Meeting Title: Uttam <> Robert Date: 2024-01-12 Meeting participants: Robert Tseng, Uttam Kumaran
WEBVTT
1 00:01:10.190 ⇒ 00:01:11.210 Uttam Kumaran: No.
2 00:01:15.350 ⇒ 00:01:17.229 Robert Tseng: hey? Hey, Tom?
3 00:01:17.500 ⇒ 00:01:23.950 Uttam Kumaran: What’s up, man? Happy Friday, yeah. Crazy.
4 00:01:24.950 ⇒ 00:01:30.070 Uttam Kumaran: awesome. Happy. Friday. Yeah. How? How everything’s been for you.
5 00:01:30.610 ⇒ 00:01:32.850 Uttam Kumaran: Things are good.
6 00:01:33.270 ⇒ 00:01:37.399 Uttam Kumaran: yeah, the stuff with both the clients are going. Well.
7 00:01:37.450 ⇒ 00:01:46.100 Uttam Kumaran: I’ve been experimenting with some stuff on the Snowflake marketplace on my listing data sets. I have one
8 00:01:46.870 ⇒ 00:02:01.089 Uttam Kumaran: client I have, like one client that may close for some like AI development work which is really cool. Like local here in Austin. And then I’m excited for our thing. So things are going.
9 00:02:01.630 ⇒ 00:02:07.630 Uttam Kumaran: yeah, it’s it’s been like, it’s been pretty productive 12 days into the year. Honestly, so nice.
10 00:02:08.199 ⇒ 00:02:09.799 Robert Tseng: Yeah, I mean, I think.
11 00:02:09.910 ⇒ 00:02:24.740 Robert Tseng: took a bit to the first week was kind of crazy like I moved my wife up to the bay. And then we’re gonna we’re gonna move from New York eventually. So that’s the plan. For like March, April is, we’re gonna move there.
12 00:02:24.850 ⇒ 00:02:36.380 Robert Tseng: well, I guess we’re looking at Jersey City or Brooklyn. So I think, after visiting enough times, I don’t think I would want to live in Manhattan. It’s a little bit too crazy.
13 00:02:36.670 ⇒ 00:02:40.910 Uttam Kumaran: That’s where I was there. Yeah, in Manhattan.
14 00:02:40.940 ⇒ 00:02:44.320 Uttam Kumaran: Yeah, I was. I was in the East village for like a few years
15 00:02:44.340 ⇒ 00:02:54.590 Uttam Kumaran: for 2 years. Okay, cool. Yeah, that’s right. So that’s why I lived. I so I sort of graduated. And I lived there for like 5 years. And oh, wow! And then I just moved here.
16 00:02:54.740 ⇒ 00:03:24.520 Uttam Kumaran: What? Like a year and a half ago? So yeah, I love this. But it is crazy. It’s it’s a great place, though, and so I live in Hoboken for a year, and I’ve I’ve a couple of friends that are in Hoboken, Jersey City, and it’s really great. It’s easy to get into the city, and then a lot of my friends who lived in Manhattan are now in Williamsburg or Park slope, or like that sort of area. We like park slope on the Brooklyn side. So
17 00:03:24.570 ⇒ 00:03:30.230 Uttam Kumaran: I would say, we’re probably gonna end up in Jersey City or Barkslow. Yeah.
18 00:03:30.270 ⇒ 00:03:31.320 Robert Tseng: So
19 00:03:31.450 ⇒ 00:03:37.669 Robert Tseng: but for now, you know, I’m just so we’ll just be here. And yeah, just grinding away. I’ve been
20 00:03:37.940 ⇒ 00:03:55.609 Robert Tseng: yeah, trying to go for like a like a shift in my, my, my, like, I’m doing like a Rebrand and like kind of niche down a bit more. They’re really hesitant, because. yeah, I mean, I don’t know just oh, it’s you’re always unsure whether or not it’s like the right, the right move. But you know, I think I’m looking to
21 00:03:56.100 ⇒ 00:04:02.600 Robert Tseng: to just do do a whole new whole new thing with sales marketing start next week. So we’ll we’ll see how that goes.
22 00:04:02.790 ⇒ 00:04:06.039 Uttam Kumaran: Yeah, I think the only steady thing is like change.
23 00:04:06.160 ⇒ 00:04:09.669 Robert Tseng: No, I think I think you just have to be trying
24 00:04:09.760 ⇒ 00:04:19.579 Uttam Kumaran: new things, and I feel very similar, like my scope when I started, was really broad, found some stuff that succeeded. And then you do have got instinct on certain things, and
25 00:04:19.620 ⇒ 00:04:30.709 Uttam Kumaran: I think if you have confidence to something, it really shows and so for most of the part, I’m like, really, so much more focused on data. And I have a lot more stuff that’s like in the pipeline for that. And then
26 00:04:30.710 ⇒ 00:04:51.119 Uttam Kumaran: I’m really hoping that if this AI thing closes and we can really succeed at that, it’ll kind of open a door for that world which is like a little bit new and then trying to do some some fun stuff on like the marketplace side. And then again, I I’ve now been doing like II stopped kind of developing every day, you know, cause I was doing product stuff. And now I’ve
27 00:04:51.140 ⇒ 00:05:08.399 Uttam Kumaran: I’m writing sequel every day, or like doing technical stuff, which is great, cause I’m like so tapped in and so it’s it’s like that’s great. And then I finally, I’m I’m onboarding and a contractor on my side hopefully, the full time at the end of this month.
28 00:05:08.440 ⇒ 00:05:22.790 Uttam Kumaran: and that’s been good because I can have him do a lot of the Dbt work. That’s like really easy for me to articulate what? And that really is easy for me to review those Prs and then have him kind of do that work. And then
29 00:05:22.790 ⇒ 00:05:44.040 Uttam Kumaran: the only thing that I’m concerned about is like my time needs to go way more into sales and marketing and like, get building relationships. So as soon as like, I had any sort of like slack in terms of like, okay, I have enough cash, and I can find someone good. It’s like I just need to do that and free of my time again. That’s what I’m trying to do this quarter. So no, that’s that’s great.
30 00:05:44.860 ⇒ 00:06:01.029 Robert Tseng: Yeah. I feel like there’s been pretty steady, steady cash flow for me the past few months. I think I have capacity to take on like one or 2 more contracts with my size at this at this point before I think about hiring. But yeah, that’s kind of my goal for Q, one like of the ones
31 00:06:01.100 ⇒ 00:06:03.449 Robert Tseng: other 3 clients that I’m working with. Like.
32 00:06:03.470 ⇒ 00:06:17.089 Robert Tseng: yeah, they. They weren’t necessarily what I expected to close. But like, maybe it’s just easier to just try to go and get like 2 more of those. And I think that would put me in a better position. So I start to think beyond that, yeah.
33 00:06:17.390 ⇒ 00:06:19.540 Uttam Kumaran: okay, dope. Good. Thanks.
34 00:06:19.600 ⇒ 00:06:47.059 Uttam Kumaran: So let’s talk about let’s talk about this stuff. So yeah, I’m happy to get more specific on the scope, and then tell me what we’re thinking about, like what’s the best timeline and like what’s expectations from their side, you think would be awesome. Maybe I’ll lead with that. So just kind of give update on them. So yeah, like, kind of the like, all the event tracking and like, kind of analytics, pipeline set as setup is kind of already there, I got it.
35 00:06:47.320 ⇒ 00:06:51.790 Yeah, basically, I’ve been going through application data. And then, like
36 00:06:51.970 ⇒ 00:07:04.420 Robert Tseng: I, you know, it really is kind of like a mix of like design product in in analytics engineering a bit. Where didn’t you know II cleaned up there. They had a like a really shoddy like
37 00:07:04.480 ⇒ 00:07:10.410 Robert Tseng: Erd, and I was like kind of incomprehensible. So I just kind of built out like a
38 00:07:10.890 ⇒ 00:07:23.799 Robert Tseng: full, full event, like like a master tracking plan, which is like pretty table stakes for like any sort of data management. And then I’ve been implementing that and and segment for them. So now, all of their core events, they’re able to track now.
39 00:07:23.800 ⇒ 00:07:41.299 Robert Tseng: And so there is a direct pipeline into their bi tool, which is amplitude. But I’m trying to put them into saying like, Okay, well, we can do that for now, just to keep the stakeholders happy. But let’s parallel in parallel. Do the State warehouse implementation, too. So I think they’re open to it, and I think we can get to it in like a week or 2.
40 00:07:41.640 ⇒ 00:07:45.860 But yeah, I think they just want to know specifically like they still not able to.
41 00:07:46.030 ⇒ 00:07:47.049 Robert Tseng: I mean.
42 00:07:47.200 ⇒ 00:08:10.550 Robert Tseng: my main stakeholder says she’s on board, but like I think the rest of the team is like oh, like, not sure like what the implementation would look like, and immediately. So I was hoping that we could kind of just chat through that I could split up a one pager and just like shoot it over to them and them to sign off on that by the end of next week. You know my work is done, so there’s no other delays in getting into that.
43 00:08:11.170 ⇒ 00:08:18.530 Uttam Kumaran: Yeah. So you know if I was just even talk out loud. The biggest things is one
44 00:08:19.060 ⇒ 00:08:32.600 Uttam Kumaran: So there’s a couple of different angles, and you can kinda tell me what resonates with them. What doesn’t? I’ll kind of just throw a couple of things out one if you’re running queries directly on events which I assume that that’s what’s going on right now.
45 00:08:33.100 ⇒ 00:08:54.879 Uttam Kumaran: it’s likely gonna be a huge data set. So like, I don’t know what the kind of bill times or how often things are loading, but one that might be a concern. Second is like again, I don’t know how the vendors are being paid or where data is currently stored. But that may be an issue. Second thing is, you’re not gonna be able to combine that data or clean that data up
46 00:08:54.880 ⇒ 00:09:07.739 Uttam Kumaran: and build any sort of like logic on top of that again. That’s that’s pretty clear. The third thing is the ability to combine that data with marketing sales. Other external data.
47 00:09:07.850 ⇒ 00:09:19.879 Uttam Kumaran: You again, you may may be able to, as features to do that on the fly. But nothing that really cements that sort of code in version controlled environment where people could come in and modify. Third, is that, again.
48 00:09:20.160 ⇒ 00:09:24.170 Uttam Kumaran: having a data warehouse isn’t some sort of like huge investment.
49 00:09:24.240 ⇒ 00:09:32.959 Uttam Kumaran: I would say, if it’s just being. If you give me a sense of like, how many queries are running and stuff like that, I doubt it’s gonna be
50 00:09:33.150 ⇒ 00:09:47.329 Uttam Kumaran: like. And again just from hearing. I doubt it’s going to be more than a couple of 100 bucks a month to even run the whole thing. Yeah, I mean, I would say, is even less so sorry to pay off. But just like for this product that we’re we would spin it up first, for
51 00:09:47.330 ⇒ 00:10:13.010 Robert Tseng: so they’re part of like Umbrella company that does that has more clients. But I’ve been building it out for one of their like new products that this is launched the past year. So they’re they’re kind of in like a Beta testing phase, or they only like have like let in like 30 customers, you know. So B, Twob staff Company 30 customers. So it’s not that much data, really. And it’s like, it’s not like people are using it ever, ever all all the time. It’s probably
52 00:10:13.340 ⇒ 00:10:24.209 Uttam Kumaran: well, I mean II can’t. I don’t wanna just like no, no, but I mean, though that makes sense. I mean the Snowflake. The pricing works is like, if you run a query, you pay $2 per
53 00:10:24.330 ⇒ 00:10:49.529 Uttam Kumaran: credit, and then like pretty much if nobody’s running queries, and there’s there’s no cost to keep that up, and storage fees is like very negligent. So you may be looking at less than 100 bucks, if if like, if that’s like what they’re concerned about, I don’t know. Like that’s it’s, I would say hopefully. Cost should not be a concern for them both. Dvt. Where we run. Keep sort of logic, Github.
54 00:10:49.530 ⇒ 00:10:57.950 Uttam Kumaran: and so flake the whole thing you can run for very, very cheap if infrastructure costs is a concern, really
55 00:10:57.950 ⇒ 00:11:22.700 Uttam Kumaran: shouldn’t be if there’s data that needs to be brought in via 5 Tran, right? Like the segment data, that’s where I would want to look at the volume of data. That’s where you know, you may have some scaling infrastructure costs. But again, you’re not gonna expect to be paying a couple of $1,000 a month. We will just be really careful with the certain types of events that’d be brought in. And you get charged just for new events.
56 00:11:22.940 ⇒ 00:11:35.010 Uttam Kumaran: And that pricing is also kind of scales pretty nicely. That’s probably the most expensive part. The lot. And the main thing is, I would say, if they don’t invest in a data warehouse now.
57 00:11:35.030 ⇒ 00:11:39.140 Uttam Kumaran: They are going to do one eventually.
58 00:11:39.200 ⇒ 00:11:42.769 Uttam Kumaran: Yeah. So I don’t see any reason to
59 00:11:43.130 ⇒ 00:12:12.049 Uttam Kumaran: not just do one. And again. If it sits there, it’s not really charging you much. And again, you’re gonna be able to do a lot more sophisticated things as you build out this product or this feature? And I think maybe what we could do is think about what that initial Mvp. Or that demonstration of value is, and kind of put a timing on like, Hey, here’s like how we could demonstrate all those different value points, both like the speed of queries or the ability to add complex logic.
60 00:12:12.070 ⇒ 00:12:18.089 Uttam Kumaran: things like that. So those are the kind of key things that I articulate to folks that
61 00:12:18.160 ⇒ 00:12:29.970 Uttam Kumaran: are like going directly from application to their product like, it’s you’re, it’s just gonna break at some point you’re gonna have to invest in this middle layer to hold out logic.
62 00:12:30.200 ⇒ 00:12:34.180 Robert Tseng: So I think so I guess you said.
63 00:12:35.290 ⇒ 00:12:43.389 Robert Tseng: well, you mentioned so must be having cost wise. I think that makes sense like II think they’re aware that it should be minimal cost, like they don’t really have. They’re not storing that much data.
64 00:12:43.400 ⇒ 00:12:52.529 Robert Tseng: You’re saying that 5 Tran will probably be kind of like where, like most of the costs, would would be and then we should talk through like.
65 00:12:52.810 ⇒ 00:12:58.040 Robert Tseng: yeah, like I, what an Mvp. Would look like to prove the value of the of the data warehouse.
66 00:12:58.090 ⇒ 00:13:06.650 Robert Tseng: Yeah. So I I’m just thinking, I what what you, what you would need for me is to give you like.
67 00:13:07.040 ⇒ 00:13:28.279 Robert Tseng: I mean, I’m just thinking through different parts of the product. Maybe there’s like one like product flow that we can talk through like would have like a quoting tool, for example, and we can kind of do something along the lines of of bringing that into the to the warehouse first, and then like layering off some complex logic and and
68 00:13:28.880 ⇒ 00:13:39.610 Robert Tseng: and and then how and and seeing where that will like, how that data is going to give it push back into the use Hubspot, for their or it was open. Yeah.
69 00:13:40.020 ⇒ 00:13:45.099 Uttam Kumaran: exactly. So it’d be great to take on like one of those tasks, and then say like.
70 00:13:45.270 ⇒ 00:13:53.009 Uttam Kumaran: and again, if I’m just thinking out loud, it would be a week to kind of get stuff set up on, get to like.
71 00:13:53.280 ⇒ 00:14:04.430 Uttam Kumaran: buy and get access to everything and get snowflake at 5 trains set up, get stuff hooked up. It’s probably like a week and then the rest of the time is really just building that logic.
72 00:14:04.530 ⇒ 00:14:20.880 Uttam Kumaran: writing all that sequel into Github into Dbt, and then either providing that flat file out to access in in Hubspot, or setting up that integration directly or demonstrating whatever value we need within amplitude.
73 00:14:21.220 ⇒ 00:14:41.499 Uttam Kumaran: so I don’t see again the only things that I get nervous on is that like we need access to stuff if they have compliance or things like that, and then but procuring and setting up snowflake and 5 train is like, definitely just a few days, if not that it’s just getting credentials. And then, setting that up, took took a long time for me as well. So okay.
74 00:14:41.830 ⇒ 00:14:55.820 Uttam Kumaran: why don’t we? Why don’t like yeah, why don’t I just put some stuff in a Google Doc? And then I’ll just maybe share with you like cause. I have this conversation, and then let’s just go from there and then, if you, I think in there, if you can really clearly articulate
75 00:14:56.020 ⇒ 00:15:23.300 Uttam Kumaran: one or 2 examples, and maybe that’s what you collaborate with them on on like identifying that. And then I’m happy to just drive towards that as fast as we can. And again, I’ll tell you whether those things are all possible, and then, if we do it, and it should hit expectations, if not exceed them, if we just can do it quicker. So that’s what we should just drive towards. I think that’s really clear. When you make that Google, Doc would lean on you to kinda help me
76 00:15:23.630 ⇒ 00:15:32.389 Robert Tseng: like, make sure. II give you the details that you need in order to like model out that that would that Mvp looks like for them.
77 00:15:32.430 ⇒ 00:15:33.560 Robert Tseng: yeah. So
78 00:15:34.030 ⇒ 00:15:46.559 Uttam Kumaran: yeah. And hopefully, that gives you, I mean again, II think the talking points for Snowflake are so clear just because you don’t pay for it unless you use it. And again, if you’re gonna wanna do, if you’re investing in building like a data product like this.
79 00:15:46.720 ⇒ 00:16:13.200 Robert Tseng: Yeah, I don’t know. You’re just gonna want to layer more stuff on. And things are gonna break. And it’s a customer facing thing. So yeah, so kind of a no brainer. I mean, I’m I’m kind of selling myself, actually, how I feel. So yeah, I mean, III feel the same way. I mean, yeah, I mean, there’s well, there’s so many companies out there that don’t that don’t know the value. And that don’t really, yeah, aren’t able to
80 00:16:13.200 ⇒ 00:16:25.690 Uttam Kumaran: to do it themselves right? Just why people like us exist. So yeah, and II also think it’s like, it’s something. There’s a infrast setup. But there’s also like it doesn’t have to be a huge long term commitment. Right? So again, it for them. It’s like
81 00:16:25.910 ⇒ 00:16:43.609 Uttam Kumaran: they’re not hiring. They’re not like hiring someone full time to do this. They’re like, we need a point solution to bring on these tools, bring the data in, do some like modeling, and then hand it back to you to kind of manage, and then we can come back on to make adjustments, or whatever, so that seems like a very fair square, like kind of operation. So
82 00:16:43.960 ⇒ 00:16:55.640 Robert Tseng: yeah, so I mean, I mean, I like, where things have been going with. II want more more conference like this, I feel like this would be easier to to like, do many reps of, yeah. So
83 00:16:55.740 ⇒ 00:16:57.970 Robert Tseng: just trying to do it. Right? Yeah.
84 00:16:58.100 ⇒ 00:17:06.660 Uttam Kumaran: yeah, okay, cool. So let me write that quick thing up. It’ll just be rough. I don’t know if you’re planning on sharing that, Doc, or just like articulating them. But like, I’m just gonna throw
85 00:17:06.800 ⇒ 00:17:19.270 Uttam Kumaran: bunch of stuff on there. Yeah, I do daily checking with them. So I’ll probably just bring it up in our next one, and we’ll walk through. Okay. So I’ll just write down all these things onto there, and then I’ll take the
86 00:17:19.339 ⇒ 00:17:30.299 Uttam Kumaran: hop spot or I’ll come up with a couple of use cases and just share some of the examples. I’ll list out all the tools we’re planning on using. The pros and cons that we listed. And then.
87 00:17:30.730 ⇒ 00:17:33.459 Uttam Kumaran: yeah, let me know. Cool. That sounds good.
88 00:17:33.980 ⇒ 00:17:37.090 Uttam Kumaran: Okay, painless.
89 00:17:37.470 ⇒ 00:17:38.570 Robert Tseng: Yeah.
90 00:17:38.970 ⇒ 00:17:43.579 Uttam Kumaran: Alright cool dude. Well, I’ll send that to you. And then, yeah, let me know what else you need. Alright sounds good.
91 00:17:44.010 ⇒ 00:17:50.499 Uttam Kumaran: Okay, alright, perfect. Well, have a good weekend. Take some rest. Yeah.