Meeting Title: Personal Meeting Room Date: 2025-03-05 Meeting participants: Luke Daque, Steven Kootz, Uttam Kumaran, Demilade Agboola, Bo Yoon, Robert Tseng, Caio Velasco
WEBVTT
1 00:00:20.030 ⇒ 00:00:22.020 Bo Yoon: Hey? Utah! Good morning! Hey?
2 00:00:22.020 ⇒ 00:00:22.730 Uttam Kumaran: Good morning!
3 00:00:24.290 ⇒ 00:00:25.330 Luke Daque: Hello! Hello!
4 00:00:34.870 ⇒ 00:00:36.330 Uttam Kumaran: Ryan, you follow basketball.
5 00:00:37.600 ⇒ 00:00:40.520 Luke Daque: Not so much at the moment like I.
6 00:00:40.520 ⇒ 00:00:42.500 Uttam Kumaran: I thought you were following like last year.
7 00:00:42.930 ⇒ 00:00:50.960 Luke Daque: I I do follow still, but like just on the feeds or what I can see, I haven’t really been watching any games already.
8 00:00:51.300 ⇒ 00:00:52.739 Luke Daque: But yeah.
9 00:00:53.810 ⇒ 00:00:55.550 Uttam Kumaran: Kyrie got injured yesterday.
10 00:00:55.920 ⇒ 00:01:00.429 Luke Daque: Yeah, I saw that in the in one of the feed. Yeah, that is that sucks.
11 00:01:00.430 ⇒ 00:01:01.379 Demilade Agboola: He’s out for the season.
12 00:01:01.960 ⇒ 00:01:02.970 Uttam Kumaran: Yeah. He’s out for the season.
13 00:01:02.970 ⇒ 00:01:05.710 Luke Daque: Injuries actually like when we also got injured.
14 00:01:05.710 ⇒ 00:01:06.630 Uttam Kumaran: Mv. Got injured.
15 00:01:07.130 ⇒ 00:01:14.230 Uttam Kumaran: 80 got gets injured all the time. Oh, and then Joella Bede injured.
16 00:01:14.510 ⇒ 00:01:16.360 Demilade Agboola: But it’s always it’s always injured.
17 00:01:16.540 ⇒ 00:01:20.270 Uttam Kumaran: He’s always injured, I mean, I’m a Lakers fan.
18 00:01:20.690 ⇒ 00:01:24.396 Uttam Kumaran: Oh, we’re gonna win! We’ll we’ll win this year. I feel good.
19 00:01:24.970 ⇒ 00:01:28.529 Uttam Kumaran: I’m not like a huge Lebron fan actually
20 00:01:29.757 ⇒ 00:01:33.599 Uttam Kumaran: but I like championships more than I hate Lebron.
21 00:01:35.260 ⇒ 00:01:40.520 Uttam Kumaran: and I love Luca. I I can believe that we made that trade. I like.
22 00:01:40.520 ⇒ 00:01:42.620 Demilade Agboola: So I need I nobody.
23 00:01:42.620 ⇒ 00:01:48.899 Demilade Agboola: I need the like someone to petition and just figure out like what happened. Who was held to, who was held by.
24 00:01:48.900 ⇒ 00:01:50.020 Demilade Agboola: I know. Right.
25 00:01:50.020 ⇒ 00:01:58.549 Demilade Agboola: Was there a gun involved? Was there like blackmail involved? Something was going on. I I but that trade is scandalous. It is.
26 00:01:58.550 ⇒ 00:01:59.560 Demilade Agboola: let me see, plus.
27 00:02:00.160 ⇒ 00:02:04.859 Uttam Kumaran: Yeah, it’s probably one of the worst trades in Nba history.
28 00:02:06.090 ⇒ 00:02:10.049 Luke Daque: Yeah, that’s like, I can’t even imagine like, why
29 00:02:10.500 ⇒ 00:02:13.700 Luke Daque: someone would trade the best player, you know.
30 00:02:14.190 ⇒ 00:02:16.270 Demilade Agboola: Who’s just 25.
31 00:02:16.540 ⇒ 00:02:18.650 Demilade Agboola: He’s 25.
32 00:02:18.650 ⇒ 00:02:20.189 Uttam Kumaran: Yeah, it’s insane.
33 00:02:20.190 ⇒ 00:02:26.600 Luke Daque: Even matter how like they’re saying, he’s getting too fat, or whatever like getting too heavy. But like he’s still
34 00:02:26.760 ⇒ 00:02:30.909 Luke Daque: one of the best players in the League, you know. I know it’s crazy.
35 00:02:31.240 ⇒ 00:02:35.119 Demilade Agboola: Okay, what stadium is that, though behind you, like as your background.
36 00:02:37.700 ⇒ 00:02:38.460 Caio Velasco: Mine.
37 00:02:38.850 ⇒ 00:02:39.750 Demilade Agboola: Yeah.
38 00:02:40.353 ⇒ 00:02:42.319 Caio Velasco: Yeah, because we had the
39 00:02:42.450 ⇒ 00:02:46.170 Caio Velasco: in the last last Friday they asked us to put some crazy
40 00:02:46.910 ⇒ 00:02:55.519 Caio Velasco: backgrounds. And this one is the Brazilian team, which is the the biggest in the world, I would say. Even
41 00:02:55.520 ⇒ 00:02:56.560 Caio Velasco: today, alone in my.
42 00:02:56.560 ⇒ 00:02:58.918 Uttam Kumaran: All right. Dude all right.
43 00:02:59.390 ⇒ 00:03:00.369 Caio Velasco: You love your word? Shit.
44 00:03:00.370 ⇒ 00:03:00.750 Caio Velasco: I don’t.
45 00:03:00.750 ⇒ 00:03:08.482 Caio Velasco: We have 42 million people all across the globe. So I think we are number one. But so yeah, I forgot to switch it back.
46 00:03:09.790 ⇒ 00:03:11.309 Demilade Agboola: I mean, I do know Flamengo.
47 00:03:11.530 ⇒ 00:03:14.139 Demilade Agboola: cause I’m a huge like football fan, too.
48 00:03:14.640 ⇒ 00:03:15.689 Uttam Kumaran: Who’s your team?
49 00:03:15.690 ⇒ 00:03:16.520 Uttam Kumaran: Amalade?
50 00:03:16.870 ⇒ 00:03:18.220 Demilade Agboola: Arsenal.
51 00:03:18.560 ⇒ 00:03:19.400 Uttam Kumaran: Wow!
52 00:03:19.400 ⇒ 00:03:23.010 Demilade Agboola: I’m usually I’m usually usually working at National Jersey. I have multiple.
53 00:03:23.080 ⇒ 00:03:28.910 Uttam Kumaran: I didn’t see last time, cause it was blue. I didn’t. I didn’t notice. I was like, I don’t know what the blue jersey is.
54 00:03:29.500 ⇒ 00:03:33.679 Demilade Agboola: I have blue, I have pink, I have black, I have red like. I have a number of them.
55 00:03:33.680 ⇒ 00:03:39.237 Uttam Kumaran: Okay. So now we have. We have. I don’t know. I feel like Luke. I forgot what Luke, your soccer team is.
56 00:03:39.750 ⇒ 00:03:43.500 Uttam Kumaran: Nico loves River Plate out of Argentina.
57 00:03:43.730 ⇒ 00:03:45.809 Demilade Agboola: He’s a big river plate fan.
58 00:03:46.320 ⇒ 00:03:51.070 Uttam Kumaran: I don’t really have a soccer team, you know. But yeah.
59 00:03:51.070 ⇒ 00:03:52.040 Luke Daque: Yeah.
60 00:03:52.040 ⇒ 00:03:54.000 Caio Velasco: If you pick Flamingo that you’re good.
61 00:03:54.995 ⇒ 00:03:56.669 Uttam Kumaran: Yeah, let’s see, I don’t want.
62 00:03:56.670 ⇒ 00:04:08.709 Uttam Kumaran: I need to pick someone that wins right. I can’t. I don’t care about anything but championships, but also like in soccer. The thing is like, I’ve been a lakers fan my whole life, so
63 00:04:09.260 ⇒ 00:04:23.206 Uttam Kumaran: I don’t have to like re pick and then get the hate for being a bandwagon I go to like if I pick if I’m like yo, okay, I like love man. City people would be like, what are you talking about like you just happen to love Man City now.
64 00:04:25.860 ⇒ 00:04:33.669 Uttam Kumaran: but I love watching on Amazon the behind the scenes, the those series where they go behind the series for the soccer teams. It’s amazing.
65 00:04:34.420 ⇒ 00:04:51.150 Demilade Agboola: Yes, it is. It’s like arsenal has one, and I love it so much in sense. Well, you get to see the ups and downs. You get to see the training methods. You get to see them, how they get shouted on quite a bit. Actually, like role players might be millionaires, but they really just be shouting on them.
66 00:04:52.190 ⇒ 00:04:52.940 Demilade Agboola: Oxford.
67 00:04:53.780 ⇒ 00:04:55.020 Demilade Agboola: It’s crazy.
68 00:04:58.080 ⇒ 00:05:03.868 Uttam Kumaran: Okay, cool. I just pinged a couple more people. But maybe we can get started. So today, I
69 00:05:05.160 ⇒ 00:05:08.895 Uttam Kumaran: I really just wanted to talk through stuff for
70 00:05:09.450 ⇒ 00:05:13.500 Uttam Kumaran: I guess 3 clients. And then I wanted to share one thing that I’m working on for
71 00:05:14.662 ⇒ 00:05:29.449 Uttam Kumaran: an upcoming client. So for Javi. Yeah, I know Robert is currently reviewing all the dashboards we we’re up for renewal. With them on Monday. So he’s preparing
72 00:05:29.560 ⇒ 00:05:33.269 Uttam Kumaran: sort of that proposal. We did a lot in the last 2 weeks.
73 00:05:33.610 ⇒ 00:05:44.830 Uttam Kumaran: like, I think, last week, we came in as like, okay, we want to get like 4 dashboards out. 2 of them have, like no data. 2 of them are pretty complicated. We basically are there.
74 00:05:46.570 ⇒ 00:05:54.280 Uttam Kumaran: so I’m extremely excited that we kind of set Robert up as best as we could for for that conversation. I think he has a couple of
75 00:05:54.480 ⇒ 00:05:56.389 Uttam Kumaran: comments, but I know
76 00:05:56.650 ⇒ 00:06:04.039 Uttam Kumaran: alright. We’ll speak of the devil, maybe. Robert. Yeah, I was just talking about Javi. I don’t know. I think it could be. I think it could be helpful
77 00:06:04.200 ⇒ 00:06:05.910 Uttam Kumaran: for this crew to just see
78 00:06:06.390 ⇒ 00:06:11.409 Uttam Kumaran: how you’re going through feedback and stuff. Cause I I mean, I’m gonna go through normal tickets. But like.
79 00:06:11.620 ⇒ 00:06:16.239 Uttam Kumaran: I kind of like the way you’re breaking things down, and I think it’s helpful for the Aes to see
80 00:06:16.350 ⇒ 00:06:20.700 Uttam Kumaran: what you’re looking like. What you’re looking at in the dashboards. I don’t know is that too much to ask? Do you feel.
81 00:06:20.995 ⇒ 00:06:27.499 Robert Tseng: That’s cool. I do have to jump off in a bit because I’m talking to segment in like 20 min again.
82 00:06:27.500 ⇒ 00:06:29.770 Uttam Kumaran: Okay, yeah, that’s fine. And then I can.
83 00:06:29.770 ⇒ 00:06:31.400 Robert Tseng: Yeah, okay, cool.
84 00:06:34.430 ⇒ 00:06:39.183 Robert Tseng: Well, I suppose I already wrote everything out, so I’ll just share my screen.
85 00:06:40.240 ⇒ 00:06:48.049 Uttam Kumaran: I like this, I mean, and I I have a conversation for the back half of this meeting, too, that we can go through. So I think this is this is really helpful for the aes and the analysts. So.
86 00:06:48.490 ⇒ 00:07:17.490 Robert Tseng: Okay, cool. Yeah. So I mean, I’ll just start with the gorgeous status I was looking@thatfirstst So yeah, this is kind of what pie is built out I already made a couple of changes. I just felt like every row, for a pie was like kind of unnecessary like I feel like we could just have like 2 2 per row, that I think it’s a bit more compact and easier for them to scan through. And then, yeah, I think, even though this is a good view of aggregate breakout. There’s no time component to this, so I can’t really tell like.
87 00:07:18.030 ⇒ 00:07:31.009 Robert Tseng: what’s the trend of general inquiry tickets like over time? So that’s kind of what I asked for here. Kind of a stacked bar chart by just like contact category. I guess reason for contact category.
88 00:07:31.890 ⇒ 00:07:41.980 Robert Tseng: because right now all we see is an overall weekly ticket aggregation. But I don’t know like what types of tickets are really contributing to that. So I think, since he broke out the
89 00:07:43.710 ⇒ 00:07:51.550 Robert Tseng: the tickets by a few different sections, thought for each section you could have like a version of this, but probably it’s stacked bar chart form
90 00:07:52.245 ⇒ 00:07:55.259 Robert Tseng: and then I feel like you can probably just
91 00:07:56.180 ⇒ 00:08:00.799 Robert Tseng: make these other sections more compact. So that was the feedback on that tab
92 00:08:01.030 ⇒ 00:08:14.089 Robert Tseng: on the agent performance one. I think. Yeah. Overall like good to see the spread. But no aggregates here have no idea like what the benchmark is. I mean, you could probably.
93 00:08:14.850 ⇒ 00:08:17.010 Robert Tseng: Yeah, we should probably sort. I mean.
94 00:08:17.010 ⇒ 00:08:17.630 Uttam Kumaran: Likewise.
95 00:08:18.230 ⇒ 00:08:20.369 Robert Tseng: I think I’m okay with it, being
96 00:08:20.670 ⇒ 00:08:27.440 Robert Tseng: like the alphabetical, because that way, like, you can just tell Sue’s performance like going all the way down, because if you sort it, then different.
97 00:08:27.440 ⇒ 00:08:28.260 Uttam Kumaran: Oh!
98 00:08:28.260 ⇒ 00:08:30.830 Robert Tseng: Like the order will be off. So I’m not.
99 00:08:30.830 ⇒ 00:08:34.530 Uttam Kumaran: I would. I would guess I would take the other side of that, because I don’t know who’s like.
100 00:08:34.740 ⇒ 00:08:38.000 Uttam Kumaran: You just want to know the winners and losers like everybody in the middle.
101 00:08:40.150 ⇒ 00:08:40.640 Uttam Kumaran: I see.
102 00:08:41.150 ⇒ 00:08:43.450 Uttam Kumaran: Yeah, I mean, I I could see that.
103 00:08:43.620 ⇒ 00:08:50.880 Uttam Kumaran: Yeah. Like to give you an example, like we worked on a lot of stuff for like sales performance Asian performance stuff. The the things that the
104 00:08:50.980 ⇒ 00:08:56.060 Uttam Kumaran: the execs or the managers are looking at is like, who’s doing really good? Who’s doing really bad?
105 00:08:56.840 ⇒ 00:09:01.870 Uttam Kumaran: And then when they find someone like Sue, they will find Sue on every chart.
106 00:09:03.100 ⇒ 00:09:05.700 Uttam Kumaran: or you can filter to sue at the top.
107 00:09:05.940 ⇒ 00:09:10.983 Robert Tseng: Exactly. So. That’s also like something that’s missing here. Just like a filter by
108 00:09:12.630 ⇒ 00:09:14.230 Luke Daque: It is like a knowledge that
109 00:09:14.230 ⇒ 00:09:19.680 Luke Daque: horizontal bar chart instead, and then sorted from high to low, or something like that.
110 00:09:19.680 ⇒ 00:09:21.850 Uttam Kumaran: Yeah, that could also be better.
111 00:09:21.850 ⇒ 00:09:22.590 Robert Tseng: Yeah.
112 00:09:23.620 ⇒ 00:09:28.639 Uttam Kumaran: This is good, though. I mean, this is actually probably much more context than I’ve ever had. I’m excited, I think
113 00:09:29.120 ⇒ 00:09:42.019 Uttam Kumaran: I mean, like it would be helpful to actually like, come to that with like, these guys are doing really well. You should try to replicate whatever they’re doing. These guys are really doing poorly, we should try to have a conversation with them.
114 00:09:42.320 ⇒ 00:09:44.540 Uttam Kumaran: I don’t know if they’ve ever had that opportunity.
115 00:09:44.900 ⇒ 00:09:45.660 Robert Tseng: Yeah.
116 00:09:46.459 ⇒ 00:09:52.200 Robert Tseng: and then there’s this like pending section and not really sure what was outstanding here. So we’re just calling that out as well.
117 00:09:55.390 ⇒ 00:09:56.330 Robert Tseng: okay.
118 00:09:57.260 ⇒ 00:10:00.339 Robert Tseng: So that’s that. I think.
119 00:10:01.750 ⇒ 00:10:10.999 Robert Tseng: Yeah, I mean, I have some other thoughts on, like, what else would go into this. But I don’t really want to complicate if we can just ship it out. I think what I’ve seen also is like.
120 00:10:12.130 ⇒ 00:10:20.740 Robert Tseng: Yeah, you you have, like your your overall, your your aggregates like on what the actual performance is. But then you have your targets, too, so that you know, like.
121 00:10:20.920 ⇒ 00:10:26.620 Robert Tseng: hey, like if overall I don’t know, satisfaction is that like
122 00:10:27.480 ⇒ 00:10:35.749 Robert Tseng: 3.5, and our target is 4.5 like sense of like, how far off they are. So
123 00:10:37.380 ⇒ 00:10:38.130 Robert Tseng: yeah, I think.
124 00:10:38.130 ⇒ 00:10:40.220 Uttam Kumaran: Have, like a horizontal line, or something.
125 00:10:40.220 ⇒ 00:10:40.850 Robert Tseng: Yeah.
126 00:10:41.510 ⇒ 00:10:47.999 Robert Tseng: showing like, where the where the benchmark is. But that’s okay. They didn’t give us benchmarks. So I’m not expecting piles to really come up with that.
127 00:10:49.840 ⇒ 00:10:51.459 Robert Tseng: Yeah. Macros.
128 00:10:53.430 ⇒ 00:11:02.120 Robert Tseng: yeah, I think. The trend section is once again kind of missing like a time component to this. This is just like all time. So I think it’d be cool to have like a
129 00:11:02.280 ⇒ 00:11:19.539 Robert Tseng: stack bar weekly kind of breakout, so you can kind of know which macros are being used, and what volumes over time. And then I just called out, like I love, I love this chart. I feel like we have. We should be using stuff like this more like I could quickly tell here that like damage, athletic bottle is like.
130 00:11:20.034 ⇒ 00:11:40.110 Robert Tseng: very like lengthy handle, or whatever or like it, just it requires an agent for for most of the issues that come up. So if there’s like a few spikes here where you can see that their macros are not like doing enough or like. I think that I don’t know what the volumes are here. I think it’s quite low. It’s only like
131 00:11:40.580 ⇒ 00:11:48.290 Robert Tseng: 8 compared to maybe like 454. So maybe this doesn’t give us the best sense of volume.
132 00:11:48.490 ⇒ 00:11:55.360 Robert Tseng: but I think it is sorted by volume. Right? No, it’s not so. Actually, we should probably sort this one by volume.
133 00:11:59.600 ⇒ 00:12:13.830 Demilade Agboola: Also, is there a way we can order it in terms of, if you want to kind of see from the highest, like ranking like agents only, or highest, ranking Macros only, so that that kind of like adjust this chart.
134 00:12:16.710 ⇒ 00:12:34.449 Robert Tseng: Yeah, I mean, yeah, I guess you just have to sort it in different ways. I would prefer them to sort it by like total ticket volume because you don’t really care too much about something that only has 8 tickets, I suppose. But you would want to know how like, you know, the biggest ones are doing.
135 00:12:36.830 ⇒ 00:12:37.920 Robert Tseng: By.
136 00:12:43.430 ⇒ 00:12:49.110 Robert Tseng: Okay? Yeah. And then
137 00:12:52.670 ⇒ 00:13:00.560 Robert Tseng: I mean, generally similar feedback on the having, like a clear.
138 00:13:01.680 ⇒ 00:13:05.620 Robert Tseng: both aggregate performance benchmark, benchmarking thing here.
139 00:13:06.663 ⇒ 00:13:09.520 Robert Tseng: Ticket actions, I think.
140 00:13:09.910 ⇒ 00:13:14.790 Robert Tseng: Yeah, this one’s pretty straightforward. I mean, he could add the same tiles. But
141 00:13:16.170 ⇒ 00:13:26.969 Robert Tseng: yeah, anyway, I think, like the the overall pieces are all here. So I think I’m I’m okay with with sending it to them that we need to clean it up or whatever we can. But
142 00:13:27.410 ⇒ 00:13:31.259 Robert Tseng: yeah, I think at least we have all the core pieces there.
143 00:13:31.870 ⇒ 00:13:40.909 Uttam Kumaran: I had a couple of questions. And you know, we’re sort of in this like playbook and building moment. So thinking a lot about process, so.
144 00:13:40.910 ⇒ 00:13:41.550 Robert Tseng: Yeah.
145 00:13:42.000 ⇒ 00:13:51.119 Uttam Kumaran: Do you have like? And this is probably in your brain, or we can all sort of have a discussion, but I think it’d be helpful to have conventions around rounding
146 00:13:51.230 ⇒ 00:13:54.609 Uttam Kumaran: sort of like what chart types to use in different scenarios
147 00:13:54.750 ⇒ 00:13:58.160 Uttam Kumaran: like, do you sort of have high level, like
148 00:13:58.390 ⇒ 00:14:03.120 Uttam Kumaran: sort of thoughts on that, because I think we could make those pretty standardized and
149 00:14:05.610 ⇒ 00:14:15.979 Robert Tseng: Yeah, I mean, I would say that like, don’t the decimal points just show one like one decimal. And then anything that’s more than a million like no decimal, and just go
150 00:14:16.120 ⇒ 00:14:23.159 Robert Tseng: and just or actually anything in the thousands. I think you just you should go. No, no decimal just go whole whole numbers.
151 00:14:23.300 ⇒ 00:14:24.899 Robert Tseng: And then.
152 00:14:26.870 ⇒ 00:14:34.089 Robert Tseng: yeah, I mean, that’s like the stylistic thing. But as far as like which which charge? For which use? Case? Yeah, I mean I I don’t.
153 00:14:34.920 ⇒ 00:14:35.870 Robert Tseng: I think you kind of.
154 00:14:35.870 ⇒ 00:14:36.779 Uttam Kumaran: Too granular. Yeah.
155 00:14:36.780 ⇒ 00:14:49.109 Robert Tseng: That’s a bit too granular I mean, for aggregate breakouts like any of the pies are great, but then these you don’t get to see the time component here. So anytime you want to see a time component that’s break broken out, you should probably use a stacked bar
156 00:14:49.753 ⇒ 00:15:05.870 Robert Tseng: and then for like, if you’re comparing multiple kpis or metrics that are moving on the same chart, then we should use lines. But if it’s just singular. And we’re doing breakouts. Then I think bars typically make more sense.
157 00:15:07.970 ⇒ 00:15:08.820 Robert Tseng: So
158 00:15:10.530 ⇒ 00:15:26.150 Robert Tseng: yeah, like, this one is a single metric bar makes sense. Because then you can kinda see, like the visually the difference. And then you also get the number like I wouldn’t. I wouldn’t present this as a line, because that’s misleading. It looks like a trend, but it’s not actually trend. We’re just looking at
159 00:15:26.450 ⇒ 00:15:27.399 Robert Tseng: scores. Yeah.
160 00:15:27.400 ⇒ 00:15:34.496 Uttam Kumaran: For these like, unless alphabetical is like important, they should all be sorted for sure.
161 00:15:35.450 ⇒ 00:15:52.789 Uttam Kumaran: The other thing I had is like, do you have an I do. You have sort of an idea about every dashboard sort of starting with a summary definitions like, there’s a couple of things one I think I I really think that we should put like looms at the top of the dashboard, which is like, watch this on how to use this dashboard right?
162 00:15:54.700 ⇒ 00:16:02.810 Uttam Kumaran: like. I think that could be helpful. I think this is a really good example of like there’s a summary of like, if you, if you care about gross margin.
163 00:16:03.000 ⇒ 00:16:07.970 Uttam Kumaran: and you have 10 seconds here it is. The rest is sort of
164 00:16:08.220 ⇒ 00:16:12.589 Uttam Kumaran: breakdown, right? And yeah, I think that’s also a really good principle, because
165 00:16:14.210 ⇒ 00:16:20.540 Uttam Kumaran: they people will not look at stuff if they don’t understand it. In fact, it’ll come back to hurt us
166 00:16:20.900 ⇒ 00:16:29.610 Uttam Kumaran: if they don’t really get what the purpose of this is, and I think this is a really good example of having big numbers, as like a principle at the top as a summary, and then, having the definitions there.
167 00:16:30.900 ⇒ 00:16:38.709 Robert Tseng: Yeah. So I built this kind of like redesigning the previous like gross margin one like, I guess maybe you guys, I don’t know who’s seen
168 00:16:39.050 ⇒ 00:16:45.156 Robert Tseng: this yet? But probably just blow it out
169 00:16:45.720 ⇒ 00:17:00.460 Robert Tseng: Yeah. So like the right was kind of like what it was before. It was just like a bunch of like scattered charts everywhere. And then I kind of just I restructured it. Add, I always add key definitions, assumptions at the top, especially if the metric calculations are not very straightforward.
170 00:17:00.828 ⇒ 00:17:28.600 Robert Tseng: And then, yeah, I don’t like to include equations in here and stuff like that. Like, I just, I feel like it’s a bit cleaner to have clear section headings. I mean, this is more of a financial metrics chart. So I actually didn’t think it was that helpful to review it in in terms of lines that I think they they want to see like the pivot table breakout, because the guy who’s using. This is the Cfo so and then, yeah, anyway. So I I kind of you guys.
171 00:17:28.730 ⇒ 00:17:30.469 Robert Tseng: yeah, I feel like I.
172 00:17:31.830 ⇒ 00:17:32.520 Robert Tseng: But I am.
173 00:17:32.520 ⇒ 00:17:38.650 Uttam Kumaran: The audience. Matters, too, like the Cfos are not gonna want to see bar charts and stuff. Everything’s gonna be table format or.
174 00:17:38.650 ⇒ 00:17:39.040 Robert Tseng: Yeah.
175 00:17:39.040 ⇒ 00:17:41.599 Uttam Kumaran: Bars, basically or lines.
176 00:17:42.530 ⇒ 00:17:42.920 Robert Tseng: Yeah.
177 00:17:42.920 ⇒ 00:17:44.120 Uttam Kumaran: Yeah, okay.
178 00:17:45.190 ⇒ 00:17:48.499 Robert Tseng: And then I think the last thing I’ll say I think
179 00:17:49.430 ⇒ 00:17:57.742 Robert Tseng: we had already. Yeah, I was. I gave feedback on those review charts as well. Yeah, I mean, this is much better. I think there’s clear clear
180 00:17:58.900 ⇒ 00:17:59.660 Robert Tseng: like
181 00:17:59.900 ⇒ 00:18:06.550 Robert Tseng: aggregates up here. I think. Just ask. I was just asking for some cleanup on like the labeling like a non.
182 00:18:06.550 ⇒ 00:18:11.319 Robert Tseng: yeah, user is not gonna be able to understand distinct values of this id.
183 00:18:11.600 ⇒ 00:18:16.539 Uttam Kumaran: Yeah, I gave a lot of feedback on that, too, in the fig jam on basically, all of the
184 00:18:16.700 ⇒ 00:18:24.240 Uttam Kumaran: axes being like super clear and consistent, like number of reviews, number of reviews, number of reviews rounding like
185 00:18:24.540 ⇒ 00:18:25.130 Uttam Kumaran: frowning.
186 00:18:25.130 ⇒ 00:18:25.510 Robert Tseng: Yeah, right?
187 00:18:25.510 ⇒ 00:18:27.830 Robert Tseng: Yeah, not need to be, yeah. So.
188 00:18:28.290 ⇒ 00:18:32.711 Uttam Kumaran: Exactly. And if you scroll down here, too, yeah, I think like,
189 00:18:33.810 ⇒ 00:18:43.060 Uttam Kumaran: yeah, we could just do like Avg rating like, it’s basically like trying to cut half the text because the text takes a lot of space and
190 00:18:43.496 ⇒ 00:18:49.187 Uttam Kumaran: really is not like super important. They need to get what the what the axes are really quickly
191 00:18:50.680 ⇒ 00:18:55.889 Uttam Kumaran: and then also for charts like the weather pieces like for charts like this, where all the values are really close.
192 00:18:56.250 ⇒ 00:18:58.899 Uttam Kumaran: It’s not a useful chart, meaning
193 00:18:59.530 ⇒ 00:19:02.769 Uttam Kumaran: what’s useful is to see these are lower.
194 00:19:03.160 ⇒ 00:19:06.249 Uttam Kumaran: And by what factor are they lower? And what’s the contribution?
195 00:19:06.450 ⇒ 00:19:07.250 Uttam Kumaran: Right?
196 00:19:08.010 ⇒ 00:19:17.549 Uttam Kumaran: If, if, like a random person comes in and sees it, they’re like, Okay, cool. We’re doing great. But that’s actually not what we’re trying to get across. We’re trying to get across. Wow, Creamer and free gifts have
197 00:19:17.890 ⇒ 00:19:33.939 Uttam Kumaran: some percent lower. And we want to look at the con contribution to that, like what is affecting that on what? What was it? A certain product was a certain shipment of products, right? And this is a very similar thing that we had in pool parts. And I’ll be talking Bo to you, and ice about this is
198 00:19:34.240 ⇒ 00:19:41.519 Uttam Kumaran: they may have bad products shipped from a bad shipment, but it’s hard for them to identify and go talk to the manufacturer, get a refund for that
199 00:19:41.800 ⇒ 00:19:43.230 Uttam Kumaran: without this data.
200 00:19:43.619 ⇒ 00:19:47.979 Uttam Kumaran: But this is a great example of like on initial look, you’re like great. This is all fine. But
201 00:19:48.512 ⇒ 00:19:53.109 Uttam Kumaran: we want to sort of highlight the differences. Same thing here. If you were to. Just look at this.
202 00:19:53.350 ⇒ 00:19:54.989 Uttam Kumaran: It’s 10 numbers.
203 00:19:55.460 ⇒ 00:20:05.739 Uttam Kumaran: We we all work with numbers here. So we’ll we can quickly analyze this. But think about someone who doesn’t. We want to highlight the stuff through conditional formatting ideally right on both on both.
204 00:20:06.820 ⇒ 00:20:16.480 Uttam Kumaran: So as much of those second layer using color to sort of highlight, what actually matters is so important. And for charts like this, where there’s no action.
205 00:20:16.710 ⇒ 00:20:18.669 Uttam Kumaran: get rid of it, I think.
206 00:20:18.990 ⇒ 00:20:22.980 Uttam Kumaran: unless we want to show this. But, like, if there’s no action here. Then
207 00:20:23.150 ⇒ 00:20:26.349 Uttam Kumaran: it’s gonna stay like this, right? They’re always hitting 4, 9,
208 00:20:26.620 ⇒ 00:20:28.750 Uttam Kumaran: probably always going to keep hitting. 4, 9.
209 00:20:29.815 ⇒ 00:20:30.670 Uttam Kumaran: Yeah.
210 00:20:30.930 ⇒ 00:20:32.850 Bo Yoon: This wasn’t like this.
211 00:20:34.000 ⇒ 00:20:45.200 Bo Yoon: I I think the the update that you push yesterday. I think that changed something in the data set, only showing approved reviews.
212 00:20:46.440 ⇒ 00:20:47.360 Uttam Kumaran: Oh, okay. Okay.
213 00:20:47.360 ⇒ 00:20:48.359 Robert Tseng: For this section.
214 00:20:49.230 ⇒ 00:20:51.099 Bo Yoon: Well, yeah, look like there’s no work pending.
215 00:20:51.100 ⇒ 00:20:55.010 Bo Yoon: I just checked the data, the the data set, and it only has approved.
216 00:20:55.010 ⇒ 00:21:00.370 Uttam Kumaran: Okay. But like you get my point, I guess. Slack me about that. I’ll go fix that. But like you get my point.
217 00:21:00.560 ⇒ 00:21:01.150 Bo Yoon: Yeah.
218 00:21:01.320 ⇒ 00:21:03.730 Uttam Kumaran: But I see I see what you mean. That’s probably affecting this. But like.
219 00:21:04.230 ⇒ 00:21:08.039 Uttam Kumaran: that’s the sort of stuff I think we want to start to get in the head of the client, and
220 00:21:08.920 ⇒ 00:21:10.400 Uttam Kumaran: and tell the story here
221 00:21:11.660 ⇒ 00:21:19.009 Uttam Kumaran: and ideally again when you wake up in the morning, and I can tell you, because this is how I wake up in the morning. You want to know what’s what’s really going well.
222 00:21:19.230 ⇒ 00:21:21.089 Uttam Kumaran: and what’s really not going well.
223 00:21:21.660 ⇒ 00:21:23.790 Uttam Kumaran: everything in the middle is noise.
224 00:21:24.330 ⇒ 00:21:28.290 Uttam Kumaran: And we want to help these guys find out what is parse, the noise
225 00:21:31.540 ⇒ 00:21:33.899 Uttam Kumaran: cool. Yeah, I love this. This is really helpful.
226 00:21:35.570 ⇒ 00:21:42.420 Robert Tseng: Cool. Yeah, I mean, we’ll we’ll keep getting better, I mean, I know, like there’s different to me. These are just drafts like, so I wasn’t like super
227 00:21:42.750 ⇒ 00:21:48.479 Robert Tseng: critical of it. But no, I think you’re you’re right. I think this is this is the right. These are the conversations we need to be having
228 00:21:48.923 ⇒ 00:22:15.576 Robert Tseng: whether it’s in a dashboard or in a deck, like the output of our work is in the insights that we show. And so whatever we build needs to be very intentional and like, we have to have a clear takeaway from the client. Help them interpret what they’re seeing. So yeah, I mean, we’re I’m we have the. We have a check in with Aman in 30 min. And probably I mean on that call. I’m gonna walk him through some of these dashboards and make some call outs, and
229 00:22:15.990 ⇒ 00:22:21.779 Robert Tseng: yeah. So I understand that we’re not gonna make all the changes right now, which is fine. I don’t think he has really an eye for it, either.
230 00:22:22.149 ⇒ 00:22:36.170 Robert Tseng: I I think he just wants to know that we’ve gotten ship stuff out the door. The main consumers are, gonna be, you know, Justin, the Ops, the Ops team, and and the other non technical folks. So
231 00:22:36.456 ⇒ 00:22:53.340 Robert Tseng: I think the changes that we’re asking for now is not really for the audience that I’ll be presenting to in 30 in 30 min. But yeah, after after that, we’re gonna have to share the links by end of day. So hopefully, we can quickly make some of these adjustments and and get this shipped out.
232 00:22:53.910 ⇒ 00:22:58.279 Uttam Kumaran: Okay, cool. And yeah, Bo, if you just want to send, send a note in the Javi Channel with that
233 00:22:58.610 ⇒ 00:23:01.630 Uttam Kumaran: issue, I can take care of it in the next few hours.
234 00:23:04.290 ⇒ 00:23:04.790 Uttam Kumaran: Okay.
235 00:23:04.790 ⇒ 00:23:08.159 Robert Tseng: Cool. Alright. I’m gonna jump off. I gotta get to the other call. But
236 00:23:08.400 ⇒ 00:23:08.850 Uttam Kumaran: Okay.
237 00:23:08.850 ⇒ 00:23:10.190 Robert Tseng: Yeah, alright. We’ll see. You guys.
238 00:23:10.190 ⇒ 00:23:10.860 Uttam Kumaran: Thank you.
239 00:23:14.230 ⇒ 00:23:16.379 Uttam Kumaran: Cool. What do we think about that guys.
240 00:23:19.750 ⇒ 00:23:29.039 Luke Daque: Yeah, I think they’ll just great as well like having a standard way to do data visualizations.
241 00:23:29.660 ⇒ 00:23:33.340 Luke Daque: Cause I I think we don’t have that that it looks like the moment.
242 00:23:37.380 ⇒ 00:23:45.460 Uttam Kumaran: Yeah, I think part of it is like, if you were running when you make the dashboard, it’s like, if you are running a customer service organization. What would you want to see?
243 00:23:46.210 ⇒ 00:23:51.230 Uttam Kumaran: Right? And I think this is where a lot of data people fall flat because we get to the point where everything’s
244 00:23:51.800 ⇒ 00:23:53.960 Uttam Kumaran: there. Everything’s modeled.
245 00:23:54.070 ⇒ 00:23:56.109 Uttam Kumaran: Everything’s available in the bi tool.
246 00:23:56.520 ⇒ 00:24:07.430 Uttam Kumaran: And then we then we missed the Pk right at the end, right like it’s it’s it’s hard, because 80, like I would say 60 to 80% of the work happens beforehand.
247 00:24:08.250 ⇒ 00:24:14.230 Uttam Kumaran: probably 30, 30, 30 between DA, you know, but like and at the end.
248 00:24:14.490 ⇒ 00:24:20.759 Uttam Kumaran: But the problem is, the client only sees that portion right? And that’s what makes this problem extremely tough
249 00:24:20.900 ⇒ 00:24:26.730 Uttam Kumaran: is that we have to make the sausage, but the people just eat it at the restaurant, and we’re sort of
250 00:24:27.310 ⇒ 00:24:30.549 Uttam Kumaran: it matters. It matters that it looks good. It’s plated. Well.
251 00:24:31.020 ⇒ 00:24:35.960 Uttam Kumaran: everything matters actually right? But I but.
252 00:24:35.960 ⇒ 00:24:38.380 Luke Daque: Yeah, this is like, yeah, go ahead.
253 00:24:38.380 ⇒ 00:24:44.359 Luke Daque: I see. And then, yeah, that’s what the client is like interacting with. So they don’t really
254 00:24:45.330 ⇒ 00:24:50.880 Luke Daque: care much about the the things going on back end. They they just see the final product. Yeah.
255 00:24:52.090 ⇒ 00:25:03.610 Demilade Agboola: I also think it might help to also identify the audience for every single like dashboard and report, because audiences react and interact with dashboards differently.
256 00:25:03.730 ⇒ 00:25:10.860 Demilade Agboola: So the way manager interacts is different from like the manager of the team interacts is different from how C-suites will interact.
257 00:25:11.400 ⇒ 00:25:15.429 Demilade Agboola: You know, the C-suite don’t necessarily care about like the granular details. Just wants to know.
258 00:25:15.710 ⇒ 00:25:28.659 Demilade Agboola: Is it going well? Is it going badly? But the manager really wants to know who is doing badly, and you know who is doing great that sort of thing. So I think, like also having that in mind when building out the dashboards could be really helpful.
259 00:25:30.100 ⇒ 00:25:34.119 Uttam Kumaran: I guess, Demilla, did you have an example from your past of like
260 00:25:34.550 ⇒ 00:25:42.480 Uttam Kumaran: a really amazing dashboard delivery process, or like where you saw this go off without a hitch
261 00:25:42.600 ⇒ 00:25:48.489 Uttam Kumaran: like you? Is there a dashboard in mind? Cause? I I mean, I have a couple from like years ago.
262 00:25:48.940 ⇒ 00:25:57.190 Uttam Kumaran: This is a really hard thing, though, like it’s not many people solve this, you know, but just curious.
263 00:25:58.140 ⇒ 00:26:19.609 Demilade Agboola: I wouldn’t say dashboard delivery process, but I do think in terms of the formulation of how we wanted to build the dashboard. We kind of would have like an understanding of what are the kpis like? What really matters, what drives growth or what drives the you know, the you know the desired growth?
264 00:26:20.240 ⇒ 00:26:27.510 Demilade Agboola: And 2, what’s the audience? So that allowed us to be able to know? Is this being used by, you know
265 00:26:27.640 ⇒ 00:26:37.180 Demilade Agboola: the managers, the, you know, regular staff who need to see the level of granularity is this being used by C. Suite, who don’t care so much about granularity, but about like
266 00:26:37.420 ⇒ 00:26:40.560 Demilade Agboola: dollar bills, or you know, just whatever it is.
267 00:26:41.305 ⇒ 00:26:47.010 Demilade Agboola: And that allowed us able to know how much level of detail and how much we should
268 00:26:47.750 ⇒ 00:26:58.250 Demilade Agboola: level of complexity, basically. So c-suite there, I would not want too many filters. Again, they’re not really digging into the data. Someone who is using
269 00:26:58.370 ⇒ 00:27:01.119 Demilade Agboola: that like someone. If you have like
270 00:27:01.300 ⇒ 00:27:04.950 Demilade Agboola: feedback forms and like, we’re having like what the
271 00:27:05.060 ⇒ 00:27:13.790 Demilade Agboola: users are having issues with, it’s possible customer care. The individual agents care to go deeper things like that. So it’s kind of like understanding.
272 00:27:14.330 ⇒ 00:27:15.430 Demilade Agboola: It’s it’s waiting.
273 00:27:15.430 ⇒ 00:27:15.750 Uttam Kumaran: Yeah.
274 00:27:16.120 ⇒ 00:27:24.419 Demilade Agboola: And what questions? By answering for that individual person, for that audience. So that’s kind of like how we structured it. And that helped us largely
275 00:27:24.580 ⇒ 00:27:34.299 Demilade Agboola: hit the mark. But then sometimes we’ll get feedback like, Hey, we don’t need this level. We need to change this way, things like that. But I think, also making an iterative process helps as well.
276 00:27:37.740 ⇒ 00:27:44.389 Uttam Kumaran: Yeah, I don’t know. Has anyone else on the team like, seen any process where, like we like, they’ve shipped a dashboard. And it’s been like.
277 00:27:45.130 ⇒ 00:27:47.029 Uttam Kumaran: okay, this is the best thing ever.
278 00:27:49.270 ⇒ 00:27:55.299 Luke Daque: Yeah, that’s always difficult. Like, I also did that before, like creating dashboards and stuff. And it.
279 00:27:55.550 ⇒ 00:27:59.820 Luke Daque: yeah, like, like them letting mentioned. It always depends on like, who’s using it.
280 00:28:00.150 ⇒ 00:28:03.730 Luke Daque: And yeah, it it.
281 00:28:03.850 ⇒ 00:28:05.320 Luke Daque: It’s difficult, because
282 00:28:06.340 ⇒ 00:28:12.990 Luke Daque: you need to use both of your brains like the left side and the right side, because it also needs to look good. Right? Not just like.
283 00:28:13.400 ⇒ 00:28:17.380 Luke Daque: yeah, organized in a way that looks good.
284 00:28:18.097 ⇒ 00:28:21.269 Luke Daque: And not very cluttered, and and stuff like that. So
285 00:28:22.690 ⇒ 00:28:27.600 Luke Daque: yeah, but it still really depends on like who really is using it
286 00:28:27.990 ⇒ 00:28:33.009 Luke Daque: is we don’t have. We don’t need to show all the details, and
287 00:28:33.120 ⇒ 00:28:35.650 Luke Daque: like the more details we showed, the less
288 00:28:36.230 ⇒ 00:28:39.359 Luke Daque: or the more complicated, or like very
289 00:28:40.140 ⇒ 00:28:48.800 Luke Daque: cloudy the dashboard becomes, because, like we know, the the person using it would wouldn’t know what to look. Because there’s just too many things going on
290 00:28:50.080 ⇒ 00:28:55.660 Luke Daque: like, yeah, it’s it’s it’s difficult to balance it. Basically, that’s what I’m saying.
291 00:28:57.910 ⇒ 00:29:06.270 Demilade Agboola: I just remembered another thing that we did that kind of helped quite a bit. Is. We had high fidelity templates.
292 00:29:06.470 ⇒ 00:29:06.830 Uttam Kumaran: So.
293 00:29:06.830 ⇒ 00:29:14.629 Demilade Agboola: We had like a, you know, just like. So we’ll see. Here would be a bar. Charts. Here would be a line chart. What filters.
294 00:29:14.630 ⇒ 00:29:23.479 Uttam Kumaran: Can I show? Can I show you an example of like, what a version of that we have? And maybe I can get you can tell me like, what’s what’s the gap between what we have and maybe what you saw worked really well.
295 00:29:23.590 ⇒ 00:29:24.199 Uttam Kumaran: like
296 00:29:25.060 ⇒ 00:29:27.140 Uttam Kumaran: Here’s what we did for Eden.
297 00:29:38.010 ⇒ 00:29:44.110 Uttam Kumaran: and we got approval. We worked on this with their team got approval.
298 00:29:44.270 ⇒ 00:29:46.240 Uttam Kumaran: Now this is moving to being.
299 00:29:46.740 ⇒ 00:29:50.960 Uttam Kumaran: Now, this is ideally, this is moving to get modeled some of this already modeled. But like
300 00:29:51.750 ⇒ 00:29:57.140 Uttam Kumaran: we basically worked with them to produce this, get the approval. And then we’re going to work on the dashboard.
301 00:29:58.330 ⇒ 00:30:02.850 Uttam Kumaran: There’s 2 benefits in my mind. One, it buys us time right, and in our business
302 00:30:03.470 ⇒ 00:30:05.680 Uttam Kumaran: time is everything. In fact.
303 00:30:06.080 ⇒ 00:30:12.000 Uttam Kumaran: like the dashboarding and analysis work is the really the shortest sla work which makes it really hard.
304 00:30:12.230 ⇒ 00:30:15.829 Uttam Kumaran: like anything that needs to happen within 24 h
305 00:30:16.030 ⇒ 00:30:35.249 Uttam Kumaran: is not good work for us, because it’s so hard to do. And it really causes a lot of issues. So this allows us to slow down the process for a greater good. Right? We’re not slowing it down just to sandbag it like consultants do. Consultants are pretty good at being like. Oh, well, we need this meeting. We need this meeting like, that’s not what we’re doing here.
306 00:30:35.420 ⇒ 00:30:39.920 Uttam Kumaran: What we’re doing is actually just making sure we’re gonna get it right on the 1st try, or at least like 90% right?
307 00:30:40.150 ⇒ 00:30:43.640 Uttam Kumaran: But I guess, like them a lot. Or anyone. If you guys see this like.
308 00:30:44.690 ⇒ 00:30:50.399 Uttam Kumaran: I think maybe we could probably make this 2030% look nicer. But like, I like, how this looks.
309 00:30:51.930 ⇒ 00:30:55.579 Demilade Agboola: Yeah, I mean to be fair. It could do with some
310 00:30:56.060 ⇒ 00:31:02.129 Demilade Agboola: some structure, but the idea of it is never really about structure. It’s about like what is important.
311 00:31:02.509 ⇒ 00:31:25.420 Demilade Agboola: And I think in this case you’re able to show them. Hey, this is what it’s gonna look like. These are what the global filters will be so what the local filters will be, and these are what you’ll be able to see. And so I might go. Hey, this looks great. But this doesn’t really answer my daily questions. And then you can say, Okay, so what are your questions? And then you can sort of modify that give them a version. 2
312 00:31:25.820 ⇒ 00:31:33.270 Demilade Agboola: have feedback. And so you’re not wasting so much time on building out a dashboard that is entirely useless to the audience.
313 00:31:35.550 ⇒ 00:31:43.459 Uttam Kumaran: I think it would be helpful as part of this like, I see this as the requirements is like, what questions can be answered, or
314 00:31:44.280 ⇒ 00:31:45.979 Uttam Kumaran: need to be answered.
315 00:31:48.100 ⇒ 00:31:53.119 Uttam Kumaran: Who is the audience, you know, and like
316 00:31:53.310 ⇒ 00:32:05.280 Uttam Kumaran: that’s that’s probably that’s probably it. For like this. And then in our data documentation, we have the link between what tables does this pull from? Right like, what tables does this asset pull from?
317 00:32:07.261 ⇒ 00:32:16.300 Uttam Kumaran: we’re. Gonna I’ll have the design team work on something like that looks really amazing. And does this. But like functionality wise, I feel pretty good. Sahana worked on this, so I’ll give her the credit.
318 00:32:16.770 ⇒ 00:32:20.009 Uttam Kumaran: I think this is probably what we’ll start to adapt across the team.
319 00:32:21.050 ⇒ 00:32:25.229 Demilade Agboola: I will suggest that the like. I don’t know how she’s doing it, but like
320 00:32:25.340 ⇒ 00:32:29.399 Demilade Agboola: there should be something that takes 1520 min tops.
321 00:32:29.530 ⇒ 00:32:33.270 Demilade Agboola: It shouldn’t be. It’s not. We’re not trying to do like a complicated thing. They should.
322 00:32:33.270 ⇒ 00:32:33.770 Uttam Kumaran: Yeah.
323 00:32:33.770 ⇒ 00:32:35.509 Demilade Agboola: Think we’re able to put together
324 00:32:36.000 ⇒ 00:32:46.340 Demilade Agboola: and just be like hit. Bar charts is here. Line charts is here, there will be these filters, and so the idea is we can turn around quickly, get feedback and be able to use it.
325 00:32:49.440 ⇒ 00:32:52.639 Uttam Kumaran: I think this one took a way longer to like.
326 00:32:52.820 ⇒ 00:32:57.350 Uttam Kumaran: Well, I mean, part of it’s tough, because part of it is like meeting with them getting the requirements. But you’re
327 00:32:57.520 ⇒ 00:33:00.110 Uttam Kumaran: it is. I will also agree that, like
328 00:33:00.260 ⇒ 00:33:03.330 Uttam Kumaran: the other challenge to dashboards is they’re living artifacts.
329 00:33:03.650 ⇒ 00:33:12.570 Uttam Kumaran: They will always change anyone that’s worked in data, you know. Listen, we’re never gonna get a hundred. And so it’s not worth trying for that.
330 00:33:12.880 ⇒ 00:33:16.119 Uttam Kumaran: It’s, in fact, worth getting something out, that’s like.
331 00:33:16.230 ⇒ 00:33:24.350 Uttam Kumaran: okay, we can answer 50% of your questions. The next part is coming. The problem we did with Javi is that it took us 2 months to get something basic out.
332 00:33:24.450 ⇒ 00:33:33.539 Uttam Kumaran: Instead, we could have got like 3 views out and been like, well, we’re working on the next views work on the next views it builds the anticipation it builds the buy in right? So it’s almost like.
333 00:33:35.950 ⇒ 00:33:41.170 Uttam Kumaran: like we need some sort of phased build approach or something like. I totally agree with you in that.
334 00:33:42.010 ⇒ 00:33:49.850 Demilade Agboola: Yeah. So when when I said 2030 min, I wasn’t referring to like the entire like process, I was referring to the building of the mock up, so the
335 00:33:50.310 ⇒ 00:33:53.719 Demilade Agboola: should not be time consuming to create.
336 00:33:53.720 ⇒ 00:33:54.070 Uttam Kumaran: Yeah.
337 00:33:54.855 ⇒ 00:33:55.640 Demilade Agboola: Yeah.
338 00:33:55.640 ⇒ 00:34:08.969 Uttam Kumaran: So one yeah. One thing that I will do with the design team is, I’m gonna we’re gonna build a mock up fig jam where you’ll have components available, they will all look. They all look branded, look beautiful, and you can just drag it in.
339 00:34:09.790 ⇒ 00:34:12.170 Uttam Kumaran: And it’s like playing with Legos. That’s it.
340 00:34:12.179 ⇒ 00:34:14.799 Demilade Agboola: Exactly. Exactly. So. That’s what I was speaking to.
341 00:34:14.989 ⇒ 00:34:15.579 Uttam Kumaran: Okay. Okay.
342 00:34:15.856 ⇒ 00:34:30.229 Demilade Agboola: But also yet the phase build up is very important. Because if the focus is, we’re gonna get that this like perfect dashboard in one attempt odds that you want, and you know the iterative process that would allow you to have that perfect dashboard will be missed out on, because they
343 00:34:30.230 ⇒ 00:34:31.340 Demilade Agboola: yeah time left.
344 00:34:34.030 ⇒ 00:34:35.429 Uttam Kumaran: No, that that helps a lot.
345 00:34:42.100 ⇒ 00:34:43.210 Uttam Kumaran: Okay, cool.
346 00:34:43.380 ⇒ 00:34:51.109 Uttam Kumaran: Yeah. And I think you know, as soon as this is ready, this will move to sort of the data platform documentation we could talk about like that handoff process, I think
347 00:34:51.719 ⇒ 00:34:53.309 Uttam Kumaran: probably a little bit more today.
348 00:34:53.540 ⇒ 00:34:59.430 Uttam Kumaran: But I think, seeing this, seeing this was really really helpful. Having this conversation was really helpful.
349 00:34:59.430 ⇒ 00:35:11.670 Demilade Agboola: Also, I think this also ties into the conversation we had in the previous call, where, if you kind of see this, and you have an idea of what the end goal is. As an analytics engineer. It’s easier to build models.
350 00:35:11.670 ⇒ 00:35:12.140 Uttam Kumaran: Yeah.
351 00:35:12.140 ⇒ 00:35:20.249 Demilade Agboola: To answer these questions, because you can see what the questions are. You can see what filters are needed, and you understand, like the granularity.
352 00:35:20.450 ⇒ 00:35:24.439 Demilade Agboola: So it kind of like helps shape how people work towards the end goal.
353 00:35:25.360 ⇒ 00:35:29.230 Uttam Kumaran: I guess one of my questions, and maybe it’s for Steven. This is for you, like
354 00:35:29.570 ⇒ 00:35:33.900 Uttam Kumaran: I wanna talk about like how the all the roles mesh here, because
355 00:35:34.370 ⇒ 00:35:41.390 Uttam Kumaran: one problem in data teams that happens is you get I I keep saying this, you get this like, throw this over the fence mentality
356 00:35:41.630 ⇒ 00:35:47.229 Uttam Kumaran: where it’s like that’s for analysts. Go do that. That’s for Aes go do that.
357 00:35:47.520 ⇒ 00:35:51.430 Uttam Kumaran: I think I think of us as having things where
358 00:35:51.560 ⇒ 00:36:04.230 Uttam Kumaran: specialized in. But I think of everybody on the data team as data. People like, I think we can go do analyst work. I think both on it can definitely come, learn. Dbt, so I just. And also that improves their redundancy across the stack.
359 00:36:04.710 ⇒ 00:36:09.219 Uttam Kumaran: So for that being said like, I also think about for the for Pm’s like.
360 00:36:09.340 ⇒ 00:36:16.499 Uttam Kumaran: I think Pm’s can even own tell. Taking a stab at a version of these, or at least collecting even the requirements from the team.
361 00:36:16.980 ⇒ 00:36:21.289 Uttam Kumaran: And again, because the as we go further down the technical stack.
362 00:36:21.510 ⇒ 00:36:24.930 Uttam Kumaran: the time becomes less and less available.
363 00:36:25.030 ⇒ 00:36:37.860 Uttam Kumaran: And so our ability to collect a lot of those requirements upfront. And for our Pm’s to build a relationship with the client through this process is really really important. It allows us to scale a lot better.
364 00:36:38.356 ⇒ 00:36:43.700 Uttam Kumaran: You know, but I guess open question, but like sort of just random thoughts on my my brain.
365 00:36:44.450 ⇒ 00:36:46.580 Steven Kootz: Okay, yeah. So you’re kind of saying, like.
366 00:36:46.720 ⇒ 00:36:53.910 Steven Kootz: you know, whenever it’s kind of pretty much done and finished, it’s up to me to kind of like click things together and like present it is that what you’re saying.
367 00:36:53.910 ⇒ 00:37:02.059 Uttam Kumaran: It’s kind of it’s kind of like. Well, no, I’m I’m also even saying just like I’m getting these from the client because I don’t know how much of these you actually
368 00:37:02.420 ⇒ 00:37:03.729 Uttam Kumaran: need to be
369 00:37:03.850 ⇒ 00:37:09.100 Uttam Kumaran: like, I think there’s probably part of this where you have to be like a super well trained analyst to do.
370 00:37:09.280 ⇒ 00:37:11.999 Uttam Kumaran: But a lot of this is asking, What do you want to see?
371 00:37:12.450 ⇒ 00:37:13.960 Uttam Kumaran: How do you want to see it?
372 00:37:14.230 ⇒ 00:37:17.679 Uttam Kumaran: What’s important, like, what are important, Kpis, for your business.
373 00:37:18.210 ⇒ 00:37:19.750 Uttam Kumaran: Those are things that
374 00:37:20.050 ⇒ 00:37:27.920 Uttam Kumaran: anyone in our business should be able to ask right? I would say anyone in the company should be able to go to one of our clients and gather that information
375 00:37:28.780 ⇒ 00:37:29.200 Uttam Kumaran: and
376 00:37:29.200 ⇒ 00:37:38.749 Uttam Kumaran: that sort of that sort of and that could be anyone on the team. But of course, like again thinking about how the the team is layered in order for us to scale our technical talent across
377 00:37:39.080 ⇒ 00:37:42.730 Uttam Kumaran: across multiple clients. I want to start to leverage
378 00:37:43.190 ⇒ 00:37:47.649 Uttam Kumaran: more on on the top of funnel, to to gather some of these, which I think would help a lot.
379 00:37:49.190 ⇒ 00:38:13.849 Steven Kootz: Yeah, I I think that makes sense, too. And I think even yesterday, too, I I mentioned, like, you know, what we do, too, is like, we said, like an intake form, to see like what clients want to see for the month, too, and like even just like generating something like that to like gain, like what they want to see, like kpis, and like all those like different breakdowns, too, of like additional information, it’s something we can think of, too. But when it comes to like, you know, kind of building everything out and looking at everything, I think. You know.
380 00:38:14.520 ⇒ 00:38:18.699 Steven Kootz: once we get like a general idea of like how the process can be like.
381 00:38:19.350 ⇒ 00:38:34.309 Steven Kootz: You know we’ve not, you know, reformatted, I’m gonna say, very loosely. You know you’ll have a good understanding of like how like what things can be added in and what things can be, you know, rearranged. So I think that’s that’s a good thing to bring up so.
382 00:38:35.370 ⇒ 00:38:36.290 Uttam Kumaran: Okay, great.
383 00:38:37.590 ⇒ 00:38:46.510 Uttam Kumaran: Okay, cool. I know we’re coming up on the end of this meeting. Yeah, I I really just have time like this afternoon, but I would love to continue.
384 00:38:46.840 ⇒ 00:38:51.628 Uttam Kumaran: We we’re having conversation the Ae. Team, I know maybe late for you, Kyle.
385 00:38:52.150 ⇒ 00:38:56.150 Uttam Kumaran: but would love to continue this conversation like the conversation we were having later today.
386 00:38:56.260 ⇒ 00:39:01.480 Uttam Kumaran: Maybe I can ping in slack and see what everybody’s availabilities are.
387 00:39:02.950 ⇒ 00:39:14.250 Uttam Kumaran: and yeah, I would love to just continue talking about that. And Steven would love to have you there as well. We were just talking a little bit about a process between analysts and aes, it was a really, it was really, really productive.
388 00:39:17.052 ⇒ 00:39:27.910 Uttam Kumaran: Cool. And then, yeah, we didn’t. We didn’t get to any actions. But I think everybody’s sort of on the ball. I think, Luke, I got your stuff from Stackblitz, Bo. I’m I think we’re sort of looping on stuff for Eden and
389 00:39:28.367 ⇒ 00:39:33.312 Uttam Kumaran: Javi, I think, Kyle, you have the next steps for Javi, I think what we’re also gonna start to do.
390 00:39:34.062 ⇒ 00:39:37.190 Uttam Kumaran: Is sort of. Now see how we can start to
391 00:39:37.440 ⇒ 00:39:40.059 Uttam Kumaran: have a little bit of spread across clients.
392 00:39:40.647 ⇒ 00:39:45.700 Uttam Kumaran: So I’ll be working on a little bit of that later this week and thinking through
393 00:39:46.145 ⇒ 00:39:51.750 Uttam Kumaran: assignments, across teams, and then demoted. When do you? When are you officially starting next week or week after.
394 00:39:52.830 ⇒ 00:39:53.860 Demilade Agboola: Next week.
395 00:39:54.300 ⇒ 00:39:56.107 Uttam Kumaran: Yes. Okay. Cool.
396 00:39:57.050 ⇒ 00:39:58.150 Uttam Kumaran: All right. Great.
397 00:40:00.160 ⇒ 00:40:02.330 Uttam Kumaran: Awesome. Any other questions.
398 00:40:05.700 ⇒ 00:40:08.800 Uttam Kumaran: Cool. Okay. Thanks. Guys. Talk soon.
399 00:40:09.830 ⇒ 00:40:10.260 Caio Velasco: Thank you.
400 00:40:10.750 ⇒ 00:40:11.280 Luke Daque: Bye.