Meeting Title: Uttam <> Patrick Date: 2024-02-01 Meeting participants: Uttam Kumaran
WEBVTT
1 00:00:47.350 ⇒ 00:00:48.560 Uttam Kumaran: Hey, dude.
2 00:00:48.940 ⇒ 00:00:49.850 Patrick’s iPhone: hey?
3 00:00:50.280 ⇒ 00:00:51.560 Uttam Kumaran: What’s up, man?
4 00:00:51.880 ⇒ 00:00:54.150 Patrick’s iPhone: Nope. you know, chillin
5 00:00:54.190 ⇒ 00:01:07.080 Uttam Kumaran: sorry about the news today. You seem awfully cheerful, though, and so it’s it’s fine, like. I mean, it sucks. But
6 00:01:07.360 ⇒ 00:01:08.750 Patrick’s iPhone: it’s I’m
7 00:01:10.250 ⇒ 00:01:12.190 Patrick’s iPhone: I’m fine. I’ll be all right. Okay.
8 00:01:12.300 ⇒ 00:01:17.030 Uttam Kumaran: what do you like? What’s your what’s your gut instinct on it like, what are you thinking about
9 00:01:18.040 ⇒ 00:01:31.510 Patrick’s iPhone: like? I wish I could go back in time and like, tell the Cba to each shit. It’s it’s kind of where my head. So I like, I’ve I’ve been there for what? 2 and a half years, and it’s like
10 00:01:31.610 ⇒ 00:01:35.510 Patrick’s iPhone: it’s it’s kinda like watching a slow car crash
11 00:01:35.650 ⇒ 00:01:44.630 Patrick’s iPhone: when I joined it, and now is totally different company, and it’s like so when I joined, it went up really fast, and then it was like.
12 00:01:45.560 ⇒ 00:01:52.319 Patrick’s iPhone: I don’t know how did like a hair PIN turn and then crash. And then it’s just kind of been like
13 00:01:53.100 ⇒ 00:01:58.080 Patrick’s iPhone: like a rest and best type of deal, and so
14 00:01:58.930 ⇒ 00:01:59.610 Patrick’s iPhone: sees.
15 00:02:00.140 ⇒ 00:02:01.480 Here’s what it is.
16 00:02:02.200 ⇒ 00:02:11.110 Uttam Kumaran: What do you think of it all for, like Mex, that you want to go like to start up, or you think about doing another company like what or what do you like figure company, or what do you think?
17 00:02:13.290 ⇒ 00:02:15.430 Patrick’s iPhone: I mean?
18 00:02:16.960 ⇒ 00:02:24.889 Patrick’s iPhone: I don’t know. I guess I haven’t thought about it. Gotten that far right now. What I’m thinking is like, where do I want to go for lunch?
19 00:02:39.130 ⇒ 00:02:41.080 Patrick’s iPhone: Yeah. yeah.
20 00:02:41.630 ⇒ 00:02:44.079 Like. I mean, I think it’ll be fine, and
21 00:02:46.700 ⇒ 00:02:49.590 Patrick’s iPhone: for it well enough, like homeless. And
22 00:02:59.300 ⇒ 00:03:00.479 Patrick’s iPhone: but
23 00:03:00.560 ⇒ 00:03:02.220 Patrick’s iPhone: yeah, so what’s up with you?
24 00:03:02.890 ⇒ 00:03:11.910 Uttam Kumaran: Stuff’s good. I’m I just feel like a lot more stuff is like coming across like my desk for opportunity. So
25 00:03:11.950 ⇒ 00:03:21.890 Uttam Kumaran: it’s just good to know, like, who’s around and like who wants to do what in particular, one of the clients that I’m working, one of the clients that I’m working on?
26 00:03:21.940 ⇒ 00:03:23.190 Uttam Kumaran: they!
27 00:03:23.440 ⇒ 00:03:42.749 Uttam Kumaran: I have done a lot of dashboarding, and so one of my team has been doing dashboarding for them. But it’s like not either of our skill sets, and it’s a top takes a shift ton of time to both like, think about what they need and like, have, like that sort of like visual language, and like, think about like information architecture like a dashboard.
28 00:03:42.970 ⇒ 00:03:50.469 Uttam Kumaran: so that’s something that I’m trying to see whether I can bring someone on to do or
29 00:03:51.340 ⇒ 00:03:58.809 Uttam Kumaran: not. Only this one client, but hopefully across a bunch of people. It’s kind of like a last mile problem. You know what I mean like.
30 00:03:58.930 ⇒ 00:04:08.949 Uttam Kumaran: but at the same time for the for the business folks. That is the actual end product that we’re giving them, and is a manifestation of like all the pipelines.
31 00:04:09.530 ⇒ 00:04:16.930 Patrick’s iPhone: Right? Yeah, that’s kind of like the the interface of everybody else that like, we’re, we’re
32 00:04:16.950 ⇒ 00:04:21.780 Patrick’s iPhone: your stop. And that’s how they interact with everyone. So
33 00:04:22.070 ⇒ 00:04:24.739 Patrick’s iPhone: yeah, that’s exactly correct. So
34 00:04:27.460 ⇒ 00:04:30.789 Uttam Kumaran: okay, cutting off a little bit, let me turn off my video. Maybe it’ll make it better.
35 00:04:31.730 ⇒ 00:04:40.300 Patrick’s iPhone: plus data I might be able to. Might be. That might be, too. I’ll probably probably don’t walk outside.
36 00:04:41.840 ⇒ 00:04:42.780 Uttam Kumaran: How’s it now?
37 00:04:43.450 ⇒ 00:04:57.660 Patrick’s iPhone: Alright, yeah, that that’s that’s good. I think I was in a weird spot. I was in a weird spot in the coffee shop. So you know you’re good. You’re good. Yes, the main. The main thing is that
38 00:04:57.770 ⇒ 00:05:04.169 Uttam Kumaran: like that sort of like thinking about the dashboard thing, about information architecture, and like working through those feedback cycles is, I think.
39 00:05:04.440 ⇒ 00:05:08.259 Uttam Kumaran: kind of a tougher skill. A lot of engineers, I know.
40 00:05:08.450 ⇒ 00:05:17.689 Uttam Kumaran: just don’t have that that sort of like visual understanding of like how to present information. And it’s not even anything complicated. It’s not like we’re not building like a New York Times infograph like.
41 00:05:17.770 ⇒ 00:05:35.979 Uttam Kumaran: it’s just bar charts line charts. But it’s also thinking about how to present the information given. The context of the data. And how do you make the dashboard? Serve them right, like, you don’t want to show sales over time, right? If that’s like a that’s like a one out of 10, and like a 10 out of 10, is like something super advanced.
42 00:05:35.980 ⇒ 00:05:56.219 Uttam Kumaran: I would say I wanted for those dashboards that are like a 6 or a 7 out of 10, where they answer the first, second, and then potentially guide you to get third level degree answers to questions. For example, if you notice that sales are up today versus yesterday and also for the month sales are up, you what your questions may be, what products are contributing to those.
43 00:05:56.240 ⇒ 00:06:13.940 Uttam Kumaran: What did what and what’s our forecast for the next few months? Those are like the second order derivative questions that I want to be able to answer the same contacts or have quick ways for the executive to kind of go address that. So it’s kind of thinking about their flows. It’s thinking about their questions. And then kind of
44 00:06:14.140 ⇒ 00:06:34.879 Uttam Kumaran: you know, the the. The actual deployment is like working within light dash and and building those charts. And also again, there’s like visual feel of like axis names having definitions kind of right there, like Co kind of colors. And think of the best ways across like line charts, area charts or bars or stacks to kind of show. Info.
45 00:06:35.020 ⇒ 00:06:37.400 Patrick’s iPhone: Oh, yeah, that sort of like that sort of stuff.
46 00:06:38.480 ⇒ 00:06:45.909 Patrick’s iPhone: Okay, yeah, yeah, no, that that makes total sense. Actually, I did a class. Have you heard of a guy named Edward Tuft?
47 00:06:46.350 ⇒ 00:06:59.879 Patrick’s iPhone: Yeah, yeah. Yeah. Of course, I have his on information design. Absolutely mind blowing
48 00:07:00.450 ⇒ 00:07:05.940 Patrick’s iPhone: it. It. It was actually in Austin. What? The fuck? Yeah.
49 00:07:05.950 ⇒ 00:07:17.180 Patrick’s iPhone: no way. That’s sick. Okay. Super jealous.
50 00:07:17.750 ⇒ 00:07:20.610 Patrick’s iPhone: quantitative. Oh, what’s the
51 00:07:20.820 ⇒ 00:07:26.119 Patrick’s iPhone: the yellow.
52 00:07:26.400 ⇒ 00:07:28.269 Patrick’s iPhone: 2 white ones, and then a green one.
53 00:07:28.880 ⇒ 00:07:38.649 Patrick’s iPhone: I didn’t know you were like that into like, there’s and like, info, like architecture stuff. Oh, yeah, yeah, yeah. I like, I love information design
54 00:07:38.860 ⇒ 00:07:44.900 Patrick’s iPhone: like, that’s that’s kinda like, my, I’m pretty well rounded in terms of
55 00:07:45.000 ⇒ 00:08:02.279 Patrick’s iPhone: the data space. And II think that’s all always been kind of like, because I’ve been in startups, usually starting from like a like a 0 to one type deal where there was like there was no such thing as like data engineering. But then there also wasn’t like analytics and analysis either.
56 00:08:02.360 ⇒ 00:08:04.140 Patrick’s iPhone: But it needed to be.
57 00:08:04.520 ⇒ 00:08:05.799 And so
58 00:08:06.000 ⇒ 00:08:16.059 Patrick’s iPhone: like like I’ve always had like in college, I had like a quantitative background. And that kind of like moved into that engineering. But then there’s also like.
59 00:08:16.170 ⇒ 00:08:20.520 Patrick’s iPhone: I wanted that creative outlet. And then being able to.
60 00:08:21.530 ⇒ 00:08:25.669 Patrick’s iPhone: You know, like distill a data model into
61 00:08:25.790 ⇒ 00:08:31.369 Patrick’s iPhone: a picture was like, I mean, that’s a huge skill. And it’s also like, Yeah, it.
62 00:08:31.420 ⇒ 00:08:46.410 Uttam Kumaran: I feel super aligned with that. I was in a very similar position, and I kind of worked my way off the stack, but I almost went towards the business side, where
63 00:08:46.540 ⇒ 00:09:15.680 Uttam Kumaran: I started like working on, you know, a lot of the procurement of these tools and actually working on how these like working on how the data gets applied to strategy. But data is always an area where I learned enough to be dangerous. Like II knew about tough and a lot of information architecture. And I started working with some designers that were really really good at that. But it’s something that, like I, it was such a last mile. Prom, that it’s like I spent 2% of my time on it. But 98% of the times I’m modeling, and like.
64 00:09:15.740 ⇒ 00:09:28.779 Uttam Kumaran: you know, a lot of that stuff. But I know enough to know what a good thing looks like, and so for me is like I. The other thing I’ll mention is this is one of the skills that nobody in data has.
65 00:09:29.000 ⇒ 00:09:30.710 Uttam Kumaran: Everybody think about it.
66 00:09:30.870 ⇒ 00:09:41.489 Uttam Kumaran: Yeah. And it’s well, part of it is because they are like, Oh, this is below me. Which I would say in some situations I think nobody in the whole stack of like
67 00:09:41.620 ⇒ 00:10:01.510 Uttam Kumaran: data is gonna care if the executives gonna be like, well, I don’t wanna hire like a data vis engineer. And then the data people are like I can just do it. It’s a bar chart, right? Everybody’s kind of like talks fit about this sort of like field. However, I think when they see the results of like a really good information architecture on a dashboard, and, like good flow of like
68 00:10:01.710 ⇒ 00:10:16.370 Uttam Kumaran: finding out where to get information from different sources. It speaks for itself. And so it’s something that, like I don’t. II don’t know. I wish IA lot of people I try to work with. Don’t have the skill set, and so I’m trying to find
69 00:10:16.500 ⇒ 00:10:40.980 Uttam Kumaran: someone that I can bring on who is like not only like a 6 out of 10 at this, but like a 9 out of 10. But the thing that’s tough about this gig is that I don’t know how many like I’m not sure you how many hours there’s gonna be, for example, it’s like, it may be just like couple of strategy sessions with the client, and then iterations on a dashboard. But of course, like the amount of billable hours, for that is
70 00:10:40.980 ⇒ 00:10:52.640 Uttam Kumaran: likely going to be so significantly less than if we were doing data modeling. And just because of the amount of effort, however, the way this scales every client that I’m working with does dashboard work.
71 00:10:52.730 ⇒ 00:10:57.799 Patrick’s iPhone: all 3 of the folks that I’m working with. And so I actually think there’s opportunity to
72 00:10:57.870 ⇒ 00:11:04.930 Uttam Kumaran: add this person with a skill set kind of leverage in all those situations, and then it becomes pretty like feasible to
73 00:11:04.980 ⇒ 00:11:12.469 Uttam Kumaran: to actually like. Have this be like a reasonable bit of cash? But kind of like my my pitch to you on this one would be.
74 00:11:12.560 ⇒ 00:11:29.420 Uttam Kumaran: I have a client where we’ve developed like maybe 4 really primary dashboards. One is like a daily view of the entire company. It’s an e-commerce company. The second is a weekly and monthly view, the third is a view of refunds and discounts, which is a huge cost center to them
75 00:11:29.460 ⇒ 00:11:32.080 Uttam Kumaran: and then the fourth is a view of their shipping.
76 00:11:32.500 ⇒ 00:11:51.879 Uttam Kumaran: The fifth one that we’ll likely develop is something around marketing, but that pretty much rounds out all of the different priorities for the company. It’s like the company health on a daily, weekly, and monthly basis. It’s everything around shipping everything around refunds and discounts and everything around marketing. That’s like, generally the whole company. But data is is like
77 00:11:51.880 ⇒ 00:12:14.119 Uttam Kumaran: pretty clean. It’s all that’s all stuff that me and my team have done. Really, the problem we’re facing is the client has a lot of asks for like dashboard updates. And even he doesn’t really know, like he doesn’t have all the language to translate like, oh, II process data, maybe just like a sitting with him watching how he does his workflows, and then, being like cool, I can build you
78 00:12:14.190 ⇒ 00:12:20.040 Uttam Kumaran: like we can model the dashboard in a way where that speeds up your workflow like 5 x.
79 00:12:20.060 ⇒ 00:12:44.590 Uttam Kumaran: That’s kind of like what I need and what I’m trying to propose to him. So he he emailed me and pretty much was like, Hey, all the dashboards are in a good place. All the models are accurate, and I’m really happy with the progress I need someone who can. I need someone in the next few weeks. Who can help me with a ux of these dashboards? And he’s like, is that something you can handle or like? Can you recommend somebody. And so I’m trying to
80 00:12:44.920 ⇒ 00:12:47.300 Uttam Kumaran: pretty much toss someone in on that.
81 00:12:47.480 ⇒ 00:12:57.499 Patrick’s iPhone: Alright, yeah, that makes sense. And to like to kind of like, bounce off that, or good little deeper into it. A lot of the time these people like they don’t even really know
82 00:12:57.620 ⇒ 00:13:05.679 Patrick’s iPhone: what they want like. It’s you’re talking about like a lot of people don’t have this like skill for biz. And it’s it’s because, like, they don’t
83 00:13:05.900 ⇒ 00:13:12.699 Patrick’s iPhone: necessarily understand the like the nuance. So it’s like when they see something good they don’t realize.
84 00:13:12.970 ⇒ 00:13:24.119 Patrick’s iPhone: or they don’t have that kind of like internalization of like. Why, it’s good and what level. After that it took to make it right. Yes, right it and it it’s like, I always think of it as like
85 00:13:24.340 ⇒ 00:13:27.360 Patrick’s iPhone: like typography, or
86 00:13:27.380 ⇒ 00:13:41.700 Patrick’s iPhone: like what it in graphic design like, yes, people kind of shoes you that a ton, but it’s like when you dig into it like it’s no I
87 00:13:41.790 ⇒ 00:14:00.570 Uttam Kumaran: is a good design or a bad design. And there’s a decision made on. There’s a decision to think about it, and there’s even decision for some people it’d be like, I don’t care about the design of a product, it’s super important, and for me to be able to grow my business and be like yo. Not only are the all the models really good, the problem with these guys, they they have to trust me that
88 00:14:00.670 ⇒ 00:14:05.989 Uttam Kumaran: like, I can’t show them the art of the infrastructure that we built, although I could describe how complicated it is and like.
89 00:14:06.090 ⇒ 00:14:09.219 Uttam Kumaran: why, we’ve done it. And we’ve done it in record speed.
90 00:14:09.310 ⇒ 00:14:22.089 Uttam Kumaran: like what I’m gonna do pull up like Github. And so that the actual actual product is the dashboard. And what I can control the development side is like it’s reliability, the accuracy, and like the timeliness. But
91 00:14:22.200 ⇒ 00:14:30.130 Uttam Kumaran: the still there, the the the interface for them is that dashboard. And I’ve never worked at a company that’s like prioritize
92 00:14:30.210 ⇒ 00:14:38.269 Uttam Kumaran: like data vis for Intel reporting. But I and II think the main thing is, there’s not like full time amount of work.
93 00:14:38.360 ⇒ 00:15:01.900 Uttam Kumaran: However, I do think that if I’m able to kind of bring that to the table for some of these clients, it’s gonna blow. It’s gonna blow their fucking mind, cause even even the shit that I’m producing, which is okay, they’re getting like they’re really like holy fuck. This is really data. I like, I think if there’s really even touch more of like information architecture, and even sitting with them and understanding like.
94 00:15:01.900 ⇒ 00:15:13.929 Uttam Kumaran: here’s how you look at the company on a daily, weekly monthly basis. The other thing I’ll say is a lot of engineers. They don’t think about the company that they’re producing data for, like.
95 00:15:14.110 ⇒ 00:15:20.959 Uttam Kumaran: Yeah, I’m like, Oh, there’s like they’re like, Oh, we pushed this thing, and then I’m like, Oh, can you just make a dashboard? It covers it. They’re like makes 4 line charts. I’m like dude.
96 00:15:21.170 ⇒ 00:15:38.549 Uttam Kumaran: We we’re not like what think about it. You’re in the position of CEO. You open the stack for what the fuck are you? You don’t care about refund over time. It’s like, yeah, that’s like the most that’s like the most basic thing I’m trying to like. Think if we if we take refunds, for example. Here, here’s like 5 questions that they would ask. One is.
97 00:15:38.650 ⇒ 00:15:40.349 Uttam Kumaran: are we friends up or down?
98 00:15:40.960 ⇒ 00:16:04.759 Uttam Kumaran: And then so what we gotta answer, then what timeframe? Okay, 7 days, 30 days this year versus last year, same month, this year versus same month last year. Okay, what products are getting refunded? More. Okay, cool. We need to do a join of refunds to to products. We need to make sure we have skews. If there’s too many skews, we need to have segmentation. Okay, great. So now we can look at which products you can refund over time. Okay, our refunds concentrated in any specific region
99 00:16:04.760 ⇒ 00:16:12.359 Uttam Kumaran: or shipment provider. Okay, let’s answer that question. Okay, what percentage of my sales are actually going to refund? And does that change
100 00:16:12.360 ⇒ 00:16:14.440 Uttam Kumaran: during the year during the seasonality?
101 00:16:14.550 ⇒ 00:16:22.159 Uttam Kumaran: Okay. And those are all things. And then the last thing is like, how can we get proactive? How can I tell you that a skew or a specific
102 00:16:22.160 ⇒ 00:16:49.979 Uttam Kumaran: like, workflow is having issues. When we see a refund spike. And I’m able to say, Hey, you should go email, your warehouse guys or your customer service guys about this skew, cause we’re seeing a spike in refunds. That’s that’s like the edge. That’s the edge right? That’s the edge that if I’m able to do that. I’m able to tell them 5 days in advance of, like something bad happening with a product that like, Hey, we’re seeing a spike in refunds. You should go send an email. Now
103 00:16:49.990 ⇒ 00:17:11.979 Uttam Kumaran: that’s gonna save them money. And that’s gonna pay for us being employed right easily just in that one decision. And so now I think about, how do we scale data to affect that across all parts of their business? So we go from like no data to like being able to see historicals. And now being able to actually take action faster. And then it’s like we’re golden. We pay for ourselves.
104 00:17:12.109 ⇒ 00:17:13.140 Uttam Kumaran: right?
105 00:17:13.540 ⇒ 00:17:31.639 Uttam Kumaran: You know. So I think all that is, I think you you, and like a lot of the folks in that chat, I think, have a really good context in that it’s just the problem is a lot of the people in the chat like me. They’re like, not visual people. So it’s actually really nice to hear that you you spent a lot of time thinking and doing that. So I’d love to see if you you even want to take a crack at it.
106 00:17:31.720 ⇒ 00:17:44.289 Patrick’s iPhone: Oh, yeah, I mean, I’ve got nothing but free time now. So it sounds pretty good. But yeah, I mean, I’m totally down to. I mean, help you out. Check this out and
107 00:17:44.350 ⇒ 00:17:45.920 Patrick’s iPhone: see where we go with it.
108 00:17:46.280 ⇒ 00:17:55.369 Uttam Kumaran: Yeah. So let me. So let me even bounce my idea off of you, of like what I think could be the best like next step. So
109 00:17:55.380 ⇒ 00:18:00.269 Uttam Kumaran: like. What do you think is a good like proof of concept?
110 00:18:00.390 ⇒ 00:18:04.800 Uttam Kumaran: III could get you on the phone with them, although, like that may do
111 00:18:05.290 ⇒ 00:18:10.689 Uttam Kumaran: more bad like. I don’t know whether that’s like, gonna Be helpful or not. I wonder if it’s like
112 00:18:10.860 ⇒ 00:18:30.479 Uttam Kumaran: like, what do you think is a good proof of concept that I can maybe just share with them. Be like, Hey, we gave you if we gave you a dashboard, here’s like an example of what we can do, and then you could maybe spend like an hour doing it, and I can just send it to them, or, like the 3 of us, hop on a call for 30 min. You can share something right? It’s like, how can I quickly get them.
113 00:18:30.510 ⇒ 00:18:33.090 Uttam Kumaran: Have confidence in your work.
114 00:18:33.230 ⇒ 00:18:43.870 Patrick’s iPhone: I think kind of going with the second thing that you said. You’re definitely right like me hopping on a call with them is probably
115 00:18:43.960 ⇒ 00:18:46.090 Patrick’s iPhone: not the best use of time.
116 00:18:46.120 ⇒ 00:18:58.940 Patrick’s iPhone: I think it’s kind of like a mix between just like a like an Mvp of a dashboard essentially kind of like more or less of a wireframe. But something that has some like
117 00:18:59.060 ⇒ 00:19:01.450 Patrick’s iPhone: actual like that relates
118 00:19:01.460 ⇒ 00:19:10.230 Patrick’s iPhone: back to their business. That’s like that’s true. And then like to be able to do that from from my side, like, I’ll
119 00:19:10.310 ⇒ 00:19:16.810 Patrick’s iPhone: I’ll want to. Kind of like, get the context of the of the data model. Right? So
120 00:19:17.040 ⇒ 00:19:23.499 Patrick’s iPhone: and like seeing what’s there or what’s it like? What’s available is going to
121 00:19:23.850 ⇒ 00:19:30.729 Patrick’s iPhone: kind of like, dictate what were able to do quickly on on the visual, on the visualization side.
122 00:19:30.810 ⇒ 00:19:42.479 Patrick’s iPhone: Because a lot of the times it’s like you can have this idea, for, like a way you want to see your numbers way you want to see your business, and you kind of have to like back your way into it and produce that data model.
123 00:19:42.640 ⇒ 00:20:05.550 Patrick’s iPhone: And so so that we already have the data model, we’ll have to just flip, flop that and see like, okay, what can we do right now and then, like on the next iteration? Next, iteration, like those derivatives that’s when we start adding in like the segmentation, and all the different like pivots or around that. And then we, that’s how you
124 00:20:05.570 ⇒ 00:20:06.830 Patrick’s iPhone: continue to.
125 00:20:06.940 ⇒ 00:20:15.320 Patrick’s iPhone: Yeah. So I would say, most of the data models we’ve done like 2 full cycles of that where we’ve like
126 00:20:15.610 ⇒ 00:20:20.370 Uttam Kumaran: created something created a dash, got feedback updated, created another dash.
127 00:20:20.380 ⇒ 00:20:34.939 Uttam Kumaran: And so I think this is a really good opportunity where we have, like, probably like 80 70 to 80% of everything there, you should have enough to work with the thing. I would say that we could do is, I wonder if I can maybe give you
128 00:20:35.030 ⇒ 00:20:38.630 Uttam Kumaran: success or give you like a Pdf
129 00:20:38.750 ⇒ 00:20:56.310 Uttam Kumaran: screenshot of like one of the dashboards. And then the meeting that we have will just be like a 30 min meeting can be. You may be going through, hey? I got access to this. Here is like a typical. Here’s like how I would suggest we improve it. And it’s almost like doing a user interview.
130 00:20:56.330 ⇒ 00:21:13.779 Uttam Kumaran: And we can kind of go through a process with that with the client, which is like, Okay, you have access. Maybe I give you access like the what the the dashboard that he looks at every morning right to pretty much look at the entirety of the business. And then you can maybe make some suggestions about like, Okay, here’s here’s questions. I would ask things I would change right off the bat, and then
131 00:21:13.900 ⇒ 00:21:30.529 Uttam Kumaran: I don’t know. I could give you access to light dash if you even want to modify stuff. But again, you you tell me, like, what do you think? The best method for a salis to give you context like the guy’s very, very visual, and it’s definitely very opinion has like a run, pretty big marketing agencies. And it’s pretty fluent with data.
132 00:21:30.530 ⇒ 00:21:47.140 Uttam Kumaran: although, like it like he like, II would say is, is really competent on that side. So I think the best thing. Maybe if I can just give you either screenshots if I can give you like a Pdf outlay and Sigma, which is kind of how I’m doing iterations and dashboards anyways, right now.
133 00:21:47.150 ⇒ 00:21:56.170 Uttam Kumaran: maybe you can make suggestions, or I could even give you access to the dashboard a copy, and and you can try and make some changes, and then
134 00:21:56.240 ⇒ 00:21:58.699 Patrick’s iPhone: we. I can throw a meeting on the calendar.
135 00:21:59.000 ⇒ 00:22:06.229 Patrick’s iPhone: Right? Yeah, yeah. So like screenshots. Pdf, like, that’s that’s great. And then, if you have like.
136 00:22:06.440 ⇒ 00:22:15.269 Patrick’s iPhone: like, you don’t have to give me the information like the actual data set. But if you have, like, you create just a fake data. Set that yes, mimics the
137 00:22:15.290 ⇒ 00:22:18.880 Patrick’s iPhone: the the shape of everything. I can use that. And just like
138 00:22:19.560 ⇒ 00:22:21.850 that, that like, build those
139 00:22:21.890 ⇒ 00:22:25.099 Patrick’s iPhone: small proof proofs of concept. And then then
140 00:22:25.160 ⇒ 00:22:26.450 Patrick’s iPhone: from there we can
141 00:22:26.790 ⇒ 00:22:31.550 Patrick’s iPhone: plug in the real stuff, and then actually build it in light dash, or wherever
142 00:22:31.700 ⇒ 00:22:39.650 Uttam Kumaran: I can. I’ll just give you access to live. I’ll just have you sign an nda. And then I mentioned to him that like, and then the only thing I would say is, Don’t spend, like
143 00:22:39.820 ⇒ 00:22:47.210 Uttam Kumaran: I, upon the time we spend enough time for you to be able to sell it. And then again, for me, this is like a kind of a new
144 00:22:47.260 ⇒ 00:23:05.359 Uttam Kumaran: thing that I want to kind of figure out how I can bundle into projects, so I’m not really sure yet. The time commitment rate, I think, will be really good, but I’m not sure yet how many hours. But let’s figure it out as we go. Why don’t I try to? I’ll add, I’ll if I can add you to slack. Would that work.
145 00:23:05.460 ⇒ 00:23:14.149 Patrick’s iPhone: Yeah, yeah, yeah. Okay, cool. Maybe I can add you to slack to my like, slack Workspace. I will share you access to a couple of things
146 00:23:14.200 ⇒ 00:23:27.789 Uttam Kumaran: in light dash is all set up so you can go explore the shape of some of this data, and then you’ll have access to the dashboards, and then maybe we can plan like, what do you think is a good timeline like if I try to grab time next week?
147 00:23:28.060 ⇒ 00:23:38.570 Patrick’s iPhone: I mean, I I’ve got nothing at the time. So you tell me I’m I’m free next week. So
148 00:23:38.860 ⇒ 00:23:44.310 Uttam Kumaran: I’m gonna be meeting with them tomorrow, and I’m gonna throw your name out there.
149 00:23:44.340 ⇒ 00:23:48.149 Uttam Kumaran: and then maybe I will aim for like
150 00:23:48.500 ⇒ 00:23:51.029 Uttam Kumaran: Wednesday or Thursday of next week.
151 00:23:51.090 ⇒ 00:24:02.420 Uttam Kumaran: and hopefully, maybe you can just spend a little bit of time taking a look at the data shape, taking a look at the existing dashboards you have, you have all the time you need from me on slack or whatever.
152 00:24:02.560 ⇒ 00:24:04.060 Patrick’s iPhone: and then.
153 00:24:04.270 ⇒ 00:24:08.290 Uttam Kumaran: like, I’ll just set up like a 30 min thing where you can run the meeting
154 00:24:08.320 ⇒ 00:24:14.479 Uttam Kumaran: pretty much. Just intro, go through your background and like kind of information design, they’re gonna they’re gonna love that
155 00:24:14.530 ⇒ 00:24:25.819 Uttam Kumaran: and then I would say, just talk about, Hey, I got access to these things. I generally understand these points about your business. Taking a look at the dashboards. Here are things I would do right off the bat.
156 00:24:25.840 ⇒ 00:24:28.190 Patrick’s iPhone: Here are a bunch of questions I would ask you.
157 00:24:28.270 ⇒ 00:24:35.899 Uttam Kumaran: and this is like what I could envision. What’s the future looking like? Like? I think we got it in the bag. So you’re able to do that
158 00:24:36.120 ⇒ 00:24:39.260 Patrick’s iPhone: cool, cool? Yeah, no, that that all sounds great.
159 00:24:40.010 ⇒ 00:24:42.519 Uttam Kumaran: Okay, sick? Oh, I’m fine, nice.
160 00:24:43.210 ⇒ 00:24:47.540 Uttam Kumaran: It’s a tough. It’s it’s like a tough. It’s a tough thing, dude cause I don’t. I don’t know many people that have
161 00:24:47.750 ⇒ 00:25:02.239 Uttam Kumaran: like a ton of interest in like information architecture, but that’s exactly what this is is. This is how they’re they’re and they’re really open to using data which is really great, like, they’re not. And again, I have a through line directly to like all the executives of the company. So
162 00:25:02.400 ⇒ 00:25:11.380 Uttam Kumaran: I’m really pumped to kind of like over deliver for them, and they have a huge opportunity in front of them like, it’s a pretty successful business. So
163 00:25:11.510 ⇒ 00:25:25.899 Uttam Kumaran: yeah, I think I think it should work out. And then, additionally, if it works out for these guys, I have another client where I’m doing dashboarding work where I think I could totally tap you in on producing those. And then
164 00:25:26.130 ⇒ 00:25:33.780 Uttam Kumaran: I’m actually going. I’m actually about to start another thing with this company that wants to build embedded dashboards into their product.
165 00:25:33.920 ⇒ 00:25:39.980 Uttam Kumaran: And they have some questions about. I’m kind of helping them think about the business model around it, and like
166 00:25:40.050 ⇒ 00:25:50.130 Uttam Kumaran: the technical aspects. But they want my help and also architecting the visual language of the dashboard. And then I’m just gonna bring you. I’m just gonna have you on that part. So
167 00:25:50.370 ⇒ 00:25:51.250 Patrick’s iPhone: oh, yeah.
168 00:25:51.410 ⇒ 00:25:54.030 Uttam Kumaran: that’d be sick. Let’s see if we can make that happen.
169 00:25:54.160 ⇒ 00:25:55.560 Patrick’s iPhone: Yeah, sounds good.
170 00:25:56.310 ⇒ 00:26:05.870 Uttam Kumaran: Okay, cool. So I’ll add you to slack. And then. yeah, we’ll go from. Now. Send you anything in terms of Nda, and then I will talk them tomorrow, and kind of mention and get something set up for next week.
171 00:26:06.310 ⇒ 00:26:07.469 Patrick’s iPhone: Alright cool.
172 00:26:07.670 ⇒ 00:26:16.009 Uttam Kumaran: Sorry to keep you busy on your new found break. But it’s all good. It’s money.
173 00:26:16.060 ⇒ 00:26:24.629 Patrick’s iPhone: Yeah, it’s I’d I’d probably be doing a lot of this stuff like in in my free time, anyway, just like out of sheer boredom. Or
174 00:26:24.760 ⇒ 00:26:32.080 Uttam Kumaran: it’s just I mean, that’s just kind of the shit that I’m into. So that’s great dude. And I actually think there’s a huge avenue for like.
175 00:26:32.430 ⇒ 00:26:35.310 Uttam Kumaran: because there’s people that do this sort of like data.
176 00:26:35.630 ⇒ 00:26:49.959 Uttam Kumaran: like data vis work that’s almost like a studio where it’s like art pieces, or like, you know, it’s like huge infograph. But these are like things where it’s like day to day, you know, and although maybe less time. I think the impact is just as high.
177 00:26:49.980 ⇒ 00:26:55.379 Uttam Kumaran: I want to kind of see how it goes and see whether this is something else I can kind of like, throw in for contracts.
178 00:26:55.580 ⇒ 00:27:07.100 Patrick’s iPhone: Oh, for sure. Yeah, yeah, this, I mean, this is a great kind of like, step into it. Then, it’s I mean, like all things, it’s like you go through one door, and then there’s more doors. And so yeah.
179 00:27:08.990 ⇒ 00:27:13.739 Uttam Kumaran: alright cool. Well, I’ll add you there, and I’ll send you anything. And then we can go from there.
180 00:27:14.050 ⇒ 00:27:16.189 Patrick’s iPhone: Hell, yeah. Alright, man sounds good.
181 00:27:16.330 ⇒ 00:27:25.600 Patrick’s iPhone: Enjoy the afternoon
182 00:27:26.230 ⇒ 00:27:33.269 Uttam Kumaran: nice. I’m gonna go to the I’m gonna I think I’m gonna go to the meeting this weekend. But yeah, go to the Aquarium I was at. I was at the Monterey Bay aquarium
183 00:27:33.510 ⇒ 00:27:34.660 Uttam Kumaran: few weeks ago.
184 00:27:34.690 ⇒ 00:27:39.190 Patrick’s iPhone: Oh, nice, nice. Yeah. Hang hang out with some fish.
185 00:27:39.450 ⇒ 00:27:48.239 Patrick’s iPhone: Huge fish, huge, huge, aquatic animals. They’re cool.
186 00:27:48.670 ⇒ 00:27:53.640 Patrick’s iPhone: alright, man. Alright. Man sounds good. Alright.