Meeting Title: Uttam <> Shivani Date: 2025-10-23 Meeting participants: Shivani Amar, Uttam Kumaran
WEBVTT
1 00:00:05.200 ⇒ 00:00:06.470 Shivani Amar: Hello!
2 00:00:54.470 ⇒ 00:00:55.320 Shivani Amar: Hello!
3 00:00:55.320 ⇒ 00:00:56.980 Uttam Kumaran: Hey! How are ya?
4 00:00:56.980 ⇒ 00:00:58.020 Shivani Amar: Thank you.
5 00:00:58.020 ⇒ 00:01:00.489 Uttam Kumaran: Nice to meet you, too. How’s the week going?
6 00:01:00.490 ⇒ 00:01:03.250 Shivani Amar: fall background, you’ve got some leaves decorating.
7 00:01:03.250 ⇒ 00:01:17.280 Uttam Kumaran: Thanks, yeah, it’s well, we decorated the house for fall, and I don’t know, I, like, I have… I’m in… I’m in Austin, so I have, like, feel lucky to have, like, an actual office room, and I’m like, okay, because I sit in meetings all day, and I’m like.
8 00:01:17.280 ⇒ 00:01:33.120 Shivani Amar: let’s just throw… my dog sometimes sits in the back. Yeah. I’m like, let’s throw some fall stuff. Yeah. Break… break up the, like, crappy small talk. Well, it’s nice to meet you. Where’d you… where… when’d you move to Austin?
9 00:01:33.120 ⇒ 00:01:45.069 Uttam Kumaran: Yeah, so, I grew up in the Bay Area, I grew up in the East Bay, and then I lived in New York for about 5 years, went to Buckdell, and then I moved here about 3 years ago. Nice.
10 00:01:45.170 ⇒ 00:01:46.949 Uttam Kumaran: So, like, right after…
11 00:01:47.270 ⇒ 00:02:06.589 Uttam Kumaran: pandemic, or kind of, like, right… kind of, like, towards the end, but I just was kind of trying to get out of New York. Like, I don’t know, where you’re based, but I was just… it was just a lot to be there, and… and I visited Austin during the… during COVID, loved it. I kind of, like, was in a remote gig at that point, and I was like.
12 00:02:06.830 ⇒ 00:02:12.340 Uttam Kumaran: I’m just gonna pack up and drive over there, and so that’s what I did, and yeah, it’s great, I love it here.
13 00:02:12.340 ⇒ 00:02:14.399 Shivani Amar: Did you find your partner in Austin, or you moved.
14 00:02:14.400 ⇒ 00:02:20.900 Uttam Kumaran: Yeah, so my girlfriend actually grew up, like, just north of Austin. So, also, like.
15 00:02:21.180 ⇒ 00:02:28.239 Uttam Kumaran: just, like, amazing. Like, very, very… I didn’t know anybody here, I just, like, liked the vibe.
16 00:02:28.530 ⇒ 00:02:34.909 Uttam Kumaran: And… and also, my family’s in the Bay Area, so flying, like, coast to coast is, like, a little bit rough.
17 00:02:34.910 ⇒ 00:02:35.420 Shivani Amar: Yeah.
18 00:02:35.420 ⇒ 00:02:41.280 Uttam Kumaran: And I was… my last job before I started Brainforge, I was going to New York almost every month.
19 00:02:41.280 ⇒ 00:02:42.410 Shivani Amar: Yeah. So…
20 00:02:42.410 ⇒ 00:02:44.910 Uttam Kumaran: I… it’s not like I was, like, completely
21 00:02:45.130 ⇒ 00:02:50.200 Uttam Kumaran: Separated from the world, and, like, startup stuff, or, like.
22 00:02:50.560 ⇒ 00:02:57.129 Uttam Kumaran: business stuff, but Austin’s growing a lot, and now we have clients that are kind of everywhere, still a lot in New York.
23 00:02:57.130 ⇒ 00:02:57.690 Shivani Amar: Totally.
24 00:02:57.730 ⇒ 00:02:59.120 Uttam Kumaran: I don’t know, I don’t think…
25 00:02:59.750 ⇒ 00:03:02.409 Uttam Kumaran: in our world, I don’t know if you need to really be
26 00:03:03.180 ⇒ 00:03:09.880 Uttam Kumaran: all in one place anymore, like, to the degree it was before. Being in office and making office friends was fun, so…
27 00:03:09.880 ⇒ 00:03:10.700 Shivani Amar: Yeah.
28 00:03:11.020 ⇒ 00:03:29.600 Shivani Amar: Okay, well, I, like, basically, I, also, I’ll try to, do this, like, AI companion thing, and then… Yeah. So, basically, I used to work in healthcare services. I’ve recently shifted to working at a CPG company called Element, which you saw from my email address, probably. Yeah. Have you ever had an Element?
29 00:03:29.770 ⇒ 00:03:44.399 Uttam Kumaran: Of course, yeah. It’s very expensive, though. Yeah. I’m looking at YouTube videos, like, how do I make this at home with, like… I think they have tutorials for how to make it, so… Yeah, they do. But then I was, like, I was on Amazon, like, I’m gonna poison myself if I wanna do this.
30 00:03:44.400 ⇒ 00:03:48.740 Shivani Amar: accident, and so maybe… Keep, keep buying.
31 00:03:48.740 ⇒ 00:03:51.330 Uttam Kumaran: What health were you… what health stuff were you in before?
32 00:03:51.330 ⇒ 00:03:55.389 Shivani Amar: I was working at Brave Health Last, which is, like, a virtual mental health care company.
33 00:03:55.390 ⇒ 00:03:59.240 Uttam Kumaran: Okay, we, we’ve, we do… one of our clients, Ellie Mental Health.
34 00:03:59.240 ⇒ 00:03:59.610 Shivani Amar: Yeah.
35 00:03:59.610 ⇒ 00:04:06.780 Uttam Kumaran: And my girlfriend worked for an ABA therapy telehealth company before, so very familiar with.
36 00:04:06.780 ⇒ 00:04:07.570 Shivani Amar: Nice.
37 00:04:07.570 ⇒ 00:04:08.469 Uttam Kumaran: that world.
38 00:04:08.470 ⇒ 00:04:16.799 Shivani Amar: So I, like, when I was there, I was overseeing BizOps, and then BI kind of rolled up to me, and then…
39 00:04:16.800 ⇒ 00:04:17.130 Uttam Kumaran: Right.
40 00:04:17.130 ⇒ 00:04:37.119 Shivani Amar: after I left, the head of data, like, started rolling up to tech, which I think was, like, a natural progression, and it works fine. But I learned a lot about, like, like, when I came in to that business, it’s like, we had a bunch of Tableau reports with, like, definitions that varied from report to report, right? Like, your standard. Yes. So I, like, learned about, like.
41 00:04:37.120 ⇒ 00:04:40.750 Shivani Amar: the merits of, like, doing a dbt layer, and, like, like, I…
42 00:04:40.750 ⇒ 00:04:41.450 Uttam Kumaran: Great!
43 00:04:41.450 ⇒ 00:04:41.890 Shivani Amar: learned.
44 00:04:41.890 ⇒ 00:04:43.870 Uttam Kumaran: Solid! Right? Yeah, yeah, yeah.
45 00:04:43.870 ⇒ 00:05:00.770 Shivani Amar: So then I’m coming into Element where we have a totally different, you know, it’s like, there’s Shopify, Amazon, it’s more e-commerce plus retail, there’s some data that we get about our retail stuff from, via, like, Emerson. I don’t know if you’re, like, familiar with… but then it’s, like, in a Snowflake warehouse.
46 00:05:00.950 ⇒ 00:05:01.540 Uttam Kumaran: Cool.
47 00:05:01.540 ⇒ 00:05:17.199 Shivani Amar: And then some people over here are using Looker for something, some people here are downloading Excel spreadsheets and, like, putting manual data in. We have QuickBooks, but we’re gonna transition to NetSuite. So it’s like, there’s a lot in flux, but I’m kind of like, cool, the financial modeling is really manual, the, like.
48 00:05:17.200 ⇒ 00:05:40.290 Shivani Amar: the… the understanding of, how our retailers are performing, like, that’s not very structured right now. Our supply chain inventory stuff, again, that’s, like, manual data pulls to understand how much inventory is on hand. Cool. So I think it’ll all get better, like, also when we implement NetSuite and things like that, but what I’m trying to get ahead of is, like, saying, what is the modern data stack that we need as a business?
49 00:05:40.420 ⇒ 00:05:51.990 Shivani Amar: And so, I, you know, I might be more familiar with, like, what we had at Brave, which was Fivetran DBT, which is now obviously merged, Fivetran DBT, Snowflake.
50 00:05:52.170 ⇒ 00:05:59.310 Shivani Amar: And we used High Touch to, like, push things back, okay? And, like, coefficient reports then in, like, Google Sheets kind of thing.
51 00:05:59.310 ⇒ 00:06:00.040 Uttam Kumaran: Perfect.
52 00:06:00.040 ⇒ 00:06:08.389 Shivani Amar: So, like, that was, like, what we had at Brave. I don’t… we don’t have to mimic that. Maybe we’ll look something like… something different here. And so…
53 00:06:08.630 ⇒ 00:06:27.600 Shivani Amar: I think I’m… I… I just, like, posted in a healthcare chat, and I spoke with Ashley, and she’s like, we really only do healthcare, and I was like, yeah, like, but she recommended you for more CPG. I looked at your website, there’s, like, an AI-leaning, like, offering, and then there’s, like, this, like, data stack offering, and so I can explore, like.
54 00:06:27.600 ⇒ 00:06:29.820 Shivani Amar: The basics, kind of, right now.
55 00:06:30.370 ⇒ 00:06:33.560 Uttam Kumaran: Yeah, so this is, like, a pretty, like…
56 00:06:33.580 ⇒ 00:06:52.400 Uttam Kumaran: clear lens where we do a lot of work. So typically, we come into companies where they’ve either taken a crack at something and, like, built a data team where they have, like, random data people, or they’re, like, kind of starting from fresh, but usually it’s a very similar situation, and, like, I’ll probably just flash a diagram up so I can use this kind of, like, when we kind of go through things, but this is, like.
57 00:06:52.780 ⇒ 00:06:59.910 Uttam Kumaran: this is, like, a sample e-com, like, architecture that we typically do. Exactly. And so, of course, like, what do you have on the left side? You have…
58 00:06:59.920 ⇒ 00:07:10.430 Uttam Kumaran: Shopify, maybe you’re selling on Amazon or Walmart, you have a bunch of marketing sources, right? You have… if you guys are doing, like, if there’s subscriptions, right, which I know is a big deal, you may be using Loop or something else.
59 00:07:10.430 ⇒ 00:07:21.260 Uttam Kumaran: You may have, like, something for, refines and for customer service. Of course, you have your ERP, and so there’s just a bunch of these things. Yes, commonly people are…
60 00:07:21.260 ⇒ 00:07:32.099 Uttam Kumaran: either spreadsheeting it up, or someone’s, like, at some point has something in S3 and has, like, a tool somewhere. Most of the people will be within these tools, and whatever the report…
61 00:07:32.250 ⇒ 00:07:32.820 Uttam Kumaran: Exactly.
62 00:07:32.820 ⇒ 00:07:35.830 Shivani Amar: Marketing is using, like, their own…
63 00:07:35.830 ⇒ 00:07:38.229 Uttam Kumaran: Whale, or North Beam, or something.
64 00:07:38.230 ⇒ 00:07:42.460 Shivani Amar: I don’t know, they have their own, like, data consultancy to try to understand the marketing attribution, right?
65 00:07:42.460 ⇒ 00:07:42.900 Uttam Kumaran: Yeah.
66 00:07:42.900 ⇒ 00:07:43.980 Shivani Amar: Okay.
67 00:07:44.550 ⇒ 00:07:54.359 Uttam Kumaran: So then… and then, so, yeah, basically the first thing we come in and do is kind of, like, wrangle all this. I’ll show you a little bit, like, usually what this… it kind of takes us, like.
68 00:07:54.590 ⇒ 00:08:07.169 Uttam Kumaran: two to four weeks to sort of, like, see everything, and then our typical motion is, like, we sort of do, like, a two to four week, like, audit, where we come in, and then we help with a couple different ways. So one, we look at all your tools, we have
69 00:08:07.170 ⇒ 00:08:15.549 Uttam Kumaran: pretty good documentation on how we structure, like, here are all the tools you have. We propose, like, how do you need to ingest it, right? So, you guys use Fivetran.
70 00:08:15.550 ⇒ 00:08:30.950 Uttam Kumaran: there are some now, better, lower-cost options that we would totally recommend. Again, but when we recommend tools, it’s a lot from, like, your budget, who’s going to be using it, and, like, things like that. So, yeah, go ahead. Were you gonna say something?
71 00:08:30.950 ⇒ 00:08:33.439 Shivani Amar: No, no, Fivetran is, like.
72 00:08:33.440 ⇒ 00:08:37.899 Uttam Kumaran: Somebody was just telling me the name of one, sort of…
73 00:08:38.870 ⇒ 00:08:46.169 Uttam Kumaran: Well, there’s Fivetran, there’s, like, Portable, there’s Polytomic, there’s Estuary, there’s, like, There’s a lot.
74 00:08:46.170 ⇒ 00:08:47.290 Shivani Amar: A ton of these, yeah.
75 00:08:47.290 ⇒ 00:08:54.500 Uttam Kumaran: But also, like, only some of them have really great support, which, like, if you’re using NetSuite or, like, if you have, like.
76 00:08:54.810 ⇒ 00:09:04.069 Uttam Kumaran: for example, you mentioned, like, you may have a random marketing tool or, like, something that you guys built. You need a tool that kind of, like, will support that, and, like, build you more connectors, so…
77 00:09:04.490 ⇒ 00:09:09.530 Uttam Kumaran: some good recommendations of folks that we worked with. Also, that will cut you, like, a very good deal.
78 00:09:09.630 ⇒ 00:09:10.270 Uttam Kumaran: Yes.
79 00:09:10.270 ⇒ 00:09:12.030 Shivani Amar: You just said the word Matillion to me.
80 00:09:12.030 ⇒ 00:09:17.940 Uttam Kumaran: Matillion? Yeah, it’s… Matillion is, like, the Power BI of this world, like.
81 00:09:19.220 ⇒ 00:09:30.689 Uttam Kumaran: like, old. But they do… they do this stuff, but it’s not… I would say if you’re thinking about this, you should consider Polyatomic. Very, very good, great team, great, like, their support is, like.
82 00:09:30.890 ⇒ 00:09:40.779 Uttam Kumaran: the number one, and then second, their pricing is actually also pretty competitive. Fivetran is, like, the name brand, but they’re the most expensive, worst support, and I’ve used Fivetran for, like.
83 00:09:40.840 ⇒ 00:09:43.070 Shivani Amar: almost 10 years now, and it’s gotten…
84 00:09:43.210 ⇒ 00:09:44.810 Uttam Kumaran: Worse over time.
85 00:09:44.810 ⇒ 00:09:45.200 Shivani Amar: Whoa.
86 00:09:45.200 ⇒ 00:09:47.470 Uttam Kumaran: I also have a lot of friends that work there, and like…
87 00:09:47.800 ⇒ 00:09:51.830 Uttam Kumaran: Yeah, I mean, they also agree, so I don’t know. This is where, like, we’re…
88 00:09:52.050 ⇒ 00:10:04.509 Uttam Kumaran: I’ve bought… we’ve bought a… we do a lot of, like, procurement in the data space, and so we’re very opinionated, so I’m happy to send, like, we have sort of diagrams on, like, what we… what our recommendations are, why or why not.
89 00:10:04.510 ⇒ 00:10:04.900 Shivani Amar: I can send.
90 00:10:04.900 ⇒ 00:10:06.390 Uttam Kumaran: That… that over too, but…
91 00:10:06.390 ⇒ 00:10:12.259 Shivani Amar: And do you regularly go Snowflake, or do you sometimes do Databricks? Like, I don’t have a preference right now, but I’m just…
92 00:10:12.260 ⇒ 00:10:30.280 Uttam Kumaran: Yeah, so I would say that our… our guidance is that it sort of depends on how much data we’re talking about and who’s going to be using it. Like, if you guys are thinking about in the future doing more serious data science, you have several analysts internally that need to use the data, you can use Snowflake. If you’re budget constrained, there are also some
93 00:10:30.370 ⇒ 00:10:48.679 Uttam Kumaran: pretty good budget options, like Mother Duck, that’s… those are new that you should try. I’ve just used Snowflake my whole career, and it’s really great. Snowflake and Databricks… I think Databricks leans way more data science, and like, it’s more… I would say it’s way less intuitive.
94 00:10:48.760 ⇒ 00:10:52.209 Uttam Kumaran: to just run simple SQL queries. Snowflake is…
95 00:10:52.420 ⇒ 00:10:55.970 Uttam Kumaran: Really great, although both of them are, like, kind of the most expensive.
96 00:10:56.070 ⇒ 00:10:57.090 Uttam Kumaran: options.
97 00:10:57.190 ⇒ 00:10:58.280 Shivani Amar: That makes sense.
98 00:10:58.430 ⇒ 00:11:05.219 Uttam Kumaran: Yeah, and then we do what kind of what you’re used to, is like, we have… everything’s version controlled, so we write a ton of SQL and dbt.
99 00:11:05.350 ⇒ 00:11:08.750 Uttam Kumaran: you can run dbt for free, it’s open source, or you can pay, it’s like.
100 00:11:08.900 ⇒ 00:11:12.379 Uttam Kumaran: dbt is, like, 50 bucks a license, but again, depends on how many
101 00:11:12.510 ⇒ 00:11:17.100 Uttam Kumaran: people you’re thinking about using it. We kind of create these, like, these marts, basically.
102 00:11:17.420 ⇒ 00:11:21.859 Uttam Kumaran: Which I’m sure you’re… I don’t know what the architecture was before, but…
103 00:11:22.090 ⇒ 00:11:33.430 Uttam Kumaran: We, like, land the data, do some modeling, create these, like, fixed marked tables, source of truth tables for customers, things like that. And then, finally, yeah, we basically take that to
104 00:11:33.430 ⇒ 00:11:43.200 Uttam Kumaran: a BI tool, or we also use a tool like Hightouch. Polyatomic also offers Reverse ETL. I know Fivetran started offering that, so if you want to send that back into
105 00:11:43.210 ⇒ 00:11:48.679 Uttam Kumaran: Klaviyo, or Salesforce, or whatever, or, like, you want to send it to your conversion platforms.
106 00:11:48.890 ⇒ 00:11:54.500 Uttam Kumaran: for ad optimization, you can send those out. So this is, like, our typical stack sounds like…
107 00:11:54.610 ⇒ 00:11:56.710 Uttam Kumaran: Pretty similar to, like, what you were used to before.
108 00:11:56.710 ⇒ 00:12:14.159 Shivani Amar: Yeah, and, like, at Brave, we did so much stuff where it was, like, coefficient reports, so, like, high touch was pumping stuff back into coefficient. I’m saying this the right way, but then it’s, like, then we built off a lot of Google Sheets off of those, like, that got updated regularly, and I don’t know…
109 00:12:14.160 ⇒ 00:12:18.499 Uttam Kumaran: I don’t know if that’s the direction we’re gonna go here, but I kinda like… I kinda like to…
110 00:12:18.620 ⇒ 00:12:34.649 Shivani Amar: be able to be like, what were my daily metrics, what were my weekly metrics, and, like, be able to, like, have things, like, refreshing regularly, like… Yeah. …that you can just, like, run math off of versus just looking at it. And so, I think that’s, like, a culture thing that I’ll have to understand better about this place.
111 00:12:34.840 ⇒ 00:12:39.809 Uttam Kumaran: Yeah, so that’s also where, like, again, like, this is an example, and we have… we sort of…
112 00:12:40.040 ⇒ 00:12:48.609 Uttam Kumaran: this is just, like, work… my background is in data, and, like, I built data teams, and so I’m very opinionated on, like, what the tools you choose and why.
113 00:12:48.960 ⇒ 00:12:50.170 Uttam Kumaran: So, we have, like.
114 00:12:50.370 ⇒ 00:13:02.590 Uttam Kumaran: we built, like, one of these for every part of the stack, and so I’m happy to send you these. Mainly, it’s just, like, choose the right tools. You can’t… at a certain level, you can’t go wrong, but, like, at some point, if you choose Power BI,
115 00:13:02.820 ⇒ 00:13:06.569 Uttam Kumaran: You are going a little bit wrong, like, it will slow down a bunch.
116 00:13:06.570 ⇒ 00:13:11.489 Shivani Amar: I don’t… I haven’t heard anybody mention Power BI, I’ve heard people mention Looker just because somebody already.
117 00:13:11.490 ⇒ 00:13:11.990 Uttam Kumaran: Okay.
118 00:13:11.990 ⇒ 00:13:13.140 Shivani Amar: Looker, but, like.
119 00:13:13.140 ⇒ 00:13:24.510 Uttam Kumaran: Like, it’s like, oh, like, that team uses Looker, so should we just use Looker everywhere? So, like, I guess, like, tell me, are you, are you gonna come in and sort of be, like, head of data, or, like, what are you thinking? Like, what, what are you, yeah, like.
120 00:13:24.510 ⇒ 00:13:25.650 Shivani Amar: Give me a sense of what your goals are.
121 00:13:25.650 ⇒ 00:13:32.980 Uttam Kumaran: the head of data, because I don’t do any coding, I don’t whatever. No, I don’t think you… I don’t think you necessarily need to in order to sort of, like.
122 00:13:33.790 ⇒ 00:13:41.180 Uttam Kumaran: Because head of data is, like, also, I think, data teams, commonly, you have a head of data, but what is every… as long as every team is using data.
123 00:13:41.330 ⇒ 00:13:45.229 Uttam Kumaran: Like, there’s more… it’s more like, are you actually using it to drive decisions, right?
124 00:13:45.230 ⇒ 00:13:49.210 Shivani Amar: Yes, so I think that… that the head of data would roll up to me.
125 00:13:49.210 ⇒ 00:13:50.099 Uttam Kumaran: Cool, okay.
126 00:13:50.100 ⇒ 00:14:01.729 Shivani Amar: Yeah, so, like, and the thing is, we run pretty scrappy, so I think, like, here’s the thing, I’m like, I could make a case that’s, like, we should just hire a head of data now, and they should start building out our stack, right? I think…
127 00:14:01.840 ⇒ 00:14:08.509 Shivani Amar: The reason to maybe work with a consultant prior is that we can have the stack be built out a little faster.
128 00:14:08.850 ⇒ 00:14:09.620 Uttam Kumaran: Yeah.
129 00:14:09.620 ⇒ 00:14:12.320 Shivani Amar: Right? And then… and then, like…
130 00:14:12.620 ⇒ 00:14:31.630 Shivani Amar: get, like, the best recommendations, like… like, if… I don’t know, my head of data I loved at Brave, and I’m like, if I could just bring him in, he could build the stack out, that’s awesome. But, like, I think having a consultancy to, like, build it out, pipe everything together up front, build the recommendations, and then theoretically hire somebody who can
131 00:14:31.630 ⇒ 00:14:45.870 Shivani Amar: both be a bit of data engineer and analytic support, which is kind of what he did, like, that profile person could be nice to then, like, hire after this. How do you feel in terms of that sequencing? Are you like, you should just hire one person, they should be able to do all of this?
132 00:14:46.110 ⇒ 00:14:54.589 Uttam Kumaran: No, I mean, I mean, I will caveat, of course, I’m very biased, but as I was on, like, I was only recently building data teams myself.
133 00:14:54.590 ⇒ 00:15:11.489 Uttam Kumaran: like, here’s a couple of reasons of, like, why, even if you don’t consider us, you should consider some… someone in this phase. One is, you may hire a head of data. One is, like, if you can’t get your guy or your gal, it’s gonna be… it’s really tough to find that great person. Yeah. So there are high odds of not…
134 00:15:11.490 ⇒ 00:15:18.800 Uttam Kumaran: finding that perfect person. Second is, it will take them time to ramp up. Like, when I mentioned we come in and in, like, a month.
135 00:15:19.150 ⇒ 00:15:38.650 Uttam Kumaran: we kind of get there. The only reason I say a month is, like, in case we can’t get access to stuff, it takes us a while, but I’m, like, two weeks, we generally have, like, a pretty good sense of, like, what you need. As you can tell, we even made it pretty far, even on just this call, like, figuring things out. So, speed is certainly one thing. Second is, like, some of these decisions you make on infrastructure.
136 00:15:38.650 ⇒ 00:15:48.069 Uttam Kumaran: that person, when you hire them, will be very thankful, and you can kind of set them up for success. The last piece is very expensive. Like, head of data.
137 00:15:48.380 ⇒ 00:15:56.730 Uttam Kumaran: Who is worth, like, their weight, very expensive, and that person commonly doesn’t also want to do the work.
138 00:15:56.820 ⇒ 00:15:57.820 Shivani Amar: Like…
139 00:15:57.820 ⇒ 00:16:02.889 Uttam Kumaran: the person you’re used to, and, like, me, or, like, it’s just, like.
140 00:16:03.010 ⇒ 00:16:09.340 Uttam Kumaran: those people are also not on the ground often. They’re like, oh, I want to be, like, head of data, is where I need 5 analysts and 3 data engineers.
141 00:16:09.340 ⇒ 00:16:10.219 Shivani Amar: Oh, no, yeah.
142 00:16:10.220 ⇒ 00:16:12.600 Uttam Kumaran: That’s not, like, the situation, especially at a company like…
143 00:16:12.600 ⇒ 00:16:16.860 Shivani Amar: I can’t hire my guy just yet, but I’m like, let’s use a consultant, and when I know.
144 00:16:16.860 ⇒ 00:16:19.939 Uttam Kumaran: No, I think you should scheme for him, and I think we should help you
145 00:16:20.400 ⇒ 00:16:24.950 Uttam Kumaran: Make the case to him that, like, hey, you’re coming into a great environment, you won’t have to do the dirty work.
146 00:16:24.950 ⇒ 00:16:25.490 Shivani Amar: Yeah.
147 00:16:25.490 ⇒ 00:16:29.250 Uttam Kumaran: for us, we’re like a… I come in and, like, throw us into the…
148 00:16:29.390 ⇒ 00:16:33.189 Uttam Kumaran: Biggest fire possible, where we can make the biggest impact.
149 00:16:33.260 ⇒ 00:16:46.830 Uttam Kumaran: Because also, like, something about data is you now have a bunch of people in the company who are doing their own thing, who nobody trusts, like, a single source of truth, right? So they’re all, like, doing their own thing. It will take these slow wins to start to build, okay, like, I trust
150 00:16:46.880 ⇒ 00:16:59.530 Uttam Kumaran: the work that Shivani’s doing, we nailed it something here, and, like, whatever, if your first goal is, like, we want to just talk about, like, shipping cost data, okay, we got a win. Oh, yeah, I’m curious, can my team have that data? Cool. And then it sort of, like, spreads, right?
151 00:16:59.530 ⇒ 00:17:00.010 Shivani Amar: Yeah.
152 00:17:00.010 ⇒ 00:17:07.219 Uttam Kumaran: That’s… that’s what we do. Like, a very similar example to this is, like, we’re working with this company, Urban Stems. They’re, like, a huge flower company.
153 00:17:07.220 ⇒ 00:17:25.290 Uttam Kumaran: one of the largest, like, D2C flower companies. They have a… it’s an interesting problem because it’s a perishable good, it is, they have really spiky periods, like Mother’s Day, Valentine’s Day. We walked in, they had… they had two data people, they’ve been in business for a long time, their looker was a mess, they had all this crap everywhere.
154 00:17:25.290 ⇒ 00:17:41.010 Uttam Kumaran: And we came in and, like, we just, like, cut a bunch of stuff, brought in DBT, like, modeled everything, built new reports, and that’s… it took us, like, around 6 months, because they had so much stuff there, like, thousands of, like, assets across all of those.
155 00:17:41.030 ⇒ 00:17:51.040 Uttam Kumaran: But we came in, we picked the right ETL tool, we picked the right warehousing solution, we slowly got them, we went one by one. We, like, solved inventory, then we solved revenue, now we’re working on marketing.
156 00:17:51.040 ⇒ 00:18:03.590 Uttam Kumaran: And then we go find the who cares the most. We, like, try to see, like, okay, can we get a win for them? So this is kind of, like, how we… we work. And so, I think a great place to use us if you’re in the position of, like, hey, we…
157 00:18:03.920 ⇒ 00:18:06.139 Uttam Kumaran: One, I just need someone to come in and assess.
158 00:18:06.250 ⇒ 00:18:07.460 Uttam Kumaran: the situation.
159 00:18:07.460 ⇒ 00:18:07.860 Shivani Amar: Yeah.
160 00:18:07.860 ⇒ 00:18:09.519 Uttam Kumaran: Give a recommendation on
161 00:18:09.640 ⇒ 00:18:20.060 Uttam Kumaran: A, do we need new tools or not? So that’s the thing also for us, is like, I don’t want… you shouldn’t buy tools if you already got it going on, and you have the stuff you need, but that’s the recommendation we’ll give, is like.
162 00:18:20.070 ⇒ 00:18:30.349 Uttam Kumaran: Is there opportunity, one, for consolidation, to save money? Second, like, do you have Looker, but you should have something cheaper? Do you not… are you missing an ETL tool? Do you need one?
163 00:18:30.490 ⇒ 00:18:45.030 Uttam Kumaran: Of course, we can help, like, with the negotiation with all those vendors, because talking to software salespeople is also very annoying, so we can help with that. And then it’s like, okay, can we drive towards that first win, whether it’s, like, a dashboard, or a report, or, like, an answer?
164 00:18:45.050 ⇒ 00:18:48.619 Shivani Amar: Right, and that’s probably the last question I have for you is, like, do you have, like, a north…
165 00:18:48.620 ⇒ 00:18:52.929 Uttam Kumaran: Star, like, question you’re trying to get answered that you can’t right now, or is it, like.
166 00:18:53.620 ⇒ 00:18:56.610 Shivani Amar: Like, I think there’s an element of, the…
167 00:18:56.830 ⇒ 00:19:12.079 Shivani Amar: the, like, retail data that we’re getting, which is, like, you have to log into an instance of Snowflake from Emerson, it’s like, that’s not, like, clean in any way. So if I were to say, like, how is Target performing versus Walmart right now, like, I don’t… I don’t know where I would find that, right?
168 00:19:12.080 ⇒ 00:19:12.470 Uttam Kumaran: Yeah.
169 00:19:12.470 ⇒ 00:19:17.160 Shivani Amar: What about Target in the Northeast versus Target in California? Again, don’t know how I would find that.
170 00:19:17.160 ⇒ 00:19:17.630 Uttam Kumaran: Sure.
171 00:19:17.630 ⇒ 00:19:23.829 Shivani Amar: I think there’s some, like, the buckets of areas that would probably be priority are retail, supply chain, and finance.
172 00:19:23.930 ⇒ 00:19:32.889 Shivani Amar: Okay. And then it gets into, like… but, like, finance, for context for you, we’re using QuickBooks, we’re gonna transition to NetSuite, so I’m like, are we… would that be…
173 00:19:32.890 ⇒ 00:19:33.720 Uttam Kumaran: last quarter?
174 00:19:34.180 ⇒ 00:19:43.850 Shivani Amar: Probably early next year. Like, right now, we’re shopping around for implementation partners for NetSuite. So I’m like, would it even make sense to pipe Quick QuickBooks data in?
175 00:19:43.850 ⇒ 00:19:57.679 Shivani Amar: Or would it make sense to wait until the transition has happened? That’s something that I don’t really know. So if I were to say, I want to work with a data consultant and get, like, these financial metrics to be way more clear, I’m like, is that even possible if you’re in the middle of an ERP transition?
176 00:19:57.970 ⇒ 00:20:08.590 Uttam Kumaran: Yeah, I mean, to answer that, it wouldn’t take long to get the QuickBooks data in. Okay. Like, if you had a really simple ETL tool, and I would just do that, because 6 months without that is, like.
177 00:20:08.890 ⇒ 00:20:13.919 Uttam Kumaran: there’s no reason to not, yeah. And guess what? The NetSuite thing is gonna get delayed, so…
178 00:20:14.250 ⇒ 00:20:16.260 Uttam Kumaran: As usual, okay.
179 00:20:16.260 ⇒ 00:20:19.619 Shivani Amar: So, that’s helpful. So then,
180 00:20:19.790 ⇒ 00:20:25.289 Shivani Amar: Okay, so, like, light discovery through to, like, initial dashboards, right? Let’s say.
181 00:20:25.290 ⇒ 00:20:36.220 Uttam Kumaran: Yeah, like, this is… what we like to do is, like, one, we… we have to do… play some short- and long-term games at the same time. Yeah. So I have to come in and sort of… we have to come see everything, make some recommendations. At the same time, like.
182 00:20:36.680 ⇒ 00:20:50.189 Uttam Kumaran: I don’t like being the consulting firm that, like, is like, okay, see ya, and then we, like, go away. Like, I want to try to say, like, even if we have to stitch together CSVs or do something, I want to get you, like, a win in that first month. That is, like, analysis
183 00:20:50.190 ⇒ 00:21:01.809 Uttam Kumaran: answering a question or, like, that target question, right? To give you an example, like, yeah, we’ve had clients where we’ve stitched together Walmart, Shopify, Amazon, and, like, retail data that we got, like, via CSV from somebody.
184 00:21:01.810 ⇒ 00:21:12.619 Uttam Kumaran: Yeah. Right, and so that’s the work, but see, that is, like, pretty heavy, like, data modeling work that would happen in dbt. Step one is, like, we need to know, like, do we have access to that in some form for all of this?
185 00:21:12.650 ⇒ 00:21:23.159 Uttam Kumaran: Second is, like, okay, then if… as long as we have access, 100% we’ll get it landed somewhere, so then we’ll have to make a decision where is that, right? And, like, get that stood up, and then…
186 00:21:23.160 ⇒ 00:21:37.980 Uttam Kumaran: as fast as we can get you to a table for you to view with the answer, because the one… also, we’re just not going to get it right the first time. So for you to say, like, oh, we missed… we missed defined this segment, or this column is wrong, or I had no idea we even had this data, so now I gotta rethink
187 00:21:38.090 ⇒ 00:21:41.799 Uttam Kumaran: That’s where we want to get to you as fast as we can.
188 00:21:43.000 ⇒ 00:21:46.099 Shivani Amar: So, this 6-month engagement that you’ve had with the… with the flowers.
189 00:21:46.100 ⇒ 00:21:58.670 Uttam Kumaran: Urban SEM. So that actually started after… we did 2 months of discovery with them. Yeah, because they had a… they had, like, a 10-year-old Redshift instance, which had, like, thousands of tables.
190 00:21:58.860 ⇒ 00:22:05.940 Uttam Kumaran: They… they have… we have almost 7 or 8 different groups we’re supporting there, so we had to go do discovery with each of them.
191 00:22:06.150 ⇒ 00:22:09.110 Uttam Kumaran: And so, then we… then we started working with them.
192 00:22:09.320 ⇒ 00:22:25.440 Uttam Kumaran: through the long term, and then it’s the… I would say it’s the usual data drama. It’s like, we create something, then it gets QA’d, okay, we need a new column, we need a new metric. Oh, like, I think we model this a little bit differently. Then we kind of get into the day-to-day support for.
193 00:22:25.440 ⇒ 00:22:26.549 Shivani Amar: For data work.
194 00:22:26.770 ⇒ 00:22:32.730 Shivani Amar: So, I guess now, if we transition the last few minutes to, like, pricing, right?
195 00:22:32.730 ⇒ 00:22:33.400 Uttam Kumaran: Sure.
196 00:22:33.400 ⇒ 00:22:52.379 Shivani Amar: how do you think about… so that turned into a two-month discovery, but, like, let’s say here it’s a few weeks of discovery, because I’ve kind of given you the lay of the land. Like, there’s all these systems, they’re not really connected, and it’s not like we already have Looker and it’s discombobulated or something across the business. We don’t have everything in Snowflake, but it’s really messy, right? Like, it’s like…
197 00:22:52.380 ⇒ 00:23:03.789 Shivani Amar: would be, like, the initial piping. To me, discovery equals, like, part of discovery, a deliverable that I would like is, like, somebody to help us come up with shared definitions of the metrics that are important to us.
198 00:23:03.790 ⇒ 00:23:04.530 Uttam Kumaran: Totally.
199 00:23:05.110 ⇒ 00:23:15.350 Shivani Amar: So, it’s like, what are the… what are the data sources? What are the, Let’s see…
200 00:23:15.350 ⇒ 00:23:24.629 Uttam Kumaran: like, if I can even show you an example of, like, a common… this is just, like, one of the deliverables we would do is, like, we put together, sort of, just, like, a pretty easy doc sheet of, like.
201 00:23:24.790 ⇒ 00:23:26.030 Uttam Kumaran: What are, like.
202 00:23:26.190 ⇒ 00:23:40.159 Uttam Kumaran: the core business context. We have, like, naming conventions, the data tools, and, like, all the contract details, pricing in one place. Your data sources, right? Like, all the sources, who is the internal owner.
203 00:23:40.430 ⇒ 00:23:40.849 Shivani Amar: is it coming.
204 00:23:40.850 ⇒ 00:23:42.460 Uttam Kumaran: it to wherever it’s going.
205 00:23:42.630 ⇒ 00:23:50.470 Uttam Kumaran: core metrics. So this is, like, would be probably more the definition piece, which is, like, okay, what are the definitions? Where are they coming from?
206 00:23:50.470 ⇒ 00:23:50.980 Shivani Amar: Yeah.
207 00:23:50.980 ⇒ 00:23:52.550 Uttam Kumaran: It could be more descriptive.
208 00:23:53.090 ⇒ 00:23:59.229 Uttam Kumaran: If we want to do dashboard definitions, we want to add stakeholders, so this is, like, the outcome, typically, of our…
209 00:23:59.230 ⇒ 00:24:12.470 Shivani Amar: And it’s funny because, like, this is similar to prob… like, I’m not gonna get to this level of detail, but, like, I think, like, I won’t get to, like, can you go back to the data tools and costs? Like, I probably won’t… I probably won’t…
210 00:24:12.840 ⇒ 00:24:19.939 Shivani Amar: do this. Like, I would probably say what we have right now, which I think is data source.
211 00:24:20.400 ⇒ 00:24:21.000 Uttam Kumaran: Because so…
212 00:24:21.000 ⇒ 00:24:27.960 Shivani Amar: currently have data tools, really, I think, so this is, like, your… almost like the recommendations, right?
213 00:24:27.960 ⇒ 00:24:33.280 Uttam Kumaran: Yeah, this is where we would show you what you have, and then I would say, hey, you need, like, dbt, you don’t have it, or, like.
214 00:24:33.470 ⇒ 00:24:39.679 Uttam Kumaran: Maybe you should consider switching, or, for example, maybe you have amplitude and post-hog, you should consolidate.
215 00:24:39.890 ⇒ 00:24:48.619 Uttam Kumaran: Like, or, you know, and again, it doesn’t seem like anybody was so opinionated, so there’s not… if there’s not much politics around it, then it’s just, like, a decision needs to get made.
216 00:24:48.620 ⇒ 00:24:49.110 Shivani Amar: Yeah.
217 00:24:49.110 ⇒ 00:24:56.860 Uttam Kumaran: There’s easy wins to save money and, like, just pick the right tool. Then is, like, the real work starts after deciding on this.
218 00:24:56.860 ⇒ 00:25:02.980 Shivani Amar: This is great, and then it’s like, so if you go back to the… which one? The data sources.
219 00:25:03.380 ⇒ 00:25:03.950 Uttam Kumaran: Yeah.
220 00:25:03.950 ⇒ 00:25:07.880 Shivani Amar: Data Sources is probably where I’m gonna start talking to people. Like, I’m like.
221 00:25:07.880 ⇒ 00:25:08.230 Uttam Kumaran: check.
222 00:25:08.230 ⇒ 00:25:17.299 Shivani Amar: talk me through… I’m actually gonna just, like… I’m like, okay, like, I’m gonna… because, like, part of my onboarding is, like, talking to people and saying, like.
223 00:25:17.300 ⇒ 00:25:17.710 Uttam Kumaran: Yes.
224 00:25:17.710 ⇒ 00:25:23.650 Shivani Amar: Are you using, like, you know, like, what do you… what are the metrics that you’re trying to glean from, like, the insights.
225 00:25:23.650 ⇒ 00:25:24.120 Uttam Kumaran: Yeah.
226 00:25:24.120 ⇒ 00:25:35.270 Shivani Amar: from this data source, walk me through it. So, I don’t want to go too deep on discovery, because I know whoever we bring in is gonna do, like, another layer, but I think, like, high level, like.
227 00:25:35.520 ⇒ 00:25:47.009 Shivani Amar: I would want to, like, start understanding what all these sources are from the different stakeholders, and then I imagine that the people that we work with would want to go even a level deeper, and I would, like, shadow those conversations, just so that I’m, like…
228 00:25:47.010 ⇒ 00:25:51.739 Uttam Kumaran: Yeah, that’s exactly how we work. I mean, I’m happy even just to send you a whole copy of this.
229 00:25:51.740 ⇒ 00:25:52.140 Shivani Amar: That’s great.
230 00:25:52.140 ⇒ 00:25:52.540 Uttam Kumaran: Right.
231 00:25:52.540 ⇒ 00:25:55.279 Shivani Amar: I can, like, honestly, like, start working on something, like.
232 00:25:55.280 ⇒ 00:26:02.550 Uttam Kumaran: Yeah, and so that’s exactly… and so to kind of talk about, like, our model, we have a pretty… we have… we do a fixed fee for, like, a month. It sounds like…
233 00:26:02.670 ⇒ 00:26:22.639 Uttam Kumaran: this is quite a bit of… like, and in that month, we try to do, like, a plethora of discovery, and then we try to… we try to still drive towards, like, one analysis or, like, an outcome, like a question answer. And then, at that point, also, you’ll have a sense of, okay, what are the clear milestones that we can then price, either on a fixed or hourly basis?
234 00:26:22.640 ⇒ 00:26:35.339 Uttam Kumaran: Because for us, we… to kind of give you a sense of, like, how we typically operate, like, we have a… the company is about 15 people, we have a mix of engineers, kind of solution architects, and then people at, like, my level that are more, like.
235 00:26:35.420 ⇒ 00:26:41.670 Uttam Kumaran: strategy, like, head of data kind of… kind of thing. And so we usually just have, like, at least
236 00:26:41.830 ⇒ 00:26:51.669 Uttam Kumaran: like, 3 people per client, where we have someone that’s, like, at my level, where we’re deciding on architecture, we’re deciding on tooling, we’re deciding on, like, you can throw me into, like.
237 00:26:51.810 ⇒ 00:27:06.499 Uttam Kumaran: any meeting sort of deal. Then we have people in solution architect who are like, okay, let’s say you have… you have Emerson, how do we even get data out of there? I need to call them, do they have APIs? And then we have just the engineers that are actually, like, build the pipeline, build the model, things like that. And so…
238 00:27:06.840 ⇒ 00:27:15.979 Uttam Kumaran: Typically, we have, like, a… our pod… our smallest pod is kind of, like, 3, and then we run pretty agile in terms of sprints, like, we run weekly sprints.
239 00:27:16.060 ⇒ 00:27:35.319 Uttam Kumaran: we typically try to do, like, at least one touchpoint a week. You’d be surprised some people don’t even want to sign up for that with us, even though we’re doing all their data stuff, but we try to just, like, meet at least once a week to show what we’re working on, but we’re all… we’re a super remote async company, so everything Stripe, everything at Slack, and, like.
240 00:27:35.440 ⇒ 00:27:39.839 Uttam Kumaran: Stuff like that is, like, our bread and butter, so we would just communicate.
241 00:27:40.150 ⇒ 00:27:41.040 Uttam Kumaran: There.
242 00:27:41.400 ⇒ 00:27:46.799 Shivani Amar: Makes sense, okay. And so, like, that fixed fee for the discovery for that month, what is that?
243 00:27:47.370 ⇒ 00:27:50.959 Uttam Kumaran: Yeah, for something like this, it would probably be around 10K.
244 00:27:50.960 ⇒ 00:27:51.450 Shivani Amar: Okay.
245 00:27:51.450 ⇒ 00:27:54.379 Uttam Kumaran: Like, that would basically get us in…
246 00:27:54.530 ⇒ 00:27:59.970 Uttam Kumaran: get us… we would… again, at that point, we’d start getting access to everything. It’d probably just be me and, like, one other person.
247 00:27:59.970 ⇒ 00:28:01.190 Shivani Amar: Yeah.
248 00:28:01.190 ⇒ 00:28:04.230 Uttam Kumaran: And then we… this, again, you… we sort of build this out.
249 00:28:04.270 ⇒ 00:28:22.870 Uttam Kumaran: if we can, at that point, isolate that, like, target question or another question to, like, sort of keep in the back of our heads for the end of the month, then we would sort of drive towards that in parallel. Additionally, if you’re having active vendor conversations that you need to toss us into, or you’re, like, deciding whether to keep Looker or not, or whatever.
250 00:28:23.180 ⇒ 00:28:30.269 Uttam Kumaran: like, that’s a great place to utilize us. And then, yeah, ideally, like, if you’re already having these conversations, then we would just come alongside you, like, we don’t wanna…
251 00:28:30.390 ⇒ 00:28:32.769 Uttam Kumaran: duplicate that work. I think where we would…
252 00:28:32.990 ⇒ 00:28:38.850 Uttam Kumaran: be helpful there, just to put structure around this whole thing and give you, like, a strategy. And then at the end of the month.
253 00:28:39.050 ⇒ 00:28:49.460 Uttam Kumaran: I can tell… I can then tell you, hey, roughly, I think for you to get to your next set of goals, it’s 3, 6, whatever, here’s the pricing. And then I can also, again, if you need help.
254 00:28:49.460 ⇒ 00:29:04.439 Uttam Kumaran: on recruiting or whatever, I’m happy to help wherever you need us. But that’s sort of, like, our path, and you can shop that around at that point, too. We would clearly outline the milestones, what we would accomplish to the best of our ability, and then go from there.
255 00:29:04.440 ⇒ 00:29:15.980 Shivani Amar: mark of, like, let’s say that we were… we did discovery, and then we were like, okay, we came up with a stack that we wanted to implement, let’s actually go ahead and start implementing that stack, and then get to a place where we have, like.
256 00:29:16.190 ⇒ 00:29:23.819 Shivani Amar: three dashboards, one for supply chain, one for retail, one for finance or something. And, like, that that’s, like, the end milestone, is that…
257 00:29:23.820 ⇒ 00:29:42.719 Shivani Amar: the stack is actually built out, not just the discovery and the recommendation. Like, I think stage getting it makes sense, because if we’re like, oh, the discovery wasn’t, like, we didn’t love working together or something, like, then we can walk away, but I’m curious, like, with all of that, let’s say discovery plus implementation of stack, plus, like, a few…
258 00:29:42.720 ⇒ 00:29:47.230 Shivani Amar: you know, implementation of a few BI tools or something for the business.
259 00:29:47.230 ⇒ 00:29:53.320 Shivani Amar: like, could we ballpark what that would be cost-wise, just so that I can, like, start comparing, I guess?
260 00:29:53.920 ⇒ 00:29:55.330 Uttam Kumaran: Yeah, so…
261 00:29:56.190 ⇒ 00:30:01.690 Uttam Kumaran: it’s hard… it depends on how much stuff we have running in parallel, and let me tell you why. So…
262 00:30:02.200 ⇒ 00:30:05.829 Uttam Kumaran: The moment that some of this data becomes accessible and clean.
263 00:30:05.950 ⇒ 00:30:13.210 Uttam Kumaran: people start adding more on. So what we have in our clients, sometimes that’s, like, low, where we have really clear scopes, sometimes it’s, like.
264 00:30:13.210 ⇒ 00:30:26.459 Uttam Kumaran: oh, finally we have the data, we need to go solve this urgent thing. So you have, like, scope creep and ad hoc. So part of it is understanding, like, the expectation for that. I would say, for us to get all the tools set up and in a healthy environment.
265 00:30:26.500 ⇒ 00:30:30.659 Uttam Kumaran: given, like, what I hear today, it’s probably, like, 3 months of work.
266 00:30:30.660 ⇒ 00:30:31.000 Shivani Amar: Yeah.
267 00:30:31.000 ⇒ 00:30:35.609 Uttam Kumaran: And… but this is, again, like, it’s not like we do that and we don’t do any dashboarding, like.
268 00:30:35.770 ⇒ 00:30:53.800 Uttam Kumaran: we’re always doing the analysis and dashboarding work in parallel. I think what I would push back on is just having, like, a milestone date on, like, what you need to have, because we can shorten the timeline if we just parallel path, but what does that mean? It means it’s more… it’ll just be higher cost.
269 00:30:53.800 ⇒ 00:30:59.110 Shivani Amar: I don’t think that there is a, like, you have to… I don’t… nobody’s, like, by…
270 00:30:59.290 ⇒ 00:30:59.620 Uttam Kumaran: Okay.
271 00:30:59.620 ⇒ 00:31:22.210 Shivani Amar: Well, I need to know this. Like, that’s… I think people are just like, this business is about to get more complex, we’re going into retail, we’re gonna start doing our own distribution, like, we’re gonna need to understand things, so let’s start setting up the infrastructure and, like, get moving. And so then, like, it’s either hiring a head of data that can, like, take it over, or it’s, like, continuing to work with people if we really like working with them.
272 00:31:22.210 ⇒ 00:31:23.829 Shivani Amar: So that’s what I’m kind of, like.
273 00:31:23.830 ⇒ 00:31:30.130 Shivani Amar: to get apples to apples, I’m like, I’m just trying to throw out this question to people, like, let’s say it was Discovery Plus.
274 00:31:30.130 ⇒ 00:31:35.590 Shivani Amar: Because, like, some people are like, Discovery’s kind of free, but then, like, whatever, like, you know, like…
275 00:31:35.590 ⇒ 00:31:41.459 Uttam Kumaran: That’s really… that’s crazy, because I don’t know what they’re going to give you at the end of Discovery, if it’s worth anything.
276 00:31:41.460 ⇒ 00:31:43.840 Shivani Amar: So, like, the,
277 00:31:44.050 ⇒ 00:32:02.680 Shivani Amar: this woman I talked to today, she was wonderful, but she was like, Discovery is, like, free, but then everything else here is gonna be way more expensive than anybody else you talk to. And I was like, okay, like, thank you. She was so straight up with me, so she was like, go with one of these smaller places, what you need is, like, pretty clear and, like, standard in some ways, so…
278 00:32:03.000 ⇒ 00:32:08.459 Shivani Amar: there’s, like… the discovery price isn’t, like, the helpful… like, I’m like, who do I want to.
279 00:32:08.460 ⇒ 00:32:10.329 Uttam Kumaran: I, I, I get you, so, like.
280 00:32:10.330 ⇒ 00:32:13.940 Shivani Amar: who do I want to implement the stack with, and, like, get a few dashboards from?
281 00:32:13.940 ⇒ 00:32:25.069 Uttam Kumaran: Yeah, so I would say roughly, like, given the scope here, it’s gonna be anywhere from, like, probably 20K a month. This is where, like, I just don’t know how much
282 00:32:25.300 ⇒ 00:32:27.080 Uttam Kumaran: The demand is gonna be…
283 00:32:27.250 ⇒ 00:32:30.080 Shivani Amar: On any… in any given month for work. Yeah.
284 00:32:30.080 ⇒ 00:32:44.280 Uttam Kumaran: like, so, to give you a sense of roughly, like, how we do the math, if we can’t arrive on clear milestones, we just do hourly, and we have fixed hourly rates for the three different roles that I mentioned. And so, we could also run it that way.
285 00:32:44.610 ⇒ 00:32:55.489 Uttam Kumaran: The more economical option for our clients is to do fixed, because we don’t, like, we build in a little breathing room, and we sort of move up and down within that.
286 00:32:55.490 ⇒ 00:33:01.869 Shivani Amar: Yeah, so, like, in that case is, like, if we were to say this all takes discovery, plus this stuff takes, like.
287 00:33:01.870 ⇒ 00:33:16.339 Uttam Kumaran: And to give you a sense of why I even gave that recommendation, we work with a lot of large e-com CPG in a very similar situation, and, like, that’s usually where they land in terms of their budget for something like this. Which is, like, how fast we can even…
288 00:33:16.340 ⇒ 00:33:18.319 Shivani Amar: 20K mark or something like that.
289 00:33:18.530 ⇒ 00:33:25.880 Uttam Kumaran: Like, again, we also have clients that are way above us, but then we come in and we sort of, like, take… we’re… that we’ve had engagements with them for the long time.
290 00:33:25.880 ⇒ 00:33:26.520 Shivani Amar: Yeah, yeah, yeah.
291 00:33:26.520 ⇒ 00:33:44.840 Uttam Kumaran: And also, we work with some clients that are, like, hundreds of millions of dollars, and we just started with them, so they’re smaller. So again, it sort of depends. If you’re pretty, like, hey, this is the scope, we want to drive, I don’t know, like, just to even tell you, I don’t know if anyone’s going to get you there in less than 6 months, unless you’re paying, like.
292 00:33:45.380 ⇒ 00:33:49.290 Uttam Kumaran: the most money ever. This is, like, quite a lot of… it’s quite a lot of work.
293 00:33:49.470 ⇒ 00:33:49.940 Shivani Amar: Yeah.
294 00:33:49.940 ⇒ 00:33:50.680 Uttam Kumaran: That being said.
295 00:33:50.680 ⇒ 00:33:53.930 Shivani Amar: I think you can accomplish… Somebody pointed me, like.
296 00:33:55.240 ⇒ 00:33:58.590 Shivani Amar: 8-ish weeks? What did he say? He was like.
297 00:33:58.730 ⇒ 00:34:07.179 Shivani Amar: 3 weeks of discovery, 8 weeks to, like, implement the stack, 4 weeks to build dash… a few dashboards. So what was that? That’s, like…
298 00:34:08.230 ⇒ 00:34:09.550 Uttam Kumaran: I mean…
299 00:34:09.550 ⇒ 00:34:10.489 Shivani Amar: 4 months.
300 00:34:11.380 ⇒ 00:34:11.940 Uttam Kumaran: Yeah.
301 00:34:11.949 ⇒ 00:34:12.469 Shivani Amar: Okay.
302 00:34:15.279 ⇒ 00:34:17.929 Shivani Amar: Which might be just adding more people and compressing.
303 00:34:17.929 ⇒ 00:34:28.379 Uttam Kumaran: Well, yeah, you know what he’s doing is he’s… he’s saying, yeah, you’re 50K a month, and so I would tell you his… he’s gonna try to do… build that for, like.
304 00:34:28.549 ⇒ 00:34:35.199 Uttam Kumaran: he’s gonna try to do that work for around $30K, and so that gets you, like, 3… that’s 3 full-time engineers.
305 00:34:35.199 ⇒ 00:34:37.089 Shivani Amar: Two to three full-time engineers.
306 00:34:37.089 ⇒ 00:34:42.349 Uttam Kumaran: Yes, if you gave me that, I could do… but, like, that’s the math that he’s doing, right?
307 00:34:42.350 ⇒ 00:34:47.459 Shivani Amar: Okay, that’s helpful. You’re like, if you gave me that, I could compress my timeline.
308 00:34:47.469 ⇒ 00:35:07.079 Uttam Kumaran: Yeah, of course, I mean, this is what I… but this is what I’m saying, the reason why you’re like, that’s crazy, because yeah, that’s a lot of money for CPG sometimes to invest in data, especially when they haven’t seen the ROI. Yeah. And so we may get to there over time, but also, again, like, a lot of the problems in CPG we’ve seen is just, like.
309 00:35:07.079 ⇒ 00:35:21.989 Uttam Kumaran: there are all these random… it takes time to go build a rapport with people, dig up where the data is, like, some of that stuff you can’t just throw more hours at. And you are just gonna… if you pay that amount of money, a lot of that time, people are just gonna sit and wait for, like, access to things.
310 00:35:21.990 ⇒ 00:35:22.420 Shivani Amar: Yeah.
311 00:35:22.420 ⇒ 00:35:25.019 Uttam Kumaran: So that’s where, like, I’m not so,
312 00:35:26.230 ⇒ 00:35:37.830 Uttam Kumaran: yeah, like, we work pretty fast, and every dollar is pretty worth it, that I would say for us, but getting any… I would say doing the length of this in less than 6 months is… would be tough.
313 00:35:38.010 ⇒ 00:35:39.839 Shivani Amar: And is your team all US-based?
314 00:35:40.100 ⇒ 00:35:44.999 Uttam Kumaran: We have mixed, so we have a bunch of people here in the States, and then we… we hire, like.
315 00:35:45.230 ⇒ 00:35:54.510 Uttam Kumaran: I’m… our team is all, like, great data people, so if I’ve hired them globally, then they’re global. We have some people here in New York, LA, in Ohio, sort of just, like.
316 00:35:54.510 ⇒ 00:36:09.869 Shivani Amar: I was, like, talking to somebody at IBM, and she was like, the pricing will determine… be based off, like, if you explicitly want US people, it’ll be higher. If you want a global team, it’ll be lower. And I was like, oh, that’s so interesting, I wouldn’t have thought of, like.
317 00:36:10.190 ⇒ 00:36:26.900 Uttam Kumaran: Yeah, like, people… sometimes people are like, for data security or something, they’re like, hey, we need all your team to be in the States. That’s fine, but it’s just… yeah, we’ll have to make it a little bit more expensive. I didn’t build, like, a global team for the explicit reason of, like, outsource so it’s cheaper, and then I can, like.
318 00:36:27.320 ⇒ 00:36:35.409 Uttam Kumaran: But it is… that is a… that is a feature of that, but I will say we have… we have straight leveling. Like, the people everywhere are the same level.
319 00:36:35.410 ⇒ 00:36:35.760 Shivani Amar: And so.
320 00:36:35.760 ⇒ 00:36:48.169 Uttam Kumaran: We don’t overcomplicate it. We have two models where I like to get every client on a fixed monthly, because we don’t have, like… there’s no confusion about what we’re doing, and then I will come and tell you, hey, we just got five more asks.
321 00:36:48.590 ⇒ 00:36:48.940 Shivani Amar: Yeah.
322 00:36:48.940 ⇒ 00:36:54.329 Uttam Kumaran: either extend the timeline, or add more this month and do it. Then that’s a conversation I’m having.
323 00:36:54.330 ⇒ 00:36:55.000 Shivani Amar: Versus…
324 00:36:55.000 ⇒ 00:37:00.989 Uttam Kumaran: hourly, if we were just to take it and do it, then at the end of the month, you’re like, I didn’t approve this, I didn’t pay for it, so we have both of those models.
325 00:37:00.990 ⇒ 00:37:05.019 Shivani Amar: So the fixed monthly is nice, because then, like, the scope is super clear each month.
326 00:37:05.270 ⇒ 00:37:12.329 Uttam Kumaran: Yeah, and again, on a monthly basis, like, kind of, like, in terms of communication, like, one, of course, like, daily and weekly, we have, like, sprint-to-sprint things.
327 00:37:12.330 ⇒ 00:37:12.650 Shivani Amar: Yeah.
328 00:37:12.650 ⇒ 00:37:17.850 Uttam Kumaran: At the end of every month, we sort of do a monthly review of, like, what are all the things that we get done. We give you, sort of.
329 00:37:18.020 ⇒ 00:37:29.779 Uttam Kumaran: ideally, deck that you can go take and show, like, what did the data team do? Like, we moved a bunch of things along, here are the new asks we got, here, like, here’s, like, opportunities we have, and, like, that’s what we try to produce,
330 00:37:29.780 ⇒ 00:37:41.340 Shivani Amar: But the way that we work, actually, is in 3-week spr… like, we work in sprints, kind of, as a business, which is… Great. But we do, like, 3-week sprints, where, like, what are you trying to get done in a 3-week span?
331 00:37:41.340 ⇒ 00:37:41.830 Uttam Kumaran: Yeah.
332 00:37:41.830 ⇒ 00:37:52.120 Shivani Amar: And then when we called Rest and Assess, we were like, how did that sprint go? What am I trying to achieve in the next sprint? So I feel like if you actually, if the data team, like, mirrored that, which is like, this is what we’re gonna get done…
333 00:37:52.120 ⇒ 00:37:52.900 Uttam Kumaran: Yeah, that’s even.
334 00:37:52.900 ⇒ 00:37:56.190 Shivani Amar: The data team needs to be doing rest and assess, like, that’s not what I’m saying.
335 00:37:57.150 ⇒ 00:38:00.349 Shivani Amar: But if the data team is like, hey, this is, like, a review for.
336 00:38:00.350 ⇒ 00:38:05.350 Uttam Kumaran: No, we should totally align to that. In fact, we go to companies where there’s no… there’s no process on anything.
337 00:38:05.350 ⇒ 00:38:05.800 Shivani Amar: Yeah.
338 00:38:05.800 ⇒ 00:38:09.329 Uttam Kumaran: So, for me and my business, every team runs on one-week sprints.
339 00:38:09.330 ⇒ 00:38:09.660 Shivani Amar: Yeah.
340 00:38:09.660 ⇒ 00:38:17.570 Uttam Kumaran: But that’s because I need to show, like, at any moment, as consultants, we’re on the chopping block by our clients, who are like, what did you do for me now, lately?
341 00:38:17.570 ⇒ 00:38:18.000 Shivani Amar: Yeah.
342 00:38:18.000 ⇒ 00:38:21.780 Uttam Kumaran: So, I always want to have wins that we got in last week.
343 00:38:21.780 ⇒ 00:38:25.109 Shivani Amar: Versus, I’ve hired a lot of data consultants, and again, like.
344 00:38:25.110 ⇒ 00:38:29.050 Uttam Kumaran: people hide behind jargon that, oh, we’re waiting for this, waiting for that, so I need to create
345 00:38:29.160 ⇒ 00:38:31.209 Uttam Kumaran: A little bit of a sense of urgency.
346 00:38:31.210 ⇒ 00:38:31.590 Shivani Amar: Yeah.
347 00:38:31.590 ⇒ 00:38:33.660 Uttam Kumaran: That’s kind of how we… we do things.
348 00:38:33.660 ⇒ 00:38:38.489 Shivani Amar: to operate. Okay, I really enjoyed this conversation, and I’m, like, I have…
349 00:38:40.230 ⇒ 00:38:46.959 Shivani Amar: 2 or 3 more companies I’ve reached out to. Like, this isn’t… I’m not trying to do a huge process here, I’m just trying to get a feel, like.
350 00:38:46.960 ⇒ 00:39:02.759 Uttam Kumaran: That’s fine, no, I think it’s good, because you’re gonna meet some people that are, like, solo people, that, like, they’re, like, running almost like a dev shop, where they’re like, I can bring in 100 people if you need it. Yeah. You’ll talk to, like, bigger companies who are, like, there’ll be 5 people on this call, and it’ll be, like, a big sales call.
351 00:39:02.760 ⇒ 00:39:03.470 Shivani Amar: Yeah.
352 00:39:03.470 ⇒ 00:39:13.039 Uttam Kumaran: we’re somewhere in the middle, like, I’m not… I’m a… like, I don’t know, we’re… we have a lot of process now, and, like, we have ideas on how we price.
353 00:39:13.070 ⇒ 00:39:29.929 Uttam Kumaran: But also, we are essentially, like, we are an outcomes-focused company, so when we come in, we’re not doing for data for data’s sake. Like, I want to move your KPIs and your goals forward, and we… I would say we… our pricing, I think you’ll find, is very, very fair. Yeah. Like, I know a lot of companies are…
354 00:39:30.330 ⇒ 00:39:35.319 Uttam Kumaran: like, I would say we’re pretty inexpensive for the damage that we come in and do.
355 00:39:35.320 ⇒ 00:39:35.710 Shivani Amar: Yeah.
356 00:39:35.710 ⇒ 00:39:36.410 Uttam Kumaran: And…
357 00:39:36.590 ⇒ 00:39:43.759 Uttam Kumaran: like, I don’t know, I think we’ve found that with our… with our clients, like, they tend to see the ROI pretty clearly, so…
358 00:39:43.970 ⇒ 00:39:45.980 Shivani Amar: That’s great. Okay, cool. So…
359 00:39:45.980 ⇒ 00:40:06.289 Shivani Amar: on my side, I’m gonna have a few… basically, the way that I’m structuring this process is I’m talking to a few companies, like, just one-to-one, and then the… like, once I’ve picked probably, like, a couple, I’m gonna bring somebody in from the tech team. Cool. And, this guy who does, like, our… a lot of, like, our finance analysis, supply-demand forecasting, and things, like, right now.
360 00:40:06.980 ⇒ 00:40:10.980 Shivani Amar: Just for the next conversation, so they can meet some of the stakeholders. And then.
361 00:40:10.980 ⇒ 00:40:12.189 Uttam Kumaran: Totally, great, yeah.
362 00:40:12.190 ⇒ 00:40:13.640 Shivani Amar: I’m hoping to, like…
363 00:40:14.580 ⇒ 00:40:29.170 Shivani Amar: I don’t know timeline-wise, I’m like, I was hoping to kick this off in January, but, like, if there’s a world that I pick somebody in November and we’re kicking off in December, like, great, right? So I think that’s kind of the timeline right now. If I can get ahead of it, then that’s even better.
364 00:40:29.460 ⇒ 00:40:38.700 Uttam Kumaran: Okay, and then, yeah, on our side, I’ll send you, sort of, the materials that we covered today. That’s awesome. And then even if, like, you don’t end up going with us, you have any questions about data stuff that I can be helpful with.
365 00:40:38.700 ⇒ 00:40:42.190 Shivani Amar: Yeah. Whatever, I’m more than happy to. This is all we do.
366 00:40:42.190 ⇒ 00:40:44.239 Uttam Kumaran: Yeah. So, no, it’s like fun.
367 00:40:44.240 ⇒ 00:40:54.339 Shivani Amar: to, like, I just… even as I’m having these conversations, I’m like, oh, Fivetran, and whatever, and people are like, oh, but, like, have you thought about… what did you say today? Mother Duck for Snowflake? I’m like, I would not have heard of Mother Duck, so…
368 00:40:54.340 ⇒ 00:41:05.659 Uttam Kumaran: Yeah, but again, these are, like, these can be pretty pricey decisions, not only in just, like, the cost of the tool, but the impact it’s gonna have on every data person, or anyone who ever accesses, like.
369 00:41:05.850 ⇒ 00:41:10.940 Uttam Kumaran: It’s a… it’s a heavy decision to make, so you just want to go with the right… the right…
370 00:41:10.940 ⇒ 00:41:14.720 Shivani Amar: And I like when people are, like, tool agnostic, and trying to think about what is this business… what’s.
371 00:41:14.720 ⇒ 00:41:26.429 Uttam Kumaran: We’ve worked also with nothing. Like, I come in a place where they’re like, you can’t buy anything, and I’m like, alright, well, we’ll do it all for free, and you’re gonna see why that kind of sucks. It’s not like we’re not gonna do it, though. Like, I’m not…
372 00:41:26.430 ⇒ 00:41:26.800 Shivani Amar: Yeah.
373 00:41:26.800 ⇒ 00:41:35.850 Uttam Kumaran: we don’t live and die by the tools, it’s just the shovels. We just… it’s the shovels we use, so if we get bad shovels, then we dig slowly, and we dig small holes, and that’s it.
374 00:41:36.450 ⇒ 00:41:38.180 Uttam Kumaran: I feel like that. That’s it.
375 00:41:38.280 ⇒ 00:41:42.139 Shivani Amar: Okay, super nice to meet you.
376 00:41:42.140 ⇒ 00:41:42.470 Uttam Kumaran: Yeah.
377 00:41:42.470 ⇒ 00:41:50.210 Shivani Amar: I will definitely want to do another conversation with you, like, for sure. So, I’m actually leaving on vacation tomorrow, going away for a week, and then…
378 00:41:50.210 ⇒ 00:41:54.009 Uttam Kumaran: Great. Funny, I just started this job, but I was like, okay.
379 00:41:54.390 ⇒ 00:42:02.199 Uttam Kumaran: No, that’s when you have to get that… you have to be like, oh, actually, as soon as you sign me off, you’ll be like, actually, also, I have an out-of-office, they’re like, yes.
380 00:42:02.520 ⇒ 00:42:02.920 Uttam Kumaran: Thank you.
381 00:42:03.000 ⇒ 00:42:04.540 Shivani Amar: That’s fine.
382 00:42:04.540 ⇒ 00:42:06.590 Uttam Kumaran: So, we’ll.
383 00:42:06.590 ⇒ 00:42:11.699 Shivani Amar: We’ll talk in November, and I’ll bring some more people into the chat. Does that sound good? Okay, cool.
384 00:42:11.700 ⇒ 00:42:12.220 Uttam Kumaran: Alright.
385 00:42:12.220 ⇒ 00:42:13.589 Shivani Amar: Thank you. Thank you, nice to meet you.
386 00:42:13.770 ⇒ 00:42:14.270 Uttam Kumaran: Bye.