2025-12-05_brainforge_x_cta_weekly_8e86aa48

Meeting Title: Brainforge x CTA: Weekly! Date: 2025-12-05 Meeting participants: Ashwini Sharma, Uttam Kumaran, Katherine Bayless

---

WEBVTT

1 00:00:37.260 ⇒ 00:00:38.490 Uttam Kumaran: Hello, sir.

2 00:00:40.420 ⇒ 00:00:41.640 Ashwini Sharma: Hello!

3 00:00:42.270 ⇒ 00:00:45.160 Uttam Kumaran: Hey, how are you? Thanks for hopping on, I know it’s late.

4 00:00:45.350 ⇒ 00:00:46.810 Ashwini Sharma: No issues, yeah.

5 00:00:48.740 ⇒ 00:00:52.479 Uttam Kumaran: I’m just grabbing some lunch, sorry, just off-camera for a second.

6 00:00:52.770 ⇒ 00:00:53.920 Ashwini Sharma: No problem, man.

7 00:00:56.670 ⇒ 00:00:58.830 Uttam Kumaran: How did you think about the other, the other meeting?

8 00:00:59.820 ⇒ 00:01:11.590 Ashwini Sharma: Yeah, I’m not very clear on the scope, but I get some, you know, very high-level understanding. You just need to get the data from spins into their warehouse, and…

9 00:01:13.410 ⇒ 00:01:18.379 Ashwini Sharma: Yeah, do they already have an ETL tool for doing that, or is it something that we need to…

10 00:01:18.380 ⇒ 00:01:20.810 Uttam Kumaran: Prefect, but I think we’re gonna…

11 00:01:21.040 ⇒ 00:01:22.800 Uttam Kumaran: I think we’re gonna write the…

12 00:01:23.990 ⇒ 00:01:26.639 Uttam Kumaran: We’re probably gonna have to write Python to move it over.

13 00:01:27.210 ⇒ 00:01:28.960 Ashwini Sharma: Oh, okay.

14 00:01:29.210 ⇒ 00:01:30.359 Ashwini Sharma: Because there is no…

15 00:01:31.160 ⇒ 00:01:35.339 Uttam Kumaran: Yeah, I don’t… I mean, it’s not clear that they have, like, another ETL tool.

16 00:01:36.790 ⇒ 00:01:41.189 Ashwini Sharma: And, where does that run? Like, where does the Python.

17 00:01:41.710 ⇒ 00:01:44.880 Uttam Kumaran: In… in Prefect itself. So, Prefect is like an airflow…

18 00:01:45.140 ⇒ 00:01:45.590 Ashwini Sharma: Okay.

19 00:01:46.100 ⇒ 00:01:47.640 Uttam Kumaran: It’s like a hosted airflow.

20 00:01:48.500 ⇒ 00:01:55.440 Ashwini Sharma: Okay, oh, okay, so they’re doing all the things that generally, like, things like Fivetran or Airbuy do?

21 00:01:56.200 ⇒ 00:02:06.089 Uttam Kumaran: I… well, I think… I don’t know, I think they… I mean, this is where I’m gonna have to… maybe it’s a good follow-up question, but… yeah, it’s not clear whether they…

22 00:02:06.370 ⇒ 00:02:09.199 Uttam Kumaran: have written all the ETL themselves or not, so…

23 00:02:09.310 ⇒ 00:02:16.790 Uttam Kumaran: But you should check it out, check out Prefect. I feel like it may… they may just have connectors, some connectors there, and then ability to write custom.

24 00:02:18.870 ⇒ 00:02:20.730 Ashwini Sharma: Prefactor it, let me note that down.

25 00:02:20.730 ⇒ 00:02:21.350 Uttam Kumaran: Yeah.

26 00:02:31.790 ⇒ 00:02:32.680 Ashwini Sharma: Hi, Catherine.

27 00:02:34.590 ⇒ 00:02:35.360 Uttam Kumaran: So…

28 00:02:35.500 ⇒ 00:02:38.249 Katherine Bayless: Sorry about that, just wrapping up my 1 o’clock.

29 00:02:39.010 ⇒ 00:02:46.049 Uttam Kumaran: No problem, I’m just, eating some lunch, so… so I will be on camera in a sec. It’s just been a back-to-back day.

30 00:02:46.490 ⇒ 00:02:49.489 Katherine Bayless: Isn’t it? It is funny, like, yeah, yeah, it’s been a back-to-back day.

31 00:02:49.490 ⇒ 00:02:51.930 Uttam Kumaran: I just need to get calories in.

32 00:02:52.120 ⇒ 00:03:04.049 Katherine Bayless: Right? Well, no, I was gonna say, it’s funny, like, in person, I’ll eat in front of humans, but I’m the same way. Like, on Zoom, like, nobody needs a Zoom recording of me cookie-monstering a sandwich. Like, that’s just, yeah, no.

33 00:03:04.050 ⇒ 00:03:08.160 Uttam Kumaran: Yeah, only our team, only when it’s internal, I’m okay, but…

34 00:03:08.160 ⇒ 00:03:08.590 Katherine Bayless: Yeah.

35 00:03:09.090 ⇒ 00:03:09.610 Katherine Bayless: Yeah.

36 00:03:10.020 ⇒ 00:03:11.870 Uttam Kumaran: How’s the week going?

37 00:03:12.490 ⇒ 00:03:20.399 Katherine Bayless: It’s good. It’s definitely, I mean, I think at this point, they’re all gonna be increasingly eventful,

38 00:03:20.400 ⇒ 00:03:21.130 Uttam Kumaran: Yes.

39 00:03:21.130 ⇒ 00:03:22.370 Katherine Bayless: Yeah.

40 00:03:22.760 ⇒ 00:03:27.690 Katherine Bayless: Yeah, I just… I was like… I was like, oh, I feel like I’ve neglected, to really even kind of.

41 00:03:27.690 ⇒ 00:03:41.919 Uttam Kumaran: No, we’ve made… we’ve made good progress on our side. I think I just wanted to… the biggest thing for me is to… we can move the Snowflake thing a little bit forward, and then the ETL stuff, but we’re establishing the repo, and, like, we looked into remembers, and so we’re still making progress.

42 00:03:42.200 ⇒ 00:03:44.530 Katherine Bayless: Okay, okay, okay, yeah.

43 00:03:45.970 ⇒ 00:03:59.949 Katherine Bayless: I, so we had to stand up with SDG this morning, they’re finally starting to kind of get legs under them, and so they are going to start working on their POC, for the, like, BI tool kind of thing. So they’ll also be.

44 00:03:59.950 ⇒ 00:04:00.300 Uttam Kumaran: Okay.

45 00:04:00.300 ⇒ 00:04:05.329 Katherine Bayless: I was like, I do want both teams to do a Sigma one. And then they’re…

46 00:04:05.480 ⇒ 00:04:11.779 Katherine Bayless: guys, I have to say this, like, off the record, I guess, maybe, but, like, they suggested Qlik, and I was like, ew, what? No, I’m not gonna…

47 00:04:11.780 ⇒ 00:04:13.130 Uttam Kumaran: Why?

48 00:04:13.130 ⇒ 00:04:20.419 Katherine Bayless: Right? I was like, I don’t care how far the product may or may not have come, I still remember Click from 15 years ago when I picked Tableau over it. Like, no, just no.

49 00:04:20.420 ⇒ 00:04:22.689 Uttam Kumaran: Oh, why are they doing that? Yeah, it’s crazy.

50 00:04:23.080 ⇒ 00:04:42.390 Katherine Bayless: I think they’re a very, very traditional shop in that way. Okay, okay. They’re gonna do… they’re gonna give us a click option, which is fine. And then they’re looking at, like, a pure snowflake, acknowledging our organization’s very frugal posture, like, you know, what if you didn’t have a different tool? And then they…

51 00:04:43.320 ⇒ 00:04:50.239 Katherine Bayless: they were going to do ThoughtSpot, but then they were like, we’ll focus on the other three, I think was kind of where they landed.

52 00:04:50.240 ⇒ 00:04:50.560 Uttam Kumaran: Okay.

53 00:04:50.560 ⇒ 00:04:51.120 Katherine Bayless: But…

54 00:04:51.480 ⇒ 00:04:51.830 Uttam Kumaran: Okay.

55 00:04:51.830 ⇒ 00:04:57.589 Katherine Bayless: Yeah. So they’ve got that going, and then, they’re also working on some of the, like.

56 00:04:58.130 ⇒ 00:05:01.379 Katherine Bayless: Power BI, sort of, like, inventory, and just, like.

57 00:05:01.380 ⇒ 00:05:02.499 Uttam Kumaran: Yeah, of what’s in there.

58 00:05:02.500 ⇒ 00:05:02.990 Katherine Bayless: Right.

59 00:05:02.990 ⇒ 00:05:03.320 Uttam Kumaran: Yeah.

60 00:05:03.320 ⇒ 00:05:17.229 Katherine Bayless: Yeah, before I jettison it all. So, I think if it makes sense, maybe next week we can get a, like, a team of teams meeting kind of going, and have you guys all meet each other, because I certainly don’t intend for you to feel like it’s, like, secret what’s happening on the other side.

61 00:05:17.230 ⇒ 00:05:19.409 Uttam Kumaran: No, I’m just interested in what they’re finding, too, and like…

62 00:05:19.630 ⇒ 00:05:26.669 Uttam Kumaran: Yeah, it’s all stuff we can learn from. I want to see, like, what’s already been reported on, and yeah.

63 00:05:27.500 ⇒ 00:05:29.149 Katherine Bayless: Yeah. Yeah, it was funny, too.

64 00:05:29.150 ⇒ 00:05:33.269 Uttam Kumaran: So what data… what data are they using for the, like, their proof-of-concept stuff?

65 00:05:33.880 ⇒ 00:05:36.320 Katherine Bayless: Hmm, yeah, so…

66 00:05:37.420 ⇒ 00:05:50.979 Katherine Bayless: they were oddly, like, very insistent, and I was like, okay, fine, I… you seem to have… you seem to care more about this than I do. They wanted to use our exhibitor history data, but only 2 years of it,

67 00:05:50.980 ⇒ 00:06:04.479 Katherine Bayless: And then also, I was like, okay, you guys are gonna, like, redact and anonymize the PII, right? And they’re like, oh, I guess. I’m like, yes. But but yeah, so they’re gonna use our exhibitor history data, kind of coming out of the…

68 00:06:05.020 ⇒ 00:06:19.269 Katherine Bayless: rationale being, like, we… those are the questions we get constantly, like, has this exhibitor come before, kind of thing. So they’re gonna be prototyping, like, dashboards that would give general information about exhibitors, but then also have that lookup kind of component to them.

69 00:06:19.390 ⇒ 00:06:36.310 Katherine Bayless: I have tried to emphasize how much I think that should be a natural language question type approach, right? Like, I really, I really think that if people could just slack a little bot and say, has somebody gone to CES before, and get an answer, I mean, instant, instant win.

70 00:06:36.310 ⇒ 00:06:39.180 Uttam Kumaran: It’s just so new that, like, I would even be sup… like…

71 00:06:39.370 ⇒ 00:06:44.770 Uttam Kumaran: I would be surprised if there are other… Places, considering it, especially…

72 00:06:45.190 ⇒ 00:06:45.600 Katherine Bayless: Yeah.

73 00:06:45.600 ⇒ 00:07:01.990 Uttam Kumaran: I don’t know, I mean, I’m thinking about it, like, all the time. We just got off with another call where we were telling a client about, sort of, like, we just, we just did sort of, like, a 7 or 8 vendor evaluation for chat with data, and I still think it’s, like, Omni is probably the best. We’ve had a couple that were pretty good, but…

74 00:07:02.120 ⇒ 00:07:07.419 Uttam Kumaran: like, it’s sort of just all happening now, so I’m not… I don’t know what they said, but…

75 00:07:07.530 ⇒ 00:07:15.409 Uttam Kumaran: Yeah, totally. It’s kind of like, if they do have a kind of a cookie-cutter approach, then they’re probably like, oh yeah, I don’t know, that’s not in scope.

76 00:07:16.180 ⇒ 00:07:24.440 Katherine Bayless: Yeah, yeah, I mean, yeah, I think they’re… they’re kind of in the same boat, where it’s like, they’re not as convinced as I am. Although, you know, maybe I realize that

77 00:07:24.680 ⇒ 00:07:31.129 Katherine Bayless: The missing data point in my, advocacy is that we have this currently, and it sucks.

78 00:07:31.460 ⇒ 00:07:49.250 Katherine Bayless: But I know it could better, because it’s just Glean. It’s like, when you ask a question in Slack, Glean tries to answer it, and because Jay has connected a bunch of our data that I don’t really think you should have, to Glean, like, it’ll try to answer those questions. And, like, it’s actively harming the reputation of our data.

79 00:07:49.250 ⇒ 00:07:50.070 Uttam Kumaran: Yeah.

80 00:07:50.070 ⇒ 00:08:02.670 Katherine Bayless: questions, and I’m like, I think if we built this, it would be better, even if it’s not perfect, and it would, like, shape the conversation very differently. Because Glean is making people actively hate AI, and it’s unfortunate.

81 00:08:03.630 ⇒ 00:08:06.380 Uttam Kumaran: Yeah, I mean, I… I… I feel that.

82 00:08:06.830 ⇒ 00:08:07.645 Katherine Bayless: Yeah.

83 00:08:08.660 ⇒ 00:08:14.600 Katherine Bayless: Yeah. But anyway, so yeah, so that’s what they’re doing, is the exhibitor data for purposes of the lookup use case.

84 00:08:15.110 ⇒ 00:08:15.650 Uttam Kumaran: Okay.

85 00:08:17.040 ⇒ 00:08:17.630 Katherine Bayless: You know.

86 00:08:19.260 ⇒ 00:08:20.190 Katherine Bayless: Yeah.

87 00:08:20.830 ⇒ 00:08:32.309 Uttam Kumaran: Cool, I mean, I think today, maybe we can start with, like, kind of the ETL conversation. I know we sent some thoughts, yeah, wondering kind of, like, what you were thinking about based on some of those notes that we sent.

88 00:08:32.409 ⇒ 00:08:40.630 Uttam Kumaran: We found some coverage across, Fivetran and Polyatomic. I think, really, the big, you know, the big…

89 00:08:40.730 ⇒ 00:08:45.979 Uttam Kumaran: Thing for us to understand is the volumes, before you can, like, get a quote.

90 00:08:46.160 ⇒ 00:08:50.399 Uttam Kumaran: Both of those tools, like, have free periods.

91 00:08:52.010 ⇒ 00:08:59.530 Uttam Kumaran: Fivetran, I think it’s a 14-day, and we can measure. The… probably the bigger consideration is just, like, some of our systems.

92 00:09:00.200 ⇒ 00:09:05.909 Uttam Kumaran: Like, if they don’t support, and they are P1, P0,

93 00:09:06.100 ⇒ 00:09:11.929 Uttam Kumaran: And they’re, like, kind of finicky, we may want to go with a provider that can build that and host that for us.

94 00:09:12.300 ⇒ 00:09:17.420 Uttam Kumaran: You know, but there are all… there also are some sources that are offline, and…

95 00:09:17.580 ⇒ 00:09:24.150 Uttam Kumaran: it’s a simple API call to get some of the data, so those we can probably build ourselves, but yeah, just, like, wanted to get your thoughts.

96 00:09:24.460 ⇒ 00:09:27.260 Uttam Kumaran: You know, we could help guide a decision.

97 00:09:28.120 ⇒ 00:09:43.189 Katherine Bayless: Yeah, I think… admittedly, I haven’t had a chance to dig in as deeply as I would normally like to, but, like, I think the polyatomic angle makes a lot of sense to me over Fivetran, because of the willingness to kind of build some of these things.

98 00:09:44.040 ⇒ 00:09:45.000 Katherine Bayless: I think…

99 00:09:45.740 ⇒ 00:10:03.689 Katherine Bayless: actually, sorry, a small point of, just logistics. I think there’s also… there is an AWS product somewhere buried in their 250 product catalog that is some of connectors, and I can’t remember if it’s Data Brew, Data Wrangler, or something else entirely, but they… the AWS…

100 00:10:03.690 ⇒ 00:10:05.960 Katherine Bayless: does have some off-the-shelf.

101 00:10:05.960 ⇒ 00:10:07.330 Uttam Kumaran: Manage UTL? Okay.

102 00:10:07.490 ⇒ 00:10:23.180 Katherine Bayless: Yeah, I just can’t remember the name of the service, but I did poke around with it, like, months ago when I was new, so it might be worth looking at that one from a frugality perspective, too. But I think the polyatomic angle makes sense. The swirly bit in my brain is, like.

103 00:10:24.650 ⇒ 00:10:41.150 Katherine Bayless: I know we have to deal with some of these systems today, but I’m also hoping that we don’t have to deal with them for much longer, or certainly not forever, and so I’m like, even if Polytomic doesn’t cover some of the stuff

104 00:10:41.580 ⇒ 00:11:04.230 Katherine Bayless: there’s probably ones where it would make sense to ask them to build something, and then others where it’s like, yeah, let’s actually just struggle bus this for a few more months, and then, like, just hope that we’re on a different product by the next time we need this data. The CES tech stack in particular is kind of where that is heavy on my brain, because I can’t remember if I’ve mentioned this or not, but the

105 00:11:04.490 ⇒ 00:11:11.159 Katherine Bayless: there is no owner for the CES tech stack at the moment, and without an owner, somebody’s gotta…

106 00:11:11.860 ⇒ 00:11:17.139 Katherine Bayless: Take care of it, and so they’re increasingly looking at throwing it all, like, the direction of my team.

107 00:11:18.400 ⇒ 00:11:43.320 Katherine Bayless: so many things, but I’m like, okay, well, if… if I am the one in charge of our CES tech stack, I mean, we’re immediately gonna start moving some of these players on the board, because the merits, which, you know, I sent over the little bit of documentation they have, they also went down during Reg last year, so, like, they literally can’t support our event. It makes no sense to stay on this platform if it doesn’t work, and it’s not integratable. We have EventPoint, which seems like a really solid event management platform.

108 00:11:43.320 ⇒ 00:11:46.240 Katherine Bayless: I think we could push in that direction.

109 00:11:46.560 ⇒ 00:11:51.840 Katherine Bayless: Things like that. So, like… Some of these players are on the board now, but won’t be…

110 00:11:51.840 ⇒ 00:12:13.000 Katherine Bayless: if we move in the direction of Polytomic, and they have that ability to be kind of hand-holdy in the early stages and grow with us, I think that’s a real story I can sell internally of, like, we will grow to meet them, because we should not continue to use Funky Fringe off the, you know, beaten path software. Like, we want to play in an ecosystem that is configurable and integratable.

111 00:12:14.120 ⇒ 00:12:20.349 Katherine Bayless: but also, you know, this vendor is lovely and willing to help us kind of get through the early days. Like, to me, that seems like a nice…

112 00:12:20.460 ⇒ 00:12:21.310 Katherine Bayless: Pitch.

113 00:12:23.060 ⇒ 00:12:24.030 Uttam Kumaran: Okay, okay.

114 00:12:24.830 ⇒ 00:12:32.290 Uttam Kumaran: So, I mean, I think the… I mean, the biggest priority is to try to land some of the Salesforce data, I feel like. That’s… that’s a huge thing, and then…

115 00:12:32.470 ⇒ 00:12:35.960 Uttam Kumaran: I think I’d… I mean, ideally…

116 00:12:35.960 ⇒ 00:12:36.929 Katherine Bayless: CRM, no.

117 00:12:37.530 ⇒ 00:12:38.919 Uttam Kumaran: Yeah, yeah, yeah, exactly.

118 00:12:39.210 ⇒ 00:12:41.680 Uttam Kumaran: So I feel like…

119 00:12:42.300 ⇒ 00:12:53.110 Uttam Kumaran: that’s what we can go forward with. I mean, so maybe we go ahead and kick off, like, a trial with… with Polyatomic. I would… I’ll also additionally get them to take a look at the additional

120 00:12:55.500 ⇒ 00:13:03.339 Uttam Kumaran: the additional things that we want to, bring in, and start to see if we can at least start syncing some stuff there.

121 00:13:03.560 ⇒ 00:13:04.980 Katherine Bayless: I actually know…

122 00:13:04.980 ⇒ 00:13:13.450 Uttam Kumaran: Galib, the CEO, pretty well, and what I was sort of looking for… I mean, I’d just done work with Fivetran for a long time, and

123 00:13:13.650 ⇒ 00:13:30.740 Uttam Kumaran: One, we were looking for a partner that could support building new connectors, and who, like, was able to give better discounts, and, like, whose pricing came in a lot… typically a lot lower. And then the last piece was, like, support. We have… we have now, I think, 2 clients on…

124 00:13:31.270 ⇒ 00:13:45.009 Uttam Kumaran: on Polytomic, and one of which has, like, they’re extremely particular about, like, the timing of their data, and they have, like, some real-time use cases, and they’ve, like, just rolled with the punches really well, so… yeah, I think it’s worth…

125 00:13:45.390 ⇒ 00:14:00.370 Uttam Kumaran: worth giving it a shot with them, and then I think, Ashwini, we can also take a look at the, the use cases, the AWS option. I would definitely like to consider that. I haven’t looked at that in a while, either. Like, I think I maybe looked at it, like, 2 or 3 years ago, so…

126 00:14:00.850 ⇒ 00:14:04.559 Katherine Bayless: Yeah, yeah, it’s like one of those products that seems to have come a long way. Sorry, go ahead.

127 00:14:09.860 ⇒ 00:14:10.850 Uttam Kumaran: Okay, cool.

128 00:14:12.080 ⇒ 00:14:22.210 Uttam Kumaran: Okay, great, so I feel like that’s… that’s a, you know, path forward on the… on the ETL side. Ashwini, do you want to talk through… Yeah, yeah, go ahead.

129 00:14:22.740 ⇒ 00:14:33.910 Katherine Bayless: Okay, just a tiny note, sorry, sorry. One advantage, too, of the Marketing Cloud, being one of the first ones we move on is, A, useful, lovely, awesome, B,

130 00:14:34.080 ⇒ 00:14:38.980 Katherine Bayless: Salesforce CRM is integrated into Marketing Cloud, they’re not using it.

131 00:14:38.990 ⇒ 00:14:55.959 Katherine Bayless: Which is a whole other story. But we can actually sneakily extract the CRM data from Marketing Cloud because of that integration, so we can kind of get our hands around a little bit of that data without bothering that team, which are in, as Ashwini so beautifully put it, do not disturb mode at the moment.

132 00:14:55.960 ⇒ 00:14:57.599 Uttam Kumaran: Okay. Okay.

133 00:14:58.930 ⇒ 00:14:59.730 Katherine Bayless: Backdoor pilot.

134 00:15:00.960 ⇒ 00:15:01.690 Uttam Kumaran: Cool.

135 00:15:06.980 ⇒ 00:15:07.820 Uttam Kumaran: Okay.

136 00:15:09.540 ⇒ 00:15:10.100 Katherine Bayless: been.

137 00:15:11.000 ⇒ 00:15:12.060 Uttam Kumaran: But, sorry, say that again.

138 00:15:12.440 ⇒ 00:15:14.400 Katherine Bayless: I forget what your question was before I interrupted with.

139 00:15:14.400 ⇒ 00:15:19.550 Uttam Kumaran: Oh, I’m sorry. No, no, no, that’s a problem. My question was gonna be,

140 00:15:20.010 ⇒ 00:15:22.970 Uttam Kumaran: More about, what’s it called?

141 00:15:23.070 ⇒ 00:15:24.460 Uttam Kumaran: the…

142 00:15:25.200 ⇒ 00:15:33.540 Uttam Kumaran: Oh, remembers data. I don’t know, Srini, if that’s a good lead-in for you to share, like, if you’ve already started modeling any of that, or kind of, like, what you.

143 00:15:33.540 ⇒ 00:15:40.199 Ashwini Sharma: Yeah, yeah, yeah, yeah. Yeah, I’ve already started standardizing, the raw data.

144 00:15:40.310 ⇒ 00:15:47.419 Ashwini Sharma: And, looking into it to understand the various relationship between the different tables that’s exposed there, and…

145 00:15:47.670 ⇒ 00:15:53.740 Ashwini Sharma: Specifically, the one that, Catherine mentioned in the Slack, right? There is something, is members, and…

146 00:15:54.180 ⇒ 00:16:07.010 Ashwini Sharma: benefits, the bi-directional relationship. I just wanted to see an example of that, and kind of struggling to see that in the table, so maybe if you could point me in the right direction, I can take a look at it.

147 00:16:07.400 ⇒ 00:16:08.050 Ashwini Sharma: Yeah.

148 00:16:08.050 ⇒ 00:16:10.440 Katherine Bayless: Actually, I can do…

149 00:16:10.440 ⇒ 00:16:13.830 Ashwini Sharma: I can… let me share my screen, I think it’ll be easier.

150 00:16:23.740 ⇒ 00:16:28.630 Ashwini Sharma: Okay, so… Let me know if you’re able to see my screen.

151 00:16:29.210 ⇒ 00:16:30.099 Katherine Bayless: Yep, yep.

152 00:16:32.780 ⇒ 00:16:40.079 Ashwini Sharma: Alright, alright, so where do I look at, like, there was something called members, or benefits, in fact, right?

153 00:16:42.010 ⇒ 00:16:58.790 Katherine Bayless: Yeah, so probably the first place that I would start with from, like, a building-out perspective would be under the, like, the CRM chunk of views. There’s a customer table in there, and so…

154 00:16:58.790 ⇒ 00:17:03.189 Katherine Bayless: This is when I worked with Impexians, or remembers… so hard.

155 00:17:03.230 ⇒ 00:17:06.210 Katherine Bayless: If they just picked a better new name, right?

156 00:17:06.550 ⇒ 00:17:09.469 Katherine Bayless: It’s just so hard to say remembers and make it sound like it makes any sense.

157 00:17:09.470 ⇒ 00:17:27.039 Katherine Bayless: But, like, this is usually where I’ve started, is, like, this is all of the entities people are interested in the system. I think the second column in the table is, like, a flag for, like, C versus I, or O versus I, yeah, so organization is a company, I is an individual.

158 00:17:27.109 ⇒ 00:17:37.609 Katherine Bayless: And then I think a lot of the flags that we’re really looking for are gonna be in this table and or come from joining this to some of the other ones.

159 00:17:37.810 ⇒ 00:17:42.880 Katherine Bayless: I’m trying to look and scan kind of on the side here.

160 00:17:43.840 ⇒ 00:17:44.620 Katherine Bayless: Let me see.

161 00:17:46.660 ⇒ 00:17:47.670 Katherine Bayless: Yeah.

162 00:17:48.040 ⇒ 00:17:52.330 Katherine Bayless: If you want to scroll a little bit in that list… Let’s see…

163 00:17:59.370 ⇒ 00:18:15.999 Katherine Bayless: Yeah, yeah, yeah. Okay, yeah, so then there’s the individual table, which if we key out to that for the records that have type equals I, we get additional information around those, and then there’s, obviously, there’s somewhere there’s an organization table, same idea.

164 00:18:16.000 ⇒ 00:18:23.960 Katherine Bayless: I think there should be a membership table somewhere in this CRM data as well.

165 00:18:26.190 ⇒ 00:18:29.389 Ashwini Sharma: This one, membership law or membership benefit?

166 00:18:29.740 ⇒ 00:18:31.350 Katherine Bayless: Hmm, great question.

167 00:18:31.920 ⇒ 00:18:32.720 Katherine Bayless: Hmm.

168 00:18:32.930 ⇒ 00:18:33.660 Katherine Bayless: Hmm.

169 00:18:35.330 ⇒ 00:18:41.859 Katherine Bayless: Let’s take a look at membership benefit, because membership log seems like… Probably gonna be strange.

170 00:18:46.810 ⇒ 00:18:51.210 Ashwini Sharma: Alright, so it has an ID, customer ID, membership product ID…

171 00:18:51.780 ⇒ 00:19:07.439 Katherine Bayless: Okay, this is the one we want. So, this one, membership name, I’m recognizing the values in that column. So, core member are the ones that we’re primarily interested in, those are the organizations. This industry associate member is, like.

172 00:19:07.670 ⇒ 00:19:12.629 Katherine Bayless: I don’t know, it’s this, like, funky, tiny program that I don’t really understand, to be totally honest.

173 00:19:12.940 ⇒ 00:19:17.890 Katherine Bayless: We have, like, our primary lane of membership is organizations, companies.

174 00:19:18.420 ⇒ 00:19:34.610 Katherine Bayless: Then we have this kind of strange concept of, like, a handful of individuals who are members because we needed them to be able to be members without their company being a member, so they can be on our committees, I guess, is kind of the idea. And then there’s also some, like, allied associations.

175 00:19:34.610 ⇒ 00:19:48.090 Katherine Bayless: that we record, and so they’re all tracked as memberships in the data, but only core member really represents CTA membership at the end of the day. So, like, that spreadsheet that I shared in Slack.

176 00:19:48.200 ⇒ 00:19:50.360 Katherine Bayless: Those are our core members.

177 00:19:53.640 ⇒ 00:19:55.550 Ashwini Sharma: Can you give me access to that sheet?

178 00:19:55.990 ⇒ 00:19:58.859 Ashwini Sharma: Right now, I think… Oh, yeah.

179 00:19:59.290 ⇒ 00:20:12.609 Katherine Bayless: I was gonna say, Utam can vouch for it. It was so painful to figure out the SharePoint stuff with Jay, so what I’ll do is I’ll just, I’ll take that file, I’ll just put it in an S3 bucket, if that’s okay? Great, sure, sure.

180 00:20:12.610 ⇒ 00:20:13.090 Ashwini Sharma: certified.

181 00:20:13.090 ⇒ 00:20:17.519 Katherine Bayless: It’s like, rather than trying to disentangle the SharePoint bullshit.

182 00:20:19.790 ⇒ 00:20:32.449 Katherine Bayless: But yeah, it’s honestly, it’s like a six-column spreadsheet. It’s, like, the organizational ID number, the name, the primary representative, and their name and email, and then just, like, their membership status, and it takes two people all day.

183 00:20:33.410 ⇒ 00:20:33.950 Katherine Bayless: Yeah.

184 00:20:33.950 ⇒ 00:20:41.400 Ashwini Sharma: Oh, KKK, can you repeat that? The last part, what is the logic to generate that report that you have shared?

185 00:20:41.970 ⇒ 00:20:44.390 Katherine Bayless: Yeah, yeah, here, I’ll, let me…

186 00:20:47.220 ⇒ 00:20:50.390 Katherine Bayless: Yeah, let me… I’ll share my screen, too.

187 00:20:51.570 ⇒ 00:20:59.120 Katherine Bayless: So it’s, and like I said, I’ll put it in the SB bucket, but, this is basically what it is. So it’s… this is…

188 00:20:59.630 ⇒ 00:21:05.140 Katherine Bayless: Actually, this is a good call-out, too. So, in the remembers data in Snowflake.

189 00:21:05.840 ⇒ 00:21:08.350 Katherine Bayless: it’ll have the GUIDs to join the tables.

190 00:21:08.360 ⇒ 00:21:21.659 Katherine Bayless: functionally, on the front end of the system, this is the only unique identifier people are going to be familiar with seeing. Like, if they see a GUID, they look like they’ve seen a ghost. We will change that, but for the moment, the record number is what we’re looking for.

191 00:21:21.660 ⇒ 00:21:29.289 Katherine Bayless: And so this is the record number for the organization that is a core member. This is the company name.

192 00:21:29.550 ⇒ 00:21:40.400 Katherine Bayless: And then we have the representative, the primary representative that’s associated with that member, their name, their record number, their email, and then

193 00:21:40.710 ⇒ 00:21:49.650 Katherine Bayless: not really necessary, but they’re all primary representatives. That value is displayed here. And this… this is… this is a full day. This is a full day of work.

194 00:21:51.530 ⇒ 00:21:52.720 Ashwini Sharma: Why? I mean…

195 00:21:53.740 ⇒ 00:22:00.580 Katherine Bayless: It’s a great question. Truthfully, because the reporting functionality in IMPEX, or remembers.

196 00:22:00.580 ⇒ 00:22:16.390 Katherine Bayless: by default is not great, and because the organization has not really invested in, like, tech, tech literacy, good systems, any of those things, and so they’re cobbling it together by, like, exporting a bunch of different CSVs, running a bunch of silly functions.

197 00:22:16.700 ⇒ 00:22:20.989 Katherine Bayless: it just needed to be a SQL statement all along, but yeah.

198 00:22:21.200 ⇒ 00:22:21.750 Katherine Bayless: Yeah.

199 00:22:21.750 ⇒ 00:22:26.390 Ashwini Sharma: And this is the only sheet over here? There’s another purple one over there.

200 00:22:27.030 ⇒ 00:22:32.909 Katherine Bayless: I think there’s… this one has a little bit more, some of the, like,

201 00:22:33.170 ⇒ 00:22:39.620 Katherine Bayless: detail around the membership itself, so, like, same idea, right? Company, the organizational ID and name.

202 00:22:39.930 ⇒ 00:22:46.090 Katherine Bayless: And then the membership type, This particular column, membership status.

203 00:22:46.610 ⇒ 00:22:54.579 Katherine Bayless: I have a feeling this is a human-inferred value as they assemble the spreadsheet, so we’ll have to, like, get the logic for it.

204 00:22:55.100 ⇒ 00:23:10.160 Katherine Bayless: this is gonna come down to understanding the grace period, and the renewal window, and all that kind of stuff, and so, like, I don’t know if this is going to be a data point that exists explicitly in that remembers data. My suspicion is no, but if we can understand, like.

205 00:23:10.160 ⇒ 00:23:21.969 Katherine Bayless: what and when is happening, then we can infer, like, okay, if they have XYZ transaction in this date window, then we call them renewed. Otherwise, maybe they’re canceled or in conversation.

206 00:23:22.010 ⇒ 00:23:27.869 Katherine Bayless: promised payment, but I think these are human… human-generated values rather than anything declared.

207 00:23:28.610 ⇒ 00:23:40.039 Katherine Bayless: The account manager, I do think, is somewhere in the data, but again, probably coming more from, like, human knowing and putting on spreadsheet, but I do think they try to track these.

208 00:23:40.140 ⇒ 00:23:43.259 Katherine Bayless: And then the dues level…

209 00:23:43.710 ⇒ 00:24:00.739 Katherine Bayless: is a… it should be pullable as a number out of the remembers data. It is a custom, like, formula. We charge membership fees based on, like, overall revenue in North America kind of a thing, so there’s a formula somewhere in the system that does that.

210 00:24:01.170 ⇒ 00:24:08.569 Katherine Bayless: Primary tag will be somewhere in the remembers data. The join data will come out of membership.

211 00:24:08.810 ⇒ 00:24:10.589 Katherine Bayless: This is just math.

212 00:24:11.810 ⇒ 00:24:22.150 Katherine Bayless: These notes are, like, the people that put these spreadsheets together. Notes, they’re not necessarily reflective of the notes that might be on a record in the system.

213 00:24:23.210 ⇒ 00:24:31.510 Katherine Bayless: And then this chunk… This is entity resolution, woes, made manifest.

214 00:24:31.750 ⇒ 00:24:39.959 Katherine Bayless: So, I think I’ve mentioned before, we have this remembers for our membership data, Salesforce CRM for selling the expo hall.

215 00:24:40.250 ⇒ 00:24:46.960 Katherine Bayless: You get a discount on your booth if you’re a member, but because those systems aren’t integrated and the data isn’t harmonized.

216 00:24:47.330 ⇒ 00:24:48.430 Katherine Bayless: we have…

217 00:24:48.730 ⇒ 00:24:57.689 Katherine Bayless: now created 7 fields to track the potential exhibitor ID. These are the Salesforce IDs that go with the Impexium ones, and as you can see.

218 00:24:58.270 ⇒ 00:24:59.520 Katherine Bayless: Some of them.

219 00:24:59.710 ⇒ 00:25:01.920 Katherine Bayless: are quite messy.

220 00:25:02.380 ⇒ 00:25:06.449 Katherine Bayless: someday this will just be one ID floating around between all the systems, but this is kind of.

221 00:25:06.450 ⇒ 00:25:07.270 Uttam Kumaran: Wow, interesting.

222 00:25:07.270 ⇒ 00:25:08.679 Katherine Bayless: either. Yeah.

223 00:25:08.680 ⇒ 00:25:09.040 Uttam Kumaran: Yeah.

224 00:25:09.120 ⇒ 00:25:28.779 Katherine Bayless: And let me tell you, man, the guy who manages ExpoCAD is pissed, about this, because his data used to be really, really clean, and now it’s getting, like, blown up with all these duplicates coming through, and he’s… he’s… he’s cranky. He’s cranky. But these should all be fields, like, custom fields somewhere in that remembers data.

225 00:25:29.630 ⇒ 00:25:34.029 Uttam Kumaran: Is there… can we get access to the Remembers UI platform? Because I’ve been.

226 00:25:34.030 ⇒ 00:25:34.510 Katherine Bayless: Basically.

227 00:25:34.510 ⇒ 00:25:36.030 Uttam Kumaran: Happy Happy QA.

228 00:25:36.850 ⇒ 00:25:39.729 Uttam Kumaran: Yeah, I was actually… I was gonna say, like.

229 00:25:39.730 ⇒ 00:25:43.660 Katherine Bayless: we should probably do that. I, to be totally honest.

230 00:25:52.870 ⇒ 00:25:54.579 Katherine Bayless: Can’t talk and type at the same time.

231 00:25:58.300 ⇒ 00:26:05.570 Katherine Bayless: So the Okta thing is something I would like to talk about, parking lot for this call. Oops, what did I do?

232 00:26:06.790 ⇒ 00:26:13.000 Katherine Bayless: Because I… I have… I have… I have a request, but for the moment, I will just fight past it, so I’ll show you this.

233 00:26:36.920 ⇒ 00:26:42.740 Katherine Bayless: Ta-da! Okay. So, this is… remembers.

234 00:26:44.670 ⇒ 00:26:50.600 Katherine Bayless: Let’s… Take a look at… a company.

235 00:26:52.370 ⇒ 00:26:54.880 Katherine Bayless: And I happened to have had to look these guys up yesterday.

236 00:26:59.710 ⇒ 00:27:02.229 Katherine Bayless: Interesting. Wonder what that one is. Okay.

237 00:27:04.500 ⇒ 00:27:08.510 Katherine Bayless: So… so yeah, so, like, when we come in to the record.

238 00:27:09.120 ⇒ 00:27:16.310 Katherine Bayless: I mean, isn’t this just the most beautiful system you’ve ever seen? So, like, a couple points of interest. So this…

239 00:27:16.550 ⇒ 00:27:30.929 Katherine Bayless: this is coming from that isMember kind of flag as well, right? So when the users are in the system, this big green highlight is often the first thing they’re looking for to understand if the member is current, that kind of thing.

240 00:27:31.150 ⇒ 00:27:37.469 Katherine Bayless: I’ll try and find an example of a subsidiary where they don’t have membership, but they do have benefits.

241 00:27:37.770 ⇒ 00:27:48.119 Katherine Bayless: This is also part of that attempt to track parents versus subsidiaries. As you can see, they have an open invoice for their membership for the year.

242 00:27:48.600 ⇒ 00:27:56.990 Katherine Bayless: These are… Probably largely incorrect, but,

243 00:27:57.380 ⇒ 00:28:14.899 Katherine Bayless: somewhere we could improve. These are the nodes that are actually in the system. There’s a big desire for this to be more utilized, but right now there’s not anything that’s, like, automated around it, so it relies on the human caring enough to come deal with a crappy interface to put the data in.

244 00:28:15.130 ⇒ 00:28:20.510 Katherine Bayless: But perhaps more usefully, let me…

245 00:28:21.750 ⇒ 00:28:27.900 Katherine Bayless: This is funny, because I always kind of forget where to find this piece I’m looking for. I want to show you the relationships part.

246 00:28:28.910 ⇒ 00:28:30.100 Katherine Bayless: Yeah, here we go, okay.

247 00:28:30.580 ⇒ 00:28:31.560 Katherine Bayless: account.

248 00:28:31.820 ⇒ 00:28:33.250 Katherine Bayless: Relationships.

249 00:28:35.510 ⇒ 00:28:39.550 Katherine Bayless: So… Oh, okay, okay, okay, yeah.

250 00:28:39.720 ⇒ 00:28:47.330 Katherine Bayless: So this is that organization parent-child thing. So, I guess when I first looked up BlueEddy, and it gave me the two results.

251 00:28:48.280 ⇒ 00:29:01.809 Katherine Bayless: we have… Blue Eddy Power is a subsidiary of Blue Eddy Power Incorporated, and Blue Eddy Power is a parent of Blue Eddy Power Incorporated. Without both relationship… I know, I know, I know.

252 00:29:02.120 ⇒ 00:29:18.260 Katherine Bayless: Without both relationships being entered, it won’t come through at all. And the same thing for the individual. So, Yanyun is primary representative of Blue Eddy, and Blue Eddy is employer of Yanyun, right?

253 00:29:18.820 ⇒ 00:29:28.800 Katherine Bayless: I managed to pick one that only had a couple people. Some of them we track tons and tons of folks for. And so these relationships will all have that reciprocal, has to have both entries.

254 00:29:29.130 ⇒ 00:29:35.499 Katherine Bayless: Whether or not they’re primary, which is a flag set separately on each of those relationships.

255 00:29:35.980 ⇒ 00:29:52.439 Katherine Bayless: They can have titles, so, like, this is where we would see primary representative, potentially, if that were entered on these folks, and then, like, start and end dates, which are not mandatory, but usually relationships will have a start date, and then, like, a null end date just indicates that they’re presently valid.

256 00:29:52.680 ⇒ 00:30:01.559 Katherine Bayless: And then we do also, or the system also keeps tabs on the, like, historical associations, even if the relationships are ended.

257 00:30:06.260 ⇒ 00:30:09.789 Katherine Bayless: Let me show you their reporting functionality while we are here.

258 00:30:16.740 ⇒ 00:30:21.910 Katherine Bayless: Alright, so we have 298 reports that we have built.

259 00:30:22.700 ⇒ 00:30:27.860 Katherine Bayless: Let’s see if we can find one for active that is probably part of that spreadsheet.

260 00:30:30.430 ⇒ 00:30:31.520 Katherine Bayless: Yeah, okay.

261 00:30:31.880 ⇒ 00:30:50.010 Katherine Bayless: This… this phrase you might see, not necessarily in the data, but just in things we share with you guys, account management is essentially, like, us identifying the most critical members to pay attention to, and then they get assigned, like, a high handhold, high touch sort of person on the side, yeah. That’s just our…

262 00:30:50.010 ⇒ 00:30:54.070 Katherine Bayless: internal jargon for it. But yeah, so if we were to take a look at this report…

263 00:30:57.220 ⇒ 00:31:00.950 Katherine Bayless: Let’s see if it’ll just let us through without entering anything else.

264 00:31:04.640 ⇒ 00:31:19.839 Katherine Bayless: Yeah, so yeah, so we get this kind of funky, sort of grid-like thing, and then they will do export to CSV, right, and then start kind of compiling those spreadsheets. But this is… this is… this is what they’ve had to deal with.

265 00:31:23.240 ⇒ 00:31:24.420 Uttam Kumaran: Okay.

266 00:31:24.930 ⇒ 00:31:33.040 Katherine Bayless: Yeah, I wish… I don’t know if I can find out where it is in here, because I actually haven’t looked at them a ton, but there is, in theory, this, like.

267 00:31:34.000 ⇒ 00:31:38.400 Katherine Bayless: Bit of dashboarding that they tried to add to the product?

268 00:31:39.150 ⇒ 00:31:43.910 Katherine Bayless: Although, I don’t… Let’s see, we’ll go back to apps.

269 00:31:47.390 ⇒ 00:32:06.800 Katherine Bayless: No, I don’t know. I’m not sure where it’s gonna be hiding. But, like, they’ve got this, like, funky sort of, like, Power BI thing kind of baked in, that is really just, like, bizarre, because it’ll tell you that we don’t have any active members, but that also we’ve gotten, like, 300 new members this year, and neither of those are true.

270 00:32:06.840 ⇒ 00:32:12.500 Katherine Bayless: Let me see if it’s just a matter of admin being needed.

271 00:32:17.050 ⇒ 00:32:28.900 Katherine Bayless: I should also say, there’s something funky with Okta and Remembers that, like, kicks you out of the administrator role, like, every time you log out and back in, and so you have to, like, add it back to yourself.

272 00:32:30.370 ⇒ 00:32:32.000 Katherine Bayless: And then you have to clear your cache.

273 00:32:32.000 ⇒ 00:32:32.610 Uttam Kumaran: staying.

274 00:32:32.610 ⇒ 00:32:33.300 Katherine Bayless: Yeah.

275 00:32:40.810 ⇒ 00:32:42.800 Katherine Bayless: Oh yeah, here we go, dashboards.

276 00:32:49.940 ⇒ 00:32:53.680 Katherine Bayless: Okay, this is not even the one that I had seen the other day.

277 00:32:58.290 ⇒ 00:32:59.569 Katherine Bayless: Here you go.

278 00:33:02.930 ⇒ 00:33:08.279 Katherine Bayless: In theory, these dashboards are drawing off of the same data as the Snowflake data share.

279 00:33:10.890 ⇒ 00:33:12.410 Katherine Bayless: Let’s see what comes up.

280 00:33:15.940 ⇒ 00:33:29.109 Katherine Bayless: Yeah, there you go, okay. So, active members, 1,658, but if we look at our, technically, source of truth, right? Oops, what did I say we had? We have, like, 1,200, 1,100?

281 00:33:31.320 ⇒ 00:33:47.330 Katherine Bayless: yeah, about 1,100 actual active members, but we’re seeing 1,658 in here. New members, not true. Like, it’s just all this strange, sort of janky stuff that I will just, replace with better.

282 00:33:53.910 ⇒ 00:33:54.780 Katherine Bayless: But yeah.

283 00:33:56.330 ⇒ 00:34:01.379 Katherine Bayless: This is a… This is life in the association world.

284 00:34:10.670 ⇒ 00:34:25.500 Katherine Bayless: I can definitely get you guys access to the system. I will figure out, Jay will have to, like, add it to you for Okta. I think Anna Rudder might also have to provision something, but I will figure out how to get you guys in here so that you can play around and actually see the data, because I think…

285 00:34:25.500 ⇒ 00:34:34.979 Katherine Bayless: it will probably make a big difference in terms of being able to go from, like, the raw backend to the things we’re trying to build. Like, okay, what does that actually look like? Yeah.

286 00:34:35.409 ⇒ 00:34:36.170 Katherine Bayless: Kiddo.

287 00:34:37.810 ⇒ 00:34:39.440 Katherine Bayless: Okay, I’ll pause my sharing.

288 00:34:41.320 ⇒ 00:34:48.479 Uttam Kumaran: Okay, perfect. Yeah, that way we can just QA, but I think, Ashwini, that’s a great, like, end-to-end scope for our first models.

289 00:34:48.639 ⇒ 00:34:49.639 Uttam Kumaran: Being able to power that.

290 00:34:49.639 ⇒ 00:34:50.819 Ashwini Sharma: Yeah, true.

291 00:34:51.769 ⇒ 00:34:52.469 Uttam Kumaran: Yeah.

292 00:34:52.759 ⇒ 00:34:55.739 Ashwini Sharma: I’m just making sure that I have access to S3.

293 00:34:56.889 ⇒ 00:34:58.729 Ashwini Sharma: It’s… it’s a weekend end.

294 00:34:58.919 ⇒ 00:35:08.949 Ashwini Sharma: once Catherine puts that file in S3, and if I’m not able to access, then I’ll have to wait until Monday. Give me… give me a second, let me just check if all the access is there.

295 00:35:08.950 ⇒ 00:35:09.530 Uttam Kumaran: Yeah.

296 00:35:09.730 ⇒ 00:35:21.339 Katherine Bayless: I mean, you know, truthfully, we’ll pretend I’m not being recorded as I say this, but it would not be the end of the world if I just sent it to you on Slack. I mean, I try to not do that, generally, but this organization…

297 00:35:21.340 ⇒ 00:35:23.030 Uttam Kumaran: You can put it in GitHub.

298 00:35:23.770 ⇒ 00:35:24.980 Uttam Kumaran: You can put in the repo.

299 00:35:24.980 ⇒ 00:35:26.229 Ashwini Sharma: Oh, yeah, yeah.

300 00:35:26.830 ⇒ 00:35:29.690 Uttam Kumaran: I could check it in the repo. So, yeah.

301 00:35:30.240 ⇒ 00:35:31.530 Katherine Bayless: Okay. Option C.

302 00:35:32.330 ⇒ 00:35:32.920 Katherine Bayless: Okay.

303 00:35:37.360 ⇒ 00:35:40.909 Katherine Bayless: Yeah, I mean, ideally, if you do have access to,

304 00:35:41.330 ⇒ 00:35:50.499 Katherine Bayless: AWS and S3, that makes the most sense, because there’s a lot of good data in there anyway to be playing with in Snowflake. But if not, then yeah, repo is a good backup.

305 00:35:50.770 ⇒ 00:35:52.410 Ashwini Sharma: Yeah, I can access S3.

306 00:35:52.990 ⇒ 00:35:54.410 Katherine Bayless: Oh, nice. Okay.

307 00:35:56.290 ⇒ 00:36:01.249 Katherine Bayless: Actually, do you want to… here, I’ll share my screen. I can give a, like, a quick, like.

308 00:36:01.380 ⇒ 00:36:07.380 Katherine Bayless: what is where in S3, just as a tiny… Orientation.

309 00:36:40.700 ⇒ 00:36:51.610 Katherine Bayless: Okay, so… This, this old one, marketing data, I had parked a bunch of, like.

310 00:36:52.240 ⇒ 00:36:54.949 Katherine Bayless: Exported old, flat files.

311 00:36:56.590 ⇒ 00:37:03.810 Katherine Bayless: Oh no, actually, I’m lying to you. I’m sorry. This one I started using, and then I abandoned. That is right. I think it’s actually archive.

312 00:37:08.240 ⇒ 00:37:12.889 Katherine Bayless: Nice job, Catherine. I wonder which one has the stuff. Okay, yeah, let’s see.

313 00:37:15.360 ⇒ 00:37:16.510 Katherine Bayless: Yeah, okay.

314 00:37:16.920 ⇒ 00:37:31.389 Katherine Bayless: So, yeah, so this bucket, the CTA DataOps Archive, this represents the SQL server that the marketing data team was using as their sort of, like, data warehouse CRM kind of thing. I just dumped it all into an S3 bucket.

315 00:37:31.390 ⇒ 00:37:38.319 Katherine Bayless: And so, on that server, there were the 6 databases, archive, Archive 2,

316 00:37:38.320 ⇒ 00:37:44.010 Katherine Bayless: 2018, 19, 20, and then the marketing one, which is kind of the bulk of everything.

317 00:37:44.460 ⇒ 00:38:03.270 Katherine Bayless: And then in there, there were a bunch of schemas, some which do or don’t have data, but again, majority of the stuff is in DBO. And so this is every table exported out of that old system, and so it’s, like, all of those, you know, 50, 60, whatever it was, data sources.

318 00:38:03.270 ⇒ 00:38:14.060 Katherine Bayless: anything that they’d ever gotten their hands on is somewhere in here. You might find interesting tea leaf type stuff, in some of the member tables, but…

319 00:38:14.450 ⇒ 00:38:29.839 Katherine Bayless: that’s not necessarily a guarantee, but, like, this is some of the way that they had been previously structuring it. I can also dump all of the old SQL code they were using into an S3 bucket, but I would say that, it might be more for, like, a good laugh on a Friday afternoon than anything terribly useful.

320 00:38:29.840 ⇒ 00:38:36.230 Katherine Bayless: But it would maybe give you, again, like, some tea leaves to kind of look at as to, like, where they were finding stuff and pulling it in from.

321 00:38:36.260 ⇒ 00:38:45.909 Katherine Bayless: I did set up the S3 stage thing in Snowflake, so that, like, in theory, I think we’d be able to pull any of this in if we wanted to.

322 00:38:46.770 ⇒ 00:38:50.630 Katherine Bayless: But, yeah, it’s a… it’s a lot of shtuff.

323 00:38:57.770 ⇒ 00:39:06.280 Katherine Bayless: Alright, let me, while we’re in here, should I make a bucket just for, like, us to collaborate over, or do you want me to just kind of put it as a folder in,

324 00:39:06.740 ⇒ 00:39:08.580 Katherine Bayless: Maybe, like, the data lake one.

325 00:39:09.180 ⇒ 00:39:10.229 Ashwini Sharma: Yeah, that’s fine.

326 00:39:10.230 ⇒ 00:39:12.750 Uttam Kumaran: Let’s… yeah, either… either one’s fine.

327 00:39:15.830 ⇒ 00:39:22.720 Katherine Bayless: Let’s… alright, let’s do the ad hoc one, which is where I’ve been parking all the random things. Create a Brief Forge folder.

328 00:39:26.730 ⇒ 00:39:34.190 Katherine Bayless: I also look forward to finding out how many times I accidentally type your name as Brian Forge, because it’s happened a few times.

329 00:39:36.050 ⇒ 00:39:39.339 Katherine Bayless: Okay, let’s see, example…

330 00:40:01.080 ⇒ 00:40:05.350 Katherine Bayless: I know I’m doing this in the silliest way, but just… Lazy Friday brain.

331 00:40:09.820 ⇒ 00:40:10.590 Katherine Bayless: Okay.

332 00:40:11.990 ⇒ 00:40:14.690 Katherine Bayless: There you go. Now you should have access to that file.

333 00:40:16.580 ⇒ 00:40:18.110 Katherine Bayless: Do with it as you please.

334 00:40:20.540 ⇒ 00:40:21.629 Ashwini Sharma: Sure, thank you.

335 00:40:30.280 ⇒ 00:40:34.120 Uttam Kumaran: Okay, perfect. Anything else on remembers?

336 00:40:34.510 ⇒ 00:40:35.420 Uttam Kumaran: Theta?

337 00:40:36.230 ⇒ 00:40:37.020 Katherine Bayless: Hmm…

338 00:40:40.090 ⇒ 00:40:57.360 Katherine Bayless: Not on my end, necessarily. We had a really great meeting with the membership team on Wednesday about the engagement score that they want to start putting together. But it’s kind of like the garden variety, stuff, you know. Has the company exhibited? Do we have good contact with them? Did they win any awards? Did they volunteer on our committees? That kind of stuff, so…

339 00:40:57.360 ⇒ 00:41:04.679 Katherine Bayless: I think that’s all things we’ll start building towards, but truthfully, there’s a lot of questions just back to them around, like, well.

340 00:41:04.810 ⇒ 00:41:16.680 Katherine Bayless: what exactly should we count as volunteering our committee, and how should we weight these things, and stuff like that. So, like, we’ll start to kind of unknit the business rules that they’re looking for. But yeah, they’re really excited to get in there and start working.

341 00:41:17.450 ⇒ 00:41:29.180 Uttam Kumaran: Cool. I guess, Ashrini, did you want to walk through, like, did we have an initial DBT structure? Do you want to walk through? Probably, like, the last thing I had, and I know you had some bonus topics, Catherine, so we can leave…

342 00:41:29.860 ⇒ 00:41:30.910 Uttam Kumaran: Time for that.

343 00:41:31.640 ⇒ 00:41:32.380 Katherine Bayless: Yeah.

344 00:41:36.260 ⇒ 00:41:38.459 Ashwini Sharma: Let me know if you’re able to see my screen.

345 00:41:39.510 ⇒ 00:41:40.170 Uttam Kumaran: Yes.

346 00:41:40.710 ⇒ 00:41:44.099 Ashwini Sharma: Alright, so this is how I’ve structured,

347 00:41:45.020 ⇒ 00:41:48.859 Ashwini Sharma: So this is not the one, huh? Hold on a second, I think this is the one.

348 00:41:49.150 ⇒ 00:41:51.789 Ashwini Sharma: Alright, so we have the CTA DataOps.

349 00:41:54.190 ⇒ 00:42:00.879 Ashwini Sharma: And inside that, I placed, a folder called dbt Project, which contains everything.

350 00:42:01.260 ⇒ 00:42:06.130 Ashwini Sharma: Which will contain everything. For the transformation, is that…

351 00:42:06.280 ⇒ 00:42:09.770 Ashwini Sharma: Basically, I just broke it down into 3 different layers.

352 00:42:10.050 ⇒ 00:42:17.159 Ashwini Sharma: If you see, here’s the staging layer, and staging layer is broken down by source.

353 00:42:17.350 ⇒ 00:42:28.200 Ashwini Sharma: So, for example, like, we don’t have this yet, but eventually we’ll have Salesforce Marketing Cloud data. Right now, all we have is the remembers data, so this is the remembers, right?

354 00:42:28.320 ⇒ 00:42:37.550 Ashwini Sharma: And, Remembers, again, it’s kind of broken down by the different, schemas within Remembers, accounting, app, Award, and so on.

355 00:42:38.090 ⇒ 00:42:42.169 Ashwini Sharma: Everything is defined over here.

356 00:42:42.980 ⇒ 00:42:55.619 Ashwini Sharma: Right, and this is just currently only the staging models that I’ve created, right? So, all it does is, basically, it standardizes, these column names into snake case.

357 00:42:56.080 ⇒ 00:43:07.289 Ashwini Sharma: And, yeah, eventually what I’ll be doing is, like, with the information that you have given, I’ll go through it and then see, you know, how I can model this data so that

358 00:43:07.410 ⇒ 00:43:14.130 Ashwini Sharma: At least we can get that report, active members report, out as soon as possible from… Snowflake, right?

359 00:43:14.630 ⇒ 00:43:15.260 Katherine Bayless: Yeah.

360 00:43:16.350 ⇒ 00:43:33.090 Katherine Bayless: I was just gonna say, small side note, also in AWS, in Secrets Manager, is where I have the, the REST API, key for, Marketing Cloud. It’s CTA, DataOps Playground, SFMC, REST API.

361 00:43:33.090 ⇒ 00:43:33.769 Uttam Kumaran: Okay, okay, great.

362 00:43:33.770 ⇒ 00:43:41.079 Katherine Bayless: There is also somewhere in the repo, I don’t remember off the top of my head what the file is called, but, like, somewhere under, I think.

363 00:43:41.610 ⇒ 00:43:56.150 Katherine Bayless: scripts, maybe? I had done some initial itty-bitty lightweight attempts at, like, making a little loop from, like, the form stack where we were taking, CES registration requests.

364 00:43:56.150 ⇒ 00:44:06.350 Katherine Bayless: And bouncing them up through NeverBounce, just to make sure it’s a valid email, and then on to Salesforce Marketing Cloud. I did not get too terribly far. But it’s in that CTA DataOps one, yeah.

365 00:44:08.670 ⇒ 00:44:11.460 Katherine Bayless: Just don’t remember exactly what I called it off the top of my head, let me see.

366 00:44:17.720 ⇒ 00:44:19.090 Ashwini Sharma: Oh…

367 00:44:25.400 ⇒ 00:44:34.049 Katherine Bayless: Yeah, under, CTA DataOps, scripts, Utilities, Email Validation, Pipeline, Test, that was where I had gotten, kind of.

368 00:44:34.350 ⇒ 00:44:35.890 Katherine Bayless: Really, I mean, like.

369 00:44:35.890 ⇒ 00:44:41.340 Ashwini Sharma: Oh, I looked at a different repo, yeah. CT DataOps, yeah.

370 00:44:41.340 ⇒ 00:44:42.479 Katherine Bayless: Yeah,

371 00:44:42.910 ⇒ 00:44:46.740 Katherine Bayless: And then under, the playground branch, and then…

372 00:44:47.510 ⇒ 00:44:50.750 Ashwini Sharma: Sorry, which branch is it? Was it? Playground?

373 00:44:50.750 ⇒ 00:44:51.660 Katherine Bayless: And, yeah.

374 00:44:52.640 ⇒ 00:44:54.720 Katherine Bayless: And then under, scripts.

375 00:44:56.120 ⇒ 00:45:02.870 Katherine Bayless: And utilities… And email validation pipeline test, that first one.

376 00:45:09.170 ⇒ 00:45:16.909 Katherine Bayless: Yeah, so, like, if you just needed a snippet of, like, somewhere in here, I had built out the calls out to the, marketing cloud.

377 00:45:20.330 ⇒ 00:45:22.260 Uttam Kumaran: Oh, great. Perfect. Okay.

378 00:45:24.250 ⇒ 00:45:33.639 Katherine Bayless: It was one of those things that, like, I, like, I think I started really early, and I was like, I’m gonna do this, and then I was like, oh, no, I am… I am definitely not gonna actually have the time to really do this, do this.

379 00:45:34.130 ⇒ 00:45:34.830 Katherine Bayless: But…

380 00:45:35.670 ⇒ 00:45:43.750 Ashwini Sharma: How does the Polyatomic Salesforce Marketing Cloud Connector work? Does it require an OAuth, or does it… I’ve not seen that.

381 00:45:43.750 ⇒ 00:45:48.759 Uttam Kumaran: We’re gonna have to figure it out next week with Galib, I think, yeah.

382 00:45:50.440 ⇒ 00:45:51.430 Katherine Bayless: Yeah, we’ll go through a basis.

383 00:45:51.430 ⇒ 00:45:54.799 Uttam Kumaran: Basically, figure out what, like, what auth requirements we have for each of them.

384 00:45:55.550 ⇒ 00:46:01.179 Katherine Bayless: Yeah, good reminder, yeah. I’m so… gravitate towards, like, the rest, yeah, like…

385 00:46:01.520 ⇒ 00:46:08.419 Katherine Bayless: But yeah, yeah, if there’s a different auth method that we need to set up, I can probably configure it on the backend in Marketing Cloud.

386 00:46:08.670 ⇒ 00:46:10.989 Katherine Bayless: I’d say, optimistically.

387 00:46:12.410 ⇒ 00:46:16.150 Katherine Bayless: I have full admin access, I just find the platform very clunky to navigate.

388 00:46:18.530 ⇒ 00:46:29.359 Katherine Bayless: But the dbt thing is cool. I think, truthfully, like, since I haven’t worked with it before, I know we kind of talked about this, maybe last week, but, like, maybe if there’s a chance to, like, kind of…

389 00:46:29.560 ⇒ 00:46:32.879 Katherine Bayless: Do, like, a tutorial, like, just, like, here’s how.

390 00:46:32.880 ⇒ 00:46:33.929 Uttam Kumaran: Oh, yeah.

391 00:46:33.930 ⇒ 00:46:34.740 Katherine Bayless: Yeah, yeah.

392 00:46:34.740 ⇒ 00:46:39.579 Uttam Kumaran: Maybe, Ashwini, what we should do next week, once you… maybe if you get to, like, a MARTS model.

393 00:46:39.720 ⇒ 00:46:45.600 Uttam Kumaran: We should basically, like, what we commonly will do is, like, we’ll just do a meeting, and we’ll have you share and ship your first

394 00:46:45.800 ⇒ 00:46:46.790 Uttam Kumaran: Like, dbt?

395 00:46:47.620 ⇒ 00:47:01.260 Uttam Kumaran: The… the tough part about dbt is, like, a lot of the config stuff, so it’s helpful that we’ll share with you, like, what the setup is, and, like, we have docs, but ideally, we won’t need to, like, touch a ton of that.

396 00:47:01.520 ⇒ 00:47:09.100 Uttam Kumaran: You know, for a while. But we are… we do… we do, in particular, a lot on, like, tagging, naming convention, folder structure, so it’s, like.

397 00:47:09.460 ⇒ 00:47:12.160 Uttam Kumaran: Yeah, I would love to explain all those things.

398 00:47:12.660 ⇒ 00:47:15.400 Katherine Bayless: Yeah, yeah, yeah. I would love to geek out and learn.

399 00:47:16.930 ⇒ 00:47:19.650 Uttam Kumaran: Perfect. Yeah, Ashwini, we were just debating.

400 00:47:19.800 ⇒ 00:47:21.420 Ashwini Sharma: Adding another layer.

401 00:47:21.480 ⇒ 00:47:24.849 Uttam Kumaran: like, and internally, because what I was telling him, like, when…

402 00:47:24.880 ⇒ 00:47:42.679 Uttam Kumaran: when we were just starting the company, and I’ve worked in sort of, like, huge dbt code bases, but, like, sometimes you go to a client, they have no appreciation for it, so they’re like, just make this happen. You’re like, okay, it’s just me. And then as we started getting bigger, and there’s finally people I can talk to about, like.

403 00:47:42.770 ⇒ 00:48:00.070 Uttam Kumaran: dbt structure naming conventions, because there’s not, like, that’s not, like, you know, not everybody’s interested in talking to me about that. But I… I’m not very, like, opinionated, but I’m, like, we need an opinion. Like, I don’t like not having an opinion on those types of things, so we’ve arrived at a pretty good,

404 00:48:00.300 ⇒ 00:48:10.419 Uttam Kumaran: pretty good setup now, I feel like, where it’s like, if you were to ask, like, I wonder where the model is for this, it’s easy for you to go in the repo and find that, you know?

405 00:48:10.420 ⇒ 00:48:25.290 Katherine Bayless: Nice, nice. I mean, that stuff, it’s true, like, a lot of folks don’t think about it or get excited about it, but, like, it makes all of the difference, and I think increasingly, like, with AI, right, like, we’re gonna have to do it if we want the robots to be able to help us, like, all of this little…

406 00:48:25.290 ⇒ 00:48:30.510 Uttam Kumaran: For us, that’s the thing. You need the context and organization there if pressure’s gonna help you.

407 00:48:30.800 ⇒ 00:48:36.569 Uttam Kumaran: write stuff, and things like that, so… that’s… that’s honestly the… a big reason is, like, we also…

408 00:48:36.680 ⇒ 00:48:46.499 Uttam Kumaran: we’ll… we’ll put in, like, an agent’s MD file, we’ll put in cursor rules, because we’ll help… it’ll just help us, like, speed up a lot of stuff, especially when you’re like, where did this logic come from, or how should

409 00:48:46.920 ⇒ 00:48:50.400 Uttam Kumaran: this, and, like, just speeding up a lot of things, so…

410 00:48:51.000 ⇒ 00:48:51.640 Katherine Bayless: Yeah.

411 00:48:52.060 ⇒ 00:49:00.589 Katherine Bayless: Yeah, I would also love to do the cursor kind of, like, intro demo kind of thing, because I think that would be… and I think Jay wants in on that, too, yeah.

412 00:49:00.590 ⇒ 00:49:03.880 Uttam Kumaran: Okay, cool, yeah, yeah, yeah. Okay, so maybe that’s two things that we’ll…

413 00:49:04.170 ⇒ 00:49:06.219 Uttam Kumaran: We can do first is, like, we just set up

414 00:49:06.440 ⇒ 00:49:11.509 Uttam Kumaran: Yeah, maybe we’ll just set up Cursor, and then we’ll also just do a walkthrough of dbt.

415 00:49:12.050 ⇒ 00:49:12.710 Uttam Kumaran: That’s great.

416 00:49:13.360 ⇒ 00:49:14.280 Katherine Bayless: I like it.

417 00:49:18.910 ⇒ 00:49:24.269 Uttam Kumaran: Okay. I feel like that’s kind of, like, all we had. If there’s other topics, Captain, we can move to that.

418 00:49:25.630 ⇒ 00:49:30.599 Katherine Bayless: Yeah, so I think, yeah, other topics, yes.

419 00:49:30.980 ⇒ 00:49:33.960 Katherine Bayless: At the risk of asking a question in a scary way.

420 00:49:33.960 ⇒ 00:49:57.019 Katherine Bayless: like, how much, how much extra bandwidth you guys got running around over there? Because, I might… I might have a whole bunch of things that, like, I don’t know, I don’t know. But there’s some, there’s some possibilities swirling, and I think it’s funny, but your… your… your assistance with the remembers thing, has… has increased your legend internally, and so I’ve… I’ve been like, well, I think there’s more stuff they could help with.

421 00:49:57.020 ⇒ 00:50:00.110 Katherine Bayless: In particular.

422 00:50:00.650 ⇒ 00:50:14.579 Katherine Bayless: we have, as you just watched my Okta, like, as I tried to log into that platform, right? It’s like, it’s going through a gazillion steps. I’m entering my password constantly. Everybody on staff is entering their password constantly. They’re getting cranky.

423 00:50:15.190 ⇒ 00:50:36.580 Katherine Bayless: Fundamentally, the challenge with Okta is that we are using it to, like, username and password authenticate our entire audience, which is just odd to me. Like, in my experience, you would use, you know, your Okta Auth0-type product for, like, your employees and your known entities, and then anybody who’s, like, interacting with you once a year to come to CES gets a magic link in their email, right?

424 00:50:36.770 ⇒ 00:50:37.470 Uttam Kumaran: Yes.

425 00:50:37.830 ⇒ 00:50:48.749 Katherine Bayless: Everybody’s got a username and password. And now we’ve got somehow two Okta tenants, and, like, Impexium, or remembers in particular, is, like, you’re kind of logging in through both tenants somehow.

426 00:50:48.750 ⇒ 00:50:49.440 Uttam Kumaran: Okay.

427 00:50:50.230 ⇒ 00:51:03.059 Katherine Bayless: Jay is using Claude Code to, like, try to disentangle a lot of these rules and stuff like that, and, I mean, he’s making progress, but I’m also, like, his time is the same as mine, right? Like, he’s getting pulled in 5,000 directions as the show approaches.

428 00:51:03.250 ⇒ 00:51:18.400 Katherine Bayless: And increasingly, I’m like, can we just throw some money at this problem? Because it’s driving everybody crazy, and it’s totally fixable, we just need the person who has the brain space to sit down and spend, like, a week on it, instead of 5 minutes here and there, which is what Jay has. So Okta.

429 00:51:18.580 ⇒ 00:51:21.500 Katherine Bayless: And then let me… let me show you a Slack channel.

430 00:51:22.280 ⇒ 00:51:28.219 Katherine Bayless: Okay. I feel like I’m gonna have to ask you guys to stop recording the calls, because I’m like…

431 00:51:28.220 ⇒ 00:51:29.539 Uttam Kumaran: Oh yeah, I could totally stop that.

432 00:51:29.540 ⇒ 00:51:41.149 Katherine Bayless: No, no, I’m just kidding. I’m just like, my coworkers are gonna be like, you showed them that? Okay, support download issues. Okay, so… and Ashwini, I think you were the one who said you used to work at Shopify briefly, right?

433 00:51:42.340 ⇒ 00:51:43.750 Ashwini Sharma: Yeah, yeah, yeah, I worked with them.

434 00:51:43.750 ⇒ 00:51:47.230 Katherine Bayless: Okay. Yeah, okay, so we have… Actually…

435 00:51:47.230 ⇒ 00:51:54.610 Uttam Kumaran: I also, by the way, have a… have a good amount of friends that work at Shopify. Surprisingly, I got… so I worked at,

436 00:51:54.760 ⇒ 00:51:59.220 Uttam Kumaran: I worked at WeWork way back, and a lot of the WeWork data crew

437 00:51:59.410 ⇒ 00:52:01.590 Uttam Kumaran: ended up at Shopify, so I have, like.

438 00:52:02.280 ⇒ 00:52:09.980 Uttam Kumaran: yeah, I have, like, 2 or 3 different friends that are on the data science team there, in case we need them to help us with something.

439 00:52:10.600 ⇒ 00:52:14.640 Katherine Bayless: Yeah, so, okay, well, that’s actually… that’s good context, too.

440 00:52:15.300 ⇒ 00:52:19.539 Katherine Bayless: Sue… We could go two directions with this, to be honest.

441 00:52:19.790 ⇒ 00:52:23.719 Katherine Bayless: nobody seems to know exactly why we got Shopify.

442 00:52:23.910 ⇒ 00:52:47.230 Katherine Bayless: we use it to sell, and in some cases they’re free, but still sell, our, like, research, basically. And so, like, members get it for free, and I think volunteers and stuff like that, and then everybody else can purchase it. It’s not a huge revenue stream for us. Impexium, remembers, does have, an ability to, like, service a storefront. I think the Okta thing, being such a pain

443 00:52:47.230 ⇒ 00:52:51.010 Katherine Bayless: for remembers made us go towards the direction of Shopify.

444 00:52:51.010 ⇒ 00:52:55.140 Katherine Bayless: We now have Shopify doing our research downloads.

445 00:52:55.180 ⇒ 00:52:57.920 Katherine Bayless: But we also have a Slack channel.

446 00:52:58.070 ⇒ 00:53:05.040 Katherine Bayless: Of people who are unable to download the research, no matter how many times they have tried logging in or clicking

447 00:53:05.710 ⇒ 00:53:13.109 Katherine Bayless: Jay built, like, a Gleep agent that can kind of, like, try to troubleshoot these, but, like.

448 00:53:13.260 ⇒ 00:53:23.530 Katherine Bayless: I just… to me, I’m, like, at the point where you have to create a Slack channel to, like, track trouble with something, it’s time to ask a deeper question. But, like.

449 00:53:23.710 ⇒ 00:53:29.680 Katherine Bayless: day after day after day, nobody can get in and download these reports. So part of me is like.

450 00:53:29.800 ⇒ 00:53:35.820 Katherine Bayless: do we really need Shopify, or do you just need to fix Okta, and then push this store back into remembers where it was before?

451 00:53:35.820 ⇒ 00:53:36.380 Uttam Kumaran: Yeah.

452 00:53:37.120 ⇒ 00:53:43.870 Katherine Bayless: But then I’m also like, Shopify’s kind of a nice product, and I know we have all of this need to push out, like, more data sharing.

453 00:53:43.870 ⇒ 00:53:46.450 Uttam Kumaran: Is there a link to the Shopify storefront?

454 00:53:47.470 ⇒ 00:53:54.450 Katherine Bayless: Mmm… that’s a great question. I… I assume I do somewhere, but I’ve actually never really gone in there myself.

455 00:53:54.810 ⇒ 00:53:57.920 Katherine Bayless: kind of curious if there’s one I can grab, like, right here while I’m…

456 00:54:01.590 ⇒ 00:54:06.830 Uttam Kumaran: Because I would be like, if these were all digital assets, like, I would just probably, like, you just do it on Stripe.

457 00:54:08.580 ⇒ 00:54:08.910 Uttam Kumaran: because you’re not.

458 00:54:08.910 ⇒ 00:54:09.260 Katherine Bayless: Thank you.

459 00:54:09.260 ⇒ 00:54:14.320 Uttam Kumaran: Selling, like, We’re not selling, like, anything. I mean, we have a lot, like.

460 00:54:14.320 ⇒ 00:54:14.759 Katherine Bayless: Half of them.

461 00:54:14.760 ⇒ 00:54:16.680 Uttam Kumaran: Our customer base is e-commerce.

462 00:54:17.220 ⇒ 00:54:21.470 Katherine Bayless: Yeah. And we… so we do a lot of Shopify work, but it’s, like, Let me see…

463 00:54:21.470 ⇒ 00:54:23.609 Uttam Kumaran: They’re just selling PDFs and stuff, like…

464 00:54:24.920 ⇒ 00:54:28.039 Uttam Kumaran: You don’t need, like, most of that product, and if.

465 00:54:28.040 ⇒ 00:54:28.689 Katherine Bayless: I’m freezing!

466 00:54:28.690 ⇒ 00:54:29.370 Uttam Kumaran: And…

467 00:54:29.950 ⇒ 00:54:33.059 Katherine Bayless: And you kind of just want to track receipts, I guess?

468 00:54:33.360 ⇒ 00:54:34.490 Uttam Kumaran: You… yeah.

469 00:54:35.050 ⇒ 00:54:43.989 Katherine Bayless: We do need to track receipts. I think we also… like, I’m not 100% sure, per se, like, what the actual, like.

470 00:54:43.990 ⇒ 00:54:53.200 Katherine Bayless: download and delivery experience was like in Impexium, so, like, it’s possible that maybe that just sucked too hard. I’m trying to see if I even have a login for it.

471 00:54:53.200 ⇒ 00:55:04.949 Katherine Bayless: Like, the Shopify thing is, like, this third rail dumpster fire that I’m like, I really don’t have any desire to get mixed up in it too soon. But, let’s see…

472 00:55:05.110 ⇒ 00:55:05.980 Katherine Bayless: Inc.

473 00:55:09.230 ⇒ 00:55:15.500 Katherine Bayless: Yeah… No, it’s gonna send a passcode out to somebody that I don’t.

474 00:55:16.320 ⇒ 00:55:24.739 Uttam Kumaran: So they’re… yeah, I guess it would be, like, it’d be cool to walk through the flow, like, if they’re… so they’re not getting a PDF, they’re, like, having to go backlog into an Impexium paywalled thing.

475 00:55:25.640 ⇒ 00:55:27.799 Katherine Bayless: I… honestly, I genuinely.

476 00:55:27.800 ⇒ 00:55:28.610 Uttam Kumaran: Okay.

477 00:55:28.610 ⇒ 00:55:33.209 Katherine Bayless: But I think that is the idea, is, like, they’re coming in via…

478 00:55:33.450 ⇒ 00:55:55.219 Katherine Bayless: via Remembers being passed off to Shopify, making the purchase, and then trying to access the download, and it’s failing. I should also say, I found a log of API calls in Remember’s, like, backend, like, admin stuff. There’s, like, hundreds of calls a day that are failing for products that, to me, look like the attempt.

479 00:55:55.220 ⇒ 00:55:55.710 Uttam Kumaran: Oh.

480 00:55:55.710 ⇒ 00:55:57.909 Katherine Bayless: that are… right?

481 00:55:59.040 ⇒ 00:56:01.810 Uttam Kumaran: Is there a pro… is there an internal product person on…

482 00:56:02.310 ⇒ 00:56:05.539 Uttam Kumaran: On this? Like, who is, like, who came up with the architecture?

483 00:56:06.190 ⇒ 00:56:17.230 Katherine Bayless: I asked on Wednesday which team owns Shopify, and I got a bunch of answers, but none of them were a team. They were all… I don’t know, and I think it was marketing, and it turns out

484 00:56:17.330 ⇒ 00:56:29.860 Katherine Bayless: the person who bought it back in the day was somebody who just got tired of dealing with Jay, and he was on the marketing team. That person has since left, and now nobody really owns Shopify. I think Jay did the initial build-out.

485 00:56:30.450 ⇒ 00:56:33.299 Katherine Bayless: And it’s just, it’s, yeah, it’s just… yeah.

486 00:56:33.300 ⇒ 00:56:39.789 Uttam Kumaran: Yeah, like, so Sam… so Sam on our team can, yeah, can totally… Do both of those.

487 00:56:39.920 ⇒ 00:56:46.029 Uttam Kumaran: So in terms of bandwidth question, yeah, we have bandwidth. We… so also to give you a sense of, like, our…

488 00:56:46.190 ⇒ 00:56:54.119 Uttam Kumaran: even our company and scope. So we do a lot of work, of course, in… no, no, no, I’ll tell you, because there’s a lot of stuff we don’t do, but…

489 00:56:54.120 ⇒ 00:56:59.670 Katherine Bayless: And I’m like, but you guys are good, do all the things, right? Yeah. I’m a classic, classic customer, yeah.

490 00:56:59.880 ⇒ 00:57:12.500 Uttam Kumaran: No, we… so we do a lot, of course, like, we do a ton in data, so warehousing, ETL, modeling, everything. We also do a ton of work in analytics. So a lot of our engagements, we’re doing a ton of…

491 00:57:12.640 ⇒ 00:57:30.379 Uttam Kumaran: like, strategic analytics, investor board deck type stuff, like, really, like, getting to the meat of, like, how does this grow? And, like, sort of… that gets more, like, we’re putting together decks of, like, hypotheses that we’re going after. So that’s, like, that’s the work that, of course, like, every data person, like, wants to

492 00:57:30.830 ⇒ 00:57:39.919 Uttam Kumaran: basically power. Then, a lot of… the other side of our business, which is starting to get closer now because of, like, data and AI things, is we do a lot of, like.

493 00:57:40.070 ⇒ 00:57:49.490 Uttam Kumaran: basically, like, application, AI application-related development. So we do have a ton of internal talent, like, full-stack Engage talent.

494 00:57:49.650 ⇒ 00:57:53.390 Uttam Kumaran: So I’m happy to have… like, Sam would be a good person to just…

495 00:57:54.040 ⇒ 00:57:56.780 Uttam Kumaran: poke at it. Like, he’s back on Monday.

496 00:57:56.950 ⇒ 00:57:58.670 Uttam Kumaran: So I can have him…

497 00:57:58.820 ⇒ 00:58:01.970 Uttam Kumaran: poke at the Okta problem, and then

498 00:58:02.260 ⇒ 00:58:05.510 Uttam Kumaran: can kind of give that and the Shopify stuff.

499 00:58:05.710 ⇒ 00:58:11.370 Uttam Kumaran: he’s a machine, like, he’ll just go, like, figure… yeah. He’s, like, the smartest guy, so… he’ll…

500 00:58:11.370 ⇒ 00:58:11.810 Katherine Bayless: Because, like.

501 00:58:11.810 ⇒ 00:58:13.020 Uttam Kumaran: it out.

502 00:58:13.020 ⇒ 00:58:21.550 Katherine Bayless: Yeah, because I, like, I think if it is something that’s remotely reasonable to ask of your shop, because, yeah, I’ve totally been on the vendor side…

503 00:58:21.550 ⇒ 00:58:31.549 Uttam Kumaran: He’ll let me know. I mean, because, yeah, he’ll let me know. I don’t mind if it’s, like, if we have the… if we truly… I don’t… the one thing I don’t want to do

504 00:58:31.710 ⇒ 00:58:51.069 Uttam Kumaran: is do what every consultancy does, be like, yeah, we can do it, and we do, like, half-baked. Like, I can’t… I can’t sleep if we, like, don’t deliver, the best. So, for me, that’s more of the… I could have him just to spend time taking a look at it, and then giving a sense of, like, what parts can we cover?

505 00:58:51.170 ⇒ 00:58:56.150 Uttam Kumaran: Well, Park Sam, like, but Sam, and then another guy on our team, Surf, they’re both, like.

506 00:58:56.460 ⇒ 00:58:58.379 Uttam Kumaran: CTO, like, architect.

507 00:58:58.750 ⇒ 00:59:00.379 Uttam Kumaran: They do a lot of auth.

508 00:59:00.530 ⇒ 00:59:07.440 Uttam Kumaran: And auth, and, like, integration, Set up stuff, like… Yeah, he… he’ll pro… he’ll…

509 00:59:07.620 ⇒ 00:59:12.599 Uttam Kumaran: be a good person to just give you, like, what’s going on here. So maybe I can tell them on Monday?

510 00:59:13.780 ⇒ 00:59:17.180 Uttam Kumaran: Yeah, I think… And because he’s already in Access and stuff, so…

511 00:59:17.870 ⇒ 00:59:25.380 Katherine Bayless: True, true, true, yeah. Yeah, and I think… so what I’d like to do, really, is, like, bring Jay into the conversation, too, and be like.

512 00:59:26.630 ⇒ 00:59:32.940 Katherine Bayless: it, you know, you guys could also… so, I mean, his stuff and my stuff, I mean, they’re the blurred lines, right? Like, between IT and…

513 00:59:32.940 ⇒ 00:59:38.660 Uttam Kumaran: He should, but also, he should be like, yeah, I mean, we could just take it off his plate if he’s just getting slammed with that stuff, you know?

514 00:59:38.840 ⇒ 00:59:43.359 Katherine Bayless: Right, right, right, exactly. And I think… I think it… it…

515 00:59:43.520 ⇒ 00:59:59.530 Katherine Bayless: I think it’s very solvable by somebody who just kind of, like, knows what needs to be done, and, like, I feel like if we, since we have the MSA in place already, like, if we just, like, slot in a little scope, you know, two weeks of debugging, and then, like, everybody’s happier by the time we get to Vegas kind of thing.

516 00:59:59.890 ⇒ 01:00:00.820 Uttam Kumaran: Okay. Okay.

517 01:00:00.820 ⇒ 01:00:05.230 Katherine Bayless: I think… I think it would also be really good for Jay to say…

518 01:00:05.270 ⇒ 01:00:16.350 Katherine Bayless: see the power of this group. Not that he doesn’t see it, that’s not what I mean, but, like, his consulting shops that he’s been working with are, like, more like, you know, traditional, strategic, advisory, like.

519 01:00:16.350 ⇒ 01:00:28.680 Katherine Bayless: Like, I’m like, no, we can get you some, like, serious tactical help for some of these pieces of our tech stack that are just not functioning the way they should be. And there’s, you know, there’s legacy and baggage and all the things, but the reality is…

520 01:00:28.800 ⇒ 01:00:38.639 Katherine Bayless: I mean, these are solvable problems if we throw the right resources at them. Yeah. And I think if he could get out of, like, tech debt hell, he might be a little bit less cranky all the time.

521 01:00:38.640 ⇒ 01:00:40.760 Uttam Kumaran: Right? Yeah.

522 01:00:40.900 ⇒ 01:00:49.250 Katherine Bayless: There is also the increasingly terrifying possibility that I will be the one in charge of some of these things, and I’m like, okay, well, if they’re gonna be mine, then we’re.

523 01:00:49.250 ⇒ 01:00:50.299 Uttam Kumaran: That’s gonna happen.

524 01:00:50.300 ⇒ 01:01:01.889 Katherine Bayless: Yeah, yeah. Yeah, so yeah, so if you guys want to talk internally or whatever first, but yeah, like, maybe on Monday or Tuesday, we can kind of connect and, like, bring Jay into, and just be like, look, I think we could put out these fires pretty, like…

525 01:01:02.460 ⇒ 01:01:09.110 Katherine Bayless: Rapidly, maybe? And hey, I could be wrong. Like, if Sam gets in there and he’s like, oh no, you guys are fucked, that’s fine. But my hypothesis.

526 01:01:09.110 ⇒ 01:01:11.900 Uttam Kumaran: No, he’ll be so nice, yeah.

527 01:01:11.900 ⇒ 01:01:12.490 Katherine Bayless: He’s amazing.

528 01:01:12.490 ⇒ 01:01:18.550 Uttam Kumaran: nicest guy ever. Like, he’ll… he will know what… he will know what to do, and then he’ll… we’ll see what he says, yeah.

529 01:01:18.860 ⇒ 01:01:29.470 Katherine Bayless: Okay, okay. Yeah. Because, like, Jake’s doing some really cool bleeding-edge stuff with, like, AI and, like, some of the cloud code. Like, he’s… he… I think if I can get his brain out of tech debt.

530 01:01:29.470 ⇒ 01:01:45.099 Katherine Bayless: and into the, like, how can we tune some of these systems to really work for the organization? The other day, I was talking to somebody in membership who said they missed Microsoft Copilot because it did better at generating meeting minutes on Teams calls, and I was like, well, we have Zoom! And she’s like, yeah, it’s just not as good. And I’m like, okay, well.

531 01:01:45.140 ⇒ 01:01:47.199 Katherine Bayless: somebody in Jay’s role could

532 01:01:47.260 ⇒ 01:01:50.869 Katherine Bayless: tune Zoom so that it works, right? Yeah, exactly. But I need him to be.

533 01:01:50.870 ⇒ 01:02:02.470 Uttam Kumaran: No, but Jay should think more about the strategy and, like, what to build, because he has, like, so much institutional knowledge, and then he doesn’t need to do it. You don’t need him to do it, the thing.

534 01:02:02.900 ⇒ 01:02:03.230 Katherine Bayless: Right.

535 01:02:03.230 ⇒ 01:02:03.800 Uttam Kumaran: You know?

536 01:02:04.400 ⇒ 01:02:05.290 Uttam Kumaran: Yeah.

537 01:02:05.290 ⇒ 01:02:09.660 Katherine Bayless: Right, right, right, right, yeah, exactly, exactly. So, we will get there.

538 01:02:09.660 ⇒ 01:02:10.040 Uttam Kumaran: Okay.

539 01:02:10.040 ⇒ 01:02:11.380 Katherine Bayless: Yeah.

540 01:02:11.500 ⇒ 01:02:29.079 Uttam Kumaran: So I’ll… so let me send a… I’ll send a little bit of, like, a follow-up on a couple things. I reached out to Polyatomic, and I think, Catherine, also, I may try to grab time between me, you, and Golub from Polyatomic, just so you can say hi. Really, really good guy. And then, yeah, let’s plan…

541 01:02:29.530 ⇒ 01:02:34.099 Uttam Kumaran: Probably a couple meetings for next week. I’ll just, like, get organized later today.

542 01:02:34.720 ⇒ 01:02:36.910 Katherine Bayless: Okay, okay, awesome.

543 01:02:36.910 ⇒ 01:02:37.320 Uttam Kumaran: Okay.

544 01:02:37.320 ⇒ 01:02:53.609 Katherine Bayless: One last little tidbit of just, like, fun. Since you mentioned the, like, you know, advanced analytics and what you want to build and stuff, I started dabbling in, like, Monday night. I was like, I need to just look at data that doesn’t suck. And so I started dabbling in a concept of, like.

545 01:02:53.940 ⇒ 01:03:13.070 Katherine Bayless: could we look at, like, changes in our audience if we created, like, a seniority score per market segment? So, like, are we seeing more C-suite people coming to robotics content versus more individual contributors? What might that suggest about the maturity of the technology at the organization and the interest in it? And then, like, how do those things change over time?

546 01:03:13.230 ⇒ 01:03:26.910 Katherine Bayless: I did some back-of-the-envelope, you know, rough work at it, like, dividing points, sort of like a weighted title system by raw attendance, right? And interestingly, yes, AI quantum robotics, top, top, top, top, all the C-suites are there, right?

547 01:03:26.910 ⇒ 01:03:28.390 Uttam Kumaran: Yeah. Supply chain.

548 01:03:28.390 ⇒ 01:03:45.109 Katherine Bayless: is seeing a massive uptick in C-suite after not having that attention on it in a while, which is, like, yeah, I mean, hardly, like, shocking, right, between tariffs and COVID and all the things. Yeah. But still really, like, possibly a more interesting way to look at the, like, attendance and engagement than what we have been doing.

549 01:03:45.110 ⇒ 01:03:45.840 Uttam Kumaran: Oh, great.

550 01:03:45.840 ⇒ 01:03:47.830 Katherine Bayless: Just raw numbers, right?

551 01:03:47.830 ⇒ 01:03:48.530 Uttam Kumaran: Yeah, he’s high school.

552 01:03:48.530 ⇒ 01:04:01.149 Katherine Bayless: socializing that to a few people this week, and I was like, look, when it has a numerator and a denominator, suddenly we have a little more context. And I feel like some of the eyes are opening, light bulbs are turning on, right? So, like, I think…

553 01:04:01.150 ⇒ 01:04:10.509 Katherine Bayless: I think if we can get the low-hanging fruit tech debt out of the way, I think there’s some really good questions to dig in on. I think we could predict mergers and acquisitions if we looked at the floor traffic at CES.

554 01:04:11.080 ⇒ 01:04:12.409 Uttam Kumaran: Yeah, yeah, yeah.

555 01:04:13.480 ⇒ 01:04:18.920 Uttam Kumaran: Wow, that’s awesome. Yeah, I would love to see that, if that was a blurb, or if that was, like, a slide or something. Yeah, that’d be great.

556 01:04:18.920 ⇒ 01:04:21.140 Katherine Bayless: Oh, yeah, yeah, yeah, I’ll kind of know.

557 01:04:21.140 ⇒ 01:04:22.140 Uttam Kumaran: Or whatever.

558 01:04:22.760 ⇒ 01:04:31.860 Katherine Bayless: I think it’s like a… it’s just a hodgepodge of Catherine’s brain dumped into READMEs, and I think there was a slide deck I put out somewhere, but yeah, I’ll dump it in the S3 bucket so you can play with it.

559 01:04:32.480 ⇒ 01:04:33.570 Uttam Kumaran: Okay, perfect.

560 01:04:34.010 ⇒ 01:04:35.100 Katherine Bayless: Yeah.

561 01:04:36.670 ⇒ 01:04:41.399 Uttam Kumaran: Alright, well, appreciate the time on a Friday, and then, yeah, I’ll follow up later today with a couple notes.

562 01:04:41.940 ⇒ 01:04:44.140 Katherine Bayless: Okay, thank you, thank you. Super appreciated.

563 01:04:44.140 ⇒ 01:04:44.690 Uttam Kumaran: Okay.

564 01:04:45.210 ⇒ 01:04:46.090 Uttam Kumaran: Perfect.

565 01:04:46.120 ⇒ 01:04:47.560 Katherine Bayless: Alright, thanks, Srini.

566 01:04:47.560 ⇒ 01:04:48.230 Uttam Kumaran: Thank you, Kevin.

567 01:04:48.230 ⇒ 01:04:49.330 Ashwini Sharma: Thank you, thank you.