Meeting Title: Brainforge x CTA: Weekly! Date: 2026-02-06 Meeting participants: Awaish Kumar, Katherine Bayless, Chi Quinn, Uttam Kumaran, Ashwini Sharma
WEBVTT
1 00:01:07.660 ⇒ 00:01:09.469 Katherine Bayless: Hey, Awish! How are you?
2 00:01:13.620 ⇒ 00:01:15.709 Awaish Kumar: Hi, I’m good, how about you?
3 00:01:16.530 ⇒ 00:01:17.440 Katherine Bayless: I’m good.
4 00:01:17.620 ⇒ 00:01:20.940 Katherine Bayless: I’m excited for the conversation today, like…
5 00:01:21.080 ⇒ 00:01:29.369 Katherine Bayless: We had such a great call with the membership team, yesterday, and so, like, feeling, like, very excited about our work.
6 00:01:30.010 ⇒ 00:01:31.670 Awaish Kumar: Yeah, it’ll be how it went…
7 00:01:34.070 ⇒ 00:01:45.790 Katherine Bayless: It was really good. I think, they were blown away at how much, like, because we had seen them, you know, the week before, and then this past, meeting, like, how much we had built in the… well, you guys had built in the meantime.
8 00:01:45.790 ⇒ 00:01:59.940 Katherine Bayless: And just, yeah, like, I think you can see, like, the little eyes going wide, right, with some of the, like, AI stuff, too, that Snowflake is rolling out, so, like, just… they’re a really great team to be our first, sort of, adopters of the new data platform, because they’re just…
9 00:02:00.190 ⇒ 00:02:13.209 Katherine Bayless: I mean, historically data-starved, and also, like, they’re just, they’re friendly and creative and, like, curious people, and so, like, we’re, you know, we’re not dealing with a group that are like, we have to learn a new tool, you know? Like, we’re dealing with people that are like.
10 00:02:13.570 ⇒ 00:02:14.609 Katherine Bayless: I have more data.
11 00:02:15.200 ⇒ 00:02:17.269 Katherine Bayless: So it’s awesome.
12 00:02:18.040 ⇒ 00:02:18.690 Uttam Kumaran: Nice.
13 00:02:20.170 ⇒ 00:02:21.490 Katherine Bayless: So, good morning, everybody!
14 00:02:21.790 ⇒ 00:02:22.630 Uttam Kumaran: Morning.
15 00:02:22.630 ⇒ 00:02:23.420 Chi Quinn: Right.
16 00:02:23.620 ⇒ 00:02:24.440 Katherine Bayless: Happy Friday.
17 00:02:25.180 ⇒ 00:02:30.250 Uttam Kumaran: Happy Friday! I think you mentioned Kyle’s out, so I think we probably have everybody here.
18 00:02:30.600 ⇒ 00:02:31.739 Katherine Bayless: Yep, yep, yep.
19 00:02:32.360 ⇒ 00:02:46.290 Uttam Kumaran: Okay, so I… I sent a little bit of an agenda. I think we actually, probably can spend a good amount of time talking about, like, what our preliminary plan is going to be on the identity stitching piece, but maybe…
20 00:02:46.540 ⇒ 00:02:51.920 Uttam Kumaran: like, we can just walk through, like, the Asana board, N.
21 00:02:52.080 ⇒ 00:02:54.520 Uttam Kumaran: Just kind of, like, start from there.
22 00:02:54.680 ⇒ 00:02:56.130 Uttam Kumaran: I’ll pull that out.
23 00:03:00.360 ⇒ 00:03:04.329 Katherine Bayless: Actually, while, which I’m pulling that up, Kai, do you want to share your comment about the priority piece?
24 00:03:04.330 ⇒ 00:03:14.250 Chi Quinn: Yes, yeah, so I saw the request to add the priority. I did add that to the field. It does have the three options, so you get to go from there.
25 00:03:14.560 ⇒ 00:03:20.989 Uttam Kumaran: Okay, okay, cool. Yeah, that way, when I’m just, like, I can just stack rank what I’m working on a little bit.
26 00:03:22.170 ⇒ 00:03:22.550 Chi Quinn: Yeah.
27 00:03:23.140 ⇒ 00:03:25.550 Katherine Bayless: I like my suggestion of oops next and meh.
28 00:03:26.030 ⇒ 00:03:26.990 Uttam Kumaran: Yeah.
29 00:03:30.780 ⇒ 00:03:33.389 Uttam Kumaran: Okay, I’m logging into Opta.
30 00:03:35.590 ⇒ 00:03:38.330 Katherine Bayless: Oh yeah, did Jay get your glean thing sorted, or don’t…
31 00:03:38.330 ⇒ 00:03:43.100 Uttam Kumaran: We’re still going back and forth. Yeah.
32 00:03:44.720 ⇒ 00:03:46.300 Katherine Bayless: I can’t remember…
33 00:03:47.910 ⇒ 00:03:54.289 Katherine Bayless: I can’t remember if I’ve experienced anybody else who doesn’t have CTA.tech that we’ve gotten in there, so, like…
34 00:03:58.180 ⇒ 00:04:02.640 Uttam Kumaran: Okay, give me one sec, it’s, like, continuing to bounce me notifications.
35 00:04:02.900 ⇒ 00:04:04.010 Katherine Bayless: Oh, yeah.
36 00:04:04.670 ⇒ 00:04:05.460 Uttam Kumaran: Yeah.
37 00:04:08.790 ⇒ 00:04:15.559 Katherine Bayless: Yeah, I noticed that my Okta has gone from often seamlessly logging in to, like, constant password and, like.
38 00:04:15.880 ⇒ 00:04:18.030 Katherine Bayless: Push notification prompting again.
39 00:04:18.640 ⇒ 00:04:24.480 Uttam Kumaran: Yeah… I’m on my laptop, and so I wonder if maybe that changed, I don’t know.
40 00:04:24.950 ⇒ 00:04:25.440 Katherine Bayless: Yeah.
41 00:04:25.780 ⇒ 00:04:29.100 Uttam Kumaran: Trying to hit as many, like…
42 00:04:29.350 ⇒ 00:04:34.670 Uttam Kumaran: Remember me, remember me. Right. Things as possible.
43 00:04:36.770 ⇒ 00:04:38.000 Uttam Kumaran: Okay, now…
44 00:04:51.070 ⇒ 00:04:52.090 Uttam Kumaran: Okay.
45 00:05:11.540 ⇒ 00:05:14.819 Uttam Kumaran: Okay, I guess I have, like, I have a… oh, here we go.
46 00:05:15.770 ⇒ 00:05:19.810 Uttam Kumaran: I signed up with my… well, I signed up with my Brainforge email, but…
47 00:05:19.910 ⇒ 00:05:22.330 Uttam Kumaran: I have a free account, and then it’s like…
48 00:05:22.490 ⇒ 00:05:29.589 Uttam Kumaran: oh, your trial expired. And I’m like, I don’t even know that I had a trial. Stop, I just want to get where I’m going.
49 00:05:29.910 ⇒ 00:05:30.300 Katherine Bayless: Yeah.
50 00:05:32.780 ⇒ 00:05:33.809 Uttam Kumaran: Okay, here.
51 00:05:33.810 ⇒ 00:05:48.979 Katherine Bayless: This is, like, one of those things I feel like software… like, GitHub solves for it pretty elegantly, I think, but, like, I think more SaaS providers are gonna have to figure out, like, what do you do when you’ve got, like, multiple identities across, you know, clients, or even… I mean, I used to.
52 00:05:48.980 ⇒ 00:05:49.780 Uttam Kumaran: Yeah.
53 00:05:49.780 ⇒ 00:05:59.130 Katherine Bayless: Where it was, like, within the job, I had 3 different identities professionally, and so, like, I had 3 different sets of access to stuff, and pay off, but anyway, carry on.
54 00:05:59.610 ⇒ 00:06:04.550 Uttam Kumaran: Yeah, it’s really brutal for us, I feel like. We’re always dealing with this.
55 00:06:04.720 ⇒ 00:06:05.100 Katherine Bayless: Yeah.
56 00:06:05.100 ⇒ 00:06:09.399 Uttam Kumaran: Okay, great. So…
57 00:06:10.400 ⇒ 00:06:30.149 Uttam Kumaran: I think we’re probably just gonna see everything on the board, so, I sort of started creating some tickets, based on our conversations yesterday, but maybe we can take a look at things that are pending and in progress, and just see if there’s anything we can move or, update or change.
58 00:06:30.540 ⇒ 00:06:33.380 Uttam Kumaran: So I feel like, maybe, Kai, if you want to go first…
59 00:06:34.130 ⇒ 00:06:47.769 Chi Quinn: Yeah, so I was, working on the CES, the data, the Power BI report. Pretty much those are done. I notified both of, the requesters of those tickets.
60 00:06:47.770 ⇒ 00:07:08.989 Chi Quinn: And pretty much they got the confirmation, they’re good. I just usually… it’s funny, my definition of pending is, I want to hear their response saying, I got it, thanks, or hey, something’s wrong. But for the bottom one, for sure, they’re good to provide scan reports for accessibility, so we can move that to complete activity.
61 00:07:09.030 ⇒ 00:07:09.940 Uttam Kumaran: Okay.
62 00:07:10.090 ⇒ 00:07:12.109 Chi Quinn: Yeah, and really, the same…
63 00:07:12.110 ⇒ 00:07:29.209 Uttam Kumaran: I’m the same way. Usually, what I… what we did on our side is, like, I just want to, like, I think part of the swim lanes is, like, to indicate, like, what is the next action. So, we could even consider putting a swim lane for waiting for approval. That way.
64 00:07:29.540 ⇒ 00:07:35.829 Uttam Kumaran: It’s, like, clear that, okay, our team’s stuff is done, and then things will probably bounce back and forth between those.
65 00:07:36.320 ⇒ 00:07:53.969 Chi Quinn: Alright, and so that’s why I kept that top one pending, because although they did, they confirmed they have the report, but they also requested, like, a spreadsheet that… it was like a spreadsheet for, just a raw list of member lounge.
66 00:07:53.970 ⇒ 00:08:07.979 Chi Quinn: raw, yeah, for the member lounge data, which I did, I gave it to them, and I haven’t heard from them, so what I usually do, if I don’t hear from them, I’ll just kind of follow up with, hey, do you have any questions about, you know.
67 00:08:07.980 ⇒ 00:08:18.039 Chi Quinn: your deliverable or anything. If not, then we can move that to complete. So I’ll actually reach out to them today to just see if they’re good before we can move that.
68 00:08:18.230 ⇒ 00:08:18.940 Uttam Kumaran: Excited.
69 00:08:19.080 ⇒ 00:08:19.820 Uttam Kumaran: Okay.
70 00:08:20.860 ⇒ 00:08:32.770 Katherine Bayless: I do like the, the idea of a swim lane, potentially, too, if we had the, like, waiting for sign-off, and then, like, if we triangulate that with the priority field sort of thing, where it’s, like.
71 00:08:32.770 ⇒ 00:08:41.279 Katherine Bayless: You know, okay, well, this thing’s been waiting for sign-off, but it’s low priority, so let’s just assume it’s done, because we haven’t heard anything in, you know, two weeks, and then we could, like, kind of automate that kind of thing.
72 00:08:41.740 ⇒ 00:08:48.609 Katherine Bayless: But yeah, I have the same work file, like, pending means, like, I’m not sure that they’re done yet, but yeah.
73 00:08:48.920 ⇒ 00:08:49.570 Chi Quinn: Yep.
74 00:08:49.570 ⇒ 00:08:50.760 Uttam Kumaran: Okay.
75 00:08:50.760 ⇒ 00:08:58.699 Katherine Bayless: For the in-progress one that’s got me on that top one, that is done. Dave actually just sent it to them himself, so I was like, fine then.
76 00:08:59.850 ⇒ 00:09:01.110 Uttam Kumaran: Okay, perfect.
77 00:09:01.640 ⇒ 00:09:13.779 Chi Quinn: And for the update Power BI reports, I am still working on that. I did create it, but I might have to recreate some of the reports again, and so that will still be in progress.
78 00:09:15.410 ⇒ 00:09:23.570 Uttam Kumaran: So then what I think, like, maybe my suggestion on this, too, is, like, we should just put the due dates as, like, Fridays every week, maybe to start.
79 00:09:23.660 ⇒ 00:09:27.320 Katherine Bayless: And that way, it at least just gives us, like, okay, this is…
80 00:09:27.320 ⇒ 00:09:29.610 Uttam Kumaran: We think this is gonna get done in the next.
81 00:09:30.040 ⇒ 00:09:43.639 Uttam Kumaran: sort of sprint. And then it’s because I think, like, the… like, the narrowness of, like, oh, this will be done on Tuesday versus Wednesday, it’s, like, doesn’t help anybody, and if we put, like, Monday, and then it takes till Wednesday, then it’s, like.
82 00:09:43.780 ⇒ 00:09:50.470 Uttam Kumaran: I think it’s helpful sometimes to be like, it’s gonna be done next week. And so Friday is, like, the ultimate due date, you know?
83 00:09:51.220 ⇒ 00:09:52.370 Chi Quinn: I didn’t stare that way.
84 00:09:53.600 ⇒ 00:10:08.080 Chi Quinn: Oh, no, I was just gonna say, is there a way, like, in terms of sprints, like, could we… I mean, I… this is my really first time working with Asana. I work more so with Jira, and usually do, like, 2-3 week sprints, and we’ll have, like.
85 00:10:08.080 ⇒ 00:10:24.320 Chi Quinn: this is for, you know, the sprint, whatever, this date, or whatever, and then, meanwhile, each ticket is still kind of based on what’s due, but I do like that for… for now, at least, to just have it on Fridays for any, tickets that are…
86 00:10:24.460 ⇒ 00:10:29.940 Chi Quinn: you know, that I guess doesn’t have a specific due date, but in this case, it could just be, like, a Friday thing.
87 00:10:32.800 ⇒ 00:10:37.080 Uttam Kumaran: Yeah, I think we basically talked about just, like, trying to time box, like.
88 00:10:37.540 ⇒ 00:10:53.060 Uttam Kumaran: on Monday, you know, what do we think we can get done this week? So it’s, like, one-week sprints, but it’s… it’s a lot… it’s a lot lighter, because a two-week, three weeks, like, what I found is just, like, tasks take up the time that you basically give them, and so…
89 00:10:53.190 ⇒ 00:11:04.800 Uttam Kumaran: I think… I know we’re such a reactive team right now, so I feel like it’s, like, we get on a Monday, and we’re like, what do we think we can get out this week? And we, like, drive towards that, you know? Versus, like.
90 00:11:04.900 ⇒ 00:11:16.530 Uttam Kumaran: Okay, we have 3 weeks, and since we’re not, like, a software team, we’re not… we don’t have the luxury of planning more than 50% of our work, you know, up front, and where you can start to
91 00:11:16.950 ⇒ 00:11:18.660 Uttam Kumaran: Bake things out, you know?
92 00:11:19.480 ⇒ 00:11:37.510 Katherine Bayless: Yeah, totally agree. I do think the other piece Kai called out that’s important is, like, the due date field is available on the request form, so, like, we can set it to be the Fridays for, like, us, but then we would want, like, if the user submits something with a Tuesday or a Wednesday, we would assume that that is actually their deadline. Okay. Yeah, yeah, yeah.
93 00:11:38.220 ⇒ 00:11:38.800 Uttam Kumaran: Okay.
94 00:11:39.660 ⇒ 00:11:40.280 Katherine Bayless: But yeah.
95 00:11:41.530 ⇒ 00:11:51.110 Uttam Kumaran: So for this guy, should I just change it to next Friday? And then, yeah, maybe I can summarize some of our Asana, like, change conversation, and we can talk about it.
96 00:11:51.700 ⇒ 00:11:53.869 Chi Quinn: Yeah, yeah, that’ll be fine.
97 00:11:54.040 ⇒ 00:11:54.730 Uttam Kumaran: Okay.
98 00:11:54.830 ⇒ 00:11:55.760 Uttam Kumaran: Yeah.
99 00:11:55.760 ⇒ 00:11:58.390 Katherine Bayless: Realistically, Kai, it’ll probably go out Monday, don’t you think?
100 00:11:59.360 ⇒ 00:12:16.199 Chi Quinn: Yeah, I mean, because it’s… it shouldn’t take too long. It’s funny, I usually try to be conservative on that, because, you know, usually I’ll say, oh, it might take a couple of days, when it might actually take one day, because, of course, anything can happen in between. But yeah, I think it shouldn’t take
101 00:12:16.300 ⇒ 00:12:20.129 Chi Quinn: that long, but I say that with an asterisk.
102 00:12:20.130 ⇒ 00:12:22.050 Katherine Bayless: Yeah, exactly. Yeah.
103 00:12:22.390 ⇒ 00:12:23.560 Uttam Kumaran: Okay, cool.
104 00:12:26.960 ⇒ 00:12:37.359 Uttam Kumaran: Okay, cool, and then this is in progress, so I’m just gonna put this as next Friday, but now that it’s here, I’m like… I… because we’ve been talking about this probably, like, for 3 weeks, so now that it’s on here, I’ll just keep pushing this forward until we…
105 00:12:37.480 ⇒ 00:12:38.860 Uttam Kumaran: Finalize.
106 00:12:39.610 ⇒ 00:12:41.380 Katherine Bayless: Cool.
107 00:12:41.380 ⇒ 00:12:44.180 Uttam Kumaran: Cool. And then, this is the last one.
108 00:12:44.180 ⇒ 00:13:09.079 Chi Quinn: Yeah, and I’m still working on that. I’m going back and forth. I did start off, just looking at the user usage metrics, how many people are currently using reports, and then, of course, I mean, can’t really determine that, but also just kind of listing things that are seriously outdated, like something… 2024, CES, blah blah blah. So I’m still making… checking the list, and then kind of going with the next step, so that is still on…
109 00:13:09.080 ⇒ 00:13:13.790 Chi Quinn: kind of… Ongoing, but you can put that for next Friday, as.
110 00:13:13.790 ⇒ 00:13:25.459 Uttam Kumaran: I mean, we could even review it, like, during this meeting next Friday. Yeah. And, like, because I think part of, like, if the audit is taking a long time, it may be that, like, we should just check in and then…
111 00:13:25.580 ⇒ 00:13:31.020 Uttam Kumaran: See if, okay, if there’s anything we can start to peel off that audit and take action on, you know?
112 00:13:31.500 ⇒ 00:13:34.180 Uttam Kumaran: Because I know these can just, like, continue to just roll.
113 00:13:34.670 ⇒ 00:13:35.150 Chi Quinn: Yeah.
114 00:13:35.150 ⇒ 00:13:39.489 Uttam Kumaran: Yeah, which is kind of the intention for it, to a certain extent, because it’s like, I think…
115 00:13:39.560 ⇒ 00:13:45.910 Katherine Bayless: To your point, where, like, we are so reactive right now, like, people are definitely letting us know what they need,
116 00:13:46.770 ⇒ 00:13:53.999 Katherine Bayless: This is kind of good, like, background work of, like, alright, well, what else is out there that might not get asked about, like, soon, but we should still have.
117 00:13:54.130 ⇒ 00:14:01.549 Uttam Kumaran: Yeah, and I think, Kai, you’re, like, the first person, like, going in here and looking, and then it’s sort of like, okay, I found all this stuff, like.
118 00:14:01.760 ⇒ 00:14:08.409 Uttam Kumaran: Should we just keep going, or should we take some of it out, and it’s, like, maybe just a check, like, a checkpoint?
119 00:14:08.830 ⇒ 00:14:09.250 Chi Quinn: Yeah.
120 00:14:09.250 ⇒ 00:14:13.580 Katherine Bayless: Yeah, a little report out each week on, like, here’s my latest findings. Yeah, yeah.
121 00:14:13.580 ⇒ 00:14:14.160 Chi Quinn: Yeah.
122 00:14:14.830 ⇒ 00:14:15.230 Katherine Bayless: like that.
123 00:14:17.200 ⇒ 00:14:18.000 Uttam Kumaran: Okay, cool.
124 00:14:20.380 ⇒ 00:14:26.330 Uttam Kumaran: Okay, great. And then these are all in… yeah.
125 00:14:26.840 ⇒ 00:14:34.550 Katherine Bayless: Yeah, sorry, no, I just, I saw that top one for the Shopify thing, and I was like, I think that’s done? If you pop it open real quick, I’ll take a look.
126 00:14:37.220 ⇒ 00:14:39.740 Katherine Bayless: Oh, that’s right, that’s right, that’s right.
127 00:14:42.000 ⇒ 00:14:45.910 Katherine Bayless: Yeah, this is… this could go into, like, a backlog, honestly, kind of thing. Okay.
128 00:14:47.220 ⇒ 00:14:54.450 Katherine Bayless: Yeah, there’s a couple of these tickets that I had put in, back in, like, December, where I was like, I have emails that, like, I don’t know, but they.
129 00:14:54.450 ⇒ 00:14:55.490 Uttam Kumaran: Okay, alright.
130 00:14:55.490 ⇒ 00:14:59.540 Katherine Bayless: email. But they’re not, like, things that we’re working right now, per se.
131 00:15:00.160 ⇒ 00:15:00.950 Uttam Kumaran: Okay.
132 00:15:11.190 ⇒ 00:15:13.060 Uttam Kumaran: Something for next week?
133 00:15:13.060 ⇒ 00:15:13.800 Katherine Bayless: Mmm.
134 00:15:14.790 ⇒ 00:15:15.349 Uttam Kumaran: Thank you.
135 00:15:15.350 ⇒ 00:15:18.760 Katherine Bayless: Oh yeah, okay. This is…
136 00:15:20.740 ⇒ 00:15:33.679 Katherine Bayless: sign this one to me. I think it’s probably done, so I see that’s Adrian on it. I ended up working with Michael on this, but I don’t know that Michael and Adrian closed the loop, so yeah, if you sign it to me and make it, just make it today, because I’ll…
137 00:15:33.680 ⇒ 00:15:34.430 Uttam Kumaran: Okay.
138 00:15:34.430 ⇒ 00:15:35.640 Katherine Bayless: Yeah, yeah.
139 00:15:36.300 ⇒ 00:15:39.089 Katherine Bayless: Yeah, I didn’t realize Adriana had put a ticket in.
140 00:15:41.300 ⇒ 00:15:57.130 Uttam Kumaran: Okay. And then, yeah, I’m working on our, like, CICD. I’m gonna move this to in progress. I have a PR. Like, how we’re doing, Snowflake rolls, and then also how we’re doing, like, CICD.
141 00:15:57.520 ⇒ 00:15:59.990 Uttam Kumaran: This is probably gonna end up as, like, two…
142 00:16:00.420 ⇒ 00:16:05.700 Uttam Kumaran: pieces of documentation in the repo. So this should also be done today.
143 00:16:07.150 ⇒ 00:16:12.050 Uttam Kumaran: And then I will kind of send that into channel, and then also see if Jay wants to review that.
144 00:16:13.340 ⇒ 00:16:19.669 Uttam Kumaran: This will be… Probably Monday, so I’m just gonna mark this as Monday.
145 00:16:19.840 ⇒ 00:16:21.450 Uttam Kumaran: So, I’m gonna actually put…
146 00:16:30.320 ⇒ 00:16:31.660 Uttam Kumaran: We’re just gonna hang.
147 00:16:32.450 ⇒ 00:16:33.520 Uttam Kumaran: That’s that.
148 00:16:33.700 ⇒ 00:16:38.709 Uttam Kumaran: Any of these ones.
149 00:16:39.220 ⇒ 00:16:44.320 Katherine Bayless: Yeah, so some of these I recognize as coming from my email ones. So, like, the…
150 00:16:44.560 ⇒ 00:16:56.780 Katherine Bayless: the import Shopify data, like, via Polyatomic. That one, honestly, could just probably be deleted, because it’ll get replaced by the actual story when it comes around.
151 00:16:57.250 ⇒ 00:16:59.500 Uttam Kumaran: I could just put this in the backlog.
152 00:17:00.440 ⇒ 00:17:02.279 Katherine Bayless: Yeah, that’s true, that’s true, that’s true.
153 00:17:02.390 ⇒ 00:17:04.160 Katherine Bayless: The same.
154 00:17:04.160 ⇒ 00:17:08.759 Uttam Kumaran: Because I’ll go through all of the backlog, and then we can think about, like.
155 00:17:08.910 ⇒ 00:17:18.570 Uttam Kumaran: I guess whatever the… Epic’s, like, variant is here. I’ll probably think… start thinking about that next week.
156 00:17:18.750 ⇒ 00:17:19.940 Uttam Kumaran: I think it’s first.
157 00:17:19.940 ⇒ 00:17:24.150 Katherine Bayless: The equivalent is, like, a project, but yeah.
158 00:17:24.150 ⇒ 00:17:43.560 Katherine Bayless: Same, yeah, with the Cat F and the media team. I mean, yeah. This one, just for everybody’s curiosity, you can put it in the backlog, but basically, it came to light in the fall when we were starting some of the CES lists that, like, it seems like nobody really is curating them, like, we’re just kind of running the same lists each year, and like…
159 00:17:43.570 ⇒ 00:17:48.789 Katherine Bayless: So, I need to kind of figure out who the staff owner is, and then, like, yeah, but…
160 00:17:48.830 ⇒ 00:17:50.690 Katherine Bayless: Yeah, backlog, for sure.
161 00:17:50.790 ⇒ 00:18:00.100 Katherine Bayless: The same with the Importing Innovation Award submissions, Impexium triggers, I think, oh.
162 00:18:00.390 ⇒ 00:18:05.989 Katherine Bayless: That one, yeah, backlog is fine. It might end up being deleted, we might have found a different workaround for those.
163 00:18:05.990 ⇒ 00:18:06.570 Uttam Kumaran: Okay.
164 00:18:06.880 ⇒ 00:18:20.549 Katherine Bayless: And then the sync contract numbers between Concur and Ironclad, that can go to backlog. It might get reprioritized out of our work stream entirely, which would be fine by me, but backlog for now is fine.
165 00:18:21.610 ⇒ 00:18:32.169 Katherine Bayless: And then the enhanced request form, probably is mostly completed, but this was… Adrienne was the first person to use it, and so I had asked her, like, you know, if you have any thoughts, kind of thing.
166 00:18:37.490 ⇒ 00:18:48.630 Uttam Kumaran: Okay, cool. Yeah, I feel like we should… typically in how we’ve set up request forms in the past is, like, you basically put, like, a must-have-by date
167 00:18:48.630 ⇒ 00:19:01.489 Uttam Kumaran: Because otherwise, if you put, like, what date do you want it by, it’ll be, like, tomorrow. Like, and everybody will put P0 tomorrow. So, I almost want to put, like, what is the latest? So…
168 00:19:01.490 ⇒ 00:19:14.040 Uttam Kumaran: And then, so that’s one thing. I think also we can… once we start to get a good sense of, like, different teams, we can start to organize requests by teams.
169 00:19:14.150 ⇒ 00:19:20.080 Uttam Kumaran: And then, just like any service team, like, we’ll need to think of some type of triage process, like.
170 00:19:20.080 ⇒ 00:19:24.350 Katherine Bayless: Maybe that’s on Monday, where we go through and look at all new requests.
171 00:19:24.350 ⇒ 00:19:31.380 Uttam Kumaran: And… we should basically… I mean, we have to try to set some SLA, so for example, it’s like.
172 00:19:31.710 ⇒ 00:19:37.719 Uttam Kumaran: we don’t take midweek requests unless it’s, like, a P0 and, like, Catherine approves.
173 00:19:37.870 ⇒ 00:19:49.459 Uttam Kumaran: So, some stuff like that. Otherwise, it’s just gonna get inundated. So that’s where, like, the sprint structure works, is because you just want to avoid sprint… sprint interrupts, or, like, unplanned.
174 00:19:49.710 ⇒ 00:19:50.880 Uttam Kumaran: So…
175 00:19:51.660 ⇒ 00:19:59.949 Uttam Kumaran: that’s, like, I think we’ll just figure that out as we go. It doesn’t seem too much right now, but it will get to be more. And so.
176 00:19:59.950 ⇒ 00:20:00.430 Katherine Bayless: Yeah.
177 00:20:00.430 ⇒ 00:20:02.849 Uttam Kumaran: We want to avoid the, like, peril of, like.
178 00:20:03.000 ⇒ 00:20:08.450 Uttam Kumaran: Tuesday, Wednesday, random stuff comes, we forget what we just planned on Monday, things like that.
179 00:20:09.140 ⇒ 00:20:14.039 Katherine Bayless: Yeah, totally, and I think Kai is an excellent asset for helping with some of this stuff, too.
180 00:20:14.040 ⇒ 00:20:14.909 Uttam Kumaran: Okay, cool.
181 00:20:14.910 ⇒ 00:20:28.300 Katherine Bayless: we’re trying to, like, you know, sort of socialize this ticket behavior internally, too, and so, like, we’re definitely still getting, like, blacks and emails, but yeah, the more we can kind of funnel into a channel, click to an SLA, all, yeah, absolutely, absolutely.
182 00:20:28.410 ⇒ 00:20:30.250 Katherine Bayless: These are the goals.
183 00:20:30.250 ⇒ 00:20:37.069 Uttam Kumaran: Yeah, maybe, Kai, like, I don’t know if you have written down anywhere, like, sort of, our thoughts on, like, how we…
184 00:20:37.170 ⇒ 00:20:41.279 Uttam Kumaran: I mean, both organize, like, Asana, but also do this, like.
185 00:20:41.640 ⇒ 00:20:44.930 Uttam Kumaran: new ticket acceptance process, but, like, I…
186 00:20:44.980 ⇒ 00:20:58.150 Uttam Kumaran: I have so many opinions on this, and, like, I’ve been… we’ve just done this for a long time, so I think we can probably skip a lot of learnings, and, like, I can share what we’ve seen that’s working.
187 00:20:58.150 ⇒ 00:21:12.359 Uttam Kumaran: Of course, like, it needs to be a process that you got… that you, in particular, can own, and then you circulate internally, but I want to make sure that we can… the back end can handle it, and we have a good, like, Monday-Friday process, you know, so…
188 00:21:12.370 ⇒ 00:21:18.580 Katherine Bayless: We can talk about that today, or we can talk about that on Monday, too. Like, happy to kind of share what I know about setting these up.
189 00:21:19.610 ⇒ 00:21:20.080 Chi Quinn: Yeah.
190 00:21:20.080 ⇒ 00:21:27.910 Katherine Bayless: Yeah, maybe let’s put that on the docket for Monday, because I know we’ve… if we get into the identity stitching stuff, time will disappear out from under us. But I, yeah.
191 00:21:27.910 ⇒ 00:21:29.740 Uttam Kumaran: Okay.
192 00:21:30.140 ⇒ 00:21:30.970 Uttam Kumaran: Good luck.
193 00:21:37.010 ⇒ 00:21:39.160 Uttam Kumaran: That was kinda weird. What was that?
194 00:21:39.990 ⇒ 00:21:48.490 Uttam Kumaran: Alright. It’s like some PM just woke up and decided to put that into this enterprise product.
195 00:21:48.740 ⇒ 00:21:57.980 Uttam Kumaran: I was like, I was one of those moments where I was like, is everybody seeing the unicorn? Okay, we’re all seeing… good, good, okay. Wait, I don’t even know where that went. Did that go somewhere?
196 00:21:58.420 ⇒ 00:22:05.399 Uttam Kumaran: Alright, I don’t know… I’m just gonna ride it again, I guess.
197 00:22:05.910 ⇒ 00:22:07.960 Katherine Bayless: Yeah, bizarre.
198 00:22:10.820 ⇒ 00:22:11.900 Uttam Kumaran: I don’t know.
199 00:22:12.810 ⇒ 00:22:14.590 Uttam Kumaran: Discussion.
200 00:22:15.380 ⇒ 00:22:16.650 Uttam Kumaran: Okay.
201 00:22:16.760 ⇒ 00:22:18.770 Uttam Kumaran: And then I have not this one.
202 00:22:20.070 ⇒ 00:22:21.699 Uttam Kumaran: What did that.
203 00:22:23.450 ⇒ 00:22:24.330 Uttam Kumaran: Okay.
204 00:22:24.660 ⇒ 00:22:26.950 Uttam Kumaran: This weekend is Monday.
205 00:22:28.020 ⇒ 00:22:40.039 Uttam Kumaran: Perfect. And then, yeah, I think, basically, we… my thought, to kind of put a pin in is, like, any way people want to ask, like, get a request to us, they should be free to.
206 00:22:40.260 ⇒ 00:22:44.140 Uttam Kumaran: And then… but we should always just try to direct them to, like.
207 00:22:44.310 ⇒ 00:22:52.000 Uttam Kumaran: either create, like, they should be able to ask in Slack, and then you can say, great, like, put here’s the request flow process, or…
208 00:22:52.230 ⇒ 00:23:02.129 Uttam Kumaran: you know, you could use the right click on Slack to create this on a ticket. So, we wanted people to be open to just tag us wherever, and then it all kind of flows into here.
209 00:23:02.560 ⇒ 00:23:10.779 Uttam Kumaran: And we want to create room for, like, the P0 things, because if everything is urgent, then we will actually miss the things that are urgent.
210 00:23:10.920 ⇒ 00:23:11.940 Katherine Bayless: So…
211 00:23:12.010 ⇒ 00:23:14.480 Uttam Kumaran: Ideally, if we can arrive at, like.
212 00:23:14.960 ⇒ 00:23:18.259 Uttam Kumaran: Your request can… will be handled in the following week.
213 00:23:18.500 ⇒ 00:23:36.610 Uttam Kumaran: That would be great, and then for anything that needs to happen mid-week, we at least have an internal triage of, like, hey, this request came in, they need this by Friday, like, we get to work on that, I need help working on this, here’s how it’s gonna impact what we planned this week, you know? But we’ll… we’re gonna… I think we’ll end up with, like.
214 00:23:36.840 ⇒ 00:23:38.450 Uttam Kumaran: Probably, like.
215 00:23:38.570 ⇒ 00:23:45.630 Uttam Kumaran: 50-50 ad hoc plan stuff until we kind of, like, figure out what our splits are, you know?
216 00:23:46.170 ⇒ 00:23:49.169 Katherine Bayless: Yeah. And our team, again, is working mostly on.
217 00:23:49.200 ⇒ 00:23:55.620 Uttam Kumaran: fixed work, so we’ll even see how, like, maybe we can support some of the ad hoc, and we can kind of split time. Sorry, go ahead.
218 00:23:56.260 ⇒ 00:24:06.059 Katherine Bayless: Yeah, when I think, too, like, as we go through the year, and I know Kai’s very excited to have this, like, data coming out of the board, is, like, I think we’ll start to really see the seasonality, right? Like, once September.
219 00:24:06.060 ⇒ 00:24:06.820 Uttam Kumaran: Yes.
220 00:24:06.820 ⇒ 00:24:11.770 Katherine Bayless: everything is ad hoc, right? Because DES is coming, and panic. But then, like.
221 00:24:11.770 ⇒ 00:24:12.420 Uttam Kumaran: Yes.
222 00:24:12.420 ⇒ 00:24:21.729 Katherine Bayless: turned out to be more eventful than I was expecting, but, like, when I started in April, it was pretty quiet. So yeah, so I think we’ll start to surface seasonality. We might wind up with, like.
223 00:24:22.040 ⇒ 00:24:27.100 Katherine Bayless: you know, seasonality-aligned, SLAs to a certain extent, but yeah.
224 00:24:27.100 ⇒ 00:24:36.530 Uttam Kumaran: Totally, yeah, like, if most of our… if it’s, like, shifts to, like, 80-20, like, during CES time, then that’s what we should…
225 00:24:36.820 ⇒ 00:24:38.519 Uttam Kumaran: Like, end up doing, and then…
226 00:24:38.840 ⇒ 00:24:46.369 Uttam Kumaran: But again, you guys are… internally are the frontline workers, right? So, that’s gonna be, that’s gonna be…
227 00:24:46.950 ⇒ 00:24:52.479 Uttam Kumaran: something we just think about, like, yeah, January, and then September, post-September. I mean, we have clients that are, like.
228 00:24:52.990 ⇒ 00:25:00.399 Uttam Kumaran: like, Valentine’s Day or Black Friday becomes really similar, so we just… we intentionally, like, kind of pause a lot of, like, big changes, or…
229 00:25:00.640 ⇒ 00:25:02.230 Uttam Kumaran: Planned work in that way.
230 00:25:02.680 ⇒ 00:25:03.910 Katherine Bayless: Yeah, yeah.
231 00:25:06.970 ⇒ 00:25:08.900 Uttam Kumaran: Okay, cool, so then I think…
232 00:25:09.210 ⇒ 00:25:21.269 Uttam Kumaran: Probably, in terms of next week, on our side, we do have some, changes on models that we’re making,
233 00:25:21.330 ⇒ 00:25:34.619 Uttam Kumaran: Today, I’ll be learning a bit more about, like, how the Snowflake AI rollout looks like, and so I’ll try my best to kind of get some of that into something so that we can see if we can ticket anything out next week.
234 00:25:34.770 ⇒ 00:25:41.870 Uttam Kumaran: I’m also going to think about breaking up these into, yeah, projects. So, in our…
235 00:25:42.090 ⇒ 00:25:46.159 Uttam Kumaran: what I would probably recommend, and I think, Kai, we could talk about this on Monday, is, like.
236 00:25:46.280 ⇒ 00:26:04.339 Uttam Kumaran: typically the… it’s, like, the types of workstreams, so typically, like, okay, this is data ingestion, this is modeling, this is BI, and then maybe some… there’s, like, an ad hoc workstream. That way, on the… on the analytics side, for when you report on Asana metrics, you’ll be able to see
237 00:26:04.510 ⇒ 00:26:21.839 Uttam Kumaran: how many tickets are coming in in which work stream, because at any point, we’re gonna have some stuff that we’re ingesting, right, for new… for, like, for example, for this chat sessions from this chat… the chat vendor, but we’re also still modeling, and then some of those models are gonna go out, and then we’re gonna begin, like, BI.
238 00:26:23.490 ⇒ 00:26:29.320 Uttam Kumaran: And then, hopefully, naturally, you’ll start to see, like, okay, we end up with, like, 5 to 10 ad hoc requests.
239 00:26:29.540 ⇒ 00:26:32.760 Uttam Kumaran: And then we’re able to take 5 to 10 of these tickets.
240 00:26:33.110 ⇒ 00:26:41.549 Uttam Kumaran: I kinda… I’m sort of moving away from, like, points being, like, super, super relevant, because…
241 00:26:42.150 ⇒ 00:26:55.230 Uttam Kumaran: And the reason being, like, I don’t know, I just, like, worked in product management for a long time, like, if we just have week-long segments, then we just know that, like, a ticket is, at minimum gonna be what can be done in a week.
242 00:26:55.240 ⇒ 00:27:09.929 Uttam Kumaran: And so we instead… tickets are… not that anybody’s trying to game them, but, like, it’s just, like, another thing we don’t have to think about, which is, like, do we have to do 10 points versus 5 points, or whatever? Instead, we’re, like, the number of tickets per week.
243 00:27:09.990 ⇒ 00:27:12.889 Uttam Kumaran: And we assume that at max, one ticket is, like.
244 00:27:12.980 ⇒ 00:27:19.220 Uttam Kumaran: can be completed in one week, right? So we shouldn’t have tickets that span multiple weeks, they should get broken up.
245 00:27:19.590 ⇒ 00:27:21.360 Uttam Kumaran: Yeah.
246 00:27:21.940 ⇒ 00:27:27.849 Katherine Bayless: Yeah, this is my style, too. I have just, like, so much time spent in rooms with people arguing over…
247 00:27:28.480 ⇒ 00:27:34.480 Katherine Bayless: versus 13 points, versus I could do that in an hour, it should only be 3 points. You’re just like, oh, kill me.
248 00:27:34.480 ⇒ 00:27:44.650 Uttam Kumaran: I, yeah. So, I don’t think we need to hash that out. It’s not… it’s not even valid. I think we should… as long as the tickets are all things that can get completed in a week.
249 00:27:45.410 ⇒ 00:27:53.879 Uttam Kumaran: like, we’re gonna be fine. And it’s… we’re mostly setting the expectations for ourselves, I feel like, you know, among the organizations, so it’s something that, like.
250 00:27:54.210 ⇒ 00:28:00.559 Uttam Kumaran: I tend to be like, we understand that there is, like, principles on how to do this, but then we pick what works for us, you know?
251 00:28:00.560 ⇒ 00:28:06.499 Katherine Bayless: Yeah, yeah, yeah, exactly. I mean, we’re lucky in that we’re kind of getting to be in the designer seat for this.
252 00:28:06.500 ⇒ 00:28:06.970 Uttam Kumaran: Yes. Pipe?
253 00:28:06.970 ⇒ 00:28:14.710 Katherine Bayless: working at this organization, right? Like, we’re not coming into a bunch of established and dusty agile practices, like, we get to sort of, like.
254 00:28:15.090 ⇒ 00:28:18.379 Katherine Bayless: show people it for the first time, and then that anchors it. Yeah.
255 00:28:18.380 ⇒ 00:28:22.129 Uttam Kumaran: Like, we’re not, like, doing, like, oh, we need 3 hours for grooming, and…
256 00:28:22.130 ⇒ 00:28:22.500 Katherine Bayless: Right.
257 00:28:22.540 ⇒ 00:28:26.729 Uttam Kumaran: Like, all types of stuff, like, it’s… it’s brutal.
258 00:28:28.240 ⇒ 00:28:29.350 Uttam Kumaran: Yeah.
259 00:28:29.600 ⇒ 00:28:34.760 Uttam Kumaran: Okay, cool, so I… Big…
260 00:28:35.220 ⇒ 00:28:45.519 Uttam Kumaran: probably coming in Monday, I will get some of this organized. We do have several things on the ingestion side. We also have… majority of our work on Brainforge team is modeling, so…
261 00:28:45.550 ⇒ 00:28:58.459 Uttam Kumaran: I want to make sure we break up our modeling tickets a bit better for the membership data. I know we are still waiting for some feedback, so as soon as that feedback comes in, if it could come in as a request, could be nice, but…
262 00:28:59.010 ⇒ 00:29:04.109 Uttam Kumaran: even however we get it. I also… we talked about, like, potentially just meeting with them directly.
263 00:29:04.350 ⇒ 00:29:07.399 Uttam Kumaran: And whatever feedback we get, we can tick it out, so…
264 00:29:08.310 ⇒ 00:29:13.679 Katherine Bayless: So my plan is, we have a training with them this afternoon at 2.30, and, like.
265 00:29:13.680 ⇒ 00:29:28.950 Katherine Bayless: since we had met with them kind of yesterday, overwhelmed them delightfully, then, you know, I know today they’re gonna have, like, all the questions. And so, after the meeting today, I’ll package everything up into, like, you know, these are the nuts and bolts price changes, these are the things they’re interested in, these are the curiosities.
266 00:29:28.950 ⇒ 00:29:30.589 Uttam Kumaran: And I’ll put it in as a request.
267 00:29:30.590 ⇒ 00:29:38.560 Katherine Bayless: And then I think on an ongoing basis, I would like Anna to add you guys to the calls that we’re having on Thursday. I think they’re Thursdays at, like…
268 00:29:38.560 ⇒ 00:29:39.780 Uttam Kumaran: Great.
269 00:29:39.780 ⇒ 00:29:53.000 Katherine Bayless: whatever, with them. And then, you know, optional, if you can’t make it, totally fine. But that’s gonna be a great opportunity to just do all of these, like, working sessions around, like, we built this, does it work? We built this, does it work? We tweaked this, is that right now? Yeah, exactly.
270 00:29:53.480 ⇒ 00:30:08.839 Uttam Kumaran: Yeah, so while… for this foundational work, like, our typical MO is, like, just ship what we have in the form that we think, so that you can QA something. Because oftentimes, like, it’s gonna get stuck in translation if we’re asking about, like, what dimensions, what metrics do you need?
271 00:30:08.950 ⇒ 00:30:19.490 Uttam Kumaran: I’m like, let’s make a… we’ve learned so much about the business, let’s just make an educated guess, get out the pipelines, and then people will naturally be like, oh, I needed this column, or this column is wrong.
272 00:30:19.680 ⇒ 00:30:39.199 Uttam Kumaran: And then, for us, like, we just don’t take it, like… we don’t, take it personally. I’m like, this is gonna be wrong. And so we’re like… and so that’s why, for some clients, we’re like, no, this is, like, the process, because they don’t know how to give… sometimes the business doesn’t know how to give feedback on things like this yet. So this is the…
273 00:30:39.420 ⇒ 00:30:57.010 Uttam Kumaran: this is the thing, so for… for as many, marts as we can get into that place will then allow us to work through the feedback. But the membership scene, I think, again, is, like, the one that I don’t want to go too far on other marts until we, like, really just nail that, because
274 00:30:57.290 ⇒ 00:31:02.180 Uttam Kumaran: Even just to… we’ll learn about, like, what it is like to model within
275 00:31:02.380 ⇒ 00:31:15.560 Uttam Kumaran: you know, CTA, it’ll help improve our request process, our planning process, and then we’ll go copy-paste it to the rest of the organization, you know? Otherwise… and because we have to start maintaining that stuff, too, so…
276 00:31:15.660 ⇒ 00:31:25.049 Uttam Kumaran: We’ll get a sense of, like, our capacity in that way, and then it’ll allow us to be like, okay, if we’re gonna go support 5 other teams, some bigger, some smaller, like.
277 00:31:25.830 ⇒ 00:31:28.139 Uttam Kumaran: Can we do that, like, reliably now?
278 00:31:29.260 ⇒ 00:31:44.570 Katherine Bayless: Yeah, and I think, you know, just to kind of pull up the thread around the, like, feedback upon familiarity, that is something that I’ve… it’s been really interesting in the time that I’ve been here, because the previous sort of marketing data team that we quasi-evolved out of…
279 00:31:44.980 ⇒ 00:32:03.590 Katherine Bayless: they weren’t very friendly, as far as I can tell, and so people are, like, actually afraid to tell us that we did something that they would like differently. And I’ve actually had a few people tell me, like, to my face, like, oh, David never let me make changes, or never let me suggest edits, and I’m like, please, like, ask, right? Like, we’re trying to also, like, socialize.
280 00:32:03.590 ⇒ 00:32:11.189 Uttam Kumaran: You know, that may be… that may be a good… that may be a good place to start, though. Right, right. But then also…
281 00:32:11.190 ⇒ 00:32:28.699 Katherine Bayless: They’re used to the requests being, like, you know, first of all, prickly response, but then, like, waiting forever, and so there’s this mindset often where, and Kai got to see it, like, in full glory in this meeting earlier this week about, doing the membership journeys in Marketing Cloud, where it’s like, they want the whole thing!
282 00:32:28.720 ⇒ 00:32:40.110 Katherine Bayless: But the whole thing is really… they can’t… they haven’t really thought it through yet, and, like, as they talk, it’s starting to change, and so it’s like, this… the behaviors you’re talking about. Give them something to react to, let them say yes, no-ish.
283 00:32:40.110 ⇒ 00:32:43.219 Uttam Kumaran: We have to trigger… you have to trigger the conflicts, basically.
284 00:32:43.220 ⇒ 00:32:48.480 Katherine Bayless: Yeah, exactly. So yeah, it’s an adventure. Fun.
285 00:32:49.060 ⇒ 00:32:55.959 Uttam Kumaran: Yeah, so we’re gonna kinda get a rough sense of, like, hey, can we go end-to-end on a mart in 8 weeks?
286 00:32:56.150 ⇒ 00:33:04.330 Uttam Kumaran: Or… and then, like, we’ll see how long memberships take. We’re also, like, this one, I think we had a lot of, like, systems set up and things like that, so… Yeah.
287 00:33:04.450 ⇒ 00:33:15.739 Uttam Kumaran: For the next one, we will not have to go through setting up a lot of systems, but at least it’ll give us the ability to, like, forecast, okay, like, if we were to go end-to-end from, like, new data sources, MARTS modeling.
288 00:33:15.860 ⇒ 00:33:19.189 Uttam Kumaran: some Power BI stuff, the feedback sessions.
289 00:33:19.620 ⇒ 00:33:28.179 Uttam Kumaran: like, okay, is that… can we do that possibly in this quarter? And can we do two at a time? Like, what… you know, so that’s what I kind of really want to take this…
290 00:33:28.340 ⇒ 00:33:32.540 Uttam Kumaran: This stakeholder, you know, as a use case there, so…
291 00:33:33.320 ⇒ 00:33:49.719 Katherine Bayless: Yeah, and the other thing that’ll pay dividends, too, is, like, the stuff that membership wants and needs is gonna be, like, so reusable and extensible across the other teams we’ll wind up working with. Like, everything we’re building now will pay, like, double and triple and quadruple duty for all the other teams.
292 00:33:50.480 ⇒ 00:33:51.350 Uttam Kumaran: Cool.
293 00:33:51.550 ⇒ 00:33:52.270 Katherine Bayless: Yeah.
294 00:33:53.460 ⇒ 00:33:53.810 Katherine Bayless: Cool.
295 00:33:53.930 ⇒ 00:34:06.329 Uttam Kumaran: Okay, so that’s everything on Asana, and then, yeah, I’ll kind of plan stuff for next week. I think the only sort of, like, working session topic we had for this week was just talking about
296 00:34:06.500 ⇒ 00:34:24.590 Uttam Kumaran: the identity stitching work. Ashwini sort of was starting to work on it. I think what our process is gonna be internally is, like, we just did some exploration. It’s basically as messy as we expected it to be. And so, maybe I just wanted to briefly talk a little bit about, like, how…
297 00:34:24.659 ⇒ 00:34:32.510 Uttam Kumaran: We’re planning on talking… on tackling it. And, you know, we have some semblance of a query that we started, but…
298 00:34:33.239 ⇒ 00:34:37.880 Uttam Kumaran: maybe we could just kind of talk out loud about, like, how we’re thinking about it overall. So…
299 00:34:37.889 ⇒ 00:34:38.299 Katherine Bayless: guilt.
300 00:34:39.860 ⇒ 00:34:48.240 Uttam Kumaran: Yeah, like, the way we’ve, you know, I’ve handled this, and I think, Awash, I’m curious your thoughts. We were talking about this this morning,
301 00:34:49.070 ⇒ 00:34:58.590 Uttam Kumaran: like, we’re gonna have basically, like, a series of, like, conditional joins and conditional mappings between IDs, depending on the data source.
302 00:34:58.800 ⇒ 00:34:59.690 Uttam Kumaran: So…
303 00:34:59.800 ⇒ 00:35:13.279 Uttam Kumaran: the ways I’ve handled this in the past is, one, you try to just create a really great mapping table that may just end up being, like, a CSV or, like, a fixed table, right, that has these mappings between these orgs.
304 00:35:13.680 ⇒ 00:35:26.450 Uttam Kumaran: after this… like, and this is all just for fixing past data, right? So, there’s gonna be situations in, like, okay, if the year is this, then pull from here, the year is this, pull from here,
305 00:35:26.900 ⇒ 00:35:39.869 Uttam Kumaran: Additionally, we are seeing, for example, like, Samsung, where it’s, like, that we can’t deduce and code what to use one way or another. So, in those situations, we won’t. Like, we will have to hard code that somewhere.
306 00:35:39.890 ⇒ 00:35:59.069 Uttam Kumaran: to clean that up, and that will most likely, hopefully, end up in, like, not in a query, but in the mapping table, which is, like, this is the… this is the split. Moving forward, though, that should end up in some system, right? So we need to go find, like, what is the source of truth system where that mapping can exist? Yeah.
307 00:35:59.540 ⇒ 00:36:01.470 Katherine Bayless: Oh, check, done.
308 00:36:01.470 ⇒ 00:36:01.960 Uttam Kumaran: Yes.
309 00:36:01.960 ⇒ 00:36:14.089 Katherine Bayless: So that was, in Remembers, that… because the membership team has been doing this work, like, kind of without realizing it, so they have the domains and the IDs in that customer link and,
310 00:36:14.100 ⇒ 00:36:19.810 Katherine Bayless: Customer alias table… sorry, yeah, domains, IDs, and, like, alternate, like, company name variants.
311 00:36:19.820 ⇒ 00:36:40.309 Katherine Bayless: And so I really want to use what they’ve done already as the backbone for the kind of beginning of this, and the behavior internally for the other staff is like, oh, did your company not join correctly? Well, we probably need to add something to remember so that we have that mapping. So yeah, formalizing the storing of those mappings into remembers is what I would like to do.
312 00:36:40.310 ⇒ 00:36:40.720 Uttam Kumaran: Okay.
313 00:36:40.720 ⇒ 00:36:49.389 Katherine Bayless: it’s a good cultural move, right? Like, there’s a lot of data there for us to use, and it’s a good positioning for that team and for us in the organization.
314 00:36:50.840 ⇒ 00:36:51.610 Uttam Kumaran: Great.
315 00:36:52.460 ⇒ 00:36:53.290 Uttam Kumaran: Okay.
316 00:36:53.670 ⇒ 00:37:06.989 Uttam Kumaran: Okay, perfect. So that’s, like… I mean, I think… so there’s gonna be some type of mix of conditional joins, a mapping table. There’s also gonna be situations where we don’t have the data, so we will have to go look to enrichment to solve some of that.
317 00:37:07.000 ⇒ 00:37:17.200 Uttam Kumaran: Right, and so that’s also something that I think we’re gonna be working on a little bit of, like, a technical design document that we will show, like, for some of these situations.
318 00:37:17.200 ⇒ 00:37:27.780 Uttam Kumaran: we need to help the membership team answer that question, and that will be through some enrichment, right? So, that will be enrichment that lives in Snowflake, and then they can use it.
319 00:37:27.980 ⇒ 00:37:45.069 Uttam Kumaran: Or, like, maybe we write that back to remembers, or, like, somehow leverage that to solve some of these. Like, a lot of what this is gonna be is, like, what we call, like, waterfall enrichment. So, we are gonna pull from remembers, if it doesn’t exist, check the enrichment. If it doesn’t exist, maybe go to the manual mapping.
320 00:37:45.300 ⇒ 00:37:53.550 Uttam Kumaran: And ultimately, like, if the join doesn’t happen, we will flag that in the code to say, like, these are all the things that need fixes. So…
321 00:37:53.690 ⇒ 00:38:06.810 Uttam Kumaran: We not only will create a column that’s, like, source of the join, like, where is the logic coming from? And if there’s no logic, flag that so that that becomes, like, the thing we, like, chip at, you know, to fix everything.
322 00:38:07.330 ⇒ 00:38:14.200 Katherine Bayless: Yeah, and I think another thing that’ll work to our advantage in that phase of it is that the membership team has, like.
323 00:38:14.270 ⇒ 00:38:31.470 Katherine Bayless: they’re… they’re already doing this sort of thing. Again, it’s kind of, like, happening in different ways, but they’re used to manually, like, looking at, like, are these two things the same, and confirming it, and so if we can translate that existing sort of habit that they have into a workflow that feeds this pipeline.
324 00:38:31.470 ⇒ 00:38:42.070 Katherine Bayless: Right? Like, I think we can actually lean on them to resolve a lot of the unjoined matches, and it won’t feel like we’re asking them to do new work. It’ll probably feel like we’re asking them to do their familiar work in a better way.
325 00:38:43.210 ⇒ 00:38:43.990 Uttam Kumaran: Yep.
326 00:38:43.990 ⇒ 00:38:48.269 Katherine Bayless: The other thing that we’re gonna run into that is quirky in our data is, like.
327 00:38:48.700 ⇒ 00:38:51.460 Katherine Bayless: There are situations where…
328 00:38:51.460 ⇒ 00:39:16.119 Katherine Bayless: a company might not genuinely be two different companies, but just the way that they want to be positioned at CES, and the way that we’ve engaged with them, they are different. Amazon is a big example of this. Like, we have separate records in remembers for, like, obviously Amazon.com versus Web Services, but then also, like, Amazon Automotive, and it’s like, they’re not quite a subsidiary, but we, like, they exist.
329 00:39:16.120 ⇒ 00:39:31.000 Katherine Bayless: exhibit differently, they behave differently, and so they have a different record number. And so, yeah, so we will wind up with a little bit of that, where it’s like a quirk of CTA-specific engagement patterns, where we’re having, like, essentially two versions of the same company, but…
330 00:39:31.090 ⇒ 00:39:40.540 Katherine Bayless: again, if they’re capturing the identifiers and the mappings that they want in your members, that’s fine, because we’ll be drawing from that. Yeah.
331 00:39:40.540 ⇒ 00:39:41.170 Uttam Kumaran: Okay.
332 00:39:41.880 ⇒ 00:39:53.180 Katherine Bayless: Yeah. I had actually, had a little SageMaker, because we had to do some, lists for membership prospecting emails, coming out of some of the, like, mixers we had at CES, and so I had a little, like, notebook
333 00:39:53.180 ⇒ 00:40:04.310 Katherine Bayless: Where I was doing the, like, okay, is there a deterministic match in the existing VMexist data to either a company, or a member, or a company record that’s a non-member company? And then if not.
334 00:40:04.590 ⇒ 00:40:07.530 Katherine Bayless: Do a little bit of soft, fuzzy matching to see if you can.
335 00:40:07.530 ⇒ 00:40:08.310 Uttam Kumaran: Yes.
336 00:40:08.310 ⇒ 00:40:09.100 Katherine Bayless: it, and then.
337 00:40:09.100 ⇒ 00:40:12.169 Uttam Kumaran: No, so we’ll do as much, like, regex and, like…
338 00:40:12.170 ⇒ 00:40:14.139 Katherine Bayless: Yeah. And conditional, like.
339 00:40:14.140 ⇒ 00:40:24.059 Uttam Kumaran: string joins, we’ll do all the magic we can do, and then… I mean, additionally, we’ll try to use some of the internal AI features also to, like, basically
340 00:40:24.320 ⇒ 00:40:31.389 Uttam Kumaran: see if it can find a match. So that’s… in that way, we’re gonna have, like, all these waterfalls that we try,
341 00:40:32.680 ⇒ 00:40:37.980 Uttam Kumaran: You know, and we will understand, like, sort of what our coverage is on that join.
342 00:40:38.550 ⇒ 00:40:47.620 Katherine Bayless: Yeah, yeah. Yeah, I think the remembers data is gonna be a huge, like, jumpstart, but yeah, it’s gonna be messy. And then, when we get to people, we’ll…
343 00:40:48.300 ⇒ 00:40:55.089 Katherine Bayless: But fortunately, I think if we focus on companies and get that out the door, I think that’s a huge win. I think people can do that.
344 00:40:55.800 ⇒ 00:40:56.570 Uttam Kumaran: Yeah.
345 00:40:56.990 ⇒ 00:40:57.750 Uttam Kumaran: Okay.
346 00:40:58.210 ⇒ 00:41:02.850 Uttam Kumaran: Awish, anything else, like, we want to add on… This.
347 00:41:04.340 ⇒ 00:41:07.960 Awaish Kumar: No, I think I just wanted… what it was crazy.
348 00:41:08.050 ⇒ 00:41:25.259 Awaish Kumar: some things which Kathy already mentioned, like, I was trying to highlight if… if team is already maintaining some kind of method or doing manual work, but she already mentioned they have, like, we can just use that, like we do for other clients, and I think,
349 00:41:25.670 ⇒ 00:41:28.149 Awaish Kumar: And we can build on top of that.
350 00:41:29.540 ⇒ 00:41:41.470 Katherine Bayless: Yeah, I did put the little Zoom video in the Slack channel, where I was showing, like, what it looks like in the UI and where it is in the Snowflake, data share, for the two tables, customer link and customer alias.
351 00:41:42.580 ⇒ 00:41:43.870 Uttam Kumaran: Okay.
352 00:41:44.070 ⇒ 00:41:44.829 Katherine Bayless: I think we…
353 00:41:44.830 ⇒ 00:41:46.469 Uttam Kumaran: Customer links…
354 00:41:46.580 ⇒ 00:41:48.300 Katherine Bayless: No, Customer Link is very clean.
355 00:41:48.320 ⇒ 00:41:48.810 Uttam Kumaran: Quickly.
356 00:41:48.810 ⇒ 00:42:05.350 Katherine Bayless: customer alias, there’s probably a little room for cleanup, because they’re both, like, just text fields, so the alias label and the alias value, obviously the alias value, pretty trustworthy, but the labels aren’t terribly consistent. Like, sometimes it’s Exhibitor ID, sometimes it’s ExpoCAD ID, sometimes it’s…
357 00:42:05.350 ⇒ 00:42:21.690 Katherine Bayless: Expo space CAD ID, right? So there might be a little bit of cleanup that needs to happen on the alias labels that are in there, but I think if we come to a standard set, we could clean up the data in the members, and they would adhere to it going forward, especially if we build them a little interface to formalize the matches.
358 00:42:21.690 ⇒ 00:42:26.770 Katherine Bayless: You kind of reduce the risk of text values getting, like… Messy.
359 00:42:29.470 ⇒ 00:42:30.270 Katherine Bayless: But yeah.
360 00:42:30.540 ⇒ 00:42:31.130 Uttam Kumaran: Okay.
361 00:42:33.720 ⇒ 00:42:41.109 Uttam Kumaran: Yeah, the things that are… I’ll… the other… so that’s, like, I think what’s gonna be the majority of our week next week, is gonna be…
362 00:42:41.420 ⇒ 00:42:44.970 Uttam Kumaran: Membership, modeling feedback and identity stitching.
363 00:42:45.240 ⇒ 00:42:49.670 Uttam Kumaran: We… we… I’m gonna continue to push as much as we can on, like,
364 00:42:50.200 ⇒ 00:42:59.469 Uttam Kumaran: like, documentation on internal Snowflake and all that stuff, and CICD, but probably gonna take, like, a month to, like, round a lot of that out.
365 00:43:02.350 ⇒ 00:43:08.989 Uttam Kumaran: And so I think, like, that is sort of the… one of the side quests, and then the other side quests is all the AI pieces, so…
366 00:43:09.150 ⇒ 00:43:17.879 Uttam Kumaran: I want… I just… we’re just gonna… I’ll just keep pushing that as, like, we get time, but the modeling piece is, like, of course, like, the P0, so…
367 00:43:18.500 ⇒ 00:43:18.920 Katherine Bayless: Yeah.
368 00:43:19.190 ⇒ 00:43:23.099 Katherine Bayless: And I think, too, like, with the Power BI stuff,
369 00:43:23.670 ⇒ 00:43:30.759 Katherine Bayless: I’m feeling increasingly confident that I think we can start to really just push, push, push towards Snowflake, especially, like.
370 00:43:30.760 ⇒ 00:43:31.800 Uttam Kumaran: Cool.
371 00:43:31.800 ⇒ 00:43:36.399 Katherine Bayless: models get built, we’ll have the things available there. I think people, like.
372 00:43:36.460 ⇒ 00:43:52.869 Katherine Bayless: generally speaking, seem to be excited, just that it’s like, okay, I don’t have to go dig around in that, like, messy, old, permissions, terrible Power BI, like, dumpster fire. And so I think the more stuff we can direct people to in Snowflake, the better, and I think we’re going to be able to do it pretty fast.
373 00:43:52.990 ⇒ 00:44:04.580 Katherine Bayless: Small possibility, we wind up doing kind of a, like, a ta-da! sort of announcement in the next few weeks around Snowflake, where we would want to bring in, like, a whole bunch more users kind of thing.
374 00:44:06.400 ⇒ 00:44:08.339 Uttam Kumaran: posture ongoing. Okay.
375 00:44:08.340 ⇒ 00:44:09.790 Katherine Bayless: Okay.
376 00:44:09.930 ⇒ 00:44:10.810 Uttam Kumaran: Great.
377 00:44:11.690 ⇒ 00:44:12.899 Katherine Bayless: Death to Power BI.
378 00:44:13.720 ⇒ 00:44:14.760 Uttam Kumaran: Yes.
379 00:44:14.760 ⇒ 00:44:33.830 Katherine Bayless: But, like, it costs a lot of money, right? And so, like, if we can actually shut down that environment this year, then, like, we would look, you know, rather, rather good in the lights of the leadership, because there is a goal for the organization this year to, like, you know, save X amount of money by ending, you know, program services subscriptions that are no longer necessary, and so I’m like, well.
380 00:44:33.830 ⇒ 00:44:34.470 Uttam Kumaran: Okay.
381 00:44:34.470 ⇒ 00:44:36.989 Katherine Bayless: contribute, like, $80,000 to that.
382 00:44:37.470 ⇒ 00:44:44.089 Uttam Kumaran: Yeah, awesome, I mean, so I think Kai, it’s, like, up to you to, like, find as much as you can in there to get rid of.
383 00:44:44.320 ⇒ 00:44:48.700 Chi Quinn: Yeah. Collecting it now, so… Yeah.
384 00:44:50.570 ⇒ 00:44:55.170 Uttam Kumaran: Okay, cool. Well, that rounds out what I… what I had to chat about, so…
385 00:44:55.950 ⇒ 00:45:03.449 Katherine Bayless: Okay, cool. Oh, the only other thing that was on my mind, similar to the identity-facing stuff, the committees piece, so…
386 00:45:03.450 ⇒ 00:45:04.420 Uttam Kumaran: the…
387 00:45:04.720 ⇒ 00:45:17.310 Katherine Bayless: I think if we… I don’t… I mean, I see to engineering judgment, but, like, because right now, the only individuals that we have in the march are the ones that are, like, the primary reps for the company.
388 00:45:17.330 ⇒ 00:45:34.270 Katherine Bayless: The committee data obviously has many people in different roles, and so we will need some way, shape, or form to go from committee member to relationship to company, so that when we look at that engaging dashboard, the people that work for that company that are volunteering on the committee.
389 00:45:34.530 ⇒ 00:45:37.769 Katherine Bayless: The trick with the dates piece, I’m not…
390 00:45:37.990 ⇒ 00:45:46.030 Katherine Bayless: It may not be as tricky as I think it could be, but, like, there will be relationships start and end dates, potentially, and committee start and end dates.
391 00:45:46.260 ⇒ 00:46:01.750 Katherine Bayless: I don’t know that it’s ever happened, but I feel like the tech industry, like many, probably has a lot of people that bounce around companies, and so I could see where somebody might… I’ve, you know, been at two-member companies and been on a couple committees, but depending on when they were at the company.
392 00:46:01.870 ⇒ 00:46:10.310 Katherine Bayless: we should associate them differently kind of a thing. But I don’t… I don’t have an example of that, it’s just a, like, potential gotcha in the data.
393 00:46:10.410 ⇒ 00:46:14.220 Uttam Kumaran: That we like to be linked to more than one company.
394 00:46:14.670 ⇒ 00:46:15.500 Katherine Bayless: Potentially.
395 00:46:15.500 ⇒ 00:46:16.110 Uttam Kumaran: Okay.
396 00:46:16.350 ⇒ 00:46:17.590 Katherine Bayless: effectively, but yeah.
397 00:46:18.720 ⇒ 00:46:19.470 Uttam Kumaran: Okay.
398 00:46:20.010 ⇒ 00:46:27.830 Katherine Bayless: But yeah, I think that, and then the research downloads, and then, yeah, like, I’ll… like I said, after the meeting today, I’ll put together, like, all of the wonderful pictures.
399 00:46:29.380 ⇒ 00:46:31.140 Uttam Kumaran: Okay.
400 00:46:31.140 ⇒ 00:46:31.960 Katherine Bayless: Feeling pretty good.
401 00:46:33.060 ⇒ 00:46:33.990 Uttam Kumaran: Okay.
402 00:46:34.220 ⇒ 00:46:35.080 Uttam Kumaran: Awesome.
403 00:46:35.270 ⇒ 00:46:37.569 Uttam Kumaran: Well, thanks, everyone, for the time, appreciate it.
404 00:46:38.030 ⇒ 00:46:39.939 Katherine Bayless: Yeah, thank you guys, and happy Friday!
405 00:46:41.490 ⇒ 00:46:44.079 Uttam Kumaran: Happy Friday. Alright, I’ll talk to you guys soon.
406 00:46:45.240 ⇒ 00:46:45.960 Chi Quinn: Bye.