2025-11-24_brainforge_x_cta_5a9b4343

Meeting Title: Brainforge x CTA Date: 2025-11-24 Meeting participants: Uttam Kumaran, Katherine Bayless

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WEBVTT

1 00:00:11.110 ⇒ 00:00:12.060 Katherine Bayless: Blue.

2 00:00:12.490 ⇒ 00:00:13.430 Uttam Kumaran: Hello!

3 00:00:14.210 ⇒ 00:00:15.060 Katherine Bayless: How are you?

4 00:00:15.380 ⇒ 00:00:16.890 Uttam Kumaran: Good! How’s everything?

5 00:00:17.160 ⇒ 00:00:25.940 Katherine Bayless: Good. I just, finished up an interview for the data engineering, candidate, or, interviewing a candidate for the data engineering role. There we go, words.

6 00:00:25.940 ⇒ 00:00:26.910 Uttam Kumaran: How’d it go?

7 00:00:27.460 ⇒ 00:00:39.770 Katherine Bayless: really good, actually, I liked him. I, I feel like my interview style is, like, guerrilla warfare. Like, people come in and they’re like, let me tell you about my strengths and weaknesses, and I’m like, yeah, I got, like, this thing on my mind, what do you think, right?

8 00:00:39.770 ⇒ 00:00:48.940 Uttam Kumaran: No, I’m the same… I’m kind of very similar. I’m like, I don’t think I’m… I’m not a great interviewer, I think I’m pretty optimistic about people, and I’m like, I see the potential, like.

9 00:00:49.120 ⇒ 00:00:50.529 Katherine Bayless: Right? And then…

10 00:00:50.530 ⇒ 00:01:02.559 Uttam Kumaran: So I’m usually, like, the closer, or the seller, like, I’m not the person in the middle, but I also, yeah, I’m like, yeah, that’s all nice, but I’m like, okay, I’m thinking… I just got out of this meeting where I’m, like, thinking about this, like.

11 00:01:02.590 ⇒ 00:01:03.759 Katherine Bayless: What do you think?

12 00:01:03.760 ⇒ 00:01:09.120 Uttam Kumaran: And… one, I think for the best people, they… that sells them, because…

13 00:01:09.560 ⇒ 00:01:22.509 Uttam Kumaran: I’m like, yeah, this is how, like, life is gonna be if you’re here. And then for the people that don’t… are very prepared, it’s, like, really brutal, which is also good, like, a lot of our work is unprepared, walking into a situation, so…

14 00:01:22.890 ⇒ 00:01:32.769 Katherine Bayless: Yeah. Yeah, and he definitely was in the former camp, where it was like, you could tell I caught him a little off guard, but he’s like, okay, you know, actually, I have a lot of questions for you. I’m like, yep.

15 00:01:32.770 ⇒ 00:01:33.340 Uttam Kumaran: Yeah.

16 00:01:34.670 ⇒ 00:01:35.610 Uttam Kumaran: Great, great, great.

17 00:01:35.610 ⇒ 00:01:36.830 Katherine Bayless: Yeah.

18 00:01:37.820 ⇒ 00:01:39.590 Uttam Kumaran: How’s the week going so far?

19 00:01:40.930 ⇒ 00:01:46.279 Katherine Bayless: Hmm, I will be honest, bit of a rocky, rocky start to the week.

20 00:01:46.280 ⇒ 00:01:46.950 Uttam Kumaran: Okay.

21 00:01:46.950 ⇒ 00:01:50.479 Katherine Bayless: you know, I’ll… I’ll hang in there. Okay.

22 00:01:50.840 ⇒ 00:02:01.610 Katherine Bayless: It’s just, like, it’s not data-related necessarily, it’s just, like, return to office stuff, and, you know, you’re just like, oh my god, like, do we really have to keep having these fights? Like, can we not let it.

23 00:02:01.610 ⇒ 00:02:02.210 Uttam Kumaran: No.

24 00:02:02.210 ⇒ 00:02:06.340 Katherine Bayless: Well, choices, and just will, like, get out of everybody’s business, but, you know.

25 00:02:06.530 ⇒ 00:02:10.540 Uttam Kumaran: What’s your, what’s the, what’s your TLDR on your perspective?

26 00:02:11.039 ⇒ 00:02:17.949 Katherine Bayless: oh, I feel like if you don’t trust your people to be getting their job done, that’s your fault, not theirs, right?

27 00:02:18.930 ⇒ 00:02:20.440 Katherine Bayless: Yeah. Yeah, like, if you don’t.

28 00:02:20.440 ⇒ 00:02:21.070 Uttam Kumaran: Yeah.

29 00:02:21.070 ⇒ 00:02:26.699 Katherine Bayless: I’m working because my butt’s not in a chair, then, I mean, that’s a problem I can’t solve.

30 00:02:26.700 ⇒ 00:02:34.679 Uttam Kumaran: Yeah, it’s hard, like, for even… I’ve just worked in remote for a long time, like, I was in person in New York, and then, after COVID, sort of stayed remote.

31 00:02:36.340 ⇒ 00:02:44.670 Uttam Kumaran: I think if you already have a great team, it would just make it more fun to be in person. But, like, I don’t want… I want to be remote.

32 00:02:45.390 ⇒ 00:03:01.800 Uttam Kumaran: So, by virtue of, like, eye for an eye in the best sense, everybody can be remote, because I don’t really care. I would like there to be some people here in Austin, like, we’re trying to hire in Austin, and that would be amazing, but it’s not a requirement at all. Yeah.

33 00:03:01.800 ⇒ 00:03:17.200 Katherine Bayless: thing, too, is like, I actually, I really do love being in person with people, but I feel like if those people are mandated to be in person with me, we don’t have any fun at all, and so I don’t like that. But yeah, yeah. Like, a cool team hanging out and doing cool stuff, sounds awesome.

34 00:03:17.200 ⇒ 00:03:17.900 Uttam Kumaran: Yeah.

35 00:03:18.150 ⇒ 00:03:23.109 Katherine Bayless: be like, everybody must be in Monday through Wednesday, or else we’ll kill you, is not fun.

36 00:03:23.950 ⇒ 00:03:30.120 Uttam Kumaran: Yeah, I wonder… yeah, and it’s just, like, it’s also such a drain on people’s lives to, like, mute in and…

37 00:03:30.700 ⇒ 00:03:31.130 Katherine Bayless: Right.

38 00:03:31.130 ⇒ 00:03:33.230 Uttam Kumaran: I don’t know, it’s a hard ask.

39 00:03:33.230 ⇒ 00:03:33.910 Katherine Bayless: Yeah.

40 00:03:34.550 ⇒ 00:03:35.790 Katherine Bayless: Yeah. But…

41 00:03:36.140 ⇒ 00:03:42.689 Katherine Bayless: But we can talk about things that are actually under my sphere of influence, instead of battles I can’t win.

42 00:03:42.960 ⇒ 00:03:57.929 Uttam Kumaran: Well, yeah, I guess, like, I wanted to sort of… maybe we can spend some time today, and really my… this week for me is, Sam’s, you know, planning out the executions for Snowflake setup. I wanted for him to just run

43 00:03:58.020 ⇒ 00:04:16.500 Uttam Kumaran: the plan by you first before we run a bunch of stuff, and also so that, like, you kind of see how we think about setting up stuffing, so he’s working on that. I went ahead and, you know, we worked on this Gantt chart, and so I think probably today can just be mainly focused on

44 00:04:16.680 ⇒ 00:04:24.380 Uttam Kumaran: on that, so I’ll… I’ll have it open on my side. Let me just share that, and then, yeah, we just, like, have a conversation about

45 00:04:25.000 ⇒ 00:04:27.970 Uttam Kumaran: How to tackle the next, you know, few weeks.

46 00:04:28.110 ⇒ 00:04:31.380 Uttam Kumaran: Let me just take… I’m just gonna also take notes.

47 00:04:38.850 ⇒ 00:04:41.589 Katherine Bayless: My brain lives mostly on sticky notes.

48 00:04:41.590 ⇒ 00:04:42.210 Uttam Kumaran: Okay.

49 00:04:42.330 ⇒ 00:04:50.209 Katherine Bayless: I had written this one down to mention to you at some point. It’s mostly a tangent, but it was like a, fold this one into the back of your brain.

50 00:04:50.210 ⇒ 00:04:50.760 Uttam Kumaran: Okay.

51 00:04:50.900 ⇒ 00:05:12.840 Katherine Bayless: So when it comes to, like, data and, like, security and protecting and all the things, like, we definitely have PII, right, we will be more sensitive about. We also have embargoed data in some cases, so, like, award winners we just can’t tell people about yet, but staff can know, and, like, I know it’s a totally normal use case, I’ve just never had to deal with embargoed versus just, like, protect.

52 00:05:12.840 ⇒ 00:05:18.220 Uttam Kumaran: Yeah, I guess, like, I haven’t worked for, like, the Oscars, or… Yeah! It’s probably the most fun use case, there’s probably some more…

53 00:05:18.390 ⇒ 00:05:26.209 Uttam Kumaran: not-so-fun use cases, but yeah, like, yeah, I get it. Especially sitting next to the Pentagon, yeah. Yes. Yeah.

54 00:05:26.320 ⇒ 00:05:27.150 Katherine Bayless: Yeah.

55 00:05:27.290 ⇒ 00:05:30.539 Katherine Bayless: But yeah, embargoed versus sensitive. I was like, huh.

56 00:05:30.540 ⇒ 00:05:37.299 Uttam Kumaran: Okay, great. Yeah, so we could definitely talk about, like, different sort of security postures for that.

57 00:05:37.620 ⇒ 00:05:38.800 Uttam Kumaran: Yeah.

58 00:05:43.910 ⇒ 00:05:49.460 Uttam Kumaran: Okay, great, so I’m just gonna… I just have a bunch of stuff open, so I’m just gonna leave this there.

59 00:05:58.940 ⇒ 00:06:00.100 Uttam Kumaran: All right.

60 00:06:10.790 ⇒ 00:06:11.650 Uttam Kumaran: Okay.

61 00:06:11.780 ⇒ 00:06:17.640 Uttam Kumaran: So… I mean, I’ll just kind of give the overall walkthrough, and then I have our Slack.

62 00:06:17.980 ⇒ 00:06:26.350 Uttam Kumaran: up as well that I could read through. But basically, I was sort of… Overall, just…

63 00:06:26.530 ⇒ 00:06:27.910 Uttam Kumaran: the first

64 00:06:28.110 ⇒ 00:06:44.069 Uttam Kumaran: parts for us is just, like, looking through all the sources, understanding, and then starting to make a couple of, like, definitions. Like, what are the core KPIs? Like, how do we… what is a member versus, like, an attendee? You know, all the different core objects, so…

65 00:06:44.480 ⇒ 00:06:51.499 Uttam Kumaran: that’s what I kind of like to work on. The second piece that we did get done last week, so I’ll just note this down, is, like.

66 00:06:51.680 ⇒ 00:06:55.450 Uttam Kumaran: getting access to… to Snowflake.

67 00:06:55.920 ⇒ 00:06:58.950 Uttam Kumaran: And then, it’s sort of… we kind of get into…

68 00:06:59.060 ⇒ 00:07:17.859 Uttam Kumaran: We’ll kind of talk through today, like, what is this phase, which is, like, kind of defining and mapping, but in terms of storage, this is, like, gonna be what Sam’s working on, which is setting up world-based asset controls, setting up the core warehouses, users, setting up service accounts. This is gonna just set the stage for

69 00:07:18.120 ⇒ 00:07:25.639 Uttam Kumaran: Anything that touches into Snowflake has a way to get access to things. We have the core…

70 00:07:25.930 ⇒ 00:07:35.499 Uttam Kumaran: flexible objects to be able to assign roles, to land things, so just, like, the general setup is there. After that is when…

71 00:07:36.040 ⇒ 00:07:37.760 Uttam Kumaran: We will start to…

72 00:07:37.860 ⇒ 00:07:45.710 Uttam Kumaran: basically talked through ingestion. And so, this is where, even today, we can get into, like, what are the P0, P1,

73 00:07:45.740 ⇒ 00:08:04.069 Uttam Kumaran: data sources, we then can use that to go and evaluate, you know, ingestion tools or building things on our own. Once we make a decision on, like, sort of the strategy around ingestion is when we can basically kick off ingestion.

74 00:08:04.110 ⇒ 00:08:07.859 Uttam Kumaran: And so that kind of, hopefully, that kind of kicks off.

75 00:08:08.510 ⇒ 00:08:09.540 Katherine Bayless: Before…

76 00:08:09.540 ⇒ 00:08:13.229 Uttam Kumaran: Christmas-ish, so that if things need to run for a while.

77 00:08:13.360 ⇒ 00:08:25.530 Uttam Kumaran: they can run during that time frame, and then we have some sort of step that, like, we confirm that, like, data has landed. Yeah.

78 00:08:25.960 ⇒ 00:08:37.100 Uttam Kumaran: So I put this kind of out here because we haven’t listed all the sources that are landing. The next piece we’re going into is as soon as we decide on the ETL tool.

79 00:08:37.309 ⇒ 00:08:43.589 Uttam Kumaran: Honestly, probably, like, during that process, if we have parallel path, we can start to set up

80 00:08:43.840 ⇒ 00:08:56.110 Uttam Kumaran: dbt Core, GitHub, so we can also make some decisions on that, and I can start working with Jay to make sure that, yeah, like, we have access to our… or he’s in the loop on these infra decisions.

81 00:08:56.290 ⇒ 00:08:59.679 Uttam Kumaran: Dbt Core is free, so we can rock with that.

82 00:09:00.020 ⇒ 00:09:06.640 Uttam Kumaran: And then consider… consider cloud. Again, I mean, we can talk about… I want to talk about your notes on the procurement strategy, too, so…

83 00:09:08.070 ⇒ 00:09:13.049 Uttam Kumaran: Then, you know, as soon as we start to model things, we’ll move into…

84 00:09:13.170 ⇒ 00:09:26.859 Uttam Kumaran: sort of building out… building out marts when… when things are down… are sort of landed. So once we kind of understand the schema, we can start to say, okay, like, what are the ultimate marts that we’re driving towards? And then…

85 00:09:27.030 ⇒ 00:09:38.990 Uttam Kumaran: ideally, we parallel a path how does data get… how do those marks get accessed, both writing queries and Snowflake, or if there needs to be… we need to consider, like, reverse ETL, and then as well as, like, a BI tool.

86 00:09:39.200 ⇒ 00:09:42.640 Uttam Kumaran: And then the BI tool is like, okay, we…

87 00:09:42.880 ⇒ 00:09:50.870 Uttam Kumaran: We have some amount of making a decision there, setting up the tool, driving towards some reports, and then training folks.

88 00:09:51.130 ⇒ 00:09:53.850 Katherine Bayless: In parallel across everything.

89 00:09:54.310 ⇒ 00:09:57.110 Uttam Kumaran: Let me just move this guy up…

90 00:09:57.420 ⇒ 00:10:10.799 Uttam Kumaran: In parallel across everything, we also have, like, these, like, cross-cutting tasks, where we’re gonna put together, sort of, our metric dictionary, we’re gonna be maintaining a data platform, like, visual architecture.

91 00:10:10.850 ⇒ 00:10:21.990 Uttam Kumaran: as we… as we drive, so at any moment, you’ll have, like, a visual of, like, what is the system. Of course, we’ll have more information noted down on, like, any of the contracts, so, like.

92 00:10:22.120 ⇒ 00:10:26.960 Uttam Kumaran: the contract, the terms, so trying to centralize all of that. Ideally, if we can… if…

93 00:10:27.190 ⇒ 00:10:32.369 Uttam Kumaran: Since you’re technical, we can centralize all of that in the repo, that’s probably perfect, you know, versus…

94 00:10:32.480 ⇒ 00:10:37.349 Uttam Kumaran: Creating more spreadsheets and things like that, so ideally, we would just put that in docs there.

95 00:10:37.610 ⇒ 00:10:46.649 Uttam Kumaran: We would also start to have documentation on, like, ETL, so, like, how often are jobs running, on the data warehouse, and then

96 00:10:47.130 ⇒ 00:10:50.290 Uttam Kumaran: After we build models, we can start to document models.

97 00:10:50.480 ⇒ 00:10:58.859 Uttam Kumaran: this is, like, sort of the zero to one. I think what, you know, I’m very curious about is, like.

98 00:10:59.230 ⇒ 00:11:01.730 Uttam Kumaran: What parts of this are…

99 00:11:01.940 ⇒ 00:11:08.649 Uttam Kumaran: more important than others. Of course, this is 0 to 1 on, like, both the infrastructure, but also

100 00:11:08.920 ⇒ 00:11:17.409 Uttam Kumaran: I’ve listed that we will model certain things. If you’re more like, hey, let’s just spend this sort of phase landing everything.

101 00:11:17.630 ⇒ 00:11:30.390 Uttam Kumaran: and then… and that’s what we focus on, then we can just prioritize that. If you’re like, actually, let’s just land the two things that we need to drive this question I have, then we can do that. So this is where I sort of…

102 00:11:30.950 ⇒ 00:11:34.279 Uttam Kumaran: We’ll need you as a guide to say, like, where to…

103 00:11:34.690 ⇒ 00:11:39.410 Uttam Kumaran: where to parallel a path, and, like, what to drive towards. But, yeah.

104 00:11:40.940 ⇒ 00:11:42.469 Katherine Bayless: Yeah, so I think…

105 00:11:42.710 ⇒ 00:11:45.160 Uttam Kumaran: So many thoughts. Yeah.

106 00:11:45.310 ⇒ 00:11:55.670 Katherine Bayless: Like, I think… I think we can focus more on landing than modeling, but landing in a…

107 00:11:55.830 ⇒ 00:11:58.920 Katherine Bayless: Slightly intelligent prioritization.

108 00:11:59.020 ⇒ 00:12:01.779 Katherine Bayless: The reason I say that is because I’m like.

109 00:12:02.300 ⇒ 00:12:13.770 Katherine Bayless: I think the modeling is going to be where we’re gonna run into, sort of, like, the upper limit on people’s existing data literacy, right, like, pretty quickly.

110 00:12:13.790 ⇒ 00:12:28.620 Katherine Bayless: There’s definitely some baseline stuff that will be modelable, but I think the business rules are gonna be a real challenge to truly sort of surface. Like, in an odd way, like, they’re everywhere, and therefore they’re nowhere. Yeah.

111 00:12:28.620 ⇒ 00:12:29.210 Uttam Kumaran: Yeah.

112 00:12:29.210 ⇒ 00:12:33.160 Katherine Bayless: like, we… so, actually, the chatbot thing that I sent you.

113 00:12:33.320 ⇒ 00:12:50.510 Katherine Bayless: we had this, like, hour-long call with the guy, at one point early on, where he was like, so I’ll ingest this source, this source, this source, you know, blah blah blah blah, right? Train the model, etc. And we were like, oh… but you need to understand all of the business rules to put that data.

114 00:12:50.510 ⇒ 00:12:50.840 Uttam Kumaran: Yeah.

115 00:12:50.840 ⇒ 00:12:55.810 Katherine Bayless: together, and he’s like, yeah, just send them to me. And we were like.

116 00:12:55.810 ⇒ 00:12:57.469 Uttam Kumaran: That’s cute. Yeah, you’re like…

117 00:12:57.470 ⇒ 00:13:15.800 Katherine Bayless: So, literally, that AI thing is getting trained off our public website, because we were like, you know what, that’s gonna be the easiest way to get this right, is like, if we put it on the website, it can go into the AI thing, because otherwise, going back to the embargoed data, we were like, if somebody’s paid us, you know, millions of dollars and told us to keep it a secret, and then the AI.

118 00:13:15.800 ⇒ 00:13:16.619 Uttam Kumaran: Yeah, you’re just…

119 00:13:16.620 ⇒ 00:13:17.290 Katherine Bayless: it today.

120 00:13:17.290 ⇒ 00:13:18.220 Uttam Kumaran: Yeah, yeah.

121 00:13:18.220 ⇒ 00:13:20.099 Katherine Bayless: Right? Yeah, so I think…

122 00:13:20.160 ⇒ 00:13:34.239 Katherine Bayless: landing, we can accomplish more, more quickly than we will with modeling. Okay. Is kind of where my head is at. The other thought, which is the more optimistic and less pessimistic flavor of it, is.

123 00:13:34.260 ⇒ 00:13:51.240 Katherine Bayless: I think there’s a lot more citizen data engineers running around than I was realizing. So I had this conversation with this guy on Friday, who’s in our market research team. He spent 10 years at, like, a market research firm working a multi-million dollar contract for Verizon, and he’s like, what are you guys doing here? And I’m like.

124 00:13:51.420 ⇒ 00:13:58.409 Katherine Bayless: help. And so, like, there’s totally teams where, like, I think if we could just land it, and hand it over, people…

125 00:13:58.410 ⇒ 00:13:58.870 Uttam Kumaran: Perfect.

126 00:13:58.870 ⇒ 00:14:03.239 Katherine Bayless: to do with it. Yeah. Okay. So, long way of saying, I think…

127 00:14:03.240 ⇒ 00:14:04.200 Uttam Kumaran: Okay, great, no, that…

128 00:14:04.200 ⇒ 00:14:05.800 Katherine Bayless: We’ll bear more fruit.

129 00:14:06.520 ⇒ 00:14:13.169 Uttam Kumaran: So I think, yeah, if we can land the data, and then we identify… Yeah, like, the…

130 00:14:13.380 ⇒ 00:14:15.230 Uttam Kumaran: Citizen, DEs.

131 00:14:16.320 ⇒ 00:14:17.360 Uttam Kumaran: AEs.

132 00:14:20.090 ⇒ 00:14:32.250 Uttam Kumaran: and enable them… like, And, potentially, gives… them, home to model.

133 00:14:32.450 ⇒ 00:14:36.410 Uttam Kumaran: In dbt, like, that is a really great way of doing things.

134 00:14:36.600 ⇒ 00:14:37.720 Uttam Kumaran: Yeah.

135 00:14:38.370 ⇒ 00:14:43.949 Uttam Kumaran: Do you have, like, the… these folks, like, a couple of them top of mind?

136 00:14:44.160 ⇒ 00:14:52.990 Uttam Kumaran: Because then, ideally, like, these are the folks who we would meet, go become friends with, and then they would drive our initial

137 00:14:53.940 ⇒ 00:14:58.980 Uttam Kumaran: you know, roadmap. Or at least I could tell you what they’re… I could go find out what exactly they want.

138 00:14:59.890 ⇒ 00:15:18.190 Katherine Bayless: Yeah, yeah, so I think, Chris Deathloff, is one, and then there’s, there are 3 people who are all named Anna, Anna P, Anna K, and Anna R. Okay. They have… they are not technical in the sense of, like, they’re… they’re great with Excel, but that’s as far as they’ve gone. However…

139 00:15:18.190 ⇒ 00:15:36.759 Katherine Bayless: they really do understand the data, and they would be able, I think, to help with the modeling if we gave them a little bit of an understanding of, like, okay, here’s the techie piece of it, now… Yeah. And then there’s another, person, Quinn, and then JC.

140 00:15:37.860 ⇒ 00:15:40.039 Katherine Bayless: Actually, even Tom, probably.

141 00:15:40.510 ⇒ 00:15:45.149 Katherine Bayless: There’s, yeah, there’s really… there’s a lot of latent talent running around. Okay, cool.

142 00:15:45.300 ⇒ 00:15:49.829 Katherine Bayless: Yeah. So, interestingly, then, so, like, if that’s kind of, like.

143 00:15:50.980 ⇒ 00:16:04.120 Katherine Bayless: recruiting some assistants in the trenches, knowing that also leadership wants to see things, right? And it’s less likely that I’m gonna find a VP that’s suddenly gonna put hands on a keyboard, which is fair. I think…

144 00:16:04.150 ⇒ 00:16:17.079 Katherine Bayless: The upward trajectory for the data is gonna be, like, we need the machinery to help us stop doing things that don’t really make us as much money as they should, or could, or…

145 00:16:17.230 ⇒ 00:16:19.430 Katherine Bayless: cost, frankly. I think it’s the.

146 00:16:19.430 ⇒ 00:16:22.439 Uttam Kumaran: Yeah, should maybe go into that a little bit deeper at that point.

147 00:16:23.560 ⇒ 00:16:40.960 Katherine Bayless: So, for example, we have the Investor Partnership Program this year, where we are charging $2,500 for an investor to purchase data on, like, startups, basically, at CES.

148 00:16:41.320 ⇒ 00:16:47.599 Katherine Bayless: But I mean, we’ve definitely… I think we have about 15 people that have signed up for it. We might get 30, but like…

149 00:16:47.800 ⇒ 00:16:54.160 Katherine Bayless: You know, we’ll definitely have spent more than $60,000 in staff time putting this program together.

150 00:16:54.190 ⇒ 00:17:05.950 Katherine Bayless: what I don’t want to do is make it sound like every idea is a bad idea, because, like, the investor thing is definitely something we should explore generally, but, like, this particular program probably was not the winner.

151 00:17:05.950 ⇒ 00:17:15.660 Katherine Bayless: Right? We have another one that’s, like, an attendee match thing, kind of similar. There’s these, like, show floor tours that bring in a little bit of money, but take a lot of manual effort.

152 00:17:15.660 ⇒ 00:17:16.630 Katherine Bayless: like…

153 00:17:17.280 ⇒ 00:17:26.540 Katherine Bayless: We just don’t have great, like, decision-making architecture around, like, what are we actually getting out of this program?

154 00:17:28.670 ⇒ 00:17:29.500 Uttam Kumaran: Yeah.

155 00:17:29.670 ⇒ 00:17:30.670 Uttam Kumaran: So, okay.

156 00:17:30.670 ⇒ 00:17:49.510 Katherine Bayless: There’s one question that’s been directly asked by leadership, which was, did we panic too early and move up a bunch of our paid marketing into this phase where CES was still, like, free registration for everybody, versus waiting to spend more money on marketing after CES.

157 00:17:49.510 ⇒ 00:17:50.150 Uttam Kumaran: Mmm.

158 00:17:50.150 ⇒ 00:18:06.570 Katherine Bayless: and cost money, and we don’t have a model to do that. And also, when I said we should focus on profit for attendee, they looked at me like I was from Mars. So, like, some of it is the tools and techniques, and some of it is the ideas and the ways of thinking.

159 00:18:07.040 ⇒ 00:18:13.220 Uttam Kumaran: Okay, so there’s definitely something around, like, what is the service line profitability?

160 00:18:13.740 ⇒ 00:18:15.680 Katherine Bayless: right.

161 00:18:15.840 ⇒ 00:18:18.019 Uttam Kumaran: So, this is very similar in…

162 00:18:18.260 ⇒ 00:18:26.060 Uttam Kumaran: I mean, in a lot of different places, they just do product-level… you want some product-level insights, so what products are we selling the most of? How much are they bringing in?

163 00:18:27.210 ⇒ 00:18:29.320 Uttam Kumaran: And that alone helped dictate

164 00:18:29.690 ⇒ 00:18:36.500 Uttam Kumaran: like, you know, what products to lean in more, or what to lean out of. Second is, like, basically.

165 00:18:36.580 ⇒ 00:18:55.870 Uttam Kumaran: like, marketing effectiveness. This you can’t do in isolation until you also look at what are the outcomes for those folks? Like, are the folks coming in free? Are they end up purchasing ancillary services? Or are you finding that the free… the people that pay end up being the people that pay again and again? And so, there’s something around

166 00:18:56.790 ⇒ 00:18:59.209 Uttam Kumaran: The free versus paid attendee.

167 00:18:59.410 ⇒ 00:19:01.500 Uttam Kumaran: Okay, great.

168 00:19:02.340 ⇒ 00:19:08.459 Katherine Bayless: Yeah, it was actually… it was earlier over the summer, there was a conversation, because I guess if you go to CES, but the next

169 00:19:08.490 ⇒ 00:19:20.849 Katherine Bayless: three years, you will get invited for free as an alumni. And so if you continue coming, obviously, that’s just perpetual. And there’s talk of switching it to be, like, you have to come every 2 years instead of three.

170 00:19:20.850 ⇒ 00:19:30.989 Katherine Bayless: And so Kyle had done some back-of-the-envelope numbers on that, but, like, those were the first time anybody had even kind of asked that question, right? Right. Yeah, yeah.

171 00:19:30.990 ⇒ 00:19:36.430 Uttam Kumaran: This is, I mean, do you consider this very similar to, like, yeah, just, like, a rewards program or, like, customer loyalty program, yeah.

172 00:19:38.230 ⇒ 00:19:44.089 Uttam Kumaran: And also, I guess, talk to me about Kyle, like, how should we best work with, Kyle on things?

173 00:19:44.880 ⇒ 00:19:46.970 Katherine Bayless: It’s a really great question.

174 00:19:47.290 ⇒ 00:20:05.279 Katherine Bayless: So, he’s really smart, smarter than he realizes, I think. Like, he hooked up, like, an ETL process using R last week, and I was like, I didn’t know you could do that, I’m gonna be honest. I really did not. I’m a Python girl, what can I say? Yeah. So, like, he’s really smart. He’s also, like, very…

175 00:20:05.280 ⇒ 00:20:21.020 Katherine Bayless: fast, right? Like, he is responsive, he’s restless, he’s impatient. I mean, he does my work faster than I can even read the email asking me to do something. So, like, you know, delightful. Just delightful combination of intelligence and restlessness.

176 00:20:21.710 ⇒ 00:20:32.629 Katherine Bayless: now, I think, where I’m, like, as a person who likes to grow the people she works with, I’m like, hmm, I feel like I need to get you to slow down and strategize.

177 00:20:32.630 ⇒ 00:20:33.240 Uttam Kumaran: Yeah, yeah, yeah.

178 00:20:33.240 ⇒ 00:20:51.239 Katherine Bayless: Right? You know, because, like, I think he is so interested in helping, he might spend all of his time helping and not enough time learning, right? And so, it might be interesting to figure out how do we really kind of pull him into stuff where he feels like he’s not slowing things down because he’s learning, which I would.

179 00:20:51.240 ⇒ 00:20:51.670 Uttam Kumaran: Yeah.

180 00:20:51.670 ⇒ 00:21:03.439 Katherine Bayless: someone to think, but I do think he might fall into that trap, at least initially. And, you know, like, yeah, yeah, yeah, yeah. I think he’s gonna race to put out fires if we don’t give him things to do.

181 00:21:03.620 ⇒ 00:21:17.059 Uttam Kumaran: Yeah, so I, this is perfect. Like, I… whenever we come in, I just had this conversation with another client, is like, who are the people that you want to make, like, heroes in your org? And so what we’ll teach them is to start

182 00:21:17.420 ⇒ 00:21:20.689 Uttam Kumaran: Doing what you’re doing, which is asking the question.

183 00:21:20.690 ⇒ 00:21:22.680 Katherine Bayless: Alright, how do we get him…

184 00:21:22.680 ⇒ 00:21:25.040 Uttam Kumaran: To not just take li… take…

185 00:21:25.460 ⇒ 00:21:32.979 Uttam Kumaran: asks and execute, but to start saying, what do you… what does the business need? How can I present that to Catherine on a weekly basis?

186 00:21:32.980 ⇒ 00:21:33.450 Katherine Bayless: Yeah.

187 00:21:33.450 ⇒ 00:21:53.239 Uttam Kumaran: answers. So, my next point here is sort of, like, what are the rituals that you’re running in a data team, and, like, how can we, you know, support that? Because we’re gonna start running rituals, for CTA. Like, what we found effective in the past, especially if you had these

188 00:21:53.340 ⇒ 00:21:55.260 Uttam Kumaran: If you have folks that are, like.

189 00:21:55.530 ⇒ 00:22:03.880 Uttam Kumaran: sort of straggling data folks that you want to be… kind of sit next to us as we start to execute these, and they can learn, and we can start

190 00:22:04.070 ⇒ 00:22:14.109 Uttam Kumaran: start to basically give them a lot of the stuff that’s, you know, as core as we can, and they basically start to level up. Like, give me a sense of, like, what the…

191 00:22:14.350 ⇒ 00:22:21.689 Uttam Kumaran: what the data team processes look like now, and, like, kind of, like, what your vision for these, like, daily, weekly, monthly, like, rituals are.

192 00:22:22.530 ⇒ 00:22:38.849 Katherine Bayless: Yeah, so, to be perfectly frank, at the moment, there is essentially nothing, except me occasionally saying, hey Kyle, we should chat again, but this is not my normal behavior, so because he was coming over from the market research team and was like, I think…

193 00:22:39.030 ⇒ 00:22:50.510 Katherine Bayless: I think they’re still trying to find the backfill? I can’t remember if they’ve got a finalist yet or not. So he’s kind of like a one-foot each world, which I do… I don’t know why, but that is where, like, everything folds apart for me, and I’m like.

194 00:22:50.510 ⇒ 00:22:51.360 Uttam Kumaran: Yes, okay.

195 00:22:51.360 ⇒ 00:22:52.370 Katherine Bayless: Right? .

196 00:22:52.370 ⇒ 00:22:52.800 Uttam Kumaran: Yeah, yeah, yeah.

197 00:22:52.800 ⇒ 00:22:54.710 Katherine Bayless: We need to establish ceremonies.

198 00:22:54.810 ⇒ 00:23:11.210 Katherine Bayless: rituals. What I have done in the past is, like, just a constant back and forth between, like, over-engineering the perfect system, and then being too exhausted to use it, and being too light-touch, and now I need to rally the troops, but I don’t have a way to get them easily.

199 00:23:11.210 ⇒ 00:23:21.510 Katherine Bayless: But I usually am, like, stand-up, weekly planning. Retros are something… retros slash, I always call it show and tell, demo, retro slash show and tell.

200 00:23:21.510 ⇒ 00:23:22.180 Uttam Kumaran: Hell yeah, yeah.

201 00:23:22.180 ⇒ 00:23:34.220 Katherine Bayless: Yeah, like, that’s one that I really want to get better at building the muscle for, and I think it’s going to be critical here, because, like, at my old place, it was a very technical staff, and so demos would always kind of die on the vine, because you’re like, yeah.

202 00:23:34.220 ⇒ 00:23:35.090 Uttam Kumaran: Yes.

203 00:23:35.090 ⇒ 00:23:38.919 Katherine Bayless: Right? But here, I mean, I think people would be like, that’s so cool, like, tell me more.

204 00:23:38.920 ⇒ 00:23:39.470 Uttam Kumaran: Yeah.

205 00:23:39.470 ⇒ 00:23:41.440 Katherine Bayless: Right? Yeah, yeah.

206 00:23:42.020 ⇒ 00:23:44.320 Uttam Kumaran: And so… and then give me a sense of, like.

207 00:23:45.100 ⇒ 00:23:51.820 Uttam Kumaran: And this may be a leading question, but, like, your capacity… because for us, like, we can…

208 00:23:52.760 ⇒ 00:24:02.979 Uttam Kumaran: I mean, I would love to help you establish these rituals, but I also don’t want to ha- don’t want to create more meetings for you to join, and like… so give me a sense of, like.

209 00:24:03.420 ⇒ 00:24:15.589 Uttam Kumaran: where you, like, for example, if a good solution is like, hey, we’re gonna be meeting about CTA on a daily basis, maybe we just have Kyle join that meeting every other day.

210 00:24:15.720 ⇒ 00:24:31.370 Uttam Kumaran: And maybe just the three of us meet. The other alternative could be, hey, maybe we present to Catherine, like, our roadmap of what we’re planning, and Kyle just kind of joins us throughout the week. And then, as we meet these folks.

211 00:24:31.960 ⇒ 00:24:37.959 Uttam Kumaran: we see who’s also interested in joining us, and, like, this is where I think it would be helpful to know, like.

212 00:24:38.260 ⇒ 00:24:53.049 Uttam Kumaran: In some other… in some… in some other clients, they’re like, oh, yeah, but that person’s not in my org, or like, I don’t want to waste their time. Other places, they’re like, oh, no, that person can totally… if we enable them, they’ll service… and they would love to just be talking to other data people.

213 00:24:53.050 ⇒ 00:24:58.680 Uttam Kumaran: So, we’re happy to kind of establish that, but what I don’t want to do is have you in just, like, another…

214 00:24:59.440 ⇒ 00:25:09.490 Uttam Kumaran: 5 or 6 more meetings, where, like, I want to… I want to teach them what it’s like to have… to, like, basically…

215 00:25:09.700 ⇒ 00:25:18.320 Uttam Kumaran: execute and deliver for, like, a stakeholder, like, an end-to-end question, or an analysis, or… and… versus…

216 00:25:18.950 ⇒ 00:25:37.489 Uttam Kumaran: like, part of not giving them a lot of your time is to show them that, like, hey, we… Catherine already mentioned the priorities, so it’s now up to us to ideate, okay, maybe there’s… it’s a modeling thing, we need to land the data, okay, then let’s come back next week, let’s codify our learnings, and then present. Like, that’s the sort of stuff I’d like

217 00:25:37.900 ⇒ 00:25:41.299 Uttam Kumaran: to sort of… Impart to them, you know?

218 00:25:41.580 ⇒ 00:25:45.280 Katherine Bayless: Yeah, that’s so funny, like…

219 00:25:45.950 ⇒ 00:26:05.259 Katherine Bayless: So, yeah, yes, yes, yes, yes, yes, yes, yes, yes, yes. It is… honestly, it’s funny, it is the biggest lesson that I learned in my, like, early years transitioning to leadership was, like, the more I show up, the less people will walk on their own. And if everybody’s just doing what they think is, like, normal.

220 00:26:05.260 ⇒ 00:26:05.880 Uttam Kumaran: Yes.

221 00:26:05.880 ⇒ 00:26:10.269 Katherine Bayless: But yeah, I have learned that neglect is the most effective technique.

222 00:26:10.450 ⇒ 00:26:10.930 Uttam Kumaran: Yeah.

223 00:26:10.930 ⇒ 00:26:11.630 Katherine Bayless: Okay.

224 00:26:11.630 ⇒ 00:26:13.589 Uttam Kumaran: Calculated, calculated neglect.

225 00:26:13.590 ⇒ 00:26:27.769 Katherine Bayless: Yeah, exactly, yeah, yeah, right? But, and honestly, it is part of, I think, what has really been kind of cool to watch with Kyle. Like, I used the one foot in each world as a bit of an excuse to a certain extent, but, like, he’s fucking flying, right?

226 00:26:27.770 ⇒ 00:26:28.470 Uttam Kumaran: Yeah, yeah, yeah.

227 00:26:28.470 ⇒ 00:26:30.990 Katherine Bayless: I feel like he has to, like, sit down next to me and look at a.

228 00:26:30.990 ⇒ 00:26:31.540 Uttam Kumaran: Yes.

229 00:26:31.540 ⇒ 00:26:34.240 Katherine Bayless: morning, you know? And so, yeah, I think…

230 00:26:34.560 ⇒ 00:26:38.059 Katherine Bayless: I… yes. Yes to everything you said. Yes. Okay. Yes.

231 00:26:38.060 ⇒ 00:26:47.230 Uttam Kumaran: So that’s what I’d like to help at least do that, and… and we can establish a cadence by which we’re meeting as a team, and I don’t think… I think we’re still gonna…

232 00:26:47.740 ⇒ 00:26:52.809 Uttam Kumaran: like, this is where I think I’m still gonna get, like, Snowflake and something set up.

233 00:26:52.880 ⇒ 00:26:57.399 Katherine Bayless: And… but I want him to really, as we start to land data and kind of do…

234 00:26:57.440 ⇒ 00:27:13.819 Uttam Kumaran: go source by source to understand it, like, if he can join and be right next to us, that would be great. And then I want to get him and anybody else to the point where if they like to join those meetings, they can join, or if it’s, like, Kyle and us are supporting one of these people because he has the…

235 00:27:13.830 ⇒ 00:27:18.579 Uttam Kumaran: Background from the market research team, but, like, we’re… then our team’s sort of more of, like.

236 00:27:18.600 ⇒ 00:27:33.290 Uttam Kumaran: does Kyle have enough data, and is he trained to run stuff in dbt and in SQL? And then is he poison enough to sort of go present to the org, or tag along with you to meetings and present, and, like, take that off of your plate? Like, that would be great.

237 00:27:34.030 ⇒ 00:27:48.299 Katherine Bayless: Yeah, yeah. And actually, you know, interestingly, like, a funny specific side note about Kyle, so the market research team does a lot of, like, presentations at CES, like, that clients sort of, like, essentially… Great. I guess? He hates it.

238 00:27:48.380 ⇒ 00:27:55.750 Katherine Bayless: hates it. He’s like, I… it’s, like, my least favorite part of my job, I cannot stand it. I think he might be decent at it, I really don’t know.

239 00:27:55.750 ⇒ 00:27:56.250 Uttam Kumaran: Yeah, yeah, yeah.

240 00:27:56.250 ⇒ 00:28:08.600 Katherine Bayless: But I think it’s because certain folks in leadership have, on occasion, popped up to correct him while talking. And I’m like, oh my god, talk about instant confidence drain. So yeah, I would love.

241 00:28:08.600 ⇒ 00:28:08.960 Uttam Kumaran: Yeah.

242 00:28:08.960 ⇒ 00:28:13.979 Katherine Bayless: build his confidence as a presenter, but there’s some trauma there at the moment. Perfect.

243 00:28:13.980 ⇒ 00:28:17.859 Uttam Kumaran: So, yeah, I would like… I mean, this is where it’s, like, you, you,

244 00:28:18.100 ⇒ 00:28:22.280 Uttam Kumaran: You bring people to situations where it’s like you safely can mess up.

245 00:28:22.700 ⇒ 00:28:37.669 Uttam Kumaran: Right? And we’ll start by just, like, working with us, we’ll just kind of show how we would present as an insights team, and then start to have him sort of take ownership of that. So that’s great. I’m glad that we at least we have a Kyle, you know, that we’re thinking about here.

246 00:28:37.870 ⇒ 00:28:48.360 Katherine Bayless: I really, yeah, I’m very lucky to have him, for sure. Yeah. The other thing I’ll say is, the organization, generally speaking, because it is so, like.

247 00:28:48.700 ⇒ 00:29:04.760 Katherine Bayless: I mean, you know, it’s like this, like, human blockchain of information, right? Like, working out loud actually seems to go over pretty well here, and so, like, I don’t know if it makes sense, but, like, a… like, a Slack channel that’s, like, open, not necessarily putting everybody in it.

248 00:29:04.760 ⇒ 00:29:16.830 Katherine Bayless: by default, but, like, you know, putting some of these folks that I think will want to be in it, and then saying, like, yeah, anybody’s welcome to drop by, right? You know, like, I think that working out loud kind of thing would actually land really well here.

249 00:29:16.830 ⇒ 00:29:19.919 Uttam Kumaran: Okay, great. Yeah, there’s some… there’s some…

250 00:29:20.130 ⇒ 00:29:31.030 Uttam Kumaran: That’s just sort of, again, like, for me to learn a little bit of the culture, but if people are genuinely, like… and this is… we have this in our company, and a lot of the companies we join, we try to do this, which is just, like.

251 00:29:31.030 ⇒ 00:29:42.010 Uttam Kumaran: engineers are always gonna be like, oh, I wanna… it’s not perfect, and I’m big on, like, just ship it in any state and get feedback from anybody you can get that is going to give you feedback.

252 00:29:42.060 ⇒ 00:29:48.219 Uttam Kumaran: Because you’re just gonna toil, and you’re gonna go down something where you should’ve got feedback 5 steps in.

253 00:29:48.240 ⇒ 00:29:49.360 Katherine Bayless: You know?

254 00:29:49.360 ⇒ 00:29:57.410 Uttam Kumaran: So if we can do some type of open data channel, and yeah, I mean, maybe it is something where, like… and this is also where I’m sort of interested in how you think about

255 00:29:57.830 ⇒ 00:30:03.719 Uttam Kumaran: like, getting more buy-in from leadership. Like, one thing that we’ve done often is we’ll be like, hey, okay.

256 00:30:03.990 ⇒ 00:30:20.909 Uttam Kumaran: we want to make sure Catherine has a deck of, like, all of our accomplishments for this month, so that when you go present, you’re like, here are some of the wins from the DE team, but we can even, like… if there isn’t an established, like, cadence of, like, a monthly business review, and that’s something that you want to start to do.

257 00:30:20.910 ⇒ 00:30:24.459 Uttam Kumaran: I don’t know if we’ll be able to necessarily do that.

258 00:30:24.750 ⇒ 00:30:36.299 Uttam Kumaran: maybe towards the end of next month or early January, but if that’s something that you’re like, okay, that… that keeps… that starts to build, like, this proactive cadence in a meeting that you own.

259 00:30:36.530 ⇒ 00:30:45.869 Uttam Kumaran: Right? And it’s less of, like, just call Captain when there’s a fire, but you set the tone, you come to that really fair, and it’s just purely about data, it’s not about, like, other activities.

260 00:30:45.990 ⇒ 00:30:51.480 Uttam Kumaran: That could be also good, and then we would just basically support Wherever needed for that.

261 00:30:52.280 ⇒ 00:30:58.409 Katherine Bayless: Yeah, so, yes, yes, let’s see, so many, so thoughts,

262 00:30:58.530 ⇒ 00:31:05.850 Katherine Bayless: I think that… well, okay, one tiny thing. So, culturally right now, right, like, everybody is freaked out, and…

263 00:31:05.850 ⇒ 00:31:08.130 Uttam Kumaran: Oh yeah, for January, yeah, yeah, yeah.

264 00:31:08.130 ⇒ 00:31:26.720 Katherine Bayless: Right. So, unlikely to get a whole lot of attention, which is good. Sure. But I was actually wanting to kind of suggest this as a, like, you know, sort of a thing that we would be, like, launching as part of, like, okay, well, this is the end of the year, and in January, we’re gonna, you know, start kind of socializing this more.

265 00:31:27.230 ⇒ 00:31:32.439 Katherine Bayless: I don’t think I can get away with going as hard as, like, OKRs out of the gate, but.

266 00:31:32.440 ⇒ 00:31:33.150 Uttam Kumaran: Yeah, yeah, yeah.

267 00:31:33.150 ⇒ 00:31:37.290 Katherine Bayless: I want to be a team that has visible public metrics, like.

268 00:31:37.290 ⇒ 00:31:37.730 Uttam Kumaran: Yes.

269 00:31:37.730 ⇒ 00:31:45.510 Katherine Bayless: LAs, and I want to report against their delivery against them. I want to… I have a number somewhere, I’ve parked it, of the, like.

270 00:31:45.660 ⇒ 00:32:10.610 Katherine Bayless: 300 and some manual processes the marketing data team was running before I started. Like, I want to actually show the amount of just bullshit we are stopping, right? Yeah. I… and I want… I want to have it be some sort of, you know, KPI, OKR type framework, because I really… eventually, I would like to move the needle on the way this organization does its annual goal setting, because right now.

271 00:32:10.610 ⇒ 00:32:13.300 Katherine Bayless: That’s just a to-do list. It’s just a to-do list.

272 00:32:13.300 ⇒ 00:32:16.919 Katherine Bayless: And I’m like, I want to start focusing them on outcomes.

273 00:32:16.920 ⇒ 00:32:18.529 Uttam Kumaran: Not. Yeah.

274 00:32:19.450 ⇒ 00:32:28.659 Uttam Kumaran: Okay, perfect. Yeah, and… and this is, like, a perfect way to leverage us as, like, the backbone, because you’re… what we… what we’re not going to be able to do is…

275 00:32:28.780 ⇒ 00:32:34.389 Uttam Kumaran: Build the relationships and build the buy-in, but we’ll totally make sure you’re armed, you know, with…

276 00:32:34.390 ⇒ 00:32:34.760 Katherine Bayless: to me.

277 00:32:34.760 ⇒ 00:32:50.089 Uttam Kumaran: With those wins, and of course, like, help accomplish those, and then set you up to really, like, nail those meetings, and get whatever you need, budget, resources, more time with executives, you know, more decision-making power.

278 00:32:50.300 ⇒ 00:32:53.360 Uttam Kumaran: You know, so, okay, great.

279 00:32:54.690 ⇒ 00:32:55.870 Uttam Kumaran: Perfect, this is great.

280 00:32:55.870 ⇒ 00:32:58.599 Katherine Bayless: To see, like… Yeah, no, 100%.

281 00:32:58.600 ⇒ 00:33:14.660 Uttam Kumaran: Right. Yeah, and that’s the only way, also, you know, it’s like, the only way you’re gonna get buy-in on going after that tech debt that, like, nobody even gets, like, what you’re talking about, or why it’s important, is if you’re like, in order to do this, I need to clear this out.

282 00:33:14.660 ⇒ 00:33:17.389 Katherine Bayless: Then it’s like, okay, yeah, clear that out, whatever.

283 00:33:17.420 ⇒ 00:33:26.939 Uttam Kumaran: But if you’re sort of, like… the problem with engineering all the time is they all… they can never articulate how the tech debt is, like, is slowing down an outcome. Instead, they’re…

284 00:33:27.070 ⇒ 00:33:37.589 Uttam Kumaran: they sort of just talk about it like an annoyance, and then they’re like, oh, it’s just annoying, so don’t deal with that right now. You need to almost… for a lot of our clients, we structure it as, like.

285 00:33:37.910 ⇒ 00:33:44.089 Uttam Kumaran: we’ve gone this far, but the reason we couldn’t go further is because of this, right? So…

286 00:33:44.090 ⇒ 00:33:44.920 Katherine Bayless: Yeah. And…

287 00:33:44.940 ⇒ 00:33:47.660 Uttam Kumaran: Structuring those stories, especially as, like.

288 00:33:47.940 ⇒ 00:33:51.960 Uttam Kumaran: As you mentioned, like, if we start to get hit with, like, rules around

289 00:33:52.130 ⇒ 00:34:00.200 Uttam Kumaran: Access, or procurement, or security, like, that’s where you’ll be able to drive, because you’ll be the one with the wins, you know?

290 00:34:00.400 ⇒ 00:34:03.549 Katherine Bayless: exactly, exactly.

291 00:34:03.550 ⇒ 00:34:04.240 Uttam Kumaran: Right.

292 00:34:04.240 ⇒ 00:34:04.810 Katherine Bayless: Bye.

293 00:34:08.020 ⇒ 00:34:19.309 Uttam Kumaran: Okay, cool. I think maybe we can… So a couple of things on, like, sort of this, like, define and map. I mean, one thing that we’re gonna start to work on is a bit of, like, a metrics glossary.

294 00:34:19.610 ⇒ 00:34:26.260 Uttam Kumaran: at this… I think this will be something that, like, we sort of… this will be sort of starting and going on

295 00:34:26.520 ⇒ 00:34:30.820 Uttam Kumaran: Until we reach that, like, okay, at least the… highest level.

296 00:34:30.980 ⇒ 00:34:36.640 Uttam Kumaran: KRs, like, you know what it’s defined as, like, and there’s…

297 00:34:37.010 ⇒ 00:34:44.039 Uttam Kumaran: Ideally, some type of cross-functional sign-off on, like, this is what a member is, and here’s how it’s defined in the logic.

298 00:34:44.060 ⇒ 00:34:58.339 Uttam Kumaran: And, like, we’re all happy with that, right? And that may take some powwows and stuff, but, like, that’s what I want to sort of start on. So we’re gonna… we’ll sort of kick that off and start to save, and as we work with Kyle and meet everybody, start to keep track of, like.

299 00:34:58.660 ⇒ 00:35:11.159 Uttam Kumaran: when folks mean revenue, like, what are they talking about? When folks mean members, what are they talking about attendees? I also know that there’s more than just the CES business, so we’ll start to learn a little bit about what the KPIs that people care about.

300 00:35:11.340 ⇒ 00:35:28.379 Uttam Kumaran: And then, ideally, as we go, once people define a KPI, we’re like, tell me the source of truth for measuring that today? Are you pulling a report? Are you looking in a UI? Something that someone calculates, or is there just, like, no, we don’t even know… we don’t reliably can’t get that, so…

301 00:35:28.860 ⇒ 00:35:31.880 Uttam Kumaran: That’s something we’ll… we’ll do. I think the rest…

302 00:35:31.880 ⇒ 00:35:45.069 Katherine Bayless: Yeah. Just on that, like, so, a couple things that might be helpful. So, one is a human, so the person that’s going to be the business intelligence analyst, her name is Kai, like the Greek letter, C-H-I.

303 00:35:45.070 ⇒ 00:36:05.430 Katherine Bayless: She starts Monday, and she comes from, like, a very buttoned-up data governance, I just put all the VPs in a room, and we define member, and everybody’s fine. I’m like, oh, welcome to the fire, you know? Yeah. But so she’s got a lot of experience in that world, so she could definitely help with, kind of, brokering. It’ll help her learn, too, the organization.

304 00:36:05.540 ⇒ 00:36:09.229 Katherine Bayless: I also think there is a lot of, like.

305 00:36:09.830 ⇒ 00:36:33.090 Katherine Bayless: latent, definition, if you will, running around out there. So, like, for example, the person who runs the CES, like, registration site with the… in conjunction with the vendor, I mean, she has this, like, 32-tab Excel spreadsheet with every definition and path, and, like, you know, if this, then can’t that, but could this, right? Like, so there is a lot of stuff that exists that I think we could kind of bring together.

306 00:36:33.090 ⇒ 00:36:43.800 Katherine Bayless: as a jumping-off point. There’s still going to be a need to get people in a room and agree on some of the pieces, but I do think we’re not starting as much from scratch as

307 00:36:43.910 ⇒ 00:36:46.960 Katherine Bayless: It might look like the rest of the chaos, if that makes sense.

308 00:36:46.960 ⇒ 00:37:02.829 Uttam Kumaran: No, totally, and every… every… every team will have something defined and something measured, but it’s like, okay, do we see that there are 5 different definitions here? Like, what are the… what’s the dimensionality we need to offer for stakeholders? What are their wants? Yeah.

309 00:37:02.830 ⇒ 00:37:12.699 Katherine Bayless: Yeah, and so, like, in particular, like, one of the things that they talked to me about, like, in my interviews, and I’m like, we will solve that, but they’re tech debt in the way, right?

310 00:37:12.900 ⇒ 00:37:28.809 Katherine Bayless: we don’t charge the correct price for a booth at CES, usually, because we don’t have any integration of that membership data into the sales system, right? So, like, if somebody calls and is like, I think we’re a member, we’re like, I don’t know, maybe you are.

311 00:37:28.810 ⇒ 00:37:42.409 Katherine Bayless: We had the same problem with our awards applications. We didn’t have exhibitor and membership status in a way that was connected. Some of that problem is the entity resolution work, that, like, these are two different systems, company names are different, they don’t match.

312 00:37:42.410 ⇒ 00:37:43.830 Katherine Bayless: Yeah. Etc.

313 00:37:43.850 ⇒ 00:37:56.220 Katherine Bayless: But I’m also suspicious, curious, I should say, because suspicious sounds mean, but, like, I’m curious if part of the reason that there’s not more trust in that membership data is just because, like.

314 00:37:56.220 ⇒ 00:38:11.449 Katherine Bayless: there’s things like grace periods, and like, you know, well, they said they’re gonna send it, you know, and like, membership transactions do tend to be more flexible than, we want 10,000 square feet of exhibit space at CES, in which case, yeah, we’re not gonna call that done until that checks in the bank, right?

315 00:38:11.450 ⇒ 00:38:11.880 Uttam Kumaran: Yeah.

316 00:38:11.880 ⇒ 00:38:17.529 Katherine Bayless: So, I think some of that, like, I don’t even know what you call it, business friction?

317 00:38:17.530 ⇒ 00:38:18.120 Uttam Kumaran: Yeah, yeah, yeah.

318 00:38:18.120 ⇒ 00:38:24.209 Katherine Bayless: Like, you know, our definition of done and your definition of done are different for important reasons.

319 00:38:24.450 ⇒ 00:38:28.719 Katherine Bayless: We need to understand that. We don’t necessarily need to change that, if that makes sense.

320 00:38:28.720 ⇒ 00:38:29.280 Uttam Kumaran: Yeah.

321 00:38:29.690 ⇒ 00:38:38.720 Uttam Kumaran: Yeah, so you just… we just need a clear underst… oh, I mean, one, we need a report to show, like, okay, like, how many people are outstanding, like, we need to somehow reconcile

322 00:38:38.910 ⇒ 00:38:52.140 Uttam Kumaran: payments collected versus that, and then also, you’ll immediately just be able to see, like, who are we undercharging, overcharging? We just did this for another SaaS client of ours, where they’re more… they’re close, they’re… they’re like a fast-powing startup, but

323 00:38:52.420 ⇒ 00:38:58.980 Uttam Kumaran: They just started, like, invoicing willy-nilly, and we basically were like, you guys are sitting on, like, almost, like, half a million dollars in uncollected.

324 00:38:59.100 ⇒ 00:39:00.550 Katherine Bayless: Poss, because…

325 00:39:00.660 ⇒ 00:39:04.179 Uttam Kumaran: You have no mechanism to, like, reconcile your seats.

326 00:39:04.380 ⇒ 00:39:15.000 Uttam Kumaran: with, like, what you’re charging people, and so you just charge them once, and then they add, like, 30 users, and you’ve never… but, like, they didn’t know how bad it was, they’re like, we’ll get to it. I’m like, no, that’s, like.

327 00:39:15.190 ⇒ 00:39:20.730 Uttam Kumaran: that’s, like, more than half a million in, like, ARR that you could just… turn on today.

328 00:39:21.130 ⇒ 00:39:23.859 Katherine Bayless: Yay! And you might do that.

329 00:39:24.180 ⇒ 00:39:35.649 Katherine Bayless: Yeah, so, like, the guy in finance who manages this, sort of, the piece of the process that’s, like, for booth space and payments, he’s got that, like, locked down, right? But he knows that it is

330 00:39:35.650 ⇒ 00:39:47.209 Katherine Bayless: like, done right, and why it’s done that way. But, like, I think he sees the potential for that to start eroding, because a salesperson is like, I just wanna… I just wanna add this to my, you know, my numbers for the week, and he’s like.

331 00:39:47.210 ⇒ 00:39:47.820 Uttam Kumaran: Yeah.

332 00:39:47.820 ⇒ 00:39:58.830 Katherine Bayless: Right? Like, that’s not how this is going to work, and so helping him defend that line might actually be good, because we don’t want to have a half million dollars in ARR that just needs to hit the go button, right?

333 00:39:58.830 ⇒ 00:39:59.530 Uttam Kumaran: Yeah.

334 00:39:59.530 ⇒ 00:40:01.090 Katherine Bayless: Yeah, yeah.

335 00:40:01.280 ⇒ 00:40:02.070 Katherine Bayless: Yeah.

336 00:40:02.680 ⇒ 00:40:05.260 Uttam Kumaran: So there’s something around, like, AR, yeah.

337 00:40:06.310 ⇒ 00:40:11.590 Katherine Bayless: Yeah, it’s like the ship is tight, but I don’t think people understand how much that matters.

338 00:40:11.590 ⇒ 00:40:12.420 Uttam Kumaran: Right. Yeah.

339 00:40:12.450 ⇒ 00:40:14.250 Katherine Bayless: Yeah, yeah.

340 00:40:15.890 ⇒ 00:40:16.570 Uttam Kumaran: Okay.

341 00:40:16.860 ⇒ 00:40:18.270 Uttam Kumaran: It’s interesting. Susan, let’s…

342 00:40:19.050 ⇒ 00:40:30.359 Uttam Kumaran: So I guess my other question is for Kai. So she’s… so our… so, if I can get this right, so Kai and Kyle are sort of rolling up to you, or what’s, like, the… okay, great.

343 00:40:30.790 ⇒ 00:40:42.290 Katherine Bayless: Yeah, so eventually, I hope it would be, like, Kyle would manage Kai and the data engineer, but because everything is so new, I’m just kind of keeping it flat that way. It’s not like, you know…

344 00:40:42.430 ⇒ 00:40:45.259 Katherine Bayless: I think sometimes it’s like, you know, too many layers, then you realize.

345 00:40:45.260 ⇒ 00:40:45.970 Uttam Kumaran: Yeah, yeah, yeah.

346 00:40:45.970 ⇒ 00:40:48.780 Katherine Bayless: kids, kind of thing, so… Yeah.

347 00:40:49.200 ⇒ 00:40:56.689 Katherine Bayless: And then the guy, like, I was just interviewing, the data engineer, like, that would also report up to me, but eventually Kyle.

348 00:40:58.250 ⇒ 00:41:02.119 Uttam Kumaran: Okay, great. So then, yeah, I mean, I think that, like, as soon as we…

349 00:41:02.410 ⇒ 00:41:14.339 Uttam Kumaran: feel good about, like, as soon as we get a couple things set up, I mean, it’s just… it’s just Kyle, so we can start talking to him. And then, yeah, I mean, if we can get… I mean, the one thing that you know is, like, you hire…

350 00:41:14.610 ⇒ 00:41:21.519 Uttam Kumaran: these three… you have these three people, and now you’re like, I need one-on-ones with everybody, I need stand-ups that you’re just never gonna get anything else done.

351 00:41:21.520 ⇒ 00:41:23.760 Katherine Bayless: So, if we can take some of that.

352 00:41:24.210 ⇒ 00:41:25.070 Uttam Kumaran: like…

353 00:41:25.500 ⇒ 00:41:39.269 Uttam Kumaran: if we could take some of that on and still guide the folks, or, like, at least be, like, our roadmap. We have a roadmap, and so that means that’s also your roadmap, so out of these pieces, like, what can you take on while we’re set up all the infra?

354 00:41:39.460 ⇒ 00:41:39.940 Katherine Bayless: Nice.

355 00:41:39.940 ⇒ 00:41:47.100 Uttam Kumaran: And they… and we help them basically chase things, but they also come with their fresh eyes, like, I think that could be really helpful.

356 00:41:47.580 ⇒ 00:42:03.120 Katherine Bayless: Yeah, yeah, exactly. Like, I think there’s a lot of potential for, like, sunshine and rainbows and unicorns to come out of that collaboration, right? Like, lots of potential. Especially if we do get a few, sort of, like, visiting guests from some of these other.

357 00:42:03.120 ⇒ 00:42:03.990 Uttam Kumaran: Yes.

358 00:42:03.990 ⇒ 00:42:04.690 Katherine Bayless: Yeah.

359 00:42:04.920 ⇒ 00:42:05.850 Katherine Bayless: Yeah. Yeah.

360 00:42:06.690 ⇒ 00:42:11.379 Uttam Kumaran: And, like, I mean, these folks are the ones that are gonna, like, because they’re new, they have a good opportunity to just, like.

361 00:42:11.680 ⇒ 00:42:15.700 Uttam Kumaran: not worry about what it was. They’re, like, sort of naive, so…

362 00:42:15.810 ⇒ 00:42:26.090 Uttam Kumaran: They just start… if they… if we start when they join about, like, we have a culture of, like, posting questions about data openly, there’s a channel with everybody, you can app people, and there’s, like.

363 00:42:26.420 ⇒ 00:42:34.160 Uttam Kumaran: They don’t have any fear because they don’t know anything, they don’t know any better, that would be great. And then, yeah, and that’s…

364 00:42:34.580 ⇒ 00:42:39.059 Uttam Kumaran: And now you have three, sort of, You have… Those three…

365 00:42:39.420 ⇒ 00:42:57.699 Uttam Kumaran: and us asking, like, all these questions, it’s gonna be, like… it’s not like one person’s nosy, it’s like we’re all, like, hey, we’re completely blocked by, like, this person or this process not moving, so that’s… it’s very… it’s, like, become very obvious, like, what’s, what’s not happening, so… But as you mentioned, like, there’s…

366 00:42:57.890 ⇒ 00:43:11.879 Uttam Kumaran: I think even when we hop on the call, like, people are gonna be fans of this work, so I think for as many people that may block us, you also have a lot of people that I want to publicly get, like, their testimonials and get them to share that it’s been effective, and like, you know, so…

367 00:43:13.100 ⇒ 00:43:26.050 Katherine Bayless: Yeah, yeah, and it’s funny, like, a couple times since I’ve been here, people have said things to me along the lines of, like, you know, like, oh, well, people… people don’t like me because I am opinionated or cranky, or whatever, and I’m like, I love people that are opinionated and cranky.

368 00:43:26.050 ⇒ 00:43:27.009 Uttam Kumaran: No, I don’t know what else.

369 00:43:28.830 ⇒ 00:43:33.139 Uttam Kumaran: It’s nice with people. Yeah. Life is… that’s what life is about.

370 00:43:33.140 ⇒ 00:43:37.099 Katherine Bayless: Right? Yeah, exactly. I’m like, the crankiness is important data. It’s telemetry.

371 00:43:37.100 ⇒ 00:43:37.700 Uttam Kumaran: Yeah.

372 00:43:37.700 ⇒ 00:43:38.980 Katherine Bayless: This is going.

373 00:43:38.980 ⇒ 00:43:40.060 Uttam Kumaran: Yeah, yeah.

374 00:43:40.360 ⇒ 00:43:41.170 Katherine Bayless: Yeah.

375 00:43:41.840 ⇒ 00:43:45.460 Katherine Bayless: If I’m blocking something, say it. I might not know.

376 00:43:46.890 ⇒ 00:43:53.689 Uttam Kumaran: Okay, so maybe let’s go through, like, maybe, like, the P0 data sources. I mean, I also have open…

377 00:43:54.060 ⇒ 00:43:57.280 Uttam Kumaran: the Excel that you sent me over.

378 00:43:59.530 ⇒ 00:44:11.819 Uttam Kumaran: So, we can use this. I guess, like, you tell me what’s best. I mean, I still think, like, our North Star that we signed up for is for, like, identity stitching, but…

379 00:44:12.230 ⇒ 00:44:13.899 Uttam Kumaran: I sort of don’t want…

380 00:44:14.060 ⇒ 00:44:19.459 Uttam Kumaran: To use, like, that to just, like, be like, okay, we’re only gonna bring in a couple things, so…

381 00:44:19.860 ⇒ 00:44:23.990 Uttam Kumaran: we can think about P0 as, like, Well, it’s like…

382 00:44:24.430 ⇒ 00:44:39.110 Uttam Kumaran: It’s… it’s the most open, or you can think about it like, well, it’s already, like, we have keys, or you can think about it like it has the most data, or you can think about it like, we need to shut down that service, so why don’t we get the data, we can kill it.

383 00:44:39.260 ⇒ 00:44:45.700 Uttam Kumaran: But, like, those are kind of a couple of the ways I would think about, sort of, like, what is P0? So if anything…

384 00:44:46.500 ⇒ 00:44:52.400 Uttam Kumaran: If anything, sort of, shouts out like that, I would like to arrive on, like.

385 00:44:52.630 ⇒ 00:44:56.369 Uttam Kumaran: At least a couple that are like that, that we can start to go after.

386 00:44:57.270 ⇒ 00:45:00.119 Katherine Bayless: Yeah, so I, so for…

387 00:45:00.570 ⇒ 00:45:09.619 Katherine Bayless: for P0, the impexium slash remembers, like, that data share, that’s kind of, like, negative one, right? Like, the.

388 00:45:09.620 ⇒ 00:45:10.769 Uttam Kumaran: Yeah, yeah, yeah.

389 00:45:10.770 ⇒ 00:45:23.220 Katherine Bayless: And for a couple of reasons, partly because it is how I managed to sneak Snowflake in. Also, because the team that has not had, like, any real, like.

390 00:45:24.510 ⇒ 00:45:35.750 Katherine Bayless: they have this incredible, like, distribution of efforts on the team in terms of, like, spreadsheets, and pulling things together and running their reports, and they, like, they’ve got a well-oiled machine going, and… Right.

391 00:45:35.750 ⇒ 00:45:36.100 Uttam Kumaran: Okay.

392 00:45:36.100 ⇒ 00:45:42.860 Katherine Bayless: I just love to be like, okay, great, send me all of those spreadsheets, they’re now snowflake dashboards, what would you like to do with your time?

393 00:45:43.280 ⇒ 00:45:44.800 Katherine Bayless: Right? Like, they’re…

394 00:45:44.800 ⇒ 00:45:45.120 Uttam Kumaran: Yeah, perfect.

395 00:45:45.120 ⇒ 00:45:59.809 Katherine Bayless: incredibly industrious team, and they are spending so much time and effort in Excel. I was talking to this woman last week who manages our digital health and our smart cities, like, councils, essentially, or boards, we call them.

396 00:45:59.940 ⇒ 00:46:06.759 Katherine Bayless: And like, these are decision-making bodies for the organization that are volunteer-driven, right? So they’re important, but she’s like.

397 00:46:07.000 ⇒ 00:46:26.889 Katherine Bayless: they’re asking me why they’re on these things, and I can’t tell them what the return on their volunteer commitment is, and I’d like to be able to do that, because I also think that one of them, it doesn’t really have much of a return on commitment, and I think I should convince CTA to maybe, you know, disband the group, and we’ll start another one if we need it again, kind of thing.

398 00:46:26.890 ⇒ 00:46:27.270 Katherine Bayless: That’s fair.

399 00:46:27.270 ⇒ 00:46:29.360 Katherine Bayless: He was like, I want to kiss you right now, right?

400 00:46:29.360 ⇒ 00:46:30.600 Uttam Kumaran: Yeah, yeah, yeah.

401 00:46:30.600 ⇒ 00:46:38.060 Katherine Bayless: There’s a lot of brainpower that is just blocked by reporting that takes too long. So yeah, Impexio.

402 00:46:38.060 ⇒ 00:46:38.620 Uttam Kumaran: Nice.

403 00:46:38.740 ⇒ 00:46:39.510 Katherine Bayless: first.

404 00:46:40.330 ⇒ 00:46:45.390 Katherine Bayless: So, then the tricky part is the Salesforce CRM.

405 00:46:45.390 ⇒ 00:46:45.810 Uttam Kumaran: Yeah.

406 00:46:45.810 ⇒ 00:46:51.339 Katherine Bayless: They would go hand-in-hand with that. The reality is, we are going to onboard

407 00:46:51.800 ⇒ 00:47:05.350 Katherine Bayless: 2,000 exhibitors between now and January, and so, like, there’s just… there’s just no time to really get in their way, and so, like, anything we can do that’s kind of gentle and invisible, great.

408 00:47:05.740 ⇒ 00:47:21.030 Katherine Bayless: But yeah, I hesitate to really kind of swat at the beehive when they’re so overwhelmed. But Marketing Cloud, however, we can totally incorporate into P0. We’ve… I’ve been working with that team most closely since I’ve started anyway.

409 00:47:21.030 ⇒ 00:47:26.429 Katherine Bayless: And there is a specific current interest in, sort of, this swirl between

410 00:47:27.390 ⇒ 00:47:34.369 Katherine Bayless: the people we’re inviting to CES, whether or not they are even receiving that invite, how they are actioning it, right? Like, that kind of thing.

411 00:47:34.370 ⇒ 00:47:34.950 Uttam Kumaran: Yeah.

412 00:47:34.950 ⇒ 00:47:52.699 Katherine Bayless: like, they want to see that pipeline. We have some pretty decent Google Analytics, like, once they’re on the website, but getting that, like, acquisition end of the puzzle together, I think, would be, good. So if we did, like, Impexium Marketing Cloud, and then this one, Merits, line 36,

413 00:47:54.800 ⇒ 00:48:09.660 Katherine Bayless: Those would be 3 really high-impact ones for, like, useful right now kind of thing. The one trick with merits is that they don’t have a single set of REST APIs. They have, like, a little bit of this and a little bit of that.

414 00:48:09.660 ⇒ 00:48:10.110 Uttam Kumaran: Okay.

415 00:48:10.110 ⇒ 00:48:19.880 Katherine Bayless: I don’t know if we’ll get too terribly far, with, like, a true integration, but we have flat files all day, so at least we could work with.

416 00:48:19.880 ⇒ 00:48:20.380 Uttam Kumaran: Okay, okay.

417 00:48:20.380 ⇒ 00:48:28.549 Katherine Bayless: and model it, it just might be kind of janky, but it’s only four more weeks, right? Like, I can move my files around in exchange for insights for four weeks, right?

418 00:48:28.690 ⇒ 00:48:30.099 Katherine Bayless: And then…

419 00:48:30.100 ⇒ 00:48:33.059 Uttam Kumaran: And so G… so GA is important as well.

420 00:48:34.000 ⇒ 00:48:36.429 Uttam Kumaran: And then, how about, like, the…

421 00:48:36.860 ⇒ 00:48:40.249 Uttam Kumaran: Formstack. Are we using that yet, or no?

422 00:48:40.500 ⇒ 00:48:43.589 Katherine Bayless: We actually, yeah, we use a lot of Formstack. Okay, great.

423 00:48:43.840 ⇒ 00:48:47.670 Katherine Bayless: And it’s pretty easy to deal with, like, I built a little webhook, for the…

424 00:48:47.670 ⇒ 00:48:48.110 Uttam Kumaran: Great, okay.

425 00:48:48.110 ⇒ 00:48:57.860 Katherine Bayless: So yeah, I would say Formstack’s a good low-hanging fruit. And then, EventPoint has really nice APIs, and they’re…

426 00:48:58.260 ⇒ 00:49:01.419 Katherine Bayless: Well, actually, okay, yeah. Yes. They have really nice APIs.

427 00:49:01.850 ⇒ 00:49:10.180 Katherine Bayless: the only data that’s in there right now is, like, our speaker data, so it’s not necessarily the most high value. However, as you’ll see above, with 3C vents listed.

428 00:49:10.910 ⇒ 00:49:22.470 Katherine Bayless: My curiosity is, like, could we do all of that Cvent stuff in EventPoint? This is a question I don’t know if anybody even wants me to ask, but it is a question I’m asking. So, like, I’m kind of curious…

429 00:49:22.630 ⇒ 00:49:38.790 Katherine Bayless: if we can get the C event sites integrated, frankly, it would make my life a little bit easier, which is selfish, I know, but… because there’s a lot of, like, ancillary CES event registrations happening over there, and so when people are like, hey, has anybody signed up for that mobility breakfast? I have to remember which C event site that is.

430 00:49:38.790 ⇒ 00:49:40.639 Uttam Kumaran: Which, yeah, yeah, yeah, okay.

431 00:49:40.820 ⇒ 00:49:42.069 Katherine Bayless: Right? And…

432 00:49:42.070 ⇒ 00:49:42.770 Uttam Kumaran: Yes, yes.

433 00:49:43.060 ⇒ 00:49:45.950 Katherine Bayless: that EventPoint is a nicer platform, and…

434 00:49:45.950 ⇒ 00:49:46.310 Uttam Kumaran: Okay.

435 00:49:46.310 ⇒ 00:49:51.600 Katherine Bayless: If we can get the data out of Cvent enough to be able to figure out, like, yeah, this is totally event pointable.

436 00:49:52.910 ⇒ 00:49:58.860 Uttam Kumaran: So, are there any other, like, consolidation opportunities, or are some of these, like.

437 00:49:59.790 ⇒ 00:50:05.310 Uttam Kumaran: I guess the… anything that’s active use… maybe I’ll start with another question. For anything that’s active use, false.

438 00:50:06.080 ⇒ 00:50:11.240 Uttam Kumaran: are those, like, we just need to get the data in and shut it off? Is it, like…

439 00:50:11.430 ⇒ 00:50:13.780 Uttam Kumaran: We’re still paying for it, and like…

440 00:50:14.020 ⇒ 00:50:16.319 Uttam Kumaran: Yeah, I guess, give me a sense of that.

441 00:50:16.920 ⇒ 00:50:29.270 Katherine Bayless: Yeah, truthfully, the meaning behind that data point is Catherine wanted to put it on the list, but not make anybody think it’s active. But the reason I put it on the list is because I don’t know the answer to your questions.

442 00:50:29.270 ⇒ 00:50:30.080 Uttam Kumaran: Alright, alright, great.

443 00:50:30.570 ⇒ 00:50:34.009 Katherine Bayless: I know we don’t have any more. I’m…

444 00:50:34.530 ⇒ 00:50:47.319 Katherine Bayless: unsure if we got data out of it before it got shut down. I think even if we didn’t extract the raw data from the platform, we do have the historical awards data in the old data warehouse.

445 00:50:47.480 ⇒ 00:51:03.590 Katherine Bayless: But, yeah, it’s a question mark. And then the other ones are the surveying platforms, and I just kind of wanted to flag it, because I’m not sure that that team would have thought about it, right? Like, oh, let’s make sure we get all the data out before we close this account. So it’s kind of like a…

446 00:51:03.610 ⇒ 00:51:08.650 Katherine Bayless: Catherine, follow up on sort of question. Okay. Just those three that I had flagged, though.

447 00:51:08.650 ⇒ 00:51:10.860 Uttam Kumaran: So for Decipher, okay, yeah.

448 00:51:10.860 ⇒ 00:51:17.290 Katherine Bayless: Yeah. Alright, yeah, I see that, okay. Okay, cool. These are great because they don’t hurt anybody, so I can ask a bunch of questions, probably.

449 00:51:19.160 ⇒ 00:51:21.859 Uttam Kumaran: like, I’ll ask Kyle, and then we’ll just be like.

450 00:51:21.860 ⇒ 00:51:22.390 Katherine Bayless: That’s cool.

451 00:51:22.390 ⇒ 00:51:24.590 Uttam Kumaran: I’ll just go down the telephone to find out.

452 00:51:25.090 ⇒ 00:51:26.880 Katherine Bayless: Yeah, yeah, start with Kyle.

453 00:51:27.910 ⇒ 00:51:34.940 Katherine Bayless: As far as the opportunities to combine things, yes?

454 00:51:35.200 ⇒ 00:51:41.049 Katherine Bayless: many, ugh, it’s like, yeah.

455 00:51:41.160 ⇒ 00:51:58.829 Katherine Bayless: So, like, okay, so Causeway, for example, this is, like, a volunteer management platform. I have yet to see it. Maybe it’s delightful, but all the team does is complain about how something is broken, and so I’m kind of like, okay, well, like, what are we really using that platform for?

456 00:51:58.830 ⇒ 00:52:07.470 Katherine Bayless: Sorry, I should back up. There’s a foundational piece I didn’t mention. There is a very strong desire internally to put more data into Impexium. People don’t.

457 00:52:07.470 ⇒ 00:52:08.010 Uttam Kumaran: Damn.

458 00:52:08.010 ⇒ 00:52:09.379 Katherine Bayless: Because it’s a pain in the ass.

459 00:52:09.810 ⇒ 00:52:10.360 Katherine Bayless: So, I…

460 00:52:10.360 ⇒ 00:52:10.740 Uttam Kumaran: Yeah.

461 00:52:10.740 ⇒ 00:52:21.590 Katherine Bayless: we can… if we can solve the data coming out, making it look really nice, they might feel more incentivized to put data in, even if it’s difficult. And then we can deal with Pexium and tell them to make their product suck less.

462 00:52:21.790 ⇒ 00:52:36.990 Katherine Bayless: But Causeway, like, if whatever is in there could be achieved through Impexium, awesome, we could combine. The same with, Quorum, like, I know what Quorum is, I used it at my last place, it’s like… Yeah.

463 00:52:37.040 ⇒ 00:52:50.779 Katherine Bayless: Yeah, but I’m like, I… I don’t know that what they’re doing in Quorum is stuff that couldn’t really be done in Impexium. I also, I mean, it’s a dismal opinion of Quorum, to be totally honest, but that’s just me being snarky.

464 00:52:50.930 ⇒ 00:53:10.460 Katherine Bayless: And then even, like, some of these things, like Qualtrics and Marketing Cloud, like, I would never say… obviously, but, like, there’s probably parts of those processes that could be elsewhere, like, reducing dependency. Formstack, for sure, we could probably, like, find things to combine in there. So yeah, all of that to say, lots of options.

465 00:53:10.460 ⇒ 00:53:17.740 Uttam Kumaran: So every… so everything around the… Survey, and then the member… And the exhibitor…

466 00:53:18.040 ⇒ 00:53:20.579 Uttam Kumaran: Basically, outside of, like, the show.

467 00:53:21.850 ⇒ 00:53:23.059 Uttam Kumaran: It’s sort of, like.

468 00:53:23.870 ⇒ 00:53:35.429 Uttam Kumaran: Is that… is that a good characterization? Because none of the items here are exclusively related to, like, putting on the show, meaning, like, the floor data, the on-site stuff…

469 00:53:35.850 ⇒ 00:53:41.219 Uttam Kumaran: Like… Yeah, anything around security…

470 00:53:42.620 ⇒ 00:53:43.030 Katherine Bayless: Yeah.

471 00:53:43.030 ⇒ 00:53:44.430 Uttam Kumaran: Rough, yeah.

472 00:53:45.160 ⇒ 00:53:51.839 Katherine Bayless: That’s actually… that’s probably not a bad column to have included, is like, is this a CES-only platform?

473 00:53:51.840 ⇒ 00:54:01.550 Uttam Kumaran: I see, yeah, yeah, yeah. Okay, yeah, maybe I like that. I just like having as many dimensions as we can have, and I’ll just… well, I could cut it a couple different ways.

474 00:54:01.790 ⇒ 00:54:05.459 Katherine Bayless: So yeah, I know, like, a lot… a couple of these I know, are…

475 00:54:05.460 ⇒ 00:54:06.820 Uttam Kumaran: Just CES.

476 00:54:07.190 ⇒ 00:54:14.349 Katherine Bayless: Yeah, so, like, the beacons, and I don’t even know what we use for them. I know they’re just CES.

477 00:54:14.350 ⇒ 00:54:18.330 Uttam Kumaran: I assume beacons may… these beacons could probably be QR… like, QR codes?

478 00:54:20.040 ⇒ 00:54:21.240 Katherine Bayless: Or these are act…

479 00:54:21.530 ⇒ 00:54:27.000 Uttam Kumaran: Because I used to work for this company called Flowcode, and I’m pretty sure Beacons was one of our big competitors.

480 00:54:27.480 ⇒ 00:54:31.229 Katherine Bayless: You know, actually, that might explain why.

481 00:54:31.230 ⇒ 00:54:32.439 Uttam Kumaran: Oh, yeah, like.

482 00:54:33.360 ⇒ 00:54:34.170 Katherine Bayless: Like, it could…

483 00:54:34.170 ⇒ 00:54:36.920 Uttam Kumaran: I mean, it could… it could be these guys?

484 00:54:37.490 ⇒ 00:54:38.410 Katherine Bayless: Like, is there, like.

485 00:54:38.410 ⇒ 00:54:41.779 Uttam Kumaran: Simple landing page development. So, like, for example.

486 00:54:42.040 ⇒ 00:54:52.030 Uttam Kumaran: you could have, like, a beacon for every exhibitor, so, like, you scan their QR code, it goes to, like, the exhibitor’s beacon, it’s, like, you have, like, 5 links, it’s, like, those really mobile-friendly pages.

487 00:54:52.520 ⇒ 00:54:58.060 Katherine Bayless: I think that is probably what it is. My brain, when I kept hearing beacons, I was always thinking, like.

488 00:54:58.060 ⇒ 00:54:59.240 Uttam Kumaran: NFC beacons.

489 00:54:59.240 ⇒ 00:54:59.860 Katherine Bayless: Yeah, yeah, exactly.

490 00:54:59.860 ⇒ 00:55:00.499 Uttam Kumaran: Yeah, yeah, yeah.

491 00:55:00.520 ⇒ 00:55:01.020 Katherine Bayless: No.

492 00:55:01.020 ⇒ 00:55:07.780 Uttam Kumaran: That’s also the first thing I thought, but then I used to think… I even worked in this field, so then I kind of re-associated, like, what it is.

493 00:55:07.780 ⇒ 00:55:08.340 Katherine Bayless: Yeah.

494 00:55:08.340 ⇒ 00:55:11.600 Uttam Kumaran: We used to, like, compete with these guys, so… okay.

495 00:55:11.760 ⇒ 00:55:26.630 Katherine Bayless: Yeah, that’s funny. Okay, yeah, so maybe that is what that is. I do know it’s CES only, but yeah. Okay, and then, DocuSign is CES only, but, I mean, it’s just kind of baked into.

496 00:55:26.630 ⇒ 00:55:27.400 Uttam Kumaran: Yeah.

497 00:55:27.400 ⇒ 00:55:34.270 Katherine Bayless: There’s, EventPoint, Event Base, ExpoCAD, those are all CES only.

498 00:55:35.090 ⇒ 00:55:38.160 Katherine Bayless: Glean In is CES only.

499 00:55:39.070 ⇒ 00:55:42.940 Katherine Bayless: the lead retrieval CES only. I do think that is.

500 00:55:42.940 ⇒ 00:55:44.269 Uttam Kumaran: But lead retrieval…

501 00:55:44.360 ⇒ 00:55:45.720 Katherine Bayless: Could be helpful.

502 00:55:46.010 ⇒ 00:55:52.699 Uttam Kumaran: Because it is almost like… It is… it also is data about the members, like, who’s attending, right?

503 00:55:53.030 ⇒ 00:56:08.539 Katherine Bayless: Yeah, so here’s a funny thing about the lead retrieval data, and this is, like, I just, at some point, need to fan out the answer to this, but, like, everybody has this weird idea that we can’t use it, because they’re like, well, it’s not our data, it belongs to the exhibitors, and I’m like.

504 00:56:09.270 ⇒ 00:56:10.650 Katherine Bayless: What? I mean…

505 00:56:10.650 ⇒ 00:56:11.679 Uttam Kumaran: What does that mean?

506 00:56:11.810 ⇒ 00:56:13.050 Katherine Bayless: Right? Exactly.

507 00:56:13.050 ⇒ 00:56:18.190 Uttam Kumaran: Oh, I see what I mean, so this is, like, when, you go up to an exhibitor, and they’re, like.

508 00:56:18.640 ⇒ 00:56:25.480 Uttam Kumaran: oh, I love your… I love your demo, like, yeah, let’s grab a fee booking, let’s grab a demo next week, and then…

509 00:56:25.480 ⇒ 00:56:27.109 Katherine Bayless: Like, can I scan your badge?

510 00:56:27.250 ⇒ 00:56:31.459 Uttam Kumaran: Yeah, yeah, yeah, oh, yeah, so I could put you in our mini CRM for follow-up.

511 00:56:31.730 ⇒ 00:56:36.719 Katherine Bayless: Right, and I’m like, I’m pretty sure we can use that data, guys. I have not yet…

512 00:56:36.720 ⇒ 00:56:43.949 Uttam Kumaran: So I guess, give me a sense of, like, you would be using… because you… you would technically… oh, so you would look at which exhibitor got, like, the most…

513 00:56:44.320 ⇒ 00:56:50.540 Uttam Kumaran: badges, Or, like, somehow slice it to show, like, Okay.

514 00:56:51.570 ⇒ 00:56:53.369 Katherine Bayless: I mean, I just… I like data.

515 00:56:53.370 ⇒ 00:56:55.079 Uttam Kumaran: Yeah, yeah, yeah, no, I know, I mean, I would…

516 00:56:55.500 ⇒ 00:57:00.460 Uttam Kumaran: like all this. We’ll figure out the use case, but trying to think about, like, if I had to go

517 00:57:00.580 ⇒ 00:57:05.189 Uttam Kumaran: talk to them about what… yeah, okay, so… I mean, I feel like this is…

518 00:57:05.920 ⇒ 00:57:09.510 Uttam Kumaran: This is also something that, say, you know the balance of, like.

519 00:57:09.700 ⇒ 00:57:15.109 Uttam Kumaran: There’s some stuff that’s essential, but there’s also some stuff that’s, like, you guys have, like, one of the most unique

520 00:57:15.670 ⇒ 00:57:24.450 Uttam Kumaran: Lore, exhibition data, You know? So, there’s probably a lot more that can help you inform things like.

521 00:57:24.640 ⇒ 00:57:32.560 Uttam Kumaran: Book traffic and form things like, oh, this vendor clearly got the most scans, so maybe we should have put them… we should just, like.

522 00:57:32.830 ⇒ 00:57:36.620 Uttam Kumaran: Put them on stage tomorrow, because they’re just blowing up, and, like, everybody wants to, like…

523 00:57:37.030 ⇒ 00:57:39.300 Uttam Kumaran: So maybe this… and this is something that, like.

524 00:57:39.630 ⇒ 00:57:47.639 Uttam Kumaran: maybe the people who are running a show on a daily basis want to look at, like, how many leads were exchanges. Okay, so I have a general, like.

525 00:57:48.100 ⇒ 00:57:49.190 Katherine Bayless: That would be cool.

526 00:57:49.350 ⇒ 00:57:55.520 Uttam Kumaran: This is, like, that’s where it sort of goes into, like, you guys uniquely are spitting out a lot of data that doesn’t exist.

527 00:57:55.850 ⇒ 00:57:56.700 Katherine Bayless: Right?

528 00:57:56.700 ⇒ 00:58:00.870 Uttam Kumaran: And so, it’s like, what products Can you guys develop?

529 00:58:01.150 ⇒ 00:58:04.900 Uttam Kumaran: around… Around that.

530 00:58:05.090 ⇒ 00:58:24.810 Katherine Bayless: Yeah, like, where my brain was, like, foot traffic plus lead retrieval gives you sort of, like, a, you know… well, in the other order, like, so lead retrieval would be, like, your numerator, foot traffic is your denominator. If we were able to knit that together in the, like, what we call Eureka Park, where all of our startups are, which, asterisk.

531 00:58:24.810 ⇒ 00:58:40.350 Katherine Bayless: going back to definitions, we define startups incorrectly, and it is going to be a business problem. I don’t know how long it will take to fix that, but, like, it’s this weird set of rules that don’t make any sense for real startups. And so when we say startups.

532 00:58:40.930 ⇒ 00:58:53.159 Katherine Bayless: it’s like a grab bag of tiny companies, basically. But if we fix that, I’m like, foot traffic and lead retrieval in Eureka Park, where the startups are, I mean, could we get a sense of

533 00:58:53.550 ⇒ 00:59:03.809 Katherine Bayless: predicting, like, how they might do, right? Like, if this is what your, you know, your ratio was that year, and you come back next year, and, like, you met with these investors, can we.

534 00:59:03.810 ⇒ 00:59:08.119 Uttam Kumaran: No, but your salespeople should also have, like, here’s how many scans you got last year.

535 00:59:08.330 ⇒ 00:59:10.309 Uttam Kumaran: Like, in… for example.

536 00:59:11.300 ⇒ 00:59:22.479 Uttam Kumaran: I worked at, so my first job was at WeWork, and one of the things I worked on was I worked on, like, the badge scanning data. That was, like, the sort of, like, mystery…

537 00:59:23.080 ⇒ 00:59:37.389 Uttam Kumaran: data set that I sort of brought to, like, life and made them a lot of money, because they were like… they were like, we have this badge scan data from our badge scan vendor, but, like, nobody’s ever modeled it. I’m like, you have every keycard scan to every WeWork door.

538 00:59:37.870 ⇒ 00:59:40.449 Uttam Kumaran: that exists, and that we had, like, a lot of WeWorks, I was like.

539 00:59:40.540 ⇒ 00:59:48.419 Uttam Kumaran: you can do so much for that, and so eventually, we sort of built a huge model around keycard scams, but not only, of course, you could do a lot of human stuff, but

540 00:59:48.450 ⇒ 01:00:04.280 Uttam Kumaran: we wanted to show that, like, hey, your people, like, for example, we would sell both floors, and then we would… companies would have, like, hot desks. And we would say, okay, you have, like, 5 hot desks, but you see there’s, like, 10 people coming and swapping in and out of that. You should open an office there, like…

541 01:00:04.340 ⇒ 01:00:07.350 Katherine Bayless: Yeah. You know, and that’s sort of what we would do. For example.

542 01:00:07.350 ⇒ 01:00:25.689 Uttam Kumaran: you could say, hey guys, you guys got, like, 100 different lead scans, but you’re in the corner, so you guys should move up to the floor, and, like, here’s what you can expect. And you can also, for your salespeople, you could say, on average, people on this side of the floor, which is X dollars, get X percent more scans than the.

543 01:00:26.420 ⇒ 01:00:30.330 Uttam Kumaran: people, right? So there… and that… that is the story for the salespeople to tell, right?

544 01:00:30.330 ⇒ 01:00:32.879 Katherine Bayless: Yes. Yes. Yes.

545 01:00:32.880 ⇒ 01:00:33.370 Uttam Kumaran: Bull.

546 01:00:33.370 ⇒ 01:00:39.410 Katherine Bayless: Yes. I, yeah. Like, to me, like, seems like there’s totally things that could be…

547 01:00:39.800 ⇒ 01:00:49.960 Uttam Kumaran: Yeah, I’m also interested, because the nice thing about this stuff is, like, if it’s… if sales drives it, and if it’s, like, your data is helping them close… net… close net new business faster.

548 01:00:50.140 ⇒ 01:00:56.070 Uttam Kumaran: Or give them a story. All doors will kind of open to follow the money a little bit.

549 01:00:56.070 ⇒ 01:00:58.249 Katherine Bayless: Like, if they’re like, we need this data, because.

550 01:00:58.250 ⇒ 01:00:59.959 Uttam Kumaran: We want to put together, like, a…

551 01:01:00.560 ⇒ 01:01:08.869 Uttam Kumaran: end of CES dashboard with, like, all the metrics, and, like… or when I go call them for next year and getting a bigger booth, I want to have, like, a…

552 01:01:08.990 ⇒ 01:01:12.599 Uttam Kumaran: customer health dashboard, right? So I want to see how they did last year.

553 01:01:13.350 ⇒ 01:01:14.360 Katherine Bayless: Yes. Biking.

554 01:01:14.360 ⇒ 01:01:17.729 Uttam Kumaran: They’re gonna eat that up, because probably they’re going in and winging it, you know?

555 01:01:18.340 ⇒ 01:01:35.499 Katherine Bayless: Right, and so, truthfully, in a funny way, like, there might be a lot of opportunity to start that work on the membership side, because the membership team sort of winds up being the, like, you know, I don’t know, like, the kid brother, like, running around behind, like, hey, us too, us too, right?

556 01:01:35.500 ⇒ 01:01:36.579 Uttam Kumaran: Yeah, I see what you mean.

557 01:01:36.580 ⇒ 01:01:37.020 Katherine Bayless: Yeah.

558 01:01:37.020 ⇒ 01:01:37.739 Uttam Kumaran: I’m not hearing you.

559 01:01:37.740 ⇒ 01:01:44.610 Katherine Bayless: there, it might help them tell their story and broker some trust with the sales team, because I think, I mean.

560 01:01:44.610 ⇒ 01:01:46.070 Uttam Kumaran: Yeah, I see what you mean.

561 01:01:46.190 ⇒ 01:01:47.370 Katherine Bayless: Yeah, the sales.

562 01:01:47.370 ⇒ 01:01:49.169 Uttam Kumaran: Sue me, because… yeah.

563 01:01:49.460 ⇒ 01:02:01.529 Katherine Bayless: They’re lovely people, but I think part of the challenge is, like, for a long time, we didn’t have to do sales. We just answered phones and took checks, right? But now, the world is different, and we have to do more selling, selling, and I don’t.

564 01:02:01.530 ⇒ 01:02:02.110 Uttam Kumaran: Yeah.

565 01:02:02.110 ⇒ 01:02:02.770 Katherine Bayless: that reality.

566 01:02:02.770 ⇒ 01:02:06.979 Uttam Kumaran: But also, like, without a data story, like, I’ve… and this is where I don’t know…

567 01:02:07.000 ⇒ 01:02:11.100 Katherine Bayless: This is really helping, because I have some friends that are in the event world.

568 01:02:11.100 ⇒ 01:02:29.350 Uttam Kumaran: all of them struggle with this type of giving data back and telling the story of the ROI, which was… it’s good and bad. It’s good because you guys can not do that and sort of continue to be the premier place to advertise, because you tell that story. It’s also tough because it’s just not used to that, so…

569 01:02:30.120 ⇒ 01:02:42.110 Uttam Kumaran: But this is where, like, you want to go to your customers, say, you had a great ROI, you should continue spending with us, versus calling them, and then both of y’all are, like, debating whether it was worth spending money on a booth, when it totally could have been, but, like.

570 01:02:42.280 ⇒ 01:02:47.070 Uttam Kumaran: Instead of just, like, showing the numbers and being like, great, cool checks in the mail.

571 01:02:47.230 ⇒ 01:02:51.549 Uttam Kumaran: You sort of have to, like, dance or use other sales tactics to figure it out.

572 01:02:52.130 ⇒ 01:02:55.229 Katherine Bayless: Right. I mean, I think I told you the Samsung story from, like, the last.

573 01:02:55.230 ⇒ 01:02:56.010 Uttam Kumaran: Yes.

574 01:02:56.010 ⇒ 01:02:58.629 Katherine Bayless: Yeah, yeah, yeah, exactly. Yeah, it’s brutal.

575 01:02:59.660 ⇒ 01:03:08.560 Katherine Bayless: Okay, let’s see, so map your show, merits… Nix Halo, more agency.

576 01:03:08.610 ⇒ 01:03:10.010 Uttam Kumaran: Pointer…

577 01:03:12.470 ⇒ 01:03:20.049 Katherine Bayless: And then… Salesforce CRM, I guess, obviously. Session scanners…

578 01:03:21.560 ⇒ 01:03:23.069 Uttam Kumaran: Oh, this is great too, yeah.

579 01:03:23.790 ⇒ 01:03:27.320 Katherine Bayless: Yeah, that data we have, and nobody.

580 01:03:27.320 ⇒ 01:03:27.720 Uttam Kumaran: Oh, okay.

581 01:03:27.720 ⇒ 01:03:35.400 Katherine Bayless: to play with. And, also, nobody knows how to import into a database, because it’s too big. And I was like, okay, okay.

582 01:03:35.400 ⇒ 01:03:43.500 Uttam Kumaran: Oh, okay. Yeah, it’ll solve that problem. That’s great. This is great, too. This is great for… to show, like, what sessions are doing well, and…

583 01:03:43.690 ⇒ 01:03:45.070 Uttam Kumaran: Yeah, 100%.

584 01:03:45.660 ⇒ 01:03:53.290 Katherine Bayless: Yeah, so then Titan, turn out now… and Zoom info.

585 01:03:54.170 ⇒ 01:03:57.209 Katherine Bayless: And that’s the extent of the, like.

586 01:03:57.210 ⇒ 01:03:59.939 Uttam Kumaran: Oh, so you guys do use ZoomInfo for enrichment?

587 01:03:59.940 ⇒ 01:04:01.049 Katherine Bayless: I don’t know.

588 01:04:01.340 ⇒ 01:04:02.099 Uttam Kumaran: Okay, okay, okay.

589 01:04:02.300 ⇒ 01:04:04.150 Katherine Bayless: I think one of the CES

590 01:04:04.290 ⇒ 01:04:13.330 Katherine Bayless: I think one of the other CES tools has it, like, baked in, and it may be… it might be… Oh. I’m not sure. But yeah, I’m not… I don’t think we have a…

591 01:04:13.330 ⇒ 01:04:15.460 Uttam Kumaran: You guys already have… okay, okay.

592 01:04:15.960 ⇒ 01:04:18.170 Uttam Kumaran: Okay, I can find out, because, yeah.

593 01:04:19.480 ⇒ 01:04:20.110 Uttam Kumaran: It’s decent.

594 01:04:21.260 ⇒ 01:04:30.939 Katherine Bayless: Yeah, well, and the other thing, too, is, like, the… like, Impexium as a product has no concept of, like, baked-in enrichment, right? Like, you know, at least in HubSpot or Salesforce.

595 01:04:30.940 ⇒ 01:04:31.570 Uttam Kumaran: Yeah, yeah, yeah.

596 01:04:31.570 ⇒ 01:04:33.169 Katherine Bayless: They’ll, like, you know, get this.

597 01:04:33.170 ⇒ 01:04:34.749 Uttam Kumaran: Goodbye through their credits, yeah.

598 01:04:34.750 ⇒ 01:04:41.270 Katherine Bayless: Yeah, but like, if we could bring any enrichment into Impexium, I think that would make their lives better.

599 01:04:41.270 ⇒ 01:04:41.650 Uttam Kumaran: Okay.

600 01:04:41.920 ⇒ 01:04:42.719 Katherine Bayless: But yeah…

601 01:04:42.720 ⇒ 01:04:48.890 Uttam Kumaran: And then talk to me about, the… Turnout now? Is this…

602 01:04:49.720 ⇒ 01:04:54.149 Uttam Kumaran: like, I guess I’m also trying to think about, okay, it’s like, we can get you, like, a…

603 01:04:54.300 ⇒ 01:05:01.560 Uttam Kumaran: a win… like, during CES, that’s, like, there’s some live data coming in to show people.

604 01:05:01.760 ⇒ 01:05:08.140 Uttam Kumaran: Like, is this important to see, like, what people are asking for? That’s something they already produced?

605 01:05:10.470 ⇒ 01:05:14.180 Uttam Kumaran: It’s like, I’m talking about more the… the chatbot.

606 01:05:14.390 ⇒ 01:05:15.600 Uttam Kumaran: like…

607 01:05:15.790 ⇒ 01:05:25.350 Uttam Kumaran: There’s probably some stuff we can do with the attendee matching, but for the chatbot, for example, if you can see that, like, people are asking a certain type of question, is that, like, anything relevant.

608 01:05:26.040 ⇒ 01:05:41.980 Katherine Bayless: So… so for the attendee matching thing, I’ll say, apparently, I don’t really understand exactly all of it, but it’s a very tiny program, and I don’t know if it’s tiny because nobody wants it, or if it’s tiny because it’s restricted to certain tiers or something like that, but it’s a teeny tiny kind of.

609 01:05:41.980 ⇒ 01:05:43.260 Uttam Kumaran: Oh, really? Okay.

610 01:05:43.430 ⇒ 01:05:44.550 Katherine Bayless: Yeah, I just…

611 01:05:44.550 ⇒ 01:05:50.739 Uttam Kumaran: I wonder if you can show that, like, people who match attendees, like, stay longer, or go to more stuff.

612 01:05:51.420 ⇒ 01:05:55.499 Katherine Bayless: Right now, we can’t get more than 30 people to sign up for it, so…

613 01:05:55.500 ⇒ 01:05:58.889 Uttam Kumaran: Okay, alright, so then there’s not much… not very significant.

614 01:05:59.100 ⇒ 01:06:03.490 Katherine Bayless: But the event co-pilot, the little AI chatbot.

615 01:06:05.380 ⇒ 01:06:08.740 Katherine Bayless: So, let’s see, I know I tell a lot of stories and talk a lot, but…

616 01:06:08.740 ⇒ 01:06:09.709 Uttam Kumaran: No, that’s fine.

617 01:06:10.060 ⇒ 01:06:15.430 Katherine Bayless: Last year, our board basically was like, this is embarrassing that we don’t have AI.

618 01:06:15.800 ⇒ 01:06:30.899 Katherine Bayless: Oh, is it? So then Panasonic was like, we will help. And so, yours truly got on a call with a woman who leads Panasonic’s AI division, because they wanted to help us build AI at CES.

619 01:06:32.400 ⇒ 01:06:34.230 Uttam Kumaran: And she asked what…

620 01:06:34.230 ⇒ 01:06:45.399 Katherine Bayless: tools we used, and I said, well, we don’t own any of the source data, and our mobile app is in ColdFusion, and I started 4 weeks ago, and they think this is a conversation that should happen.

621 01:06:45.570 ⇒ 01:06:46.269 Uttam Kumaran: Yeah, yeah, yeah.

622 01:06:46.270 ⇒ 01:07:04.079 Katherine Bayless: We had a good chat, and then Panasonic politely bowed out, and I said, we’re gonna hire somebody, because whatever AI we throw at CES in 7 months is gonna fail, and I am not sending my career down with that ship. No.

623 01:07:04.080 ⇒ 01:07:05.309 Uttam Kumaran: Yeah, yeah, yeah.

624 01:07:05.310 ⇒ 01:07:15.000 Katherine Bayless: So, I feel bad. The guy is a little bit of a… I mean, he’s an interesting character, but, like, this thing’s gonna get eaten alive. It is gonna get eaten alive, because it’s terrible.

625 01:07:15.590 ⇒ 01:07:17.840 Uttam Kumaran: Yeah, no, I saw the… I saw the thing.

626 01:07:18.050 ⇒ 01:07:18.900 Katherine Bayless: Damn.

627 01:07:18.900 ⇒ 01:07:20.909 Uttam Kumaran: I wish we… I wish I had, like…

628 01:07:20.910 ⇒ 01:07:21.680 Katherine Bayless: It’s worth the call.

629 01:07:21.680 ⇒ 01:07:23.039 Uttam Kumaran: Should’ve started a little earlier.

630 01:07:23.870 ⇒ 01:07:24.480 Katherine Bayless: I know.

631 01:07:24.480 ⇒ 01:07:26.570 Uttam Kumaran: Yeah, this is tough. I mean…

632 01:07:26.720 ⇒ 01:07:27.420 Katherine Bayless: I know.

633 01:07:27.760 ⇒ 01:07:41.499 Katherine Bayless: Now, that being said, I do think, in a weird way, if we… because he has shown us what the analytics will look like. I think we get, like, a daily digest, but there is the ability to consume in real time. I just need to read his email and see what the details were. But, like.

634 01:07:42.100 ⇒ 01:07:44.970 Katherine Bayless: We could maybe do a, like…

635 01:07:45.530 ⇒ 01:07:54.030 Katherine Bayless: siphoning the data out, surfacing what’s going well versus not going so well, like, at least we’re learning what we need to fix.

636 01:07:54.030 ⇒ 01:07:57.370 Uttam Kumaran: And who’s this… and you said it’s guys, this is a one-guy company?

637 01:07:58.000 ⇒ 01:08:09.759 Katherine Bayless: Well, the CEO and co-founder is the guy that’s joining our technical calls and telling us what he’s building, so I’m assuming it’s not too many more people than him, but it’s definitely one guy, but it’s not many.

638 01:08:09.760 ⇒ 01:08:13.280 Uttam Kumaran: So I think, at minimum, to just be able to say, like.

639 01:08:14.440 ⇒ 01:08:18.500 Uttam Kumaran: So, but… and then, is Casey on our… on our side…

640 01:08:19.270 ⇒ 01:08:26.080 Uttam Kumaran: like, is he owning the, like, basically the fact that it works? Is it more about whatever that guy needs?

641 01:08:26.229 ⇒ 01:08:26.750 Uttam Kumaran: Like…

642 01:08:26.750 ⇒ 01:08:46.749 Katherine Bayless: So, Casey, so she basically is just kind of stuck with this, so… Okay, great. So we had a woman named Emily Kaiser who worked here until, I think, about September, before she moved on, because she doesn’t live in the area, we did the RTO thing, going back to what I explained about at the beginning of the call, right?

643 01:08:46.790 ⇒ 01:08:52.409 Katherine Bayless: Emily had been managing the… she was intending to manage the AI chatbot, she’s been managing.

644 01:08:52.410 ⇒ 01:08:53.100 Uttam Kumaran: God damn.

645 01:08:53.109 ⇒ 01:08:53.899 Katherine Bayless: app.

646 01:08:54.629 ⇒ 01:08:58.339 Katherine Bayless: Casey is gonna be on maternity leave, probably?

647 01:08:58.340 ⇒ 01:08:59.040 Uttam Kumaran: Oh, damn.

648 01:08:59.040 ⇒ 01:09:11.680 Katherine Bayless: But she’s been holding together the website, the mobile app, and the AI chatbot, and probably 10 other things, and somebody needs to buy her an all-expenses ClubMed vacation when she’s done, because I don’t know.

649 01:09:11.680 ⇒ 01:09:12.240 Uttam Kumaran: Yeah.

650 01:09:12.240 ⇒ 01:09:17.570 Katherine Bayless: I mean, I know I’m driving her insane, because I’m, like, day late, dollar short to everything she needs.

651 01:09:17.850 ⇒ 01:09:27.879 Katherine Bayless: And I can feel it in her eye contact, and it’s very fair. I’m not objecting at all. But yeah, so, like, she’s way underwater, but somehow holding it down.

652 01:09:28.359 ⇒ 01:09:37.799 Uttam Kumaran: So at least, like, maybe we can just get the data from the chatbot, and so at least there’s, like, one other level of, like, is this thing working?

653 01:09:38.050 ⇒ 01:09:38.420 Katherine Bayless: Yeah.

654 01:09:38.420 ⇒ 01:09:55.750 Uttam Kumaran: And you’re not, like, relying on the vendor to tell you that it is or not, or, like, anecdotally, like, it’s lagging or whatever, so… And if it’s just one dude, then, yeah, it’s one email away from being like, okay, we need this data, thanks, and I can start that. Again, like, even… that’s… that seems like…

655 01:09:56.210 ⇒ 01:10:01.570 Uttam Kumaran: Low effort, because it’s, like, one guy, one couple emails to get access.

656 01:10:01.780 ⇒ 01:10:04.380 Uttam Kumaran: Okay.

657 01:10:04.950 ⇒ 01:10:05.690 Katherine Bayless: Yeah.

658 01:10:05.690 ⇒ 01:10:10.909 Uttam Kumaran: And in the spirit of not making too many things P0, because then they’re not P0, I feel like this is probably…

659 01:10:10.940 ⇒ 01:10:14.730 Katherine Bayless: half decent list.

660 01:10:15.240 ⇒ 01:10:25.130 Uttam Kumaran: I know you mentioned P negative-1, so I’ll think about that. But a lot of these are great, and then I guess,

661 01:10:29.430 ⇒ 01:10:37.650 Uttam Kumaran: Yeah, I mean, I think we at least… I wanna… I wanna sort of see the… the GA data, I want to see the membership

662 01:10:38.400 ⇒ 01:10:41.150 Uttam Kumaran: data…

663 01:10:41.360 ⇒ 01:10:49.379 Uttam Kumaran: And then, the only, like, bonus win is if we can get some of the live… if we can get some of the on-the-floor CES data.

664 01:10:49.770 ⇒ 01:10:54.210 Uttam Kumaran: piped in, it could be a really good win for you, if we, like, something can come out of that, or, like.

665 01:10:54.700 ⇒ 01:11:10.500 Uttam Kumaran: for example, I assume, like, if a good example is, like, Kyle, you have access to all this live CES data, we’re testing it the week before, now you can start sending reports live, you know, that could be a good win, but I’m… if that’s, like… we’ll just see. If that’s, like, a…

666 01:11:10.850 ⇒ 01:11:18.990 Uttam Kumaran: if I can… we can also get that as a win, then it’d be a win, but there also is some good consolidation opportunities to start just asking questions about some of these sources, like.

667 01:11:19.320 ⇒ 01:11:29.340 Uttam Kumaran: finally get an answer on some of these exist. Can we, as I start learning about Impexium, I can start giving, you know, my opinion on, like, what we can consolidate in there? Things like that.

668 01:11:29.920 ⇒ 01:11:37.729 Katherine Bayless: Yeah, yeah. And I think… so, also along the consolidation lines, another person that I…

669 01:11:37.830 ⇒ 01:11:42.290 Katherine Bayless: I do really want to kind of bring pretty closely into the project, I think, is Jay.

670 01:11:42.810 ⇒ 01:11:43.760 Uttam Kumaran: Yeah, okay.

671 01:11:43.760 ⇒ 01:11:57.149 Katherine Bayless: you know, he’s a… he really… he’s a very compelling cast member in my mind, because he’s been here 20 years, he has every right to be burnt out and all the things, and yet, like, he’s still, like, slacking me, you know, in the night, like, hey, what do you think about blah blah blah? And I’m like, you care.

672 01:11:57.150 ⇒ 01:11:57.550 Uttam Kumaran: Yes.

673 01:11:57.550 ⇒ 01:12:02.700 Katherine Bayless: So much, right? I’m like, I wish you had a staging environment and a bigger team, but…

674 01:12:02.700 ⇒ 01:12:03.410 Uttam Kumaran: Yeah, yeah, yeah.

675 01:12:03.410 ⇒ 01:12:06.789 Katherine Bayless: I’m smart, and he cares, and I think, like.

676 01:12:08.050 ⇒ 01:12:13.329 Katherine Bayless: I actually think the consolidation and the procurement and, like, some of these things

677 01:12:13.390 ⇒ 01:12:30.900 Katherine Bayless: would be… I mean, he has the, like, the seat at the right table for, right? Like, and it would probably help him establish a different relationship between IT and the organization, because I think he’s kind of victim to the thing that, you know, happens to anybody who stays along. He’s like, they still see him as the help desk guy from…

678 01:12:30.900 ⇒ 01:12:31.780 Uttam Kumaran: Yeah, yeah, yeah, yeah, yeah.

679 01:12:31.780 ⇒ 01:12:50.959 Katherine Bayless: Right? Like, reset my password, why doesn’t Outlook, you know, whatever, whatever. And so it’s like, no, I want to put him in that strategic spot where it is like, no, I’m advising on, you know, consolidating these platforms and staffing up in this area because I see the technical needs of the organization. He does, just nobody thinks about him that way.

680 01:12:51.420 ⇒ 01:12:52.100 Uttam Kumaran: Yeah.

681 01:12:52.570 ⇒ 01:12:54.199 Katherine Bayless: I want to make him look good.

682 01:12:55.220 ⇒ 01:12:57.350 Katherine Bayless: Okay, great, so…

683 01:12:57.790 ⇒ 01:13:02.460 Uttam Kumaran: Yeah, no, I think that’s perfect, and so I’m thinking that at least, sort of, like, we should make…

684 01:13:02.570 ⇒ 01:13:08.989 Uttam Kumaran: a core data team channel, I think that could be the one we have now. I think it would be great to start, like, a data IT channel.

685 01:13:08.990 ⇒ 01:13:10.859 Katherine Bayless: And then…

686 01:13:11.170 ⇒ 01:13:19.430 Uttam Kumaran: as we start getting some wins, we can start a larger, like, show-and-tell channel. The reason I don’t want to bring… this needs to be, like, the core…

687 01:13:19.600 ⇒ 01:13:26.360 Uttam Kumaran: Kind of crew, because… The, every channel will become a, like, this looks wrong situation.

688 01:13:26.560 ⇒ 01:13:39.129 Uttam Kumaran: So I just wanna, like, I just wanna not have every channel be like that. And, like, unless you’ve… unless you’ve, like, seen that before, which I’ve seen now so many times, you… you don’t, like, learn your lesson, so try to, like.

689 01:13:39.330 ⇒ 01:13:44.879 Uttam Kumaran: have a core crew that can lean on each other, and it’s like, hey, this is wrong, like, can I call you real quick?

690 01:13:44.880 ⇒ 01:13:46.890 Katherine Bayless: To fix this, and then we have, like.

691 01:13:46.890 ⇒ 01:13:50.669 Uttam Kumaran: these, like, external-facing ones, I think that’s a good way to start.

692 01:13:50.960 ⇒ 01:13:51.780 Uttam Kumaran: Yeah, yeah.

693 01:13:51.780 ⇒ 01:14:03.200 Katherine Bayless: I think if we can build the muscle of failing out loud and getting more comfortable, that’s great, but we are totally in the stage right now… You know, and I don’t want to get started. Please don’t just put my dirty laundry out there, right?

694 01:14:03.200 ⇒ 01:14:08.019 Uttam Kumaran: No, no, no, I agree, and I don’t… I want… I want these… the folks that we support, I want them to, like.

695 01:14:08.350 ⇒ 01:14:14.550 Uttam Kumaran: Be able to, like, Fail first with us, get the sense, and then start to, like.

696 01:14:14.670 ⇒ 01:14:16.859 Uttam Kumaran: Works for some of the external folks.

697 01:14:16.860 ⇒ 01:14:20.669 Katherine Bayless: Exactly. And then also have a team to fall back on when eventually they, like.

698 01:14:21.280 ⇒ 01:14:24.479 Uttam Kumaran: They get to a meeting, they don’t have the right thing, or something breaks, like.

699 01:14:24.690 ⇒ 01:14:33.390 Uttam Kumaran: Versus, like, oh, no one… how do I… I ship some dbt models, and, like, no one knows about it, and, like, build a real, like, data team, you know? So…

700 01:14:33.970 ⇒ 01:14:34.620 Katherine Bayless: Yeah.

701 01:14:34.760 ⇒ 01:14:37.259 Katherine Bayless: Yeah, exactly, exactly.

702 01:14:38.740 ⇒ 01:14:46.700 Uttam Kumaran: So, I think the last piece… I mean, I read through your message. I think this is really helpful, so I will… I’ll keep this in mind.

703 01:14:46.780 ⇒ 01:14:47.810 Katherine Bayless: I mean…

704 01:14:47.810 ⇒ 01:14:58.109 Uttam Kumaran: the lovely thing is, like, if you… if… one perspective I have, given your situation, is to try to consolidate as much stuff into Snowflake as possible.

705 01:14:58.570 ⇒ 01:15:03.200 Uttam Kumaran: I also just talked to Snowflake today, and they’re actually offering some really great, like.

706 01:15:03.720 ⇒ 01:15:08.810 Uttam Kumaran: some really great, like, discounts and things that I don’t… I don’t think they were previously advertising. For example.

707 01:15:09.080 ⇒ 01:15:14.850 Uttam Kumaran: I talked to the guy today, he’s like, yeah, we’re offering, like… I think he offers, like, marketing partner discounts, where basically…

708 01:15:15.190 ⇒ 01:15:19.779 Uttam Kumaran: 25% of your Snowflake contract can go to approved partners.

709 01:15:20.720 ⇒ 01:15:33.020 Uttam Kumaran: And I was like, when did this release? He’s like, just recently. I’m like, dude, that’s, like, great, because now, if I, like, can find a tool that fits their partnership on the ETL side, and you can adapt some of that money, that could be…

710 01:15:33.810 ⇒ 01:15:35.209 Uttam Kumaran: You know, a good win.

711 01:15:36.550 ⇒ 01:15:57.389 Katherine Bayless: the Snowflake rep that I was talking to actually mentioned that too, and it was, like, one of those that I was like, I filed this away for, like, that’s a later problem, or opportunity, but yeah. And he said, technically, it’s 49%, because it just can’t be 50, or else it’s technically money laundering, right? Yeah. Like, so we’re saying 25, but he’s like, it can be more, and what.

712 01:15:57.390 ⇒ 01:15:58.380 Uttam Kumaran: Oh, yeah.

713 01:15:58.380 ⇒ 01:16:00.459 Katherine Bayless: Yeah, but but I actually…

714 01:16:00.460 ⇒ 01:16:10.689 Uttam Kumaran: And he said… he said you… even if you were to take that amount out, you would still get the discount tier from the top level. So, my guy told me the same spiel that your guy did then.

715 01:16:11.030 ⇒ 01:16:17.829 Katherine Bayless: Yeah, you were better poised to understand and think about it than I was thinking. Cool. Yeah, I think, I mean…

716 01:16:17.830 ⇒ 01:16:31.169 Katherine Bayless: I did run it by our finance team, who are a little, you know, maybe on the older school side. Really lovely, but, you know, I was like, you said we have money to burn, end of year, right? Use it or lose it, like, can I buy a bunch of Snowflake credits? And they were like, well, but…

717 01:16:31.500 ⇒ 01:16:33.129 Katherine Bayless: Would we use them this year?

718 01:16:33.850 ⇒ 01:16:34.699 Uttam Kumaran: Yeah, yeah.

719 01:16:36.730 ⇒ 01:16:39.950 Katherine Bayless: So… but I do think in the new year.

720 01:16:39.950 ⇒ 01:16:46.849 Uttam Kumaran: What did, like, how did… like, what did they… are they… are they concerned about, like, what is… how do you win them over on decisions currently?

721 01:16:48.370 ⇒ 01:16:50.980 Uttam Kumaran: Like, yeah, like, how do they… yeah.

722 01:16:51.960 ⇒ 01:17:07.610 Katherine Bayless: Yeah, I… I… I… I don’t… I don’t actually know yet, to be honest, right? Like, I… I have not yet figured out, because that… that whole, like, pay for, deliver for, that is a brick wall that I am just fascinated by. I’m like… Yeah.

723 01:17:07.610 ⇒ 01:17:23.259 Katherine Bayless: I just… it doesn’t have to be that way, right? I mean, I also know that they are one of the teams that refuses to move off of their map network drive. Like, sometimes payroll gets, like, held up because the drive goes down, which is just weird to me. I’m like, what?

724 01:17:23.260 ⇒ 01:17:24.230 Uttam Kumaran: Yeah.

725 01:17:24.540 ⇒ 01:17:37.280 Katherine Bayless: So, like, I think, truthfully, there’s a part of me that’s like, okay, can I get all the pieces on the chessboard lined up just right, where, like, Jay, who has really strong relationships with them… Okay. …could be the, like.

726 01:17:37.280 ⇒ 01:17:38.149 Uttam Kumaran: It’s fighting for that.

727 01:17:38.150 ⇒ 01:17:49.449 Katherine Bayless: Right, because it’s like, that whole accounting and finance team needs an overhaul of, like, just technology writ large, but, like, they’re not gonna let me in right away, but they know.

728 01:17:49.450 ⇒ 01:17:50.380 Uttam Kumaran: Yeah, yeah.

729 01:17:50.630 ⇒ 01:17:52.370 Katherine Bayless: They might. Yeah.

730 01:17:52.370 ⇒ 01:17:59.130 Uttam Kumaran: So I just want to know whether they’re looking for… if it’s, like, we’re investing in the software for growth, or is it, like, we’re getting a good deal?

731 01:17:59.420 ⇒ 01:18:00.370 Uttam Kumaran: like…

732 01:18:00.370 ⇒ 01:18:00.690 Katherine Bayless: Definitely.

733 01:18:00.690 ⇒ 01:18:04.720 Uttam Kumaran: I mean, we fight for the… we’ll fight for discounts and the deals and stuff, but sometimes…

734 01:18:05.710 ⇒ 01:18:12.699 Uttam Kumaran: it’s, like, for… for no use, so it’s like, okay. But some people are really, like, I want to know that we got, like, the highest discount.

735 01:18:12.990 ⇒ 01:18:15.579 Uttam Kumaran: Possible, and we, like, pressured, and so…

736 01:18:16.120 ⇒ 01:18:23.559 Katherine Bayless: Yeah, I think they just want to spend as little as possible. Okay, okay. Yeah, it is interesting.

737 01:18:23.770 ⇒ 01:18:28.280 Uttam Kumaran: I can ask Jay, too. I’ll ask Jay when I… when I meet with him about, like, what he thinks.

738 01:18:28.650 ⇒ 01:18:29.230 Katherine Bayless: Yeah.

739 01:18:29.230 ⇒ 01:18:35.209 Uttam Kumaran: But it’s nice to know that, like, we want to procure stuff through AWS Marketplace, so that’s great.

740 01:18:35.650 ⇒ 01:18:43.040 Uttam Kumaran: So that’s, like, kind of will be my… I mean, that’s gonna be kind of, like, my principled approach, apart from bringing in other tools, and…

741 01:18:43.150 ⇒ 01:18:47.699 Uttam Kumaran: for those other tools, I’m gonna tell them, you get on the marketplace, because we can’t do this deal, basically, so…

742 01:18:47.700 ⇒ 01:18:48.610 Katherine Bayless: Right.

743 01:18:48.610 ⇒ 01:18:49.280 Uttam Kumaran: But that’s…

744 01:18:49.280 ⇒ 01:18:51.019 Katherine Bayless: They can do that, so, yeah.

745 01:18:51.020 ⇒ 01:18:56.470 Uttam Kumaran: Yeah, and if I know Fivetrane you can procure, I should look at some of the other ones, but, okay, that’s helpful.

746 01:18:56.710 ⇒ 01:19:01.950 Uttam Kumaran: So the next, I mean, the next biggest decision for us to…

747 01:19:02.110 ⇒ 01:19:11.809 Uttam Kumaran: probably make is, like, ETL, so what I’m gonna start to do is… is look through for these tools, like, what platform can do some or all of them.

748 01:19:11.980 ⇒ 01:19:25.579 Uttam Kumaran: Also, you know, like, a lot of these ETL platforms, they offer free trials. Some will build connectors, so if it is, like, hey, we’re gonna roll with two until, like, one of our preferred ones builds them all out.

749 01:19:25.840 ⇒ 01:19:29.289 Uttam Kumaran: Like, we can do some interesting things.

750 01:19:29.420 ⇒ 01:19:34.350 Uttam Kumaran: And so, like, Fivetran is a really generous trial program, and I can call them and get more.

751 01:19:34.460 ⇒ 01:19:45.289 Uttam Kumaran: And so there’s opportunities. The other thing is, like, you can actually do a lot of consolidation within Snowflake, so, like, dbt, you can run entirely within Snowflake, so I’m gonna look into that as well.

752 01:19:45.460 ⇒ 01:19:50.550 Uttam Kumaran: If it’s all flat files into S3, we can use Snowpipe, so we don’t even have to go through…

753 01:19:52.060 ⇒ 01:19:54.229 Katherine Bayless: Yeah. ETL for a lot of stuff.

754 01:19:55.420 ⇒ 01:20:03.940 Katherine Bayless: I think, in general, like, having a, like, a pretty nice, like, flat file ingester into Snowflake…

755 01:20:03.940 ⇒ 01:20:05.450 Uttam Kumaran: Totally, it’s the best.

756 01:20:05.640 ⇒ 01:20:21.990 Katherine Bayless: Yeah, well, and like, part of me is like, even for some of these systems that might have a connector, I think there’s gonna be so much legacy… and, like, I mean, I go back and forth, and like, on the one hand, if I support it, they’ll continue doing it. On the other hand, like, I think there’s so much legacy stuff that people are gonna be like, well.

757 01:20:21.990 ⇒ 01:20:27.509 Katherine Bayless: I don’t know how to, like, find it in all of that, but I have my spreadsheet, and I want to use it, right?

758 01:20:27.510 ⇒ 01:20:28.110 Uttam Kumaran: Yeah.

759 01:20:28.330 ⇒ 01:20:30.910 Katherine Bayless: Maybe there’s a grace period where I allow spreadsheets.

760 01:20:32.290 ⇒ 01:20:32.930 Uttam Kumaran: Yeah.

761 01:20:33.100 ⇒ 01:20:34.210 Katherine Bayless: And…

762 01:20:34.210 ⇒ 01:20:39.679 Uttam Kumaran: Yeah, that’ll be up to you. That’d be how much.

763 01:20:39.680 ⇒ 01:20:40.389 Katherine Bayless: Can’t remember.

764 01:20:40.390 ⇒ 01:20:45.540 Uttam Kumaran: stick you want to do on the BI side, but… Yeah, I think,

765 01:20:46.260 ⇒ 01:20:50.270 Uttam Kumaran: So I think I… this is a good place to start. I think…

766 01:20:51.540 ⇒ 01:20:56.079 Uttam Kumaran: Yeah, as much as we can consolidate into Snowflake, I think would be really helpful here. And then,

767 01:20:59.390 ⇒ 01:21:02.860 Uttam Kumaran: Yeah, I kinda… I’ll keep kind of your… your notes,

768 01:21:03.190 ⇒ 01:21:07.899 Uttam Kumaran: in my… I don’t think anything… Shouldn’t necessarily be above that.

769 01:21:08.470 ⇒ 01:21:13.980 Uttam Kumaran: Price point. But the ETL decision is gonna be the one to make

770 01:21:15.130 ⇒ 01:21:22.339 Uttam Kumaran: soon, but really, I’m gonna go through and map out all the ways we can get data out, and then start to identify, like, some vendors, and

771 01:21:22.780 ⇒ 01:21:24.530 Uttam Kumaran: I mean, I would say, like.

772 01:21:24.820 ⇒ 01:21:28.630 Uttam Kumaran: We’re good at just getting… if we can even… a lot of these folks

773 01:21:28.990 ⇒ 01:21:34.069 Uttam Kumaran: They’re just, like… they’re just software vendors, so you can really put pressure on them and get really good pricing.

774 01:21:34.190 ⇒ 01:21:39.370 Uttam Kumaran: Especially because you guys are great logo, and like, yeah, so we’re good at, like, really

775 01:21:39.530 ⇒ 01:21:42.069 Uttam Kumaran: Getting good deals for these folks.

776 01:21:42.720 ⇒ 01:21:47.619 Katherine Bayless: Yeah, yeah. Yeah, and we do allow use of our logo, so…

777 01:21:47.800 ⇒ 01:21:48.570 Uttam Kumaran: It’s great.

778 01:21:48.860 ⇒ 01:21:49.580 Katherine Bayless: Yeah.

779 01:21:49.580 ⇒ 01:21:54.010 Uttam Kumaran: Okay, cool. I feel like I’ve took… taken… yeah.

780 01:21:54.010 ⇒ 01:21:58.450 Katherine Bayless: I was gonna say, the Impexium thing, I keep forgetting, it did come with…

781 01:21:58.600 ⇒ 01:22:01.469 Katherine Bayless: Power Automate, by the way, like, so…

782 01:22:01.470 ⇒ 01:22:02.620 Uttam Kumaran: Oh, okay.

783 01:22:02.620 ⇒ 01:22:17.330 Katherine Bayless: Power Automate, a key… I’ve literally never used Power Automate, no idea, like, what exactly this all entails, and they emailed us the key in plain text, so I sent back, please rotate this, and send me again in a secure way.

784 01:22:17.380 ⇒ 01:22:22.779 Katherine Bayless: But but yes, I don’t know if there’s any, like, leverageable potential on that one.

785 01:22:22.780 ⇒ 01:22:23.550 Uttam Kumaran: I got it.

786 01:22:23.550 ⇒ 01:22:24.160 Katherine Bayless: Yeah.

787 01:22:24.460 ⇒ 01:22:33.280 Uttam Kumaran: Yeah, the other… the other thing I was gonna mention is, like, yeah, typically we would implement… I mean, if most of your stuff is modeling, then we can get away with just dbt, but…

788 01:22:33.820 ⇒ 01:22:47.250 Uttam Kumaran: like, we would typically implement, like, a Dagster or, like, an Airflow, but the other thing is, you… Snowflake has now a pretty good task-based orchestrator that can execute Python workloads directly in Snowflake.

789 01:22:47.420 ⇒ 01:22:54.709 Uttam Kumaran: So this is a fun… I like this gig, because it’s, like, we just try to consolidate as much into Snowflake as possible, and

790 01:22:55.630 ⇒ 01:23:03.100 Uttam Kumaran: Yeah, it’s actually, like, it’s a great… now that Stuff like has a lot of those tools, I feel like we should be just fine with that, for the most part.

791 01:23:03.960 ⇒ 01:23:05.829 Katherine Bayless: Actually, I think that’s a good…

792 01:23:06.210 ⇒ 01:23:25.069 Katherine Bayless: It is actually… it’s a good way to think about it, too, from, like, the educating and growing people’s, like, comfort with some of this stuff, is, like, we can probably get really far in Snowflake, but also start telling the story of, like, look, we’re pushing it to its limits. If we really want to get further, we, you know.

793 01:23:25.070 ⇒ 01:23:28.449 Uttam Kumaran: Yeah. This tool to it, right? Yeah. Yeah.

794 01:23:28.470 ⇒ 01:23:29.000 Katherine Bayless: Yeah.

795 01:23:29.000 ⇒ 01:23:41.100 Uttam Kumaran: But I wouldn’t, like, I would say 2 years ago, or even 3 years ago, like, it would not make sense to do a lot of this in Snowflake. They were just really immature in some of these other things, but they’ve built the platform in a way where you can do everything in there.

796 01:23:41.240 ⇒ 01:23:42.410 Uttam Kumaran: Yeah.

797 01:23:42.560 ⇒ 01:23:45.990 Uttam Kumaran: And then we can decide, basically,

798 01:23:46.410 ⇒ 01:23:50.340 Uttam Kumaran: You know, to split things out pretty easily, but…

799 01:23:50.630 ⇒ 01:23:56.239 Uttam Kumaran: Given that the tax year is actually not the budget, but more of, like, the approval side.

800 01:23:56.630 ⇒ 01:23:58.780 Uttam Kumaran: That’s gonna kill all momentum.

801 01:23:58.780 ⇒ 01:24:01.229 Katherine Bayless: You know? And, like.

802 01:24:01.230 ⇒ 01:24:07.559 Uttam Kumaran: Snowflake will… for our IT and InfoSec folks, that’s great, because it’s just one.

803 01:24:08.050 ⇒ 01:24:08.600 Katherine Bayless: True.

804 01:24:08.600 ⇒ 01:24:09.400 Uttam Kumaran: place.

805 01:24:09.590 ⇒ 01:24:10.780 Katherine Bayless: True, yeah, yeah.

806 01:24:10.780 ⇒ 01:24:11.380 Uttam Kumaran: You know?

807 01:24:11.380 ⇒ 01:24:12.769 Katherine Bayless: It is something that I do.

808 01:24:12.770 ⇒ 01:24:15.629 Uttam Kumaran: And, like, Snowflake has, like, the AI features, too.

809 01:24:16.010 ⇒ 01:24:20.459 Uttam Kumaran: So… Yeah, I think maybe I kind of think about that.

810 01:24:22.480 ⇒ 01:24:29.129 Uttam Kumaran: It’s such a… yeah, I feel like some places, like, it’s low budget, but, like, you can get stuff signed. Here, it’s a little bit opposite.

811 01:24:29.260 ⇒ 01:24:35.489 Uttam Kumaran: Right. So, I want to, like, make sure that we’re not, like, bogged down in, like, getting a big vendor for 6 months.

812 01:24:35.860 ⇒ 01:24:44.400 Uttam Kumaran: And then we just sort of, like, die, you know? There’s just, like, no momentum. So I don’t want to do that. I just want to be like, we got Snowflake signed off.

813 01:24:44.800 ⇒ 01:24:51.899 Uttam Kumaran: Great, I’m gonna… we’ve become the poster child for, like, leveraging all Snowflake’s features.

814 01:24:51.900 ⇒ 01:25:02.749 Katherine Bayless: Yeah, like, when I… the guy I interviewed earlier, he was, also, like, elbow-deep in AWS Glue, and I was like, how do you like it? And he’s like, it sucks, but I know exactly how to do everything I need, and.

815 01:25:02.750 ⇒ 01:25:03.390 Uttam Kumaran: Yeah.

816 01:25:03.390 ⇒ 01:25:04.760 Katherine Bayless: Perfect. Yeah, yeah.

817 01:25:04.760 ⇒ 01:25:05.280 Uttam Kumaran: Yeah.

818 01:25:05.790 ⇒ 01:25:09.680 Uttam Kumaran: I mean, it’s good for security, but, like, you could tell, they just, like, don’t… they don’t need to…

819 01:25:10.120 ⇒ 01:25:14.140 Uttam Kumaran: Build out that product, because it’s, like, comes bundled. Like, it’s…

820 01:25:14.510 ⇒ 01:25:19.099 Uttam Kumaran: You buy everything at one shop, so they’re not gonna innovate on, like, every single thing.

821 01:25:19.320 ⇒ 01:25:20.000 Uttam Kumaran: Yeah.

822 01:25:20.250 ⇒ 01:25:21.870 Katherine Bayless: Exactly.

823 01:25:21.870 ⇒ 01:25:22.780 Uttam Kumaran: So…

824 01:25:23.510 ⇒ 01:25:40.839 Katherine Bayless: There is maybe some room and interest in the new year for, like, a big picture observability tool, like Datadog or something like that. Like, Jay’s very interested in, like, right now, he’s like, my kingdom is tiny, but I’d like it to be bigger, and if it’s gonna be bigger, I need to be able to watch it, so…

825 01:25:41.370 ⇒ 01:25:46.420 Uttam Kumaran: Yeah, so let me talk to him about that. That’s also a lot of stuff that I’ve done in the past.

826 01:25:46.560 ⇒ 01:25:52.679 Uttam Kumaran: like, we’re evaluating all the major data observability logging platforms. I mean, Datadog is sort of, like.

827 01:25:53.540 ⇒ 01:25:57.590 Uttam Kumaran: Kind of, like, king there right now, but there are a lot of options.

828 01:25:57.710 ⇒ 01:26:02.580 Uttam Kumaran: So I can… I’m gonna… I’ll put that on my notes to talk to him about.

829 01:26:03.180 ⇒ 01:26:10.619 Katherine Bayless: Okay. Yeah, we used Datadog at my last place, and I will say that I managed to harness, like, 3%, I think, of the functionality.

830 01:26:10.620 ⇒ 01:26:13.570 Uttam Kumaran: Extremely confusing, and now it’s massive.

831 01:26:13.570 ⇒ 01:26:13.950 Katherine Bayless: Yeah.

832 01:26:13.950 ⇒ 01:26:18.809 Uttam Kumaran: There’s, like, a couple of other firms that are really good, like, LogRock is good.

833 01:26:18.810 ⇒ 01:26:19.410 Katherine Bayless: There’s a couple.

834 01:26:19.410 ⇒ 01:26:20.970 Uttam Kumaran: other companies. I’ll sort of ask.

835 01:26:21.200 ⇒ 01:26:24.580 Uttam Kumaran: him, because ideally, yeah, I want to make sure to use it for…

836 01:26:24.910 ⇒ 01:26:29.480 Uttam Kumaran: Our stuff on the reporting side, and then if he can get stuff out of that, too.

837 01:26:29.800 ⇒ 01:26:31.460 Uttam Kumaran: That would be great, so…

838 01:26:32.060 ⇒ 01:26:41.029 Katherine Bayless: Yeah. In the meantime, I did at least set up, like, you know, CloudTrail and AWS Config and some of those things, so that we’re at least monitoring all the stuff that’s happening in there, but…

839 01:26:41.230 ⇒ 01:26:41.850 Uttam Kumaran: Yeah.

840 01:26:43.510 ⇒ 01:26:44.180 Katherine Bayless: Yeah, but…

841 01:26:44.180 ⇒ 01:26:45.680 Uttam Kumaran: Okay, perfect.

842 01:26:45.790 ⇒ 01:26:50.310 Uttam Kumaran: This is great. I have so many… so much stuff answered. This is amazing.

843 01:26:51.100 ⇒ 01:26:55.510 Katherine Bayless: It’s funny, like, I feel like every time I talk to you, I’m like, okay, I can do this, I can do this.

844 01:26:55.510 ⇒ 01:27:05.109 Uttam Kumaran: No, you could totally do this! Yeah, no, I just want to make sure that, like, we’re not… we’re not like, oh, it would be great to run, like, this ritual, and then, like, you run it. It’s like, I’m like.

845 01:27:05.230 ⇒ 01:27:23.030 Uttam Kumaran: I will take on… like, we can take that stuff on and have people run with us, and then eventually, like, I’m gonna start having them try to run some of those. Yeah. Because you want us in the… in, like, whatever the worst situation is. Or, like, whatever the hardest, or the thing where you’re like, just go figure this out.

846 01:27:23.610 ⇒ 01:27:26.840 Uttam Kumaran: That’s where you want us? Did you want to save your people from…

847 01:27:27.020 ⇒ 01:27:45.790 Uttam Kumaran: that, because that… that… we won’t… like, I… I won’t get emotional, I guess is what I’m saying. So, like, I just… I come in, and I’m like, I’m just a consultant, like, I’m asking these questions, like, I’m working for you. But, like, the internal people, I want to make sure they actually, like, learn, and they do that safely.

848 01:27:46.730 ⇒ 01:27:53.139 Katherine Bayless: Yeah, exactly, exactly, yes. Yes. No, it’s awesome, really awesome.

849 01:27:53.260 ⇒ 01:27:59.960 Katherine Bayless: like, yeah, I’ve already… I’ve mentioned you to a couple other friends, I don’t know if you’ll ever get any phone calls necessarily, but .

850 01:27:59.960 ⇒ 01:28:01.550 Uttam Kumaran: Oh, I appreciate that.

851 01:28:01.550 ⇒ 01:28:08.680 Katherine Bayless: former boss of mine, who’s now at the American Association of Physicists in Medicine, I think? Yeah, yeah, yeah, Physicists in Medicine.

852 01:28:08.680 ⇒ 01:28:23.880 Katherine Bayless: and he’s looking for a director of IT membership and marketing for $120,000 a year, and I was like, good luck, dear. But he’s like, girl, it’s crazy over here. Physicists invented the internet, so they think they’re technical, so they built everything in-house, and they are not…

853 01:28:23.880 ⇒ 01:28:24.630 Uttam Kumaran: Yeah…

854 01:28:24.880 ⇒ 01:28:28.029 Katherine Bayless: the people who should have done that, and I was like, you should call this guy out.

855 01:28:28.030 ⇒ 01:28:38.540 Uttam Kumaran: I did… I did okay at physics, yeah, but none of my work is physics, like, it’s like, if I’m lucky, we get to do some multiplication around here, like, maybe some division.

856 01:28:38.780 ⇒ 01:28:49.749 Uttam Kumaran: only once sometimes in my career were they doing, like, derivatives and things like that for SQL, but yeah. No, I’m happy to… happy to talk and help anyone out, you know, that’s awesome, I appreciate that.

857 01:28:50.280 ⇒ 01:28:56.110 Katherine Bayless: Yeah, yeah. I’m just really excited. The collaboration is much appreciated, and I feel like…

858 01:28:56.370 ⇒ 01:28:59.619 Katherine Bayless: Yeah, courage to fight one more day.

859 01:28:59.620 ⇒ 01:29:17.119 Uttam Kumaran: Yeah, definitely. No, and we’ll… we’re good at, like, trying to codify these things and documents and keep them fairly up-to-date, so hopefully, as you go to meetings, like, you have… you’ll have this Gantt chart, you’ll have some project plans, and then as we start to roll, and we have more check-ins, we’ll have decks and things that you can repurpose, so…

860 01:29:17.220 ⇒ 01:29:21.609 Uttam Kumaran: Just be easy for you to share wins, you know, and things like that, so…

861 01:29:22.190 ⇒ 01:29:34.559 Katherine Bayless: Yeah, yeah, yeah, I think the OKRs and stuff will be really helpful, too, because, like, I’ll be leading the charge with saying, like, here’s a way that I am measuring my performance, and sharing it openly, and be like, what?

862 01:29:34.940 ⇒ 01:29:37.310 Uttam Kumaran: Yeah, I know, it’s rare.

863 01:29:37.310 ⇒ 01:29:40.580 Katherine Bayless: Right. Right. For slide decks, do you use Gamma?

864 01:29:41.680 ⇒ 01:29:50.820 Uttam Kumaran: We started using Gamma, I… I was using it, and then the new version released, and I haven’t used it. I’m trying to get my team to build

865 01:29:51.120 ⇒ 01:30:03.960 Uttam Kumaran: using on the API, because we have, we have, like, we have a pretty good deck format, and we have, like, our brand book, and I’m like, guys, the new API just came out, like, can we start to automate some of these? So that’s, like, gonna be someone’s, like.

866 01:30:04.400 ⇒ 01:30:19.269 Uttam Kumaran: Christmas project to, like, think about how to do, because we do a lot of deck work, and we’re… we’re pretty good, and, like, but I would like it to be all prompt-based, so we’re not spending time, like, doing Google Slides things, but we… I… we’ve used… we use… some people in the company use Canva, yeah.

867 01:30:19.660 ⇒ 01:30:20.820 Katherine Bayless: Yeah, yeah, yeah, I’ve…

868 01:30:20.820 ⇒ 01:30:21.630 Uttam Kumaran: Pretty good.

869 01:30:21.910 ⇒ 01:30:27.269 Katherine Bayless: Yeah, I mean, like, obviously in my world, I don’t have to make slides that often, it’s more like if I’m giving a talk,

870 01:30:27.270 ⇒ 01:30:27.910 Uttam Kumaran: Yay.

871 01:30:27.910 ⇒ 01:30:38.900 Katherine Bayless: I do like it. I increasingly… I’m getting to the point where I’m like, hmm, it’s very good at design and layout and all that kind of stuff, but I’m like, I wish it would also, like, comment on your narrative arc, or like, you know…

872 01:30:38.900 ⇒ 01:30:42.769 Uttam Kumaran: Yeah, usually I’m exporting it to PDF and putting it in the GPT.

873 01:30:42.900 ⇒ 01:30:46.859 Uttam Kumaran: And asking for, like, the copy. What I found is actually, like.

874 01:30:47.150 ⇒ 01:30:51.869 Uttam Kumaran: Doing an ugly version first, nailing the content, and then…

875 01:30:52.590 ⇒ 01:30:55.680 Uttam Kumaran: Shove it into there, versus I think people start there.

876 01:30:55.980 ⇒ 01:30:59.019 Uttam Kumaran: Because it’ll just, like, hallucinate a bunch of stuff.

877 01:30:59.020 ⇒ 01:30:59.840 Katherine Bayless: Yeah.

878 01:30:59.840 ⇒ 01:31:04.039 Uttam Kumaran: And so I usually make an ugly version. I almost treat it like I would…

879 01:31:04.140 ⇒ 01:31:14.700 Uttam Kumaran: some of our decks, where I’m like, I make a really lucky version and have someone on our marketing team help me just, like, make it look way better. But, like, I couldn’t… but, like, they… they’re good at that. They can’t…

880 01:31:14.770 ⇒ 01:31:27.670 Uttam Kumaran: they wouldn’t have been able to take what’s in my brain and start it there, so… I’m almost trying to treat it more like, okay, if it was, like, a marketing intern, I would have to give it, like, the first ugly version or something, so that it… it doesn’t, like, try other stuff, you know?

881 01:31:28.000 ⇒ 01:31:34.229 Katherine Bayless: Right, right, exactly, exactly. Like, I feel like I think in sticky note, and, like, one line at a time.

882 01:31:34.840 ⇒ 01:31:41.700 Katherine Bayless: Yeah. So I’m like, yeah, whatever this is can’t be a prompt, whether it’s to a human or an AI. I’ve gotta, like.

883 01:31:41.700 ⇒ 01:31:48.439 Uttam Kumaran: Are you… do you use, like, Whisper Flow at all? Or anything like, voice-to-text?

884 01:31:49.120 ⇒ 01:31:51.830 Katherine Bayless: Hmm, no, what is this?

885 01:31:51.830 ⇒ 01:31:58.680 Uttam Kumaran: consider Whisper. I’ll send it to you, you should consider using it. So, a lot of times, like, when you’re… when you’re working with AI,

886 01:31:58.800 ⇒ 01:32:04.430 Uttam Kumaran: you’re, like, doing a lot of typing, and basically, of course, the AI’s typically

887 01:32:04.730 ⇒ 01:32:07.849 Uttam Kumaran: Small changes to your prompt can actually improve the…

888 01:32:08.070 ⇒ 01:32:21.969 Uttam Kumaran: like, the outcomes a lot, and so usually my… my rule of thumb is that, like, it’s only as good as your context, so you need to just give it a lot more. And so typically, of course, people are typing, and typing is not as effective as if you were just to speak.

889 01:32:22.210 ⇒ 01:32:40.190 Uttam Kumaran: Typically, you can speak and talk, and talk, and talk, and you get 2 minutes of prompt, versus, like… I don’t know if I could have written that, like, you’re… it doesn’t flow that way. I’m not a writer, so it doesn’t flow directly to the hand. So, I’ve been using it to do speech-to-text for almost, like, a year now, and it’s amazing, because I just sit here, click the button, talk.

890 01:32:40.470 ⇒ 01:32:45.180 Uttam Kumaran: Press… it types it out, and it’s not like, when you use a voice mode where

891 01:32:45.320 ⇒ 01:32:48.170 Uttam Kumaran: it talks… I still want the output to come back in text.

892 01:32:48.280 ⇒ 01:32:59.029 Uttam Kumaran: But instead, I’m able to give it way more input. Very similarly, like, if I have to type out a long answer to somebody these days, I don’t type it at all. I’m usually just, like, fucking it out.

893 01:32:59.210 ⇒ 01:33:01.110 Uttam Kumaran: It’s very, very helpful.

894 01:33:01.530 ⇒ 01:33:08.720 Katherine Bayless: Yeah, I mean, I use voice a lot, but, like, I’ve… it sounds silly, but I’ve never thought about a tool?

895 01:33:09.410 ⇒ 01:33:11.960 Katherine Bayless: Like, and even I’m, like, really.

896 01:33:11.960 ⇒ 01:33:16.260 Uttam Kumaran: No, but you used to have Dragon, right? Dragon used to be, like, the big speech-to-text.

897 01:33:16.590 ⇒ 01:33:21.009 Uttam Kumaran: But it used to be for, like, writers, and, like, it was kind of janky. Now.

898 01:33:21.010 ⇒ 01:33:22.379 Katherine Bayless: the LLM…

899 01:33:22.390 ⇒ 01:33:26.849 Uttam Kumaran: Voice understanding is very, very good, and basically real-time.

900 01:33:27.090 ⇒ 01:33:34.539 Katherine Bayless: Well, so what I’ll do a lot of times is, like, I’ll do… like, I like opening eyes, because it’s actually processing the raw audio versus transcribing and writing.

901 01:33:34.540 ⇒ 01:33:35.339 Uttam Kumaran: Yeah, yeah, yeah.

902 01:33:35.340 ⇒ 01:33:42.390 Katherine Bayless: I’ll talk to ChatGPT voice mode, and then I’ll say, like, okay, now write a prompt that I can take to Claude.

903 01:33:44.620 ⇒ 01:33:51.229 Uttam Kumaran: Yeah, you know, I mean, to each their own, yeah, you should be… you should just be doing this all in cursor.

904 01:33:51.970 ⇒ 01:33:53.580 Uttam Kumaran: You could easily switch, you know?

905 01:33:53.730 ⇒ 01:34:03.629 Katherine Bayless: Yeah, I actually… Jay was interested in, like, hearing from you, actually, probably, and anybody else. He’s like, he’s… because he’s like, all the cool kids are using cursor, but I can’t figure out, like, should I really.

906 01:34:03.630 ⇒ 01:34:16.140 Uttam Kumaran: Oh, I would… I would love to show him. He’ll be, like, in there all day. Yeah, it’s just… I was just talking to a friend yesterday, and I was like, I want… I think everybody should… every role in my company should be in Kirscher, because

907 01:34:16.230 ⇒ 01:34:24.490 Uttam Kumaran: You could write, you could do project planning, you could do mermaid diagrams, you can ship PRs, you can call external things.

908 01:34:24.690 ⇒ 01:34:26.640 Uttam Kumaran: You can build apps.

909 01:34:26.960 ⇒ 01:34:39.509 Uttam Kumaran: I’m like, well, maybe every… maybe I should just force everybody to do their job in cursor, because for, like, for you guys, right, we’ll create a repo, we’ll show… I’ll put in the project plans, I’ll put in our meetings, I’ll put in these notes.

910 01:34:39.860 ⇒ 01:34:49.800 Uttam Kumaran: And then it makes it way easier to iterate, like, when I’m writing a dbt model, I actually do want it to have context on, like, what we’re here to do, so that it makes the right trade-offs or focuses on the right thing.

911 01:34:50.460 ⇒ 01:34:52.000 Uttam Kumaran: And I’m like, okay.

912 01:34:52.380 ⇒ 01:34:57.789 Uttam Kumaran: Yeah, like, I want everybody to be working in there, you know? Even the… even our PMs, like, I want them to start there.

913 01:34:58.120 ⇒ 01:34:58.750 Katherine Bayless: Yeah.

914 01:34:59.330 ⇒ 01:35:00.410 Uttam Kumaran: Because, yeah.

915 01:35:00.970 ⇒ 01:35:03.830 Katherine Bayless: like, that context ops is everything. I mean, like, I…

916 01:35:03.830 ⇒ 01:35:04.360 Uttam Kumaran: Yes.

917 01:35:04.360 ⇒ 01:35:13.210 Katherine Bayless: To a certain extent, that’s ultimately what Jay and Mai’s team will become someday in the future, right? It’s, like, as your context ops team, and some of the people… Yes, this is knowledge engineering is the… yeah.

918 01:35:13.210 ⇒ 01:35:23.190 Katherine Bayless: Yeah, right? Exactly. Like, I mean, some of the projects that I built out in Claude when I started were basically, like, I would… because I’m a… I do do handwritten notes, because it helps me focus.

919 01:35:23.190 ⇒ 01:35:41.400 Katherine Bayless: And so I had it, like, trained on my handwriting so I could take a picture of my notes, and it would transcribe them, and then kind of create these little, like, atomic knowledge cards, and then I would put those into the project where I was building on AWS, and so, like, I could ask it, you know, like, to use the instruction set for the, you know, console expert with the context of all these meetings.

920 01:35:41.400 ⇒ 01:35:41.890 Uttam Kumaran: Yes.

921 01:35:41.890 ⇒ 01:35:48.519 Katherine Bayless: And, like, I mean, I blew people’s minds when I would be like, hey, what was that thing I needed to ask Emily about? You know, that kind of thing.

922 01:35:48.520 ⇒ 01:35:48.860 Uttam Kumaran: Yeah.

923 01:35:48.860 ⇒ 01:35:51.440 Katherine Bayless: It makes a difference, like, yeah. Huge difference.

924 01:35:51.440 ⇒ 01:35:52.130 Uttam Kumaran: reference.

925 01:35:52.310 ⇒ 01:35:52.900 Katherine Bayless: Yeah.

926 01:35:52.900 ⇒ 01:36:01.309 Uttam Kumaran: Okay, now I’ll show them our, like, cursor setup. Actually, maybe I’ll set up your repo for cursor and stuff, and then I’ll send it to them, yeah.

927 01:36:01.710 ⇒ 01:36:08.609 Katherine Bayless: Yeah, yeah, yeah, yeah, yeah, that would be good, yeah, because I think I can keep you going for it, and I would love to, like, move in that direction.

928 01:36:08.940 ⇒ 01:36:09.760 Uttam Kumaran: Yeah.

929 01:36:10.930 ⇒ 01:36:22.339 Uttam Kumaran: Okay, perfect. Well, I appreciate the time. I’m gonna do kind of my best to get some of this organized. Maybe… are you off all week, or are you working until Thursday, or…

930 01:36:22.660 ⇒ 01:36:33.840 Katherine Bayless: I am, yeah, I’m around. Basically, from noon to 2 on Thursday, I will be forced to do family things, but otherwise, please save me from the holidays, yeah.

931 01:36:33.840 ⇒ 01:36:49.209 Uttam Kumaran: Okay, so if I can get something organized and start to get us to think about one of these directions, that’d be great, and then ideally may… yeah, I know a lot of folks may be off or not really responsive, so maybe we wait to

932 01:36:49.320 ⇒ 01:36:54.689 Uttam Kumaran: hit people up for access and stuff if I need to for next week, but at least I can start to…

933 01:36:54.930 ⇒ 01:36:56.879 Uttam Kumaran: Oh, okay, okay. Yeah, yeah. Okay, cool.

934 01:36:56.880 ⇒ 01:37:01.940 Katherine Bayless: Actually, I was gonna say, like, yeah, a lot of people are out, but I’m here, and so it’s a great time to, like, soak.

935 01:37:01.940 ⇒ 01:37:02.640 Uttam Kumaran: Oh, great.

936 01:37:02.640 ⇒ 01:37:07.019 Katherine Bayless: time and attention, but yeah, everything that’s in that list, I can grant…

937 01:37:07.020 ⇒ 01:37:07.520 Uttam Kumaran: Perfect.

938 01:37:07.520 ⇒ 01:37:10.550 Katherine Bayless: or whatever. Yeah, yeah, yeah, yeah, so just.

939 01:37:10.550 ⇒ 01:37:11.430 Uttam Kumaran: Okay, okay.

940 01:37:11.750 ⇒ 01:37:12.079 Katherine Bayless: And then you…

941 01:37:12.080 ⇒ 01:37:12.430 Uttam Kumaran: Perfect.

942 01:37:12.430 ⇒ 01:37:16.899 Katherine Bayless: They did send us that, data share in the other EIA.

943 01:37:16.900 ⇒ 01:37:17.440 Uttam Kumaran: Yeah.

944 01:37:17.870 ⇒ 01:37:22.710 Katherine Bayless: Yeah, whatever makes sense for getting the data out and into somewhere else.

945 01:37:23.020 ⇒ 01:37:25.730 Katherine Bayless: Or even just looking at it in there, I guess, but…

946 01:37:25.730 ⇒ 01:37:26.310 Uttam Kumaran: Okay.

947 01:37:26.840 ⇒ 01:37:31.929 Katherine Bayless: But yeah, definitely don’t be shy, and like, I think I have no meeting tomorrow or Wednesday, so I am.

948 01:37:31.930 ⇒ 01:37:36.749 Uttam Kumaran: Oh, great, okay, then I’m gonna try to jam on some stuff, probably tomorrow, and then see what I can get out.

949 01:37:37.450 ⇒ 01:37:38.060 Katherine Bayless: Okay.

950 01:37:38.460 ⇒ 01:37:39.080 Katherine Bayless: I like it.

951 01:37:39.080 ⇒ 01:37:39.770 Uttam Kumaran: Okay.

952 01:37:39.820 ⇒ 01:37:44.589 Katherine Bayless: Awesome. He tries around tomorrow and Wednesday, I think he’s out Thursday, Friday.

953 01:37:44.590 ⇒ 01:37:48.879 Uttam Kumaran: Yeah, maybe I’ll see if, like, maybe the three of us can hop on, or… yeah.

954 01:37:49.190 ⇒ 01:37:56.580 Uttam Kumaran: just say hi again, and, you know, start thinking about overall plan, and I can brief them on, sort of, like, what we’re… where we’re going, so…

955 01:37:56.940 ⇒ 01:37:58.980 Katherine Bayless: Yeah, yeah, yeah, that’d be good.

956 01:37:59.100 ⇒ 01:37:59.910 Katherine Bayless: Yeah.

957 01:37:59.910 ⇒ 01:38:00.580 Uttam Kumaran: Okay.

958 01:38:00.760 ⇒ 01:38:03.880 Uttam Kumaran: Perfect. Well, appreciate it. Thank you so much.

959 01:38:04.140 ⇒ 01:38:07.380 Katherine Bayless: Thank you. I think my brain is about to, like, flatline, say.

960 01:38:07.380 ⇒ 01:38:09.929 Uttam Kumaran: No, this was a lot. This was a lot to go through, so…

961 01:38:10.140 ⇒ 01:38:14.050 Uttam Kumaran: This is good. But very helpful. Just once, hopefully, on a lot of these.

962 01:38:14.050 ⇒ 01:38:15.069 Katherine Bayless: Don’t worry, don’t be shy.

963 01:38:15.750 ⇒ 01:38:20.580 Katherine Bayless: But they say it takes an adult, like, 6 times before they actually remember a fact or something like that?

964 01:38:20.890 ⇒ 01:38:29.009 Uttam Kumaran: Oh, yeah, maybe, probably. Well, that’s why we need the AR, so we could get that down to one… one or two. Exactly.

965 01:38:29.010 ⇒ 01:38:32.369 Katherine Bayless: But… Cool. Well, thank you, thank you. Okay, alright.

966 01:38:32.370 ⇒ 01:38:33.990 Uttam Kumaran: Thank you. Glad to see you.

967 01:38:33.990 ⇒ 01:38:34.770 Katherine Bayless: Thank you.