Meeting Title: LMNT Stand Up Date: 2026-05-11 Meeting participants: Greg Stoutenburg, Advait Nandakumar Menon, Uttam Kumaran, Jasmin Multani
WEBVTT
1 00:01:09.620 ⇒ 00:01:10.940 Greg Stoutenburg: How’s it going, innovate?
2 00:01:12.860 ⇒ 00:01:14.850 Advait Nandakumar Menon: Hey, I’m doing good, how about you?
3 00:01:15.590 ⇒ 00:01:17.790 Greg Stoutenburg: Doing well. Do you have a good weekend?
4 00:01:19.260 ⇒ 00:01:22.279 Advait Nandakumar Menon: Yeah, it was relaxing, relaxed a bit.
5 00:01:22.480 ⇒ 00:01:26.210 Advait Nandakumar Menon: Nice. Yeah, just stayed in most of the time, so…
6 00:01:26.400 ⇒ 00:01:27.060 Greg Stoutenburg: Yeah.
7 00:01:27.060 ⇒ 00:01:29.649 Advait Nandakumar Menon: What was your… I think you went somewhere, right?
8 00:01:29.880 ⇒ 00:01:35.550 Greg Stoutenburg: Yeah, I went to a wedding. Katie and I went to a wedding. Her cousins in New Jersey.
9 00:01:35.710 ⇒ 00:01:39.660 Greg Stoutenburg: Which they… it was great, great, you know, great couple.
10 00:01:39.800 ⇒ 00:01:46.890 Greg Stoutenburg: We… they did a very small, like, actual wedding ceremony. Hey, Tom.
11 00:01:47.240 ⇒ 00:01:47.650 Advait Nandakumar Menon: Awesome.
12 00:01:48.280 ⇒ 00:01:59.040 Greg Stoutenburg: They did a very small actual ceremony that was, like, private, and we weren’t invited to that part, and so basically we drove… the trip was… we went out there to just go to a giant party for, like, 6 hours.
13 00:01:59.040 ⇒ 00:01:59.840 Advait Nandakumar Menon: Okay.
14 00:01:59.840 ⇒ 00:02:07.689 Greg Stoutenburg: On the… and this piece is fun, on the property of a billionaire. And.
15 00:02:07.690 ⇒ 00:02:08.180 Advait Nandakumar Menon: Oh my god.
16 00:02:08.180 ⇒ 00:02:08.869 Greg Stoutenburg: With a B.
17 00:02:08.870 ⇒ 00:02:10.659 Uttam Kumaran: There’s not that many of them, so…
18 00:02:10.669 ⇒ 00:02:13.349 Greg Stoutenburg: Yeah, yeah, Bill Huff.
19 00:02:13.699 ⇒ 00:02:22.219 Greg Stoutenburg: There’s, you can find a Forbes profile on him from, like, 20 years ago, when he was merely in the hundreds of millions.
20 00:02:22.949 ⇒ 00:02:30.329 Greg Stoutenburg: And, I mean, I guess I should say, I presumed billion since 20 years ago. It was,
21 00:02:31.019 ⇒ 00:02:32.609 Greg Stoutenburg: Approaching that. Anyway…
22 00:02:33.540 ⇒ 00:02:34.310 Uttam Kumaran: Nice.
23 00:02:34.310 ⇒ 00:02:38.630 Greg Stoutenburg: Yeah, I was just saying presumed, it’s not like I have the… I don’t have the tax returns.
24 00:02:38.630 ⇒ 00:02:41.040 Uttam Kumaran: Okay, he could be a hundred millionaire, that’s fine.
25 00:02:41.360 ⇒ 00:02:41.950 Greg Stoutenburg: Yes.
26 00:02:41.950 ⇒ 00:02:44.249 Uttam Kumaran: Get that away from him. I’m sure he’ll be angry.
27 00:02:44.250 ⇒ 00:02:55.839 Greg Stoutenburg: So, like… so, like, yeah, so yesterday we were treated to the, you know, to, like, a vault of a room with a bunch of gems in it. Any one of which is just…
28 00:02:56.610 ⇒ 00:02:58.149 Greg Stoutenburg: Probably worth…
29 00:02:58.150 ⇒ 00:03:01.490 Uttam Kumaran: We did an Indiana Jones cosplay party.
30 00:03:01.490 ⇒ 00:03:02.200 Greg Stoutenburg: Yeah.
31 00:03:02.200 ⇒ 00:03:05.170 Uttam Kumaran: Gems, gold, treasure.
32 00:03:06.180 ⇒ 00:03:11.539 Greg Stoutenburg: Tell you what, if you have, like, an ungodly amount of money, you can throw quite the party.
33 00:03:11.910 ⇒ 00:03:21.249 Greg Stoutenburg: And, yeah, all three of their kids were married in the last year, and every one of them, it’s like… it’s like, I would not normally be allowed to go to something like this.
34 00:03:22.130 ⇒ 00:03:26.479 Uttam Kumaran: Dude, why? You should’ve… did you not sell a single Brainforge service line while.
35 00:03:26.480 ⇒ 00:03:27.650 Greg Stoutenburg: Sorry.
36 00:03:27.650 ⇒ 00:03:29.549 Uttam Kumaran: That’s a party, like…
37 00:03:29.550 ⇒ 00:03:30.080 Greg Stoutenburg: Come on, guys.
38 00:03:30.470 ⇒ 00:03:35.829 Uttam Kumaran: You just… you’re telling me you just ate the canopes. You didn’t sell a single data project.
39 00:03:35.830 ⇒ 00:03:45.530 Greg Stoutenburg: No one, yeah, no, it turns out no one wanted the data project, so I think we were all there pretty much for the same reason, which is, we got invited to this wedding, and we’re gonna let her rip.
40 00:03:45.850 ⇒ 00:03:47.210 Greg Stoutenburg: From now until then.
41 00:03:47.470 ⇒ 00:03:56.279 Uttam Kumaran: My trouble these days is, like, someone says hi to me, and then, of course, it’s pretty stale, like, what do you do? And I’m like, I just do data stuff. Like, no, no, but, like, what else? I’m like.
42 00:03:56.470 ⇒ 00:03:59.850 Uttam Kumaran: Okay, well, yeah, we’re brain forwards, we do this, they’re like.
43 00:03:59.990 ⇒ 00:04:05.389 Uttam Kumaran: oh my god, like, I’ve heard about Snowflake, but everything comes back to work somehow.
44 00:04:05.390 ⇒ 00:04:05.860 Greg Stoutenburg: Yeah.
45 00:04:05.860 ⇒ 00:04:14.639 Uttam Kumaran: So then I’m just like, alright, this is great, I don’t have to talk about whatever small talk, you know, it’s either work or small talk, and I could just blab about us.
46 00:04:14.760 ⇒ 00:04:18.889 Uttam Kumaran: And I’m like, this passes the time, this is great. Yeah, yeah.
47 00:04:18.890 ⇒ 00:04:23.670 Greg Stoutenburg: Yeah, well, these are all finance people, yeah, so…
48 00:04:24.710 ⇒ 00:04:25.460 Uttam Kumaran: Nice.
49 00:04:26.250 ⇒ 00:04:29.830 Greg Stoutenburg: Utam, I need you to tell me where I should be and when tomorrow.
50 00:04:29.830 ⇒ 00:04:30.920 Uttam Kumaran: Yeah, I know, I know.
51 00:04:30.920 ⇒ 00:04:35.120 Greg Stoutenburg: All I know right now is just, like, go to Washington, D.C. tomorrow.
52 00:04:35.120 ⇒ 00:04:42.089 Uttam Kumaran: I’ll tell you the address in Arlington. Okay. Yeah, if you don’t mind coming for dinner, I’ll try to get us out.
53 00:04:42.240 ⇒ 00:04:45.299 Uttam Kumaran: Eating as early as I can, so you can drive back.
54 00:04:45.300 ⇒ 00:04:46.719 Greg Stoutenburg: Yeah, cool. That’d be great.
55 00:04:47.660 ⇒ 00:04:51.299 Uttam Kumaran: But also, if you want to take a car, You could take a car…
56 00:04:52.180 ⇒ 00:04:57.489 Uttam Kumaran: you could check how much it costs, but it would be really nice to have you there, so… I’ll text you, I’m like, just…
57 00:04:58.540 ⇒ 00:05:08.110 Uttam Kumaran: Catching up, and trying to also, I need to mute some channels. I’m trying to see where am I getting all this… where am I getting all the noise from? I’m like, cool, I’m not gonna be in that, I’m gonna do that, so… Yeah.
58 00:05:08.440 ⇒ 00:05:09.570 Greg Stoutenburg: Yeah, sounds good.
59 00:05:13.240 ⇒ 00:05:16.859 Uttam Kumaran: Okay, so I haven’t done a single thing for Element today, but I’ll tell you my update.
60 00:05:17.180 ⇒ 00:05:23.430 Uttam Kumaran: what I’m gonna do. So, I owe Shivani something for recruiting, I owe some follow-up on muffin data.
61 00:05:23.650 ⇒ 00:05:28.170 Uttam Kumaran: So I’m just gonna continue to push stuff on the ingestion side.
62 00:05:28.520 ⇒ 00:05:40.859 Uttam Kumaran: I wasn’t able to make a ton of progress on observability, but I’m also gonna put together some of my notes that I talked to the tech team on, like, an observability plan, so I can start hooking it to Datadog, so I think…
63 00:05:41.020 ⇒ 00:05:53.239 Uttam Kumaran: brag, like, this is just another nice, I feel like, thing that we can just continue to talk about. So I feel good on the ingestion and observability side. I also think last week was really great. I feel like I got to spend
64 00:05:53.420 ⇒ 00:05:57.389 Uttam Kumaran: plenty of time with Shivani on interviews and stuff like that, so…
65 00:05:57.780 ⇒ 00:06:00.190 Uttam Kumaran: I think, Greg, maybe one thing for this week is, like.
66 00:06:00.650 ⇒ 00:06:06.439 Uttam Kumaran: any opportunity where I can come on a call and just talk about a subject, I think people are enjoying that, and so…
67 00:06:06.720 ⇒ 00:06:22.830 Uttam Kumaran: I think that’s… the tech team meeting was really good, and I’ve been helping on recruiting and strategy, and, like, I feel like that ends up buying us, a bunch of time. And then on Datadog… so Datadog, we’re gonna use, basically, Jasmine for, like, observability of, like, our whole data system.
68 00:06:22.970 ⇒ 00:06:25.799 Uttam Kumaran: So right now we have no alerting or things like that, so…
69 00:06:25.800 ⇒ 00:06:26.419 Jasmin Multani: I don’t.
70 00:06:26.420 ⇒ 00:06:35.329 Uttam Kumaran: They are already a Datadoc customer, so we are just gonna kind of integrate with them, and then Muffin Data may potentially replace Emerson.
71 00:06:35.610 ⇒ 00:06:37.140 Uttam Kumaran: As a source.
72 00:06:37.140 ⇒ 00:06:37.780 Jasmin Multani: Hopefully.
73 00:06:37.780 ⇒ 00:06:44.329 Uttam Kumaran: Hopefully it shouldn’t affect your work stream, because ideally, even if we replace the sources, we’ll just…
74 00:06:44.490 ⇒ 00:06:49.079 Uttam Kumaran: We’ll just, shift what’s underneath the MART models that you guys are already using.
75 00:06:49.250 ⇒ 00:06:53.500 Uttam Kumaran: So hopefully not something that should affect, like.
76 00:06:53.770 ⇒ 00:07:06.549 Uttam Kumaran: you guys immediately, although the way it does affect you guys is, like, I’m not able to get, like, zip code data right now. I’m not able to give you guys a firm SLA on some of the data sources until this decision is made. I also…
77 00:07:06.810 ⇒ 00:07:08.900 Greg Stoutenburg: Oh, that’s also those Emerson blockers, right?
78 00:07:08.900 ⇒ 00:07:17.719 Uttam Kumaran: Yeah, that’s Emerson Blocker. I also don’t have, like, Costco data for you. I don’t have, any of the other retailer data for you, so…
79 00:07:18.050 ⇒ 00:07:20.160 Uttam Kumaran: That’s our… that’s all this through line.
80 00:07:20.370 ⇒ 00:07:21.449 Greg Stoutenburg: Yeah, okay.
81 00:07:22.200 ⇒ 00:07:23.330 Uttam Kumaran: Babe, were you gonna say something?
82 00:07:24.140 ⇒ 00:07:25.109 Advait Nandakumar Menon: No, no, no.
83 00:07:25.110 ⇒ 00:07:32.310 Uttam Kumaran: Okay, okay. And then, maybe one other piece on the, Amazon e-commerce side, so I’m gonna follow up with Awash, who I think has been doing the
84 00:07:32.470 ⇒ 00:07:38.720 Uttam Kumaran: flat file ingestion for all the Amazon data. So, I don’t know if you guys are still
85 00:07:38.890 ⇒ 00:07:41.350 Uttam Kumaran: Kind of on, like, supply chain or retail.
86 00:07:41.500 ⇒ 00:07:44.480 Uttam Kumaran: Or I would love to kind of hear, like, how far we are from
87 00:07:44.980 ⇒ 00:07:48.139 Uttam Kumaran: Trying to produce something on the e-commerce side that is…
88 00:07:48.350 ⇒ 00:07:50.789 Uttam Kumaran: As all of our e-commerce sources, because
89 00:07:51.110 ⇒ 00:07:56.909 Uttam Kumaran: I would like to finish out having everything for Amazon and Shopify in one place modeled this week.
90 00:07:57.080 ⇒ 00:07:58.980 Uttam Kumaran: So that you guys have that ready.
91 00:07:59.190 ⇒ 00:08:01.280 Uttam Kumaran: so.
92 00:08:01.790 ⇒ 00:08:05.380 Jasmin Multani: Yeah, I… I just have to QA the Amazon.
93 00:08:05.740 ⇒ 00:08:16.489 Jasmin Multani: data that’s, so I’m able to pull the raw information from Amazon Seller. I’m just trying to truncate the data and actually be able to compare it.
94 00:08:16.700 ⇒ 00:08:27.169 Jasmin Multani: to our data tables, but because Awash walked us through how to set up Snowflake, a connection through Cursor, it could just be as simple as me
95 00:08:27.460 ⇒ 00:08:37.480 Jasmin Multani: uploading the raw data from Amazon Seller, and then asking Cursor, hey, cross-check it with our existing data tables, for me to QA.
96 00:08:38.539 ⇒ 00:08:39.099 Uttam Kumaran: Okay.
97 00:08:41.620 ⇒ 00:08:51.330 Jasmin Multani: And then, in supply chain, Greg and I talked about next steps, and I think the next deliverable should be a high-level mapping of
98 00:08:53.500 ⇒ 00:08:58.770 Jasmin Multani: Saying, hey, here are the external stakeholders, external to Element.
99 00:08:58.950 ⇒ 00:09:01.409 Jasmin Multani: This is how they pass in information.
100 00:09:01.750 ⇒ 00:09:09.750 Jasmin Multani: And then the black box is going to be Element as a team making decisions based off of that data, and then the output will be, like.
101 00:09:10.900 ⇒ 00:09:15.920 Jasmin Multani: where is this data then sent to downstream from Element? I think we need to make
102 00:09:16.740 ⇒ 00:09:24.949 Jasmin Multani: High-level mapping of that, just from the discovery calls that we’ve had.
103 00:09:26.140 ⇒ 00:09:38.230 Jasmin Multani: I think the reason is that these are going to be our critical points, and then we, from there, we work with engineering to decide, hey, these files are being ingested through emails.
104 00:09:38.450 ⇒ 00:09:41.799 Jasmin Multani: These are being gestured through portals.
105 00:09:42.210 ⇒ 00:09:47.000 Jasmin Multani: What’s our plan, to start integrating this information?
106 00:09:47.790 ⇒ 00:09:50.699 Jasmin Multani: the reason why I want to separate this
107 00:09:50.910 ⇒ 00:09:59.930 Jasmin Multani: external stakeholder versus internal stakeholder sprint is because this… the way data is manipulated and packaged
108 00:10:00.520 ⇒ 00:10:04.440 Jasmin Multani: to, amongst, like, the internal element folks.
109 00:10:05.570 ⇒ 00:10:08.799 Jasmin Multani: That often requires a lot of data manipulation.
110 00:10:08.930 ⇒ 00:10:15.359 Jasmin Multani: an iteration that the Element team is still moving… having as a moving target.
111 00:10:15.790 ⇒ 00:10:33.580 Jasmin Multani: So, the same data requests, or, like, the same question of, like, hey, how much resources, how much raw material should we allocated to this warehouse? They have an equation for that, and a spreadsheet set up for that, but we’ve noticed that they keep iterating on it. So that’s why I wanted to…
112 00:10:33.810 ⇒ 00:10:36.889 Jasmin Multani: First, take a stab of, hey, what are the whole numbers?
113 00:10:37.160 ⇒ 00:10:45.049 Jasmin Multani: Of the data? What are the external stakeholders? What are the input and output points?
114 00:10:45.380 ⇒ 00:10:52.930 Jasmin Multani: And, once we get that stage, then that’s when I suggest we start looking at the black box of
115 00:10:53.620 ⇒ 00:10:56.790 Jasmin Multani: Internal element, data manipulation itself.
116 00:10:57.310 ⇒ 00:10:57.890 Uttam Kumaran: Okay.
117 00:10:59.250 ⇒ 00:11:03.499 Jasmin Multani: So I can at least, like, get, like, a V1.
118 00:11:03.770 ⇒ 00:11:06.610 Jasmin Multani: by end of week, and then see…
119 00:11:07.380 ⇒ 00:11:12.320 Jasmin Multani: get Shivani to sign off on it, and feel like it’s thorough enough for her.
120 00:11:12.980 ⇒ 00:11:13.300 Uttam Kumaran: guess what?
121 00:11:13.300 ⇒ 00:11:14.420 Jasmin Multani: I don’t think…
122 00:11:14.830 ⇒ 00:11:19.820 Uttam Kumaran: Yeah, one thing I would love from you guys is, is basically…
123 00:11:20.290 ⇒ 00:11:39.349 Uttam Kumaran: like, a list of the ingestion sources and, like, where you want it to land. I think that would make it really clean for me and Awash, to just… basically, it’s like, there’s this UI that we need to scrape something from. There’s this document that gets emailed to somebody. There’s this document that you have to log in and download. Like, even at that level, like, I don’t need you guys to,
124 00:11:39.460 ⇒ 00:11:45.930 Uttam Kumaran: ideate on how it gets done, it’s just, like, this is the source, this is how they’re currently accessing it, I need it
125 00:11:46.290 ⇒ 00:11:54.069 Uttam Kumaran: I need it here, like, in this table, basically, or if you just hand me that, and then give me, like, a prior list,
126 00:11:54.340 ⇒ 00:11:58.380 Uttam Kumaran: we can start executing. Ideally,
127 00:11:58.770 ⇒ 00:12:02.729 Uttam Kumaran: That would be great. If you… for bonus points, we could just shove that into our…
128 00:12:02.970 ⇒ 00:12:07.069 Uttam Kumaran: Ingestion source list that’s in the platform documentation.
129 00:12:07.790 ⇒ 00:12:10.159 Uttam Kumaran: And you can just mark them all as P1.
130 00:12:11.470 ⇒ 00:12:22.690 Uttam Kumaran: that would be, like, gold, because ultimately, everything… all of our ingestion sources are all already documented in that list, and it’ll get brownie points with Shivani and CPUs and that stuff.
131 00:12:23.160 ⇒ 00:12:24.890 Jasmin Multani: Yeah, I can do that.
132 00:12:25.540 ⇒ 00:12:32.249 Uttam Kumaran: And then you can also, I think for you guys to tell her that, basically, like, we’ve already started collaborating with,
133 00:12:32.790 ⇒ 00:12:46.230 Uttam Kumaran: with the ingestion folks on how to get these, and then we’re… you can tell your side. That way, it just looks like you’re owning that versus, oh, I’m waiting for… it sort of continues to show that we’re one unit, basically, you know? It’s like, yeah.
134 00:12:46.870 ⇒ 00:12:50.349 Greg Stoutenburg: Utam, is this the area you were referring to?
135 00:12:50.660 ⇒ 00:12:52.160 Greg Stoutenburg: Where all the data sources are laid out.
136 00:12:52.160 ⇒ 00:12:55.969 Uttam Kumaran: Yes, sir, yeah, so if you just add these as extra on the bottom.
137 00:12:56.150 ⇒ 00:13:06.759 Uttam Kumaran: Some of these look like they’re already supply chain ones, so someone added it, or… so, yeah, so you guys just add these and fill out as many columns as you feel comfortable filling out, and then just…
138 00:13:07.520 ⇒ 00:13:10.419 Uttam Kumaran: ping us, Awesha and I could start moving on it.
139 00:13:10.420 ⇒ 00:13:11.050 Greg Stoutenburg: Okay.
140 00:13:12.750 ⇒ 00:13:20.059 Greg Stoutenburg: Sounds good. Jasmine, is that something that you can roll into the, data diagram that you said you were gonna pick up?
141 00:13:20.290 ⇒ 00:13:26.250 Jasmin Multani: Yes, yeah, it’s just a matter of, like, adding, copy-pasting. It’s the same…
142 00:13:26.250 ⇒ 00:13:26.599 Greg Stoutenburg: Pretty cool.
143 00:13:26.600 ⇒ 00:13:28.279 Jasmin Multani: thinking, it’s just…
144 00:13:29.050 ⇒ 00:13:29.440 Greg Stoutenburg: Yep.
145 00:13:29.440 ⇒ 00:13:30.959 Jasmin Multani: Getting into different things, yeah.
146 00:13:31.480 ⇒ 00:13:32.700 Greg Stoutenburg: Yep, cool.
147 00:13:33.220 ⇒ 00:13:37.500 Jasmin Multani: And I’m placing this in our 101 notes from today.
148 00:13:37.500 ⇒ 00:13:38.190 Greg Stoutenburg: Perfect.
149 00:13:38.380 ⇒ 00:13:56.910 Greg Stoutenburg: And then I think I can update the external channel and say hi to Shivani, and that we’re working on this with a view toward making sure that we have all the plumbing in place to begin to scope out what the, you know, what the final state of a supply chain Omni dashboard set will look like.
150 00:14:11.160 ⇒ 00:14:19.299 Jasmin Multani: Apart from that, we’re just waiting for Shivani to give us a thumbs up on the three dashboards that Ed Booth published last week.
151 00:14:19.730 ⇒ 00:14:23.740 Jasmin Multani: Yep.
152 00:14:27.080 ⇒ 00:14:27.720 Greg Stoutenburg: Yeah, go ahead.
153 00:14:27.720 ⇒ 00:14:28.770 Jasmin Multani: Yum.
154 00:14:30.780 ⇒ 00:14:38.870 Advait Nandakumar Menon: Yeah, so I’m looking into that stockpile analysis ticket you created for me, Justin, right now. So, for the order funnel.
155 00:14:40.300 ⇒ 00:14:44.790 Advait Nandakumar Menon: Did you get anywhere with the findings I shared with you on Friday?
156 00:14:45.510 ⇒ 00:14:50.860 Jasmin Multani: No, I… okay, so let’s… we can bring this up today. So…
157 00:14:52.150 ⇒ 00:14:55.809 Jasmin Multani: We have been able to look into the spreadsheet.
158 00:14:55.960 ⇒ 00:15:02.879 Jasmin Multani: And we noticed that the calculations of the… within the spreadsheets are not adding up to one another.
159 00:15:03.100 ⇒ 00:15:06.219 Jasmin Multani: The, totals for…
160 00:15:07.050 ⇒ 00:15:16.000 Jasmin Multani: drink and sparkling on wholesale, right? Wholesale is coming out as one number, and then when you look at the same
161 00:15:16.400 ⇒ 00:15:19.220 Jasmin Multani: Grain for partner segments.
162 00:15:19.620 ⇒ 00:15:21.579 Jasmin Multani: It’s coming out as a different number.
163 00:15:22.020 ⇒ 00:15:30.159 Jasmin Multani: So, Otam, should we lean in with you, or Awash, to figure out… How to finalize. Okay.
164 00:15:30.760 ⇒ 00:15:33.770 Jasmin Multani: Okay.
165 00:15:34.010 ⇒ 00:15:35.020 Advait Nandakumar Menon: Answer that question.
166 00:15:37.820 ⇒ 00:15:41.959 Advait Nandakumar Menon: Yeah, that’s one thing, and the… Other thing is,
167 00:15:42.230 ⇒ 00:15:47.639 Advait Nandakumar Menon: Jasmine, about the order funnel, like, the first order, the second order, and third order, so…
168 00:15:47.860 ⇒ 00:15:51.150 Advait Nandakumar Menon: That’s un… that’s something I’m looking into as well right now.
169 00:15:52.310 ⇒ 00:15:56.359 Jasmin Multani: Yeah, just keep that… yeah, Greg, for your visibility,
170 00:15:56.830 ⇒ 00:16:04.330 Jasmin Multani: Shivani felt like the way the information is being calculated for first, second, and third order.
171 00:16:04.680 ⇒ 00:16:09.090 Jasmin Multani: her gut says that it’s… it’s too optimistic, like, that third order…
172 00:16:09.890 ⇒ 00:16:12.850 Jasmin Multani: Makes it look like our retention is amazing.
173 00:16:13.040 ⇒ 00:16:15.360 Jasmin Multani: And…
174 00:16:15.540 ⇒ 00:16:22.679 Jasmin Multani: Even though, like, our spreadsheet, like, the way we built out the dashboard was to mirror what’s in the spreadsheet.
175 00:16:23.530 ⇒ 00:16:30.950 Jasmin Multani: So that’s done, but… She wants to now double-click into the data integrity of the spreadsheet itself.
176 00:16:31.900 ⇒ 00:16:38.319 Jasmin Multani: So, she’s saying, like, oh, this third order funnel thing, it’s too optimistic.
177 00:16:39.260 ⇒ 00:16:47.759 Jasmin Multani: And so we spent, like, an hour just going through SQL, showing her, running things CTE by CTE, and…
178 00:16:49.420 ⇒ 00:16:53.490 Jasmin Multani: I feel like Awash set it up correctly. I don’t.
179 00:16:53.490 ⇒ 00:17:10.980 Uttam Kumaran: Why did that… like, I think my… my kind of, like, point is, like, why did we get that far on a call? Like, if that… if that’s, like, a question about modeling, we should just toss it to me and Awash to give, because ultimately, unless you feel, like, super tight to walk through how Awash and I modeled everything.
180 00:17:11.150 ⇒ 00:17:13.349 Uttam Kumaran: You’re gonna get into a jam, probably.
181 00:17:13.970 ⇒ 00:17:17.519 Jasmin Multani: She literally was like, show me… show this to me on Snowflake right now.
182 00:17:18.060 ⇒ 00:17:21.710 Uttam Kumaran: Okay. I think, yeah.
183 00:17:22.079 ⇒ 00:17:36.819 Jasmin Multani: So, we at least were able to be like, hey, Shivani, like, this looks accurate to me, and like, I even told her the commands, like, the row number command is something that’s standard practice. Yes, I see that in the code, like, everything looks logical to me.
184 00:17:36.820 ⇒ 00:17:37.370 Uttam Kumaran: Yeah.
185 00:17:37.750 ⇒ 00:17:38.760 Jasmin Multani: Yeah.
186 00:17:38.760 ⇒ 00:17:42.590 Uttam Kumaran: So there’s still a piece of QA, so why don’t we just take that? I feel totally fine.
187 00:17:43.050 ⇒ 00:17:47.190 Uttam Kumaran: Just, like, taking whatever question still needs to be answered, and our team can go answer that.
188 00:17:47.490 ⇒ 00:17:50.570 Jasmin Multani: Okay, sure, week.
189 00:17:50.570 ⇒ 00:17:58.009 Uttam Kumaran: Basically, the question is, like, is this thing actually acting? Like, is the logic actually producing that output that we expect?
190 00:17:58.590 ⇒ 00:18:01.100 Uttam Kumaran: She looked at the number and was like, this seems high.
191 00:18:01.210 ⇒ 00:18:01.920 Uttam Kumaran: Platinum, right?
192 00:18:01.920 ⇒ 00:18:18.600 Jasmin Multani: but I think the reason that we thought about it was it’s not looking at the secure pool, right? So we compared the numbers to what we see in Shopify, and what we noticed is that even deliveries that have $0 are being sequenced in.
193 00:18:18.620 ⇒ 00:18:30.050 Jasmin Multani: And so when she walked us through Shopify, she’s like, oh, these orders that are $0 are actually Element sending over free gifts, like stands and stuff. So that’s.
194 00:18:30.050 ⇒ 00:18:31.009 Uttam Kumaran: Oh, okay, okay.
195 00:18:31.010 ⇒ 00:18:32.030 Jasmin Multani: So we can exclud.
196 00:18:32.030 ⇒ 00:18:34.790 Uttam Kumaran: Yeah, we can exclude 100% discount.
197 00:18:34.930 ⇒ 00:18:37.480 Uttam Kumaran: Yeah, I feel like we definitely didn’t account for that.
198 00:18:38.300 ⇒ 00:18:47.230 Jasmin Multani: Okay, so that’s just something, one thing we found out. The ask is now, Adviv, is to explore
199 00:18:47.520 ⇒ 00:18:55.629 Jasmin Multani: the data, and see for… track for other trends similar to this, that is, Inflating the number even higher.
200 00:18:55.780 ⇒ 00:19:15.290 Jasmin Multani: So there are other cuts that she asked about, too. It’s like, hey, if someone is making 4 orders in the same day, but what if they have, like, four different locations? Should we be sequencing that as 4 sec- like, four? Or should we be sequencing that as, like, one order? So, the ask to Advaid is, like.
201 00:19:15.520 ⇒ 00:19:26.430 Jasmin Multani: look at how often these things happen, these edge cases happen. Those should be, like, a really light exploratory analysis, and then from there, we can sign it off with Siobhan and be like, hey.
202 00:19:27.090 ⇒ 00:19:30.649 Jasmin Multani: You know, cut this out, keep this in the pool, so on and so forth.
203 00:19:31.010 ⇒ 00:19:37.110 Jasmin Multani: And then the idea, I think, is to hand it off to you and Awash to further validate, or like… Okay.
204 00:19:37.480 ⇒ 00:19:41.120 Uttam Kumaran: Yeah, this seems totally fine. Yeah, I mean, there’s no way we would have…
205 00:19:41.730 ⇒ 00:19:45.710 Uttam Kumaran: Yeah, I think this is… this seems a pretty natural QA process.
206 00:19:45.710 ⇒ 00:19:47.350 Jasmin Multani: Yeah, yeah.
207 00:19:47.350 ⇒ 00:19:55.049 Uttam Kumaran: So I think the biggest thing is, like, don’t promise a timeline as much as promise, like, we’re gonna add a ticket, and then we’ll get back to you on the timeline.
208 00:19:55.050 ⇒ 00:19:55.460 Jasmin Multani: Yes.
209 00:19:55.460 ⇒ 00:20:01.499 Uttam Kumaran: So I think, basically, If we could… if we try to avoid same-week changes like this.
210 00:20:03.010 ⇒ 00:20:14.980 Uttam Kumaran: Because that way it gives us a little bit of time, because when a waste goes back in, I want him to make, like, 4 or 5 improvements every time. Right, right, right. And then… and then send it to you guys, and then…
211 00:20:15.870 ⇒ 00:20:19.519 Uttam Kumaran: the next week, at least, we, like, present it, right? So…
212 00:20:19.520 ⇒ 00:20:20.580 Jasmin Multani: Okay.
213 00:20:20.680 ⇒ 00:20:33.510 Jasmin Multani: Yeah, right now, it’s clear to her that, like, this is just exploration, and then she’s gonna sign off on, like, whatever. Cool. I had another question, so Avid was able to find…
214 00:20:34.000 ⇒ 00:20:37.500 Jasmin Multani: Some data integrity issues on the wholesale spreadsheet.
215 00:20:37.630 ⇒ 00:20:40.139 Jasmin Multani: In which, for the same dates.
216 00:20:40.600 ⇒ 00:20:47.590 Jasmin Multani: If you just add up the numbers for drink plus sparkling, that comes out to one number.
217 00:20:47.700 ⇒ 00:20:55.820 Jasmin Multani: But if you track for the same dates, but track for the partner segment cuts, Those numbers,
218 00:20:56.690 ⇒ 00:20:58.220 Jasmin Multani: Add up to something else.
219 00:20:58.450 ⇒ 00:21:03.680 Jasmin Multani: So… Should we go to you or Awage for that? And should we cut it?
220 00:21:03.680 ⇒ 00:21:06.159 Uttam Kumaran: It’s all Oasis. Awashi’s first on everything modeling.
221 00:21:06.160 ⇒ 00:21:06.770 Jasmin Multani: Okay.
222 00:21:07.150 ⇒ 00:21:10.369 Uttam Kumaran: Yeah, I’m not taking anything modeling, unless I’m, like, backup.
223 00:21:11.220 ⇒ 00:21:15.129 Jasmin Multani: Do you know what other information he needs from us to make
224 00:21:15.350 ⇒ 00:21:17.209 Jasmin Multani: To make this easier on him.
225 00:21:17.530 ⇒ 00:21:19.730 Uttam Kumaran: Yeah, I would create a ticket, I would…
226 00:21:20.500 ⇒ 00:21:27.919 Uttam Kumaran: Send whatever evidence, whether it’s a link to the spreadsheet or screenshots, throw that all onto the ticket, and then ping it.
227 00:21:27.970 ⇒ 00:21:35.719 Jasmin Multani: Okay, and then he… does he make the final decision of, like, hey, this is what the real data should look like? Or should… where… should we lean in?
228 00:21:36.480 ⇒ 00:21:39.369 Uttam Kumaran: He’ll investigate, and then he’ll probably ping you.
229 00:21:39.370 ⇒ 00:21:40.830 Jasmin Multani: Okay, cool, cool, cool.
230 00:21:41.560 ⇒ 00:21:42.240 Uttam Kumaran: Yeah.
231 00:21:42.620 ⇒ 00:21:45.280 Jasmin Multani: Okay, cool, so I’ll cut that ticket right now, and then…
232 00:21:45.280 ⇒ 00:21:53.040 Uttam Kumaran: Like, ultimately, prior, we were, like, me and Awash were on the hook for, like, what should it look like, and how do we model it, and how do we ingest this, and now I’m kind of like.
233 00:21:53.360 ⇒ 00:21:57.540 Uttam Kumaran: You guys decide on whatever the ingestion format
234 00:21:57.970 ⇒ 00:22:04.210 Uttam Kumaran: Like, if you’re… if you have control over, like, whatever we’re ingesting, then I would suggest making it there, and then we’ll be… we’ll handle
235 00:22:04.410 ⇒ 00:22:09.680 Uttam Kumaran: how do we get it into the system, how do we model it, and then put it in ARTS so that you can take it from there?
236 00:22:12.450 ⇒ 00:22:14.709 Jasmin Multani: Okay, okay, okay, that makes sense.
237 00:22:16.410 ⇒ 00:22:21.779 Jasmin Multani: I’ll cut that ticket, and then I’ll CC Advaith and I into that ticket, so…
238 00:22:22.380 ⇒ 00:22:29.489 Jasmin Multani: we know what the answer is, in case Shivani asks. It’s not something that Shivani has caught so far, so…
239 00:22:29.640 ⇒ 00:22:33.050 Jasmin Multani: Shout out to… for being a step ahead.
240 00:22:33.760 ⇒ 00:22:34.470 Uttam Kumaran: Cool.
241 00:22:34.470 ⇒ 00:22:35.040 Advait Nandakumar Menon: Yep.
242 00:22:37.290 ⇒ 00:22:37.870 Jasmin Multani: Okay.
243 00:22:38.300 ⇒ 00:22:39.750 Jasmin Multani: Other than that…
244 00:22:41.700 ⇒ 00:22:49.670 Greg Stoutenburg: Jasmine, can you show me what’s currently the source of truth for the order in which we’re supposed to take on
245 00:22:49.800 ⇒ 00:22:53.709 Greg Stoutenburg: Topics, like, subject matters for reporting.
246 00:22:53.860 ⇒ 00:23:02.100 Greg Stoutenburg: I’m asking because she’s just asked in the channel about e-commerce dashboarding?
247 00:23:02.560 ⇒ 00:23:08.909 Greg Stoutenburg: And I thought that we’re currently working on supply chain. That’s what I thought it was coming into last week, so…
248 00:23:08.910 ⇒ 00:23:13.260 Jasmin Multani: No, e-commerce… e-commerce should be for May 22nd.
249 00:23:14.070 ⇒ 00:23:16.589 Greg Stoutenburg: that we deliver e-commerce on May 22nd?
250 00:23:16.590 ⇒ 00:23:17.400 Jasmin Multani: Yeah.
251 00:23:18.440 ⇒ 00:23:21.610 Greg Stoutenburg: Okay. Can you show me what I can reference?
252 00:23:21.820 ⇒ 00:23:22.910 Jasmin Multani: Yeah, that I’ve got there.
253 00:23:22.910 ⇒ 00:23:23.450 Greg Stoutenburg: Right?
254 00:23:23.660 ⇒ 00:23:26.120 Jasmin Multani: Data documentation…
255 00:23:35.150 ⇒ 00:23:36.089 Jasmin Multani: This one.
256 00:23:37.200 ⇒ 00:23:42.850 Jasmin Multani: I don’t know where stored information would fall under that.
257 00:23:44.690 ⇒ 00:23:48.319 Greg Stoutenburg: Oh, we’re using… we’re using that one. So that’s the one that… okay, that’s the one that she came up with.
258 00:23:50.000 ⇒ 00:23:56.659 Jasmin Multani: She came up with, and then we leaned in. Okay. Because we moved away from the pilot stuff.
259 00:23:56.660 ⇒ 00:23:57.280 Greg Stoutenburg: Yep.
260 00:23:58.140 ⇒ 00:24:00.840 Jasmin Multani: And then, so, from here on forward, like.
261 00:24:00.980 ⇒ 00:24:03.190 Jasmin Multani: If anything needs to get pushed.
262 00:24:03.620 ⇒ 00:24:05.459 Jasmin Multani: This is the source of truth.
263 00:24:05.460 ⇒ 00:24:07.319 Greg Stoutenburg: Got it. Okay. Alright.
264 00:24:10.240 ⇒ 00:24:12.869 Jasmin Multani: And even though, like, supply chain is being…
265 00:24:13.760 ⇒ 00:24:18.170 Jasmin Multani: in landing in July, we should still be… because it’s such a big…
266 00:24:19.640 ⇒ 00:24:21.890 Jasmin Multani: Workstream, we should be working on it.
267 00:24:22.030 ⇒ 00:24:22.560 Jasmin Multani: Today.
268 00:24:22.560 ⇒ 00:24:25.380 Greg Stoutenburg: Yeah. Yeah, yeah, and we are.
269 00:24:25.830 ⇒ 00:24:28.629 Greg Stoutenburg: Just wanted to clarify about e-commerce, then.
270 00:24:29.150 ⇒ 00:24:33.580 Jasmin Multani: I think we… Yeah, I think we were spinning our wheels because…
271 00:24:34.070 ⇒ 00:24:42.789 Jasmin Multani: because we didn’t know where we stood with E. Emerson, and I didn’t totally understand how Emerson played into it. But now I think it’s…
272 00:24:43.170 ⇒ 00:24:49.150 Jasmin Multani: We can just, like, hit run on what we have, so that she can at least sign off on the shapes of the visuals.
273 00:24:49.520 ⇒ 00:24:53.470 Jasmin Multani: And then, we can go from there.
274 00:24:54.920 ⇒ 00:25:01.859 Greg Stoutenburg: Yeah, I… I want to firm that up, and make sure we’re crisp on…
275 00:25:02.940 ⇒ 00:25:06.099 Greg Stoutenburg: What we’re delivering, because we’re only looking at 11 days here.
276 00:25:06.420 ⇒ 00:25:06.940 Jasmin Multani: Yeah.
277 00:25:06.940 ⇒ 00:25:07.490 Greg Stoutenburg: Mmm.
278 00:25:08.410 ⇒ 00:25:20.090 Uttam Kumaran: If you want to draft something, Greg, I can put in the ingestion piece. I mean, basically, today, I mean, I could just send a note to Awash right now asking, like, how we’re doing.
279 00:25:20.320 ⇒ 00:25:24.890 Uttam Kumaran: on the… Amazon ingestion.
280 00:25:25.540 ⇒ 00:25:33.030 Uttam Kumaran: But for Shopify, we should be, like, able to produce any dashboard on Shopify, and we have a portion of Amazon ready. Okay.
281 00:25:33.870 ⇒ 00:25:37.090 Greg Stoutenburg: Alright, let’s see…
282 00:25:37.860 ⇒ 00:25:43.650 Jasmin Multani: But that’s gonna change once… does that change once we, introduce muffin data.
283 00:25:44.360 ⇒ 00:25:47.589 Uttam Kumaran: No, no, that’s what I’m saying, you guys… you guys don’t need to care about that.
284 00:25:47.590 ⇒ 00:25:48.180 Jasmin Multani: Okay.
285 00:25:48.180 ⇒ 00:25:51.779 Uttam Kumaran: Because you just could keep consuming from Martz, don’t… don’t worry.
286 00:25:52.560 ⇒ 00:25:53.139 Uttam Kumaran: Yeah, yeah.
287 00:25:54.690 ⇒ 00:25:58.670 Uttam Kumaran: We’ll swap in… yeah, we’ll swap in, and you’ll… the structure will maintain.
288 00:25:59.190 ⇒ 00:25:59.790 Greg Stoutenburg: Okay.
289 00:25:59.790 ⇒ 00:26:00.920 Jasmin Multani: Okay, sounds good.
290 00:26:01.070 ⇒ 00:26:02.420 Greg Stoutenburg: Okay,
291 00:26:03.500 ⇒ 00:26:14.250 Greg Stoutenburg: Did, Jasmine, did Shivani approve any spec, or any kind of outline for what’s going to be shared for dashboards?
292 00:26:15.300 ⇒ 00:26:18.879 Jasmin Multani: No, not yet, but I can send that over today.
293 00:26:19.420 ⇒ 00:26:20.040 Greg Stoutenburg: Okay.
294 00:26:20.440 ⇒ 00:26:21.800 Greg Stoutenburg: Yeah, let’s.
295 00:26:21.800 ⇒ 00:26:22.690 Jasmin Multani: Yeah, we were able to…
296 00:26:22.690 ⇒ 00:26:24.909 Greg Stoutenburg: Make sure we both reviewed it before we run it by her.
297 00:26:25.210 ⇒ 00:26:26.029 Jasmin Multani: Okay, okay.
298 00:26:26.470 ⇒ 00:26:34.849 Greg Stoutenburg: Yeah, and let’s just, just, you know, as we’re discussing during our one-on-one, to continue to iteratively improve on the way that we’re
299 00:26:35.030 ⇒ 00:26:51.020 Greg Stoutenburg: Moving things along with her. Yeah, Utam, I can draft a message, internally, and then, you know, you can add your part, and then, I’ll share it out. I need to leave in just a couple minutes to go pick my kids up from school, but, you know, I mean, maybe we’ll just do it the way that we did.
300 00:26:51.470 ⇒ 00:26:58.010 Greg Stoutenburg: the Element update on Friday when I was on the road, which is… sort of like, we collaborate, I copy and paste it, that’s pretty easy.
301 00:26:58.010 ⇒ 00:26:58.540 Uttam Kumaran: Yes.
302 00:26:59.140 ⇒ 00:26:59.770 Uttam Kumaran: Yeah.
303 00:27:00.650 ⇒ 00:27:06.599 Greg Stoutenburg: Okay. And then, Jasmine, yeah, if you can send over that draft spec?
304 00:27:07.440 ⇒ 00:27:11.899 Greg Stoutenburg: We can review it, get her feedback, and then…
305 00:27:12.210 ⇒ 00:27:16.179 Greg Stoutenburg: Yeah, I mean, it sounds like ingestion’s in a good place, and so,
306 00:27:16.380 ⇒ 00:27:20.439 Greg Stoutenburg: Yeah, I think we can be confident we’ll… we’ll hit the… we’ll hit the deadline.
307 00:27:20.440 ⇒ 00:27:31.419 Uttam Kumaran: Yeah, like, that stuff for ingestion, I feel good about, like, I’m able to poke in every other day or so, and, like, keep moving things along. Yeah. And so I just kind of, like, checking the box, but…
308 00:27:31.420 ⇒ 00:27:42.690 Uttam Kumaran: like, I think what that allows for is, like, there’s still some noise there. We’re gonna… but all kind of eyes, Jasmine, are gonna be on… your output is ultimately what she can see and feel.
309 00:27:42.710 ⇒ 00:27:49.660 Uttam Kumaran: So… but you guys are doing a good job, and I just think… just try to stray from promising anything same week.
310 00:27:49.830 ⇒ 00:27:50.410 Jasmin Multani: Yes.
311 00:27:50.410 ⇒ 00:28:06.939 Uttam Kumaran: That’s gonna be the best way for us to take your input, make the change, give it to you to look at, and then you deliver it in whatever form, whether it’s like, hey, the number’s changed, and here’s what it looks like, or, like, we added this column, or whatever that is.
312 00:28:07.000 ⇒ 00:28:17.169 Uttam Kumaran: I would… I think as time goes on this project, those will move to have, like, longer cycles between them, because once the dashboards are live, we can’t, like, make
313 00:28:17.250 ⇒ 00:28:29.489 Uttam Kumaran: we’re not gonna be able to move this fast and make those tweaks and QA it. So, probably what’s gonna happen is that during the month, we gather these, like, QA fixes, make them during, like, rest and assess.
314 00:28:29.970 ⇒ 00:28:33.820 Uttam Kumaran: And then, like, allow you to kind of present them next or something, but…
315 00:28:33.920 ⇒ 00:28:46.120 Uttam Kumaran: like, I think, ultimately, I think what you’re gonna see probably this week and next week is she’s trying to ram a bunch of stuff through before she goes out on leave, so just be aware of that. So as much as we can do to sort of, like.
316 00:28:46.120 ⇒ 00:28:54.120 Uttam Kumaran: show her that we’re still moving at a high pace, and also, Greg, I think, ultimately, you giving her confidence on, like, what we’re gonna achieve.
317 00:28:54.170 ⇒ 00:28:57.019 Uttam Kumaran: In that 2 weeks. Yeah.
318 00:28:57.790 ⇒ 00:29:01.720 Uttam Kumaran: That’s, like, a great way to… for us to crack this one.
319 00:29:02.130 ⇒ 00:29:02.680 Greg Stoutenburg: Yep.
320 00:29:07.380 ⇒ 00:29:15.399 Greg Stoutenburg: Yeah, sounds good. I’m just drafting this, and then I’ll drop it in the channel. I’ll just keep it brief and on topic.
321 00:29:16.400 ⇒ 00:29:18.299 Greg Stoutenburg: Yeah, cool, that’s it from me.
322 00:29:21.740 ⇒ 00:29:23.130 Jasmin Multani: Okay, sounds good.
323 00:29:23.400 ⇒ 00:29:24.410 Uttam Kumaran: Thanks, guys, great job.
324 00:29:24.820 ⇒ 00:29:26.089 Greg Stoutenburg: I’ll talk to you soon.
325 00:29:26.690 ⇒ 00:29:27.230 Greg Stoutenburg: Bye.