right? So, that’s… this is all kind of open, like, the ERP, Atomic, our project, all to get supply chain data.
267 00:30:25.310 ⇒ 00:30:25.880 Uttam Kumaran: Yes.
268 00:30:25.880 ⇒ 00:30:36.970 Shivani Amar: And so, I’m, like, working through that with folks, but yeah, I just feel like I can synthesize that a little bit for Brian, and just be like, hey, like, we have a few different projects running in parallel.
269 00:30:36.970 ⇒ 00:30:37.400 Uttam Kumaran: Yeah.
270 00:30:37.400 ⇒ 00:30:43.350 Shivani Amar: trying to figure out the best ingestion pathways, but, like, that’s, like, the discussion I want to have with the leadership on the team.
271 00:30:43.630 ⇒ 00:30:55.029 Uttam Kumaran: Yeah, it needs to be even further, more derivative, like, yeah. So there’s, like, I think there’s a document in the middle here that we have to produce that’s, like, perspective from me on the modeling ingestion side.
272 00:30:55.030 ⇒ 00:31:06.129 Uttam Kumaran: from Robert on the more analysis strategy side, like, we can answer these questions, we’ve learned all the reporting, and then it’s like a… we’re ready to… this is what we think you should…
273 00:31:06.150 ⇒ 00:31:07.859 Uttam Kumaran: report up to the VP.
274 00:31:07.860 ⇒ 00:31:08.190 Shivani Amar: Yeah.
275 00:31:08.190 ⇒ 00:31:14.120 Uttam Kumaran: In terms of status, and if we’re not happy with that, then we do… we go back, you know, we keep iterating on that doc.
276 00:31:14.780 ⇒ 00:31:15.580 Shivani Amar: Okay.
277 00:31:15.960 ⇒ 00:31:22.599 Shivani Amar: Cool. And then, for you guys, so, like, if you’re gonna be on these calls with the follow-up with the…
278 00:31:22.600 ⇒ 00:31:23.180 Uttam Kumaran: Yeah.
279 00:31:23.180 ⇒ 00:31:37.379 Shivani Amar: with the supply chain team. Should I go ahead and get those scheduled with the other people? Like, have you already clicked into some of the reports? If you have questions… because now we’re getting into what are these reports, what are these metrics, how do we ingest them kind of territory.
280 00:31:37.380 ⇒ 00:31:47.279 Uttam Kumaran: Yeah, I have seen the list of reports, I haven’t clicked into them, but my… again, my pattern matching is, like, okay, there’s, like, custom Excel stuff, I’m less about,
281 00:31:47.680 ⇒ 00:31:54.010 Uttam Kumaran: I’m less like, what’s in the report? Like, how are they getting it? It’s through UI? Okay, let me go look through the APIs, and things like that.
282 00:31:54.010 ⇒ 00:31:54.540 Shivani Amar: Yeah.
283 00:31:54.540 ⇒ 00:32:11.359 Uttam Kumaran: Versus, like, okay, this report has this ingestion method. I’m like, am I seeing anything, like, out of bounds that we can’t handle, roughly? And so that’s what I’ll put into that doc. And so then maybe, Robert, we try to, like, ship a second draft of this, like, Shivani-facing synthesis.
284 00:32:11.760 ⇒ 00:32:15.010 Uttam Kumaran: And then that’s what we… we have a conversation review.
285 00:32:15.440 ⇒ 00:32:16.040 Shivani Amar: Okay.
286 00:32:16.040 ⇒ 00:32:17.000 Robert Tseng: Yeah, let’s do that.
287 00:32:17.000 ⇒ 00:32:23.389 Shivani Amar: But then the follow-up conversations with, like, Kelton and Hannah and people all scheduled with you and Jasmine, right, with them?
288 00:32:23.760 ⇒ 00:32:24.450 Uttam Kumaran: Yes.
289 00:32:24.450 ⇒ 00:32:30.930 Shivani Amar: Okay, and then, my last piece of minor feedback is that
290 00:32:31.250 ⇒ 00:32:35.520 Shivani Amar: Jasmine is lovely, and I think she’s, like, understanding where we’re trying to get to.
291 00:32:35.850 ⇒ 00:32:41.890 Shivani Amar: I think there are times that, like, I’m like, okay, she’s gonna leave this call, and then I immediately have to take the call over.
292 00:32:42.760 ⇒ 00:32:46.509 Shivani Amar: Because she starts with a very nebulous question, and I see blank stares.
293 00:32:46.690 ⇒ 00:32:47.500 Shivani Amar: And…
294 00:32:48.930 ⇒ 00:32:56.319 Shivani Amar: So, that’s my feedback, and I can talk to her about it myself, but I just want you to, like, keep an eye out for that.
295 00:32:56.320 ⇒ 00:32:57.110 Uttam Kumaran: Yeah, yeah.
296 00:32:57.340 ⇒ 00:33:02.289 Shivani Amar: That she gets a little excited about, like, one of the principles that I’ve been, like, holding…
297 00:33:02.360 ⇒ 00:33:11.910 Shivani Amar: is that I want to just make sure that the what is correct. What is happening in the business? Are the numbers flowing? Like, before we roll anything out to people, let’s just make sure we know the what.
298 00:33:11.970 ⇒ 00:33:24.620 Shivani Amar: I think Jasmine is kind of eager to be like, let’s analyze it, because she’s an analyst, right? So she’s gonna be like, let’s analyze, like, the efficacy of sampling on future revenue.
299 00:33:24.970 ⇒ 00:33:28.779 Shivani Amar: And I’m like, I just want to see if you can get the sampling data to be accurate.
300 00:33:28.780 ⇒ 00:33:29.749 Uttam Kumaran: Yeah, yeah, yeah.
301 00:33:29.750 ⇒ 00:33:30.479 Shivani Amar: That’s what I meant.
302 00:33:30.480 ⇒ 00:33:34.829 Uttam Kumaran: The same thing was a good example that we all discussed last week, which was, like.
303 00:33:35.390 ⇒ 00:33:41.229 Uttam Kumaran: It’s really rich, but also, the goal here is just get, like, a line graph of, like, samples by month.
304 00:33:41.230 ⇒ 00:33:41.820 Shivani Amar: Yeah.
305 00:33:41.820 ⇒ 00:33:43.290 Uttam Kumaran: Can we just arrive at that point?
306 00:33:43.290 ⇒ 00:33:50.619 Shivani Amar: I sent an example earlier today, somebody goes, like, how much… how many 30-count of pink lemonade have been sold so far? What are our weeks of stock?
307 00:33:50.750 ⇒ 00:33:53.469 Shivani Amar: Like, so much of this people digging up the what.
308 00:33:53.700 ⇒ 00:33:54.440 Uttam Kumaran: Yes.
309 00:33:54.660 ⇒ 00:33:57.990 Shivani Amar: And, like, if we can serve up the what faster.
310 00:33:57.990 ⇒ 00:33:58.470 Uttam Kumaran: Yeah.
311 00:33:58.470 ⇒ 00:33:59.720 Shivani Amar: You can analyze it.
312 00:33:59.920 ⇒ 00:34:01.090 Uttam Kumaran: Yeah, yeah, yeah, yeah.
313 00:34:01.090 ⇒ 00:34:06.099 Shivani Amar: The inaccurate version of the what is, like, my mantra for, like, the next.
314 00:34:06.100 ⇒ 00:34:06.630 Uttam Kumaran: Yeah.
315 00:34:06.630 ⇒ 00:34:12.190 Shivani Amar: Basically. Like, next 3 months. It’s like, let’s just, like, really nail the what.
316 00:34:12.920 ⇒ 00:34:13.380 Uttam Kumaran: Yeah.
317 00:34:13.389 ⇒ 00:34:24.709 Shivani Amar: Okay? And so, like, that’s, like, a theme I can share with the team, but I’m like, if Omni can get people to the what really quickly, we will have succeeded in our mission to get the what defined for, like, all these
318 00:34:24.839 ⇒ 00:34:26.609 Shivani Amar: Essential parts of the business.
319 00:34:27.000 ⇒ 00:34:28.149 Uttam Kumaran: Yeah, yeah, okay, okay.
320 00:34:28.150 ⇒ 00:34:29.020 Shivani Amar: Okay?
321 00:34:29.020 ⇒ 00:34:29.550 Uttam Kumaran: Yeah.
322 00:34:29.550 ⇒ 00:34:35.290 Shivani Amar: Okay, that’s my feedback. The last thing was just, you talked about bringing Greg back in. Do you want to share.
323 00:34:35.290 ⇒ 00:34:35.750 Uttam Kumaran: Yes.
324 00:34:35.750 ⇒ 00:34:37.689 Shivani Amar: What he’s gonna be doing.
325 00:34:37.699 ⇒ 00:34:39.539 Uttam Kumaran: Yeah, Robert, I can let you take that.
326 00:34:40.670 ⇒ 00:34:50.380 Robert Tseng: Yeah, so, I mean, I think… this is kind of… I would also like to get some feedback for you on, like, why you felt like there it didn’t add value, but I think,
327 00:34:50.510 ⇒ 00:35:00.190 Robert Tseng: the role that he’s playing here, I mean, one, kind of get… making sure that the what is very clear. So, accompanying… well, on the calls when I was with Jasmine, I felt like we…
328 00:35:00.300 ⇒ 00:35:17.249 Robert Tseng: I think we handled those well. I understand what you mean, that she tends to, like, really zero in on something that you may not really kind of be able to get to right away, or it’s definitely somebody with no contacts who’s never talked to her before, kind of a hard time connecting. I think Greg is kind of going to be somebody who will help
329 00:35:17.280 ⇒ 00:35:29.780 Robert Tseng: like, start with the what, and, like, can really guide… guide those conversations. But then also, just from, like, an async comms perspective, like, not just, like, putting a bunch of noise in your inbox, I mean, your Slack channels of, like.
330 00:35:29.810 ⇒ 00:35:36.399 Robert Tseng: all these tickets. I mean, I think these… these rituals we want to keep to some extent, in terms of, like, getting you
331 00:35:36.410 ⇒ 00:35:53.829 Robert Tseng: previews before we jump on, like, our weekly meetings, also giving you updates that are more relevant to the tickets that you have. I think, like, maybe… I mean, our assumption is that you felt like a lot of that was just noise coming from Garrett, but, yeah, Gregory’s gonna take over… is gonna take over that part as well.
332 00:35:54.140 ⇒ 00:35:54.460 Shivani Amar: Yeah.
333 00:35:54.460 ⇒ 00:36:04.170 Uttam Kumaran: And maybe if I could just… if I just say one thing on the engineering side, like, the opposite of this is, like, sort of no noise, and so I always try to encourage our team to, like.
334 00:36:04.260 ⇒ 00:36:06.210 Uttam Kumaran: Just share, and… and…
335 00:36:06.230 ⇒ 00:36:23.340 Uttam Kumaran: one thing I can say is, like, not all of the updates will be, like, immediately relevant, but I want you to see, like, we’re executing tickets, and all of our stuff actually goes through an engineering flow. It’s… it’s… that’s more in tune for, like, daily updates. We’re not gonna go… I don’t want us to go all the way in depth on, like.
336 00:36:23.340 ⇒ 00:36:29.739 Uttam Kumaran: ticket by ticket on a Thursday call, but I want the team to sort of share that openly, but if you feel like it’s…
337 00:36:29.740 ⇒ 00:36:49.160 Uttam Kumaran: it’s sort of noise, then I’ll have them, you know, limit that. I don’t like… I didn’t like the long daily updates where there’s a lot of repeating. I think something daily where it’s like, here’s when we move forward, or, like, here’s a flag… Yeah. …that leads to a nice discussion, is like… is, like, what I’m encouraging the team to send.
338 00:36:49.860 ⇒ 00:36:52.780 Uttam Kumaran: On a… on, like, a… basically, like, a daily progress level.
339 00:36:53.300 ⇒ 00:36:58.550 Shivani Amar: Yeah, I think that feels aligned. There were, like, multiple days in a row where he was like.
340 00:36:58.720 ⇒ 00:37:04.220 Shivani Amar: Just got off… we just hopped off the phone with Muffin Data, and I feel like that sent.
341 00:37:04.220 ⇒ 00:37:05.699 Uttam Kumaran: Yeah, yeah, and I said…
342 00:37:05.700 ⇒ 00:37:11.620 Shivani Amar: multiple times, like, it was, like, multiple days in a row that says we just hopped off the phone with Muffin Data, and I was like.
343 00:37:12.070 ⇒ 00:37:26.289 Shivani Amar: why is he copying and pasting this sentence again when this happened, like, 3 days ago? So, it was like, I was just kind of… then I get to a place where I’m ignoring it, instead of actually reading it, which I think you probably intuited, which is why you changed things up.
344 00:37:26.500 ⇒ 00:37:29.200 Shivani Amar: But… but going forward, so Greg is gonna, like.
345 00:37:29.420 ⇒ 00:37:32.010 Shivani Amar: Pick up some of this, like, project management?
346 00:37:32.010 ⇒ 00:37:33.149 Uttam Kumaran: Right, so I won’t…
347 00:37:33.150 ⇒ 00:37:36.489 Shivani Amar: Omni account manager, like, he’s gonna…
348 00:37:36.490 ⇒ 00:37:36.850 Robert Tseng: Yeah.
349 00:37:36.850 ⇒ 00:37:37.749 Shivani Amar: Do a little bit, okay.
350 00:37:37.750 ⇒ 00:37:42.050 Uttam Kumaran: Yeah, so I kind of wanted to work in two ways. One, be, like, the daily pulse.
351 00:37:42.460 ⇒ 00:37:47.850 Uttam Kumaran: And be, like, sort of got… like, he’s good at basically coming to a call and saying, like, I’m gonna keep this call on track.
352 00:37:47.850 ⇒ 00:37:48.410 Shivani Amar: Yeah.
353 00:37:48.410 ⇒ 00:38:04.369 Uttam Kumaran: And then whoever the internal… whoever the subject matter expert is alongside of him, is sort of the execution. So, you know, I think that’s gonna be a good foil to, like, sort of the Jasmine-style tendency sometimes of, like, going too deep. So, like, that’s… that’s who he’s gonna be, and I think…
354 00:38:04.530 ⇒ 00:38:15.300 Uttam Kumaran: That’s gonna be great. So, on my side, I’m using him to say, hey, make sure tickets, and we’re laddering into the Thursday meeting in a strong way, and that we’re laddering into the month-long, the sprint objectives.
355 00:38:15.300 ⇒ 00:38:15.630 Shivani Amar: Yeah.
356 00:38:15.630 ⇒ 00:38:22.570 Uttam Kumaran: I can just, like, push on a daily basis on all of the various threads on ingestion and modeling. Yeah.
357 00:38:23.470 ⇒ 00:38:36.140 Uttam Kumaran: And then e-commerce is coming… is kind of coming next, so I think what we’re gonna do on our side, and Robert, we can talk about this, is, like, as we seem, like, for example, if we want to do another round around e-com, like, do we need more…
358 00:38:36.190 ⇒ 00:38:49.550 Uttam Kumaran: you know, support from our team on that, right? So I think that’s where we’re kind of working on, like, okay, Jasmine’s fully occupied, we still have stuff on supply chain, should we bring in more people to do that? And so Greg, Robert, and I will sort of make that decision.
359 00:38:49.550 ⇒ 00:38:50.470 Shivani Amar: Okay.
360 00:38:50.790 ⇒ 00:38:56.679 Uttam Kumaran: So you should see change. Again, I want to make sure that the updates are helpful, but I also don’t want there to be no noise.
361 00:38:57.190 ⇒ 00:39:02.640 Uttam Kumaran: Or, like, yeah, I don’t want us… I want us to kind of have some rhythm during the week, you know, mainly.
362 00:39:02.640 ⇒ 00:39:05.200 Shivani Amar: Yeah, I think it’s… and, like, I’d say…
363 00:39:05.760 ⇒ 00:39:15.179 Uttam Kumaran: It’s getting… I think it’s getting a lot better, like, we are sending things, but I think having just the motion of, like, send us something with just, here’s what we got done, because we are pushing every day on several things, you know?
364 00:39:15.180 ⇒ 00:39:25.639 Shivani Amar: Okay, that sounds good. And then… Sorry, what was I gonna… That sounds good. I…
365 00:39:25.990 ⇒ 00:39:28.549 Shivani Amar: I think Greg is a nice communicator.
366 00:39:28.880 ⇒ 00:39:33.709 Shivani Amar: But it’s helpful to know that it’s not just, like, omni product…
367 00:39:33.710 ⇒ 00:39:34.260 Uttam Kumaran: Yeah.
368 00:39:34.260 ⇒ 00:39:41.630 Shivani Amar: that it’s, like, expanded. And, like, if I have… if I’m having a day where I’m, like, I’m not hearing enough
369 00:39:41.750 ⇒ 00:39:43.150 Shivani Amar: Like, will he be the person.
370 00:39:43.150 ⇒ 00:39:43.630 Uttam Kumaran: Yes.
371 00:39:43.630 ⇒ 00:39:45.230 Shivani Amar: Yes. Okay.
372 00:39:45.230 ⇒ 00:39:46.000 Uttam Kumaran: Yes, yeah.
373 00:39:46.000 ⇒ 00:39:46.540 Shivani Amar: Beh.
374 00:39:46.700 ⇒ 00:39:49.290 Shivani Amar: Yeah. Next slide.
375 00:39:49.630 ⇒ 00:40:06.569 Uttam Kumaran: So yeah, I’m expecting him to come to meetings, drive towards the outcome of the meeting, but then pull… call the right… call on the right folks, and then basically own… own our four box, and, like, the fact that today we have clearly what we’re trying to accomplish this sprint, and, like, in every one of our meetings, we’re, like.
376 00:40:07.570 ⇒ 00:40:12.910 Uttam Kumaran: Driving back to that, and then that way, that responsibility is sort of off of all the individual folks that are
377 00:40:13.050 ⇒ 00:40:14.679 Uttam Kumaran: Just pushing things along.
378 00:40:14.680 ⇒ 00:40:17.400 Shivani Amar: Okay, that sounds good. And then,
379 00:40:18.740 ⇒ 00:40:22.450 Shivani Amar: Is there an e-commerce table, topic now in Omni?
380 00:40:22.700 ⇒ 00:40:27.790 Uttam Kumaran: Yeah, there is, but we don’t have the dashboards.
381 00:40:28.220 ⇒ 00:40:28.760 Uttam Kumaran: Ready.
382 00:40:28.760 ⇒ 00:40:32.170 Shivani Amar: There are no dashboards, but can I start chatting with Omni about e-commerce?
383 00:40:32.170 ⇒ 00:40:36.909 Uttam Kumaran: Yeah, let me, let me message, Advance and just, like, confirm.
384 00:40:37.240 ⇒ 00:40:37.890 Shivani Amar: Okay.
385 00:40:38.270 ⇒ 00:40:39.130 Shivani Amar: Because…
386 00:40:39.130 ⇒ 00:40:43.050 Robert Tseng: Yeah, they were queuing that on Friday last week, and I think it should be ready to go.
387 00:40:43.440 ⇒ 00:40:46.149 Uttam Kumaran: Yeah, we’re gonna… we’re pushing the Amazon stuff.
388 00:40:46.280 ⇒ 00:40:48.020 Uttam Kumaran: Today, and yeah, so…
389 00:40:48.020 ⇒ 00:40:49.819 Robert Tseng: Yeah, that’s what’s remaining.
390 00:40:50.030 ⇒ 00:40:51.110 Uttam Kumaran: Okay, cool.
391 00:40:53.410 ⇒ 00:40:59.680 Shivani Amar: Like… Let’s just go through this example, I’m just curious, like… Can it…
392 00:41:00.850 ⇒ 00:41:06.800 Shivani Amar: Okay, so it says, like, let’s look up e… I haven’t seen e-commerce orders and product performance before, so it.
393 00:41:06.800 ⇒ 00:41:09.240 Uttam Kumaran: Yeah, this must have been what you guys were QAing last week.
394 00:41:09.240 ⇒ 00:41:10.330 Shivani Amar: Okay, cool.
395 00:41:10.810 ⇒ 00:41:12.100 Shivani Amar: Okay, this is great.
396 00:41:12.260 ⇒ 00:41:20.369 Shivani Amar: Matching pink lemonade zero values, so it’s like, yeah, it’s,
397 00:41:23.860 ⇒ 00:41:42.249 Shivani Amar: Yeah. Okay, cool. And then the SKU, like, the last thing is, like, I know there’s this whole SKU map idea that would be beneficial, and then I, like, sent something that the supply chain team maintains. I think Hannah maintains it. And so when we do our call with Hannah, I want to be prepped to say, like, is that sufficient? Do we need other fields added to that? Like…
398 00:41:43.080 ⇒ 00:41:44.760 Shivani Amar: just understanding.
399 00:41:45.810 ⇒ 00:41:49.439 Shivani Amar: This is interesting how it’s trying to find pink lemonade.
400 00:41:59.800 ⇒ 00:42:00.660 Shivani Amar: Hmm.
401 00:42:02.530 ⇒ 00:42:06.979 Robert Tseng: I’m gonna screenshot the… Where it got stuck.
402 00:42:06.980 ⇒ 00:42:09.049 Uttam Kumaran: Yeah, it looks like it got the SKU.
403 00:42:09.540 ⇒ 00:42:11.440 Uttam Kumaran: And it got units sold.
404 00:42:12.150 ⇒ 00:42:13.429 Shivani Amar: So let’s see what they say.
405 00:42:13.430 ⇒ 00:42:16.849 Uttam Kumaran: Oh, they just got the, product type.
406 00:42:16.850 ⇒ 00:42:27.059 Shivani Amar: sis… Supply, demand, when they asked this question last week, they said Shopify was 85,524 units of 30 counts.
407 00:42:29.530 ⇒ 00:42:31.009 Shivani Amar: And this is 5,000.
408 00:42:31.310 ⇒ 00:42:33.679 Uttam Kumaran: Yeah, I don’t know what source this is.
409 00:42:46.300 ⇒ 00:42:48.130 Shivani Amar: So this is Shopify only.
410 00:42:48.330 ⇒ 00:42:49.110 Uttam Kumaran: Okay.
411 00:42:49.110 ⇒ 00:42:52.580 Shivani Amar: And she said Shopify was $85,000.
412 00:42:54.630 ⇒ 00:42:56.720 Shivani Amar: So… so let me know.
413 00:42:56.720 ⇒ 00:43:01.910 Uttam Kumaran: Yeah, send this credit to me, Craig, Robert.
414 00:43:01.910 ⇒ 00:43:04.120 Robert Tseng: Yeah, yeah, yeah, I did.
415 00:43:04.120 ⇒ 00:43:05.939 Shivani Amar: That’s hilarious. Okay.
416 00:43:06.380 ⇒ 00:43:07.540 Uttam Kumaran: Oh, at least it’s there.
417 00:43:07.690 ⇒ 00:43:08.680 Shivani Amar: Yeah.
418 00:43:09.370 ⇒ 00:43:14.710 Uttam Kumaran: Hey, that was the first question! All you asked was, was it there?
419 00:43:14.710 ⇒ 00:43:20.180 Shivani Amar: Okay, alright, thank you for taking the feedback, guys.
420 00:43:20.530 ⇒ 00:43:23.570 Shivani Amar: I’m open to the iteration on…
421 00:43:24.040 ⇒ 00:43:30.120 Shivani Amar: the flow of, like, what happens after discovery, basically, right? Like, that’s, like, what we’re trying to land.
422 00:43:30.580 ⇒ 00:43:31.250 Shivani Amar: And, like…
423 00:43:31.500 ⇒ 00:43:35.109 Uttam Kumaran: And can you send me, Shivani, that, that, playbook?
424 00:43:35.920 ⇒ 00:43:37.960 Uttam Kumaran: Yeah.
425 00:43:38.860 ⇒ 00:43:43.920 Shivani Amar: Let me send it to you guys, so… with them, Robert.
426 00:43:44.990 ⇒ 00:43:45.860 Shivani Amar: It’s like…
427 00:43:46.230 ⇒ 00:44:01.659 Shivani Amar: this is, like, the premise of this document is so that anybody at Element is like, okay, like, what is the data and analytics project? Like, what is it? And what’s the high-level thing that we’re doing? And then, what does it mean to engage right now? And then what you can see is, like, Phase 1
428 00:44:01.660 ⇒ 00:44:09.429 Shivani Amar: is building the warehouse, Element Data Warehouse, and dashboards, like, the dashboards at IQA. But, like, Phase 2 will be, like.
429 00:44:09.620 ⇒ 00:44:13.960 Shivani Amar: working with VPs to, like, make their dashboards exactly what they want.
430 00:44:15.120 ⇒ 00:44:20.679 Shivani Amar: Yeah. But right now, the dashboards are so that I can QA stuff and try to get it closest to what I think that they might want.
431 00:44:20.860 ⇒ 00:44:26.519 Shivani Amar: If that makes sense. So, like, Phase 2 is what will be, like, September onward.
432 00:44:28.440 ⇒ 00:44:29.450 Shivani Amar: Okay.
433 00:44:29.800 ⇒ 00:44:47.230 Shivani Amar: Yeah. Then it has, what is a divisional audit? I put your little image here, and I was like, hey, this is what we’ve done so far. We’ve done retail, e-commerce. I was like, I don’t know if we truly, like, we sort of touched e-commerce and marketing at the same time, but I almost kind of want to…
434 00:44:47.350 ⇒ 00:44:59.479 Shivani Amar: separate them in my head, even if they’re not even different divisions, because it’s like, what is the marketing level discovery that we need? And then what is, like, just, like, the e-commerce, Shopify, Amazon, Walmart.com stuff? Does that make sense?
435 00:45:01.700 ⇒ 00:45:02.200 Shivani Amar: I don’t.
436 00:45:02.200 ⇒ 00:45:02.540 Uttam Kumaran: Yeah.
437 00:45:02.540 ⇒ 00:45:04.959 Shivani Amar: talked to Carlos, I had it, like, separate. Like, I almost.
438 00:45:04.960 ⇒ 00:45:05.310 Uttam Kumaran: No.
439 00:45:05.830 ⇒ 00:45:09.079 Shivani Amar: I almost want, like, this document.
440 00:45:09.250 ⇒ 00:45:12.219 Shivani Amar: As a paper trail, like you said, with them, for each discovery that.
441 00:45:12.220 ⇒ 00:45:16.209 Uttam Kumaran: Yeah, exactly. So, so we have, we have that on the source level now.
442 00:45:16.210 ⇒ 00:45:16.820 Shivani Amar: Yeah.
443 00:45:16.820 ⇒ 00:45:23.059 Uttam Kumaran: And we do have the past notes from the discovery, but yes, I kind of want, like, the domain level, like.
444 00:45:23.220 ⇒ 00:45:24.289 Shivani Amar: What did we learn about.
445 00:45:24.290 ⇒ 00:45:24.730 Uttam Kumaran: e-call.
446 00:45:24.730 ⇒ 00:45:32.879 Shivani Amar: commerce at Element. What did we learn about retail at Element? What did we learn? And, like, that’s a very… it’s helpful for my brain, it’s helpful for your brain, right?
447 00:45:32.880 ⇒ 00:45:42.209 Uttam Kumaran: Yeah, so I think we should agree on, like, that, and then exactly, like, that format. We actually have… I actually can easily write that for wholesale and for… for e-com.
448 00:45:42.210 ⇒ 00:45:42.860 Shivani Amar: Okay.
449 00:45:42.980 ⇒ 00:45:46.470 Uttam Kumaran: And then partnerships.
450 00:45:47.210 ⇒ 00:45:53.019 Shivani Amar: Okay, thank you. He had to hop for a call, right?
451 00:45:53.020 ⇒ 00:45:53.750 Uttam Kumaran: Yeah.
452 00:45:53.750 ⇒ 00:45:54.410 Shivani Amar: Okay.
453 00:45:55.160 ⇒ 00:45:59.190 Shivani Amar: Okay. Is the recording still happening? Can you stop the recording?
454 00:45:59.190 ⇒ 00:45:59.970 Uttam Kumaran: Yeah.