Meeting Title: LMNT January Planning Date: 2025-12-30 Meeting participants: Awaish Kumar, Uttam Kumaran, Shivani Amar
WEBVTT
1 00:00:23.080 ⇒ 00:00:23.790 Uttam Kumaran: Anyway…
2 00:00:24.360 ⇒ 00:00:25.220 Awaish Kumar: Hello.
3 00:00:57.190 ⇒ 00:00:58.470 Shivani Amar: Hello!
4 00:00:59.160 ⇒ 00:00:59.920 Uttam Kumaran: Hey!
5 00:01:00.270 ⇒ 00:01:01.349 Shivani Amar: Good to see you guys. How’s everything?
6 00:01:02.260 ⇒ 00:01:02.770 Shivani Amar: Oh, thank you.
7 00:01:02.770 ⇒ 00:01:03.140 Uttam Kumaran: Good.
8 00:01:03.140 ⇒ 00:01:04.949 Shivani Amar: Good. How about you guys?
9 00:01:05.530 ⇒ 00:01:07.300 Uttam Kumaran: Good. Yeah, relaxed.
10 00:01:08.760 ⇒ 00:01:11.500 Uttam Kumaran: Yeah, it’s been a relaxing two weeks.
11 00:01:11.500 ⇒ 00:01:13.360 Shivani Amar: Good. Ush, how are you doing?
12 00:01:13.730 ⇒ 00:01:17.089 Awaish Kumar: Yeah, all good. It has been nice. How about you?
13 00:01:17.810 ⇒ 00:01:30.749 Shivani Amar: It’s really cold out, but otherwise, life is good. It’s like the first winter… I don’t know, actually, we’ll see, because we still have time, but I’m like, my spirits are pretty up relative to the weather outside, so…
14 00:01:30.750 ⇒ 00:01:35.169 Uttam Kumaran: Got a hammer of vitamin D, I feel like.
15 00:01:35.780 ⇒ 00:01:43.389 Shivani Amar: And… Yeah, I have the beautiful Urban Stems flowers in my apartment, so it was very nice.
16 00:01:43.390 ⇒ 00:01:46.040 Uttam Kumaran: Yay, nice.
17 00:01:46.040 ⇒ 00:01:59.589 Shivani Amar: Yeah, there’s… I feel like there… like, in this January sprint, you have an agenda of things we can talk about, but, like, one of the things I’ll just add that I was thinking about was, like.
18 00:02:00.310 ⇒ 00:02:16.800 Shivani Amar: like… but then, like, a time for me and you, or I don’t know, whatever, for us to, like, really sit with what Source Medium has done, because I know if we say, like, okay, theoretically, one day Source Medium is phased out, right? Like, let’s say that that’s the goal, but not, like, pressing.
19 00:02:16.910 ⇒ 00:02:18.049 Shivani Amar: And it’s like…
20 00:02:18.330 ⇒ 00:02:23.529 Shivani Amar: Is the way that they’re showing the metrics the most useful, or are there better ways to, like, deliver.
21 00:02:23.870 ⇒ 00:02:38.259 Shivani Amar: insights to people, and so I don’t know if we want to try and do that in January, just like a working session, like, the three of us, or the two of us, or whatever, but I was staring at it yesterday, and I was like, it’s, like, got the right data, I guess, but it’s, like, but it’s…
22 00:02:38.370 ⇒ 00:02:54.379 Shivani Amar: it’s messy, and, like, if I were to… if I were Carlos, like, what would I want answered readily? And I’m like, I feel like that could be a nice thing to aim towards now that we’ve done the discovery with, like, e-commerce and stuff like that. Does that resonate with you?
23 00:02:54.840 ⇒ 00:03:00.319 Uttam Kumaran: Yeah, and it’s also, there’s no, like, owner internally, kind of clearly, right? Yeah. And…
24 00:03:00.500 ⇒ 00:03:10.020 Uttam Kumaran: I feel like it’s a lot of, like, okay, it works, and, like, we’re getting the right data, but then nobody’s probably looked into, like, the inner workings in a long time. Someone’s like.
25 00:03:10.320 ⇒ 00:03:12.720 Uttam Kumaran: We’re just gonna trust it, you know?
26 00:03:12.720 ⇒ 00:03:13.170 Shivani Amar: Exactly.
27 00:03:13.170 ⇒ 00:03:24.050 Uttam Kumaran: You can tell that’s happening, and so it’s up to us to sort of break that open and say, like, are we comfortable with this? There is also gonna definitely be, like, redundancy there versus what’s coming out of this system.
28 00:03:24.560 ⇒ 00:03:25.360 Uttam Kumaran: So…
29 00:03:25.360 ⇒ 00:03:26.159 Shivani Amar: 100%, yeah.
30 00:03:26.160 ⇒ 00:03:27.610 Uttam Kumaran: Yeah, yeah.
31 00:03:27.940 ⇒ 00:03:37.279 Shivani Amar: that was just, like, another thing in my mind. I was like, what can we, like, you know, like, what are we digesting? What are we discovering? But then, like, what are we actually, like, generating? Like, again, it’s like.
32 00:03:37.630 ⇒ 00:03:54.199 Shivani Amar: I… hold me accountable, right? If I start thinking, like, let’s generate some insights, and it’s like, it’s… to be deliberate equals getting definitions laid out, and like, there’s still a lot of work to do, like, help me understand where we are in that journey, right? Because maybe my…
33 00:03:54.480 ⇒ 00:04:00.200 Shivani Amar: maybe I was, like, just thinking about it, I was like, oh, let’s get wholesale, like, a version of a dashboard, but, like.
34 00:04:00.200 ⇒ 00:04:17.230 Shivani Amar: if we still have a lot of cleanup we need to do, definitions we need to align, that’s, like, where the good stuff is, right? So, I just want to preface, it’s like, I might start getting excited about things myself, and we all need to say, like, what is the, like, super methodical way of doing this, versus, like, the racing to something…
35 00:04:17.230 ⇒ 00:04:20.099 Shivani Amar: Some answer to one question that’s not gonna be, like.
36 00:04:20.660 ⇒ 00:04:25.490 Shivani Amar: You know, trusted long-term because we didn’t do our, like, thorough work kind of thing.
37 00:04:25.490 ⇒ 00:04:26.040 Uttam Kumaran: Yeah.
38 00:04:26.250 ⇒ 00:04:34.829 Uttam Kumaran: Yeah, I think… I think that’s what we’ll talk about today, is, like, what is our goal for Jan, basically, and start to see, like, do we want to…
39 00:04:34.870 ⇒ 00:04:47.969 Uttam Kumaran: continue to drive towards, like, a BI tool? Is it more necessary to drive towards, like, a decision on source medium, or, like, a preliminary report? Because also, wholesale is getting, like, no support.
40 00:04:48.100 ⇒ 00:04:57.129 Uttam Kumaran: Right? So, when we’re thinking about that, that’s, like, something we’ll discuss today, is, like, okay, should we solve their problems? If they’re, like, sort of off in the ether.
41 00:04:57.200 ⇒ 00:05:11.930 Uttam Kumaran: Or should we focus on, like, Carlos’ world, where he’s already getting some support from source medium? It may not be, like, trusted and accurate, but at least they’re getting data, versus on wholesale, we saw that they’re very limited on, like, their…
42 00:05:12.040 ⇒ 00:05:14.140 Uttam Kumaran: What they’re able to get out of the system.
43 00:05:14.210 ⇒ 00:05:15.270 Shivani Amar: Right.
44 00:05:15.430 ⇒ 00:05:31.699 Uttam Kumaran: And then I’m also curious on, like… like, I would like to know more about the contract details on Source Medium, like, how much are we paying? What’s, like, what is our scope with them? Like, maybe they can support a small slice of things that… and we can phase them out on a longer sort of cycle.
45 00:05:31.700 ⇒ 00:05:47.109 Shivani Amar: I think phasing them out longer term is probably the move, right? Like, I would say that that’s… we don’t need, like, multiple data consultants and stuff like that, so I think that’s aligned. I don’t even know what the contra… the cost is, like, kind of irrelevant to me. Like, it’s like, that’ll go in.
46 00:05:47.110 ⇒ 00:06:04.400 Uttam Kumaran: More is just so I can… I can know about, yeah, more of, like, I just want to know what the details are, what you guys are paying them, so we can see, okay, is there still some value? If you’re signing up for them for another year, then it’s like, okay, maybe we see if we can get… they can still support some… and sort of figure out the phased-out plan.
47 00:06:04.420 ⇒ 00:06:10.679 Uttam Kumaran: Versus moving on to ours. Versus if they’re, like, if they’re coming up on, like, okay, they’re gonna be phased out by, like, February.
48 00:06:10.990 ⇒ 00:06:15.010 Uttam Kumaran: then it’s like, okay, I need to sort of think a little bit about, like, the timing.
49 00:06:15.580 ⇒ 00:06:29.049 Shivani Amar: I don’t think we have a timeline to phase them out. Okay. So, I also don’t… I haven’t heard anything about, like, we’re locked in with them until X, nor have I heard… I’m sure there’s, like, similar to you guys, there’s probably a period of notice.
50 00:06:29.270 ⇒ 00:06:44.729 Shivani Amar: Right? Like, which is, like, for you guys, it’s 14 days. I don’t know what their contract says, but let’s say it’s 30 or 60, and it’s like, then that’s fine. It’s like, when we decide that we feel ready to, like, phase that out, I think it’s us leading it versus, like, their contract leading it.
51 00:06:45.220 ⇒ 00:06:46.170 Uttam Kumaran: Okay, okay, cool.
52 00:06:47.090 ⇒ 00:06:56.049 Shivani Amar: Cool. Okay, so… you had an agenda, or I had sent some topics, but do you want to go through, like, I feel like, how do you want to structure this time?
53 00:06:56.050 ⇒ 00:06:57.670 Uttam Kumaran: Yeah, Awish, do you want to lead?
54 00:07:00.470 ⇒ 00:07:03.810 Awaish Kumar: Yeah, I… I’ll just share the slides.
55 00:07:16.340 ⇒ 00:07:17.170 Awaish Kumar: Okay.
56 00:07:17.740 ⇒ 00:07:23.080 Awaish Kumar: So yeah, we, mainly just looking to discuss, like, the…
57 00:07:23.390 ⇒ 00:07:29.530 Awaish Kumar: Gantt chart, and then they review it, and then review the ingestion blockers.
58 00:07:29.840 ⇒ 00:07:34.550 Awaish Kumar: Then what we are looking to deliver in, January.
59 00:07:35.070 ⇒ 00:07:46.430 Awaish Kumar: And then there are some topics to discuss. We can go over it in more details. So I can just start with the slide congestion blockers.
60 00:07:47.150 ⇒ 00:07:52.790 Awaish Kumar: So, yeah, we started ingesting data on 22nd of December.
61 00:07:52.970 ⇒ 00:07:57.329 Awaish Kumar: We set up the Polytomic and Shopify and Recharge connections.
62 00:07:57.560 ⇒ 00:08:09.330 Awaish Kumar: Shopify data is flowing in for orders and most of this stuff, which we want to do analytics on, but there is some data regarding refunds and payments.
63 00:08:09.350 ⇒ 00:08:20.309 Awaish Kumar: So that’s not flowing in because of some permission issues. So we, like, we need to ask tech team to increase permission and scope for that token, which is being used in Polytomic.
64 00:08:20.380 ⇒ 00:08:24.929 Awaish Kumar: So we just get more permissions, and we can get refunds data.
65 00:08:25.050 ⇒ 00:08:28.400 Awaish Kumar: Which would be, like.
66 00:08:28.570 ⇒ 00:08:31.750 Awaish Kumar: Relevant to where we are building a sales model.
67 00:08:31.910 ⇒ 00:08:49.480 Awaish Kumar: Then for immersion, so we have access to data, but that’s on different Snowflake instance, now that we have our, like, internal, Snowflake instance, where all the data is being landed on, so we just want them, like the immersion team, to…
68 00:08:49.500 ⇒ 00:08:54.799 Awaish Kumar: directly share it with the elements in the Snowflake instance, which we have created.
69 00:08:54.890 ⇒ 00:09:00.729 Awaish Kumar: instead the one they shared, and we can’t do that because of some,
70 00:09:00.950 ⇒ 00:09:03.929 Awaish Kumar: Because it’s already a share, so we can’t share a share.
71 00:09:03.930 ⇒ 00:09:05.140 Shivani Amar: Okay, gotcha.
72 00:09:07.750 ⇒ 00:09:08.370 Shivani Amar: Okay.
73 00:09:08.370 ⇒ 00:09:15.020 Uttam Kumaran: So this… so for this item, I think it’s just… we’ll just draft the email. I think if Jason has the contact there, then I can…
74 00:09:15.780 ⇒ 00:09:19.410 Uttam Kumaran: He can send that over, or if you’d like us to send it directly, we can go direct.
75 00:09:19.800 ⇒ 00:09:22.660 Shivani Amar: Yeah, yeah. Have you been put on an email chain with them?
76 00:09:23.520 ⇒ 00:09:33.069 Uttam Kumaran: I have not been on a chain with them. I just got forwarded the last… we were asking them just, like, what are other ways to get the Emerson data?
77 00:09:33.420 ⇒ 00:09:34.800 Uttam Kumaran: I just have.
78 00:09:34.800 ⇒ 00:09:36.120 Shivani Amar: forwarded the reply.
79 00:09:36.230 ⇒ 00:09:44.610 Shivani Amar: Yeah, that’s fine. I guess then… I guess draft it, and then when Jason comes… when we’re all back, we can figure out, like, does it make sense for you to have a direct line with Emerson at some point?
80 00:09:46.380 ⇒ 00:09:46.920 Awaish Kumar: Okay.
81 00:09:47.270 ⇒ 00:09:57.959 Awaish Kumar: But after immersion, we have Spins, so we have been waiting on Matt Davis from Spins, so we have this, like, the email thread, so we need to follow there.
82 00:09:58.540 ⇒ 00:10:02.989 Awaish Kumar: Like, what’s the status, and how long it’s going to take to get this data?
83 00:10:03.440 ⇒ 00:10:06.639 Shivani Amar: Matt Davis. Okay, so Spins Access…
84 00:10:06.870 ⇒ 00:10:13.590 Shivani Amar: Your signature, maybe I just didn’t review the documents, sorry. I thought I did. Let me…
85 00:10:13.770 ⇒ 00:10:20.510 Shivani Amar: Let me send him a note, and… Say…
86 00:10:21.480 ⇒ 00:10:23.950 Shivani Amar: Let me send him a note right now.
87 00:10:51.480 ⇒ 00:10:52.720 Shivani Amar: Okay.
88 00:10:56.180 ⇒ 00:11:04.829 Awaish Kumar: Okay, after that, we have Amazon, Where to Go, and Walmart. These are the sources, for which Polyatomic is building the connector.
89 00:11:05.100 ⇒ 00:11:10.239 Awaish Kumar: And they’re not working, like, this week, so they will be back on Gen 5, and then…
90 00:11:10.370 ⇒ 00:11:12.670 Awaish Kumar: They can start delivering these connectors.
91 00:11:13.400 ⇒ 00:11:19.510 Shivani Amar: Out of curiosity, did, did Fivetran have connectors to this easily?
92 00:11:20.010 ⇒ 00:11:20.620 Shivani Amar: Just out of…
93 00:11:20.620 ⇒ 00:11:23.529 Uttam Kumaran: Fran just has… just has the Amazon one.
94 00:11:24.090 ⇒ 00:11:24.730 Shivani Amar: Project.
95 00:11:25.540 ⇒ 00:11:26.040 Uttam Kumaran: Yeah.
96 00:11:26.560 ⇒ 00:11:27.240 Shivani Amar: Gotcha.
97 00:11:27.800 ⇒ 00:11:30.169 Shivani Amar: But you’re not, you’re like, this will happen quickly.
98 00:11:31.220 ⇒ 00:11:35.470 Uttam Kumaran: Yeah, they would have had it by this week, it’s literally just Christmas week.
99 00:11:35.470 ⇒ 00:11:35.870 Shivani Amar: Okay, so…
100 00:11:35.870 ⇒ 00:11:37.010 Uttam Kumaran: It should happen in one week.
101 00:11:37.010 ⇒ 00:11:38.020 Shivani Amar: Okay, perfect.
102 00:11:38.670 ⇒ 00:11:41.079 Awaish Kumar: Yeah, so we… Yeah, we can.
103 00:11:41.080 ⇒ 00:11:47.859 Uttam Kumaran: The Walmart connectors on Fivetran are not good at all, and then there’s nowhere to go in Fivetran, so…
104 00:11:47.860 ⇒ 00:11:49.280 Shivani Amar: Gotcha. Okay.
105 00:11:49.280 ⇒ 00:11:49.990 Uttam Kumaran: Yeah.
106 00:11:49.990 ⇒ 00:11:55.760 Awaish Kumar: Yeah, on this, we don’t need… there’s no action needed from our side. We will be just waiting on Polyatomic.
107 00:11:56.060 ⇒ 00:12:02.190 Awaish Kumar: So that’s all for the condition stuff. Then we have these.
108 00:12:02.510 ⇒ 00:12:10.760 Shivani Amar: Actually, can I ask a question? So, yes, there are blockers, but do you, like, if I were just to say, like, how do you feel about the data you’ve gotten so far? You’re like, we’ve gotten, like.
109 00:12:11.060 ⇒ 00:12:13.700 Shivani Amar: Rich amount of data, like… like…
110 00:12:14.190 ⇒ 00:12:18.849 Shivani Amar: We’re feeling the blockers mean that we can’t make a nice transaction table.
111 00:12:18.850 ⇒ 00:12:25.470 Uttam Kumaran: Can you go back a wish? Like, refunds and payments are, like, not gonna be the highest priority on Shopify.
112 00:12:25.470 ⇒ 00:12:26.120 Shivani Amar: Cool.
113 00:12:26.350 ⇒ 00:12:29.400 Uttam Kumaran: Like, payments… yeah, payments is, like.
114 00:12:30.200 ⇒ 00:12:35.860 Uttam Kumaran: We get some, like, credit card details, so typically clients ask us for that if they’re, like, interested in, like.
115 00:12:36.170 ⇒ 00:12:47.550 Uttam Kumaran: okay, are people who use Amex versus this, like, better customers, stuff like that, but it’s not really a priority. Refunds, refunds is important, but both of these will get cleared up, like.
116 00:12:48.120 ⇒ 00:12:48.760 Uttam Kumaran: Very, very.
117 00:12:48.760 ⇒ 00:12:49.250 Shivani Amar: Quincy.
118 00:12:49.250 ⇒ 00:12:57.030 Uttam Kumaran: It’s just, like, one press of a button in Shopify. The Emerson data we’ve already walked through, and then we’ll… the…
119 00:12:57.550 ⇒ 00:13:12.440 Uttam Kumaran: I’m excited to see the spin data, in particular, and the where to go data. We’re already familiar with Amazon and Walmart, what you get, so we’ll get everything we can from them. The Amazon will be, like, really, really rich.
120 00:13:12.440 ⇒ 00:13:18.560 Shivani Amar: And, like, now that you already… now that you already have a decent amount of Shopify data, like…
121 00:13:18.880 ⇒ 00:13:35.929 Shivani Amar: I’m curious, like, yes, there are blockers, but you’re like, hey, these aren’t the highest priority, like, things to have in the dataset right away. Like, now that you have a wealth of Shopify data, which is, I guess, what the source medium has in BigQuery or whatever, right? But what is that…
122 00:13:36.320 ⇒ 00:13:37.639 Shivani Amar: What is that, like…
123 00:13:38.280 ⇒ 00:13:45.530 Shivani Amar: Generate for you in terms of excitement about, like, like, okay, the tables that we can produce and… and things like that.
124 00:13:46.650 ⇒ 00:13:48.989 Uttam Kumaran: Yeah, so it’s… yeah, go ahead, Awish.
125 00:13:49.390 ⇒ 00:13:51.220 Awaish Kumar: So, like, it’s kind of… we have…
126 00:13:51.550 ⇒ 00:14:07.999 Awaish Kumar: almost all the data we need to build the models, like SalesMart, like, we have all the orders information, the total revenue generated out of this, and the customer information, we can generate, like, dim customer kind of tables out of this.
127 00:14:08.130 ⇒ 00:14:14.510 Awaish Kumar: We can calculate metrics like LTV on Shopify channel.
128 00:14:14.850 ⇒ 00:14:21.969 Awaish Kumar: And also, like, it will have wholesale data for Shopify, so he can generate, like.
129 00:14:22.310 ⇒ 00:14:38.770 Awaish Kumar: metrics for wholesale, wholesale customers mod, and if we pair it with, like, Google Sheets from… from Wholesale team, that can make it, like, very rich, dim customer… customer smart for us, for Wholesale team. So, like, we have, kind of.
130 00:14:39.040 ⇒ 00:14:42.600 Awaish Kumar: You can say we have 99% of the data we need.
131 00:14:42.600 ⇒ 00:14:57.539 Shivani Amar: Awesome. That’s awesome. Like, the, like… like, I even think about how… yeah, I just think about how people are, like, downloading snapshots at a time, and I’m like, the fact that we’re about to get, like, the history of something is just exciting to me, even though I don’t exactly.
132 00:14:57.540 ⇒ 00:14:59.740 Uttam Kumaran: And also from the source, right?
133 00:14:59.740 ⇒ 00:15:00.130 Shivani Amar: Yeah.
134 00:15:00.130 ⇒ 00:15:05.789 Uttam Kumaran: You’re basically, like… There’s no… there’s no intermediary now.
135 00:15:06.100 ⇒ 00:15:06.580 Uttam Kumaran: Yeah.
136 00:15:06.870 ⇒ 00:15:23.439 Uttam Kumaran: we don’t… the data we’re getting from Polyatomic is an exact reflection of what’s in the system, so every step of the way we build on top of it is documented and, like, understood. Versus right now, we’re getting it from source medium, and as you saw from everybody we talked to, they’re like, Source Medium
137 00:15:23.540 ⇒ 00:15:36.180 Uttam Kumaran: is, like, how we get stuff out of Shopify. But then, there is… there are models, as source media mentioned, between Shopify and their Looker dashboard, and there’s logic on the dashboard side that nobody…
138 00:15:36.710 ⇒ 00:15:42.949 Uttam Kumaran: Really, like, was, like, articulated, or it was done maybe, like, more than a year ago, and so…
139 00:15:43.370 ⇒ 00:15:53.549 Uttam Kumaran: That all gets removed, you know, in this process. And yeah, for each of these, we’re gonna land as much as… as much as possible, and then, yeah, you basically can go all the way back.
140 00:15:53.940 ⇒ 00:15:54.980 Shivani Amar: Perfect.
141 00:15:54.990 ⇒ 00:15:59.659 Uttam Kumaran: To have a source of truth for, like, order data and understand the transformations
142 00:15:59.810 ⇒ 00:16:03.599 Uttam Kumaran: You know, what makes the sausage at the end is, like, what this is solving.
143 00:16:03.990 ⇒ 00:16:04.450 Shivani Amar: Yeah.
144 00:16:05.640 ⇒ 00:16:06.810 Awaish Kumar: Yeah, and then…
145 00:16:06.810 ⇒ 00:16:13.040 Uttam Kumaran: I mentioned the stuff from Spins, Emerson, Where to Go, like, Walmart, those are all, you know, net new.
146 00:16:13.950 ⇒ 00:16:14.630 Shivani Amar: Cool.
147 00:16:15.680 ⇒ 00:16:22.110 Awaish Kumar: Yeah, for Shopify, yeah, the Polytomic is bringing in the historical data as well, so it will be…
148 00:16:22.670 ⇒ 00:16:26.030 Awaish Kumar: It will go back as much as they can.
149 00:16:26.480 ⇒ 00:16:27.940 Awaish Kumar: To bring in the data.
150 00:16:28.540 ⇒ 00:16:33.670 Awaish Kumar: So, yeah, that’s… So that’s for January. We… what we want to achieve.
151 00:16:33.870 ⇒ 00:16:35.830 Awaish Kumar: In terms of deliverables?
152 00:16:35.960 ⇒ 00:16:40.060 Awaish Kumar: So I’ve divided into 3 different… Categories?
153 00:16:40.210 ⇒ 00:16:46.240 Awaish Kumar: ingestion modeling and reporting. For the ingestion part, like, we just want to solve immersion thing with
154 00:16:46.830 ⇒ 00:16:54.939 Awaish Kumar: Which will be… which is a quick thing, but I just mentioned it. But then we have Google Sheets for Wholesale team, so the… all the Google Sheets that
155 00:16:55.210 ⇒ 00:17:02.319 Awaish Kumar: which the team is managing, and maybe using for their analysis, we are going to bring that in warehouse.
156 00:17:03.370 ⇒ 00:17:12.030 Awaish Kumar: And somehow also make a connection, so if they update anything, we get those updates in our warehouse. Then we are going to… yeah.
157 00:17:12.650 ⇒ 00:17:14.600 Shivani Amar: So, what does ERP mean here?
158 00:17:14.730 ⇒ 00:17:16.289 Shivani Amar: Do we mean CRM?
159 00:17:16.900 ⇒ 00:17:17.869 Awaish Kumar: Yeah.
160 00:17:18.329 ⇒ 00:17:31.049 Shivani Amar: Okay, okay, okay, okay. I was like, I was just getting confused by the slide. Okay, so, so, sorry, okay, so Google Sheets for Wholesale team, the CRM that they have, that’s what you’re saying, let’s actually ingest it so we can do the mapping.
161 00:17:31.190 ⇒ 00:17:31.980 Awaish Kumar: You got him.
162 00:17:32.140 ⇒ 00:17:34.070 Shivani Amar: Okay, okay, I’m with you.
163 00:17:34.450 ⇒ 00:17:38.059 Awaish Kumar: So that CRM is, like, they will have the customer’s
164 00:17:38.240 ⇒ 00:17:51.780 Awaish Kumar: like, majority of the customer’s information is… lives in those sheets, and the Shopify is… is… just have… will have, like, orders information. So we are going to connect those both, and generate some sales marks, some customer.
165 00:17:51.780 ⇒ 00:18:03.829 Uttam Kumaran: And so the logic… their logic that they’re doing… so there’s… there’s gonna be, like, VLOOKUPs and joins and stuff in sheets that we will basically replicate in SQL, and then produce the same outputs.
166 00:18:03.990 ⇒ 00:18:12.910 Uttam Kumaran: And then, of course, ideally, when NetSuite comes, there’s no sheet, like, there’s no sheet. Source of truth for much, right? The source of truth.
167 00:18:12.910 ⇒ 00:18:13.790 Shivani Amar: So, networks.
168 00:18:13.790 ⇒ 00:18:14.350 Uttam Kumaran: In, like, a…
169 00:18:14.350 ⇒ 00:18:21.309 Shivani Amar: is… I’m getting confused when we’re talking about NetSuite versus… that’s why I’m like, ERP versus CRM.
170 00:18:22.410 ⇒ 00:18:23.190 Shivani Amar: This is happening.
171 00:18:23.190 ⇒ 00:18:29.279 Uttam Kumaran: do with… yeah, meaning, right now, they’re actually… their actual source of truth for, like.
172 00:18:29.720 ⇒ 00:18:33.389 Uttam Kumaran: their customer information for the wholesale is that Google Sheet.
173 00:18:34.070 ⇒ 00:18:34.440 Shivani Amar: Okay, yes.
174 00:18:34.440 ⇒ 00:18:41.710 Uttam Kumaran: they’re merging in Shopify data with some source data that only lived in that Google Sheet to produce their final reporting.
175 00:18:42.130 ⇒ 00:18:46.490 Shivani Amar: Well, I think I’m getting thrown off. Why is NetSuite coming into this topic?
176 00:18:47.170 ⇒ 00:18:55.799 Uttam Kumaran: Because after NetSuite is included, you will no longer have a Google Sheet that is source of truth for customer, like, information.
177 00:18:55.800 ⇒ 00:18:59.459 Shivani Amar: But I don’t think we’re saying that NetSuite becomes the CRM.
178 00:19:00.430 ⇒ 00:19:05.540 Uttam Kumaran: Well, but I guess my point is that where, like, where’s… where is the…
179 00:19:06.080 ⇒ 00:19:09.910 Uttam Kumaran: Customer information for wholesale gonna end up living longer term?
180 00:19:09.910 ⇒ 00:19:27.880 Shivani Amar: Okay, so that’s not NetSuite, right? Like, Phil and I said maybe Salesforce, HubSpot, like, I don’t know what the… but, like, let’s really… I’m like, let’s… let’s actually just change the slide right now to say CRM, because otherwise I’m, like, I’m, like, getting very… when we say NetSuite, and then in this topic, I’m like, it feels so separate to me.
181 00:19:27.970 ⇒ 00:19:37.789 Shivani Amar: Unless NetSuite has a CRM functionality that I’m not aware of, but nobody has said that. Everybody’s like, at some point, we gotta get the wholesale team to be, like, working off of Salesforce or something like that.
182 00:19:37.790 ⇒ 00:19:43.040 Uttam Kumaran: Okay, yeah, so… so what… wherever the… I guess more of what I mean is, like, the source of truth for…
183 00:19:43.330 ⇒ 00:19:46.240 Uttam Kumaran: That spreadsheet will move into another system.
184 00:19:46.690 ⇒ 00:19:48.020 Shivani Amar: At some point, yes.
185 00:19:48.020 ⇒ 00:19:53.249 Uttam Kumaran: At some point, before that, we have to ingest the Google Sheet in order to replicate their reporting, yeah.
186 00:19:53.250 ⇒ 00:19:59.019 Shivani Amar: Perfect. Okay. Cool. Aligned. Just like ERP, NetSuite, separate, separate, separate.
187 00:19:59.020 ⇒ 00:19:59.600 Uttam Kumaran: Okay, okay.
188 00:20:00.200 ⇒ 00:20:02.480 Shivani Amar: Okay.
189 00:20:03.180 ⇒ 00:20:11.250 Shivani Amar: for you on the ingestion piece, okay? This is continuing with the commercial team. Phil has asked if we could get Gorgeous
190 00:20:11.350 ⇒ 00:20:23.390 Shivani Amar: ingested in Jan… or, like, I don’t know if it’s ingested in January, but, like, start conversations with Gorgeous, with the CX team. And so, like, where, like, are you like, hey, this is gonna keep our
191 00:20:23.390 ⇒ 00:20:33.119 Shivani Amar: pipes busy, like, all of this ingestion, and there’s not gonna be… our pipes are gonna be… have a cube. Or you’re like, yeah, we could be, like, we could be ingesting gorgeous.
192 00:20:33.120 ⇒ 00:20:33.670 Uttam Kumaran: I’m talking about it.
193 00:20:33.670 ⇒ 00:20:34.150 Shivani Amar: feel.
194 00:20:34.150 ⇒ 00:20:41.789 Uttam Kumaran: So, the bottleneck here is gonna be on the modeling side. On the ingestion side, there’s no, like.
195 00:20:41.970 ⇒ 00:20:56.400 Uttam Kumaran: we don’t… we just turn it on. There’s, like, yeah, it’s… maybe that… there’s not an… I don’t know what the fair analogy is, but it doesn’t… it doesn’t… like, there’s no… we could just keep turning on more connectors. What’s gonna happen, though, is that our ability to model it into a finalized data mart
196 00:20:56.590 ⇒ 00:21:03.940 Uttam Kumaran: So you have, like, you have things you can easily query, that’s where the bottleneck is gonna be.
197 00:21:04.060 ⇒ 00:21:08.679 Uttam Kumaran: So, in this, you know, maybe I wish, can we show, like, the Gantt chart?
198 00:21:08.940 ⇒ 00:21:16.400 Uttam Kumaran: I just want to show, like… and this is where we’re gonna… we’ll loop in one more person on our team to support with modeling, basically next week.
199 00:21:16.520 ⇒ 00:21:21.210 Uttam Kumaran: Because now that we have all the core data landed.
200 00:21:21.350 ⇒ 00:21:25.740 Uttam Kumaran: Our… the core mission now is to start to build these
201 00:21:26.170 ⇒ 00:21:30.000 Uttam Kumaran: you know, commercial marts. So, that’s where…
202 00:21:30.150 ⇒ 00:21:33.920 Uttam Kumaran: We can land the gorgeous data, Pretty quick, but…
203 00:21:34.310 ⇒ 00:21:39.129 Uttam Kumaran: to start to build, like, the data mart on top of it is going to be…
204 00:21:39.440 ⇒ 00:21:50.980 Uttam Kumaran: the bottleneck. What is possible, though, is if someone on that team just wants to get raw data, and they want us to just run queries to pull that raw data out, or they want to do some modeling in Excel, we can easily give that to them.
205 00:21:53.030 ⇒ 00:21:58.809 Shivani Amar: Cool. So I think… I’m trying to figure out how to articulate what you just said in, like, a…
206 00:22:00.370 ⇒ 00:22:05.679 Shivani Amar: To say, like, one… let me give you one more way of saying it. We’re… ultimately, we are driving towards…
207 00:22:05.680 ⇒ 00:22:06.530 Uttam Kumaran: like…
208 00:22:06.780 ⇒ 00:22:23.800 Uttam Kumaran: a data mart with our core company objects. DIM customer, DIM orders, fact transactions, right? So the source of truth for these really particular objects, we didn’t… we didn’t sort of factor in time for
209 00:22:24.030 ⇒ 00:22:27.989 Uttam Kumaran: Modeling out a similar data mark for customer experience, what could be
210 00:22:28.160 ⇒ 00:22:33.380 Uttam Kumaran: back tickets, right? Like, things about, like, ticket health, ticket speed.
211 00:22:33.510 ⇒ 00:22:38.290 Uttam Kumaran: NPS, right? Everything that goes into Customer CX, we just didn’t…
212 00:22:38.850 ⇒ 00:22:41.230 Uttam Kumaran: playing for that. You know.
213 00:22:41.230 ⇒ 00:22:43.970 Shivani Amar: That makes total sense. So,
214 00:22:45.650 ⇒ 00:22:54.760 Shivani Amar: Okay, that was… that was wordy. Let me try to play it back, okay? So, I’m saying…
215 00:22:55.060 ⇒ 00:22:57.080 Shivani Amar: Let me think about, like…
216 00:22:58.200 ⇒ 00:23:10.570 Shivani Amar: Okay, if we think about who we’re doing discovery with, okay, in this upcoming month, we haven’t done discovery yet with retail, right? With retail stakeholders.
217 00:23:11.510 ⇒ 00:23:20.330 Uttam Kumaran: Yes, so on our… on our… yeah, the next slide was more… we wanted to do a discovery call with finance, marketing.
218 00:23:20.720 ⇒ 00:23:32.290 Uttam Kumaran: And it’s gonna be, depending on what our JAN deliverables are, we will need to do another call with wholesale, because we will have the… we’ll have the data landed, and we’ll be modeling.
219 00:23:32.500 ⇒ 00:23:33.020 Shivani Amar: Love.
220 00:23:33.020 ⇒ 00:23:38.989 Uttam Kumaran: Primarily for their use case. So this is where we can now decide who we wanna, yeah, talk to.
221 00:23:39.740 ⇒ 00:23:48.470 Shivani Amar: Okay, marketing team… is… nebulous to me. Okay, so… Okay.
222 00:23:48.470 ⇒ 00:23:51.579 Uttam Kumaran: That’s what I was talking to Awash today, we just didn’t know whether…
223 00:23:52.290 ⇒ 00:23:55.860 Uttam Kumaran: Yeah, we just didn’t know whether you wanted us to slot that in, or if there’s another.
224 00:23:55.860 ⇒ 00:23:59.379 Shivani Amar: No, marketing team equals, like, kind of, like, Carlos.
225 00:23:59.590 ⇒ 00:24:00.150 Shivani Amar: The plug.
226 00:24:00.150 ⇒ 00:24:01.040 Uttam Kumaran: Yeah.
227 00:24:01.230 ⇒ 00:24:02.770 Shivani Amar: You know what I mean? Blake…
228 00:24:03.390 ⇒ 00:24:04.320 Uttam Kumaran: Yeah, yeah.
229 00:24:04.520 ⇒ 00:24:13.659 Shivani Amar: And then, like, retail is doing their own, like, retail is like, okay, we’re gonna have the ladder at Target that has, like, the end… what is it called? The.
230 00:24:13.880 ⇒ 00:24:14.969 Uttam Kumaran: End cards, yeah.
231 00:24:14.970 ⇒ 00:24:25.520 Shivani Amar: And cars, yeah, like, like, we’re gonna do that, right? And, like, we’ll trade spend and stuff like that. So what I think is, like, really needed is that you need to get to know Russell on the retail side.
232 00:24:26.350 ⇒ 00:24:33.739 Uttam Kumaran: Yeah, oh wait, what did you mention to me this morning? Why did we want to try to do another… why did we want to do the call with marketing? Yeah, what we discussed?
233 00:24:33.740 ⇒ 00:24:47.490 Awaish Kumar: So, like, we had a call with Carlos, and that was more focused on, like, e-com kind of stuff, like, what he’s measuring, how the revenue is being generated, what is in Shopify, what is in Amazon.
234 00:24:47.810 ⇒ 00:25:05.330 Awaish Kumar: So, even though there’s no separate marketing team, but what I mean by marketing team discovery call is that I want to understand how they spend money, like, what platforms are being used, how money is being spent, and how can we ingest that? Like, if someone is spending on meta, we can bring that…
235 00:25:05.330 ⇒ 00:25:11.869 Shivani Amar: Oh, okay, okay, okay, perfect, perfect, perfect. That makes sense to me. So that’s… when you say marketing team, you just mean another round with Carlos, basically.
236 00:25:11.870 ⇒ 00:25:12.779 Awaish Kumar: Yep, okay.
237 00:25:12.780 ⇒ 00:25:16.059 Shivani Amar: Okay, gotcha, because I was, like, I think I was just trying to share, I was like, you’ve sort of already.
238 00:25:16.060 ⇒ 00:25:23.940 Uttam Kumaran: No, I forgot, we just talked about this, like, an hour ago, I was like, what did… I feel like, I was like, we had the answer, I forgot, because I asked the same question? Alright, cool, great.
239 00:25:24.890 ⇒ 00:25:33.190 Shivani Amar: Okay, so let’s say, like, the discovery calls we want to do are with retail, finance.
240 00:25:33.320 ⇒ 00:25:40.610 Shivani Amar: Carlos with the lens of marketing, more so than, like, more so than Shopify, okay?
241 00:25:40.940 ⇒ 00:25:41.480 Awaish Kumar: Yep.
242 00:25:41.860 ⇒ 00:25:55.909 Shivani Amar: And then we want to do one… like, we’ll do the discovery call with EX just to understand, like, the questions and stuff they’re answering, noting that, note we can ingest gorgeous data
243 00:25:56.130 ⇒ 00:26:08.259 Shivani Amar: But modeling is not prioritized for, this 3-month project with Brain Forge.
244 00:26:08.260 ⇒ 00:26:08.750 Uttam Kumaran: Yeah.
245 00:26:08.750 ⇒ 00:26:15.729 Shivani Amar: Okay, perfect. Then we’re saying we want to ingest… I’m just taking notes for myself to make sure I’m, like, super clear on this, right?
246 00:26:15.730 ⇒ 00:26:16.110 Uttam Kumaran: Yeah.
247 00:26:16.110 ⇒ 00:26:18.949 Shivani Amar: We want to… can you go back to the ingestion slide, I wish?
248 00:26:19.160 ⇒ 00:26:26.959 Shivani Amar: Okay, so we want to ingest… ingest, Emerson data, wholesale Google Sheet, parentheses, CRM.
249 00:26:27.330 ⇒ 00:26:37.630 Shivani Amar: Emerson, into, like, into our snowflake, into our italicized snowflake instance, okay?
250 00:26:38.050 ⇒ 00:26:39.290 Awaish Kumar: Here’s that feel.
251 00:26:39.320 ⇒ 00:26:42.430 Shivani Amar: Is it our Snowflake warehouse? Warehouse? Where…
252 00:26:42.430 ⇒ 00:26:43.750 Uttam Kumaran: Yes, warehouse. Warehouse.
253 00:26:43.750 ⇒ 00:26:45.729 Shivani Amar: Welcome to our Snowflake warehouse.
254 00:26:46.670 ⇒ 00:26:55.690 Shivani Amar: As opposed to… in a shared… warehouse, okay? Where to go.
255 00:26:58.380 ⇒ 00:27:10.270 Shivani Amar: And then spins… Walmart… Walmart.com. That’s what that is, right?
256 00:27:10.970 ⇒ 00:27:12.440 Shivani Amar: Walmart.com.
257 00:27:13.410 ⇒ 00:27:17.679 Shivani Amar: Walmart online. I’ll just put Walmart online, and then Amazon.
258 00:27:18.820 ⇒ 00:27:19.630 Shivani Amar: Okay.
259 00:27:20.050 ⇒ 00:27:26.760 Shivani Amar: Love it, so that feels clear, and then I’ll say gorgeous. We’re gonna also ingest gorgeous, let’s just say that that’s…
260 00:27:26.760 ⇒ 00:27:33.080 Uttam Kumaran: Did Phil mention, like, what… What he wanted… what he was… wanted them out of that, or…
261 00:27:33.080 ⇒ 00:27:38.079 Shivani Amar: I think he was, like, he was like, hey, by the way, did you… let me… let me actually just… gorgeous.
262 00:27:38.080 ⇒ 00:27:40.040 Uttam Kumaran: Cause we talk… I think we talked about it…
263 00:27:40.310 ⇒ 00:27:45.860 Uttam Kumaran: like, maybe a month and a half ago, where I think they were debating between Gorgeous and, like, a new system.
264 00:27:46.270 ⇒ 00:27:52.209 Shivani Amar: he was like, well, regardless if they choose a different system, we’d want the gorgeous data. And so he.
265 00:27:52.210 ⇒ 00:27:52.830 Uttam Kumaran: He’s a kid.
266 00:27:53.140 ⇒ 00:27:58.399 Shivani Amar: He was just like, can you have the Brainforge and CX team start to get to know each other, basically?
267 00:27:58.400 ⇒ 00:27:58.870 Uttam Kumaran: Okay, cool.
268 00:27:58.870 ⇒ 00:28:12.470 Shivani Amar: I was like, yeah, sure, like… and then I think we can make that clear. That’s, like, there’s… we’re not going to be able to deliver a lot of insights for you, but if we want to just, like, understand, like, the purpose for that discovery call is, like, lay of the land on, like, the tools that.
269 00:28:12.470 ⇒ 00:28:17.880 Uttam Kumaran: Yeah, you know how we do the disco calls already, so we’ll just be… we’ll ask them 100 questions, and then…
270 00:28:17.880 ⇒ 00:28:18.660 Shivani Amar: Yeah.
271 00:28:18.660 ⇒ 00:28:20.449 Uttam Kumaran: We’ll, we’ll have that, yeah.
272 00:28:20.450 ⇒ 00:28:30.110 Shivani Amar: So I’m adding… basically, I’m adding just to your ingestion, okay? Then we have modeling, okay? I’m like, okay, the… Yeah.
273 00:28:31.780 ⇒ 00:28:38.959 Shivani Amar: modeling… Okay, I feel complete on ingestion, basically, so let’s get into modeling.
274 00:28:40.870 ⇒ 00:28:49.679 Awaish Kumar: Okay, from our side, what we are looking to do is, like, for data modeling, we need to have an infrastructure ready to run the…
275 00:28:49.900 ⇒ 00:29:01.519 Awaish Kumar: like, the queries we are… the models we are going to write, the SQL queries, they need to be run every day, or on some cadence, and also for the PR, like, the… whenever we are
276 00:29:01.980 ⇒ 00:29:17.590 Awaish Kumar: creating new models, we need some place to validate them before it goes to production. So, we need that infrastructure set up, and we are looking to do that by Gen 9. After that, we are… like, we plan, basically, these mods for wholesale.
277 00:29:17.810 ⇒ 00:29:25.289 Awaish Kumar: If we are aligned, we can, like, have… like, we have Shopify wholesale data, we have… we can ingest Google Sheets for…
278 00:29:25.410 ⇒ 00:29:31.839 Awaish Kumar: Wholesale team, and then we can start to build customer mart and the sales mart, basically, for the wholesale.
279 00:29:32.460 ⇒ 00:29:35.299 Shivani Amar: That sounds good. So it’s like, if I were to put that in, like.
280 00:29:35.500 ⇒ 00:29:39.359 Shivani Amar: plain language for me, it’s like,
281 00:29:39.480 ⇒ 00:29:46.710 Shivani Amar: maybe Customer Mart is, like, the way, but I’m… I think of it as, like, a really clean…
282 00:29:47.450 ⇒ 00:30:05.350 Shivani Amar: Actually, maybe you can explain it to me, because I’m like, I actually… I’m like, I see this, and I’m like, we’re gonna get a really clean table, we’re gonna get a couple really clean tables for wholesale that can, like, be the foundation of… of dashboards in the future, but then I’m like, you have your customer Martin, your sales mart, and I’m like, actually don’t know how to describe.
283 00:30:05.350 ⇒ 00:30:13.460 Uttam Kumaran: So this is where, like, these are all semantic definitions of, like, data objects, meaning customer mart is, like.
284 00:30:13.570 ⇒ 00:30:33.559 Uttam Kumaran: things about customers, sales is things about sales, you will get several clean tables that describe the customer, that describe sales, right? Tables that refresh on time, have all the necessary information, and are, like, validated and trusted, right? And so that’s, like, what delivering the customer mart… the mart
285 00:30:33.920 ⇒ 00:30:39.000 Uttam Kumaran: is more of, like, a place to come get that information, meaning nobody should be going…
286 00:30:39.070 ⇒ 00:30:57.320 Uttam Kumaran: directly into the Shopify raw data. Nobody should be, like, writing complicated queries again. If they say, I have a question about orders, okay, check out DIM orders. Some people, that may look like, okay, you can go into your dashboard, and any order-related information is powered by DIM orders.
287 00:30:57.430 ⇒ 00:31:12.739 Uttam Kumaran: Right? But dim orders is gonna be, like, we have to join 5 sources together, Amazon is slightly different than Shopify, we’ll have to combine, combine IDs, do unions, like, all of the SQL logic that goes into that.
288 00:31:12.940 ⇒ 00:31:23.950 Uttam Kumaran: is the iceberg underneath this, like, one dim orders table? Of course, yeah. So, like, when you’re saying dim orders, I’m like, that gets into our omni-channel of, like, like, all the orders, right?
289 00:31:23.950 ⇒ 00:31:31.959 Shivani Amar: That’s not… that’s not slated for January, because… because we still need to do the ingestion of Emerson, blah blah blah blah blah.
290 00:31:31.960 ⇒ 00:31:32.350 Uttam Kumaran: Yeah.
291 00:31:32.510 ⇒ 00:31:33.970 Shivani Amar: the February output.
292 00:31:34.670 ⇒ 00:31:35.739 Uttam Kumaran: Yeah, that’s correct.
293 00:31:35.740 ⇒ 00:31:43.019 Awaish Kumar: put a, like, the string, like, wholesale. So, like, for this, for the part of January, like, we can have a table.
294 00:31:43.280 ⇒ 00:32:02.589 Awaish Kumar: showing all the orders for wholesale team, and… and data about all the customers who we are… who are making, like, wholesale orders, right? And then we can continue to build like this, then we can have, for e-com, similar tables, and then for retail. And when we have all of them, we can just, like, have a one joint table, which combines them all into one.
295 00:32:04.750 ⇒ 00:32:05.300 Shivani Amar: Great.
296 00:32:05.300 ⇒ 00:32:09.050 Uttam Kumaran: So, wholesale customer mart, wholesale sales mart will be delivered.
297 00:32:09.480 ⇒ 00:32:10.240 Shivani Amar: Exactly.
298 00:32:10.490 ⇒ 00:32:11.350 Shivani Amar: Okay.
299 00:32:11.630 ⇒ 00:32:17.449 Shivani Amar: cool. I feel aligned on this, and like, so, when we think about…
300 00:32:17.450 ⇒ 00:32:22.940 Uttam Kumaran: This is where… this is where our… our work is. Like, really the net… yeah, yeah.
301 00:32:23.390 ⇒ 00:32:32.920 Shivani Amar: So when we think about this, and, like, you’re like, hey, we’re gonna need more discovery time, right? Like, I’m trying to think about how do I flag to Laura that this is, like.
302 00:32:33.100 ⇒ 00:32:40.219 Shivani Amar: what is my two sentences I’m sharing with Laura, so she can prepare in her sprint to say, hey.
303 00:32:40.800 ⇒ 00:32:47.110 Shivani Amar: We’re going to, like… like… Let me think…
304 00:32:47.110 ⇒ 00:32:53.640 Uttam Kumaran: When we go to replicate the logic that’s in her spreadsheet, we are going to most likely have some questions.
305 00:32:53.740 ⇒ 00:32:54.880 Shivani Amar: Yeah.
306 00:32:54.880 ⇒ 00:32:58.759 Uttam Kumaran: about, like, the way it’s replicated. There will be things we…
307 00:32:58.870 ⇒ 00:33:09.709 Uttam Kumaran: we may find that, like, okay, they’re legacy logic, should we be replicating this? Are there additional things that you couldn’t accomplish in your spreadsheet that you want to accomplish in the sales mart?
308 00:33:10.720 ⇒ 00:33:23.170 Shivani Amar: Okay, so let’s say Brainforge’s goal is for January is to deliver some, like, beyond clean tables, like, like, is to… because you’re saying, like, it’s, like, to basically…
309 00:33:23.930 ⇒ 00:33:30.160 Shivani Amar: not have to go into Shopify to get the data, right? You’re like, to be able to have .
310 00:33:32.180 ⇒ 00:33:37.090 Uttam Kumaran: So we should save them on the manual work that… some of the manual work that they’re doing right now.
311 00:33:37.710 ⇒ 00:33:39.499 Uttam Kumaran: That we should save them on that.
312 00:33:39.720 ⇒ 00:33:43.519 Uttam Kumaran: Similarly, there is probably parts of the spreadsheet that they’re, like.
313 00:33:44.200 ⇒ 00:33:46.860 Uttam Kumaran: This is sort of being held together by duct tape.
314 00:33:47.010 ⇒ 00:33:54.680 Uttam Kumaran: we want to sort of, like, move that out of that type of environment. Third is there’s going to be types of analysis that they haven’t been able to do.
315 00:33:54.810 ⇒ 00:34:01.780 Uttam Kumaran: And we want to show them that they can now accomplish those analyses, given these… clean tables.
316 00:34:02.040 ⇒ 00:34:05.279 Shivani Amar: And the clean tables, like, live in Snowflake, right?
317 00:34:06.080 ⇒ 00:34:07.710 Uttam Kumaran: They live in Snowflake, and…
318 00:34:07.710 ⇒ 00:34:13.599 Shivani Amar: Would it be, like, you know how, like, at Brave, I had, coefficient reports?
319 00:34:13.790 ⇒ 00:34:15.959 Shivani Amar: In Google Sheets? Is that the idea?
320 00:34:15.960 ⇒ 00:34:25.200 Uttam Kumaran: Yeah, so we will understand from them whether we need to bring that back into Google Sheets for their analysis, whether they’re equipped to come get that directly out of Snowflake.
321 00:34:25.909 ⇒ 00:34:39.099 Uttam Kumaran: moment, we don’t have the BI tool, right? So, in order to get value out of the modeling, A, they can come in to Snowflake and get that. B, we can also write that back to Google Sheets if they’d like it there.
322 00:34:39.489 ⇒ 00:34:42.689 Shivani Amar: It will operate very similarly to what you…
323 00:34:42.689 ⇒ 00:34:46.849 Uttam Kumaran: felt with coefficient, where something’s writing to a Google Sheet and you’re pulling it out of there.
324 00:34:46.850 ⇒ 00:34:47.679 Shivani Amar: Yeah.
325 00:34:47.960 ⇒ 00:34:56.850 Shivani Amar: Okay, I’m still trying to think about the zoom out for myself, and sorry, I’m going a little slow on this, but it’s like, e-commerce has source medium.
326 00:34:57.390 ⇒ 00:35:05.750 Shivani Amar: Wholesale is kind of, like, we’re, like, we’re aware that they’re doing a lot of manual data pulling. So part of the reason we are, like.
327 00:35:05.750 ⇒ 00:35:22.699 Shivani Amar: making… I’m trying to articulate this for myself, like, if Phil asks, or anybody asks, like, why are you focusing so much on wholesale? It’s like, we have… we don’t have all the data ingested yet to jump to, like, omni-channel overview, like, you know, create the full order table and join everything together.
328 00:35:22.720 ⇒ 00:35:32.329 Shivani Amar: We’re still in the process, but in the meantime, we want to deliver these stakeholders out of a high manual… high manual amount of work to do to, like, glean insights.
329 00:35:32.380 ⇒ 00:35:39.609 Shivani Amar: And to, like, understand their data. We want to deliver them clean tables that can be their reference point for future analysis.
330 00:35:40.270 ⇒ 00:35:47.020 Shivani Amar: And, like, in order for us to continue doing… in order for us to achieve that this quarter, it means we’re gonna need some more FaceTime with them.
331 00:35:48.010 ⇒ 00:35:48.700 Uttam Kumaran: Correct.
332 00:35:48.900 ⇒ 00:35:49.360 Shivani Amar: Okay.
333 00:35:49.360 ⇒ 00:35:55.929 Uttam Kumaran: And ideally, what I want to say, one more point, is not just replicating what they’re doing, this should ideally unlock
334 00:35:56.330 ⇒ 00:36:02.400 Uttam Kumaran: More time and more flexibility to do more analysis, you know, versus just the things that they’re doing now.
335 00:36:02.810 ⇒ 00:36:03.660 Shivani Amar: Yeah.
336 00:36:04.870 ⇒ 00:36:10.649 Awaish Kumar: Like, yeah, like, cleaning, standardizing the tags that she was talking about.
337 00:36:11.140 ⇒ 00:36:21.540 Uttam Kumaran: Yeah, the tag work, like, all the stuff Robert and her sort of went into, which is a lot of the tag work, a lot of the, is this the right way we’re modeling this data, like, yeah.
338 00:36:22.600 ⇒ 00:36:31.599 Uttam Kumaran: I think… and also, this is just a… I would say, another way… why to focus on this. It’s… they’re… they’re… they’re really managing a lot
339 00:36:32.040 ⇒ 00:36:49.650 Uttam Kumaran: in a Google Sheet, and the data is actually not as complicated as, like, the e-commerce and the other data. So it is… it is ripe for us to not only tackle that, but in this process, as you know, we’re using wholesale as a way to make sure we have ingestion set up, Snowflake set up, dbt set up.
340 00:36:49.690 ⇒ 00:36:54.830 Uttam Kumaran: And… and really do the one pass at the end-to-end for, like, one stakeholder.
341 00:36:55.380 ⇒ 00:37:09.029 Uttam Kumaran: And this is really… every stakeholder after that, we’re not setting up dbt again, we’re not setting up Snowflake again, we’re not setting up, like, an ingestion tool again. So every next stakeholder benefits from us going all the way through this.
342 00:37:09.230 ⇒ 00:37:13.990 Uttam Kumaran: And the next set of stakeholders, there’s a lot… there’s, like, a lot more complexity in the… in the…
343 00:37:14.160 ⇒ 00:37:16.590 Uttam Kumaran: Like, in the e-com business,
344 00:37:17.130 ⇒ 00:37:20.390 Uttam Kumaran: And in the retail part, there’s a lot of complexity.
345 00:37:24.940 ⇒ 00:37:37.869 Shivani Amar: And the e-commerce business is more complex because we’re also joining, because you’re joining… Shopify with Amazon.
346 00:37:38.610 ⇒ 00:37:41.250 Uttam Kumaran: With the marketing, with some of the spend data.
347 00:37:41.500 ⇒ 00:37:45.270 Shivani Amar: Walmart.com, and marketing spend.
348 00:37:45.270 ⇒ 00:37:50.030 Uttam Kumaran: Yeah, there’s not only sources that the team knows, there’s also these news sources.
349 00:37:50.480 ⇒ 00:37:58.559 Uttam Kumaran: And there’s, like, there’s just… there’s a much larger set of metrics that we have to support.
350 00:37:58.560 ⇒ 00:37:59.170 Shivani Amar: You know.
351 00:37:59.350 ⇒ 00:38:03.330 Awaish Kumar: We might also have to run shipping data, And all of that.
352 00:38:03.610 ⇒ 00:38:06.190 Awaish Kumar: In the… in the e-com sales model.
353 00:38:06.570 ⇒ 00:38:09.910 Shivani Amar: Like, yeah, with wholesale, it’s really just Shopify and the CRM.
354 00:38:10.150 ⇒ 00:38:11.080 Awaish Kumar: Yes.
355 00:38:13.910 ⇒ 00:38:22.530 Uttam Kumaran: So that’s… and that’s why I don’t want to say that it’s less complex in terms of the business, but in terms of the data, it’s one source, one Google Sheet, you know?
356 00:38:22.530 ⇒ 00:38:23.250 Shivani Amar: Yeah.
357 00:38:23.480 ⇒ 00:38:27.490 Uttam Kumaran: So we can… we can test out every phase of the… like…
358 00:38:27.830 ⇒ 00:38:28.540 Awaish Kumar: Unless, yeah.
359 00:38:28.540 ⇒ 00:38:30.410 Uttam Kumaran: data modeling, yeah.
360 00:38:31.010 ⇒ 00:38:33.670 Awaish Kumar: Unless they are spending anything on marketing, I don’t know.
361 00:38:34.250 ⇒ 00:38:37.479 Shivani Amar: I mean, I also, like, love the idea of, like.
362 00:38:37.630 ⇒ 00:38:44.879 Shivani Amar: deciding on a BI tool this month, that feels like good, like, momentum to, like, keep the stack conversation.
363 00:38:44.880 ⇒ 00:38:52.900 Uttam Kumaran: Yeah, Wish, you want to go back to the Gantt chart? Let’s… we could talk about that. This is also… I was like… I just want to confirm, because this is what we had on our original plan.
364 00:38:53.080 ⇒ 00:38:53.800 Uttam Kumaran: And…
365 00:38:53.800 ⇒ 00:38:54.410 Shivani Amar: Yeah.
366 00:38:54.730 ⇒ 00:38:55.160 Awaish Kumar: Yeah.
367 00:38:55.160 ⇒ 00:38:59.890 Uttam Kumaran: And this is what we would… I would want to pair with the source medium.
368 00:39:00.330 ⇒ 00:39:08.909 Uttam Kumaran: Shivani, is, like, we do the source medium decision. In addition, we can show source medium versus any other tool we’re deciding on, and, like.
369 00:39:09.260 ⇒ 00:39:12.130 Uttam Kumaran: Wrap that all into a decision by the end of the month.
370 00:39:12.460 ⇒ 00:39:18.700 Uttam Kumaran: It’s like this… but I would say… I would say this is still… I told a waste that I felt like this was…
371 00:39:19.680 ⇒ 00:39:31.280 Uttam Kumaran: aggressive, because I… I always worry that we may find more on the wholesale side than the team is currently aware of, because we’re going to be pulling directly from Shopify, like.
372 00:39:31.440 ⇒ 00:39:32.350 Uttam Kumaran: So…
373 00:39:32.960 ⇒ 00:39:38.390 Uttam Kumaran: I think we just need to define a way, like, what is the customer mart and what is the sales mart for wholesale?
374 00:39:38.980 ⇒ 00:39:43.779 Shivani Amar: in terms of what tables we are delivering, and in case there’s, like, a Phase 1 or a Phase 2.
375 00:39:44.290 ⇒ 00:39:49.390 Uttam Kumaran: The BI tool decision, though, as soon as we have some clean tables, we can start to…
376 00:39:49.650 ⇒ 00:39:50.630 Uttam Kumaran: Work on that.
377 00:39:51.570 ⇒ 00:39:52.230 Shivani Amar: Yeah.
378 00:39:52.500 ⇒ 00:39:55.910 Uttam Kumaran: But, like, the data… building data models and data mart is, like.
379 00:39:56.980 ⇒ 00:40:04.090 Uttam Kumaran: we will be doing that our whole time here at Element. You know, it’s gonna… it’s gonna be something we work on for a while, and these will… they’re…
380 00:40:04.300 ⇒ 00:40:12.410 Uttam Kumaran: data marts mature, meaning we have a lot of clients, after, like, 6 months to 8 months of working on a data mart, we’re no longer making, like.
381 00:40:12.490 ⇒ 00:40:26.220 Uttam Kumaran: really big new tables or changes, but we are, like, adding new columns or changing some logic as things develop. So these data marks mature over time, but most of the next 6 months is gonna be
382 00:40:26.880 ⇒ 00:40:41.389 Uttam Kumaran: doing these data marts, and then they’re gonna have to get displayed somewhere. So that’s also what I’m interested to see, like, out of the people we meet, are there people capable of coming into Snowflake? Or is everything gonna sort of have to come through a BI tool or into Google Sheets?
383 00:40:41.930 ⇒ 00:40:45.059 Uttam Kumaran: Let’s also… wrapping up the next set of discovery will help me
384 00:40:45.490 ⇒ 00:40:47.050 Uttam Kumaran: Sort of hear more about that.
385 00:40:47.560 ⇒ 00:40:50.640 Shivani Amar: Yeah, my instinct… but I don’t know.
386 00:40:50.640 ⇒ 00:40:54.289 Uttam Kumaran: Is the mic. I think I agree with you, probably.
387 00:40:54.290 ⇒ 00:40:55.510 Shivani Amar: Yeah, I’m just like, I don’t know.
388 00:40:55.510 ⇒ 00:40:56.270 Uttam Kumaran: People are BI tool.
389 00:40:56.270 ⇒ 00:41:04.150 Shivani Amar: Like, it’s overwhelming, and so it’s like… Yeah. I think the instinct is clean up tables that you can, like, pull into coefficient, and then.
390 00:41:04.150 ⇒ 00:41:04.490 Uttam Kumaran: Yeah.
391 00:41:04.970 ⇒ 00:41:09.390 Shivani Amar: you can, like, still do analysis in Google Sheets, like, that’s my instinct.
392 00:41:09.390 ⇒ 00:41:09.710 Uttam Kumaran: Exactly.
393 00:41:09.710 ⇒ 00:41:11.439 Shivani Amar: like, sometimes…
394 00:41:11.440 ⇒ 00:41:13.759 Uttam Kumaran: On the BI tool as well, this is where we’ll do…
395 00:41:13.880 ⇒ 00:41:17.230 Uttam Kumaran: Yeah, the BI tool is also where we’ll talk about all the AI stuff.
396 00:41:17.650 ⇒ 00:41:18.130 Shivani Amar: Yeah.
397 00:41:18.130 ⇒ 00:41:19.950 Uttam Kumaran: Because… so…
398 00:41:20.170 ⇒ 00:41:25.780 Shivani Amar: That’s one thing that is gonna be a knock on source medium, is there’s no… Is it aggressive?
399 00:41:25.780 ⇒ 00:41:26.520 Uttam Kumaran: It’s aggressive.
400 00:41:26.520 ⇒ 00:41:27.040 Shivani Amar: Hmm.
401 00:41:27.740 ⇒ 00:41:28.940 Uttam Kumaran: Yeah, sorry, sorry, go, you go ahead.
402 00:41:29.220 ⇒ 00:41:37.669 Shivani Amar: Is it aggressive? It should be, like, should we say that, like, we’re gonna tee up the BI conversation, but not make the decision? Like, that’s, like, where I’m like, is this too much?
403 00:41:37.950 ⇒ 00:41:45.159 Shivani Amar: Yeah, I would like to say that. Because this is… the reason why is this… this is a very.
404 00:41:45.790 ⇒ 00:42:03.349 Uttam Kumaran: it’s more subjective at this layer of the stack, and it’s really tuned to what you guys need, versus a customer data mart… like, a whole, a Shopify customer data mart is not something… there will be some parts that are unique to Element, but it’s not… it’s very objective, like, we’re driving toward the clean table.
405 00:42:03.470 ⇒ 00:42:06.360 Uttam Kumaran: whether the team likes one BI tool or another.
406 00:42:07.150 ⇒ 00:42:09.870 Uttam Kumaran: is… will take some time. Also, just, like.
407 00:42:09.870 ⇒ 00:42:10.400 Shivani Amar: Thank you.
408 00:42:10.830 ⇒ 00:42:15.820 Uttam Kumaran: Those proof of concepts may take, like, anywhere from 2 to 4 weeks to do.
409 00:42:15.970 ⇒ 00:42:20.169 Shivani Amar: Dude, yeah, I think it’s, like, I don’t… it already… it feels.
410 00:42:20.170 ⇒ 00:42:23.150 Uttam Kumaran: And it’s hard… this is hard to roll back, by the way. The BI tool is
411 00:42:23.400 ⇒ 00:42:29.470 Uttam Kumaran: very hard to roll back, and I don’t know, also, if we’re gonna be able to get non-annual contracts.
412 00:42:29.630 ⇒ 00:42:31.430 Uttam Kumaran: To be… to be quite honest.
413 00:42:31.730 ⇒ 00:42:32.050 Shivani Amar: Yeah.
414 00:42:32.050 ⇒ 00:42:37.320 Uttam Kumaran: So, I wanna, like, Yeah, it’s just gonna be a…
415 00:42:37.770 ⇒ 00:42:44.710 Uttam Kumaran: And this is also gonna be the area where Phil, like, exec team, everybody is in and pulling data out.
416 00:42:45.180 ⇒ 00:43:03.030 Shivani Amar: Yeah. So, like, getting their blessing, like, nobody’s gonna… This feels like high stakes to me, like, versus Snowflake and pipes in the background kind of thing, so… I agree. I’m, like, I feel very comfortable being like, this is a decision for February, and you begin teeing up the conversation in January.
417 00:43:03.670 ⇒ 00:43:04.330 Uttam Kumaran: Okay.
418 00:43:04.540 ⇒ 00:43:15.850 Shivani Amar: Yeah, like, to me… In parallel, I’m feeling it in my body, and I think that’s the signal. Like, at this business, it’s like, if you’re feeling it in your body, that it’s, like, feeling, like, stressful, then it’s, like, not the right way to approach it.
419 00:43:16.450 ⇒ 00:43:32.079 Uttam Kumaran: Okay, okay, good to know. Then I would like to put that to February. The benefit is, we will have the warehouse… we will have the wholesale, data marts ready for that proof of concept phase, for the BI tool proof of concept, and, like, BI tool evaluation.
420 00:43:33.840 ⇒ 00:43:38.640 Uttam Kumaran: And the last piece is, if there is value we can deliver to the wholesale team.
421 00:43:39.430 ⇒ 00:43:43.049 Uttam Kumaran: prior to the… whatever the BI tool exists.
422 00:43:43.430 ⇒ 00:43:58.409 Uttam Kumaran: I would like to vote that we try to do that, because they’re… I could… they’re doing a lot of manual reporting, whether that is us pulling them a report out of Snowflake and giving it to them, whether that is us landing data in a Google Sheet, or there’s a third option, I’m not sure yet.
423 00:43:58.680 ⇒ 00:44:02.870 Uttam Kumaran: I would like to do that, and then all the tagging stuff that Robert highlighted.
424 00:44:03.300 ⇒ 00:44:06.189 Uttam Kumaran: So, more of my point here is, like.
425 00:44:06.520 ⇒ 00:44:15.409 Uttam Kumaran: we’re still continuing on our infrastructure path, but finally, we’re also deciding on a stakeholder to try to deliver some value from. My question is that, is that the right stakeholder?
426 00:44:16.840 ⇒ 00:44:22.170 Uttam Kumaran: And, like, are we comfortable with, like, those… like, is wholesale the right stakeholder to try to deliver for our…
427 00:44:22.380 ⇒ 00:44:25.550 Uttam Kumaran: In this next, like, Month or so.
428 00:44:25.550 ⇒ 00:44:29.019 Shivani Amar: So, okay, so you’re pressure testing the thing that we’re talking about, right? And, like.
429 00:44:29.020 ⇒ 00:44:31.800 Uttam Kumaran: Well, we’ve met a lot of other stakeholders as well.
430 00:44:33.340 ⇒ 00:44:36.130 Shivani Amar: what I… my notes, can I share my screen for a second?
431 00:44:36.840 ⇒ 00:44:37.770 Awaish Kumar: Yep.
432 00:44:39.460 ⇒ 00:44:41.010 Shivani Amar: Bomp boom, boom!
433 00:44:41.520 ⇒ 00:44:47.980 Shivani Amar: Okay, wait, give me, give me… I’m like, I’m trying to, like, deliver it in a way that makes sense to me, so give me one second.
434 00:44:48.330 ⇒ 00:44:55.140 Shivani Amar: The other data ingestion… Gorgeous.
435 00:44:58.900 ⇒ 00:45:00.550 Shivani Amar: One second…
436 00:45:24.300 ⇒ 00:45:31.510 Shivani Amar: Okay, so I’m basically, like, in my base, okay.
437 00:45:33.110 ⇒ 00:45:51.539 Shivani Amar: I’m just, like, showing you guys what I’m doing, right? So it’s like, I do this, like, sprint setting document, which is just, like, anybody at, Element can, like, pop into my sprint and be like, what’s going on with, like, the data project, okay? Because I’m not expecting them to look at your slides. I can link your slides, whatever, but… or the Gantt chart or something. But I’m like, look.
438 00:45:51.540 ⇒ 00:45:58.779 Shivani Amar: Like, the discovery is… Like, with retail, finance, like, whatever.
439 00:45:58.780 ⇒ 00:46:16.609 Shivani Amar: CX with the lens of… Carlos with the lens of marketing, CX, although, like, note that this thing is out of scope. And then it’s, like, continued… continued exploration with wholesale, and then I kind of want to articulate that it’s, like.
440 00:46:16.790 ⇒ 00:46:22.680 Shivani Amar: the, like, first team that’s going to be, like, getting something. Yes.
441 00:46:23.010 ⇒ 00:46:23.630 Uttam Kumaran: Yes.
442 00:46:23.630 ⇒ 00:46:26.360 Shivani Amar: that’s where I’m kind of, like, trying to put.
443 00:46:27.890 ⇒ 00:46:29.720 Uttam Kumaran: So I would… so I would say.
444 00:46:29.720 ⇒ 00:46:45.300 Shivani Amar: That’s actually kind of helpful, right? Like, I’m like, I’m, like, I keep being, like, we’re doing three things at once in January. End-to-end delivery for wholesale, not exactly, because it’s not BI, but whatever. We’re doing parallel ingestion of other critical data sources, parallel discovery with other stakeholders, right?
445 00:46:45.550 ⇒ 00:46:49.620 Shivani Amar: Like, I’m trying to figure out… do you see how what I’m doing? I’m, like, trying to figure out how to, like, make it, like.
446 00:46:49.620 ⇒ 00:46:54.350 Uttam Kumaran: Yeah, so I would say we are doing some…
447 00:46:56.990 ⇒ 00:46:59.379 Uttam Kumaran: Yeah, let me think about the wording.
448 00:46:59.380 ⇒ 00:47:10.050 Shivani Amar: Think this. Wholesale is intentionally selected as the first stakeholder to go fully end-to-end because fewer data sources, high manual overhead today, tagging reconciliation, lower modeling complexity, and fast path to trust and usability.
449 00:47:10.050 ⇒ 00:47:11.940 Uttam Kumaran: Yeah, yeah, and then the last.
450 00:47:11.940 ⇒ 00:47:14.699 Shivani Amar: I’m like, I’m trying to, like, take notes and, like.
451 00:47:14.700 ⇒ 00:47:22.719 Uttam Kumaran: No, no, I think that’s… I think that’s good. The one thing I would mention, though, is that it’s also a great use case for us to do the BI
452 00:47:23.110 ⇒ 00:47:30.380 Uttam Kumaran: evaluation on top of that data, because there’s not gonna be many other sources. Like, we couldn’t do…
453 00:47:30.590 ⇒ 00:47:32.529 Uttam Kumaran: End-to-end e-com modeling.
454 00:47:32.870 ⇒ 00:47:33.990 Uttam Kumaran: within…
455 00:47:34.200 ⇒ 00:47:41.230 Uttam Kumaran: like, 3, 4 weeks. There’s, like, too much there. But I feel pretty confident we can deliver the wholesale modeling.
456 00:47:41.530 ⇒ 00:47:44.950 Uttam Kumaran: And use the wholesale data to…
457 00:47:45.580 ⇒ 00:47:47.960 Uttam Kumaran: measured that to evaluate the BI tool.
458 00:47:50.010 ⇒ 00:47:51.340 Shivani Amar: Hmm…
459 00:47:51.840 ⇒ 00:47:56.250 Uttam Kumaran: And so, and then this is also where I think it’s gonna be up to us to figure out how many…
460 00:47:56.370 ⇒ 00:47:58.010 Uttam Kumaran: like, ultimately.
461 00:47:58.220 ⇒ 00:48:05.889 Uttam Kumaran: how many parallel paths do we want to do? And that’s how, like, I’ll… we’ll kind of scale up our team.
462 00:48:06.040 ⇒ 00:48:07.600 Uttam Kumaran: Right? Because we have been discovered…
463 00:48:07.600 ⇒ 00:48:12.139 Shivani Amar: like… I’m like, okay, the parallel ingestion… I’m… parallel ingestion…
464 00:48:12.140 ⇒ 00:48:19.629 Uttam Kumaran: I would say the parallel ingestion is not the risk, because even if they’re like, okay, ingest the next nth connector.
465 00:48:19.790 ⇒ 00:48:22.190 Uttam Kumaran: We will request it, or we will add it.
466 00:48:22.540 ⇒ 00:48:22.960 Shivani Amar: Yeah.
467 00:48:22.970 ⇒ 00:48:29.370 Uttam Kumaran: There’s no, like, in terms of bandwidth risk on us, that’s not where it’s gonna be coming from.
468 00:48:32.260 ⇒ 00:48:33.210 Shivani Amar: Hmm…
469 00:48:36.720 ⇒ 00:48:42.040 Uttam Kumaran: So there’s an… the other… like, do… for example, if you were like, well, I actually wanna… I actually think we should…
470 00:48:42.740 ⇒ 00:48:45.309 Uttam Kumaran: start modeling for CX?
471 00:48:46.120 ⇒ 00:48:54.390 Uttam Kumaran: then I’m like, okay, I need to… right? Or, like, I need to start modeling for finance, like, so I’m just trying to get that we’re gonna agree on, like, trying to deliver something.
472 00:48:55.030 ⇒ 00:48:58.370 Shivani Amar: The thing that we’re holding is omni-channel modeling.
473 00:48:58.790 ⇒ 00:49:07.789 Uttam Kumaran: Yeah, okay, then I think wholesale is something we have to do. We’ll quickly move into, basically, e-com and retail right after that.
474 00:49:07.900 ⇒ 00:49:13.889 Shivani Amar: Yeah, so to me, it’s like, okay, we’ve got January, then February output is really,
475 00:49:14.830 ⇒ 00:49:18.540 Shivani Amar: the order… dim order table, right? Like, it’s like…
476 00:49:18.540 ⇒ 00:49:27.700 Uttam Kumaran: Yes, but also for those, we need to finish the metrics dictionary, we need to think about how we’re displaying the KPIs, right? So those are… on the Gantt chart, there’s still, like.
477 00:49:28.080 ⇒ 00:49:31.060 Uttam Kumaran: At the bottom of the game, there’s line items for, like.
478 00:49:31.200 ⇒ 00:49:33.929 Uttam Kumaran: The metrics dictionary kind of being signed off.
479 00:49:34.140 ⇒ 00:49:34.870 Shivani Amar: like.
480 00:49:34.870 ⇒ 00:49:36.360 Uttam Kumaran: How are we gonna report
481 00:49:36.950 ⇒ 00:49:43.679 Uttam Kumaran: on this data, like, how does your team versus other teams report? That’s all fed, like, Feb stuff.
482 00:49:44.260 ⇒ 00:49:45.090 Shivani Amar: Yeah.
483 00:49:45.290 ⇒ 00:49:49.530 Shivani Amar: Okay, so I think here is where…
484 00:49:49.850 ⇒ 00:49:54.390 Shivani Amar: Okay, perfect. So, I think with wholesale, I liked the…
485 00:49:54.920 ⇒ 00:49:58.399 Uttam Kumaran: I can also give you this meeting transcript, by the way, if you want to use it.
486 00:49:58.800 ⇒ 00:50:05.980 Shivani Amar: It’s okay. I’m like… We’re doing it live, and I’m like, are you not gonna rework my notes after?
487 00:50:06.380 ⇒ 00:50:17.460 Shivani Amar: So, wholesale is in, like, I’m gonna say, oops, not here. I was trying to show you that marketing is sitting wherever, but I was gonna say that I can, like.
488 00:50:17.670 ⇒ 00:50:24.109 Shivani Amar: to Laura specifically, right, like, I can, like, like, this is the way this thing works, is, like.
489 00:50:24.380 ⇒ 00:50:28.650 Shivani Amar: Context for Laura… Okay?
490 00:50:30.060 ⇒ 00:50:33.949 Shivani Amar: Wholesale is in… we are intentionally selecting
491 00:50:38.280 ⇒ 00:50:39.730 Shivani Amar: Wholesale.
492 00:50:40.660 ⇒ 00:50:47.650 Shivani Amar: Can you bold in the comment note? As the first stakeholder to go… end-to-end is weird language here.
493 00:50:48.820 ⇒ 00:50:52.220 Uttam Kumaran: Not end-to-end, it’s sort of, like, to deliver…
494 00:50:52.740 ⇒ 00:50:57.199 Uttam Kumaran: It’s like, to deliver clean… data models for.
495 00:50:57.640 ⇒ 00:51:00.709 Uttam Kumaran: It’s from what it is from our perspective.
496 00:51:15.700 ⇒ 00:51:18.200 Uttam Kumaran: I mean, and this is also the thing, like, we…
497 00:51:18.370 ⇒ 00:51:35.439 Uttam Kumaran: the data team overall needs to start building trust with the organization, and we will find out more ways about how people report on things. But this is a stakeholder that now starts to have requirements for us, right? Every few weeks, and we start to build stuff for them. So we will also get a sense of, like, what is the…
498 00:51:35.550 ⇒ 00:51:38.830 Uttam Kumaran: Like, how much time needs to be dedicated now to…
499 00:51:39.220 ⇒ 00:51:45.060 Uttam Kumaran: Wholesale per week, in addition to continuing to build, continue to do larger things, so…
500 00:51:45.480 ⇒ 00:52:01.150 Uttam Kumaran: it’s, again, just helping us understand, like, bandwidth, and really, like, this is, like, when… if we… if we’re planning after February, we will at least by then know how much time it takes for us to support wholesale, can use that to project a little bit about how much time it’s gonna take to support retail.
501 00:52:01.470 ⇒ 00:52:03.720 Uttam Kumaran: And… e-comm.
502 00:52:04.300 ⇒ 00:52:04.810 Shivani Amar: Yeah.
503 00:52:04.810 ⇒ 00:52:13.810 Uttam Kumaran: And omni-channel support doesn’t really, like, exist, so maybe consider that, like, a third stakeholder, and then you can kind of get a sense of, like, okay, how much time is needed?
504 00:52:14.490 ⇒ 00:52:15.900 Shivani Amar: Yeah. Okay, cool.
505 00:52:15.900 ⇒ 00:52:21.580 Uttam Kumaran: So, that’s what I want to, like, land at by 4 Feb, so you can also get a sense of, like, what is the…
506 00:52:21.890 ⇒ 00:52:23.200 Uttam Kumaran: what’s the…
507 00:52:23.510 ⇒ 00:52:30.539 Uttam Kumaran: scope, you know, that we’re gonna take on. Because ultimately, the worst thing we can make as a data team is to go and support a
508 00:52:30.740 ⇒ 00:52:36.699 Uttam Kumaran: A team, say we’re gonna deliver something, then just kick it until, like, 6 months.
509 00:52:36.820 ⇒ 00:52:38.619 Uttam Kumaran: Which we haven’t done yet, but like…
510 00:52:38.620 ⇒ 00:52:42.170 Shivani Amar: I genuinely think Laura is very well respected in the org, and…
511 00:52:42.170 ⇒ 00:52:42.600 Uttam Kumaran: K.
512 00:52:42.810 ⇒ 00:52:53.539 Shivani Amar: people are aware that, like… like, she flies to Bozeman when she has to present things about, like, Shopify business versus Shopify e-commerce, like, you know, like, I feel like she’s… she’s…
513 00:52:53.770 ⇒ 00:52:54.870 Shivani Amar: Delivering first.
514 00:52:54.870 ⇒ 00:52:56.750 Uttam Kumaran: Or could it be a great win for this team, then we.
515 00:52:56.900 ⇒ 00:52:57.430 Shivani Amar: I agree.
516 00:52:57.980 ⇒ 00:53:07.170 Shivani Amar: I actually don’t even feel, like, murky on that. Like, I’m like, if Phil was like, why are you delivering for wholesale? I would be like, here’s the why.
517 00:53:07.170 ⇒ 00:53:25.320 Shivani Amar: It’s fewer data sources, we want to show that we can deliver some, like, we can… it’s not even deliver a dashboard, it’s just, like, this is where we’re starting off with the clean tables, and then the master clean table for omnichannel is still going to take some, like, work on, like, making sure we’re all super aligned on definitions and things like that. Yeah.
518 00:53:25.320 ⇒ 00:53:32.570 Uttam Kumaran: We may find that other teams we may also be able to deliver for before the BI tool, you know? And that’s a good win for others that we can figure.
519 00:53:32.570 ⇒ 00:53:33.250 Shivani Amar: Yeah.
520 00:53:33.500 ⇒ 00:53:37.480 Shivani Amar: And, like, so, like, very tactically on this wholesale piece.
521 00:53:37.590 ⇒ 00:53:41.960 Shivani Amar: Would you say that it’s, like, it would be nice for me to have an hour a week
522 00:53:42.140 ⇒ 00:53:44.440 Shivani Amar: scheduled with Laura and Madison.
523 00:53:44.740 ⇒ 00:53:58.510 Shivani Amar: Or is that too much? Are you, like, 30 minutes a week just for me to ask questions? Like, think about how you’re like, we want to get to this clean table place, like, what would be your flow for working with these stakeholders in the span of January?
524 00:53:58.960 ⇒ 00:54:00.520 Uttam Kumaran: Yeah, Awish, what do you think?
525 00:54:02.370 ⇒ 00:54:05.570 Awaish Kumar: Yeah, I think we can…
526 00:54:05.960 ⇒ 00:54:08.829 Awaish Kumar: Like, we can meet with them weekly.
527 00:54:09.490 ⇒ 00:54:10.510 Awaish Kumar: Once a week.
528 00:54:12.220 ⇒ 00:54:18.879 Uttam Kumaran: And then… I think once a week in Jan, and then we will wind it down. We will wind it to something more…
529 00:54:19.050 ⇒ 00:54:21.609 Uttam Kumaran: Infrequent, plus Slack, basically.
530 00:54:21.900 ⇒ 00:54:24.710 Shivani Amar: Yeah, and so, for how long would you want?
531 00:54:25.760 ⇒ 00:54:28.219 Uttam Kumaran: I would ask for an hour a week.
532 00:54:32.220 ⇒ 00:54:39.520 Uttam Kumaran: And yeah, you can let them know that over time, most of our stuff will move into Slack, and then we can think about
533 00:54:39.720 ⇒ 00:54:43.689 Uttam Kumaran: Which parts of the org need what type of reporting support?
534 00:54:44.210 ⇒ 00:54:46.640 Uttam Kumaran: And then another thing that…
535 00:54:46.640 ⇒ 00:54:47.300 Shivani Amar: the…
536 00:54:47.380 ⇒ 00:54:58.470 Shivani Amar: I like this as, like, a flow that’s like, when we’re in the zone of delivering your team clean tables, it’s gonna mean that we’re, like, having a weekly. The discovery is a kickoff, but if I’m in the zone of, like.
537 00:54:58.540 ⇒ 00:55:10.669 Shivani Amar: okay, I’m, like, really trying to deliver something for this team, it’s a weekly touchpoint, and so, like, come February, it might mean that you’re weekly meeting with Carlos, or Russell, or something on retail, right?
538 00:55:10.670 ⇒ 00:55:11.440 Uttam Kumaran: Exactly, exactly.
539 00:55:11.440 ⇒ 00:55:18.250 Shivani Amar: But you’re not switching to the weekly with CX, because we’re not trying to deliver them, like, cleaned up tables, we’re just doing the ingestion right now.
540 00:55:18.630 ⇒ 00:55:19.330 Uttam Kumaran: Correct, correct.
541 00:55:19.330 ⇒ 00:55:19.980 Shivani Amar: Okay.
542 00:55:21.440 ⇒ 00:55:24.860 Uttam Kumaran: And I could show you different ways that, data team
543 00:55:25.070 ⇒ 00:55:28.080 Uttam Kumaran: Like, there’s a lot of different support models we can consider.
544 00:55:28.450 ⇒ 00:55:37.680 Uttam Kumaran: You know, as we start to set the first data marts, and then those start to mature. Because they will already have… they will… as soon as they get new stuff, it’s like.
545 00:55:37.830 ⇒ 00:55:44.099 Uttam Kumaran: oh, like, now that I know we can do this, like, they’ll get… we’ll get more requests. So then we’ll start to understand our bandwidth.
546 00:55:45.170 ⇒ 00:55:52.669 Shivani Amar: Gotcha. So then, like, okay, another way that… I’m still figuring out the rhythms, but this is just, like, you’re just getting a peek into.
547 00:55:52.670 ⇒ 00:55:55.349 Uttam Kumaran: It’s also interesting, because its tech team is the only, like.
548 00:55:55.710 ⇒ 00:56:01.000 Uttam Kumaran: engineering team, so we’re… I’m trying to understand also, like, how people are meeting, yeah.
549 00:56:01.800 ⇒ 00:56:04.630 Shivani Amar: So, like, if I go to Laura’s, okay?
550 00:56:05.520 ⇒ 00:56:11.680 Shivani Amar: I’ll just show you guys how this stuff works. Laura… where’s her assessment brief? I guess I just searched Laura.
551 00:56:12.450 ⇒ 00:56:15.850 Shivani Amar: Laura Putnam, I guess is hers. Okay, so she started
552 00:56:16.400 ⇒ 00:56:19.989 Shivani Amar: planning Jan 26th. She’s, like, already in here, right? She’s in here right now.
553 00:56:20.200 ⇒ 00:56:24.839 Shivani Amar: So she’s trying to draft and present a 3-year wholesale vision, so she’s like, okay, she’s…
554 00:56:25.020 ⇒ 00:56:31.299 Shivani Amar: Or that was what she was trying to do, reviewing December. Then she’s writing reflections. Then she’s planning her sprint ahead.
555 00:56:31.410 ⇒ 00:56:33.369 Shivani Amar: Refine the 3-year vision.
556 00:56:35.890 ⇒ 00:56:37.929 Shivani Amar: Finalized specialty retail.
557 00:56:38.740 ⇒ 00:56:45.149 Shivani Amar: and then custom fridge designs, right? And then here, I can say, like.
558 00:56:45.850 ⇒ 00:57:04.690 Shivani Amar: like, so basically, it’s like, she has to think, like, am I already slammed with, like, trying to make these fridges happen, right? Like, is the data work? Like, do I have time to dedicate how we make the data work? So it’s like, that makes sense. I’m gonna show you how Element works, because it’s, like, very, like, you can go into somebody else’s document and be like, what is their thing for this, like…
559 00:57:04.690 ⇒ 00:57:05.430 Uttam Kumaran: Yeah.
560 00:57:05.430 ⇒ 00:57:06.599 Shivani Amar: Four weeks ahead.
561 00:57:06.720 ⇒ 00:57:07.280 Shivani Amar: Yeah.
562 00:57:07.280 ⇒ 00:57:12.980 Uttam Kumaran: If she ends up being like, there’s no way I can do that, then we can think about what’s the minimum viable time
563 00:57:14.110 ⇒ 00:57:16.279 Uttam Kumaran: You know, that we would need with her?
564 00:57:16.560 ⇒ 00:57:17.270 Shivani Amar: Yeah.
565 00:57:17.270 ⇒ 00:57:31.039 Uttam Kumaran: Which, which at minimum, if we can at least get one hour meeting this next month, we can still deliver a lot. We learned a lot in the last one. And also it’s… I know, I forgot who the other woman on her team was, but maybe it’s working directly with her.
566 00:57:32.380 ⇒ 00:57:33.709 Shivani Amar: Madison, right? Like…
567 00:57:33.710 ⇒ 00:57:38.150 Uttam Kumaran: Yeah, if Madison is the primary person putting stuff together, then…
568 00:57:38.440 ⇒ 00:57:42.389 Uttam Kumaran: I actually don’t… I’d rather just work with her directly, because…
569 00:57:42.390 ⇒ 00:57:44.850 Shivani Amar: More of my job’s gonna be to empower her.
570 00:57:44.880 ⇒ 00:57:48.940 Uttam Kumaran: And it doesn’t break up their sinks at all, you know?
571 00:57:49.320 ⇒ 00:57:50.020 Shivani Amar: Yeah.
572 00:57:50.870 ⇒ 00:57:51.560 Shivani Amar: And ultimately.
573 00:57:51.560 ⇒ 00:58:00.140 Uttam Kumaran: Definitely, like, we’re… the way I try to do this is we just layer it on, and then we can take it all back later, you know? If we don’t need that frequent meetings, or we find it’s not, like.
574 00:58:00.780 ⇒ 00:58:02.289 Uttam Kumaran: We could do a lot async.
575 00:58:18.060 ⇒ 00:58:23.479 Shivani Amar: So, goal is to get wholesale a couple of clean tables that exist within Snowflake that will be a foundational source of truth.
576 00:58:23.660 ⇒ 00:58:30.589 Shivani Amar: Regarding… Regarding wholesale customers and sales.
577 00:58:31.800 ⇒ 00:58:36.350 Awaish Kumar: I don’t know, to get a couple… to wholesale a couple of clean tables that exist.
578 00:58:36.700 ⇒ 00:58:42.619 Shivani Amar: within Snowflake, there’ll be a foundational source of truth regarding Customers and sales.
579 00:58:44.340 ⇒ 00:58:49.750 Shivani Amar: And, like, the nice thing is, like, it will go back to the beginning of time, kind of, right?
580 00:58:49.750 ⇒ 00:58:50.240 Uttam Kumaran: Yes.
581 00:59:02.920 ⇒ 00:59:04.889 Shivani Amar: Okay.
582 00:59:23.510 ⇒ 00:59:38.069 Shivani Amar: So, my work, right, this week, if I, like, think about what I’m doing, is, like, tee up the things with the right stakeholders, so everybody knows what to expect. It’s kind of like a blessing that we have these weeks to, like, do this, because otherwise it just gets very, like, jumbled and frazzled.
583 00:59:38.140 ⇒ 00:59:44.699 Shivani Amar: And so, like, the other person that I want to message is Esther.
584 00:59:44.830 ⇒ 00:59:54.210 Shivani Amar: And she’s the head of, like, she’s a leader in the CX org. And…
585 00:59:55.510 ⇒ 01:00:03.959 Shivani Amar: I had pinged her, like, in November, saying, with your Brainforge Discovery proposal, and, like, I think…
586 01:00:04.450 ⇒ 01:00:17.109 Shivani Amar: or, sorry, like, Brain Forge Discovery Project Summary, like, whatever your project summary was. And I said, no action needed from CX right now, but when we get to your team in the discovery phase, I’ll let you know, and we can set up time with the right teammates. So, I think I can say, like.
587 01:00:17.360 ⇒ 01:00:26.099 Shivani Amar: Hey, well, like… modeling… Basically, it’s like.
588 01:00:26.530 ⇒ 01:00:43.279 Shivani Amar: we’ve started to ingest data across the business, right? And I’m not sure where you’ve landed with the gorgeous decision, but we figure we can at least, like, start capturing the data in our warehouse this sprint. And so, like, who on your team would you recommend we have Brainforge connect with?
589 01:00:43.600 ⇒ 01:00:45.789 Shivani Amar: Does that sound right? Okay, perfect.
590 01:00:45.790 ⇒ 01:00:52.140 Uttam Kumaran: And anything we learn will end up being valuable if we end up working with them 3 months or 6 months.
591 01:00:52.380 ⇒ 01:00:55.189 Uttam Kumaran: Like, all the context is gonna be useful there, so…
592 01:00:55.190 ⇒ 01:00:55.760 Shivani Amar: Yeah.
593 01:01:03.050 ⇒ 01:01:04.090 Shivani Amar: Perfect.
594 01:01:08.300 ⇒ 01:01:09.190 Shivani Amar: Okay.
595 01:01:09.670 ⇒ 01:01:13.880 Shivani Amar: And then I will tee up I will tee up…
596 01:01:14.310 ⇒ 01:01:29.839 Shivani Amar: discovery with retail, and then I think the one that I’m feeling nebulous on is, like, why… discovery with finance, talk me through why… like, I think that’s the one that I’m, like, from an omnichannel… is it definitions? Is it, like, going into, like, how they define things? Yeah. Okay.
597 01:01:29.840 ⇒ 01:01:35.320 Shivani Amar: So it’s less about finance data systems, it’s more about vernacular.
598 01:01:35.320 ⇒ 01:01:41.369 Uttam Kumaran: Unless that’s… unless that’s… relevant? If not, then… They’re gonna be the…
599 01:01:41.580 ⇒ 01:01:49.540 Uttam Kumaran: accounting and finance stakeholder for, like, metric sign-off. So I’m gonna go through and understand their perspective on
600 01:01:49.860 ⇒ 01:01:53.559 Uttam Kumaran: All of the most common revenue metrics, and…
601 01:01:53.790 ⇒ 01:01:59.129 Uttam Kumaran: kind of see, like, if they… if they have an opinion on operational ones, too, like.
602 01:01:59.370 ⇒ 01:02:04.500 Uttam Kumaran: Because there are gonna be some metrics that they’re like, if you use this metric, it needs to be defined like this, like.
603 01:02:04.620 ⇒ 01:02:13.250 Uttam Kumaran: full stop. There’s gonna be other things that are, like, those are operational, like, they’re not… they don’t have to be, like, GAP, you know? So that’s what I want to understand.
604 01:02:13.550 ⇒ 01:02:17.849 Shivani Amar: Totally. So, I think the right person for that one is Jacob.
605 01:02:18.170 ⇒ 01:02:19.800 Shivani Amar: Maybe Dan, also.
606 01:02:21.360 ⇒ 01:02:25.840 Uttam Kumaran: I know we talked to Dan in the, like, when we were just starting to chat.
607 01:02:26.360 ⇒ 01:02:27.850 Shivani Amar: Yeah.
608 01:02:27.850 ⇒ 01:02:38.430 Uttam Kumaran: And if we talk to Jacob, then I can… I’ll have some stuff, at least, I can send to Dan async for him to review, because we’ll be working through a larger document, like, on metrics, like, definitions we’re seeing.
609 01:02:38.720 ⇒ 01:02:49.090 Shivani Amar: I think Jacob and Dan in that meeting together would be good, so I can… I can actually just, like, look at… when would you want to do that? Because I can look at calendars and kind of just, like, make that happen also.
610 01:02:52.440 ⇒ 01:02:55.750 Uttam Kumaran: Yeah, I wanna do, like, there… I wanna do,
611 01:03:01.040 ⇒ 01:03:07.060 Uttam Kumaran: There’s two conversations I want to have, so there’s one on… Metric 62.
612 01:03:08.850 ⇒ 01:03:12.770 Uttam Kumaran: Yeah, probably, like, second or third week of Jan.
613 01:03:13.430 ⇒ 01:03:17.190 Uttam Kumaran: Because after I get their input, I can start working on
614 01:03:19.290 ⇒ 01:03:21.570 Uttam Kumaran: They basically have the entire metrics.
615 01:03:21.950 ⇒ 01:03:25.979 Uttam Kumaran: dictionary and, like, kind of KPI standardization process.
616 01:03:27.410 ⇒ 01:03:27.940 Uttam Kumaran: But then.
617 01:03:27.940 ⇒ 01:03:37.769 Shivani Amar: Do they have their one-on-one… They have a one-on-one, on Jan 12th.
618 01:03:38.340 ⇒ 01:03:42.609 Shivani Amar: 1.30 to 2 p.m. Eastern, and it’d be interesting if, like.
619 01:03:42.900 ⇒ 01:03:49.209 Shivani Amar: Yeah, I feel like if we talk to them right after their one-on-one at 2pm Eastern on the 12th, that could be good, because…
620 01:03:49.430 ⇒ 01:03:53.680 Shivani Amar: If you’ve teed up something with them before, they can even, like, get aligned on it.
621 01:03:53.810 ⇒ 01:03:54.450 Shivani Amar: Prior.
622 01:03:54.450 ⇒ 01:03:55.010 Uttam Kumaran: Okay.
623 01:03:55.500 ⇒ 01:04:00.999 Shivani Amar: Awish, does that time work for you? 2PM Eastern? Yeah.
624 01:04:01.040 ⇒ 01:04:02.140 Awaish Kumar: So…
625 01:04:02.350 ⇒ 01:04:06.020 Shivani Amar: Okay, so I’m gonna just send that invitation right now, one second, so…
626 01:04:06.580 ⇒ 01:04:14.359 Shivani Amar: Brainforge metric… Brainforge X Element Metrics Discussion.
627 01:04:14.760 ⇒ 01:04:18.519 Shivani Amar: Okay? And then, let me add you both.
628 01:04:19.610 ⇒ 01:04:26.530 Shivani Amar: But then, like, if you have, like, a…
629 01:04:27.040 ⇒ 01:04:46.459 Shivani Amar: two-sentence thing you want to Slack me that I can put in the description of that, that will help. Just like, okay, we want to go through which metrics are GAAP, not GAAP, like, whatever you just said after this meeting. Yeah. I can add it to the… like, I’ll just send the invite now to hold it, but, if you can do that, then I’ll add it to the description later.
630 01:04:48.350 ⇒ 01:04:49.210 Shivani Amar: Cool.
631 01:04:50.270 ⇒ 01:04:55.500 Shivani Amar: So… Let’s see… .
632 01:04:55.500 ⇒ 01:05:03.759 Uttam Kumaran: And then one other item, if that’s good, we… I told Awash that maybe it’d be great for us to meet with the tech team every two weeks to sort of, like.
633 01:05:04.080 ⇒ 01:05:06.840 Uttam Kumaran: Just show them, like, what we’ve been doing.
634 01:05:10.600 ⇒ 01:05:17.780 Uttam Kumaran: So that they’re just aware of, like, what the setup is that we did in Polyatomic, we’re gonna set up dbt here shortly, Snowflake.
635 01:05:18.490 ⇒ 01:05:21.109 Uttam Kumaran: I thought it would just be good to hold time with them.
636 01:05:22.060 ⇒ 01:05:23.530 Uttam Kumaran: Basically, bi-weekly.
637 01:05:23.820 ⇒ 01:05:26.709 Shivani Amar: And then me, you, and Jason already talk weekly, so…
638 01:05:26.900 ⇒ 01:05:29.690 Uttam Kumaran: That way, I don’t have to cover as much of that there.
639 01:05:30.170 ⇒ 01:05:33.600 Shivani Amar: Jason, Andy, Steve…
640 01:05:33.820 ⇒ 01:05:46.229 Shivani Amar: Okay, let me look at calendars. So, if we go… so maybe we’d want something like Feb… Jan 7th, let’s say, because you want to talk about the blockers, or whatever, like, you want something early, right?
641 01:05:47.170 ⇒ 01:05:53.820 Uttam Kumaran: Yeah, like, we would talk… if Jan 7, we can clear up the Shopify and the Emerson stuff, then that would be great.
642 01:05:54.610 ⇒ 01:06:01.060 Uttam Kumaran: Ideally, we can do that… we can do that on… if we could do it on Wednesdays, that… that way, our calls are on Thursdays, and so…
643 01:06:01.640 ⇒ 01:06:02.540 Uttam Kumaran: Yeah, it’s…
644 01:06:02.540 ⇒ 01:06:04.610 Shivani Amar: How about 2PM Eastern that day?
645 01:06:06.930 ⇒ 01:06:08.700 Shivani Amar: On the Wednesday. Yeah, that works for me.
646 01:06:09.250 ⇒ 01:06:15.720 Shivani Amar: Okay, so… The thing is, I’m like…
647 01:06:15.820 ⇒ 01:06:26.260 Shivani Amar: Okay, actually, so there’s a note on all this that I’m gonna be off some days here and there in January, because I’m going on vacation with my family. So,
648 01:06:27.310 ⇒ 01:06:42.479 Shivani Amar: Can you send the invitation to Wish, so it’s from your Zoom, for 2PM on the 7th, and then I guess, the 21st, if you want to do it bi-weekly, because I don’t want to be on point to start a Zoom if I’m not able to join.
649 01:06:42.750 ⇒ 01:06:47.609 Shivani Amar: And I can join this Wednesday, but basically I’m looking at it, I’m like, I might not be able to join on the 21st.
650 01:06:48.900 ⇒ 01:06:49.900 Awaish Kumar: Okay, yeah, obviously.
651 01:06:50.240 ⇒ 01:06:51.140 Shivani Amar: Okay?
652 01:06:51.440 ⇒ 01:06:54.800 Shivani Amar: Let’s see…
653 01:06:55.430 ⇒ 01:06:59.310 Uttam Kumaran: And then we can also maybe plan on doing, like, a source medium…
654 01:07:00.780 ⇒ 01:07:05.570 Uttam Kumaran: Like, 2 hours, sort of, like, Deep dive sometime next month.
655 01:07:05.790 ⇒ 01:07:07.380 Shivani Amar: You wanna do that me and you?
656 01:07:07.900 ⇒ 01:07:08.520 Uttam Kumaran: Yeah.
657 01:07:08.690 ⇒ 01:07:12.919 Shivani Amar: like, the three of us. I think that’s just, like, a workshop for the three of us, because, like, I don’t…
658 01:07:12.920 ⇒ 01:07:17.620 Uttam Kumaran: That’s… No, no, no, just us, yeah. So we can go through, and then I can,
659 01:07:18.720 ⇒ 01:07:21.029 Uttam Kumaran: We can start to put together, like.
660 01:07:21.030 ⇒ 01:07:21.360 Shivani Amar: How about.
661 01:07:21.360 ⇒ 01:07:22.040 Uttam Kumaran: At least we’ll have.
662 01:07:22.040 ⇒ 01:07:24.140 Shivani Amar: It’s very open for me, the 9th.
663 01:07:27.310 ⇒ 01:07:28.960 Uttam Kumaran: Yeah, I can do the 9th.
664 01:07:29.360 ⇒ 01:07:29.860 Uttam Kumaran: Like…
665 01:07:30.560 ⇒ 01:07:33.540 Shivani Amar: Meaningful, like, chunk with you guys.
666 01:07:35.230 ⇒ 01:07:39.790 Uttam Kumaran: Yeah, I mean, I’m basically… I can do, like, 2PM Eastern.
667 01:07:40.370 ⇒ 01:07:42.800 Uttam Kumaran: 2 to 4, or I can do…
668 01:07:44.040 ⇒ 01:07:50.649 Uttam Kumaran: We have… we… we have a Friday, like, all hands at 1 o’clock Eastern, so if we can do two, that would be…
669 01:07:51.070 ⇒ 01:07:54.329 Shivani Amar: why don’t we do… why don’t we just do an… like, I can go.
670 01:07:54.330 ⇒ 01:07:55.709 Uttam Kumaran: Or just do an hour.
671 01:07:55.710 ⇒ 01:08:01.539 Shivani Amar: Let’s do an hour, because otherwise we might be, like, eyes glaze over on a Friday, I don’t know, like.
672 01:08:02.010 ⇒ 01:08:05.329 Uttam Kumaran: I just want to click around with it with you and sort of give you our commentary, and then…
673 01:08:05.330 ⇒ 01:08:08.010 Shivani Amar: We’ll start to put our memo in there.
674 01:08:08.170 ⇒ 01:08:27.719 Shivani Amar: Okay, I’m sending that to you guys, source medium review for 2 to 3 p.m. on Friday. This is great. Hope you’re having a wonderful RNA period and holidays with your family. Wanted to give you a data project update. We’ve started to ingest some of our data from our commercial side of the business, Shopify Retail, etc, and Phil suggested we started ingesting
675 01:08:27.779 ⇒ 01:08:29.790 Shivani Amar: Gorgeous data as well.
676 01:08:29.950 ⇒ 01:08:38.729 Shivani Amar: While we don’t… In this 3-month project.
677 01:08:39.810 ⇒ 01:08:41.559 Shivani Amar: with brain words.
678 01:08:42.700 ⇒ 01:08:49.769 Shivani Amar: Don’t have… data modeling… Slated for CX data.
679 01:08:50.189 ⇒ 01:08:50.960 Shivani Amar: Bye-bye.
680 01:08:51.720 ⇒ 01:08:52.859 Shivani Amar: Kicking off.
681 01:08:58.130 ⇒ 01:08:59.140 Shivani Amar: Setting up.
682 01:08:59.300 ⇒ 01:09:00.670 Shivani Amar: data pipes.
683 01:09:01.000 ⇒ 01:09:02.360 Shivani Amar: Sounds right.
684 01:09:19.590 ⇒ 01:09:23.210 Shivani Amar: Okay, perfect. I will send Esther a note.
685 01:09:40.899 ⇒ 01:09:54.939 Shivani Amar: Okay, I’m saying… hope… I’m, like, running everything through ChatGPT today, because I’m still getting my brain activated. Hope you’re having a wonderful RNA period and enjoying the holidays with family. Wanted to share a quick update. We’ve started ingesting some of the commercial side data, and Phil…
686 01:09:55.360 ⇒ 01:09:58.379 Shivani Amar: Bill, also, why did… why did Chad keep T safe?
687 01:09:58.550 ⇒ 01:10:03.390 Shivani Amar: And Phil also suggested that we begin ingesting gorgeous data as part of this effort.
688 01:10:03.570 ⇒ 01:10:09.959 Shivani Amar: We’re not planning to prioritize CX data modeling, but I do think it makes sense to kick off the discovery.
689 01:10:10.370 ⇒ 01:10:20.969 Shivani Amar: to get the data pipeline set up in parallel so we’re well-positioned for future work. Let me know who on your team you’d like to include on a discovery call with BrainForge, and I’m happy to take a look at colors to get something scheduled. Bus, that’s it. Does that sound good?
690 01:10:21.520 ⇒ 01:10:27.210 Shivani Amar: Because I’m like, this feels like a little bit, like, I’m like, I don’t want to over-commit anything, like, to CX.
691 01:10:27.210 ⇒ 01:10:27.940 Uttam Kumaran: Yeah.
692 01:10:28.810 ⇒ 01:10:33.800 Shivani Amar: Like, if they’re, like… like, but you… but I guess a connection point that’s interesting with them?
693 01:10:34.030 ⇒ 01:10:37.170 Shivani Amar: It’s like, the…
694 01:10:41.030 ⇒ 01:10:43.389 Shivani Amar: Do I have it? Let’s see if I have it.
695 01:10:43.890 ⇒ 01:10:55.510 Shivani Amar: saved. There’s, like, this wholesale a CX kind of collab. Let’s see if I can find it. Wholesale CX.
696 01:10:55.690 ⇒ 01:11:02.140 Shivani Amar: There’s, like, this document that…
697 01:11:03.780 ⇒ 01:11:08.309 Uttam Kumaran: Yeah, that’s interesting for me to just hear about, like, what the CX roadmap is, because…
698 01:11:09.700 ⇒ 01:11:17.050 Uttam Kumaran: Oftentimes, it just becomes, like, a black hole for data teams, because the business sometimes doesn’t care, but the CX… there’s a lot…
699 01:11:17.400 ⇒ 01:11:18.680 Uttam Kumaran: Of data involved?
700 01:11:18.680 ⇒ 01:11:24.550 Shivani Amar: The team probably wants to optimize, optimize, optimize, and, like, everybody else is like, you gotta be doing what you need to be doing, like.
701 01:11:24.550 ⇒ 01:11:25.810 Uttam Kumaran: Yeah, yeah, yeah.
702 01:11:26.030 ⇒ 01:11:28.909 Uttam Kumaran: So that’s why I want to know, like, what is it that they’re missing?
703 01:11:30.620 ⇒ 01:11:35.560 Shivani Amar: I’ll read through their briefs also, and kind of get a feel for it, and can follow up with you.
704 01:11:35.560 ⇒ 01:11:46.270 Uttam Kumaran: People may be like, oh, we need this… we need to make sure that we use a CX Insights-informed product, and there’s no link, and so you need data to try to… can we use data to find out what customers are saying? Okay, like…
705 01:11:46.770 ⇒ 01:11:50.150 Uttam Kumaran: It’s, like, one thing, so I’m just trying to understand that, yeah.
706 01:11:58.350 ⇒ 01:12:06.309 Shivani Amar: what I was gonna say is, like, there’s a file that… will I be able to find right now? No. But there’s a file where CX copies and pastes
707 01:12:06.630 ⇒ 01:12:09.620 Shivani Amar: wholesale-specific the ex-concert.
708 01:12:09.620 ⇒ 01:12:10.160 Uttam Kumaran: Oh.
709 01:12:10.160 ⇒ 01:12:11.349 Shivani Amar: Laura to review.
710 01:12:12.040 ⇒ 01:12:17.200 Shivani Amar: I see. That’s what I’m trying to get at. So I’m like, there’s a world in which Laura
711 01:12:17.300 ⇒ 01:12:22.460 Shivani Amar: in particular is, like, when does CX get inbound from my customers?
712 01:12:22.460 ⇒ 01:12:23.240 Uttam Kumaran: I see.
713 01:12:23.490 ⇒ 01:12:26.449 Shivani Amar: Because she’s trying to make sure that her customers are happy.
714 01:12:26.890 ⇒ 01:12:31.520 Shivani Amar: And that’s the thing people are, like, manually putting together, that’s what I’m, like, trying to find right now, and I’m like…
715 01:12:31.750 ⇒ 01:12:33.109 Shivani Amar: I don’t know…
716 01:12:33.430 ⇒ 01:12:47.230 Shivani Amar: I don’t know, like, maybe, like, if I look at what Laura has shared with me, maybe I’ll find it, but, like, yeah, like, I’m like, I would need to re-find that document, but, like, there was a moment in time where
717 01:12:47.630 ⇒ 01:12:50.269 Shivani Amar: There was a moment in time where
718 01:12:50.810 ⇒ 01:12:57.949 Shivani Amar: Laura rolled out a change to some wholesale partners, saying, like, okay, we’re not gonna sell you 30-count sticks anymore, we’re gonna sell you
719 01:12:58.330 ⇒ 01:13:18.160 Shivani Amar: 12-count sticks or something, and she got, like, a surge of inbound of people messaging CX, being like, I’m pretty upset about this, because, like, that’s where the CRM stuff gets a little complicated, right? Yeah. Because then you’re like, who’s a… who’s a wholesale partner that I need to flag this to wholesale that this came in via CX?
720 01:13:18.750 ⇒ 01:13:19.110 Uttam Kumaran: gap.
721 01:13:20.700 ⇒ 01:13:27.360 Shivani Amar: So if we’re trying to deliver something for CX, and we end up ingesting data, gorgeous data, that might be, like, a link that we can aspire to.
722 01:13:27.870 ⇒ 01:13:28.770 Uttam Kumaran: Okay, okay.
723 01:13:29.110 ⇒ 01:13:35.089 Shivani Amar: That’s all I’m saying. Okay. Long-winded way of saying that, but I was like, there’s something here that’s beneficial.
724 01:13:35.340 ⇒ 01:13:37.060 Shivani Amar: For wholesale in the end.
725 01:13:37.060 ⇒ 01:13:37.760 Uttam Kumaran: Okay, okay.
726 01:13:37.760 ⇒ 01:13:38.520 Shivani Amar: Okay.
727 01:13:39.200 ⇒ 01:13:40.160 Shivani Amar: Cool.
728 01:13:40.540 ⇒ 01:13:44.970 Uttam Kumaran: So I think next week, too, I can follow up with this directly on Monday, but we’ll…
729 01:13:45.160 ⇒ 01:13:46.770 Uttam Kumaran: The spins follow up?
730 01:13:47.290 ⇒ 01:13:48.410 Uttam Kumaran: To…
731 01:13:48.410 ⇒ 01:13:51.450 Shivani Amar: I sent them an email being like, did I send them the thing?
732 01:13:52.420 ⇒ 01:14:02.009 Uttam Kumaran: Okay, cool. I didn’t get anything, so yeah, okay, and then the Emerson follow-up, we’ll talk to Jason on Wednesday. We have our source medium call, we have a tech team call.
733 01:14:02.330 ⇒ 01:14:07.449 Uttam Kumaran: we’ve made a decision on, like, still going after the BI tool, but more in February.
734 01:14:07.730 ⇒ 01:14:12.969 Uttam Kumaran: There’s, like, there’s a lot I will tee up, like, as much as I can in Jamf for that.
735 01:14:12.970 ⇒ 01:14:15.620 Shivani Amar: Is your instinct Omni, on BI?
736 01:14:16.850 ⇒ 01:14:22.570 Uttam Kumaran: I hate to say… I just don’t like saying things like that, because I just don’t… this… I have to really…
737 01:14:22.570 ⇒ 01:14:26.359 Shivani Amar: Tell me! I’m not… turn off your recorder, like, whatever.
738 01:14:26.360 ⇒ 01:14:29.679 Uttam Kumaran: No, no, no, it’s not about recording, it’s more about, like…
739 01:14:29.950 ⇒ 01:14:37.489 Uttam Kumaran: It’s just… it depends, like, Omni is a really good one, Sigma is a really good one, they’re… they’re all…
740 01:14:37.490 ⇒ 01:14:38.590 Shivani Amar: What’s your instinct?
741 01:14:40.010 ⇒ 01:14:42.499 Uttam Kumaran: Aisha, what do you think?
742 01:14:44.800 ⇒ 01:14:53.029 Awaish Kumar: Like, it’s pretty… like, we are exploring the AI part, so basically, we have been previously using multiple tools, like Tableau, Power BI,
743 01:14:53.160 ⇒ 01:15:00.430 Awaish Kumar: Omni, and also some tools which you… which provide, like, BI as a code.
744 01:15:01.510 ⇒ 01:15:06.340 Awaish Kumar: But then, like… It depends on the client, like, some people…
745 01:15:06.590 ⇒ 01:15:19.010 Awaish Kumar: are comfortable with Tableau, they just want to also explore themselves, come in the Tableau, try to build a chart themselves, so they’re really good at it, and they just want us to use Tableau. We don’t have.
746 01:15:19.010 ⇒ 01:15:33.509 Shivani Amar: anybody in the business is, like, really good at making their own Tableau charts, I think, so you’re not coming in… like, we use Tableau at Brave, but, like, I also thought it had… like, I would love to be able to query data, and, like, if it’s… you know what I mean? Like…
747 01:15:34.020 ⇒ 01:15:36.449 Uttam Kumaran: I have a feeling it’ll be Omni or Sigma.
748 01:15:36.600 ⇒ 01:15:37.120 Awaish Kumar: Yeah.
749 01:15:37.120 ⇒ 01:15:40.400 Shivani Amar: But, like, also, like, I’m a weirdo, and I don’t even like…
750 01:15:40.640 ⇒ 01:15:43.170 Shivani Amar: Data visualization for me is, like.
751 01:15:43.390 ⇒ 01:15:45.220 Shivani Amar: If I have a clean table.
752 01:15:45.990 ⇒ 01:15:55.090 Shivani Amar: that then I can, like, see trends, or, like, I can, like… I’m more of the, put it back into Google Sheets so I can mess around with it type of person.
753 01:15:55.570 ⇒ 01:16:07.659 Shivani Amar: Probably. And, like, there are, like, some things that I, like, really, like… if we just do this very quickly, which then I’ll let you guys go, because I’m just taking up a lot of your time, but if… if we look at this really quickly, and I’m like, this is…
754 01:16:08.740 ⇒ 01:16:20.749 Shivani Amar: It’s so chaotic looking, and I’m like, what are the timeframes that we’re using here? Okay, it’s like month to date, I guess? I don’t… I’m like, why isn’t it December 1st? Like, I’m just confused when I look at this.
755 01:16:20.750 ⇒ 01:16:26.099 Uttam Kumaran: So there’s a lot of dashboard design here that seems… so the reason why these guys… because these guys will give you what they’re giving to everybody.
756 01:16:26.360 ⇒ 01:16:26.980 Uttam Kumaran: maybe.
757 01:16:26.980 ⇒ 01:16:28.480 Shivani Amar: And I don’t like it.
758 01:16:28.480 ⇒ 01:16:29.600 Uttam Kumaran: Yeah, I’m not…
759 01:16:29.600 ⇒ 01:16:45.430 Shivani Amar: Like, what are the questions that we’re actually trying to ask ourselves regularly? And if it is, how are orders progressing weekly? Then give me a data table that shows that, right? Like, what do I actually want to understand? And it’s like, I want to understand how orders evolve weekly over the business.
760 01:16:45.540 ⇒ 01:16:53.939 Shivani Amar: And, like, relative to what I thought it was gonna be, or whatever, like, what is the question I’m actually trying to answer? Because this is just a hot mess to me.
761 01:16:54.870 ⇒ 01:16:55.210 Uttam Kumaran: Yeah.
762 01:16:55.210 ⇒ 01:17:04.270 Shivani Amar: It’s not bad data, but I’m like, I don’t know how to get one insight out of this when I look at this. Not one, because it’s daily data. Daily data does nothing for my brain.
763 01:17:04.270 ⇒ 01:17:07.649 Uttam Kumaran: It’s like, yeah, it doesn’t.
764 01:17:07.650 ⇒ 01:17:15.369 Shivani Amar: Why would I ever want data daily in this scenario? Like, and like, with little graphs, I’m like, I want to know… I feel like I’m yelling, but I’m like.
765 01:17:15.370 ⇒ 01:17:17.040 Uttam Kumaran: No, no, no, I agree with you, it’s like not…
766 01:17:17.040 ⇒ 01:17:25.280 Shivani Amar: I want to know, like, weekly trends and whatever, and then I’m, like, able to be like, yeah, that’s seasonality, that’s what we expected, that’s not blah blah blah.
767 01:17:25.850 ⇒ 01:17:38.479 Uttam Kumaran: So this is also where it’s like, if our… if our team… if, like, the data team is going to be modeling and developing the dashboards, then, like, it’s gonna be Tableau, Sigma, or Omni.
768 01:17:39.040 ⇒ 01:17:50.479 Uttam Kumaran: The other thing is I just drew… there are some new tools. The problem is that you may select them, and they may go out of business. And so I can’t recommend one of the new… some of these new tools.
769 01:17:50.670 ⇒ 01:17:54.210 Uttam Kumaran: Because, yeah, they may be, like, really flashy in AI, but…
770 01:17:54.620 ⇒ 01:17:57.790 Uttam Kumaran: Some of them will go out of business, and like…
771 01:17:58.090 ⇒ 01:18:00.020 Shivani Amar: I want a demo of Omni.
772 01:18:00.270 ⇒ 01:18:00.820 Shivani Amar: Can you just…
773 01:18:00.820 ⇒ 01:18:01.590 Uttam Kumaran: Yeah, I think it…
774 01:18:01.590 ⇒ 01:18:03.679 Shivani Amar: Can we just do a demo of Omni? Like.
775 01:18:03.680 ⇒ 01:18:08.550 Uttam Kumaran: Totally, I can demo you our Omni… we have an Omni instance for BainProach, I can show you everything.
776 01:18:08.550 ⇒ 01:18:09.360 Shivani Amar: Okay, cool.
777 01:18:09.360 ⇒ 01:18:10.900 Uttam Kumaran: how the AI stuff works.
778 01:18:11.290 ⇒ 01:18:16.119 Shivani Amar: Let’s do a mini omni… how much… like, 30 minutes, an hour? How much would an Omni demo be?
779 01:18:16.650 ⇒ 01:18:18.929 Uttam Kumaran: Yeah, we could do 30 minutes, and then…
780 01:18:19.510 ⇒ 01:18:26.819 Uttam Kumaran: Yeah, like, I can even give you access to ours to play around, because we have sample data in there, sample e-com data, so you can go in there and mess around and do stuff.
781 01:18:27.210 ⇒ 01:18:28.000 Shivani Amar: Okay.
782 01:18:28.120 ⇒ 01:18:31.190 Shivani Amar: Can we do…
783 01:18:32.540 ⇒ 01:18:37.950 Uttam Kumaran: Yeah, we should just do sometime, like, next week. I mean, we could do it during… if we’re gonna talk Friday, we can just do it…
784 01:18:38.610 ⇒ 01:18:40.119 Uttam Kumaran: Part of our Friday call.
785 01:18:40.430 ⇒ 01:18:51.020 Shivani Amar: Yeah. Or, like, immediately after the Thursday call, like, the three of us stay on for a little… you know, like, we have the call with Jason, and then we just tack on 30 minutes to look at Omni.
786 01:18:51.330 ⇒ 01:18:52.070 Shivani Amar: Does that work?
787 01:18:52.070 ⇒ 01:18:56.970 Uttam Kumaran: I have a… I want to bring in, Awesu, on our team.
788 01:18:57.180 ⇒ 01:18:59.760 Uttam Kumaran: is, like, doing a lot of Omni stuff right now, like Demolade?
789 01:19:00.150 ⇒ 01:19:00.770 Awaish Kumar: Yeah, we can…
790 01:19:00.770 ⇒ 01:19:01.810 Uttam Kumaran: I kinda would want…
791 01:19:02.310 ⇒ 01:19:03.020 Awaish Kumar: Beautiful.
792 01:19:03.020 ⇒ 01:19:05.709 Uttam Kumaran: Yeah, I kinda want him to come and do the demo.
793 01:19:06.050 ⇒ 01:19:11.990 Shivani Amar: Does 3 to 3.30 work for, like, 3 to 3.30 Eastern work right after our Thursday call with Jason?
794 01:19:11.990 ⇒ 01:19:14.150 Uttam Kumaran: I have a 3-330.
795 01:19:14.150 ⇒ 01:19:18.250 Shivani Amar: Or, or 3.30 to 4, or 4 to 4, like, just basically that day. We could do…
796 01:19:18.570 ⇒ 01:19:20.359 Uttam Kumaran: Yeah, we can do 3.30 to 4.
797 01:19:20.910 ⇒ 01:19:23.949 Shivani Amar: Let’s do that. And then I’ll have someone from… You wanna send it?
798 01:19:23.950 ⇒ 01:19:24.530 Uttam Kumaran: Doing a lot of…
799 01:19:24.530 ⇒ 01:19:26.410 Shivani Amar: So you can include your person.
800 01:19:26.780 ⇒ 01:19:27.590 Uttam Kumaran: Yeah, yeah.
801 01:19:27.920 ⇒ 01:19:28.640 Shivani Amar: Okay.
802 01:19:29.650 ⇒ 01:19:31.499 Shivani Amar: Perfect. Guys, we’re cruising.
803 01:19:31.500 ⇒ 01:19:37.050 Uttam Kumaran: And we’ll add you, we’ll add you to our, our… Omni instance.
804 01:19:37.260 ⇒ 01:19:42.240 Uttam Kumaran: And you can… so you can mess around and kind of see, like, what a fake data set in there, like.
805 01:19:42.460 ⇒ 01:19:45.680 Uttam Kumaran: try to create dashboards and sort of check out the AI features.
806 01:19:45.860 ⇒ 01:19:51.089 Shivani Amar: Yeah, okay, perfect. Like, look, like, Laura just updated her document, and she wrote.
807 01:19:51.760 ⇒ 01:19:57.260 Shivani Amar: BI onboarding and set up with volume and brain functions. I’m like, I don’t know if that’s the right way to articulate that.
808 01:19:58.460 ⇒ 01:20:02.989 Shivani Amar: Okay, at least you have it on your, on your list. I don’t really know what you just wrote.
809 01:20:02.990 ⇒ 01:20:14.489 Uttam Kumaran: They’re like, I’m like, I’m like, please set us up for success here with VI onboarding. She’s like, she’s like, oh yeah, great, they crushed it over Christmas, they got everything going, we’re gonna be good.
810 01:20:16.870 ⇒ 01:20:18.620 Shivani Amar: Okay.
811 01:20:19.040 ⇒ 01:20:24.240 Shivani Amar: We’ll let it… we’ll let it stay as she has it. Cool.
812 01:20:24.910 ⇒ 01:20:27.340 Shivani Amar: Do you want, Jason to be on that?
813 01:20:28.810 ⇒ 01:20:29.410 Shivani Amar: Sure.
814 01:20:29.410 ⇒ 01:20:33.630 Uttam Kumaran: I can also… okay, we can also… we can send the recording, too, after.
815 01:20:33.630 ⇒ 01:20:34.840 Shivani Amar: That’s fine, yeah.
816 01:20:35.110 ⇒ 01:20:38.580 Shivani Amar: Okay…
817 01:20:39.640 ⇒ 01:20:52.680 Shivani Amar: How do you guys feel? Are you like, cool, it’s gonna be a packed sprint, but it feels doable, we’ve punted the BI decision, that felt like some breathing room, we’re committing to, we’re not doing any data modeling on CX, we’re just doing a discovery call with them, and…
818 01:20:52.970 ⇒ 01:20:58.710 Shivani Amar: Like, are you, like, cool, like, this feels, like, doable, manageable, the things that we’ve aligned on?
819 01:20:59.900 ⇒ 01:21:06.289 Uttam Kumaran: Yeah, I feel like it. I think, the biggest thing for you is to just keep pressure on Polytomic the next two weeks.
820 01:21:06.480 ⇒ 01:21:12.230 Uttam Kumaran: And then we’re gonna loop in one more person to start helping with modeling Shivani next week. We’re just deciding on, like.
821 01:21:12.470 ⇒ 01:21:18.589 Uttam Kumaran: Our allocations for… Next quarter. That way, it’ll be me, Awaish, and one other person.
822 01:21:18.750 ⇒ 01:21:22.859 Uttam Kumaran: Usually, we staff all of our teams with, like, 3 anyways, but…
823 01:21:23.000 ⇒ 01:21:26.390 Uttam Kumaran: That way, we’ll… we’ll be able to continue, because right now, we’ll have, like.
824 01:21:26.670 ⇒ 01:21:32.850 Uttam Kumaran: Roughly 2 or 3 different parallel paths, so… Yeah.
825 01:21:33.910 ⇒ 01:21:39.700 Shivani Amar: For… that sounds great. For the… retail discovery.
826 01:21:39.890 ⇒ 01:21:57.040 Shivani Amar: you want to draft the Emerson email, but you have retail data, right? So, when you think about, like, let’s say Russell is the guy who’s kind of, like, over retail right now, and he’s the one… when I think about his role… okay, let me actually just look at something for a second, because I’m like…
827 01:21:57.360 ⇒ 01:22:01.380 Shivani Amar: There’s, mmm, let me show you something.
828 01:22:02.180 ⇒ 01:22:03.450 Shivani Amar: Russell…
829 01:22:04.370 ⇒ 01:22:05.130 Awaish Kumar: Sweet.
830 01:22:07.170 ⇒ 01:22:18.320 Shivani Amar: Like, what he’s doing, like, if we look at what he’s up to, right, planning January, he’s, like, securing the test… he’s, like, the one talking to all the buyers.
831 01:22:18.710 ⇒ 01:22:19.610 Shivani Amar: Okay?
832 01:22:19.610 ⇒ 01:22:20.690 Uttam Kumaran: Okay.
833 01:22:21.070 ⇒ 01:22:33.159 Shivani Amar: Okay, that’s, like, his role. So then it’s like, he’s the one talking to all the buyers. Now, is he the one looking at retail data velocity performance, right? Like, let’s look back at the org chart for a second.
834 01:22:33.690 ⇒ 01:22:38.110 Shivani Amar: So you’ve got… Commercial.
835 01:22:38.240 ⇒ 01:22:41.510 Shivani Amar: And…
836 01:22:41.510 ⇒ 01:22:43.459 Uttam Kumaran: Can I get, like, a copy of this?
837 01:22:43.700 ⇒ 01:22:47.119 Shivani Amar: I, I… Or like… I hope so. Okay.
838 01:22:47.120 ⇒ 01:22:50.690 Uttam Kumaran: Or even, like, an image export, whatever.
839 01:22:50.690 ⇒ 01:22:55.930 Shivani Amar: Yeah, I’m like… Let’s do this. And I’m like… Okay…
840 01:23:05.020 ⇒ 01:23:05.940 Shivani Amar: Okay.
841 01:23:09.860 ⇒ 01:23:11.040 Shivani Amar: Oh ho.
842 01:23:11.450 ⇒ 01:23:12.390 Shivani Amar: Whatever.
843 01:23:12.390 ⇒ 01:23:14.750 Uttam Kumaran: You’re multi-talented, pick what you do most often.
844 01:23:14.750 ⇒ 01:23:18.830 Shivani Amar: I’m like, what? I’m like… Data analytics.
845 01:23:19.360 ⇒ 01:23:20.000 Shivani Amar: One moment.
846 01:23:21.790 ⇒ 01:23:24.900 Shivani Amar: Leave me alone, Figma. I’m just trying to do something. Okay.
847 01:23:25.410 ⇒ 01:23:29.739 Shivani Amar: Okay, maybe visible to other people.
848 01:23:29.920 ⇒ 01:23:33.350 Shivani Amar: Okay. Share.
849 01:23:33.690 ⇒ 01:23:35.950 Shivani Amar: Now, can I share it with you?
850 01:23:42.080 ⇒ 01:23:43.830 Shivani Amar: Can edit? No, can view.
851 01:23:43.830 ⇒ 01:23:45.200 Uttam Kumaran: I don’t need edit access, yeah.
852 01:23:45.200 ⇒ 01:23:45.830 Shivani Amar: Or do…
853 01:23:45.830 ⇒ 01:23:49.229 Uttam Kumaran: But it looks like it’s global anyways. Yeah, it looks like it’s.
854 01:23:49.230 ⇒ 01:23:49.900 Shivani Amar: this.
855 01:23:50.000 ⇒ 01:23:52.270 Shivani Amar: If I just send it to you…
856 01:23:52.650 ⇒ 01:23:53.020 Uttam Kumaran: Well, yeah.
857 01:23:53.020 ⇒ 01:23:53.410 Shivani Amar: I think Ed.
858 01:23:53.410 ⇒ 01:23:56.339 Uttam Kumaran: Anyone can access it right now, just to… even if you just send me the…
859 01:23:56.340 ⇒ 01:23:58.499 Shivani Amar: I wasn’t logged in, right? So…
860 01:23:59.640 ⇒ 01:24:04.409 Shivani Amar: Did that link work that I just sent you? But anyway, okay, let’s just look at revenue for a second on my screen.
861 01:24:04.410 ⇒ 01:24:04.910 Uttam Kumaran: Yeah, it’s working.
862 01:24:04.910 ⇒ 01:24:05.800 Shivani Amar: You got Will.
863 01:24:06.250 ⇒ 01:24:12.510 Shivani Amar: Okay? And he’s, like, overseeing all of these humans. Then you’ve got retail, and it just says Russell, Key Accounts.
864 01:24:12.710 ⇒ 01:24:21.840 Shivani Amar: Now, he’s owning the accounts, is he the best person to say, how’s our drink mix actually, how is our sparkling velocity relative to what we thought it was gonna be?
865 01:24:22.310 ⇒ 01:24:23.120 Shivani Amar: That’s ultimate…
866 01:24:23.120 ⇒ 01:24:26.359 Uttam Kumaran: may not have a Madison, or who is… yeah, if it’s gonna be Will.
867 01:24:26.360 ⇒ 01:24:39.060 Shivani Amar: That’s ultimately Will, right? Like, please don’t make groups or artboards, but the revenue team makes it feel… okay, whatever, people are adding notes. Like, I’m like, Will… Will is the person, but, like.
868 01:24:39.530 ⇒ 01:24:41.930 Shivani Amar: He is the person that is the busiest.
869 01:24:42.630 ⇒ 01:24:44.729 Shivani Amar: Right? So I’m trying to think about, like.
870 01:24:45.130 ⇒ 01:24:58.950 Shivani Amar: yes, you should have a touchpoint with Russell, and just, like, talk to him, and be like, what kind of things are you looking at? But it’s… I’m… what I’m trying to flag to you is that it might not be the most fruitful from a data perspective, and that might ultimately be me.
871 01:24:59.000 ⇒ 01:25:06.070 Shivani Amar: That’s, like, trying to help us craft it. And then if we look, look at the, oKRs…
872 01:25:06.140 ⇒ 01:25:10.660 Shivani Amar: deliverables, right? Like, it’s like… What is he tracking?
873 01:25:11.360 ⇒ 01:25:17.799 Shivani Amar: commercial, will e-commerce, retail. So they’re, like, they’re putting in, you know.
874 01:25:17.980 ⇒ 01:25:25.809 Shivani Amar: okay, what are our drink mix point of sales? Drink sales? This is, like, probably, like, Russell and Will just, like, adding the data in.
875 01:25:27.440 ⇒ 01:25:39.829 Shivani Amar: Drink makes point of sales, drink mixed sales, drink makes trade spend, sparkling point of sale, sparkling sales, drink makes point of sales in Target, right, so this was overall, then you’ve got Target, then you’ve got Walmart.
876 01:25:39.940 ⇒ 01:25:41.400 Shivani Amar: Costco Canada.
877 01:25:41.540 ⇒ 01:25:47.659 Shivani Amar: Costco US, Sam’s Club, Vitamin Shop, and you’re kind of, like, building off of the, like, retailers that you’re in.
878 01:25:49.220 ⇒ 01:25:49.870 Uttam Kumaran: Yeah.
879 01:25:49.870 ⇒ 01:25:55.739 Shivani Amar: So I think, like, going through this sheet with… with Russell is fine, right? But…
880 01:25:56.570 ⇒ 01:25:57.789 Shivani Amar: I’m just kind of floating.
881 01:25:57.790 ⇒ 01:26:01.819 Uttam Kumaran: No, yeah, if no one’s gonna end up being… yeah, then it may just be this team.
882 01:26:02.360 ⇒ 01:26:15.850 Shivani Amar: Yeah, so I think… let’s look at this quickly. So, if we look at Russell’s calendar, if you want to do the discovery call with Russell and just be like, hey, we’d love to, like, go through your OKRs, ask some questions and stuff, he’s available on the Thursday, if you guys are.
883 01:26:17.790 ⇒ 01:26:19.669 Uttam Kumaran: Yeah, we can do right before our call.
884 01:26:19.780 ⇒ 01:26:21.329 Uttam Kumaran: So, noon?
885 01:26:21.790 ⇒ 01:26:22.540 Shivani Amar: Okay.
886 01:26:22.730 ⇒ 01:26:24.340 Shivani Amar: Noon your time.
887 01:26:24.930 ⇒ 01:26:29.390 Shivani Amar: Okay, Brain Forge, X Element Retail.
888 01:26:29.500 ⇒ 01:26:31.630 Shivani Amar: Discovery. Cool.
889 01:26:32.160 ⇒ 01:26:34.460 Shivani Amar: But, ish?
890 01:26:35.230 ⇒ 01:26:37.190 Shivani Amar: And then added…
891 01:26:41.640 ⇒ 01:26:42.420 Shivani Amar: Okay.
892 01:26:42.940 ⇒ 01:26:43.750 Shivani Amar: Cool.
893 01:26:45.640 ⇒ 01:26:47.180 Shivani Amar: I think that’s good.
894 01:26:48.210 ⇒ 01:26:50.239 Shivani Amar: This was a good planning session.
895 01:26:50.630 ⇒ 01:26:51.659 Uttam Kumaran: How do you feel?
896 01:26:52.580 ⇒ 01:27:00.810 Shivani Amar: I forgot that I have a massage next Sunday, so that made me feel better when I looked at my own calendar. I was like, oh, that was smart of me to book the day before work really starts again.
897 01:27:03.030 ⇒ 01:27:03.750 Shivani Amar: I was like, oh god.
898 01:27:03.750 ⇒ 01:27:04.639 Uttam Kumaran: Me too.
899 01:27:04.830 ⇒ 01:27:07.369 Shivani Amar: That was some forward-looking thinking, girl.
900 01:27:07.370 ⇒ 01:27:16.339 Uttam Kumaran: Yeah, me and… me and my girlfriend, we’re going to Boston tomorrow. We’re going to Maine, actually, for New Year’s. Spent some time with some friends, so that’ll be nice.
901 01:27:16.650 ⇒ 01:27:18.379 Shivani Amar: Awish, what are you gonna do for New Year’s?
902 01:27:19.090 ⇒ 01:27:23.519 Awaish Kumar: Yeah, I will be visiting some places here in Karachi.
903 01:27:23.740 ⇒ 01:27:29.159 Awaish Kumar: We have, like, a place where there’s an event for fireworks and all.
904 01:27:29.620 ⇒ 01:27:30.730 Shivani Amar: That sounds fun.
905 01:27:31.470 ⇒ 01:27:32.230 Uttam Kumaran: Nice.
906 01:27:32.780 ⇒ 01:27:39.910 Shivani Amar: That’s awesome. I’m gonna be in Delhi, in… early March.
907 01:27:40.900 ⇒ 01:27:42.510 Uttam Kumaran: Oh, no way.
908 01:27:42.510 ⇒ 01:27:43.200 Shivani Amar: Yeah.
909 01:27:43.670 ⇒ 01:27:46.839 Uttam Kumaran: During our Rest and Assess week, so it’s like…
910 01:27:47.510 ⇒ 01:27:57.099 Shivani Amar: I wish I’ll be more in your time zone, and I might do a little bit of work that week, because I’m, I got engaged over the holidays.
911 01:27:57.100 ⇒ 01:27:58.300 Uttam Kumaran: Oh, congrats!
912 01:27:58.300 ⇒ 01:28:02.910 Shivani Amar: It was funny, because you guys sent the flowers, and that was the day that he proposed, and I was like, this is perfect, and a lot of other people.
913 01:28:02.910 ⇒ 01:28:03.390 Uttam Kumaran: Wow!
914 01:28:03.390 ⇒ 01:28:08.699 Shivani Amar: And then… and so, I’m going to India to, like, shop.
915 01:28:10.690 ⇒ 01:28:12.259 Uttam Kumaran: Hell yeah, let’s go.
916 01:28:12.440 ⇒ 01:28:15.199 Shivani Amar: Yeah, so that’s… that’s the flow, so…
917 01:28:15.200 ⇒ 01:28:22.499 Uttam Kumaran: Oh, I’m glad the flowers were timely then. Yeah, the flowers worked out. I was like, the vibes are good.
918 01:28:23.140 ⇒ 01:28:24.520 Shivani Amar: So…
919 01:28:24.520 ⇒ 01:28:24.940 Uttam Kumaran: Nice.
920 01:28:24.940 ⇒ 01:28:31.029 Shivani Amar: Yeah, so I’ll be in India and in your time zone for a little while. The rest and assess week, the beginning of March.
921 01:28:31.550 ⇒ 01:28:33.979 Shivani Amar: And… so we might do some…
922 01:28:34.100 ⇒ 01:28:42.860 Shivani Amar: we’ll figure out a… I really liked what we did today, which is like… like, if… I guess by the beginning of March, your contract is kind of done, right?
923 01:28:43.700 ⇒ 01:28:44.560 Shivani Amar: Yeah. Technically.
924 01:28:45.180 ⇒ 01:28:46.000 Uttam Kumaran: But I…
925 01:28:46.000 ⇒ 01:28:58.009 Shivani Amar: I imagine it will continue, and then if we… that’s what I imagine, we’ll talk to Phil and stuff, but if we’re continuing, then we could do some sort of call like this, maybe before I leave for a year, to say, like, how do we want to prep the next sprint? Because this was super helpful.
926 01:28:58.010 ⇒ 01:28:58.510 Uttam Kumaran: Yeah, so…
927 01:28:58.510 ⇒ 01:28:59.230 Shivani Amar: Obviously, we’ll…
928 01:28:59.230 ⇒ 01:28:59.900 Uttam Kumaran: I think you’ll start to…
929 01:28:59.900 ⇒ 01:29:02.450 Shivani Amar: something… We’ll do the how to be.
930 01:29:02.450 ⇒ 01:29:14.500 Uttam Kumaran: to get a sense of our speed, basically. Like, that’s why in this first two months, you’re gonna see, like, what it’s like for us to support one customer, we set up all this stuff, and then we can start to use that as a unit to be like.
931 01:29:14.820 ⇒ 01:29:24.129 Uttam Kumaran: okay, like, if we’re gonna support 3… do we need to support 3 people? Do we support 5 people? How should… how does Brain Forge’s resources scale to hit that? And so that… that’s all…
932 01:29:24.500 ⇒ 01:29:34.940 Uttam Kumaran: that’s all we need to kind of look to for the next sort of contract, so… I think we’ll be in a good spot by then. But then, I’m, like, really hopeful we can get BI set up,
933 01:29:35.580 ⇒ 01:29:43.939 Uttam Kumaran: And, like, I really want us to start using some of the AI tools within the… within some of these BIs that we select, because that’s really, like, some of the innovation that’s happening right now, so…
934 01:29:44.460 ⇒ 01:29:45.280 Shivani Amar: Perfect.
935 01:29:45.720 ⇒ 01:29:46.070 Uttam Kumaran: Yeah.
936 01:29:46.070 ⇒ 01:29:51.370 Shivani Amar: Dude, okay, great session. I was like, we could do 45 minutes. No, it was an hour and a half.
937 01:29:51.890 ⇒ 01:29:53.949 Shivani Amar: No problem, it’s a… it’s a cho-.
938 01:29:53.950 ⇒ 01:30:01.499 Uttam Kumaran: Yeah, a lot of clients are off right now, and so some of our team’s off, so we’re just also planning our, like, Q1 this week, so…
939 01:30:01.500 ⇒ 01:30:03.550 Shivani Amar: Yeah. It’s good, I’m happy to spend time.
940 01:30:03.700 ⇒ 01:30:16.000 Shivani Amar: Thank you guys so much, have a happy, safe entry to the new year, and then we have some great sessions set up, and if I have questions on, like, how I’m describing things, I’ll just ping you async, I think, but I… Perfect.
941 01:30:16.000 ⇒ 01:30:19.180 Uttam Kumaran: And I’ll send you a little blurb for the… for the,
942 01:30:19.430 ⇒ 01:30:21.619 Uttam Kumaran: What was it for the finance meeting? Yeah, yeah.
943 01:30:21.620 ⇒ 01:30:27.530 Shivani Amar: Yeah, and I think at least I’m feeling more clear within myself on what we’re trying to do this month, so that’s really nice.
944 01:30:28.340 ⇒ 01:30:29.439 Uttam Kumaran: Okay, perfect.
945 01:30:29.880 ⇒ 01:30:31.150 Shivani Amar: Okay, thank you!
946 01:30:31.150 ⇒ 01:30:33.090 Uttam Kumaran: Alright, thank you. Talk to you soon.
947 01:30:33.090 ⇒ 01:30:33.610 Shivani Amar: bye.
948 01:30:34.160 ⇒ 01:30:34.700 Uttam Kumaran: Bye.