Meeting Title: Brainforge x CTA: Weekly! Date: 2026-02-20 Meeting participants: Awaish Kumar, Uttam Kumaran, Chi Quinn, Kyle Wandel, Katherine Bayless, Ashwini Sharma
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1 00:00:11.380 ⇒ 00:00:12.400 Uttam Kumaran: I wish.
2 00:00:14.490 ⇒ 00:00:15.190 Awaish Kumar: Hello.
3 00:00:37.160 ⇒ 00:00:38.309 Kyle Wandel: Hey, good morning.
4 00:00:39.040 ⇒ 00:00:39.990 Uttam Kumaran: Hey, good morning.
5 00:00:39.990 ⇒ 00:00:40.940 Chi Quinn: Mine.
6 00:00:42.770 ⇒ 00:00:44.129 Kyle Wandel: How’s it going this day?
7 00:00:45.150 ⇒ 00:00:46.479 Uttam Kumaran: Good! How about you?
8 00:00:47.480 ⇒ 00:00:53.900 Kyle Wandel: Good. We got a little… fun little request from Kinsey, so, working on that this morning, but otherwise, good.
9 00:00:54.250 ⇒ 00:00:55.340 Uttam Kumaran: Okay, cool.
10 00:00:55.890 ⇒ 00:00:57.309 Uttam Kumaran: Yeah, this week’s been good.
11 00:00:57.680 ⇒ 00:01:02.400 Uttam Kumaran: I briefly slacked with Catherine, yesterday and got some…
12 00:01:02.500 ⇒ 00:01:16.959 Uttam Kumaran: priorities to work on, but yeah, we cleaned up a bunch of, Snowflake, and so I think, you know, one of the things is we just want to make sure everybody who needs it can get access to the new roles, like, the new streamlit roles, so…
13 00:01:17.840 ⇒ 00:01:21.489 Uttam Kumaran: Yeah, we could do that today, or, you know, whenever, but…
14 00:01:24.510 ⇒ 00:01:28.049 Kyle Wandel: People are definitely interested. Catherine, I see you on for the first time, so welcome back.
15 00:01:28.240 ⇒ 00:01:34.580 Katherine Bayless: Yeah, I, I realized that I needed to do all the performance review stuff in ADP.
16 00:01:35.160 ⇒ 00:01:36.500 Kyle Wandel: Yeah, I gotta do that to go, so…
17 00:01:36.900 ⇒ 00:01:48.879 Katherine Bayless: Yeah, so I was like, okay, well, let me sneak on, work for a little bit this morning, clean up my inbox a bit, and I figured I’d join this in case I’m useful, but since I’ve been out all week, I’ll just be the observer, unless you guys need me to chime in on anything, but yeah.
18 00:01:50.740 ⇒ 00:01:56.190 Kyle Wandel: No, I think we’re just catching up a little bit. I mean, I’ve been working on a lot of ad hoc stuff, and
19 00:01:57.580 ⇒ 00:02:06.140 Kyle Wandel: list generation, basically, and I think you saw the one for this morning from Kinsey, and then also working on cleaning up some of the pipelines,
20 00:02:06.250 ⇒ 00:02:15.389 Kyle Wandel: there’s a couple PRs that need to be approved, I think, and then merged, and then we can kind of move over the registration slash attendee stuff to the member engagement.
21 00:02:15.770 ⇒ 00:02:21.449 Kyle Wandel: Part of it, and then that should be updated. But that’s mostly what I’ve been doing this week.
22 00:02:23.250 ⇒ 00:02:40.319 Chi Quinn: And for me, I’ve pretty much been working on the, Power BI inventory. I do have a list of reports that we can start taking down, right now, because they’re pretty much outdated. As far as other requests, some requests have come in this week, one from Dave.
23 00:02:40.420 ⇒ 00:03:00.390 Chi Quinn: He wanted to add a couple of fields to the, conference session attendees list, and then another request, I think somebody… I think Lori Rucker wants a… the badge scanner report, so I just need to send her the link, and I was actually checking it, and I realized that
24 00:03:00.520 ⇒ 00:03:14.429 Chi Quinn: the reports, or I guess the codes that she’s looking for, or it’s not under a specific session type, so I kind of got lost for a second, so I just have to rearrange some items in the Power BI report, then I’ll let her know.
25 00:03:18.720 ⇒ 00:03:23.700 Uttam Kumaran: Cool. I guess my other ask, Kai, was gonna be about the Power BI audit report.
26 00:03:24.180 ⇒ 00:03:24.850 Chi Quinn: Yeah. Just, like…
27 00:03:24.850 ⇒ 00:03:25.970 Uttam Kumaran: Kind of status there.
28 00:03:26.570 ⇒ 00:03:45.339 Chi Quinn: Yeah, so that’s… so that’s what I was working on pretty much this week. I was looking at more so for the CES, because there’s different, workspaces in regards… related to CES, so I was looking into those, the CES, the conferences, the programs, and…
29 00:03:45.380 ⇒ 00:03:52.299 Chi Quinn: mobile apps, even. So I do have a spreadsheet, and I’m just kind of
30 00:03:53.000 ⇒ 00:04:08.540 Chi Quinn: kind of, like, looking at who owns the report, what’s… what the report is about, does it have… is it pretty much ready to be in Snowflake, for example? Does it have the data? Is the data available? And so I do have just a…
31 00:04:08.730 ⇒ 00:04:11.170 Chi Quinn: Yes, no, and just… it’s kind of…
32 00:04:12.210 ⇒ 00:04:26.620 Chi Quinn: I have it listed, and then I have the… what my suggestions are, just based on how, I guess, from the viewers, who’s been looking into it, and I guess for… just for me, if someone’s looking with fresh eyes, it’s…
33 00:04:26.620 ⇒ 00:04:35.990 Chi Quinn: some of the reports might be outdated, but it gives an idea, like, oh, this might be useful, to use sometime later. So I just kind of wrote, like, a…
34 00:04:36.010 ⇒ 00:04:55.160 Chi Quinn: final decision list of whether it was snowflake ready, maybe there’s some gaps, some data gaps that we might need to bridge if needed, and then some others that are not even ready, the data’s outdated, and I personally need more information. I’m not sure who owns the report, so it’s just…
35 00:04:55.160 ⇒ 00:04:58.359 Chi Quinn: I have it categorized in that way, so I will…
36 00:04:58.430 ⇒ 00:05:06.140 Chi Quinn: have it, up in that ticket. And speaking of that ticket, I’ve been wanting to close that ticket, but because…
37 00:05:06.490 ⇒ 00:05:07.890 Chi Quinn: I,
38 00:05:07.950 ⇒ 00:05:15.079 Chi Quinn: Because it is a lot of reports that I have to look into, and I know we’re trying to work in one week… one-week sprints.
39 00:05:15.080 ⇒ 00:05:39.400 Chi Quinn: So, I was thinking of either just to put the plan into this ticket of what I’m working on, how I’m, going by… going about with this report, and then just close that, and then create new tickets for each week, for each section that I’m working on. So right now, I have 4 workspaces down, basically. So that’s what I was planning to do today, at least, to kind of just close that off, and then just kind of
40 00:05:39.400 ⇒ 00:05:43.990 Chi Quinn: break it down into smaller, digestible, I guess, one week.
41 00:05:43.990 ⇒ 00:05:50.950 Chi Quinn: Work… worth of working on those reports, or the auditory… excuse me, the audit.
42 00:05:51.440 ⇒ 00:05:53.920 Uttam Kumaran: Yeah, I think that’s best. I think it’s sort of like…
43 00:05:54.030 ⇒ 00:06:05.980 Uttam Kumaran: Yeah, you’re thinking about it exactly the right way. It’s like, okay, this task is gonna take several weeks, but this week, our goal is to accomplish creating the plan, and next week, it’s gonna be executing the plan. And so, that’s exactly…
44 00:06:06.100 ⇒ 00:06:23.579 Uttam Kumaran: the way to think about it. I also just think, like, if you… if you want to host something just with… with this team on, like, reviewing that, or getting, like, ideas, like, we should just go ahead and do that. That way, I know it’s been a few weeks, like, we’ve been working on, sort of, the Power IBI plan, so if it’s… it’s helpful to just, like.
45 00:06:24.230 ⇒ 00:06:27.979 Uttam Kumaran: Get fresh eyes on something, like, we should totally do that.
46 00:06:28.500 ⇒ 00:06:29.760 Chi Quinn: Yeah, absolutely.
47 00:06:33.590 ⇒ 00:06:34.140 Uttam Kumaran: Cool.
48 00:06:35.820 ⇒ 00:06:37.559 Uttam Kumaran: Anything else, Kai?
49 00:06:40.280 ⇒ 00:06:51.060 Chi Quinn: And then, oh, I guess for the Asana board. So, Catherine, I’m not sure if I have access to edit some of the sections, so I think for the…
50 00:06:51.150 ⇒ 00:07:06.490 Chi Quinn: board, you know, you have the not started, in progress. I wanted to change the no value. I was thinking of just adding backlog, to that no value, but I couldn’t change it, or I don’t have access, or I have no way of changing it, so…
51 00:07:07.160 ⇒ 00:07:24.430 Katherine Bayless: Yeah, sure. Let me make sure I can just take a quick look and see if, like, you guys should be, like, admins on this space, but I wonder if, like, the way the board is tied to the team, it’s, like, doesn’t… I don’t know. I’ve definitely… Asana permissions are somewhat mysterious to me, but I can take a quick look right now.
52 00:07:25.250 ⇒ 00:07:28.730 Chi Quinn: Okay, I mean, it might be something, I just know I tried…
53 00:07:28.950 ⇒ 00:07:40.680 Chi Quinn: various places, and… or at least the option to update the, section was not available for me. So it might be there, I might have not just looked deeper.
54 00:07:40.890 ⇒ 00:07:57.280 Chi Quinn: But that’s why the… that ticket is still pending, because I’ve pretty much added the other fields. I added the GitHub, field into the task, to the… for the task, as well. So, yeah, so that’s pretty much it, but that’s why it’s on pending.
55 00:07:59.320 ⇒ 00:08:09.139 Katherine Bayless: Okay, give it another shot now, and see if you’ve got… well, it doesn’t have to be right this second, but I think, yeah, you were not a project admin, you were an editor, so…
56 00:08:09.590 ⇒ 00:08:10.370 Chi Quinn: Got it.
57 00:08:10.660 ⇒ 00:08:13.159 Katherine Bayless: Now it should give you more abilities.
58 00:08:14.990 ⇒ 00:08:16.120 Chi Quinn: Got it.
59 00:08:19.670 ⇒ 00:08:20.350 Chi Quinn: Yeah.
60 00:08:20.600 ⇒ 00:08:23.129 Chi Quinn: Okay, yeah, I think… yep, looks like it.
61 00:08:23.580 ⇒ 00:08:25.040 Chi Quinn: Awesome, thank you.
62 00:08:26.650 ⇒ 00:08:27.220 Katherine Bayless: Nice.
63 00:08:28.430 ⇒ 00:08:31.949 Uttam Kumaran: Okay, perfect. And then, on our side, yeah, we,
64 00:08:32.150 ⇒ 00:08:37.710 Uttam Kumaran: We ship to all of the, like, Snowflake, you know, governance and role changes.
65 00:08:37.880 ⇒ 00:08:57.459 Uttam Kumaran: So I think probably my question, Catherine, is just, like, if… if you have a list of who needs what, or if you have, like, a little org chart or something, I’m happy to assign people, and then also just make sure everybody’s invited, but… and just… main thing is just to assign them to the proper new roles that we… that we created.
66 00:08:58.670 ⇒ 00:09:00.650 Uttam Kumaran: So, yeah, just a question there.
67 00:09:01.330 ⇒ 00:09:07.619 Katherine Bayless: Okay, yeah, so I think the people that we want to give… so the new role is the…
68 00:09:07.790 ⇒ 00:09:21.789 Katherine Bayless: Actually, so we were talking about two roles initially. One was, like, one that just couldn’t see some of the extra stuff, so it was a little bit more blinders on. That one, we would just give anybody who was previously in, Prod Read that new one.
69 00:09:21.790 ⇒ 00:09:35.179 Katherine Bayless: For the other one, the one that was, like, the ability to create stream-led apps but not cause chaos, that one we’d want to give to Anna Rutter, David Fallis, and Anna Kay.
70 00:09:35.180 ⇒ 00:09:47.369 Katherine Bayless: Krakova, because they’re kind of the three folks that are most interested in, or most willing and interested in, in diving into some of the more advanced features. Dave Hennessy, probably, as well.
71 00:09:47.880 ⇒ 00:09:58.679 Katherine Bayless: But yeah, I think those four people to start, if they could get the streamlid safe one, and then everybody else could just move from Prodread to the sort of, like, more limited, less distracting one.
72 00:09:59.170 ⇒ 00:10:00.399 Uttam Kumaran: Okay, okay, great.
73 00:10:03.010 ⇒ 00:10:04.380 Uttam Kumaran: Great, and then…
74 00:10:04.380 ⇒ 00:10:08.469 Katherine Bayless: setting that up, because I think that’ll be really powerful. I was like, I don’t want to, like, go back on my.
75 00:10:08.470 ⇒ 00:10:09.690 Uttam Kumaran: No, no, no, of course.
76 00:10:09.690 ⇒ 00:10:14.189 Katherine Bayless: stuff in Snowflake, but I do want them to be able to create, and normally that’s challenging to configure.
77 00:10:14.570 ⇒ 00:10:23.900 Uttam Kumaran: Yeah, no, no, that’s perfect, and again, I think, like, as we start to have more of these, like, oh, someone should just get access to, like, this and this, it’s not really easy for us to do that, actually.
78 00:10:24.120 ⇒ 00:10:31.039 Uttam Kumaran: And all of this is now documented also in the repo, on, like, how to run this and, like, all the commands we’ve run.
79 00:10:31.350 ⇒ 00:10:32.170 Uttam Kumaran: Thanks.
80 00:10:33.500 ⇒ 00:10:34.300 Uttam Kumaran: Great.
81 00:10:34.990 ⇒ 00:10:41.869 Uttam Kumaran: Probably, like, my last thing is gonna be on scanner data, so, like, one update in between there is, like, we,
82 00:10:42.050 ⇒ 00:10:52.259 Uttam Kumaran: our team started getting more, in the weeds on Cortex and Cortex AI, so I mentioned that, I’d like us to work on a…
83 00:10:52.300 ⇒ 00:11:04.400 Uttam Kumaran: just a plan on, like, how we’re gonna stand… do a couple things, right? Stand, up just, like, a base level, and, like, make sure we’re using all the features, or there’s a… there is some type of, like.
84 00:11:04.770 ⇒ 00:11:13.489 Uttam Kumaran: guidance on, like, what features are available within Cortex. I think the second piece is going to be establishing, you know, the semantic layer within Snowflake.
85 00:11:13.680 ⇒ 00:11:24.919 Uttam Kumaran: You know, on our side, we… we have been writing a ton of documentation, and I think we… a lot of that will… will naturally just end up in Snowflake powering Cortex, so that’ll be,
86 00:11:25.320 ⇒ 00:11:28.939 Uttam Kumaran: That’ll be really great. I think certainly once we get the
87 00:11:29.310 ⇒ 00:11:41.260 Uttam Kumaran: stuff stood up, it’ll be a lot of testing internally, and then sort of hopefully starting to train some people. And then the last piece is observability. So, we talked about, like, okay, is there a feedback loop system?
88 00:11:41.260 ⇒ 00:11:49.700 Uttam Kumaran: Can we look at, like, what the queries people are asking? How much does it cost? So, those are sort of the four pillars of, like, that project.
89 00:11:49.850 ⇒ 00:11:53.280 Uttam Kumaran: I think it’s helpful for us to…
90 00:11:53.420 ⇒ 00:12:11.480 Uttam Kumaran: continue to do these things, sort of, as part of, like, one business unit, or one deliverable to one business unit. So, like, if memberships is sort of still the star, then they’ll get sort of the love, but again, all the things we’re establishing in Cortex will speed up the next
91 00:12:11.610 ⇒ 00:12:13.170 Uttam Kumaran: tenant,
92 00:12:13.420 ⇒ 00:12:19.979 Uttam Kumaran: And so that is something that I want to work on next week, like, just a plan on, like, what that looks like.
93 00:12:20.150 ⇒ 00:12:24.230 Uttam Kumaran: I feel excited, like, we’ve been doing a lot of testing internally, and…
94 00:12:24.390 ⇒ 00:12:41.709 Uttam Kumaran: It’s working pretty well. I think, like, there’s definitely, like, we need to spend a lot of time on the semantic layer, but also looking at where it is today, I feel really confident that, like, in a few months, like, a lot of those small issues they’ll have fixed.
95 00:12:42.090 ⇒ 00:12:45.829 Uttam Kumaran: So yeah, that’s just that item.
96 00:12:46.670 ⇒ 00:13:11.560 Katherine Bayless: Yeah, I mean, strong, strong upvote. Like, you know what I mean, I noticed, even just working with Dave Hennessy the, you know, couple weeks ago, like, just adding the metadata to the table with, like, these are the standard filters, and then the little bit of metadata for the columns that were kind of, like, most critical, like, it made a dramatic improvement in his ability to get good answers out of it, you know, knowing that, like, it wasn’t, like, and now we’re done. But I was like, okay, this is definitely gonna be worth
97 00:13:11.560 ⇒ 00:13:17.500 Katherine Bayless: that, like, worth it to put in up front, because it’ll pay so much dividends downstream, I think.
98 00:13:18.670 ⇒ 00:13:19.730 Uttam Kumaran: Okay, perfect.
99 00:13:22.450 ⇒ 00:13:34.829 Uttam Kumaran: Great. And then, yeah, I think the last… I mean, we pushed through, you know, several, I think, model changes. I think the last item on our side is really just to, get some clarity on
100 00:13:35.030 ⇒ 00:13:36.619 Uttam Kumaran: on scanner data.
101 00:13:36.860 ⇒ 00:13:52.229 Uttam Kumaran: I think Ashwini has already, you know, loaded some of it, but yeah, I mean, that’s, I think, you know, Catherine, from what I heard from you, that’s the core priority, and so I just want to make sure that our team is aligned on, like, what the expectations are, and then we can just start
102 00:13:52.480 ⇒ 00:13:57.170 Uttam Kumaran: start driving towards that data being available in March, you know, for reporting.
103 00:13:58.760 ⇒ 00:14:03.900 Katherine Bayless: Yeah, so I think, let’s see, rambles, but, so, like, yeah, I…
104 00:14:04.600 ⇒ 00:14:14.350 Katherine Bayless: I definitely, as I was, like, you know, heading out and feeling like, oh god, there are so many things that I wished I’d left in a better place before getting on a plane, like…
105 00:14:14.430 ⇒ 00:14:30.940 Katherine Bayless: I think we’re kind of entering one of those, like, death-by-a-thousand-cut type… little bit of a, you know, window with the CES, sort of, the… sorry, the CES metrics, that’s what I’m looking for, and then, like, the scanner data, because I know, like, Kyle’s done an incredible amount of work with both.
106 00:14:30.940 ⇒ 00:14:41.619 Katherine Bayless: But then, like, now there’s kind of, like, two… two pieces of the cuts. One is the tendrils of, like, the digital exhaust on the way to the kind of final version, and so I just wanted to make sure that we do…
107 00:14:41.640 ⇒ 00:15:06.529 Katherine Bayless: cleanup, and like, I am totally also guilty of this. Like, clean up anywhere we had a version of the scanner data or the CES data along the way that might not necessarily be in the correct final state, just so nobody pulls numbers from the wrong place, and then they don’t agree, and then we look like we don’t know what we’re doing, that kind of thing. Like, I don’t want the work Kyle’s done to clean the data, have the trust be undermined by Catherine being like, yeah, let me grab that from over here, and it’s like, that wasn’t supposed to be
108 00:15:06.530 ⇒ 00:15:12.439 Katherine Bayless: there anymore, actually. So just kind of cleaning up behind us, I think, really is that kind of component.
109 00:15:12.490 ⇒ 00:15:33.839 Katherine Bayless: And then I think what I’m also noticing, and admittedly, I think probably I could have seen this coming a little better with Gary finally sort of announcing the retirement, like, date, or transition date, rather. Like, there’s now this really intense interest, not just in some of those, like, pre-audit numbers that we needed for goal setting and baseline and board reporting.
110 00:15:33.840 ⇒ 00:15:46.740 Katherine Bayless: But, like, now we’re starting to see just a lot more questions around, like, you know, what about this, and what about that? And I think we’re gonna start to see that kind of, like, legacy angle on stuff, like, this was the biggest CES in a while, and that puts a lot of pressure on the next one.
111 00:15:46.740 ⇒ 00:16:01.030 Katherine Bayless: Gary’s last, you know, kind of in that CEO role, like, right? Like, I’m seeing a lot of pressure on the data to be really available, really consultable, really, like, robustly usable by AI, which gets back to the Cortex stuff we were talking about.
112 00:16:01.030 ⇒ 00:16:08.950 Katherine Bayless: And so, I think putting some full-court press energy into supporting, you know, kind of the work Kyle’s been doing already around
113 00:16:09.800 ⇒ 00:16:23.219 Katherine Bayless: properly structuring that CES registration, and it’s essentially a mart, right, already for merits, but, like, properly structuring that and the historical years data into Snowflake so that we both are kind of, like.
114 00:16:23.220 ⇒ 00:16:34.650 Katherine Bayless: Able to more easily answer questions right now, and also we can kind of know exactly what we need to pipe stuff into next year for good continuity of fast, you know, analytics and responses.
115 00:16:34.920 ⇒ 00:16:38.790 Katherine Bayless: The scanner data, too… sorry, I should have mentioned this on that piece, like…
116 00:16:38.790 ⇒ 00:17:01.079 Katherine Bayless: I think this is probably me and my blind spots being new, but, like, it feels like I know it’s done and available, and I’m, like, I’m thinking just, like, we need to clean up behind ourselves. But there’s also, like, people still kind of coming to me with requests for, like, when is that going to be ready? And so, like, that was where I started then thinking about the communications piece of, like, maybe we could have a Slack channel or something, and, you know, to your point, Ucham, like.
117 00:17:01.080 ⇒ 00:17:04.210 Katherine Bayless: Not necessarily open it up to everybody right away, but kind of…
118 00:17:04.250 ⇒ 00:17:14.710 Katherine Bayless: make it a public channel, but just invite in the people we’re kind of currently working with, or that are, you know, looking for the scanner data. And that way, folks feel like they have a place to kind of keep up with.
119 00:17:14.740 ⇒ 00:17:18.899 Katherine Bayless: What we’re rolling out, and where it is, and that kind of thing, because it’s probably…
120 00:17:18.900 ⇒ 00:17:43.730 Katherine Bayless: it’s not a data availability problem, it’s just an awareness problem, I think, more than anything. So yeah, so it’s just kind of like a whole bunch of swirling thoughts around, I don’t want us to be constantly kind of tripping over the CES data and the scanner data when we’re trying to really, like, you know, make moves with the membership stuff and start landing additional data sources, and so that was where my head went to the, like, okay, let’s take a couple
121 00:17:43.730 ⇒ 00:17:51.219 Katherine Bayless: And just get this data really nicely packaged up and pipelined so that we’re kind of able to just glide forward.
122 00:17:52.410 ⇒ 00:17:52.970 Uttam Kumaran: Okay.
123 00:17:54.150 ⇒ 00:17:56.639 Uttam Kumaran: Any questions away, Shashweeney?
124 00:18:00.340 ⇒ 00:18:01.739 Awaish Kumar: Nope, not right now.
125 00:18:04.180 ⇒ 00:18:12.190 Ashwini Sharma: Sorry, I have questions on that, decommissioning of Postgres.
126 00:18:13.250 ⇒ 00:18:19.329 Ashwini Sharma: Yeah, so just wanted to understand where is the Postgres getting its feed from right now?
127 00:18:19.640 ⇒ 00:18:29.270 Ashwini Sharma: And yeah, so that, you know, we can decide how we can put it into Snowflake, and then there are a few other questions on top of that, but let’s start with that.
128 00:18:29.910 ⇒ 00:18:45.830 Katherine Bayless: Yeah, yeah, great question, actually, because I forgot about that piece of it. So thank you for the reminder. So the Postgres database is something that I spun up when I started, just because I don’t like SQL Server, and I was like, I just want to be in my familiar, happy place. And so.
129 00:18:45.830 ⇒ 00:18:57.839 Katherine Bayless: The decommissioning is both, like, there’s probably not a lot to really worry about, like, it’s not actually tied in live to too much. There is the one outbound stream of the CES data into Power BI that Kyle’s wired up.
130 00:18:57.840 ⇒ 00:19:11.999 Katherine Bayless: But there really isn’t any active ingestion going into it. So I think from a decommissioning standpoint, it’s mostly probably me and Kyle, that need to kind of go through and just say, like, okay, these tables, we just don’t need anymore.
131 00:19:12.000 ⇒ 00:19:19.130 Katherine Bayless: these tables, we can do one final export, make sure it’s in the data lake, and then that’s sufficient to kind of then bring it into Snowflake.
132 00:19:20.140 ⇒ 00:19:23.530 Katherine Bayless: the only… Well, two things.
133 00:19:23.800 ⇒ 00:19:43.019 Katherine Bayless: The only other piece of interest in there that might be worth a little bit of time and focus on is I was using the Postgres database to power that CES invites process this past year, and so we’ve got plenty of time before that comes up again, but if we wanted to, like, figure out how we want to, you know, kind of either
134 00:19:43.470 ⇒ 00:20:01.780 Katherine Bayless: document capture, or whatever, how that process was working in Postgres just before we kind of shut it all down, but I’ve got some documentation in the GitHub repo. We’ll always have a snapshot of the Postgres server if we wanted to spin it back up. So it’s not really too much about losing the data, it’s more just, like, it is kind of
135 00:20:01.780 ⇒ 00:20:09.170 Katherine Bayless: part of the artifact of the logic we were using for that process, but nothing’s actively flowing through that right now.
136 00:20:09.800 ⇒ 00:20:23.620 Katherine Bayless: Oh, and then the other one is, and this is me admitting a guilty behavior. Like, because I like Postgres, I keep gravitating back to it when I have, like, a small sort of, like, ad hoc task. So, like, for example.
137 00:20:23.620 ⇒ 00:20:26.970 Katherine Bayless: the week before I left, the marketing team needed to send out
138 00:20:26.970 ⇒ 00:20:51.810 Katherine Bayless: I don’t even remember what at this point. Oh, the announcement about Gary’s transition date, and they had exported his Outlook contacts and needed me to put them into Marketing Cloud, and I was like, we’re gonna wanna do a little cleaning on this before it goes in, and so my lazy brain was like, alright, I’ll just import the Outlook contacts file into the Postgres database, and then I can do my little cleanup, run it through our email validation service, and kick it out to Marketing Cloud.
139 00:20:51.900 ⇒ 00:21:07.489 Katherine Bayless: And so, like, I just need a place where I can do that in Snowflake, like, bring something in, use it, run SQL against it, and then I can kind of trash it later, so that I can kind of, like, really sort of fully break my addiction to Postgres.
140 00:21:07.490 ⇒ 00:21:23.030 Katherine Bayless: So yeah, I don’t know what that kind of looks like, but, like, almost an ad hockey sandbox, just a place where we’re not going to get confused and think that this is, like, a formal pipeline we need to build, but a place where the data team does need to do a little bit of, like, programmatic work that doesn’t have to persist forever.
141 00:21:26.850 ⇒ 00:21:28.389 Ashwini Sharma: Got it, cool.
142 00:21:29.060 ⇒ 00:21:34.929 Ashwini Sharma: There were some more questions around… okay, so basically this Postgres data is just an ad hoc thing.
143 00:21:36.780 ⇒ 00:21:37.260 Katherine Bayless: Yeah.
144 00:21:37.260 ⇒ 00:21:42.009 Ashwini Sharma: Which, okay, so basically no KPIs or, you know.
145 00:21:42.170 ⇒ 00:21:49.230 Ashwini Sharma: questions that need to… we need to answer on top of that data, right? It’s just, like, sending out emails, like the example that you shared.
146 00:21:49.980 ⇒ 00:21:56.860 Ashwini Sharma: Yeah, exactly. We were using it for stuff more, intensively in the fall, but right now it’s just kind of hanging out.
147 00:21:59.170 ⇒ 00:22:05.149 Ashwini Sharma: Okay, there was one more, thing. Let me open that up. This was,
148 00:22:06.200 ⇒ 00:22:11.429 Ashwini Sharma: Salesforce Marketing Cloud Pipeline, Snowflake to FTP.
149 00:22:14.410 ⇒ 00:22:18.229 Ashwini Sharma: So basically, like, there is some data somewhere which we want to put
150 00:22:18.380 ⇒ 00:22:22.570 Ashwini Sharma: to FTP via Snowflake, right? That’s my understanding.
151 00:22:24.250 ⇒ 00:22:32.829 Katherine Bayless: Yeah, so there are… so for this… so there’s two… kind of two pieces to this one, too. So, in Salesforce Marketing Cloud.
152 00:22:33.180 ⇒ 00:22:48.520 Katherine Bayless: We aren’t really running them right now since CES is over and we’re in between registrations, but we did build out a bunch of data extensions to model all of the CES registration data in Marketing Cloud that then powered some downstream curated lists.
153 00:22:48.570 ⇒ 00:23:02.339 Katherine Bayless: We’ll be repeating that exercise with the membership data, because they’re going to want to start doing journey automations as well, and so Kai and I will start with kind of building out some of the initial scaffolding in Marketing Cloud for those data extensions.
154 00:23:02.340 ⇒ 00:23:12.580 Katherine Bayless: And then the next step is to bring the data in to those via… if it lands on the FTP server, Marketing Cloud’s automation engine can kind of pick it up and take it from there.
155 00:23:12.580 ⇒ 00:23:21.089 Katherine Bayless: So from that standpoint, we’d want to be able to use the FTP to power, sort of, like, a recurring… probably weekly at first is enough.
156 00:23:21.190 ⇒ 00:23:29.579 Katherine Bayless: Automation that would maybe build towards daily as we get a little bit more, like, timely in our, journey send events.
157 00:23:30.170 ⇒ 00:23:46.140 Katherine Bayless: Then there’s, again, the ad hoc piece, right? Like, we need to send a one-off email to announce Gary’s transition date, and here’s, you know, a dump of his Outlook contacts. Like, that’s never gonna be something we’re gonna, like, formally model into the data extensions in Marketing Cloud.
158 00:23:46.140 ⇒ 00:23:56.300 Katherine Bayless: But having a way… and I wouldn’t say that this is, like, the most burning priority, but having a way to go from, you know, an Excel or a CSV data source, clean the data.
159 00:23:56.300 ⇒ 00:24:13.989 Katherine Bayless: check that the emails are deliverable and valid, either, you know, based on the data that we already have, or running any net new ones through NeverBounce, and then kicking that over to the FTP, where it could get picked up and pulled into a one-off data extension over there. It’s kind of that same ad hoc use case again.
160 00:24:13.990 ⇒ 00:24:23.180 Katherine Bayless: But doesn’t necessarily have a great kind of pipeline at the moment. But yeah, the ideal would be some sort of intake point from the staff.
161 00:24:23.270 ⇒ 00:24:48.179 Katherine Bayless: then it would go into Snowflake for cleaning and ultimately processing on to Marketing Cloud. I mean, you could probably skip the Snowflake step to a certain extent, just depends on what we connect to where. I do know I am delinquent a little bit on making a call on the ETL platform. I was explaining to each time yesterday, I was like, I’m just a little leery of the free trial thing, but I do think we can
162 00:24:48.180 ⇒ 00:25:11.890 Katherine Bayless: probably start using either maybe a sort of very rudimentary automation that just gets FTP files pushed up, and then we can leverage the Marketing Cloud Automation Engine from there, or looking at, you know, onboarding Fivetran maybe just for this pipeline. But that’s something I’m kind of actively doing my homework on today, so we can make a call by the time we’re in planning on Monday.
163 00:25:14.290 ⇒ 00:25:16.070 Uttam Kumaran: Yeah, I mean, I feel…
164 00:25:16.380 ⇒ 00:25:28.389 Uttam Kumaran: I feel good on that either way. I mean, I hear you on the, you know, using a tool for free trial, not sure whether we’re gonna use it long-term. I mean, there’s several other options, like, you know, there’s,
165 00:25:28.450 ⇒ 00:25:36.760 Uttam Kumaran: Snowflake has some tools for us to manage that. Like, we can orchestrate some of that ourselves, so… I mean, I think it’s worth trying that, and then…
166 00:25:37.030 ⇒ 00:25:43.800 Uttam Kumaran: I mean, we will sort of see what it takes to kind of maintain those, and then maybe deciding on a ETL platform longer term.
167 00:25:44.750 ⇒ 00:25:58.900 Katherine Bayless: Yeah, yeah. I did also, as part of my homework doing, I did go back and take a look at the, like, specific budget line item, that I had put in this year to cover, sort of all of our various software and subscription-y kind of things, so this would be covering
168 00:25:58.900 ⇒ 00:26:22.780 Katherine Bayless: just our AWS account, not, like, the… any others in the organization, but, like, our data AWS account, Snowflake instance, ETL tool choice, data viz tool choice, and then a tiny corner of amount for our Cloud subscription. And I think I parked 120 in there, and so, like, we’ve got plenty of room, considering current usage, considering it’s already almost the end of February.
169 00:26:22.850 ⇒ 00:26:39.820 Katherine Bayless: So it’s not necessarily a budget problem, it’s just a, like, I need to, like, make the choice that makes the most sense for our stack, and then socialize the price tag internally, because I am still trying to kind of help get folks used to what it costs to really run a data pipeline on proper infrastructure and not intern tiers, so…
170 00:26:46.500 ⇒ 00:26:57.540 Uttam Kumaran: Okay, and then the other piece is, you know, we… we weren’t able to, sort of, kind of move forward on anything on the ID stitching side, but sort of hearing the scanner data piece, I wonder if we should just focus on that.
171 00:26:57.890 ⇒ 00:27:07.780 Uttam Kumaran: I mean, we still have our document that we wrote, and there are next steps, but I was wondering if we should just continue to focus on scanner data for this next week, and knocking that out.
172 00:27:08.410 ⇒ 00:27:29.169 Katherine Bayless: Yeah, I mean, I think that makes sense to me, because I think the scanner data in particular, I think Kyle’s already done the really hard part of, like, figuring out what on earth these scans are actually for, and kind of doing the cleanup of the raw data that we received. And so for that, I’m like, yeah, we just kind of need to clean up behind us, and then also figure out how to let everybody know who might need it, that it is available.
173 00:27:29.170 ⇒ 00:27:40.560 Katherine Bayless: And then I think, yeah, the next focus really then becomes getting that CES reg data into a good shape, and that’s where that entity identity stitching stuff is going to be really clutch, because
174 00:27:40.560 ⇒ 00:27:45.400 Katherine Bayless: Any question that says, you know, how many of these? Well, so that, at CES, is
175 00:27:45.490 ⇒ 00:27:57.800 Katherine Bayless: probably gonna need us to join based on that, like, company name or email domain match. And so I do think, still, we can start with, like, leveraging what’s in Remember as, like, the backbone there, but…
176 00:27:57.800 ⇒ 00:28:11.179 Katherine Bayless: But yeah, I think figuring out the CES Reg data model and the identity stitching, you know, V0, in tandem makes sense. But Kyle, I noticed you came off mute a couple times, and I didn’t stop talking, so…
177 00:28:11.180 ⇒ 00:28:12.169 Kyle Wandel: No, you’re good.
178 00:28:12.310 ⇒ 00:28:36.210 Kyle Wandel: The CS Reg pipeline is in ProdMarch, that should be good, because me and Nishwini pushed it, last week. I did update and kind of do an attendee pipeline, looking at, basically the logic is if before 20214, CS status equals attended, if after 2022, then you have to do the verified and all the big stuff, and then 2026 is similar to what we’ve talked about, which is the… what the big.
179 00:28:36.440 ⇒ 00:28:43.040 Kyle Wandel: 4 or 5 different filters, basically. So that is in… that…
180 00:28:43.660 ⇒ 00:29:00.989 Kyle Wandel: sorry, that PR is committed, so just waiting for a merge for that, or review and merger for that, so that should be the attendee pipeline. And then I also… there’s another pipe… I think there’s another PR for exhibitor pipeline, so I exported all of the raw… whatever, the data from…
181 00:29:01.050 ⇒ 00:29:12.459 Kyle Wandel: Postgres, and then for the exhibitor booth from 1967 to 2025, built that logic out, and that should be good. I don’t think it’s in prod, I think I just have it in staging for right now.
182 00:29:12.510 ⇒ 00:29:21.769 Kyle Wandel: Because I want to get… I know you’re still trying to get, information or, like, a raw data set from sales, or Expo, or whatever, Matt, my show.
183 00:29:21.830 ⇒ 00:29:33.079 Kyle Wandel: So I don’t want to put in 26 yet because of that, but, we could if we need to, because the columns are a lot different, based on what sales has and what we have, so…
184 00:29:33.970 ⇒ 00:29:48.020 Katherine Bayless: Yeah, and I think, actually, that’s a perfect segue into, like, really, kind of, what I think is the next round of work on this CES data is, like, it… it feels a little silly if you think about it too much, because it’s, like, basically.
185 00:29:48.120 ⇒ 00:30:01.979 Katherine Bayless: like, merits and some of these other reports that we’ve been consuming, they were kind of already martifying the data, and now we need to, like, un-mart it, and then remark it in a better way. I know it sounds redundant, but I swear I think it has to happen.
186 00:30:01.980 ⇒ 00:30:25.880 Katherine Bayless: Because I think what we’re gonna run into is, like, stitching the columns and then the values in the columns together across the years, right? So, like, we need to know, you know, the different things that the field was called historically so that we can always kind of reliably union or join them, and then also, like, inside some of those field values that had, preset options, so, like, the product interests, for example.
187 00:30:25.880 ⇒ 00:30:42.859 Katherine Bayless: Like, we’ve seen inconsistency in those throughout the years, so if somebody said, what’s the trend in attendance across vehicle tech? Well, we need to know that that was vehicle tech and mobility this year, but it was vehicle tech ampersand mobility last year, and it used to just be vehicle tech, and before that, it was like cars.
188 00:30:42.860 ⇒ 00:30:54.870 Katherine Bayless: Right? And so, like, some of these sort of data governance pieces are gonna really matter in our ability to, like, deliver answers off the CES data quickly. And so, like I said, I know it seems silly to, like.
189 00:30:54.870 ⇒ 00:31:05.729 Katherine Bayless: take a pre-built mart and, like, break it, rebuild it, and put it back together on the other side, but I think it’s the necessary next step in order for this data to really become as usable as possible.
190 00:31:08.100 ⇒ 00:31:16.180 Kyle Wandel: I will say they’ve done a decent job to make it usable based on what the previous regime has done and data team has done, so that’s been nice. Like, it has all of the whatever the
191 00:31:17.000 ⇒ 00:31:31.409 Kyle Wandel: category interest, or, like, the product codes and stuff like that. And then, the biggest thing is cleaning up the booth information. That’s the most difficult thing, is understanding how to calculate the dimensions, or the booth size, basically. I’d love to get there. Again, I don’t know what the raw export looks like.
192 00:31:31.580 ⇒ 00:31:42.889 Kyle Wandel: Obviously, I mean, it’s just a lot… in the 2025, whatever, 67 data, 2025 data is a lot different than what Jay has, so…
193 00:31:43.300 ⇒ 00:31:55.709 Katherine Bayless: Yeah. So actually, I mean, that might be interesting, then, for… especially for you and I to work on together, kind of, Kyle, because, like, if we maybe start with our focus on the unbuild rebuild for the registration side.
194 00:31:55.710 ⇒ 00:32:20.670 Katherine Bayless: the exhibitor stuff, since Jay has turned that, like, over to us to do that report this year, A, we probably should get started on figuring out how that’s gonna work sooner than later, and B, it does mean that in order to do that, we need to go actually, like, take a look at and see what that source looks like, because now we’ve got the keys and stuff to get in there, and so I think maybe, in a couple weeks, you and I could take a couple sort of spikes and just do a really deep dive into
195 00:32:20.670 ⇒ 00:32:33.090 Katherine Bayless: So, like, okay, what does this really look like at the source? What level of translation is Jay’s report doing? How correct is that translation? Debatable? And then figuring out how we actually want to handle this piece.
196 00:32:33.150 ⇒ 00:32:35.709 Katherine Bayless: Yeah. Yeah.
197 00:32:36.650 ⇒ 00:32:37.629 Kyle Wandel: Yeah, that makes sense.
198 00:32:40.010 ⇒ 00:32:55.500 Katherine Bayless: Yeah, I think, like, the work can move fairly quickly in terms of, like, the, you know, the code and stuff, thanks to the work Kyle’s done. I think it’s just a matter of figuring out, like, what is this actually, like, conceptually, what kind of data model do we need to be able to capture these mappings? Like.
199 00:32:55.500 ⇒ 00:33:04.400 Katherine Bayless: I’m thinking it’s probably worth coming up with, like, the dimension and fact tables around this dataset, and like, how are we actually gonna structure it when it’s in the unbuilt state?
200 00:33:04.400 ⇒ 00:33:12.160 Katherine Bayless: Even if once rebuilt, it ultimately kind of looks the same as it did before, it’s just with a really much better middle. We’re building a better middle for the Oreo.
201 00:33:14.560 ⇒ 00:33:16.309 Uttam Kumaran: Nice. Heck yeah.
202 00:33:16.510 ⇒ 00:33:17.450 Uttam Kumaran: Double stuff.
203 00:33:17.450 ⇒ 00:33:20.470 Katherine Bayless: I’m like…
204 00:33:20.470 ⇒ 00:33:27.270 Uttam Kumaran: I only eat the stuffing. I’m, like, a really, like, weird person. I don’t even like the cookie, I feel like.
205 00:33:28.200 ⇒ 00:33:39.209 Katherine Bayless: I did successfully one time scrape the filling out, replace it with toothpaste, and get my brother to eat one. Oh, no. One of my proudest life achievements.
206 00:33:39.900 ⇒ 00:33:42.380 Uttam Kumaran: It’s like minty fresh, yes.
207 00:33:42.380 ⇒ 00:33:44.349 Katherine Bayless: He’s so mad.
208 00:33:46.600 ⇒ 00:33:47.460 Katherine Bayless: Anyway.
209 00:33:48.710 ⇒ 00:33:59.949 Uttam Kumaran: Okay, and I think last question, Catherine, and I don’t know if we wanted to just even just brainstorm, but we briefly discussed, like, potentially, like, Slack channel directly with, you know, a few stakeholders.
210 00:34:00.110 ⇒ 00:34:05.720 Uttam Kumaran: You know, maybe I’ll just give my… my two cents. So, after being sort of, like, kind of, like.
211 00:34:06.680 ⇒ 00:34:12.969 Uttam Kumaran: I’ve established many a data Slack help channel, and…
212 00:34:13.150 ⇒ 00:34:20.709 Uttam Kumaran: In particular, like, at my, you know, one of my first roles at WeWork, it was, like, a 10,000-person company, and we had a…
213 00:34:20.900 ⇒ 00:34:22.639 Uttam Kumaran: Data Help Slack channel.
214 00:34:23.159 ⇒ 00:34:38.610 Uttam Kumaran: With, like, 10 of us managing, and it was absolutely a nightmare. And one thing I want to share, but kind of the pros and cons. So, one is I do think, you know, and you’ll see us always try to establish a direct line with
215 00:34:39.210 ⇒ 00:34:44.629 Uttam Kumaran: how many stakeholders, so that we can at least put a face to a name, and they know that we’re here to support them, and so…
216 00:34:44.810 ⇒ 00:34:47.740 Uttam Kumaran: I… I am a big fan of trying to
217 00:34:47.850 ⇒ 00:35:07.079 Uttam Kumaran: break down those walls, whether it’s like, oh, we only communicate with email, well, let’s have a… let’s have a Slack group. Oh, it’s a Slack group, well, let’s maybe try to meet once a month to show what we’re building for you. So I do really like, you know, the ability to get closer. The… really, the thing is just setting expectations super, super, clearly.
218 00:35:08.090 ⇒ 00:35:16.040 Uttam Kumaran: One of the things we want to avoid is that when things happen in data, for that person that it’s happening to, it’s oftentimes, like.
219 00:35:16.170 ⇒ 00:35:29.660 Uttam Kumaran: the craziest thing, like, it breaks everything. For us, it’s just another day on the job, and so one of the things I just want to establish before we do that is, like, okay, what is our, like, SLA to respond to
220 00:35:30.500 ⇒ 00:35:40.039 Uttam Kumaran: you know, asks, making sure that we have ticketing in place, and that any ask from someone that can’t get resolved in a Slack thread moves to a ticket.
221 00:35:40.180 ⇒ 00:35:45.100 Uttam Kumaran: Additionally, I would really recommend having some type of, like, on-call, or, like.
222 00:35:45.460 ⇒ 00:35:51.219 Uttam Kumaran: For someone to be the lead on that channel once a week, and that’s like a floating ticket, basically.
223 00:35:51.340 ⇒ 00:36:01.849 Uttam Kumaran: If everybody on the team is, like, staring at a channel that’s getting pinged a bunch, it will drive us nuts. We will… it’ll lead to duplicate work. And…
224 00:36:01.990 ⇒ 00:36:07.970 Uttam Kumaran: At times, people will come into that channel and be like, oh my god, everything’s breaking, like…
225 00:36:08.070 ⇒ 00:36:14.650 Uttam Kumaran: And… I think that’s, that’s just something that, we need to be aware of.
226 00:36:15.250 ⇒ 00:36:18.360 Uttam Kumaran: So those are just a few of my things. I think on the,
227 00:36:18.520 ⇒ 00:36:24.580 Uttam Kumaran: On the con side, like, yes, for those reasons, like, it can often be really, really stressful.
228 00:36:25.690 ⇒ 00:36:28.130 Uttam Kumaran: And so that’s what I just want to make sure that, like.
229 00:36:28.260 ⇒ 00:36:31.540 Uttam Kumaran: we can consider, A, creating one channel for every
230 00:36:31.680 ⇒ 00:36:37.800 Uttam Kumaran: like, core business unit that we’re supporting. That way it’s not, like, a pile-on,
231 00:36:38.600 ⇒ 00:36:46.729 Uttam Kumaran: The other thing is, like, that could also be used two ways. Like, we can announce our changes to them there, and… and get direct feedback from people.
232 00:36:46.960 ⇒ 00:36:51.010 Uttam Kumaran: So that’s just a ramble on, like, My thoughts here.
233 00:36:51.810 ⇒ 00:37:07.479 Katherine Bayless: No, no, I think very, very wise rambles, and while I have never had to deal with 10,000 coworkers, I’ll say even 150 is plenty. And so, yeah, I think, I think your cautions are salient. And I was thinking about this, too, and, like.
234 00:37:07.820 ⇒ 00:37:11.099 Katherine Bayless: I think, for the purposes of the, like…
235 00:37:11.330 ⇒ 00:37:16.570 Katherine Bayless: So, like, the, you know, if I try to think about what are the problems I’m trying to solve, like, one is just…
236 00:37:16.570 ⇒ 00:37:40.929 Katherine Bayless: I feel like we need some way to say, like, hey everybody, if you’re looking for scanner data, it’s here. And we don’t need to do a whole Slack channel for that. I think we could probably post in the CES Ask Anything channel, and just do kind of a general purpose announcement of, like, if anybody’s interested, or if anybody not interested, but, like, if anybody’s looking for it, you know, this is how you can find it, and if you have any questions or need something slightly different, here’s our Asana form, right?
237 00:37:41.000 ⇒ 00:37:59.599 Katherine Bayless: The nice thing about posting it in that channel is that most all of the staff are in there anyway, certainly probably most of the people that’d be looking for the data, and because it’s a more public channel, that’ll surface that sort of through Glean, and so if somebody were to go to Glean and ask about scanner data, you know, they would kind of get routed back to us.
238 00:37:59.670 ⇒ 00:38:06.790 Katherine Bayless: And so I think the existing CES Slack channel solves the announcement thing around the scanner data for the moment.
239 00:38:06.930 ⇒ 00:38:24.009 Katherine Bayless: I think then, to your point too, like, there is a difference between supporting a team that we’re really actively engaging with, like what we’re doing with the membership team right now, and, like, yeah, having a kind of general channel for, like, data and announcements and stuff like that. And I think, yeah, let’s…
240 00:38:24.010 ⇒ 00:38:36.960 Katherine Bayless: we can kind of table the, maybe, data announcements channel for a little bit longer. Maybe that’s something we try to be able to launch in, like, the midsummer, as we’re kind of getting closer to, like, CES Reg Start.
241 00:38:36.960 ⇒ 00:38:40.180 Katherine Bayless: But still, in the meantime, set one up that’s…
242 00:38:40.180 ⇒ 00:39:05.159 Katherine Bayless: you know, whatever we want to call it, but, like, something where it’s all of us and all of the folks on the membership team that we’re working with currently, and then we can bring new folks in, as we start working with them, or create similar department-specific channels, if we feel like that’s the better way to do it. But yeah, I like the approach of starting small with the folks that we’re really actively engaging in, leveraging existing channels for sort of any sort of announcement-type
243 00:39:05.160 ⇒ 00:39:23.559 Katherine Bayless: stuff that we need for the moment. And then, yeah, continuing to direct things back to the Asana board so that we don’t start getting into firefighter reactive mode. Or, honestly, I also would probably be the one that would get in trouble for, like, here’s a Slack channel, and then never actually responding to stuff, because that is my Achilles heel.
244 00:39:23.560 ⇒ 00:39:34.800 Katherine Bayless: So yeah, I think you’re wise to say, scope it small, solve the problems we actually have, and then we can scale out to more, like, bigger Slack platforms later, but yeah.
245 00:39:35.580 ⇒ 00:39:36.120 Uttam Kumaran: Great.
246 00:39:37.870 ⇒ 00:39:50.610 Uttam Kumaran: Yeah, so even, like, an on-call strategy could be great here, and that person doesn’t fix everything. That person’s just the first line of defense, and… and is, like, the eyeball emoji and the, hey, we heard you, we’ll get back to you.
247 00:39:50.880 ⇒ 00:39:54.370 Uttam Kumaran: And then everybody can keep that channel on mute for a week.
248 00:39:54.540 ⇒ 00:39:55.310 Uttam Kumaran: So…
249 00:39:56.320 ⇒ 00:40:01.570 Uttam Kumaran: Yeah, I mean, I’m a fan of it, as long as we walk into it, you know, kind of unified, so…
250 00:40:02.220 ⇒ 00:40:15.440 Katherine Bayless: Yeah. No, no, like I said, I think it’s really wise, guidance, because yeah, it… it is a drag when you wind up in a situation where you’re like, I tried to be helpful, and now I’m just inundated with so many things, I can’t be helpful anymore.
251 00:40:19.230 ⇒ 00:40:19.850 Uttam Kumaran: Perfect.
252 00:40:31.140 ⇒ 00:40:33.930 Uttam Kumaran: Great. I think that’s… that’s sort of all I had today.
253 00:40:36.570 ⇒ 00:40:38.789 Katherine Bayless: Did I miss anything really fun while I was out?
254 00:40:40.060 ⇒ 00:40:53.620 Kyle Wandel: Just a couple fires. Me, actually, Kai and I were sitting in your office meeting about Salesforce Marketing Cloud, and then, lo and behold, Kenzie just walks in the door. Oh, where’s Catherine? I was like, well, she’s on the PTO. I was like, well, we can help ya. I was like, okay, so…
255 00:40:53.620 ⇒ 00:40:54.569 Katherine Bayless: That was fun.
256 00:40:55.150 ⇒ 00:40:56.330 Chi Quinn: That is actually…
257 00:40:56.440 ⇒ 00:40:58.280 Katherine Bayless: Serendipitous fun.
258 00:40:59.560 ⇒ 00:41:18.599 Katherine Bayless: I got a phishing email from Kinsey, to a Gmail account that, like, I don’t even know how the, like… like, I’m still trying to figure out, like, how did this wind up in the web? Because it’s not the one that I used for any of my interviewing, applying, or ADPing. And so I was like, fascinating. I did not buy the gift cards that she was asking about, don’t worry.
259 00:41:18.600 ⇒ 00:41:22.619 Katherine Bayless: But but yeah, it was just funny, I was like, how did this pop in over here?
260 00:41:23.340 ⇒ 00:41:29.209 Kyle Wandel: I haven’t… I haven’t received one from Kinsey yet. I’ve gotten a few from Gary, but, that’s interesting that they’re using Kinsey now.
261 00:41:29.710 ⇒ 00:41:33.789 Katherine Bayless: Right? I was like, okay, I’m savvy, they’re at least up-to-date on the LinkedIn news.
262 00:41:40.350 ⇒ 00:41:41.880 Kyle Wandel: That seems pretty much it for me.
263 00:41:42.950 ⇒ 00:41:43.500 Katherine Bayless: Cool.
264 00:41:44.600 ⇒ 00:41:50.709 Katherine Bayless: Well, I can dive back into performance reviewering, and, email cleanup.
265 00:41:52.800 ⇒ 00:41:54.140 Uttam Kumaran: Okay, perfect.
266 00:41:54.380 ⇒ 00:41:55.939 Uttam Kumaran: Thank you, everyone. Appreciate it.
267 00:41:56.540 ⇒ 00:41:57.849 Katherine Bayless: Thank you.
268 00:41:57.850 ⇒ 00:41:58.700 Chi Quinn: Thank you.
269 00:41:58.700 ⇒ 00:41:59.080 Ashwini Sharma: Thank you.