Meeting Title: Element Project Weekly Sync Date: 2026-05-11 Meeting participants: Greg Stoutenburg, Jasmin Multani
WEBVTT
1 00:02:43.200 ⇒ 00:02:44.679 Jasmin Multani: Hey Greg, how’s it going?
2 00:02:47.250 ⇒ 00:02:48.590 Greg Stoutenburg: Hey, Jasmine, happy Monday!
3 00:02:48.590 ⇒ 00:02:50.150 Jasmin Multani: Happy Monday!
4 00:02:52.130 ⇒ 00:02:53.220 Greg Stoutenburg: How was your weekend?
5 00:02:53.490 ⇒ 00:03:01.170 Jasmin Multani: It was good. Got my mom’s skincare for Mother’s Day, I hope she uses it instead of stealing my skincare masks.
6 00:03:02.240 ⇒ 00:03:04.890 Greg Stoutenburg: Nice. Bought my mom a robe.
7 00:03:05.200 ⇒ 00:03:05.750 Jasmin Multani: Nice.
8 00:03:06.360 ⇒ 00:03:09.499 Greg Stoutenburg: Yep. She, she just retired a few weeks ago.
9 00:03:09.720 ⇒ 00:03:11.379 Jasmin Multani: Oh, congrats to her!
10 00:03:11.380 ⇒ 00:03:16.419 Greg Stoutenburg: Yeah, so I wanted to get her something that was like, Happy Mother’s Day, also relax.
11 00:03:16.420 ⇒ 00:03:18.770 Jasmin Multani: No, this is the start of a new wardrobe.
12 00:03:19.020 ⇒ 00:03:19.360 Greg Stoutenburg: Yeah.
13 00:03:19.360 ⇒ 00:03:21.960 Jasmin Multani: Just at-home leisure clothes.
14 00:03:21.960 ⇒ 00:03:26.609 Greg Stoutenburg: Yeah, I know, it really might be. I mean, like, she… you know, I mean, she graduated high school, and…
15 00:03:26.730 ⇒ 00:03:27.739 Greg Stoutenburg: Does this work since?
16 00:03:28.150 ⇒ 00:03:29.840 Jasmin Multani: You know, so…
17 00:03:29.840 ⇒ 00:03:32.129 Greg Stoutenburg: It’s a long, long time working.
18 00:03:32.130 ⇒ 00:03:34.549 Jasmin Multani: Yeah, what’s she… what does she do?
19 00:03:34.840 ⇒ 00:03:48.920 Greg Stoutenburg: I mean, she’s been, like, I mean, variously, various administrative roles, so, you know, I mean, everything from, being a, like, an executive assistant, for,
20 00:03:49.170 ⇒ 00:03:50.450 Greg Stoutenburg: What’s the word I’m looking for?
21 00:03:50.820 ⇒ 00:04:07.329 Greg Stoutenburg: for energy executives, at a company based off of, based out of Detroit, to, now she’s been at this marketing agency for a while, but, like, I mean, variously, you know, human resources, administrative stuff, and just, you know, like, 50 years of it.
22 00:04:07.330 ⇒ 00:04:11.900 Jasmin Multani: Oh, I know. A lot of, like, emotional stuff, too, I feel like with those roles.
23 00:04:11.900 ⇒ 00:04:17.269 Greg Stoutenburg: Yep, yep, so she’s… she’s seen some things, so… yeah, anyway, so that’s a good thing.
24 00:04:17.279 ⇒ 00:04:22.569 Jasmin Multani: Yeah, she made… she had a good run. She got to put… on her own. Yeah.
25 00:04:22.570 ⇒ 00:04:34.490 Greg Stoutenburg: Yeah, and as much as, like, you know, you kind of always want to go out on a high note with things, right? But she’s also someone who’s just, like, so accustomed to working that if things were good and happy, she would probably just keep on doing it, like.
26 00:04:34.600 ⇒ 00:04:44.179 Greg Stoutenburg: too long, you know? So, her company instituted a return-to-office policy, like, a year and a half ago, and she’s just been counting down the days to get.
27 00:04:44.180 ⇒ 00:04:44.690 Jasmin Multani: Oh, no.
28 00:04:44.690 ⇒ 00:04:45.260 Greg Stoutenburg: ever since.
29 00:04:45.260 ⇒ 00:04:45.800 Jasmin Multani: funny.
30 00:04:45.800 ⇒ 00:05:01.369 Greg Stoutenburg: Yeah, she’s like, I… she’s like, I got used to remote, it’s better, I cannot go back to the office. Or it was, like, one day in the office a week, which is a lot better than three, when your commute is into, you know, like, a city center from the suburbs.
31 00:05:01.370 ⇒ 00:05:02.140 Jasmin Multani: Yeah, yeah.
32 00:05:02.140 ⇒ 00:05:03.360 Greg Stoutenburg: the hassle, so…
33 00:05:03.360 ⇒ 00:05:05.599 Jasmin Multani: She’s a… she’s a real Gen Z at heart.
34 00:05:05.880 ⇒ 00:05:09.549 Greg Stoutenburg: Yeah, I know, right, I guess I was like, forget it. Well, then I’ll just quit forever.
35 00:05:09.550 ⇒ 00:05:10.100 Jasmin Multani: Yeah.
36 00:05:10.100 ⇒ 00:05:11.370 Greg Stoutenburg: I’ll quit forever.
37 00:05:11.790 ⇒ 00:05:12.350 Greg Stoutenburg: again.
38 00:05:12.350 ⇒ 00:05:15.210 Jasmin Multani: No, I love that, I love the energy about her.
39 00:05:15.210 ⇒ 00:05:15.880 Greg Stoutenburg: Yeah.
40 00:05:15.880 ⇒ 00:05:16.850 Jasmin Multani: Full circle.
41 00:05:16.850 ⇒ 00:05:32.430 Greg Stoutenburg: Yep, yeah, so… Yeah, alright, yeah, so one-on-one stuff. I just put some… yeah, I… I, like, totally forgot that you’d already made a doc, and that we’ve both contributed to it, and then I was just like, oh, I don’t see it, so…
42 00:05:32.730 ⇒ 00:05:35.320 Greg Stoutenburg: My mind is on Element,
43 00:05:35.450 ⇒ 00:05:48.100 Greg Stoutenburg: So, I know we’ve got some dashboards that were ready, I believe ready to review with Shivani, I just wasn’t sure if we had those teed up. I know we’ve been doing a couple of QA calls a week,
44 00:05:48.270 ⇒ 00:05:56.960 Greg Stoutenburg: Just wanted to see if we had, like, you know, a plan… an explicit plan, like, okay, Tuesday we’re gonna review wholesale Executive Pulse, for example.
45 00:05:57.480 ⇒ 00:06:00.530 Jasmin Multani: I mean, I pinged her about all three.
46 00:06:00.530 ⇒ 00:06:01.190 Greg Stoutenburg: Okay.
47 00:06:01.340 ⇒ 00:06:03.310 Jasmin Multani: And… since Thursday.
48 00:06:03.520 ⇒ 00:06:15.429 Jasmin Multani: So I can… maybe it’s worth pinging her again today, being like, hey, we plan on reviewing these live tomorrow, can you take a look? And then next on my agenda is going to be supply chain.
49 00:06:15.560 ⇒ 00:06:31.610 Jasmin Multani: Just giving me feedback, and I think my first stab of doing a supply chain journey is mapping out who the external vendors are that send us information, because that creates a dependency, right? I want to map out all the dependencies.
50 00:06:31.850 ⇒ 00:06:37.820 Jasmin Multani: Where external people are offering information into internal element folks.
51 00:06:38.070 ⇒ 00:06:43.939 Jasmin Multani: And map that out separately from, okay, now at what point do internal element folks
52 00:06:44.600 ⇒ 00:06:48.340 Jasmin Multani: Sprint on manipulating the data and hand it over internally.
53 00:06:48.340 ⇒ 00:06:48.860 Greg Stoutenburg: Yep.
54 00:06:49.460 ⇒ 00:06:52.440 Jasmin Multani: I feel like that’s worthwhile, because it A creates
55 00:06:52.800 ⇒ 00:07:03.539 Jasmin Multani: a critical point that we need to map out, be like, hey, we need to coordinate with these people. We need to match to their cadence, and map out to their,
56 00:07:03.970 ⇒ 00:07:09.490 Jasmin Multani: methods of… Sending stuff, so it’s like, sometimes it’s email, sometimes it’s a portal.
57 00:07:09.850 ⇒ 00:07:11.589 Jasmin Multani: And I feel like that’s messier.
58 00:07:11.740 ⇒ 00:07:18.000 Jasmin Multani: And once that’s teed away, then we can start thinking about… All the internal stuff…
59 00:07:18.470 ⇒ 00:07:20.829 Jasmin Multani: internal manipulations, that’s gonna be a…
60 00:07:21.940 ⇒ 00:07:27.950 Jasmin Multani: Trickier because, people are still, to this day, iterating on how they
61 00:07:28.940 ⇒ 00:07:34.059 Jasmin Multani: manipulate the data, and the way they calculate the numerator and the denominator.
62 00:07:34.240 ⇒ 00:07:34.750 Greg Stoutenburg: Yep.
63 00:07:34.750 ⇒ 00:07:41.549 Jasmin Multani: And… I want to give them the freedom to keep doing that, but we also need to create, like, a…
64 00:07:43.470 ⇒ 00:07:45.130 Jasmin Multani: standard table.
65 00:07:45.130 ⇒ 00:07:48.600 Greg Stoutenburg: Yep, the direction that we’re going, we’re gonna need to lock it down.
66 00:07:48.600 ⇒ 00:07:49.000 Jasmin Multani: Yes.
67 00:07:49.000 ⇒ 00:08:07.149 Greg Stoutenburg: So, yeah, no, I think that makes total sense to understand the present state, so that we can do that, and then sort of just, you know, surface it to Shivani for review, and we make a suggestion on how we think it should be measured, and then get her to say, you know, yes or no, or revise.
68 00:08:08.250 ⇒ 00:08:08.690 Jasmin Multani: Yeah.
69 00:08:08.690 ⇒ 00:08:19.380 Greg Stoutenburg: Yeah, okay. Sounds good. Yeah, and similarly, I just spent a little bit of time in cursor reviewing context, and like, you know, I’m preparing… I think after our…
70 00:08:19.560 ⇒ 00:08:30.859 Greg Stoutenburg: after a couple conversations we had last week with Shivani, I want to make sure that when we go, alright, we’re… we’re QA… QAing the supply chain topic, I realize this is… this is, like, months out, but,
71 00:08:31.020 ⇒ 00:08:33.719 Greg Stoutenburg: Well, maybe that’s not quite so.
72 00:08:33.720 ⇒ 00:08:35.100 Jasmin Multani: We may as well do it.
73 00:08:36.100 ⇒ 00:08:41.260 Greg Stoutenburg: I think, I think it would make a lot of sense to have, like, because I’m looking at our current documentation.
74 00:08:41.730 ⇒ 00:08:47.670 Greg Stoutenburg: she provided Questions for supply chain?
75 00:08:47.800 ⇒ 00:08:51.780 Greg Stoutenburg: I think we want to go ahead and boost that out to, like, 20, you know? Yeah.
76 00:08:51.780 ⇒ 00:08:53.599 Jasmin Multani: And I don’t think she’s gonna provide us…
77 00:08:53.900 ⇒ 00:09:13.700 Greg Stoutenburg: No. I think we should… I think we should start taking a first pass, right? We’re gonna have plenty… we’re gonna have other topics to work on this as well, but I think just as we’re evolving that process that we’ll continue to rely on for Element, and then whoever we set up Omni for next, we can just use AI and best practices and our understanding of the client
78 00:09:13.700 ⇒ 00:09:16.979 Greg Stoutenburg: To sort of tee up a bunch of questions for them.
79 00:09:17.260 ⇒ 00:09:17.640 Jasmin Multani: And then…
80 00:09:17.640 ⇒ 00:09:21.590 Greg Stoutenburg: Say, how about we offer these as success criteria, and then get them to weigh in?
81 00:09:21.780 ⇒ 00:09:24.710 Jasmin Multani: Yeah, yeah, and Utho made a really good point, because I was like…
82 00:09:25.050 ⇒ 00:09:38.670 Jasmin Multani: I don’t want to be keep biasing the questions towards my voice and my interpretation, and he was like, well, just use the discovery calls, those recordings, and get cursor… Yep. You formulate questions based off of, like, their voice.
83 00:09:38.670 ⇒ 00:09:40.090 Greg Stoutenburg: Yep.
84 00:09:40.090 ⇒ 00:09:46.359 Jasmin Multani: So I’m like, okay, that… that makes sense, yeah, very straightforward. And I don’t want to, like, bias it towards one profile.
85 00:09:46.360 ⇒ 00:09:57.719 Greg Stoutenburg: Sure, yep, no, totally. And, I mean, well, a cursor should be doing that, should just be relying on transcripts. So we can, you know, I mean, sometimes it’ll just pick up a sentence that someone has uttered.
86 00:09:57.830 ⇒ 00:10:00.850 Greg Stoutenburg: And it just gives them a good place to get started, right?
87 00:10:00.850 ⇒ 00:10:01.500 Jasmin Multani: Yeah.
88 00:10:01.500 ⇒ 00:10:16.719 Greg Stoutenburg: that maybe… maybe Shivani’s not immediately thinking of 20 questions that she might type into, supply chain. I mean, she sort of hinted at that when it came to the retail stuff, or the wholesale stuff, last week. She was like, well, I didn’t know these were, like, only… the only questions. Like, yeah, they’re not supposed to be the only questions, but…
89 00:10:17.130 ⇒ 00:10:29.750 Greg Stoutenburg: we do want to be relatively comprehensive. Like, we want it to be the case that if Blobby gives the right answer for the questions that we say are the success criteria questions, we consider the topic to be functional.
90 00:10:30.510 ⇒ 00:10:31.230 Greg Stoutenburg: Nope.
91 00:10:31.590 ⇒ 00:10:37.610 Jasmin Multani: Okay, so I can draft up those questions for retail, wholesale, e-commerce, and Spotify, Shopify.
92 00:10:37.610 ⇒ 00:10:38.130 Greg Stoutenburg: Yeah.
93 00:10:38.130 ⇒ 00:10:38.870 Jasmin Multani: Supply.
94 00:10:39.120 ⇒ 00:10:50.649 Greg Stoutenburg: Yeah. Yeah, I mean, retail and wholesale, I think we can just keep iterating on that, since we’re already this far along, and looking at sign-off for all that work, what, end of next week?
95 00:10:51.000 ⇒ 00:10:57.649 Greg Stoutenburg: I think we should consider supply chain to be a sort of pivot in the way that we’re doing the QA stuff.
96 00:10:58.300 ⇒ 00:11:00.190 Jasmin Multani: Okay, pivot in what sense?
97 00:11:00.500 ⇒ 00:11:11.409 Greg Stoutenburg: I mean, like, as far as the way that we approach the process, I think it can be a pivot in that we’ll suggest more questions, we’ll be very, very explicit, these are success criteria for this to work.
98 00:11:11.410 ⇒ 00:11:11.730 Jasmin Multani: Okay.
99 00:11:11.730 ⇒ 00:11:22.799 Greg Stoutenburg: more cleanly distinguishing QAing a topic from QAing the reporting that we’ve built on the topic, which, in the past, we’d sometimes, like.
100 00:11:23.040 ⇒ 00:11:24.490 Greg Stoutenburg: distinguish them.
101 00:11:24.700 ⇒ 00:11:36.509 Greg Stoutenburg: verbally, but we didn’t actually do anything different. We just showed the reports, and then we said, okay, now ask questions about the reports, and all of this is QA. But not really, given the way that Omni is structured, so…
102 00:11:37.310 ⇒ 00:11:40.609 Greg Stoutenburg: That’s all I meant. Yeah, pivot’s a strong word. That’s not really…
103 00:11:40.610 ⇒ 00:11:44.679 Jasmin Multani: No, no, no, I see what you mean, but it’s, like, V2, like, to make the most sense. Yeah, right.
104 00:11:44.850 ⇒ 00:11:49.550 Greg Stoutenburg: Yeah, here’s what we learned the last time we did this, and let’s, you know…
105 00:11:50.150 ⇒ 00:11:56.999 Greg Stoutenburg: let’s refine these pieces of it. Actually, I’ll… I’ll give you, some… some questions.
106 00:11:57.460 ⇒ 00:12:14.729 Jasmin Multani: Yeah, perfect. And now that, Awash this morning showed Adith and I how to connect Snowflake to cursor, so we can now also be like, hey, given these questions, map out the tables to Snowflake that’ll answer these questions.
107 00:12:14.730 ⇒ 00:12:15.140 Greg Stoutenburg: Yeah.
108 00:12:15.140 ⇒ 00:12:19.740 Jasmin Multani: So, we could, like, make… cursor our blobby.
109 00:12:19.940 ⇒ 00:12:20.650 Greg Stoutenburg: Yeah, yeah.
110 00:12:20.650 ⇒ 00:12:21.769 Jasmin Multani: We pissed our blog.
111 00:12:21.770 ⇒ 00:12:22.150 Greg Stoutenburg: Totally.
112 00:12:22.870 ⇒ 00:12:27.810 Greg Stoutenburg: At least for this discovery, yeah, because then we can go, alright, well, you know, you’ve got these…
113 00:12:28.740 ⇒ 00:12:40.060 Greg Stoutenburg: a thousand, different, Google Sheets charts that they’re sending around every week. We can go, hey, you know, we know what’s really important, and we’re gonna sketch out this path to
114 00:12:40.490 ⇒ 00:12:57.079 Greg Stoutenburg: I don’t know how many dashboards, I don’t know, 10 instead of so many, and and then we can roadmap that we understand what the tables are that would be needed to provide the information that they want, and, you know, and just have used Snowflake and hooked up the cursor for that.
115 00:12:58.090 ⇒ 00:13:00.169 Jasmin Multani: perfect!
116 00:13:00.440 ⇒ 00:13:10.820 Jasmin Multani: Yeah, I’m also curious, like, what is their… current… Spreadsheet for e-commerce, right now.
117 00:13:12.150 ⇒ 00:13:18.229 Greg Stoutenburg: I don’t know the answer to that question. Okay. So… that was one thing I was going to bring up.
118 00:13:18.420 ⇒ 00:13:24.349 Greg Stoutenburg: is… I’m… you know, as I’ve gotten back onto the project.
119 00:13:24.350 ⇒ 00:13:24.700 Jasmin Multani: Yeah.
120 00:13:24.700 ⇒ 00:13:31.579 Greg Stoutenburg: I usually focus on the supply chain stuff, and so one of the things I put on the spread… on the agenda for us today was just, like.
121 00:13:31.740 ⇒ 00:13:34.460 Greg Stoutenburg: What can you tell me about what’s going on with e-commerce?
122 00:13:34.710 ⇒ 00:13:41.030 Jasmin Multani: Yeah, e-commerce… I think we’re… I think, from what I understand, we’re getting partial data.
123 00:13:41.240 ⇒ 00:13:47.849 Jasmin Multani: Because Emerson hasn’t given us… The full data, so…
124 00:13:47.960 ⇒ 00:13:50.830 Jasmin Multani: But we can at least, like, work with Amazon queuing.
125 00:13:51.700 ⇒ 00:13:58.159 Jasmin Multani: And I was able to download for the last 30 days, now I’m gonna use…
126 00:13:59.510 ⇒ 00:14:05.220 Jasmin Multani: but it’s not being able to load on Google Sheets, because the file’s so massive, so I’m gonna use…
127 00:14:06.190 ⇒ 00:14:24.280 Jasmin Multani: cursor to truncate that file to be, hey, just give me, like, the top 100 lines, so I can get a sample, and then start sampling it across our tables, which, again, now that we have Snowflake hooked up to the cursor, I can just be like, hey.
128 00:14:25.110 ⇒ 00:14:27.580 Jasmin Multani: What is…
129 00:14:28.050 ⇒ 00:14:38.880 Jasmin Multani: map out the right columns to this sample data that I’m writing, so that gives me an idea of, like, where to look for columns, and then I can sample the data from there.
130 00:14:39.180 ⇒ 00:14:44.189 Greg Stoutenburg: Okay, is that what the sampling initiative that was mentioned last week? That’s what that’s about? We already have a sample?
131 00:14:45.020 ⇒ 00:14:57.759 Jasmin Multani: From Amazon, yeah, but I don’t know what the full picture is, and I think that’s what, I get lost in the emails. I’m like, I don’t really know what… and I tried to ask her, sir.
132 00:14:57.890 ⇒ 00:15:00.859 Jasmin Multani: What’s the difference between Emerson and Polytechnic?
133 00:15:01.100 ⇒ 00:15:07.030 Jasmin Multani: Okay. Am I… am I even using that word correct? Polly something.
134 00:15:07.030 ⇒ 00:15:07.870 Greg Stoutenburg: polyatomic.
135 00:15:07.870 ⇒ 00:15:09.080 Jasmin Multani: Polytomic, yeah.
136 00:15:09.080 ⇒ 00:15:13.160 Greg Stoutenburg: Yeah, Polytomic is a connector for data systems,
137 00:15:15.410 ⇒ 00:15:30.960 Greg Stoutenburg: Whereas Emerson is, like, that’s the… that is the source of data. So, yeah, Polytomic, I mean, this is just from the snippet on their website, combines ETL and reverse ETL in a single platform, so you can move your data to and from your data warehouse without managing pipelines.
138 00:15:31.080 ⇒ 00:15:36.839 Greg Stoutenburg: So… so polytomic… you’d use polytomic to hook up to Emerson.
139 00:15:37.510 ⇒ 00:15:38.550 Jasmin Multani: Yeah, okay, okay.
140 00:15:38.550 ⇒ 00:15:44.109 Greg Stoutenburg: hook Snowflake up to Emerson. Now, I’m not positive if that’s the way that we have it built, but just as an example.
141 00:15:44.550 ⇒ 00:15:51.800 Jasmin Multani: Yeah, yeah. And I’m like, okay, so we have Amazon, we have Shopify, but what is… how does Amazon… how does Emerson…
142 00:15:52.230 ⇒ 00:15:58.769 Jasmin Multani: How’s Emerson different from, like, what we get from Amazon and Shopify? Is it the same thing? Yeah. Is it, like…
143 00:15:59.900 ⇒ 00:16:00.940 Jasmin Multani: Clayton can ask.
144 00:16:00.940 ⇒ 00:16:05.600 Greg Stoutenburg: Yeah, I don’t know why what is in Emerson is in Emerson, rather than…
145 00:16:05.990 ⇒ 00:16:15.769 Greg Stoutenburg: from elsewhere. I mean, well, for one thing, it might not be for long, because Shivani and Utam are frustrated with Emerson, so they might be moving to… I think they said Muffin Data?
146 00:16:16.160 ⇒ 00:16:18.149 Jasmin Multani: Yeah, I also don’t know what muffin data, like…
147 00:16:18.150 ⇒ 00:16:19.310 Greg Stoutenburg: Yeah, yeah.
148 00:16:19.620 ⇒ 00:16:25.789 Greg Stoutenburg: So here, I mean, the critical thing is just, like, what, right, like, what kind of service is it?
149 00:16:26.120 ⇒ 00:16:30.649 Greg Stoutenburg: I think Muffin Data is just a competitor for…
150 00:16:32.050 ⇒ 00:16:36.889 Greg Stoutenburg: Analytics platform for emerging brands, okay. Yeah, so Muffin Data is specifically
151 00:16:37.690 ⇒ 00:16:41.740 Greg Stoutenburg: Data and analytics for food and beverage brands.
152 00:16:42.940 ⇒ 00:16:45.490 Greg Stoutenburg: They’re specifically for CPG. Okay.
153 00:16:45.490 ⇒ 00:16:52.970 Jasmin Multani: I’m also… I’m also leaning on Cursor right now to find Emerson, and it’s like, okay, so for the client.
154 00:16:53.140 ⇒ 00:16:54.140 Jasmin Multani: element.
155 00:16:54.380 ⇒ 00:16:58.890 Jasmin Multani: Emerson equals the shared Target plus Walmart retail data set.
156 00:17:00.340 ⇒ 00:17:02.810 Jasmin Multani: So I guess that’s the e-commerce side of…
157 00:17:03.120 ⇒ 00:17:10.410 Greg Stoutenburg: I mean, well, we know that they were using them for some of retail,
158 00:17:12.130 ⇒ 00:17:13.859 Greg Stoutenburg: Or wait, no, or is that wholesale?
159 00:17:17.050 ⇒ 00:17:18.119 Greg Stoutenburg: One moment.
160 00:17:19.339 ⇒ 00:17:21.259 Jasmin Multani: Yeah, I should map this out with a wage.
161 00:17:21.560 ⇒ 00:17:25.400 Greg Stoutenburg: I think it’s actually already in there. So, in the data platform documentation.
162 00:17:29.790 ⇒ 00:17:32.620 Greg Stoutenburg: We can actually look at Topics here?
163 00:17:32.620 ⇒ 00:17:33.260 Jasmin Multani: really the biggest area.
164 00:17:33.260 ⇒ 00:17:40.579 Greg Stoutenburg: And then look over to sources and systems. So, for retail, yeah, we’ve got a lot that we’re getting from Emerson.
165 00:17:41.050 ⇒ 00:17:43.640 Greg Stoutenburg: From Target and Walmart.
166 00:17:45.100 ⇒ 00:17:49.650 Greg Stoutenburg: for… Oh, it just says stored. Stored comes from stored.
167 00:17:49.910 ⇒ 00:17:53.570 Greg Stoutenburg: Not exactly informative, but we’ll take it.
168 00:17:53.920 ⇒ 00:17:59.749 Greg Stoutenburg: Wholesale is coming from… Shopify, internal spreadsheets, and wholesale orders.
169 00:18:00.950 ⇒ 00:18:02.340 Jasmin Multani: internal spreadsheets…
170 00:18:02.900 ⇒ 00:18:06.979 Greg Stoutenburg: Some of supply chain… yeah, I think… I think that’s probably referring to these.
171 00:18:07.630 ⇒ 00:18:12.930 Jasmin Multani: Mmm… Hmm, okay.
172 00:18:13.510 ⇒ 00:18:20.749 Greg Stoutenburg: Yeah, so here, for example, this order detail with delivery date is a spreadsheet that’s coming from Emerson.
173 00:18:24.150 ⇒ 00:18:27.289 Jasmin Multani: Soon to be SBS this time.
174 00:18:28.980 ⇒ 00:18:30.900 Greg Stoutenburg: Yeah, I don’t know what SPS is.
175 00:18:34.550 ⇒ 00:18:36.270 Jasmin Multani: They’re getting the EPR, right?
176 00:18:41.300 ⇒ 00:18:43.029 Greg Stoutenburg: Would that be one of these reports, you mean?
177 00:18:47.040 ⇒ 00:18:55.849 Jasmin Multani: No, not the reports. They say that they’re gonna integrate an APR, which will, like, eliminate the need for certain reports.
178 00:18:57.330 ⇒ 00:18:58.810 Greg Stoutenburg: What’s EPR?
179 00:18:59.820 ⇒ 00:19:02.210 Jasmin Multani: I think it’s, like, an accounting software.
180 00:19:03.420 ⇒ 00:19:04.230 Greg Stoutenburg: Okay.
181 00:19:05.640 ⇒ 00:19:07.510 Jasmin Multani: What is EPR and supply?
182 00:19:07.510 ⇒ 00:19:09.140 Greg Stoutenburg: You mean ERP? ERP.
183 00:19:09.470 ⇒ 00:19:16.869 Greg Stoutenburg: Oh, okay, alright, sorry. That one’s on me, because I do know what ERP means. So, ERP is, like, that’s like,
184 00:19:17.140 ⇒ 00:19:31.229 Greg Stoutenburg: for a business that’s, like, executive level, production management, tracking, order handling, forecasting, that kind of thing. What does it stand for? I will look it up, but I want to see if I can still do this.
185 00:19:32.550 ⇒ 00:19:35.799 Greg Stoutenburg: I think it’s Executive Reporting Platform. Let’s see how I did.
186 00:19:36.600 ⇒ 00:19:37.969 Jasmin Multani: Enterprise Resources.
187 00:19:38.340 ⇒ 00:19:40.029 Greg Stoutenburg: Enterprise, that’s it.
188 00:19:40.900 ⇒ 00:19:41.610 Jasmin Multani: Close.
189 00:19:41.910 ⇒ 00:19:45.170 Greg Stoutenburg: Yeah. Enterprise Resource Planning. Okay.
190 00:19:46.000 ⇒ 00:19:51.930 Greg Stoutenburg: Yeah, okay. So… but this is just… that’s just a concept. This doesn’t tell us, like, what…
191 00:19:52.390 ⇒ 00:19:55.050 Greg Stoutenburg: tool they would have in mind, and Omni is not such a tool.
192 00:19:55.510 ⇒ 00:19:56.660 Jasmin Multani: Is NetSuite?
193 00:19:57.780 ⇒ 00:19:59.049 Greg Stoutenburg: I think NetSuite might be.
194 00:19:59.260 ⇒ 00:20:01.190 Jasmin Multani: ERP.
195 00:20:02.330 ⇒ 00:20:03.080 Greg Stoutenburg: Yeah, there you go.
196 00:20:03.340 ⇒ 00:20:10.729 Jasmin Multani: NetSuite, yeah. I think I heard NetSuite come up, but I don’t know what, like, the timeline for that is.
197 00:20:10.730 ⇒ 00:20:11.360 Greg Stoutenburg: Yeah.
198 00:20:11.960 ⇒ 00:20:14.859 Greg Stoutenburg: Yeah, they’ve mentioned… they have mentioned NetSuite.
199 00:20:15.240 ⇒ 00:20:18.090 Greg Stoutenburg: Okay.
200 00:20:19.810 ⇒ 00:20:22.440 Greg Stoutenburg: Yeah, that would be an Ouation Utam question.
201 00:20:24.160 ⇒ 00:20:27.300 Greg Stoutenburg: Yeah, okay.
202 00:20:27.420 ⇒ 00:20:28.220 Jasmin Multani: Yeah.
203 00:20:29.060 ⇒ 00:20:31.550 Jasmin Multani: But then back to,
204 00:20:33.290 ⇒ 00:20:40.639 Jasmin Multani: back to the data documentation. Yes. If we… if we go through with a muffin, which of those
205 00:20:41.890 ⇒ 00:20:46.349 Jasmin Multani: data sources will be swapped out for muffin data.
206 00:20:46.690 ⇒ 00:20:50.469 Greg Stoutenburg: I believe, as of right now, that it would be…
207 00:20:51.480 ⇒ 00:20:55.099 Greg Stoutenburg: I think just anything for supply chain.
208 00:20:56.830 ⇒ 00:21:00.320 Greg Stoutenburg: or wholesale that relies on Emerson.
209 00:21:00.990 ⇒ 00:21:04.989 Greg Stoutenburg: This just came up last week in the.
210 00:21:09.140 ⇒ 00:21:14.100 Greg Stoutenburg: Yeah, in… we talked about this in the presentation, let me see.
211 00:21:23.730 ⇒ 00:21:31.750 Greg Stoutenburg: Retail, okay, muffin data for retail data availability, so then that would cut out… Probably all these.
212 00:21:35.160 ⇒ 00:21:37.299 Jasmin Multani: person in retail.
213 00:21:37.300 ⇒ 00:21:37.960 Greg Stoutenburg: Nope.
214 00:21:38.340 ⇒ 00:21:39.670 Greg Stoutenburg: And then…
215 00:21:45.950 ⇒ 00:21:50.389 Greg Stoutenburg: Yeah, see, it’s Muffin Data versus Emerson for retail. Retailer…
216 00:21:57.770 ⇒ 00:21:58.520 Greg Stoutenburg: Yup.
217 00:21:59.060 ⇒ 00:21:59.730 Greg Stoutenburg: Go.
218 00:22:03.510 ⇒ 00:22:04.320 Greg Stoutenburg: Okay.
219 00:22:05.420 ⇒ 00:22:07.120 Greg Stoutenburg: Okay.
220 00:22:07.250 ⇒ 00:22:11.450 Greg Stoutenburg: In our last bit here… where did I put it?
221 00:22:12.210 ⇒ 00:22:15.830 Greg Stoutenburg: Okay, so we’ve just talked about e-commerce. It sounds like we…
222 00:22:16.100 ⇒ 00:22:21.179 Greg Stoutenburg: We need to get, we both need to do some homework and get caught up on that.
223 00:22:21.510 ⇒ 00:22:30.830 Greg Stoutenburg: To understand where we’re at with that, and then I’m just… this item 4 there is just me stating I know ticket cleanup is on me.
224 00:22:31.510 ⇒ 00:22:35.530 Jasmin Multani: I’ve been… I’ve been, like, trying to do, like, clear things out, as.
225 00:22:35.670 ⇒ 00:22:36.270 Greg Stoutenburg: Are they…
226 00:22:36.270 ⇒ 00:22:37.430 Jasmin Multani: not have been…
227 00:22:38.040 ⇒ 00:22:48.029 Jasmin Multani: trying to, get through the dashboard stuff, but there is also, like, a backlog of things that… from a month ago, that I have to be like, okay, wait, I already did this, I just never…
228 00:22:48.320 ⇒ 00:22:55.660 Greg Stoutenburg: Yeah, right, or this is the… yeah, I never updated the ticket, or actually, I never even made a ticket to do this, yeah. Yeah, I think there’ll be things like that, and I think…
229 00:22:56.090 ⇒ 00:23:10.499 Greg Stoutenburg: I think the biggest thing for the linear tickets is just making sure that the direction that we’re going in is accounted for, and for that, I need to, I just nudged him a couple hours ago, but I need to get some clarity from Robert on
230 00:23:10.500 ⇒ 00:23:24.019 Greg Stoutenburg: who is owning, laying out the roadmap for what we’re doing for supply chain. Because, you know, we’ve got all the discovery stuff, and then there’s comments in there, like, you know, Garrett was supposed to do this, I’m picking it up now, so I just need to make sure that I’ve got,
231 00:23:24.030 ⇒ 00:23:26.870 Greg Stoutenburg: I’ve gotten a solid understanding from him on what we’re…
232 00:23:26.940 ⇒ 00:23:32.610 Greg Stoutenburg: what we’re shooting for, for the roadmap for this, and then either he’ll run with it or I’ll run with it, I guess.
233 00:23:32.610 ⇒ 00:23:39.510 Jasmin Multani: Yeah, I think, the first layer should be just, like, one map.
234 00:23:39.650 ⇒ 00:23:49.709 Jasmin Multani: End-to-end. That’s literally, like, who do we get? Who are the external people that we work with? Alright, this includes, like, warehouses, this includes the softwares.
235 00:23:49.760 ⇒ 00:23:50.310 Greg Stoutenburg: Yeah.
236 00:23:50.310 ⇒ 00:23:59.049 Jasmin Multani: How do they go into… what information do they send into Element, and what information gets shoot out of Element?
237 00:23:59.180 ⇒ 00:24:06.110 Jasmin Multani: Yes. From this black box of, like, what Element is doing, To decide, like.
238 00:24:06.320 ⇒ 00:24:16.770 Jasmin Multani: allocation, demand planning, that should be treated as a separate box, because it’s such a moving target, and we want to make that piece as flexible as we can.
239 00:24:17.140 ⇒ 00:24:23.590 Jasmin Multani: And, like, that’s gonna have to be, like, partially automated, partially flexible, so that they can still, like… have…
240 00:24:23.760 ⇒ 00:24:25.830 Jasmin Multani: The chance to keep iterating.
241 00:24:26.420 ⇒ 00:24:30.019 Jasmin Multani: But my feed… when… my feedback to Ashwini was like.
242 00:24:30.400 ⇒ 00:24:42.499 Jasmin Multani: instead of doing, like, writing KPIs, just design stored content, stored data as, like, whole numbers. Let’s focus on whole numbers instead of being like, this is the numerator, this is the denominator, right?
243 00:24:42.860 ⇒ 00:24:44.570 Jasmin Multani: It’s just like, let’s figure out…
244 00:24:44.810 ⇒ 00:24:54.650 Jasmin Multani: Whatever we think is the numerator and denominator, let’s store them as separate pieces, standalone pieces, before we start, like,
245 00:24:55.050 ⇒ 00:24:57.660 Jasmin Multani: Doing additions, calculations, whatever.
246 00:24:57.660 ⇒ 00:24:58.230 Greg Stoutenburg: Yeah.
247 00:24:58.230 ⇒ 00:25:01.809 Jasmin Multani: Let’s confirm those first. That’s what my gut tells me.
248 00:25:02.030 ⇒ 00:25:04.800 Greg Stoutenburg: Sounds good. Yeah. Okay. I’m on board.
249 00:25:05.000 ⇒ 00:25:07.280 Greg Stoutenburg: Yeah, cool.
250 00:25:07.650 ⇒ 00:25:10.540 Jasmin Multani: I usually Or, like, the actual IC work.
251 00:25:10.540 ⇒ 00:25:10.970 Greg Stoutenburg: Yeah, right.
252 00:25:10.970 ⇒ 00:25:11.879 Jasmin Multani: It’s our time.
253 00:25:11.880 ⇒ 00:25:16.740 Greg Stoutenburg: How to do it! Yeah, and that’s the time-consuming part. Yeah, yeah, videos are great.
254 00:25:16.740 ⇒ 00:25:17.340 Jasmin Multani: on it.
255 00:25:17.500 ⇒ 00:25:21.340 Jasmin Multani: Pushing out the dashboards and, like, Fetting things?
256 00:25:21.340 ⇒ 00:25:21.890 Greg Stoutenburg: Yeah.
257 00:25:21.890 ⇒ 00:25:29.929 Jasmin Multani: And, like, getting alignment that, like, hey, there are all these, like, asks for dashboards May 22nd, or something else.
258 00:25:29.930 ⇒ 00:25:32.600 Greg Stoutenburg: Right, right, right, right, yeah, yeah.
259 00:25:33.280 ⇒ 00:25:39.760 Greg Stoutenburg: Yes. Yes. That… that does happen, and I think, again, you know, the cleanup linear is going to help us.
260 00:25:40.690 ⇒ 00:25:57.789 Greg Stoutenburg: go, like, hey, here’s our… as far as, like, what we’ve promised to deliver, you can see it all laid out here in linear. So, if you want to add something, we can do it, but, you know, we’re gonna be conscious of the fact that that’s going to affect something else, and we need to be clear about what the priorities are in any given sprint.
261 00:25:59.660 ⇒ 00:26:05.869 Greg Stoutenburg: Do we have, like, a handy map somewhere? I was thinking about this this morning. Do we have, like, a handy map that shows, like, what the sprint dates are?
262 00:26:06.070 ⇒ 00:26:08.929 Greg Stoutenburg: For Element, say, going forward a year?
263 00:26:09.430 ⇒ 00:26:10.790 Jasmin Multani: He has it.
264 00:26:11.000 ⇒ 00:26:12.020 Greg Stoutenburg: Okay.
265 00:26:12.020 ⇒ 00:26:17.500 Jasmin Multani: She’s using it internally, and, like, we’ve literally asked her for it. I think,
266 00:26:17.610 ⇒ 00:26:22.030 Jasmin Multani: I think somewhere, Robert took, like, a sneaky screenshot.
267 00:26:22.280 ⇒ 00:26:22.780 Greg Stoutenburg: Okay.
268 00:26:22.780 ⇒ 00:26:31.670 Jasmin Multani: after we literally asked her, and she just ignored us, so I think he took a sneaky screenshot of how she draws up the sprinting.
269 00:26:31.670 ⇒ 00:26:32.010 Greg Stoutenburg: Yeah.
270 00:26:32.010 ⇒ 00:26:33.669 Jasmin Multani: There is no, like…
271 00:26:34.720 ⇒ 00:26:36.650 Greg Stoutenburg: She’s not, like, published a calendar for us.
272 00:26:36.650 ⇒ 00:26:41.290 Jasmin Multani: No, and she… it looks like she, Robert, and Uta meet…
273 00:26:41.440 ⇒ 00:26:46.259 Jasmin Multani: once a month to plan it out. So, I think her split plannings are…
274 00:26:46.700 ⇒ 00:26:55.320 Jasmin Multani: with a month forward look ahead, where it’s like, I think I… because I asked Robert about this, too, because of the OKR stuff,
275 00:26:55.430 ⇒ 00:27:00.159 Jasmin Multani: But it looks like hers are, like, a month ahead, ours are, like, a week by weeks.
276 00:27:00.560 ⇒ 00:27:00.970 Greg Stoutenburg: Okay.
277 00:27:00.970 ⇒ 00:27:10.159 Jasmin Multani: So that’s also what I, like, asked for clarity on. It’s like, hey, as we’re being measured for, like, ticket completion and e-book completion, it’s like, how…
278 00:27:10.380 ⇒ 00:27:12.039 Greg Stoutenburg: Against what? Yeah, it’s like…
279 00:27:12.090 ⇒ 00:27:18.170 Jasmin Multani: Does this mean we’re making… if there are 52 weeks, does this mean we’re making 52 playbooks by the end of the year?
280 00:27:18.170 ⇒ 00:27:20.770 Greg Stoutenburg: Right, right. Yeah. Hope not.
281 00:27:20.770 ⇒ 00:27:22.110 Jasmin Multani: Yeah, I don’t.
282 00:27:22.110 ⇒ 00:27:23.520 Greg Stoutenburg: We would do with 52 playbooks.
283 00:27:23.520 ⇒ 00:27:32.960 Jasmin Multani: Yeah, yeah. His response was, like, as long as it’s, like, an addition, or… it doesn’t have to be a net new creation, it could just be, like, an uplift, or an iteration, or whatever.
284 00:27:33.340 ⇒ 00:27:33.990 Greg Stoutenburg: Okay.
285 00:27:34.710 ⇒ 00:27:35.560 Greg Stoutenburg: But… Okay.
286 00:27:35.560 ⇒ 00:27:39.709 Jasmin Multani: Yeah, expectation is, like, to have more than 3. Yeah.
287 00:27:40.440 ⇒ 00:27:49.379 Greg Stoutenburg: Yeah, yeah, I mean… I mean, really the goal just being, like, we’ve got routine patterns that we follow when we’re doing work that’s gonna be delivered more than one time.
288 00:27:49.690 ⇒ 00:27:52.490 Greg Stoutenburg: So… Yeah.
289 00:27:52.810 ⇒ 00:27:54.340 Greg Stoutenburg: That’s the spirit of it.
290 00:27:55.760 ⇒ 00:27:56.310 Greg Stoutenburg: Yeah.
291 00:27:57.270 ⇒ 00:27:59.679 Greg Stoutenburg: Cool, alright, anything else?
292 00:28:00.060 ⇒ 00:28:04.650 Jasmin Multani: No, nothing else. Thank you so much for providing structure. I really, really appreciate it.
293 00:28:04.650 ⇒ 00:28:08.320 Greg Stoutenburg: Yep, cool, yep, let’s keep it rolling. Alright, see you in a bit.
294 00:28:08.650 ⇒ 00:28:09.440 Jasmin Multani: Bye.
295 00:28:09.440 ⇒ 00:28:09.960 Greg Stoutenburg: Bye.