Meeting Title: Uttam Kumaran Date: 2025-02-21 Meeting participants: Caio Velasco, Jakob Kagel, Uttam Kumaran
WEBVTT
1 00:01:20.540 ⇒ 00:01:21.520 Uttam Kumaran: Hey, guys.
2 00:01:22.040 ⇒ 00:01:23.329 Jakob Kagel: How’s it going.
3 00:01:23.600 ⇒ 00:01:24.530 Caio Velasco: Hello!
4 00:01:31.240 ⇒ 00:01:45.349 Uttam Kumaran: cool. I I guess I wanted to. Just one introduce Kyle to Jacob. Jacob Kyle is an analytics engineer who joined this week on the team, and is sort of working on everything related to Javi.
5 00:01:45.960 ⇒ 00:01:58.900 Uttam Kumaran: And Jacob is sort of handling a lot of the analysis work for Javi. So kind of wanted to bring you guys together. We’re sort of work starting to work on things for
6 00:01:59.388 ⇒ 00:02:15.260 Uttam Kumaran: the next set of dashboards. And we actually have, like a new process by which we’re hopefully streamlining your side, Jacob, which is like, okay, what metrics do I need to do this, and then our side, which is making sure that you have that, and where where you can go to find those.
7 00:02:16.005 ⇒ 00:02:37.120 Uttam Kumaran: So I was hoping that we could start to use that probably seen probably seen some version of this before. But yeah, maybe, Kyle, I’ll let you drive. I just have to step out for like a minute or maybe a few minutes. But I’ll be right back. But maybe, Kyle, Jacob, you could just outline what the requirements are.
8 00:02:37.370 ⇒ 00:02:41.199 Uttam Kumaran: And then I think, Kyle, if you want to go through and walk through how Jacob can
9 00:02:41.370 ⇒ 00:02:46.599 Uttam Kumaran: use the the sheets to sort of build that out. I think that would be a really great place to start.
10 00:02:47.340 ⇒ 00:02:48.669 Caio Velasco: Okay. Perfect. Who knows?
11 00:02:48.670 ⇒ 00:02:49.390 Jakob Kagel: Sounds good.
12 00:02:49.630 ⇒ 00:02:50.350 Uttam Kumaran: Cool.
13 00:02:53.800 ⇒ 00:02:55.700 Caio Velasco: Okay. Hey? Jacob.
14 00:02:55.700 ⇒ 00:02:56.360 Jakob Kagel: To you.
15 00:02:56.650 ⇒ 00:03:06.000 Caio Velasco: Nice to meet you and let me check here. Should I share my screen with the with the spreadsheet?
16 00:03:06.000 ⇒ 00:03:17.299 Jakob Kagel: Yeah, that might be a good place for us to start. For sure. I think we can just kind of talk through it. I mean, yeah, I guess, like, you know, from my end, it’s like
17 00:03:18.110 ⇒ 00:03:30.010 Jakob Kagel: it. It’s really about like I, I think we just wanna align right like what they’re asking for. And then, like what we can actually like produce based on like the data that we have. So yeah.
18 00:03:30.840 ⇒ 00:03:51.929 Caio Velasco: No perfect perfect. Yeah. So I started this week. So I’m still like learning about everything in both sides of the client and us. So everything’s quite new to me. But so I understand. For example, now I have. I have a test to to build a gorgeous dashboard, for example. And I was trying to understand, like my ticket, and what was requested
19 00:03:52.368 ⇒ 00:04:00.000 Caio Velasco: all the details and everything. And well, let me guide you through what I saw, it would be, let me put it here.
20 00:04:05.360 ⇒ 00:04:07.520 Caio Velasco: Okay, so my notion is easier.
21 00:04:10.140 ⇒ 00:04:18.029 Caio Velasco: For example, I received this ticket and I saw some like business questions. Let’s say.
22 00:04:18.420 ⇒ 00:04:23.529 Caio Velasco: then, I was looking at the 1st one and it’s like, Okay, which Macro gonna be used. The most
23 00:04:23.650 ⇒ 00:04:33.240 Caio Velasco: one thing that initially, when I start. Well, first, st I don’t know what a macro is so like. We always start like with those very simple questions like, What is a macro
24 00:04:33.840 ⇒ 00:04:36.360 Caio Velasco: and then I went to the
25 00:04:36.630 ⇒ 00:04:48.689 Caio Velasco: to the website, like to to Gorge’s website just to see if I could find some information that I found something here which might be or might not be related to to this macro specifically, whatever that is.
26 00:04:49.125 ⇒ 00:05:08.639 Caio Velasco: So usually, that’s how I how I do, and then I go into the database and try to see if I can find, like a table or a column name something that can be related to this to this idea. So this is how I started, and well for each business questions, I would do the same.
27 00:05:09.056 ⇒ 00:05:16.099 Caio Velasco: And then, for example, when I go to the database and start checking things, then I start to have a lot of questions.
28 00:05:17.063 ⇒ 00:05:26.530 Caio Velasco: And I think that’s why I initially proposed this well, this spreadsheet idea
29 00:05:27.006 ⇒ 00:05:35.370 Caio Velasco: which initially, I think, at least on the on the analyst side or on the client side, maybe
30 00:05:35.980 ⇒ 00:05:55.889 Caio Velasco: what is needed, something like as simple as it can be. I saw that there were some things we requested here. Then I also like to know? I mean, what do you think about like? What do you think? It’s really important for you? Because at the end of the day when I was building I’m trying to like at the end of the day for us.
31 00:05:56.591 ⇒ 00:06:04.809 Caio Velasco: I’m trying to track everything, I mean, at least, whenever I try to track everything. Usually we, we can be consistent
32 00:06:04.920 ⇒ 00:06:11.309 Caio Velasco: across all databases and data marks, and also the analyst side with the metric side.
33 00:06:11.828 ⇒ 00:06:32.190 Caio Velasco: So let’s say that here we have everything in the snowflake that would be somehow pointing to a mac macro at the end of the day. That’s what I’m trying to do. But this can be done by us on the engineering side, I’d say, but this things here would probably come from. Let’s say your work or anyone else’s work?
34 00:06:32.878 ⇒ 00:06:42.439 Caio Velasco: So do you think that starting with this, it’s sufficient to to understand, like, what exactly do you need? Or if there is a problem coming up? Do you think.
35 00:06:42.440 ⇒ 00:06:43.040 Jakob Kagel: Yeah, I.
36 00:06:43.040 ⇒ 00:06:45.769 Caio Velasco: Need this column or something else. Yeah.
37 00:06:45.770 ⇒ 00:07:00.129 Jakob Kagel: No, I mean, I think kind of like, you know, simple is good like less is more, a little bit, I mean, I think right. It should be so like in the ticket. Right? It’s like we have the requirements like, what questions are we trying to answer? Right? So
38 00:07:00.760 ⇒ 00:07:05.670 Jakob Kagel: I think it should be like right dashboard question.
39 00:07:06.070 ⇒ 00:07:24.500 Jakob Kagel: If you go back to the sheet you just had a it should be like dashboard on the left, right? Then it should be question, then it should be metric name like which metrics you’re using to answer this question because some questions you may use multiple metrics. I mean it’s maybe the case right? You only have one metric per question. But then
40 00:07:24.500 ⇒ 00:07:50.929 Jakob Kagel: and then, yeah, it should be metric definition, and then it should be tables like used for that metric. Basically like, if all the fields, like in the metric definition, are from one table, then I think that makes sense. But then, like, you know, there may be a situation where you have to join. So yeah, I think that, I think is the format, I think makes the most sense. I mean, we can talk to U Tom.
41 00:07:52.110 ⇒ 00:07:59.866 Jakob Kagel: you know, and see. Maybe like what he thinks as well. I mean, I’m happy to help like filling this out as well.
42 00:08:00.760 ⇒ 00:08:03.969 Jakob Kagel: I don’t know. I think it’s sort of a little bit too like
43 00:08:05.190 ⇒ 00:08:11.089 Jakob Kagel: I I don’t know. I’m also kind of new. I just joined, maybe like 2 or 3 weeks ago.
44 00:08:11.610 ⇒ 00:08:26.890 Jakob Kagel: so I think it depends kind of like, you know, how we’re engaging with the client like, how much are we? Are they just gonna are they like, you know? Gonna give us like these open, ended questions. And it’s up to us to kind of like, define the metrics.
45 00:08:26.890 ⇒ 00:08:43.650 Jakob Kagel: or, like, you know, do we have to kind of like, go back and forth with them and get sort of sign off on the metrics that we’re using. But I think in principle, I think this like setup, makes the most sense for me. I think it’s like the easiest to interpret. And
46 00:08:43.929 ⇒ 00:08:46.560 Jakob Kagel: you know it’s not just like sort of.
47 00:08:47.000 ⇒ 00:08:52.910 Jakob Kagel: you know, putting a whole bunch of like unnecessary stuff like into the sheet, which I think we want to avoid a little bit.
48 00:08:53.720 ⇒ 00:09:19.980 Caio Velasco: Yeah, yeah, no, totally. I agree. Yeah. So like, that’s the thing like, I think, cause. Nicholas is not here now, but I can talk to him on on Monday. He’s like building the ticket and everything, and we are always like, Hey, we need more details. And that. And he was like, yeah, like, what kind of details? So I think at the end of the day, of course, we can always start with the tickets for for us to work and know what we have to do. And over there he has. He needs to to have, like the
49 00:09:20.280 ⇒ 00:09:21.660 Caio Velasco: while the
50 00:09:21.870 ⇒ 00:09:29.229 Caio Velasco: like, sufficient information like no more, not less. And then we come here. And I think here that we can well do this
51 00:09:29.970 ⇒ 00:09:34.650 Caio Velasco: like holding hands right like someone does this part like more to the
52 00:09:34.770 ⇒ 00:09:38.550 Caio Velasco: let’s say to the analyst, to the dashboard side, to the.
53 00:09:38.550 ⇒ 00:09:38.980 Jakob Kagel: Right.
54 00:09:38.980 ⇒ 00:09:52.269 Caio Velasco: User side. And then we come here. And then we make sure that we are following everything from that information. And we have all the the data marks and and the sources and all everything related to the pipeline.
55 00:09:52.758 ⇒ 00:09:57.220 Caio Velasco: But I think, starting here, it’s a it’s a good way I believe?
56 00:09:57.689 ⇒ 00:10:13.410 Caio Velasco: And when just a question, when when you have the this part like metric definition, is it because there are calculations being done in the data visualization tool like for the Meta base or something. Because when I saw here I saw this, for example.
57 00:10:13.410 ⇒ 00:10:21.490 Jakob Kagel: Right? Exactly. So that’s like, yeah, that’s how I would think about it. It’s like, yeah, just like the calculation. I think
58 00:10:21.710 ⇒ 00:10:28.240 Jakob Kagel: I don’t think that we need to include. This is my personal opinion, but I don’t.
59 00:10:28.740 ⇒ 00:10:33.099 Jakob Kagel: I don’t know that we need to include these like case wins. And like
60 00:10:33.640 ⇒ 00:10:53.369 Jakob Kagel: these are not really like metrics to me, I mean, like, maybe they go into like a separate line or something, but I think we should have it like, you know, the metrics is like, you know, all the numerical values. And then we have, like the categorical ones, maybe like separate, you know, and I don’t know if that maybe should be like a different breakout, too, but
61 00:10:53.510 ⇒ 00:10:58.729 Jakob Kagel: I think the best like order of operations for us, like in terms of dashboarding.
62 00:10:59.130 ⇒ 00:11:15.540 Jakob Kagel: It’s better if we do this where we define all the metrics like in the sheet, we align on the metric definitions. We do the data like quality checks like, you know, we make sure there are no like excessive null values and no sort of like missing values there
63 00:11:15.680 ⇒ 00:11:24.110 Jakob Kagel: and then we build the dashboard off of that, because, actually, like building the dashboard is sort of like the easiest part. If the metrics are.
64 00:11:24.200 ⇒ 00:11:50.520 Jakob Kagel: you know, predefined and pre vetted. Basically, and that’s, I think, where we ran into a little bit of trouble, just like in the past, is like we tried to build the dashboard essentially first, st and we didn’t have sort of like clearly aligned definitions on the metrics. And you know, it ended up. We had to go back and do a lot of work to sort of like validate or sort of make changes like to the underlying tables. So
65 00:11:50.520 ⇒ 00:12:02.229 Jakob Kagel: yeah, I think in my personal opinion, I feel like the metrics really should just be like the numerical values. I think the categorical ones, like really kind of like, speak for themselves, especially like
66 00:12:02.230 ⇒ 00:12:26.629 Jakob Kagel: I would even think about. Maybe we do like a separate tab, just for, like the categorical ones like product name, because it’s like we’re just gonna use this same sort of like product name grouping across every dashboard where we have product name like, we’re not gonna do different product name, probably on any dashboard. So it’s like we could really just have a tab that has, like the categorical ones.
67 00:12:26.630 ⇒ 00:12:34.574 Jakob Kagel: And then, like, really, the only thing that’s gonna change like from dashboard to dashboard would be like these numerical like metrics like,
68 00:12:35.050 ⇒ 00:12:38.859 Jakob Kagel: you know, based on the data source. Does that make sense what I’m saying?
69 00:12:39.020 ⇒ 00:12:42.796 Caio Velasco: Yeah, yeah. But could you give like an example of this?
70 00:12:43.970 ⇒ 00:12:46.420 Caio Velasco: like numeric versus categorical? Just so that.
71 00:12:46.420 ⇒ 00:13:03.589 Jakob Kagel: Right like. So, for instance, like Numeric, like gross margin, or something right where we calculate like total line item cost. And we like subtract out all the cogs, and then, you know, we end up with like a gross margin value. And then the categorical is really just what you’re grouping by. So like.
72 00:13:03.650 ⇒ 00:13:29.559 Jakob Kagel: if you’re grouping by like the product type, like latte versus creamer versus cold brew, you know. That’s like what I’m talking about when I’m saying categorical. It’s just like when you create these like case, when like custom fields where it’s like, Okay, if product name contains like, you know, frother, then it’s like an accessory or something like that, right? But there’s actually no like numeric calculation in that. Does that make sense.
73 00:13:29.700 ⇒ 00:13:31.199 Caio Velasco: Yeah, yeah, no, it does.
74 00:13:31.200 ⇒ 00:13:35.360 Jakob Kagel: Like we don’t need to like sort of spell out sort of like
75 00:13:35.540 ⇒ 00:13:53.100 Jakob Kagel: every logic, for every view in the dashboard, like we don’t need to say, like, okay, we’re showing gross margin, like total gross margin in this view. But then, in this view, we’re showing gross margin by product category, it’s like we don’t need to just include, like all that logic for.
76 00:13:53.550 ⇒ 00:14:16.025 Jakob Kagel: And then, like you say, we have, like another one where it’s like gross margin by like subscriber type or something. It’s like we just need to include the calculation for gross margin, because it’s gonna be the same across all of those. And then we just need to show like, okay, you know, these are the same like, the categorical splits are going to be the same, or they should remain the same, no matter what dashboard like you’re working on
77 00:14:16.300 ⇒ 00:14:44.939 Jakob Kagel: Because, like, we, wanna, we also want to like strive for consistency. Right? So it’s like, if we’re getting into the situation right where we maybe have 2 dashboards now. But like we’re building up to like 3, 4, 5, 6 dashboards, it’s important that we keep the consistency of those groupings like the same across all the dashboards like. What we don’t want is like somebody, you know, if it happens like we have 2 people or something, it’s probably just gonna be me. But if there is like another person or somebody has to come in and create a view
78 00:14:44.940 ⇒ 00:15:11.840 Jakob Kagel: that they okay instead of using, you know, when product class or like product name like latte, they use something else like, you know, they say when product name like vanilla or something, but it’s not the same, you know, and then it’s like it creates like these slight mix mismatches, or like the logic is not aligned, but really like things like that where it’s like subscriber type or like product group like those can really remain the same across all the dashboards. Does that make sense.
79 00:15:12.530 ⇒ 00:15:16.139 Caio Velasco: Yeah, yeah, it does. Okay, so
80 00:15:17.880 ⇒ 00:15:19.669 Caio Velasco: yeah, so I think we started from.
81 00:15:19.670 ⇒ 00:15:24.354 Jakob Kagel: Yes, I’m happy to help with this, too. For sure.
82 00:15:24.990 ⇒ 00:15:37.464 Jakob Kagel: So you know, I can maybe help just by creating that tab. That’s like the categorical like groupings. And you know, you can just focus like on the metrics.
83 00:15:39.400 ⇒ 00:15:52.271 Caio Velasco: Yeah, yeah, we can. We can definitely start with that. Yeah, because I don’t have the clear end to end picture. So I might have more questions after I started start like hands on in everything.
84 00:15:53.070 ⇒ 00:15:58.579 Caio Velasco: but yeah, no, I think so far, I understand. Like both sides. Maybe. Yeah, we can start with this, and
85 00:15:58.950 ⇒ 00:16:01.090 Caio Velasco: and as long as well
86 00:16:01.370 ⇒ 00:16:10.579 Caio Velasco: during the process, if I have more questions I can definitely reach out to you. And if you want to do the categorical part, as you said, feel free for sure.
87 00:16:12.420 ⇒ 00:16:13.153 Jakob Kagel: Sounds good.
88 00:16:13.520 ⇒ 00:16:14.320 Caio Velasco: Yeah, yeah.
89 00:16:14.320 ⇒ 00:16:21.760 Jakob Kagel: Today real quick. It’ll only take me a couple minutes, I guess. What we need utam like to talk to Utam about. To like is just.
90 00:16:22.520 ⇒ 00:16:22.900 Uttam Kumaran: Doesn’t matter.
91 00:16:22.900 ⇒ 00:16:25.399 Jakob Kagel: Oh, there! Oh, hey! You’re back
92 00:16:25.530 ⇒ 00:16:40.480 Jakob Kagel: cool. Okay. So we were just talking through sort of like organizing right like these metric views. So I was proposing something like this right where it’s like the dashboard. The question, the metric name, the definition, and then the table.
93 00:16:40.530 ⇒ 00:16:59.149 Jakob Kagel: and what I was proposing also is like, Okay, we do this for all, like the numerical metrics. But then we have a separate tab like in this sheet, where we do all of like our categorical groupings, like the product group, like, you know, subscriber versus non subscriber. Things like that. I think that
94 00:16:59.510 ⇒ 00:17:10.099 Jakob Kagel: you know what we want to avoid is like, if we’re 1st of all, it’s like, yeah, we want to use consistency in the product grouping across all the dashboards which you know, if it’s just me making the dashboards that
95 00:17:10.560 ⇒ 00:17:23.230 Jakob Kagel: issue at all. But I think also, like what we want to avoid is like what we want to do is like, keep these tabs like as simple as possible where it’s like, okay, say, we have 3 different views for gross margin or something. And we have.
96 00:17:23.240 ⇒ 00:17:48.229 Jakob Kagel: you know, gross margin overall, gross margin by product group, and then gross margin by subscriber. I don’t think that we need to spell out 3 separate calculations for that. I think we just need to say like, Okay, you know, this is the gross margin calculation, and these are the splits, and just refer to the splits tab that has the logic for the groupings, because this should remain the same across all the dashboards, no matter how many dashboards we get to
97 00:17:48.390 ⇒ 00:17:56.559 Jakob Kagel: like. If we go, we have 2 dashboards. Now we go to 6 dashboards. We should have the same product group grouping across all the dashboards. What do you think about that?
98 00:17:57.470 ⇒ 00:18:04.550 Uttam Kumaran: That makes sense. I mean, that’s just the concept of where do we pull the dimensionality right? Like, I agree that at the moment
99 00:18:04.680 ⇒ 00:18:10.299 Uttam Kumaran: we started off, mainly focusing on the on the. So it’s kind of 2 problems, one.
100 00:18:10.850 ⇒ 00:18:20.939 Uttam Kumaran: we have been working sort of backwards, and that the Ae team sort of puts out March, and then you go look for what’s there, and then you’re like, it’s missing this. We’re trying to shift it. So
101 00:18:21.050 ⇒ 00:18:24.289 Uttam Kumaran: it comes in as the form of requirements first.st
102 00:18:24.620 ⇒ 00:18:49.400 Uttam Kumaran: So I’m more inclined to. I mean one. You’re right in that. We want to make sure that if 6 dashboards are using gross margin. We don’t. We’re not defining that 6 times, however, I do think that for every dashboard there may be one or 2 additional things. For example, the gorgeous dashboard, the net margin dashboard, the Amazon dashboard. There may be another supply chain dashboard they may all use
103 00:18:49.690 ⇒ 00:19:00.399 Uttam Kumaran: like one metric from one save table. So that’s for us to say where to go. Get that? I think, Kyle, I’m open to understanding how we should do dimensionality, because right now we don’t have
104 00:19:01.411 ⇒ 00:19:05.679 Uttam Kumaran: the notion of dimensionality in this
105 00:19:07.630 ⇒ 00:19:36.640 Jakob Kagel: Yeah, I wanna I just wanna clarify some real quick. That I’m saying is like, I agree with what you’re saying. Like, I I think for the actual, like numerical metrics, for every dashboard should be split out separately on every tab, like, if gross margin is in 3 different dashboards, 4 different dashboards. The calculation should still be in all the dashboards. What I’m saying is more like on the grouping right like, if we’re grouping by product group or something like that, like, I don’t think that we like.
106 00:19:37.160 ⇒ 00:19:59.950 Jakob Kagel: I don’t think that we should basically have 3 different rows, or something like for 3 different gross margin calculations, like in one dashboard, like gross margin, should just be in one place, and you can see it. And then you can say, Okay, here are the groupings, and then refer to the groupings tab, which will be the same like across all the dashboards, or should be the same across all of the dashboards. Does that make sense? I guess.
107 00:19:59.950 ⇒ 00:20:00.979 Uttam Kumaran: That makes sense.
108 00:20:00.980 ⇒ 00:20:01.440 Jakob Kagel: Yeah.
109 00:20:03.810 ⇒ 00:20:08.720 Uttam Kumaran: yeah, I think we’ll maybe have to think about that. Kyle, in like an a team meeting about how we solve it, because.
110 00:20:08.720 ⇒ 00:20:19.929 Jakob Kagel: But I do agree with you in principle. This is a much better approach, like, yeah, if we we should work from like the bottom up. It’s like we should define the metrics. Then we should like validate. And you know.
111 00:20:20.460 ⇒ 00:20:24.800 Jakob Kagel: you know, test the data or whatnot, and then we should build a dashboard off of that.
112 00:20:29.280 ⇒ 00:20:39.839 Caio Velasco: Yeah, well, I I agree, and and well sorry for the the simple question. But let’s say that we were restart all the conversation and ask, like.
113 00:20:40.980 ⇒ 00:21:10.109 Caio Velasco: what is the end goal? I mean? I know that is building a dashboard. But still, what is the end goal of using this metric spreadsheet. For example, we would start with the metrics definition, which, okay, we need definitely those things to build the dashboard. But then I’m trying to see as as you done said like. At some point we have to to get into the data marks. And in the source situation, which is what I started to do in this part, which is more the ae work. But I’m trying just trying to see if we.
114 00:21:10.510 ⇒ 00:21:17.789 Caio Velasco: if both are sufficient with both, are enough, or if you need more things or less things, or if it’s making sense.
115 00:21:17.970 ⇒ 00:21:22.289 Caio Velasco: trying to have, like a general view about the deliverable at the end of the day.
116 00:21:25.840 ⇒ 00:21:30.199 Uttam Kumaran: I mean, I think the analyst team should have to fill out this
117 00:21:30.400 ⇒ 00:21:34.860 Uttam Kumaran: left side piece before the Ae. Team can work on anything.
118 00:21:35.170 ⇒ 00:21:35.830 Caio Velasco: Okay.
119 00:21:35.830 ⇒ 00:21:38.579 Uttam Kumaran: Or it should happen in parallel, because.
120 00:21:38.970 ⇒ 00:22:05.709 Uttam Kumaran: like, we can go create like customers, tables, and all this stuff one, we’re gonna get left with a bunch of junk that we probably don’t need. Second, we’re sort of building blindly, I think, like we will probably do a mix of both one we know, for every e-commerce customer. They want to see orders. They want to see customers. They want to see transactions. They want to see Zendesk right? So there’s definitely like 80% of the way we can go by. Just like, okay, they’re an Ecom customer.
121 00:22:05.800 ⇒ 00:22:19.110 Uttam Kumaran: But in order to match it up with a dashboard, which I feel like. Really, this exercise for gross margin was, we need some set of requirements from the analyst team. On what do we need for the dashboard, and then we can fill out
122 00:22:19.782 ⇒ 00:22:21.949 Uttam Kumaran: where that’s available. Now
123 00:22:22.070 ⇒ 00:22:28.410 Uttam Kumaran: and then. It also allows us to say, Okay, we said, this is available at the moment it looks like
124 00:22:28.840 ⇒ 00:22:36.899 Uttam Kumaran: at the moment, maybe it’s not available in the right way. Okay, we need a new metric, right? That process is allowed to happen through this.
125 00:22:37.080 ⇒ 00:22:38.600 Uttam Kumaran: So for the
126 00:22:38.980 ⇒ 00:22:53.950 Uttam Kumaran: for the like. And and I, I honestly don’t think that the analyst you guys shouldn’t. It shouldn’t matter where it’s coming from as long as it’s in a concise table, and it’s accurate. And you have the dimensionality, right? So I sort of want to start with
127 00:22:54.610 ⇒ 00:22:56.919 Uttam Kumaran: the what the needs are.
128 00:22:57.330 ⇒ 00:23:06.119 Uttam Kumaran: And we, I think we don’t have to solve the dimensionality problem. Now, maybe we can just add metric, and then the amount of the cuts you want.
129 00:23:06.360 ⇒ 00:23:09.569 Uttam Kumaran: and we can sort of go from there in the short term.
130 00:23:09.700 ⇒ 00:23:12.349 Uttam Kumaran: But I do think that’s sort of like
131 00:23:12.500 ⇒ 00:23:19.370 Uttam Kumaran: this process, I think, is what we would need for this next dashboard to start to fill out what models we need?
132 00:23:22.330 ⇒ 00:23:26.639 Uttam Kumaran: so I don’t know, Jacob like. What do you think, do you have enough context to sort of go through and just
133 00:23:26.920 ⇒ 00:23:34.229 Uttam Kumaran: build these with just metrics? And then we can even create something about cuts. And then we can go fill out where to get this, because we haven’t built the mark yet.
134 00:23:34.910 ⇒ 00:23:42.349 Jakob Kagel: Yeah, okay, I mean this one. So this one, specifically, I just need.
135 00:23:42.720 ⇒ 00:23:50.300 Jakob Kagel: I need a little bit of context. So like, I’m not from what is gorgeous like. What is that? I’ve never heard of that.
136 00:23:50.827 ⇒ 00:23:53.462 Uttam Kumaran: Is is like a
137 00:23:55.320 ⇒ 00:24:00.200 Uttam Kumaran: It’s like a I think it’s a customer support platform. Basically, yeah.
138 00:24:00.200 ⇒ 00:24:05.279 Jakob Kagel: Okay. And you’re saying, basically like, we don’t have any tables yet from them. Right? We have to build that.
139 00:24:05.540 ⇒ 00:24:06.200 Uttam Kumaran: Yeah.
140 00:24:06.200 ⇒ 00:24:08.697 Jakob Kagel: Okay, okay, yeah,
141 00:24:10.650 ⇒ 00:24:22.369 Jakob Kagel: that’s fine. I mean, yeah, I I can go through. And I can. You know, I can try to understand their product and sort of like what kind of you know data that we should be able to get from them.
142 00:24:22.370 ⇒ 00:24:25.609 Uttam Kumaran: But I but there’s there already is like questions
143 00:24:25.940 ⇒ 00:24:32.080 Uttam Kumaran: in here, that Nico source which is like what the questions that the the team, the team
144 00:24:32.080 ⇒ 00:24:39.740 Uttam Kumaran: right? I saw that in the ticket. Yeah, I guess. Like, I I guess sorry. What I meant, I guess rather is more like metric definitions.
145 00:24:40.550 ⇒ 00:24:43.549 Jakob Kagel: Or metric names. And yeah,
146 00:24:44.480 ⇒ 00:24:47.879 Jakob Kagel: but yeah, I’m happy to help with that, for sure. No problem.
147 00:24:48.740 ⇒ 00:25:02.530 Uttam Kumaran: Yeah, ideally, before we start on like getting the models created. I would love to just see what metrics we need, because I’m looking at the depth, the the requirements. It’s like, okay, Macros, which Macros need need to be using most. Okay, so we probably
148 00:25:03.260 ⇒ 00:25:16.579 Uttam Kumaran: is it like we? And then again, you can go into the Api definition or go into gorgeous and see, okay, we need like ticket name ticket, id agent. Name Macro. We need. So then that will give us a sense. Okay, we need to create, like
149 00:25:16.770 ⇒ 00:25:21.470 Uttam Kumaran: the Macros fact macro usage. We need to create refunds.
150 00:25:23.770 ⇒ 00:25:24.870 Uttam Kumaran: Exactly.
151 00:25:24.870 ⇒ 00:25:40.629 Jakob Kagel: Yeah, sure. So is this the one that you want to prioritize? I mean, I saw there was like a lot of tickets kind of like they came in or they were like in that thread. There was also this one, I think, that Nico Active asked about, that was like move the auction account.
152 00:25:40.630 ⇒ 00:25:41.000 Uttam Kumaran: No.
153 00:25:41.000 ⇒ 00:25:43.194 Jakob Kagel: I don’t know how you say.
154 00:25:43.700 ⇒ 00:25:49.499 Jakob Kagel: but that one from amplitude. The Meta base like is this the one that you want to prioritize or like.
155 00:25:51.380 ⇒ 00:25:53.660 Uttam Kumaran: Both of them are important. So.
156 00:25:53.660 ⇒ 00:25:54.200 Jakob Kagel: Okay.
157 00:25:54.200 ⇒ 00:25:54.900 Uttam Kumaran: I’m.
158 00:25:55.310 ⇒ 00:25:58.100 Jakob Kagel: That’s fine. If you want to do both. I was just asking, yeah.
159 00:25:58.100 ⇒ 00:26:04.740 Uttam Kumaran: Both of them are, I would say, similar priorities. So feel free to just take one or the other, maybe the
160 00:26:04.950 ⇒ 00:26:10.700 Uttam Kumaran: the the Oquendo one, maybe easier, because there’s already something there.
161 00:26:11.520 ⇒ 00:26:12.530 Jakob Kagel: Right? Exactly.
162 00:26:13.330 ⇒ 00:26:17.590 Jakob Kagel: That’s what I was asking, I guess to like in the thread, right is like
163 00:26:18.210 ⇒ 00:26:27.509 Jakob Kagel: this one. Are we doing? Is the fact, it’s like, what fact reviews or something is the table? Is that the table that has the data from this? Or is it.
164 00:26:31.230 ⇒ 00:26:35.799 Uttam Kumaran: No, that’s why would say, don’t worry about where it’s I don’t know. At the moment.
165 00:26:35.800 ⇒ 00:26:36.330 Jakob Kagel: Okay.
166 00:26:36.500 ⇒ 00:26:39.439 Uttam Kumaran: Like, assume we don’t have any data for either of these. Because.
167 00:26:39.700 ⇒ 00:26:47.570 Uttam Kumaran: like, let’s not, I don’t. Wanna I wanna start from like what you need basically, so that we can go look back and see what we have. I’m not sure yet.
168 00:26:47.810 ⇒ 00:26:52.860 Jakob Kagel: Okay? Alright. Well, that’s fine. Okay, that makes sense.
169 00:26:59.820 ⇒ 00:27:02.169 Uttam Kumaran: Cool. So I
170 00:27:02.780 ⇒ 00:27:10.349 Uttam Kumaran: I didn’t. I I guess I think, Kyle, we want to create 2 dashboards, one for Okendo, one for gorgeous. Yeah. And if we can
171 00:27:10.830 ⇒ 00:27:18.290 Uttam Kumaran: start to fill out that spreadsheet like, what do you think about like? Should I try to grab another time on like Monday to to review that.
172 00:27:18.290 ⇒ 00:27:28.529 Jakob Kagel: Yeah, I’m fine with that, like, Monday afternoon is, good morning is tough for me, I? But yeah, Monday afternoon, I think is good. Yeah.
173 00:27:28.700 ⇒ 00:27:29.360 Uttam Kumaran: Okay.
174 00:27:36.580 ⇒ 00:27:43.950 Uttam Kumaran: okay, cool. I think that’s all I had. I think. Yeah, we’re gonna start to use this process across every client. And honestly.
175 00:27:44.150 ⇒ 00:27:52.919 Uttam Kumaran: Part of the dashboard development process, too, is like doing mock ups and sort of getting approval from the client. So right now, we’re sort of a little bit blind on Javi, which
176 00:27:53.462 ⇒ 00:27:55.759 Uttam Kumaran: is okay, for now we’re gonna
177 00:27:55.880 ⇒ 00:28:03.069 Uttam Kumaran: we’re getting a little bit better. But yeah. And I know, Jacob, I know, even looking at some of these tickets. There’s there’s not enough
178 00:28:03.360 ⇒ 00:28:05.630 Uttam Kumaran: in here to even like, get started. Like.
179 00:28:05.740 ⇒ 00:28:17.249 Uttam Kumaran: I just see basically the link to the dashboard. Everything else. Looks like kind of like AI generated. So I’ll I’ll message Nico about trying to give you more time. Give you more information.
180 00:28:17.380 ⇒ 00:28:21.590 Uttam Kumaran: Honestly, I would just put any questions you have about this dashboard.
181 00:28:21.740 ⇒ 00:28:24.149 Uttam Kumaran: like I would just put right in
182 00:28:24.340 ⇒ 00:28:26.380 Uttam Kumaran: the ticket or right in slack
183 00:28:26.690 ⇒ 00:28:31.589 Uttam Kumaran: and sort of just keep putting pressure on until you get those answered, because.
184 00:28:31.890 ⇒ 00:28:37.280 Uttam Kumaran: yeah, I’m I’m only looking at it now. And it’s just basically just the
185 00:28:37.950 ⇒ 00:28:40.279 Uttam Kumaran: it’s just the dashboard that’s there. There’s like, no content.
186 00:28:40.940 ⇒ 00:28:43.660 Jakob Kagel: Right? Okay. Sounds good.
187 00:28:45.560 ⇒ 00:28:48.470 Uttam Kumaran: Okay, perfect. Thanks everyone. I appreciate it.
188 00:28:48.470 ⇒ 00:28:49.794 Caio Velasco: Thank you. Thank you. Appreciate.
189 00:28:50.060 ⇒ 00:28:51.360 Uttam Kumaran: Talk soon bye.
190 00:28:51.360 ⇒ 00:28:51.890 Caio Velasco: Right.