2025-12-16_cta_brainforge_discovery_scopes_q1_plan

Meeting Title: CTA<> Brainforge: Discovery Scopes + Q1 Plan Date: 2025-12-16 Meeting participants: Samuel Roberts, Katherine Bayless, Ashwini Sharma, Uttam Kumaran

WEBVTT

1 00:00:28.440 ⇒ 00:00:29.420 Katherine Bayless: Hey, Sam.

2 00:00:31.090 ⇒ 00:00:32.159 Samuel Roberts: Hello.

3 00:00:32.580 ⇒ 00:00:33.440 Katherine Bayless: We’re going.

4 00:00:34.360 ⇒ 00:00:36.389 Samuel Roberts: Doing alright. How about yourself?

5 00:00:36.860 ⇒ 00:00:38.660 Katherine Bayless: Yeah, it’s going.

6 00:00:38.660 ⇒ 00:00:39.490 Samuel Roberts: Yeah.

7 00:00:39.490 ⇒ 00:00:46.630 Katherine Bayless: It is one of those days, like, that’s not a bad day by any stretch, but, like, my brain just feels like there’s extra, like, latency, like, it’s buzzy. I’m just like…

8 00:00:46.760 ⇒ 00:00:55.040 Samuel Roberts: I keep losing track of what day it is. I think this… the holidays approaching, and everything, it’s just, I… you know, family’s coming to town one day, and then another day, and I don’t know what… yeah, it’s just…

9 00:00:55.370 ⇒ 00:00:58.320 Samuel Roberts: Yeah, it’s like my mental model is collapsing. Yeah, yeah.

10 00:00:58.400 ⇒ 00:01:02.130 Katherine Bayless: yeah. Yeah.

11 00:01:02.350 ⇒ 00:01:19.939 Katherine Bayless: Yeah, we, I just got off another call with, that, the AMS vendor remembers, where they were trying to tell us how invoices work, and I was like, it sounds like your system just doesn’t support a pro forma invoice, which is clearly what we need, and they’re like, well, I mean, nobody else has ever asked for that. I’m like, okay, no.

12 00:01:20.260 ⇒ 00:01:21.060 Samuel Roberts: Alright.

13 00:01:22.560 ⇒ 00:01:24.029 Katherine Bayless: They fascinate me.

14 00:01:24.030 ⇒ 00:01:25.200 Samuel Roberts: Yeah.

15 00:01:26.100 ⇒ 00:01:26.810 Katherine Bayless: But…

16 00:01:27.150 ⇒ 00:01:33.569 Katherine Bayless: But yeah, I’m excited to have this conversation, even though I’m probably a little bit behind on some of the action items that I need to do.

17 00:01:34.230 ⇒ 00:01:38.670 Katherine Bayless: Oh, actually, you know, I can do the Asana thing while we’re here, if we want.

18 00:01:47.760 ⇒ 00:01:49.299 Katherine Bayless: We’ve got those from the thread.

19 00:02:03.160 ⇒ 00:02:03.990 Katherine Bayless: Done.

20 00:02:06.040 ⇒ 00:02:06.720 Samuel Roberts: Hmm.

21 00:02:13.540 ⇒ 00:02:17.410 Samuel Roberts: Okay… I’m gonna ping Utena, make sure he’s…

22 00:02:29.090 ⇒ 00:02:34.149 Katherine Bayless: Okay, so I still owe… I need the S3 bucket with the flat files, which I can do that.

23 00:02:34.640 ⇒ 00:02:47.809 Katherine Bayless: do that one today. My delay has just been because I admit they are not very organized on my end, and then so I was like, I should clean these up a bit first, and now I’m like, no, I should just send the one set of files and the cleanup can be later.

24 00:02:47.810 ⇒ 00:02:48.570 Samuel Roberts: Yeah.

25 00:02:48.570 ⇒ 00:03:00.819 Katherine Bayless: is a common problem for my brain. And then the Git integration thing, I’ll have to set up as well. I realized… I started on that path, but then, Ashwini, you had called out, like, the service account thing, and I was like, oh yeah, probably…

26 00:03:01.390 ⇒ 00:03:10.440 Katherine Bayless: probably do need at least one or two service accounts for the team. I think I requested one at some point, but I’m not sure if it ever got created.

27 00:03:10.580 ⇒ 00:03:16.399 Katherine Bayless: So that might be a homework item to get those in place, because right now, everything is just some human’s credentials.

28 00:03:16.950 ⇒ 00:03:17.680 Samuel Roberts: Right.

29 00:03:20.430 ⇒ 00:03:21.730 Uttam Kumaran: Hello!

30 00:03:21.730 ⇒ 00:03:22.210 Katherine Bayless: Hello!

31 00:03:23.560 ⇒ 00:03:24.700 Uttam Kumaran: How’s everyone?

32 00:03:25.630 ⇒ 00:03:26.860 Katherine Bayless: Good. How are you?

33 00:03:26.860 ⇒ 00:03:27.590 Samuel Roberts: Alright. Cool.

34 00:03:27.590 ⇒ 00:03:28.370 Uttam Kumaran: Good.

35 00:03:28.770 ⇒ 00:03:31.469 Uttam Kumaran: Doing well. I need another coffee.

36 00:03:31.630 ⇒ 00:03:33.650 Samuel Roberts: No, I was just, I was just pouring more.

37 00:03:33.650 ⇒ 00:03:35.709 Uttam Kumaran: I need another coffee.

38 00:03:35.710 ⇒ 00:03:36.900 Katherine Bayless: what I should do.

39 00:03:37.930 ⇒ 00:03:49.980 Uttam Kumaran: Cool. I guess, what’s a good place to start? Maybe as Sweeney, do you want to start by sharing sort of where we are on the… on the dbt side? And then I want to carve out time to talk about…

40 00:03:50.150 ⇒ 00:03:54.390 Uttam Kumaran: the SOWs as well. So, yeah, maybe, Ashwini, you want to start there.

41 00:03:54.790 ⇒ 00:04:05.089 Ashwini Sharma: Jaw, so, on the DPT side, right, second… where are… where is my browser icon? Okay.

42 00:04:05.830 ⇒ 00:04:21.370 Ashwini Sharma: Let me just quickly show how it looks like, right? So there is an integration that I said, right? I used my user ID and access token to create this, so normally, like, what you would see is something like this.

43 00:04:21.459 ⇒ 00:04:28.410 Ashwini Sharma: dbt project, and this is all CTA dataOps, the same repo, GitHub repo that we have, that we are using, right?

44 00:04:28.620 ⇒ 00:04:34.109 Ashwini Sharma: And any changes to that repo has to be synced, right now, it’s, it’s…

45 00:04:34.250 ⇒ 00:04:38.329 Ashwini Sharma: Manually, like, you have to do a pull, and then it refreshes.

46 00:04:38.500 ⇒ 00:04:43.009 Ashwini Sharma: But the cool thing that you can do from here is, while it pulls.

47 00:04:43.170 ⇒ 00:04:52.589 Ashwini Sharma: you can, you get this kind of screen, right? Once you have a dbt project, it loads the profile file that we have specified in dbt project.

48 00:04:52.730 ⇒ 00:05:12.400 Ashwini Sharma: And the profile indicates, like, where do you want to generate your models, depending on what area that you are running, right? So, for example, like, let’s say somebody has pushed a PR into the dev, right? And there are going to be multiple developers who are going to be working on different, different models, different areas, and you don’t want

49 00:05:12.820 ⇒ 00:05:16.340 Ashwini Sharma: I mean, at one point, you,

50 00:05:17.500 ⇒ 00:05:21.779 Ashwini Sharma: During the initial phase, right, you don’t want developers to mess up each other’s models.

51 00:05:22.170 ⇒ 00:05:30.619 Ashwini Sharma: And the way they are going to work is all the work is going to happen locally, they’re going to run dbt commands from their local laptop.

52 00:05:30.730 ⇒ 00:05:36.729 Ashwini Sharma: And when that happens, it creates the models within their user schema, right? So, for example, right,

53 00:05:37.740 ⇒ 00:05:45.779 Ashwini Sharma: See if I can… Show you something over here, oh, right over here, right.

54 00:05:48.130 ⇒ 00:05:50.230 Ashwini Sharma: So, if you see this,

55 00:05:50.450 ⇒ 00:05:59.459 Ashwini Sharma: Catalog Explorer, right? Dev staging… you can see these schemas that are appended with my name, right?

56 00:05:59.510 ⇒ 00:06:06.699 Ashwini Sharma: So that’s, like, I am a developer, I am running things locally, but when things run from,

57 00:06:06.730 ⇒ 00:06:20.129 Ashwini Sharma: dbt project within Snowflake, everything gets dumped over here, right? And as we move across different environments, right, for example, like, okay, 5 developers did some work on dbt.

58 00:06:20.360 ⇒ 00:06:23.239 Ashwini Sharma: Two of the developers are going to promote it to QA,

59 00:06:23.300 ⇒ 00:06:27.699 Ashwini Sharma: Right, where… and in that case, we’ll move to staging.

60 00:06:27.710 ⇒ 00:06:39.389 Ashwini Sharma: Where we are going to create those mods, and then out of those two, one of them gets a QC clearance, all good to move to production, we move it, and then we’ll create it in the production.

61 00:06:39.390 ⇒ 00:06:55.969 Ashwini Sharma: And everything happens through this dbt run, for example, like, it’s relatively easy, right? You can directly click run, or you can specify some arguments on top of that, and then you can click run, in which case it will run it. And you can do this through tasks.

62 00:06:56.070 ⇒ 00:06:59.099 Ashwini Sharma: You can, which is nothing but a Quran.

63 00:06:59.360 ⇒ 00:07:04.029 Ashwini Sharma: And you can schedule, like, at what point do you want it to run.

64 00:07:04.270 ⇒ 00:07:13.929 Ashwini Sharma: The other thing that I wanted to do, which I will be doing, is basically triggering these things from GitHub, right? So…

65 00:07:14.080 ⇒ 00:07:24.619 Ashwini Sharma: essentially, like, when a developer pushes a PR, we need to run some basic tests, ensure that the schema that is generated out of those models are correct.

66 00:07:25.280 ⇒ 00:07:31.959 Ashwini Sharma: And that should happen via GitHub Actions, which I’ll be incorporating in the repo.

67 00:07:32.200 ⇒ 00:07:39.010 Ashwini Sharma: As well as we need to refresh this repo, right, every time somebody is merging PRs into different branches.

68 00:07:39.230 ⇒ 00:07:43.030 Ashwini Sharma: So that will happen through some kind of snow CLI.

69 00:07:45.160 ⇒ 00:07:51.070 Ashwini Sharma: Yeah, so I’m working on those kind of things, as well as I’m working on that, active members report,

70 00:07:51.520 ⇒ 00:07:53.429 Ashwini Sharma: which I should be done… done.

71 00:07:53.550 ⇒ 00:08:01.060 Ashwini Sharma: Max by tomorrow. I’m sorry, I’ve been telling you, I’ll do it, I’ll do it. But yeah, some other things that are coming up in between.

72 00:08:01.380 ⇒ 00:08:12.400 Katherine Bayless: Alright, I mean, I’m late on sending my stuff too, so… Terminology question, so is a DBT model, like.

73 00:08:12.700 ⇒ 00:08:16.279 Katherine Bayless: What is it? What is it as a unit of code?

74 00:08:17.200 ⇒ 00:08:24.699 Ashwini Sharma: It’s, so dbt is basically a glorified templated SQL, right?

75 00:08:24.860 ⇒ 00:08:30.860 Ashwini Sharma: Right? And the way it looks like is… like this.

76 00:08:31.950 ⇒ 00:08:34.409 Katherine Bayless: Are you able to see my screen? Yes.

77 00:08:34.520 ⇒ 00:08:35.740 Ashwini Sharma: Okay, right.

78 00:08:36.210 ⇒ 00:08:39.819 Ashwini Sharma: So, we have something called models, right?

79 00:08:40.140 ⇒ 00:08:43.730 Ashwini Sharma: And the basic unit of model is a SQL file.

80 00:08:43.850 ⇒ 00:08:47.430 Ashwini Sharma: That translates to a table in the warehouse.

81 00:08:47.670 ⇒ 00:08:51.479 Ashwini Sharma: Right? And then you build on top of those models, right? So…

82 00:08:51.620 ⇒ 00:09:04.419 Ashwini Sharma: So, in the extremely low level, we have those raw tables, the reimbursed raw tables, and the first step is to ensure that we follow a standard naming convention, which is clear.

83 00:09:04.580 ⇒ 00:09:09.509 Ashwini Sharma: Easily understandable and consistent across all different data models, so…

84 00:09:09.520 ⇒ 00:09:29.409 Ashwini Sharma: That is what I’ve tried to do in the staging layer. So if you go in staging, right, right now we only have source for remembers, but eventually we’re going to have a source for Salesforce Marketing Cloud, we’re going to have something for Shopify, and so on, right? So, that accumulates over here. And if you look into CRM, we had the CRM, sorry.

85 00:09:29.610 ⇒ 00:09:37.909 Ashwini Sharma: remembers data was, you know, grouped together into different functional areas. One of them was CRM.

86 00:09:38.020 ⇒ 00:09:44.349 Ashwini Sharma: So, if you look into some of the CRM staging table, right, for example, country, what I’ve tried to do here is, like.

87 00:09:44.550 ⇒ 00:09:48.300 Ashwini Sharma: Column names like this are typecasted into a…

88 00:09:48.300 ⇒ 00:09:49.070 Uttam Kumaran: Yeah.

89 00:09:49.070 ⇒ 00:09:49.910 Ashwini Sharma: Yeah.

90 00:09:50.510 ⇒ 00:09:51.890 Ashwini Sharma: You know,

91 00:09:52.060 ⇒ 00:10:04.300 Ashwini Sharma: I’m using a snake case, right? And what this does is it will create a table in the backend, right? In this case, it’s a view, not exactly a table, you don’t want to replicate tables.

92 00:10:04.320 ⇒ 00:10:13.609 Ashwini Sharma: In the staging layer. And it’s just typecasted into something consistent, and it follows the standard naming convention across

93 00:10:13.670 ⇒ 00:10:15.320 Ashwini Sharma: All the different columns.

94 00:10:15.990 ⇒ 00:10:30.529 Ashwini Sharma: And once you have the staging layer ready, what you can do is, you can go over to the intermediate layer, where you, again, group it based on, different functional areas. For example, like, I’ll create some of the models for CRM,

95 00:10:30.670 ⇒ 00:10:37.719 Ashwini Sharma: Which is, which is… which is going to be entity-specific, right? So, like, for example, a customer entity.

96 00:10:37.990 ⇒ 00:10:48.169 Ashwini Sharma: Or organization entity, and so on. And then, in the mods, what we’ll do is we’re going to do the dimensional modeling, right? So we’re going to have a DIM customer, or…

97 00:10:48.500 ⇒ 00:10:56.390 Ashwini Sharma: dim organization, or dim country, or things like that. And then we’re going to have facts, which are utilizing this dimension tables.

98 00:10:56.970 ⇒ 00:11:03.239 Ashwini Sharma: And also, like, some of them are not going to be facts, just facts and dimensions. Some of them are going to be reports, right, which…

99 00:11:03.350 ⇒ 00:11:13.309 Ashwini Sharma: we are working on right now. So, for example, this active membership report is just a report. So, what you’ll get in Snowflake is these tables, which will be exposed to analytics.

100 00:11:13.510 ⇒ 00:11:25.759 Ashwini Sharma: and reports, which will also be exposed to analytics. The other tables can lie there, you know, in the staging layers and the intermediate layers, but everything in the MART

101 00:11:26.090 ⇒ 00:11:29.240 Ashwini Sharma: Is what will be used for analysis.

102 00:11:31.240 ⇒ 00:11:32.120 Katherine Bayless: Cool.

103 00:11:32.950 ⇒ 00:11:33.560 Katherine Bayless: So it’s.

104 00:11:33.560 ⇒ 00:11:47.150 Uttam Kumaran: sort of, like, all of… but all of this, like, all the naming convention, folder, structure, schema is all us. Like, dbt does not… they have guidelines, of course, but they does not prevent you from not doing that. This is sort of after…

105 00:11:47.350 ⇒ 00:11:48.650 Uttam Kumaran: years of…

106 00:11:48.720 ⇒ 00:11:50.749 Katherine Bayless: just doing dbt work.

107 00:11:50.750 ⇒ 00:11:57.379 Uttam Kumaran: And we haven’t… we can… I’m happy to… we actually have a doc about how we do DPT that I’m happy to share that is…

108 00:11:57.380 ⇒ 00:11:57.920 Katherine Bayless: Hmm.

109 00:11:57.920 ⇒ 00:12:02.610 Uttam Kumaran: Sort of, like, mix of, like, several different, like, Great.

110 00:12:02.690 ⇒ 00:12:17.770 Uttam Kumaran: dbt programs that exist, like, how dbt actually structures their own dbt project, how, like, GitLab does it, and we felt like this environment structure, it usually works. The best… the thing we want to enable, and I particularly care about, is I don’t want…

111 00:12:17.820 ⇒ 00:12:32.120 Uttam Kumaran: engineers to come in and have to think about where does a file go, how do I name stuff, and where can I go find… like, I ran something, where do I go find it? Those are all, like, extremely taxing things that, like, are just, like.

112 00:12:32.300 ⇒ 00:12:34.290 Uttam Kumaran: Really can… can just, like.

113 00:12:34.500 ⇒ 00:12:42.800 Uttam Kumaran: cause a delay in the pace at which you do things in data, so we’ve tried to solve a lot of that here. Additionally, given that

114 00:12:42.820 ⇒ 00:12:54.649 Uttam Kumaran: things are now being built a lot by cursor, cursor-assisted. Having a really clear naming convention structure is, like, so much better for that, to just follow that, you know, versus it makes its own stuff up.

115 00:12:55.980 ⇒ 00:13:05.259 Uttam Kumaran: You know, so that’s, like, generally how it is. And then I think the innovation here that I pushed Ashwini to think about is, like, it’s… I don’t… traditionally, we wouldn’t…

116 00:13:05.410 ⇒ 00:13:08.860 Uttam Kumaran: centralize this in Snowflake, but given…

117 00:13:08.990 ⇒ 00:13:22.530 Uttam Kumaran: one, like, CTA and your guidance on, like, hey, as much as we can do in the tools that we have, do it. And, like, they started building a tighter connection with dbt. I was like, hey, maybe we should go after doing this directly in Snowflake this time.

118 00:13:22.530 ⇒ 00:13:34.590 Uttam Kumaran: And then we’ll figure out the kinks in GitHub Actions and merging back to Snowflake, like, I’m not worried about that, but I’m happy to see that because it sort of reduces having to do it, get another

119 00:13:35.160 ⇒ 00:13:38.090 Uttam Kumaran: Vendor, you know, and you get the exact same functionality.

120 00:13:38.720 ⇒ 00:14:02.689 Katherine Bayless: Yeah, no, no, I think, I mean, I’m super excited, to actually be able to start using this, because, like, I… I don’t know if you’ve noticed in the repo, but there’s totally, like, the migrations, branch in there, and, like, it’s my fumbling attempt at a similar idea of version controlling a database, right, at the table and, like, data level. So this is… this is gonna be interesting. I feel like it’s gonna take a minute for my head to, like, learn the new vocabulary, as always, right?

121 00:14:02.690 ⇒ 00:14:04.810 Katherine Bayless: But yeah, I think…

122 00:14:05.100 ⇒ 00:14:13.960 Katherine Bayless: I think a really in-depth, like, training for Kyle would be good too, since presumably he’ll be one of the people writing a lot of the models for this stuff as we.

123 00:14:13.960 ⇒ 00:14:14.690 Uttam Kumaran: Yeah.

124 00:14:14.690 ⇒ 00:14:17.649 Katherine Bayless: So yeah. But yeah, no, I’m… I think this is cool. This is really cool.

125 00:14:18.170 ⇒ 00:14:21.649 Uttam Kumaran: Yeah, so ideally, like, folks like Kyle and others come in and they…

126 00:14:21.980 ⇒ 00:14:29.919 Uttam Kumaran: they work in a couple ways. One, they find, like, some intermediate tables that they can use to create marts, or, you know, eventually they’re able to

127 00:14:30.070 ⇒ 00:14:48.919 Uttam Kumaran: the data gets landed, and they can sort of build a lineage, but ultimately, the time isn’t really on, like, writing that, it’s actually just, like, a debugging. So, like, oh, this column’s empty, like, so I need to trace back where it came from. Or, okay, I need to… I want to get this code reviewed. And so, like, that’s where having a PR review process is really great.

128 00:14:48.920 ⇒ 00:14:56.060 Uttam Kumaran: as part of the PR process, we will run CICD checks. Is this… is this running in Snowflake? We can run testing there, so…

129 00:14:56.090 ⇒ 00:15:01.219 Uttam Kumaran: Just getting this right now just, like, sets us… sets a lot up.

130 00:15:01.620 ⇒ 00:15:05.940 Uttam Kumaran: For, like, the people that are gonna join to start building on top of this, you know, so…

131 00:15:06.410 ⇒ 00:15:08.310 Katherine Bayless: Yeah, yeah, totally.

132 00:15:08.930 ⇒ 00:15:09.780 Katherine Bayless: Yeah.

133 00:15:09.910 ⇒ 00:15:13.399 Katherine Bayless: I’m like, I’m looking forward to the day where we have multiple developers, and would.

134 00:15:13.400 ⇒ 00:15:20.989 Uttam Kumaran: Yes. I think soon, right? Soon, so… It’s already set up in a way where I think, like, probably by the end of this week.

135 00:15:21.350 ⇒ 00:15:26.889 Uttam Kumaran: I think, at least Kyle, we can start to onboard and show him, like, how to… how to get access to some of this stuff.

136 00:15:28.230 ⇒ 00:15:35.889 Uttam Kumaran: You know, and then he can start pulling… at least pulling stuff from Martz would be great, because then he can start to have requirements for us to develop, so…

137 00:15:35.890 ⇒ 00:15:57.079 Katherine Bayless: Yeah, I think, I mean, that is definitely the right sort of entry point, because I don’t think he has any GitHub, experience. If he does, he hasn’t, mentioned. So, like, I think that’s a new model for him to work in, and then I know, like, he’s okay in SQL, and he’s very aware that AI is great at generating SQL, if you know what you’re looking for. And so, like, building out some of those muscles, too, yeah.

138 00:15:57.080 ⇒ 00:15:57.460 Katherine Bayless: Yeah.

139 00:15:57.460 ⇒ 00:15:58.040 Uttam Kumaran: Right.

140 00:15:58.220 ⇒ 00:15:59.240 Katherine Bayless: He’s a quick learner.

141 00:16:01.880 ⇒ 00:16:09.270 Uttam Kumaran: Cool, so I think that’s… that’s it. So, Sweeney, you’re… you should have on your side a report ready this week, at least probably tomorrow or so.

142 00:16:09.960 ⇒ 00:16:11.490 Ashwini Sharma: I’ll have that ready, right?

143 00:16:11.490 ⇒ 00:16:15.060 Uttam Kumaran: Any other… anything else, Catherine? Like, I want to make sure that we also…

144 00:16:15.170 ⇒ 00:16:33.800 Uttam Kumaran: document a lot of this effectively. Like, we’ll… in the repo, we actually do have a lot of documentation, on, like, name… we’ll have documentation on naming conventions. I think also we’ll… we should make sure that we need to also put in, like, any Snowflake-related grants and things that we’re running.

145 00:16:33.850 ⇒ 00:16:35.999 Uttam Kumaran: But is there, like, anything else

146 00:16:36.580 ⇒ 00:16:39.880 Uttam Kumaran: Catherine, like, we should keep aware of there, or, like.

147 00:16:40.130 ⇒ 00:16:45.460 Uttam Kumaran: you want us, like, while we’re in this sort of, like, initialization phase, to, like, save. We’re also happy to, like.

148 00:16:46.230 ⇒ 00:16:52.240 Uttam Kumaran: walk through, like, what is dbt and what is Snowflake to, like, ever… for, like, other people, too, if it’s, like, it would be helpful.

149 00:16:52.760 ⇒ 00:17:09.100 Katherine Bayless: Yeah, I mean, I think for the team, generally, that walkthrough would be good. I mean, it’s kind of funny, because, like, Kyle and Kai are both, like, they’re like, you know, we need snowflake training, and I’m like, I don’t know, I’ve never used it before either, I just click buttons. But yeah, so, like, I think, I think something like that would be useful. I mean, I don’t have…

150 00:17:09.310 ⇒ 00:17:15.900 Katherine Bayless: tons of questions that I feel like I need to ask on this call about DBT specifically, but… yeah.

151 00:17:16.050 ⇒ 00:17:19.780 Katherine Bayless: It takes some time to, like, just kind of get familiar with the vibes.

152 00:17:20.140 ⇒ 00:17:21.650 Uttam Kumaran: Okay, okay, cool.

153 00:17:22.150 ⇒ 00:17:22.770 Katherine Bayless: Yeah.

154 00:17:23.780 ⇒ 00:17:26.640 Uttam Kumaran: Great. I think we can…

155 00:17:27.030 ⇒ 00:17:30.339 Uttam Kumaran: Maybe pivot to talking about those scopes?

156 00:17:30.510 ⇒ 00:17:33.890 Uttam Kumaran: I don’t know, Sam, maybe do you wanna…

157 00:17:34.620 ⇒ 00:17:35.990 Uttam Kumaran: Share screen, and then we can…

158 00:17:36.170 ⇒ 00:17:52.390 Ashwini Sharma: Yeah, go ahead. A question, regarding the remembers data that has been shared to us. At one point, we were thinking, like, maybe we should ingest that data into the Snowflake instance that you have, because it’s a third party that is sharing in

159 00:17:53.100 ⇒ 00:17:55.730 Ashwini Sharma: through Snowflake Share.

160 00:17:55.930 ⇒ 00:18:03.520 Ashwini Sharma: I mean, how reliable is that data? Is that going to be available to us all the time?

161 00:18:03.740 ⇒ 00:18:11.240 Ashwini Sharma: As long as we use remembers, or is it, at one point of time when you stop using it?

162 00:18:11.390 ⇒ 00:18:13.519 Ashwini Sharma: If you do that, right?

163 00:18:13.760 ⇒ 00:18:14.340 Ashwini Sharma: That’s true.

164 00:18:14.830 ⇒ 00:18:15.550 Ashwini Sharma: ovation.

165 00:18:15.550 ⇒ 00:18:30.360 Katherine Bayless: It’s a good question. So, they… we have a… like, it’s part of our contract with them now, so we’re paying an additional annual fee for them to deliver the data this way four times a day. So, in theory, if that breaks, then we get to go yell at them, because they are not delivering on their contract.

166 00:18:30.360 ⇒ 00:18:37.570 Katherine Bayless: However, it is absolutely our intention to get a new AMS at the end of this two-year contract, right? So, we were actually…

167 00:18:37.570 ⇒ 00:18:53.820 Katherine Bayless: the team was interested in going out to bid when I started, and I was like, can we just wait? Because it’s a big move. I mean, it’s one of our biggest CRMs in-house, and I want to make sure we get that transition right. So we signed a two-year renewal that’ll start Jan 1st, and at the end of that, we will be on a different system.

168 00:18:54.650 ⇒ 00:18:55.160 Uttam Kumaran: Okay.

169 00:18:55.350 ⇒ 00:18:57.069 Katherine Bayless: Yep. I don’t know what, I think.

170 00:18:57.070 ⇒ 00:19:01.499 Uttam Kumaran: I think, Ashwini, we should just… you should just keep, like, run maybe one backup.

171 00:19:01.890 ⇒ 00:19:05.400 Uttam Kumaran: Script, and just store it, and then we should just execute that.

172 00:19:06.770 ⇒ 00:19:13.970 Uttam Kumaran: every month, or something, where we just drop and clone that. Like, I don’t think you… I think just continue to use the share, because if there’s an issue.

173 00:19:14.080 ⇒ 00:19:21.590 Uttam Kumaran: I… I just want to avoid the fact that, like, maybe we copied something, or we’re using stale data, but maybe we’ll just keep a copy every month or something.

174 00:19:22.750 ⇒ 00:19:47.710 Katherine Bayless: Yeah, I think holding onto a backup isn’t a bad idea. And actually, actually, yeah, the monthly backup is not a bad idea, because I have run into this before with, like, AMS vendors. They don’t do a great amount of, like, changelogging, and so sometimes, if you want to be able to see, like, granular changelog-type data around, like, a membership and, like, how often it was updated, you have to be storing that data along the way. Like, they’re not going to be able to give you every

175 00:19:47.710 ⇒ 00:19:50.689 Katherine Bayless: thing that happened to, Roe, which is…

176 00:19:51.130 ⇒ 00:19:54.889 Katherine Bayless: An annoying limitation. But yeah, so a monthly backup of it is not a bad idea.

177 00:19:54.890 ⇒ 00:19:55.490 Uttam Kumaran: Okay.

178 00:19:56.070 ⇒ 00:19:56.570 Ashwini Sharma: Yeah.

179 00:19:57.730 ⇒ 00:19:59.379 Ashwini Sharma: Yeah, cool. Alright, yeah.

180 00:19:59.380 ⇒ 00:20:01.430 Katherine Bayless: Also, I’m a data hoarder.

181 00:20:01.430 ⇒ 00:20:05.139 Uttam Kumaran: Yeah, I know, that’s exactly how I am. Let’s just save it somewhere.

182 00:20:05.140 ⇒ 00:20:06.199 Katherine Bayless: Storage is cheap.

183 00:20:06.200 ⇒ 00:20:06.830 Samuel Roberts: Yeah.

184 00:20:10.170 ⇒ 00:20:10.920 Samuel Roberts: Alright.

185 00:20:11.850 ⇒ 00:20:13.440 Samuel Roberts: So, shifting gears a little bit…

186 00:20:13.810 ⇒ 00:20:15.380 Katherine Bayless: Let me…

187 00:20:16.710 ⇒ 00:20:22.809 Samuel Roberts: Share the right, yeah… Okay, so this is also in the repo, actually.

188 00:20:23.040 ⇒ 00:20:24.100 Katherine Bayless: Okay, cool.

189 00:20:24.370 ⇒ 00:20:31.730 Samuel Roberts: So I’m just looking at it in GitHub, because it’s all just marked down right now, but, we can go wherever, but we looked at the…

190 00:20:32.340 ⇒ 00:20:41.080 Samuel Roberts: two things we discussed before, which was the Okta being used for all the registration, and then, Shopify.

191 00:20:41.450 ⇒ 00:20:49.580 Samuel Roberts: Kind of side of things. And so, really, I mean, this is just kind of talking about doing some discovery work on that for the Okta side.

192 00:20:49.780 ⇒ 00:20:56.299 Samuel Roberts: Specifically, I believe Jay was fine with workforce stuff. It was really just that.

193 00:20:56.610 ⇒ 00:20:58.199 Samuel Roberts: And so…

194 00:20:58.710 ⇒ 00:21:06.799 Samuel Roberts: This just walks through evaluating different vendors, looking at the different tools that are out there. This highlighted Auth0 and Clark.

195 00:21:07.100 ⇒ 00:21:11.859 Samuel Roberts: But, you know, there’s a number of different ones we could look at. And just…

196 00:21:12.040 ⇒ 00:21:14.600 Samuel Roberts: Trying to figure out the best way to start with that?

197 00:21:17.070 ⇒ 00:21:20.960 Samuel Roberts: Excuse me, sorry. Yeah.

198 00:21:21.100 ⇒ 00:21:24.579 Katherine Bayless: Sorry, one sec. I think the coffee went down the wrong way.

199 00:21:24.580 ⇒ 00:21:25.370 Uttam Kumaran: You’re all good.

200 00:21:25.980 ⇒ 00:21:26.600 Samuel Roberts: Yeah.

201 00:21:27.830 ⇒ 00:21:29.029 Katherine Bayless: Yeah, I will say…

202 00:21:29.030 ⇒ 00:21:30.510 Samuel Roberts: in there. Yeah, sorry, go ahead.

203 00:21:30.510 ⇒ 00:21:39.679 Katherine Bayless: I was gonna say, just to buy you time, Jay was looking into Clark a little bit, and he did seem quite intrigued. His wheels are turning around this, yeah.

204 00:21:39.680 ⇒ 00:21:40.910 Samuel Roberts: Okay, okay, great.

205 00:21:41.340 ⇒ 00:21:59.389 Samuel Roberts: Yeah, I know we had… I think we had discussed that one a little bit. The other side of that is, I think, is it clerk that also does payment stuff, too? So that might be a way these two things dovetail together, potentially, but that’s not really outlined here. That would be after we actually do the work and figure that out, but,

206 00:22:00.230 ⇒ 00:22:05.990 Samuel Roberts: Yeah, I mean, I think the only thing that we were wondering here was,

207 00:22:06.860 ⇒ 00:22:10.990 Samuel Roberts: Outstanding questions… oh, there’s outstanding questions as well, but was there anyone else that would be…

208 00:22:11.390 ⇒ 00:22:14.080 Samuel Roberts: Need to be looped in on some of this stuff.

209 00:22:14.080 ⇒ 00:22:14.820 Katherine Bayless: Hmm…

210 00:22:14.820 ⇒ 00:22:15.620 Samuel Roberts: Or…

211 00:22:16.410 ⇒ 00:22:22.960 Samuel Roberts: I mean… And we talked about, like, some customer… like, there were some issues, like, we weren’t… I wasn’t sure… who else was…

212 00:22:23.910 ⇒ 00:22:42.859 Katherine Bayless: Yeah, I mean, it’s kind of one of those things where it’s like, there… really, it’s me and Jay, but there should be a different answer to that question, right? Like, I’m trying to get him to start, like, you know, if you’re the strategy, you know, VP-level guy, like, you shouldn’t be in the weeds on Okta, but at the present, it would be him, so… Yeah, okay. Yeah, yeah.

213 00:22:45.620 ⇒ 00:22:57.259 Katherine Bayless: I think, I mean, I don’t know if it, like, makes sense as something to call out, like, in a scope per se, but, like, just kind of one of the, like, lenses on my brain as we go through this is, like, you know, Jay is very, sort of.

214 00:22:57.260 ⇒ 00:23:08.440 Katherine Bayless: he believes that, and I don’t disagree with him, that makes it sound like I don’t believe him, but, like, he thinks a lot of the trouble with Okta is not, like, necessarily the configuration, but just, like, the things we ask it to do based on our policies.

215 00:23:08.440 ⇒ 00:23:32.330 Katherine Bayless: And I’m encouraging him to also start, like, surfacing those things, like, not just with Okta, but across the board. Like, if we have a rule that we made 30 years ago, and it’s causing us to need, like, you know, custom config, or a whole bunch of, like, spaghetti code, then, like, we should change the policy, or at least revisit the spirit of it, rather than just kind of continue to code around stuff. I mean, I see it as, like, my team doesn’t clean dirty data, that’s the, you know, data stewards owner.

216 00:23:32.480 ⇒ 00:23:38.440 Katherine Bayless: his team shouldn’t be responsible for cleaning up dirty policies. Right, right. So, like…

217 00:23:38.650 ⇒ 00:23:41.839 Katherine Bayless: Yeah, surfacing some of that through this is, like, good.

218 00:23:41.840 ⇒ 00:23:46.990 Samuel Roberts: Yeah, I mean, I think… I mean, I think that would be a big part of, like, actually doing… like, figuring out… because I…

219 00:23:47.680 ⇒ 00:23:53.259 Samuel Roberts: it still seems odd to me that Okta is the tool that’s being used for however many, I forget exactly what the number was, but…

220 00:23:53.730 ⇒ 00:23:54.890 Uttam Kumaran: 140.

221 00:23:55.100 ⇒ 00:23:57.959 Samuel Roberts: Yeah, the 150, yeah, that’s right. So I was just, like.

222 00:23:58.130 ⇒ 00:24:04.140 Samuel Roberts: in my experience, that seemed, you know… but I don’t know what policies are, you know, coming down from other places.

223 00:24:04.140 ⇒ 00:24:05.070 Katherine Bayless: Right.

224 00:24:05.070 ⇒ 00:24:07.140 Samuel Roberts: For… but that would be what we would, you know…

225 00:24:07.390 ⇒ 00:24:20.929 Uttam Kumaran: It’s also, like, again, if they had to pick… if they had to sort of pick in a jiffy, or if it was, like, we’re already using this, just use them, and there wasn’t, like, a discovery process, then it’s not surprising. And so that’s what I kind of, like… for Jay.

226 00:24:21.130 ⇒ 00:24:36.969 Uttam Kumaran: I want to think about how this process makes him, like, either we’re confident that, like, we can do this in Okta, so it’s not like we… we’ll see, like, if we were to support this use case in Okta, what changes, right? And, like, possible or not possible. We will also explore the other, so at least you’re, like.

227 00:24:37.240 ⇒ 00:24:55.000 Uttam Kumaran: for the next 5 years, you, like, have a canonical, like, we thought about this, you know? And so, at least you’re like, what if next year we’re like, what do we… why don’t we decide this? The answer is, like, at least that team at one point decided, like, they have this document that you can reference, you know?

228 00:24:55.000 ⇒ 00:24:58.200 Katherine Bayless: Absolutely, yeah,

229 00:24:58.200 ⇒ 00:25:08.659 Uttam Kumaran: Sort of, like, how we’re thinking about this repo, too, is, like, I… the thing about coming in as a new data team or a new member is, like, the speed by which you can ramp up or understand why decisions were made. Well, one, hopefully.

230 00:25:08.900 ⇒ 00:25:22.680 Uttam Kumaran: allow you to, like, not question those if you’re like, oh, I understand, like, the reason why we did it, but also able to hit your… whatever your first deliverable is much faster than being like, I still haven’t been able to get it running locally, or, like, couldn’t find this thing, you know? So…

231 00:25:22.970 ⇒ 00:25:27.990 Samuel Roberts: I was hitting that just accessing the GitHub repo with Okta, where…

232 00:25:28.260 ⇒ 00:25:39.640 Samuel Roberts: GitHub kept directing me to cta.octa.com, whatever, but I only was able to log in properly through octa.ctta.tech.

233 00:25:39.770 ⇒ 00:25:54.289 Samuel Roberts: slash whatever, and I spent way too long, and I was about to message Jay and realized, wait a minute, these URLs are different, and all I did was replace that, and everything just worked, and I was… so I don’t know, I mean, there might be something that surfaced here, too, but I… Yes.

234 00:25:54.290 ⇒ 00:26:11.890 Katherine Bayless: Yes, yes. No, honestly, like, that’s another one, because I, like, I know in his mind it’s not, like, that big of a deal, but I’m like, there are definitely people in this office who get very annoyed when they see it go from screen to screen to screen to screen to screen, right? Like, that’s not a pleasant experience, right? And so…

235 00:26:11.890 ⇒ 00:26:13.919 Samuel Roberts: And I was completely locked out, and if I hadn’t.

236 00:26:13.920 ⇒ 00:26:14.250 Katherine Bayless: Yeah.

237 00:26:14.250 ⇒ 00:26:29.930 Samuel Roberts: I don’t know what someone else might have done if they didn’t, you know… if they’re just a little less technical than me, because that took me a while to find that, too. I thought there were internet issues, that was… it was a whole, yeah, it went down a long one, but anyway, that’s, you know, kind of proving the point here, I guess, that there’s some…

238 00:26:29.930 ⇒ 00:26:34.070 Katherine Bayless: Yeah, exactly. But yeah, agreed, agreed, agreed.

239 00:26:34.230 ⇒ 00:26:47.480 Samuel Roberts: Sorry. Yeah, but yeah, I think, you know, the idea here is just to put that together, do the evaluation, come up with some recommendations, and then I think, yeah, with optional implementation here as well, so. But yeah, definitely take a look at this.

240 00:26:47.760 ⇒ 00:26:49.559 Katherine Bayless: One other, like, tiny question, which.

241 00:26:49.560 ⇒ 00:26:50.090 Samuel Roberts: Yeah.

242 00:26:50.270 ⇒ 00:26:58.149 Katherine Bayless: don’t over-interpret, I don’t know that it necessarily belongs in this, but it occurred to me earlier, like, Okta has the dashboard that you land on, like.

243 00:26:58.390 ⇒ 00:27:07.220 Katherine Bayless: if we do decide to continue using the customer dashboard, it might make sense to, like, actually leverage that. I mean, right now, people tend to only hit on it by accident.

244 00:27:07.220 ⇒ 00:27:19.839 Katherine Bayless: But if we… if we’re gonna pay all this money to have that level of solution for that big of an audience, I mean, we might as well at least see if there’s something useful we can do with that dashboard, because it’s probably the only single touchpoint

245 00:27:19.840 ⇒ 00:27:28.309 Katherine Bayless: that you could reasonably expect any audience member to hit. Like, not everybody will hit ImpactCM or re-members, not everybody will hit… Yeah. Yeah, exactly, so… yeah.

246 00:27:28.310 ⇒ 00:27:29.040 Samuel Roberts: the point.

247 00:27:29.700 ⇒ 00:27:30.480 Samuel Roberts: Yeah.

248 00:27:30.890 ⇒ 00:27:35.810 Samuel Roberts: Yeah, I… and that’s the one where it would got me one way to GitHub, but not back the other way.

249 00:27:35.810 ⇒ 00:27:36.290 Katherine Bayless: Yeah.

250 00:27:36.290 ⇒ 00:27:41.390 Samuel Roberts: Yeah, it was weird. But there’s definitely… even using that, I’m sure there’s… there’s all kinds of things to sort out.

251 00:27:41.560 ⇒ 00:27:42.010 Katherine Bayless: Yeah.

252 00:27:42.220 ⇒ 00:27:43.590 Samuel Roberts: Interesting, okay.

253 00:27:44.790 ⇒ 00:27:46.010 Samuel Roberts: Excuse me.

254 00:27:46.350 ⇒ 00:27:51.349 Samuel Roberts: Alright, so that’s, yeah, out of scope, requirements and inputs, I don’t know.

255 00:27:51.960 ⇒ 00:27:56.870 Samuel Roberts: Yeah, deliverables, the discovery, evaluation, recommendations,

256 00:27:58.170 ⇒ 00:28:03.509 Samuel Roberts: I don’t know if there’s more to actually dive through into all this, if you want to take a look. I don’t know if you want me to read through it or not, but I, you know…

257 00:28:03.510 ⇒ 00:28:04.900 Katherine Bayless: No, no, no, yeah, I can dig in on it.

258 00:28:04.900 ⇒ 00:28:08.339 Uttam Kumaran: Maybe let’s go through… can we check out other open questions on this?

259 00:28:08.340 ⇒ 00:28:08.940 Samuel Roberts: Oh, yes.

260 00:28:10.580 ⇒ 00:28:12.090 Uttam Kumaran: I think they’re all at the bottom.

261 00:28:12.090 ⇒ 00:28:12.979 Samuel Roberts: Yeah, they are.

262 00:28:17.880 ⇒ 00:28:20.960 Uttam Kumaran: Yeah, so, like, this was sort of, like, what we…

263 00:28:21.670 ⇒ 00:28:23.540 Uttam Kumaran: Are going to try to answer.

264 00:28:24.930 ⇒ 00:28:30.329 Uttam Kumaran: kind of, like, net-net here, is like, okay, what is this… I kind of also want to see, like.

265 00:28:30.440 ⇒ 00:28:32.479 Uttam Kumaran: How many tickets are we getting?

266 00:28:32.620 ⇒ 00:28:38.639 Uttam Kumaran: Like, what are all the current systems that are going through it? So you can kind of basically… yeah, go ahead.

267 00:28:38.640 ⇒ 00:28:41.379 Katherine Bayless: Oh yeah, I was gonna… no, you can fish a sentence, but I do have a comment on that one.

268 00:28:41.380 ⇒ 00:28:45.830 Uttam Kumaran: Yeah, and then sort of so we kind of scope, like, how much this impacts, and then…

269 00:28:46.460 ⇒ 00:28:47.810 Uttam Kumaran: Talk about, like.

270 00:28:48.160 ⇒ 00:29:02.800 Uttam Kumaran: okay, like, what… what did… we’ll kind of circle back to see, like, did we talk to Clerk? Did we talk to these guys? Like, why… why did we, why not? Looking, and then basically try to see, like, okay, can we… can we arrive at, like, an option or two to try, you know?

271 00:29:02.800 ⇒ 00:29:03.390 Katherine Bayless: Mmm…

272 00:29:03.390 ⇒ 00:29:03.960 Uttam Kumaran: So…

273 00:29:04.430 ⇒ 00:29:25.630 Katherine Bayless: Yeah, so I was gonna say, like, definitely with the system integration thing, there are a handful of the apps that go through Okta, but not everything does, and as much as I think Jay is kind of like, ugh, when I say these things, I’m like, everything needs to be behind the SSO. And so, like, what it is currently is definitely not what it should be.

274 00:29:25.630 ⇒ 00:29:33.780 Katherine Bayless: And I’m slowly making headway getting people to want to bring platforms under enterprise governance, and so, like, the ability to, like.

275 00:29:34.000 ⇒ 00:29:40.089 Katherine Bayless: Onboard and, like, set up new systems behind it more rapidly, even if they aren’t

276 00:29:40.310 ⇒ 00:29:56.390 Katherine Bayless: like, brand new… like, it was easy to do Snowflake, because it was brand new out of the box, right? Yes. But, like, if he’s gonna bring Salesforce Marketing Cloud, for example, that’s one that would like to go through Okta starting next year. Like, he’s gonna have to be able to, like, preserve all of the roles and permissions that are currently in there, all that, you know, blah blah blah, so yeah.

277 00:29:56.390 ⇒ 00:29:56.900 Uttam Kumaran: Yeah.

278 00:29:57.160 ⇒ 00:29:58.700 Katherine Bayless: more enterprise governance.

279 00:30:00.160 ⇒ 00:30:00.810 Uttam Kumaran: Okay.

280 00:30:03.560 ⇒ 00:30:04.150 Samuel Roberts: Alright.

281 00:30:04.150 ⇒ 00:30:16.299 Katherine Bayless: The tickets thing, I don’t know if we’re gonna have that data, to be totally honest. I mean, Jay can correct me, but there’s certainly some of it, I’m sure, with our MSP, and probably some we could pull out of, like, his email and Slack, but…

282 00:30:16.710 ⇒ 00:30:22.449 Katherine Bayless: Yeah, ticketing has not been a strong behavior at the organization, historically.

283 00:30:22.800 ⇒ 00:30:23.820 Uttam Kumaran: Okay, okay.

284 00:30:24.330 ⇒ 00:30:24.920 Samuel Roberts: Okay.

285 00:30:26.120 ⇒ 00:30:33.800 Uttam Kumaran: So yeah, I think that kind of… maybe we can… yeah, I think the ask here, Catherine, is for you just to look through the phases, and then be like, is this…

286 00:30:34.940 ⇒ 00:30:40.100 Uttam Kumaran: Does this roughly seem fair? Is there stuff that we want to add, or stuff we want to remove?

287 00:30:40.740 ⇒ 00:30:41.570 Katherine Bayless: Yeah, okay.

288 00:30:41.570 ⇒ 00:30:42.170 Uttam Kumaran: But…

289 00:30:43.240 ⇒ 00:30:50.920 Uttam Kumaran: Yeah, I mean, I think out of the end of this, we should be tired of talking about this problem, is my hope. You know, and we have some clear guidelines.

290 00:30:54.160 ⇒ 00:30:56.620 Uttam Kumaran: Cool. We can stand, we can talk about the other one.

291 00:30:57.080 ⇒ 00:31:10.160 Samuel Roberts: Yeah, the other one was the digital asset, delivery on Shopify, which, is very similar discovery, making sure we’re using the right tool kind of thing, you know, fix it, change it.

292 00:31:10.290 ⇒ 00:31:14.739 Samuel Roberts: decide it’s fine as is. Whatever that is, kind of the same idea.

293 00:31:16.950 ⇒ 00:31:17.770 Samuel Roberts: Excuse me.

294 00:31:18.640 ⇒ 00:31:19.920 Samuel Roberts: So,

295 00:31:20.520 ⇒ 00:31:26.189 Samuel Roberts: But yeah, that’s, I mean, roughly the same thing here. What are the open questions on this one, I wonder?

296 00:31:29.840 ⇒ 00:31:32.509 Samuel Roberts: Yeah, everything, all the details,

297 00:31:33.150 ⇒ 00:31:39.339 Samuel Roberts: Ruby stuff, Shopify Okta, yeah, all that, yeah. So, they’re related as well, but.

298 00:31:39.340 ⇒ 00:31:44.340 Uttam Kumaran: So this is where, like, yeah, it’s just for us to understand, like, what is being sold through there.

299 00:31:44.480 ⇒ 00:31:48.540 Uttam Kumaran: and kind of give you, like, one document on just, like, I think…

300 00:31:49.190 ⇒ 00:31:54.320 Uttam Kumaran: like, okay, what is… what is Shopify being used for, and then what are other…

301 00:31:54.930 ⇒ 00:31:57.639 Uttam Kumaran: You know, opportunities to streamline this.

302 00:31:57.800 ⇒ 00:32:01.379 Uttam Kumaran: I think we kind of go through, like, pieces of, like.

303 00:32:02.120 ⇒ 00:32:08.020 Uttam Kumaran: sharing digital assets and selling digital assets, like, what options are there? But, like, this is sort of, again, the…

304 00:32:08.640 ⇒ 00:32:14.849 Uttam Kumaran: the questions that we want to answer, and, like, if we were to pick another thing, like, how do we run both, and test, and…

305 00:32:15.570 ⇒ 00:32:19.139 Uttam Kumaran: Yeah, how is the team able to customize this more easily?

306 00:32:20.540 ⇒ 00:32:29.190 Katherine Bayless: Yeah, yeah, because I think, like, finding out that our sponsorships are being sold through Shopify kind of made me, like, oh, okay, interesting, like.

307 00:32:29.190 ⇒ 00:32:29.600 Uttam Kumaran: Yeah.

308 00:32:29.880 ⇒ 00:32:48.860 Katherine Bayless: it still doesn’t make me feel very, like, committed to the Shopify solution, but it does raise my level of curiosity about this and how we’re using it to solve problems, and then, like, if it does wind up, you know, Sam, to your point, that we decide to keep it, make it a little better, but keep it, I’m like, okay, well, what else are we selling internally that we probably should then use Shopify for?

309 00:32:48.860 ⇒ 00:32:49.500 Samuel Roberts: Right, yeah.

310 00:32:49.500 ⇒ 00:32:59.779 Katherine Bayless: I mean, I’m all about, like, consolidation into, like, single lanes for certain types of things, and I have a feeling we are selling lots of stuff piecemeal in different systems that, like.

311 00:33:00.020 ⇒ 00:33:04.020 Katherine Bayless: Again, if we keep Shopify, maybe push those that direction.

312 00:33:04.020 ⇒ 00:33:04.770 Samuel Roberts: Right.

313 00:33:06.740 ⇒ 00:33:07.520 Samuel Roberts: Okay.

314 00:33:08.790 ⇒ 00:33:14.990 Katherine Bayless: Also, one of the folks on our market research team said he can’t understand why anyone would ever purchase our research, which is funny.

315 00:33:19.550 ⇒ 00:33:20.110 Uttam Kumaran: Oh…

316 00:33:20.780 ⇒ 00:33:29.410 Uttam Kumaran: So, yeah, I mean, I think the biggest thing in the first phase is, like, we’ll get access to a bunch of stuff and basically be like, here’s… here’s, like, what’s in Shopify. I think…

317 00:33:29.410 ⇒ 00:33:30.150 Samuel Roberts: Yeah.

318 00:33:30.150 ⇒ 00:33:43.709 Uttam Kumaran: I mean, for me, the biggest things here are, like, you… can you… should you build your own on top of Stripe? Are there other easy digital asset storefront opportunities that are worth considering? I think the other piece of this is, like.

319 00:33:43.950 ⇒ 00:33:49.349 Uttam Kumaran: for us to think about, Katherine, is, like, this is something you own? Is this, like, a mix of you and some people? Or, like…

320 00:33:49.540 ⇒ 00:33:56.530 Uttam Kumaran: like, what is the ownership over this? And, like, I think that’s, like, really what we highlighted in Phase 3, which is, like.

321 00:33:56.810 ⇒ 00:34:00.389 Uttam Kumaran: What is the ownership model of a tool like this internally?

322 00:34:01.870 ⇒ 00:34:04.140 Uttam Kumaran: And… and then the other piece is, like.

323 00:34:04.420 ⇒ 00:34:09.719 Uttam Kumaran: reporting. Like, how is this data getting into reporting? You know, so that’s, like, that’s the full circle thing here.

324 00:34:10.949 ⇒ 00:34:28.889 Katherine Bayless: Yeah. Yeah, actually, I mean, the who owns this system question will probably become timely very soon, because the current quasi-owner for it is a very lovely woman on our marketing team who does not want this responsibility and is about to go out on parental leave, so… I have a feeling I’ll wind up being the temporary custodian.

325 00:34:28.889 ⇒ 00:34:32.960 Uttam Kumaran: When is that? I think she’s going any day now. Okay, okay.

326 00:34:32.960 ⇒ 00:34:33.310 Samuel Roberts: Okay.

327 00:34:33.310 ⇒ 00:34:40.379 Katherine Bayless: Yeah, yeah, and she’s, like, the only person who’s, like, got the actual, like, account owner status, and so, yeah, I don’t…

328 00:34:40.389 ⇒ 00:34:47.449 Uttam Kumaran: So I guess a question on this is, like, when… are people adding new digital assets? Are they go… are they, like, going through her to just basically get it added?

329 00:34:47.730 ⇒ 00:34:50.219 Katherine Bayless: Yeah, I think… so if they’re…

330 00:34:50.300 ⇒ 00:35:00.159 Uttam Kumaran: Because Shopify, there is a lot of configur… there’s, like, I’m just… it’s just so crazy, there’s a lot of configuration. It’s hard to run a single person Shopify store.

331 00:35:00.160 ⇒ 00:35:02.970 Katherine Bayless: Well, I think she’s really not even necessarily, like…

332 00:35:03.110 ⇒ 00:35:05.980 Katherine Bayless: Necessarily, like, actively doing or managing.

333 00:35:05.980 ⇒ 00:35:06.710 Samuel Roberts: So, like, okay.

334 00:35:07.080 ⇒ 00:35:19.560 Katherine Bayless: Yeah, like, I think… I think most of the digital assets flow through a probably kind of broken integration from remembers to Shopify. Right. The sponsorship stuff, I have no idea.

335 00:35:19.960 ⇒ 00:35:21.380 Katherine Bayless: But, yeah, like…

336 00:35:21.380 ⇒ 00:35:21.750 Uttam Kumaran: Okay.

337 00:35:21.750 ⇒ 00:35:33.139 Katherine Bayless: Casey doesn’t want to have to deal with this, but I can’t just, like, say, like, yeah, just give it to me, right? I want to give her the, like, here is why we will transition this ownership to either my team or the IT team, but…

338 00:35:33.290 ⇒ 00:35:34.189 Uttam Kumaran: Yeah, I wonder if it’s a mix.

339 00:35:34.190 ⇒ 00:35:35.479 Katherine Bayless: I’m a doctor.

340 00:35:35.480 ⇒ 00:35:42.490 Uttam Kumaran: Yeah, and I wonder if it’s a mix of you owning the platform, but then empowering people to go in and, like, update

341 00:35:42.690 ⇒ 00:35:48.300 Uttam Kumaran: Products, or, like, change pricing, or, like, you know, versus…

342 00:35:48.530 ⇒ 00:35:53.399 Uttam Kumaran: you taking it all over and then just basically becoming, like, storefront owner is, like, yeah, that’s… Yeah.

343 00:35:53.400 ⇒ 00:35:54.030 Katherine Bayless: Yeah.

344 00:35:54.030 ⇒ 00:35:58.060 Uttam Kumaran: Because this is a lot of work, I mean, we support a lot of e-commerce clients, and…

345 00:35:58.240 ⇒ 00:36:03.700 Uttam Kumaran: Yeah, it’s just, like, a huge system. I mean, it’s just, like, too much to have to manage in there.

346 00:36:04.190 ⇒ 00:36:13.030 Uttam Kumaran: So that’s why I don’t know if, like, if it’s… if it would be easier to, like… again, if we do it on Stripe, and you can have, like, an admin panel, and you’re just cha… people are just… all they’re doing is, like.

347 00:36:13.340 ⇒ 00:36:18.169 Uttam Kumaran: changing prices or titles and stuff, like, I don’t know, I wonder, like, if there’s a more…

348 00:36:19.070 ⇒ 00:36:24.179 Uttam Kumaran: if there’s an easier option, I would have to just kind of look at, like, what… how it works right now, but…

349 00:36:24.740 ⇒ 00:36:27.280 Katherine Bayless: Yeah, yeah, yeah, totally.

350 00:36:29.790 ⇒ 00:36:30.609 Samuel Roberts: No problem.

351 00:36:31.890 ⇒ 00:36:38.420 Uttam Kumaran: Cool, so I think that’s both of the scopes here. I think, Catherine, from you, it’d just be, like, confirming that these are, and then I can sort of…

352 00:36:38.970 ⇒ 00:36:45.409 Uttam Kumaran: try to give you a sense of, like, pricing. I… I know that the… I don’t know if any of these are sort of…

353 00:36:45.590 ⇒ 00:36:48.330 Uttam Kumaran: like, is the CES registration one, like.

354 00:36:48.460 ⇒ 00:36:50.460 Uttam Kumaran: We’re trying to solve that before…

355 00:36:50.990 ⇒ 00:36:56.120 Uttam Kumaran: the CES, or is this sort of like, we would like to kick off something that maybe there’s… if there are any wins.

356 00:36:56.320 ⇒ 00:36:59.829 Uttam Kumaran: Like, well, how do you think about that? I assume, like, the Shopify one is…

357 00:37:00.060 ⇒ 00:37:02.920 Uttam Kumaran: A lower priority if you had to choose, but…

358 00:37:03.260 ⇒ 00:37:14.370 Katherine Bayless: Yeah, definitely the Shopify thing is the lower priority, but, still… If you had to choose, yeah. Right, yeah, exactly. Now, the registration thing, so…

359 00:37:15.570 ⇒ 00:37:23.810 Katherine Bayless: I mean, yeah, it’s a… it’s a bit of a rock and a hard place for me, to be honest, because it’s like, should we solve it before CES? Yes.

360 00:37:23.970 ⇒ 00:37:24.390 Samuel Roberts: Yeah.

361 00:37:24.390 ⇒ 00:37:28.319 Katherine Bayless: Should we really put a solution from me in place before CEOs?

362 00:37:28.320 ⇒ 00:37:28.820 Uttam Kumaran: Yes.

363 00:37:28.820 ⇒ 00:37:29.340 Katherine Bayless: Right?

364 00:37:29.340 ⇒ 00:37:32.660 Samuel Roberts: That’s what I… yeah, I kind of made the assumption here that it was not…

365 00:37:32.980 ⇒ 00:37:36.530 Uttam Kumaran: No, and that’s also what I… I made that… I tried to make that clear, too, but…

366 00:37:36.570 ⇒ 00:37:39.320 Katherine Bayless: I’m, like, I’m just asking again for…

367 00:37:40.140 ⇒ 00:37:41.470 Uttam Kumaran: Confirmation.

368 00:37:41.770 ⇒ 00:37:44.140 Samuel Roberts: I mean, Discovery, who… you know, we can… but yeah.

369 00:37:44.140 ⇒ 00:37:47.050 Uttam Kumaran: No, it’s tough, like, I really think that you can make a big…

370 00:37:47.860 ⇒ 00:37:51.260 Uttam Kumaran: mistake here, like, changing some of this, so I wonder, like…

371 00:37:51.260 ⇒ 00:37:51.680 Katherine Bayless: Right.

372 00:37:51.680 ⇒ 00:37:52.890 Uttam Kumaran: I don’t know. I mean…

373 00:37:53.620 ⇒ 00:38:09.610 Katherine Bayless: Yeah, I genuinely, I think probably what we will do is limp across the finish line and just vow to not let it be this way next year. Okay. I think that might be the most correct… it’s like the Batman thing, right? It’s the answer we need, not the answer we deserve.

374 00:38:09.610 ⇒ 00:38:10.110 Uttam Kumaran: Yeah.

375 00:38:10.110 ⇒ 00:38:12.240 Katherine Bayless: But, I also think, like.

376 00:38:13.140 ⇒ 00:38:29.699 Katherine Bayless: there might also be some ways to kind of creatively salvage some of the Okta stuff. I was doing a little digging, like, so the one piece of the immediate use case, which is really nothing to do with authentication per se, but the reason that this was also floating to the top of the pile around CES was that

377 00:38:29.860 ⇒ 00:38:44.050 Katherine Bayless: without Okta IDs from the workforce tenant in that registration system, then they’re using email address as the parameter in some of the, like, API calls that are going around between these daisy chain integrations, and so…

378 00:38:44.050 ⇒ 00:38:51.480 Katherine Bayless: in order to not use the email address as the query parameter, we need to have an ID present on every record that we can use, right?

379 00:38:51.710 ⇒ 00:39:02.099 Katherine Bayless: the original, like, alarm bell that went off was that Okta IDs aren’t in merits. However, we have learned it is Okta Workforce ID, or people who exist in the workforce tenant.

380 00:39:02.120 ⇒ 00:39:23.500 Katherine Bayless: don’t have a customer Okta ID in merits, so it’s only, like, 485 people. To be fair, it is 485 of the most important people, probably. But I’m like, okay, well, can we hotfix that? Like, I can patch those IDs into merits, and then we could use that Okta ID in the query parameter. It doesn’t solve any of the authentication piece, but it does solve that, like, security flow.

381 00:39:23.500 ⇒ 00:39:31.459 Katherine Bayless: So, I think we might be able to at least solve that narrow piece of the problem with a little bit of duct tape and popsicle sticks, and then, to your guys’ point, right, like…

382 00:39:31.460 ⇒ 00:39:36.390 Katherine Bayless: Do this the right way with enough time to not, like, over-promise and under-deliver.

383 00:39:36.560 ⇒ 00:39:37.090 Uttam Kumaran: Yeah.

384 00:39:38.730 ⇒ 00:39:39.720 Katherine Bayless: Fingers crossed.

385 00:39:40.180 ⇒ 00:39:47.529 Uttam Kumaran: So that’s why it’s also, like, if we end up doing this, and we start in January, then at least one of us is also there poking around.

386 00:39:48.290 ⇒ 00:39:50.819 Uttam Kumaran: It’s like, then that makes 3.

387 00:39:50.990 ⇒ 00:39:51.350 Katherine Bayless: I guess.

388 00:39:51.350 ⇒ 00:39:55.320 Uttam Kumaran: You know, or I guess we’re, like, one entity, but yeah, like, tech…

389 00:39:55.320 ⇒ 00:39:55.860 Samuel Roberts: Yeah, yeah.

390 00:39:55.860 ⇒ 00:40:02.730 Uttam Kumaran: So… That may also help if there is, like, Just another eyeball, and again.

391 00:40:03.090 ⇒ 00:40:07.580 Uttam Kumaran: it’s like, I know you and Jay are… that’s, like, 5 or 10% of your time, so…

392 00:40:08.290 ⇒ 00:40:11.259 Uttam Kumaran: That could be nice to have someone else just look around.

393 00:40:11.540 ⇒ 00:40:13.570 Katherine Bayless: Yeah, yeah, yeah.

394 00:40:15.550 ⇒ 00:40:16.270 Uttam Kumaran: Okay.

395 00:40:16.270 ⇒ 00:40:26.219 Katherine Bayless: Yeah. But yeah, I’ll take a look at both of these kind of in detail, I’ll get Jay’s thoughts on them, but yeah, I think should be able to get you guys feedback pretty quickly. And they look great at first blush, so…

396 00:40:26.510 ⇒ 00:40:37.149 Uttam Kumaran: Yeah, I just want to make sure that for all of these, I sort of, like, I’m like, just let’s put everything we’ve heard about the problem, so you can always be like, okay, that’s not… we did that, or that’s not worth… instead of…

397 00:40:37.920 ⇒ 00:40:43.339 Uttam Kumaran: Instead of not having not making it verbose and then missing items, you know, so…

398 00:40:43.560 ⇒ 00:40:51.280 Uttam Kumaran: Okay, cool. And then I think, yeah, this week, I want to make sure that we deliver the first report, and then we’ll also have, like, DBT

399 00:40:51.430 ⇒ 00:40:58.820 Uttam Kumaran: stood up, we have, sort of, Snowflake now running. I think a good goal, honestly, Ashwini, is maybe we can try to, like.

400 00:40:58.960 ⇒ 00:41:03.099 Uttam Kumaran: I don’t know, I mean, it’s already kind of tight, but maybe we can try to…

401 00:41:03.540 ⇒ 00:41:05.679 Uttam Kumaran: see if Kyle wants to meet,

402 00:41:05.950 ⇒ 00:41:10.210 Uttam Kumaran: Take… at least take a look at some of that data so we can get them set up in the repo this week.

403 00:41:10.360 ⇒ 00:41:15.700 Uttam Kumaran: That way… And Casey’s poking around, I don’t know, Catherine, are you guys all out next two weeks?

404 00:41:16.090 ⇒ 00:41:26.850 Katherine Bayless: So no, yeah, with the show, like, I mean, some people are bold and they take leave, but for the most part, we’re kind of in the 3 days and then off the 2 for the holiday.

405 00:41:26.850 ⇒ 00:41:37.480 Katherine Bayless: I think Kyle is traveling to Utah, so I think he has a couple additional days of PTO between Christmas and New Year’s, maybe? Okay. But you definitely will be able to catch us next week.

406 00:41:37.480 ⇒ 00:41:37.950 Uttam Kumaran: Oh, okay, okay.

407 00:41:37.950 ⇒ 00:41:38.380 Katherine Bayless: Or later.

408 00:41:38.380 ⇒ 00:41:45.019 Uttam Kumaran: Alright. Okay, great, because, yeah, I’m just, like, we’re… we’re just trying to… a lot of people are off, a lot of our clients are off, like, the last two weeks, so…

409 00:41:45.020 ⇒ 00:41:45.430 Katherine Bayless: Yeah.

410 00:41:45.430 ⇒ 00:41:55.420 Uttam Kumaran: And then maybe, Ashwini, we can see where we land here, and I would love to just do a little bit of, like, a dbt Snowflake onboarding for Kyle, and that way, also, we can record that and leave that in the repo, too.

411 00:41:56.150 ⇒ 00:41:57.490 Katherine Bayless: Yeah, that’d be awesome.

412 00:41:57.810 ⇒ 00:41:58.340 Uttam Kumaran: Okay.

413 00:42:01.970 ⇒ 00:42:10.029 Uttam Kumaran: And then, yeah, I haven’t heard anything back from Polyatomic yet. I think he said… he mentioned he was gonna try to grab time, I don’t know if that ended up happening.

414 00:42:10.030 ⇒ 00:42:10.800 Katherine Bayless: He did, yes.

415 00:42:10.800 ⇒ 00:42:11.190 Uttam Kumaran: Okay.

416 00:42:11.190 ⇒ 00:42:20.900 Katherine Bayless: I think we’re gonna call Thursday at 11, I wanna say, off the top of my head. Yeah, so we need to get that on books. I sent him over a few things, that I had to hand.

417 00:42:20.900 ⇒ 00:42:40.449 Katherine Bayless: I also… I did hear back from the guy who’s doing the event co-pilot, like, the little chatbot thing, for CES this year, and he said they don’t have an API yet, but he was very… I mean, he was very kind and responsive, and he’s like, we can build one! I’m like, that’s okay, just asking. And he’s like, we can actually send you all of the raw data directly to an S3 bucket. I’m like, well, that sounds better than an API.

418 00:42:40.450 ⇒ 00:42:42.810 Uttam Kumaran: Please, just… we can get anything?

419 00:42:43.020 ⇒ 00:42:59.489 Katherine Bayless: Right, yeah, yeah, exactly. Right now, we get an email digest. But the other thing of interest that I found that I’ll mention to, the Polyatomic guy on Thursday, is apparently we do actually get some requests for reports on scanner data at CE.

420 00:42:59.490 ⇒ 00:43:00.750 Uttam Kumaran: Oh, nice!

421 00:43:01.000 ⇒ 00:43:25.689 Katherine Bayless: apparently… I don’t… so I don’t think Merits has an endpoint for that data, but, like, they’ll give us two flat files a day, basically, which is fascinating to me. But yeah, I need to… I need to do a little, like, legwork before the call Thursday and figure out, like, okay, are we getting flat files because there’s truly no endpoint, or is there one and we just didn’t, like, set it up in the past? Because if we’re getting on-site requests to be, like, reporting on that data, then I would much

422 00:43:25.690 ⇒ 00:43:29.010 Katherine Bayless: I’d rather have it coming in programmatically than via flat files.

423 00:43:29.060 ⇒ 00:43:35.749 Katherine Bayless: And I think building out a single workflow around one endpoint is not the end of the world, even if it’s not the most, like.

424 00:43:35.990 ⇒ 00:43:37.639 Katherine Bayless: Forever code.

425 00:43:38.220 ⇒ 00:43:39.369 Uttam Kumaran: Yeah, okay, okay.

426 00:43:39.370 ⇒ 00:43:39.740 Katherine Bayless: Yeah.

427 00:43:39.740 ⇒ 00:43:45.849 Uttam Kumaran: Yeah, that would be helpful. I mean, I would love… I would love to see that data, and then, yeah, I mean, maybe that is something that gets…

428 00:43:46.230 ⇒ 00:44:00.039 Uttam Kumaran: monetized through whatever the new system is, but… this is basically the exact work that I did at WeWork, where we basically took that, and as part of the QBRs for our enterprise clients, there’s a huge section on looking at the swipe data for

429 00:44:00.490 ⇒ 00:44:17.970 Uttam Kumaran: the people that went into the WeWorks, like, their employees, and it basically led to, like, oh, you need to buy… again, it’s all upsell related, so it was like, oh, you need to buy more space, or you had some people come in in Berlin, maybe you did an office there, like, things like that. I’m sure the salespeople, if we could produce them, like, a nice dashboard, they would be, like.

430 00:44:18.340 ⇒ 00:44:24.259 Uttam Kumaran: Cool, perfect. This is what I needed to go sell more space and more… more ads, you know?

431 00:44:25.160 ⇒ 00:44:43.830 Katherine Bayless: I… this is where I’m like, ugh, I feel bad for all the things the AI companion hears me say. No, the big innovation from our sales team this year is adding another day to the in-person space selection, where people can go stand in a tiny, crowded room and get really angry for a few hours and try to book their space for next year.

432 00:44:44.280 ⇒ 00:44:47.869 Uttam Kumaran: Wait, what is the in-person… what is the in-person, what is that?

433 00:44:47.870 ⇒ 00:44:52.579 Katherine Bayless: So yeah, so on-site at CES, you can purchase your booth for next year.

434 00:44:52.580 ⇒ 00:44:53.360 Uttam Kumaran: Oh, okay.

435 00:44:53.360 ⇒ 00:45:03.189 Katherine Bayless: you are willing to go stand in a tiny room at the Venetian and get angry, and we also, we bring literal, physical, cabled, networked… we set up a LAN.

436 00:45:04.210 ⇒ 00:45:05.529 Uttam Kumaran: Oh, okay.

437 00:45:06.110 ⇒ 00:45:13.800 Katherine Bayless: Yeah, so we’re gonna spend an additional $12,000 this year to have a few more hours of that be available, and I’m like, iPads, guys. iPads.

438 00:45:13.800 ⇒ 00:45:14.530 Uttam Kumaran: iPads.

439 00:45:14.530 ⇒ 00:45:16.900 Katherine Bayless: to the booth on the floor with an iPad.

440 00:45:16.900 ⇒ 00:45:18.510 Uttam Kumaran: Yeah, yeah.

441 00:45:19.140 ⇒ 00:45:20.300 Katherine Bayless: We’ll get there. We’ll get there.

442 00:45:20.300 ⇒ 00:45:23.099 Uttam Kumaran: Yeah, we’ll get there, we’ll get there. Yeah.

443 00:45:23.100 ⇒ 00:45:26.469 Katherine Bayless: you know, space selection, yeah, there’s a lot of servers. Yeah, it’s.

444 00:45:26.470 ⇒ 00:45:32.240 Uttam Kumaran: Yeah, we did a lot of space planning. It’s so weird, this is, like, very similar to some of the stuff we did at WeWork, but…

445 00:45:32.640 ⇒ 00:45:42.430 Uttam Kumaran: Yeah, I mean, we use the key swipe data, we use the badge data to basically show that, like, oh, all your people are getting desks, like, they definitely need an office, you should sign long-term space here, you should grow.

446 00:45:42.560 ⇒ 00:45:47.689 Uttam Kumaran: And, it was an easy story, because without that, it’s, like, all sort of, like.

447 00:45:48.240 ⇒ 00:45:55.620 Uttam Kumaran: anecdotal, and the account… I supported the account management team for a while, and it’s hard for them to tell the story, but that is, like, one of the key things, and I think…

448 00:45:56.020 ⇒ 00:46:04.900 Uttam Kumaran: I don’t know what other data store you can, like, the data team can support at CES. It’s like, it is the ba- it is sort of like, how many leads did you get? Maybe what…

449 00:46:05.390 ⇒ 00:46:09.619 Uttam Kumaran: Other types of digital engagement story you can tell, but it is the floor.

450 00:46:09.670 ⇒ 00:46:11.010 Katherine Bayless: Theta, and like…

451 00:46:11.160 ⇒ 00:46:21.290 Uttam Kumaran: badge data, and you can say, like, oh, well, this year we saw that, like, there was so much more peop… the people that got this type of booth got so much more, like, maybe you should consider that.

452 00:46:21.790 ⇒ 00:46:22.450 Katherine Bayless: Right.

453 00:46:22.450 ⇒ 00:46:22.910 Samuel Roberts: Right.

454 00:46:22.910 ⇒ 00:46:30.030 Uttam Kumaran: I don’t know what… I mean, I’m sure they’re… they’re very crafty, but I think that’s… there’s a… that’s probably the only real…

455 00:46:30.140 ⇒ 00:46:36.510 Uttam Kumaran: measurable data story, right? Like, apart from just total attendees and, like, Potential impression volume.

456 00:46:37.350 ⇒ 00:46:48.119 Katherine Bayless: Right, yeah, I mean, yeah, at present, that’s kind of the upper limit on what we’d be able to do. I have many ideas about where we can go in the future, but yeah, it would mostly be, like, traffic, right? And, like.

457 00:46:48.120 ⇒ 00:46:48.780 Uttam Kumaran: Yeah.

458 00:46:48.780 ⇒ 00:46:52.220 Katherine Bayless: Yeah. I, I, yeah, I mean…

459 00:46:52.600 ⇒ 00:47:11.400 Katherine Bayless: There’s so much potential. I did find out, that Merits does do the, like, RFID in a badge kind of thing, so you can, like, track people everywhere, and the woman that runs that sort of team is open to revisiting. I think we had looked at them in the past, and it was, like, prohibitively expensive, but I’m also questioning what prohibitively was, to be honest.

460 00:47:11.400 ⇒ 00:47:17.729 Uttam Kumaran: Cause I know there’s some sticker shock troubles, and I’m like… But if you can tell the sales story on top of that data, then it’s like…

461 00:47:17.730 ⇒ 00:47:18.280 Katherine Bayless: Right?

462 00:47:18.280 ⇒ 00:47:30.579 Uttam Kumaran: And what you can… basically what we should do is we should ask them… we can ask them for a sample, put something together, and be like, guys, if you had a version of this, like, what could we sell? Could you sell more space? Like, if you had, like, this sort of, like.

463 00:47:30.860 ⇒ 00:47:35.260 Uttam Kumaran: a CES review doc that showed foot traffic and stuff like that, you know, so…

464 00:47:35.650 ⇒ 00:47:38.679 Katherine Bayless: like, somewhere I have a place where I, like, kind of sketched one out.

465 00:47:38.680 ⇒ 00:47:39.110 Uttam Kumaran: Yeah.

466 00:47:39.110 ⇒ 00:47:55.979 Katherine Bayless: Interestingly, the team that’s hungriest for it is the membership team, because they do their sales prospecting largely off of, like, CES exhibitors and attendees and stuff like that, and so they’ve asked for this. They’re like, we want to know, like, if an exhibitor got 3,000 comp badges because of their booth size, where did they go? What did they do? Who did they talk to?

467 00:47:55.980 ⇒ 00:47:56.330 Uttam Kumaran: October.

468 00:47:56.330 ⇒ 00:47:59.100 Katherine Bayless: all of that kind of stuff, and so, like, the membership team is really.

469 00:47:59.100 ⇒ 00:47:59.510 Uttam Kumaran: Yeah.

470 00:47:59.510 ⇒ 00:48:12.440 Katherine Bayless: for sales data, and conveniently, now that we have to remember stuff, like, I think we can start to really knit that together, even if it’s just in Snowflake and reporting for the moment, eventually pushing it back into their system, maybe, but yeah.

471 00:48:12.440 ⇒ 00:48:20.259 Katherine Bayless: Yeah, they’re hungry. Actually, we’re actively working on building out a member engagement, like, report with them. Kyle and Kai are working on that.

472 00:48:20.260 ⇒ 00:48:35.440 Katherine Bayless: And some of it is, like, just, like, did they attend CES? Did they have a booth? But I think we can get more granular. If you want to see the historical scan data, it is in S3, so it’s… I mean, I can post a link in Slack, I won’t remember, but it’s, like, under that archive bucket.

473 00:48:36.500 ⇒ 00:48:53.060 Katherine Bayless: DBO, or marketing database, and then DBO, and then it’s, like, CES scan history or something, but it, it actually goes back further than I thought, so we have the scans for all the sessions going back to 2019, in there. So, yeah. Okay, okay, great. We’re kind of like, oh, hmm, not bad.

474 00:48:53.060 ⇒ 00:48:53.910 Uttam Kumaran: Nice.

475 00:48:53.910 ⇒ 00:48:57.330 Katherine Bayless: Yeah, so I’ll figure out how it’s coming in this year, but… Yeah.

476 00:48:58.050 ⇒ 00:48:58.690 Katherine Bayless: Potentially.

477 00:48:58.690 ⇒ 00:48:59.350 Uttam Kumaran: Okay.

478 00:48:59.350 ⇒ 00:49:00.070 Katherine Bayless: Potential.

479 00:49:01.230 ⇒ 00:49:03.980 Uttam Kumaran: Okay, perfect. I think that’s all I had.

480 00:49:04.270 ⇒ 00:49:05.960 Katherine Bayless: Okay, I have a couple questions.

481 00:49:05.960 ⇒ 00:49:06.640 Uttam Kumaran: Yes.

482 00:49:07.090 ⇒ 00:49:24.570 Katherine Bayless: So one of them is a practical matter, so that, like, webhooks database that I had set up so that I could, share the show floor tour survey responses with the guy in Market Research, eventually, I won’t need it anymore, because the show will be over, and he will no longer be collecting show tour information. Should I plan to just…

483 00:49:24.870 ⇒ 00:49:28.389 Katherine Bayless: delete it at the end of CES, or do we want to move…

484 00:49:28.610 ⇒ 00:49:33.200 Katherine Bayless: any of what I have done into the structures we are building.

485 00:49:33.200 ⇒ 00:49:51.439 Katherine Bayless: I guess maybe that’s a clumsy way of saying, like, it’s kind of a V0 version of a form stack data mart, right? I mean, because that’s what the data is coming through. I’ve just filtered it to be, like, the one form so that Chris could see it. But I don’t know if that means there’s anything really of true utility in terms of the stuff that we need to build out.

486 00:49:52.670 ⇒ 00:49:53.920 Uttam Kumaran: What do you think, Ashwini?

487 00:49:55.560 ⇒ 00:49:57.069 Katherine Bayless: You also don’t have to answer it right this second.

488 00:49:57.070 ⇒ 00:49:57.460 Uttam Kumaran: Yeah.

489 00:49:57.460 ⇒ 00:49:58.590 Katherine Bayless: Got it, but yeah.

490 00:49:59.930 ⇒ 00:50:01.759 Ashwini Sharma: I don’t know,

491 00:50:05.970 ⇒ 00:50:08.399 Ashwini Sharma: I mean, any data, let’s just keep it, right?

492 00:50:08.400 ⇒ 00:50:08.900 Uttam Kumaran: I would…

493 00:50:08.900 ⇒ 00:50:11.929 Ashwini Sharma: You never know when it is going to.

494 00:50:11.930 ⇒ 00:50:20.770 Katherine Bayless: They’re all data hoarders. Yeah, it was more just like, should I delete, like, the webhooks, like, database that I created in Snowflake, when we’re done with the show?

495 00:50:22.320 ⇒ 00:50:24.500 Katherine Bayless: Yeah. Like, I’m gonna say raw data.

496 00:50:24.500 ⇒ 00:50:28.099 Uttam Kumaran: I would just… I think we should save it, and we store it somewhere in Snowflake Raw, yeah.

497 00:50:28.640 ⇒ 00:50:37.669 Katherine Bayless: Okay, so we’ll migrate it from the structures. Okay, okay. Similarly, so the role-based access control stuff.

498 00:50:39.520 ⇒ 00:50:54.369 Katherine Bayless: I could use some help figuring out, like, what the… not policies in terms of, like, literal policies, but, like, me approaching this, like, 150 people work here right now, I’m asking Jay every time, like, can you give somebody access to Snowflake?

499 00:50:54.430 ⇒ 00:51:02.449 Katherine Bayless: I think probably it makes more sense for him to, like, set it up so that all new employees, maybe at manager and above.

500 00:51:02.450 ⇒ 00:51:03.210 Uttam Kumaran: Yeah, yeah, yeah.

501 00:51:03.210 ⇒ 00:51:10.470 Katherine Bayless: like, by default, and then based on, like, what team they’re on or what role they’re in, like, I’m giving them, like, maybe light read roles on production.

502 00:51:10.470 ⇒ 00:51:10.840 Uttam Kumaran: Yes.

503 00:51:10.840 ⇒ 00:51:16.250 Katherine Bayless: Like, that’s the piece that I kind of need to understand, is like, how do I…

504 00:51:16.810 ⇒ 00:51:19.060 Katherine Bayless: how do I get people into using Snowflake?

505 00:51:19.060 ⇒ 00:51:38.169 Uttam Kumaran: Totally, like, if we’re at that, if we’re at that point today, then we, we, we typically run, like, we have a script of, like, how we architect Snowflake role-based asset control. Typically it’s a mix of, like, reader-writer roles for various environments. So, like, you have devs, staging, and marts.

506 00:51:38.330 ⇒ 00:51:44.999 Uttam Kumaran: Basically, you have read or writer on each of those, and then, for example,

507 00:51:45.190 ⇒ 00:52:02.979 Uttam Kumaran: Polyatomic just needs read and write on, on a certain database, and then something needs another. Like, dbt needs just transform roles, and the BI tool just needs… so you’re able to do that, and then you can assign reader-writer roles to higher levels. So, example, you may have a…

508 00:52:02.990 ⇒ 00:52:19.880 Uttam Kumaran: a data engineer role, you may have a data analysis, an analyst role, you may just have a CTA employee role that is, like, read on just marts and, like, nothing else. So, it’s very easy for us to architect in that way. The thing to be worried about here is, like.

509 00:52:19.950 ⇒ 00:52:26.790 Uttam Kumaran: One, you don’t want to do direct grants to users. Second, you don’t want to do… you want to have these, like.

510 00:52:26.910 ⇒ 00:52:37.699 Uttam Kumaran: functional roll-ups, it just makes it a little bit easier to be like, okay, I have a user coming in, like, they need reader here, they need writer here, and that sort of rolls up for the most part.

511 00:52:38.240 ⇒ 00:52:44.270 Uttam Kumaran: The data engineer role will maybe be able to create, like, pipes and ghemas and all that stuff, but…

512 00:52:44.390 ⇒ 00:52:45.490 Uttam Kumaran: really, like.

513 00:52:45.920 ⇒ 00:53:00.870 Uttam Kumaran: it also allows us to support service accounts, so, like, the DPT, like, if we were to have a BI tool service account, it would just have read access to marts, so that someone can’t come in and, like, read from RAW, and then mess up by, like, reading something super upstream, you know?

514 00:53:00.870 ⇒ 00:53:02.549 Katherine Bayless: Yeah. Yeah.

515 00:53:03.280 ⇒ 00:53:10.659 Katherine Bayless: Yeah, so similar to the webhooks thing, like, my quick and dirty, like, get this out the door, I did… I put Chris…

516 00:53:10.660 ⇒ 00:53:31.199 Katherine Bayless: into the, like, Snowflake analyst role or something like that, and then I gave that role permissions on the webhooks thing, all of which I assume will be undone and redone in the correct structures. I just was like, I don’t know, this gets you in there now. But yeah, we have people who I would like to be able to get into Snowflake to look at, at the very least, like, dashboards built on marts.

517 00:53:31.770 ⇒ 00:53:32.370 Katherine Bayless: Okay.

518 00:53:32.580 ⇒ 00:53:33.440 Uttam Kumaran: Okay.

519 00:53:33.440 ⇒ 00:53:36.649 Katherine Bayless: And so, Anna, the brother, and Chris, and Erica.

520 00:53:36.650 ⇒ 00:53:37.040 Uttam Kumaran: Great.

521 00:53:37.040 ⇒ 00:53:43.149 Katherine Bayless: are all in there currently, but I’d really… I mean, I’ve got people, like, ready to go as soon as I can give them a green light, so…

522 00:53:43.430 ⇒ 00:53:52.300 Uttam Kumaran: So, Srini, we should run through our typical… I don’t know if you ended up running that, but we should… we could run Catherine through what we typically set up, because I also want to talk about

523 00:53:52.860 ⇒ 00:53:54.130 Uttam Kumaran: warehouses.

524 00:53:54.360 ⇒ 00:53:54.870 Katherine Bayless: Yeah.

525 00:53:54.870 ⇒ 00:54:05.939 Uttam Kumaran: we set up, like, the different warehouses for the different workload types, typically. So something for ETL, something for Transform, something for BI, and reporting. And so it’s all based… we have this, like.

526 00:54:06.100 ⇒ 00:54:19.309 Uttam Kumaran: typical long script that we just, like, run through. So we can share that and then make any tweaks, that we want there. But hopefully that will allow a very flexible access structure.

527 00:54:19.500 ⇒ 00:54:22.089 Uttam Kumaran: And so you, you have to worry more about

528 00:54:22.240 ⇒ 00:54:33.489 Uttam Kumaran: giving… putting someone into… like, just granting one role to someone, versus, like, oh, this person… like, you basically shouldn’t have to run grants, you can do all this through the UI, basically, like…

529 00:54:33.490 ⇒ 00:54:35.500 Katherine Bayless: Add a new user, they have the…

530 00:54:35.720 ⇒ 00:54:41.180 Uttam Kumaran: CTA employee role, and then if they graduate, maybe they have the data analyst, and then it sort of, like, goes from there.

531 00:54:41.530 ⇒ 00:55:00.669 Katherine Bayless: Okay, okay, okay, yeah. Yeah, I would… if we can do that, that’d be awesome, because I think there are enough of these people who are really hungry to get in there, and enough things that I could, like, really throw together in, like, little dashboards, for CES reporting, some of the things that people are going to be asking on site. I think

532 00:55:00.670 ⇒ 00:55:10.230 Katherine Bayless: you know, not everybody’s gonna be like, oh, let me fire up Snowflake, but there are people who will, and I feel like if we can meet their needs, this year, then that’s a huge win for us, so…

533 00:55:10.400 ⇒ 00:55:11.450 Uttam Kumaran: Yeah. Okay.

534 00:55:11.450 ⇒ 00:55:20.250 Katherine Bayless: Okay. Okay, let’s see, there was another question that I had for… Bold.

535 00:55:26.980 ⇒ 00:55:27.780 Katherine Bayless: Oh.

536 00:55:30.850 ⇒ 00:55:34.009 Katherine Bayless: So, okay, this might… this might be, like, a…

537 00:55:34.200 ⇒ 00:55:37.939 Katherine Bayless: I’m not sure how to ask the question, or in a way that it makes sense.

538 00:55:38.100 ⇒ 00:55:44.709 Katherine Bayless: So, like, we have that, enterprise, like, AI intranet search thing, Glean.

539 00:55:45.040 ⇒ 00:55:45.470 Uttam Kumaran: Yes.

540 00:55:45.470 ⇒ 00:56:10.380 Katherine Bayless: And, like, Jay has tried in the past, like, connect it to raw data, and I’m like, please, for the love of God, do not do that. But what I would like to do is give Glean somehow, someway, some visibility into, like, the data catalog in Snowflake. Eventually, it doesn’t have to be, like, tomorrow, but, like, that way, if somebody asked a question in Glean around, like, you know, I don’t know, how many active members do we have in the vehicle tech space, it could try to answer it out of the intranet data

541 00:56:10.380 ⇒ 00:56:13.720 Katherine Bayless: But also, say, like, oh, if you’re looking for the most current information.

542 00:56:13.720 ⇒ 00:56:14.210 Uttam Kumaran: Mmm.

543 00:56:14.210 ⇒ 00:56:19.359 Katherine Bayless: you can go into Snowflake and look for this dashboard, right? So, like, it might not necessarily, like.

544 00:56:20.220 ⇒ 00:56:30.200 Katherine Bayless: push the data into Glean, but it would direct them to the stuff that we have built. Also open to other ways that that works, but, like, I do want Glean to help people find our dashboards in Snowflake.

545 00:56:30.930 ⇒ 00:56:32.780 Uttam Kumaran: Yeah, I think,

546 00:56:33.130 ⇒ 00:56:42.430 Uttam Kumaran: if we can… if you can add me to either the channel with Glean, or we can create a separate Glean channel, so… or we can just add Glean to our existing channel, I would love to test out

547 00:56:42.570 ⇒ 00:56:47.470 Uttam Kumaran: like, its answers now. I haven’t used Glean before, but I would totally love to…

548 00:56:47.900 ⇒ 00:56:58.419 Uttam Kumaran: you know, just see what we can do there. I think one is totally, I think it wouldn’t be too hard for us to pass Glean information about what is in the warehouse, and I don’t know…

549 00:56:58.990 ⇒ 00:57:03.520 Uttam Kumaran: that could be pulling live from the Snowflake catalog, that could also be done in…

550 00:57:03.900 ⇒ 00:57:11.649 Uttam Kumaran: several other ways, so I would love to see, like, what the ergonomics are for Glean to be able to do that.

551 00:57:12.060 ⇒ 00:57:17.489 Uttam Kumaran: And then, yeah, kind of just play around with, like, if you were to ask different questions, how it prioritizes

552 00:57:17.590 ⇒ 00:57:21.160 Uttam Kumaran: what’s in the intranet versus, like, what’s in here in Snowflake, and…

553 00:57:21.160 ⇒ 00:57:22.100 Katherine Bayless: Yeah.

554 00:57:22.770 ⇒ 00:57:23.520 Katherine Bayless: Yeah.

555 00:57:23.520 ⇒ 00:57:26.070 Uttam Kumaran: And then also be able to show, like, how many questions are…

556 00:57:26.220 ⇒ 00:57:45.039 Uttam Kumaran: potentially being… like, I don’t know what the reporting out of Glean looks like, so, like, can we identify how many questions could be answered? Like, the big thing I… when we go look at a BI tool is, like, I want to measure how many questions… like, both how many questions are being reported on by a person that could be used by AI, then how many questions are being asked that… that just, like.

557 00:57:45.200 ⇒ 00:58:01.830 Uttam Kumaran: someone just gave up, or, like, they didn’t ask, you know? So those are, like, it’s sort of both taking care of some of the existing questions, and increasing the TAM of questions that, like, people are like, oh, now that I have AI, I’m not embarrassed to ask this, or, like, I can just sort of, like, just ask it, like, not worry about, like.

558 00:58:01.830 ⇒ 00:58:05.420 Uttam Kumaran: how to ask it, right? Like, those… that’s kind of how I’m thinking about it, you know?

559 00:58:05.900 ⇒ 00:58:23.670 Katherine Bayless: Yeah, yeah, yeah, totally. I’m not sure… Glean does have pretty decent reporting, I have not really played with it, I know Jay has, but we’re also… we’re bringing… I think we have, like, a small bucket of hours with their ProServe team, and so they’re going to be doing some work with us in the new year around, like, adoption and enablement, that kind of stuff, and…

560 00:58:23.670 ⇒ 00:58:32.080 Katherine Bayless: Truthfully, the muscle we need to start building is, like, clean tends to do okay. Where it falls apart is, like.

561 00:58:32.260 ⇒ 00:58:34.789 Katherine Bayless: the SharePoint dumpster fire underneath of it, right?

562 00:58:34.790 ⇒ 00:58:35.280 Uttam Kumaran: Yeah.

563 00:58:35.280 ⇒ 00:58:42.430 Katherine Bayless: Somebody will build, like, a glean agent for a use case, and then share it with their team, and everybody’s like, oh, your agent sucks, but it’s like, actually, they just don’t have

564 00:58:42.430 ⇒ 00:58:57.929 Katherine Bayless: that, like, SharePoint access they needed for the agent to do its job, kind of a thing, right? Yes. Or you’ve got 25 versions of a document, and it has no idea, like, which one should be correct. There are some ways, to your point, around, like, weighting, and so, like, if… if we

565 00:58:58.180 ⇒ 00:59:04.789 Katherine Bayless: If we got into some of those settings, you could probably tinker with it to say, like, if people ask data questions, like, you know, lean heavily on this information.

566 00:59:04.790 ⇒ 00:59:11.109 Uttam Kumaran: Yeah, so that’s what I’m interested to see, like, what options it has for that. And at minimum, we should…

567 00:59:11.290 ⇒ 00:59:13.540 Uttam Kumaran: we should advertise, like, use Glean.

568 00:59:13.660 ⇒ 00:59:20.189 Uttam Kumaran: To start to ask questions, and then we can, as a data team, we want people to be asking questions, whether we can serve them.

569 00:59:20.720 ⇒ 00:59:21.040 Katherine Bayless: Yeah.

570 00:59:21.040 ⇒ 00:59:34.640 Uttam Kumaran: annually or not is, like, up to us to figure out. But, like, I do want to think about how do we make it more open for people to ask questions. Maybe they… maybe Glean is our first option now. And that way, when we evaluate BI tools, we show, like, this is what’s possible in Glean.

571 00:59:34.800 ⇒ 00:59:37.940 Uttam Kumaran: this is what’s possible in something that’s more AI-native, you know?

572 00:59:38.140 ⇒ 00:59:44.830 Katherine Bayless: Yeah, yeah, yeah. Yeah, I hated Glean for a long time. It’s gotten a little better, so… Okay. Yeah, yeah, yeah.

573 00:59:44.830 ⇒ 00:59:50.370 Uttam Kumaran: Yeah, I would love to try it, especially, like, if, like, we’re seeing… we’re able to see what questions people are asking.

574 00:59:51.940 ⇒ 01:00:10.679 Katherine Bayless: Yeah, I’ll see if, yeah, I’ll see. We can definitely get you guys access. I’ll see if we can also get you, like, admin, or if not, I can export. I also… I realize I… I’m sure Glean is on my system’s inventory. I wouldn’t have put it down as, like, P1 or P0, but I’m assuming they have an API we could consume.

575 01:00:10.680 ⇒ 01:00:12.370 Uttam Kumaran: I assume so, yes.

576 01:00:12.780 ⇒ 01:00:14.540 Katherine Bayless: Yeah, yeah. Okay.

577 01:00:14.540 ⇒ 01:00:17.630 Uttam Kumaran: I mean, even if I can just look in what the reporting is now, like…

578 01:00:18.030 ⇒ 01:00:20.829 Uttam Kumaran: what are we using it for? I can give you a good sense of, like.

579 01:00:21.040 ⇒ 01:00:21.620 Katherine Bayless: Yeah.

580 01:00:21.890 ⇒ 01:00:28.069 Uttam Kumaran: How… what percentage of these questions are, like, questions that the data team could have answered that maybe people are trying, right?

581 01:00:28.500 ⇒ 01:00:30.929 Katherine Bayless: Yeah, yeah, yeah, yeah, yeah.

582 01:00:31.430 ⇒ 01:00:37.400 Uttam Kumaran: Yeah, and I’m also interested to see, like, if CTA is enabling people to build agents, maybe our job is more of, like.

583 01:00:37.840 ⇒ 01:00:42.699 Uttam Kumaran: give them the right MCP, or give them the right tools, versus, like.

584 01:00:43.170 ⇒ 01:00:52.430 Uttam Kumaran: I mean, it’s great to hear that people are building on top of some of these things, because maybe more of the job is not to, like, build an analysts, but, like, be like, well, here’s the tool you can use to query Stove Lake, like, you go…

585 01:00:52.910 ⇒ 01:00:54.360 Uttam Kumaran: You know, build whatever you need.

586 01:00:54.880 ⇒ 01:01:12.460 Katherine Bayless: Yeah, yeah, yeah. Yeah, people are pretty… they’re pretty resourceful, I will say. I mean, it’s like, it’s good and bad. Sometimes I get more nervous about, like, oh gosh, like, things, like, running away too fast, like, if somebody kind of, like, hacks something together sort of thing, and then I’m also, like, I don’t know, I guess better than having a bunch of non-self-starters.

587 01:01:13.420 ⇒ 01:01:24.210 Katherine Bayless: But yeah, no, we’ve got quite a few people in there who have built agents, some of which are actually not bad. Often, they tend to have really, really complex branching logic that’s largely unnecessary.

588 01:01:24.720 ⇒ 01:01:25.200 Katherine Bayless: you know.

589 01:01:25.200 ⇒ 01:01:25.970 Uttam Kumaran: Yeah.

590 01:01:25.970 ⇒ 01:01:27.070 Katherine Bayless: Yeah.

591 01:01:27.870 ⇒ 01:01:28.750 Uttam Kumaran: Yeah.

592 01:01:28.750 ⇒ 01:01:30.830 Katherine Bayless: Yeah.

593 01:01:30.970 ⇒ 01:01:37.109 Katherine Bayless: I created a, Claude project to generate my, like, CES schedule, and Jay wants to kind of migrate it into a Blean agent.

594 01:01:37.110 ⇒ 01:01:37.920 Uttam Kumaran: Oh, nice.

595 01:01:37.920 ⇒ 01:01:39.950 Katherine Bayless: Sure, why not? Yeah. See what happens.

596 01:01:42.330 ⇒ 01:01:43.410 Katherine Bayless: Awesome.

597 01:01:43.750 ⇒ 01:01:44.230 Uttam Kumaran: Okay.

598 01:01:44.230 ⇒ 01:01:48.810 Katherine Bayless: Perfect. Okay, I think those were the questions I had, like, off the top of my head.

599 01:01:49.980 ⇒ 01:01:50.990 Katherine Bayless: Yeah.

600 01:01:52.790 ⇒ 01:01:54.640 Katherine Bayless: Yeah, yeah.

601 01:01:55.560 ⇒ 01:01:56.669 Katherine Bayless: So many things.

602 01:01:56.960 ⇒ 01:01:59.999 Uttam Kumaran: I’m gonna send… I’m gonna summarize our meeting, and I’ll send it out.

603 01:02:00.750 ⇒ 01:02:01.550 Katherine Bayless: Okay, sounds good.

604 01:02:02.060 ⇒ 01:02:06.129 Katherine Bayless: Oh, I was gonna say, for the 2026 scopes, do we.

605 01:02:06.130 ⇒ 01:02:06.590 Uttam Kumaran: Yes.

606 01:02:06.590 ⇒ 01:02:09.490 Katherine Bayless: Will you guys do one for just, like, the continued, like, more.

607 01:02:09.490 ⇒ 01:02:09.980 Uttam Kumaran: Yes.

608 01:02:09.980 ⇒ 01:02:15.289 Katherine Bayless: More models, okay, okay, okay. Yeah. Because that one, at the very least, I’ll ship through as soon as I get my hands on it.

609 01:02:15.510 ⇒ 01:02:26.020 Uttam Kumaran: Yeah, so I guess that’s, I guess how… how would you want to, like, should we… did I just produce that and sort of, like, do that Q1? Do you want to bundle all these? Like, what’s best on your side?

610 01:02:26.880 ⇒ 01:02:35.200 Katherine Bayless: Let’s not bundle Okta and Shopify, only because it isn’t 100% my decision to make as to whether or not we do that work.

611 01:02:35.200 ⇒ 01:02:39.799 Uttam Kumaran: So if Jay is like, I do want to do it, but not now, you know, whatever, that kind of thing. Okay, perfect.

612 01:02:39.800 ⇒ 01:02:58.440 Katherine Bayless: in terms of, like, for the stuff that I do control the decision rights on, yeah, I mean, I’d really like to get something in that’s just, like, the 2026 scope. Like, we’ve kind of talked about, like, just even if it’s just Q1, and then we’ll figure out what queues 2 through 4 should look like, but just to kind of keep scaling out the data that’s landing, and the marts, and the available.

613 01:02:58.440 ⇒ 01:03:03.090 Uttam Kumaran: Okay, great. Yeah, that was my question. I didn’t know whether we wanted a bundle, so perfect. So I’ll… I can have that this week for you to have.

614 01:03:03.090 ⇒ 01:03:03.650 Katherine Bayless: Okay.

615 01:03:03.650 ⇒ 01:03:05.789 Uttam Kumaran: And then, the other stuff, yeah, you let me know.

616 01:03:06.110 ⇒ 01:03:07.489 Katherine Bayless: Okay, cool, cool, cool.

617 01:03:07.490 ⇒ 01:03:08.180 Uttam Kumaran: Okay.

618 01:03:08.500 ⇒ 01:03:10.040 Uttam Kumaran: Alright, thank you, everyone.

619 01:03:10.040 ⇒ 01:03:11.189 Katherine Bayless: Yeah, thanks guys.

620 01:03:11.670 ⇒ 01:03:12.110 Samuel Roberts: Thank you.

621 01:03:12.320 ⇒ 01:03:12.890 Uttam Kumaran: Bye.

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