Meeting Title: Awaish - Hannah - Mattermore Case Study Date: 2025-10-01 Meeting participants: Hannah Wang, Awaish Kumar
WEBVTT
1 00:00:20.640 ⇒ 00:00:21.610 Awaish Kumar: Hello.
2 00:00:23.950 ⇒ 00:00:27.830 Hannah Wang: Hello? Hi. Yeah, sorry, Hannah, I…
3 00:00:28.950 ⇒ 00:00:34.470 Awaish Kumar: like, I don’t… I have some electricity issue here, I… that’s why I can’t open my camera.
4 00:00:34.470 ⇒ 00:00:43.590 Hannah Wang: Oh, that’s okay. I feel like most people don’t… don’t turn on their cameras anyway for meetings, except, like, the Friday one.
5 00:00:44.270 ⇒ 00:00:51.509 Hannah Wang: But… Okay, I feel like this is our first time kind of doing a…
6 00:00:52.440 ⇒ 00:01:11.340 Hannah Wang: interview style for the case studies. I’m probably going to ask you for a bunch of these later down the line, just because I think before, we were trying to get case studies from Utam and Robert, but that made them the bottleneck, and
7 00:01:11.700 ⇒ 00:01:14.020 Hannah Wang: Yeah, we… we can’t do that, so…
8 00:01:14.160 ⇒ 00:01:19.820 Hannah Wang: I don’t really have any context for the Mattermore
9 00:01:19.960 ⇒ 00:01:30.619 Hannah Wang: work that, I guess, Luke did before he left. I know we’ve been wanting to make a case study for it, and then there was, like, an issue with
10 00:01:31.510 ⇒ 00:01:46.040 Hannah Wang: I guess, like, not being able to use real data, so Utam was just saying synthetic data. I don’t really know what that means. Like, I get it, but in the context of Matter More, I don’t really know what that means, so you can just…
11 00:01:46.360 ⇒ 00:01:50.919 Hannah Wang: walk me through, I’m gonna ask you questions.
12 00:01:52.410 ⇒ 00:01:58.669 Hannah Wang: And even if the answer’s super obvious, or if you have to repeat yourself, like, just…
13 00:01:59.040 ⇒ 00:02:08.030 Hannah Wang: do that, because I take the transcript from this meeting, and I run it through AI to help me get the copy for the case study, so…
14 00:02:08.030 ⇒ 00:02:08.500 Awaish Kumar: Okay.
15 00:02:08.500 ⇒ 00:02:11.670 Hannah Wang: Yeah, I’m just gonna start off…
16 00:02:12.120 ⇒ 00:02:23.150 Hannah Wang: Hold on, let me… did not disturb my laptop, and… okay, so the first question is, like, what…
17 00:02:23.270 ⇒ 00:02:25.330 Hannah Wang: I guess, what is the project?
18 00:02:25.570 ⇒ 00:02:33.779 Hannah Wang: Type? Like, is it a… Deprecation? Is it, like, a dashboard? Like, yeah, what was the Mattermore project?
19 00:02:35.120 ⇒ 00:02:35.870 Awaish Kumar: Okay.
20 00:02:36.230 ⇒ 00:02:43.459 Awaish Kumar: So, I want to start with Mattermore as a client. So, Mattermore, basically, Was the client building a…
21 00:02:43.820 ⇒ 00:02:56.700 Awaish Kumar: Like, a product-based company, which was building a platform for… for different… to sell the… the… the… the employee from productivity.
22 00:02:57.520 ⇒ 00:03:07.410 Awaish Kumar: kind of dashboards, right? So, but… and… but they were… they didn’t… they don’t have… they didn’t have the real clients, and they didn’t have, actually, the…
23 00:03:07.550 ⇒ 00:03:16.010 Awaish Kumar: the real data, right? So, what they wanted from us as an analytics
24 00:03:16.300 ⇒ 00:03:26.220 Awaish Kumar: people were expert in analytics and reporting dashboarding, right? But they wanted us to basically, build the…
25 00:03:26.430 ⇒ 00:03:28.370 Awaish Kumar: the dashboard.
26 00:03:28.720 ⇒ 00:03:30.110 Awaish Kumar: for them?
27 00:03:30.620 ⇒ 00:03:34.500 Awaish Kumar: And, NFL, first of all.
28 00:03:34.580 ⇒ 00:03:43.799 Awaish Kumar: Generate the data, analyze it, help them figure out what are different metrics to measure the employee productivity.
29 00:03:43.800 ⇒ 00:03:54.589 Awaish Kumar: And… and employee interactions, right? For example, how employees are meeting, or how much time they spend in meetings, how much time they spend in…
30 00:03:54.590 ⇒ 00:04:00.809 Awaish Kumar: Talking to each other, and how much they spend just working, and things like that.
31 00:04:00.820 ⇒ 00:04:02.329 Awaish Kumar: And,
32 00:04:04.060 ⇒ 00:04:18.340 Awaish Kumar: after that, after we analyze, we figured out what… we worked basically totally building some, demo charts in the PowerPoint, and we used, like, writing Python scripts to basically,
33 00:04:18.800 ⇒ 00:04:25.180 Awaish Kumar: We generated those charts, and we generally… and further, as a, what to say.
34 00:04:25.330 ⇒ 00:04:28.440 Awaish Kumar: Kind of, we were building a mockup for them.
35 00:04:29.120 ⇒ 00:04:32.360 Awaish Kumar: What different kind of…
36 00:04:33.860 ⇒ 00:04:47.969 Awaish Kumar: metrics we should be using, and how should we be translating them into charts to measure the employee productivity, and then finally help them build those charts in Power BI.
37 00:04:48.460 ⇒ 00:04:50.179 Hannah Wang: Okay, I see.
38 00:04:50.180 ⇒ 00:04:50.740 Awaish Kumar: Yes.
39 00:04:53.280 ⇒ 00:05:03.320 Hannah Wang: Okay, I’m gonna ask probably more nitty-gritty questions later, so I guess my question… second question is, how long did the project take? Like, was it during Q…
40 00:05:03.420 ⇒ 00:05:11.709 Hannah Wang: 1, Q2 of last year, like, I know it was a while ago, so when… when was the project, and how long did it take, do you know?
41 00:05:12.070 ⇒ 00:05:15.220 Awaish Kumar: I think it was in this year, Q2.
42 00:05:15.400 ⇒ 00:05:16.709 Hannah Wang: Q2. But it looked…
43 00:05:17.100 ⇒ 00:05:20.569 Awaish Kumar: I think it took, like, 6 to 8 weeks, basically.
44 00:05:20.570 ⇒ 00:05:25.059 Hannah Wang: Okay. So, like, 3 months? 2-3 months? Okay.
45 00:05:25.060 ⇒ 00:05:25.740 Awaish Kumar: Yeah.
46 00:05:26.890 ⇒ 00:05:32.620 Hannah Wang: And then, I know Luke was the primary one who worked on it, I’m assuming?
47 00:05:32.860 ⇒ 00:05:44.660 Awaish Kumar: Yeah, Luke was the primary, the data engineer or data analytics engineer, and then we had, Annie as a…
48 00:05:44.820 ⇒ 00:05:50.870 Awaish Kumar: As an expert for data investigation and the dashboarding work.
49 00:05:51.720 ⇒ 00:05:54.380 Hannah Wang: Okay, well, I don’t know if I can put them on…
50 00:05:54.560 ⇒ 00:06:04.549 Hannah Wang: the case study, because they’re not here, so I just might put you later down the line, but that’s not a big deal.
51 00:06:04.550 ⇒ 00:06:08.150 Awaish Kumar: Yeah, I just… I basically take lead.
52 00:06:08.150 ⇒ 00:06:08.709 Hannah Wang: Oh, okay.
53 00:06:08.710 ⇒ 00:06:13.069 Awaish Kumar: that project, and they were the ones actually developing.
54 00:06:13.070 ⇒ 00:06:17.650 Hannah Wang: Okay, I see. Gotcha. Okay, so,
55 00:06:17.900 ⇒ 00:06:27.500 Hannah Wang: I know that we were, like, building this dashboard for Matamore. Did they… have they, like, tried to do it previously, or was this kind of a new…
56 00:06:27.610 ⇒ 00:06:34.390 Hannah Wang: thing that they asked us to build. Like, my question is, like, have they tried doing this before, and if…
57 00:06:34.390 ⇒ 00:06:35.080 Awaish Kumar: No, no, no.
58 00:06:35.080 ⇒ 00:06:35.980 Hannah Wang: Oh, okay.
59 00:06:36.840 ⇒ 00:06:43.629 Awaish Kumar: Yeah, like, you can think of it like they are a product company, and they want to build a…
60 00:06:43.960 ⇒ 00:06:56.579 Awaish Kumar: Like, what they want to build is they want to sell their product as, like, we are the one, we will show you the employee productivity, right? So just connect your Microsoft account
61 00:06:56.580 ⇒ 00:07:06.459 Awaish Kumar: With our platform, and then we are going to get the data and everything from those platforms automatically, and then we are going to…
62 00:07:06.710 ⇒ 00:07:11.890 Awaish Kumar: Just… Create the… the full… fully-fledged dashboard.
63 00:07:11.920 ⇒ 00:07:14.349 Hannah Wang: I see. Like, plug-and-play kind of thing.
64 00:07:14.630 ⇒ 00:07:19.740 Hannah Wang: Is this the only… product they have, or do they just…
65 00:07:19.740 ⇒ 00:07:20.339 Awaish Kumar: I think…
66 00:07:20.340 ⇒ 00:07:21.290 Hannah Wang: Okay.
67 00:07:23.350 ⇒ 00:07:29.730 Hannah Wang: So, like, how does the bike exist before we help them build it? Are they a new company, and they were, like.
68 00:07:29.730 ⇒ 00:07:47.799 Awaish Kumar: They were a new company. They were a new company. They didn’t hire any real clients, like, we were just helping them build out everything, so when they go in the market and get a first client, they just reconnect, and boom, here it is.
69 00:07:48.210 ⇒ 00:07:54.140 Hannah Wang: Oh, wow, they, like, trusted us to basically help them go to market. That’s… that’s crazy.
70 00:07:54.140 ⇒ 00:07:54.590 Awaish Kumar: Yes.
71 00:07:56.170 ⇒ 00:08:01.260 Hannah Wang: Okay, so… Yeah, I’m just asking these questions… this is, like, the context.
72 00:08:01.440 ⇒ 00:08:07.420 Hannah Wang: section, so I’m just trying to understand, like, the environment and the context.
73 00:08:07.420 ⇒ 00:08:08.780 Awaish Kumar: That’s tough.
74 00:08:08.780 ⇒ 00:08:22.029 Hannah Wang: project. So yeah, there were… this was a new… new project. They were a new, company, and we were basically helping them build their product so that they can launch it.
75 00:08:22.530 ⇒ 00:08:23.899 Hannah Wang: And do you know.
76 00:08:24.810 ⇒ 00:08:30.639 Hannah Wang: I guess this is more of, like, a why-they-exist type of question, but, like, do you know…
77 00:08:30.780 ⇒ 00:08:45.290 Hannah Wang: I don’t know if this is necessarily going into the case study, but, like, why… do you know why they wanted to make this, like, employee productivity product in the first place? Like, what their goal was with, like, building this?
78 00:08:45.290 ⇒ 00:08:45.850 Awaish Kumar: Okay.
79 00:08:46.520 ⇒ 00:08:54.760 Awaish Kumar: So, like, I… I don’t know what… what was going into their mind, but what I could understand from the project itself is that
80 00:08:54.850 ⇒ 00:09:11.649 Awaish Kumar: We, like, there are a lot of companies, they want to measure how employees spend their time, are they productive, where the time is going, are they spending too much on the meetings, right? And very, very little in actually delivering things, like, things like that.
81 00:09:11.650 ⇒ 00:09:20.250 Awaish Kumar: And there are, like, companies, big, big companies, like, with thousands of employees, and if every employee just spends half of their time
82 00:09:20.600 ⇒ 00:09:23.299 Awaish Kumar: In meetings, like, that’s a lot.
83 00:09:23.710 ⇒ 00:09:24.110 Hannah Wang: True, yeah.
84 00:09:24.110 ⇒ 00:09:30.609 Awaish Kumar: And that’s why they… and they… they want to… the… as a CEO, or as a…
85 00:09:30.800 ⇒ 00:09:38.809 Awaish Kumar: Someone in… manager in the company want to see how my employees are doing, and we don’t have any…
86 00:09:39.050 ⇒ 00:09:48.309 Awaish Kumar: like, product, that kind of product, showing you exactly, like, your employee, for example, Avish, spends,
87 00:09:48.460 ⇒ 00:10:05.699 Awaish Kumar: 20% of time on chatting in Slack, 30% time on, meetings, and other 10% with one-on-ones, and then he… he’s replying, and then he spends, like.
88 00:10:05.910 ⇒ 00:10:14.060 Awaish Kumar: a few hours and writing emails, and then he only works for, like, one hour. So that’s… Yeah.
89 00:10:14.620 ⇒ 00:10:18.489 Awaish Kumar: That’s what they wanted to show to the customer.
90 00:10:18.810 ⇒ 00:10:29.300 Hannah Wang: I mean, that’s what we’re doing currently at this company, I’m sure, with all the PM work that’s going on, and, like, what is it? The hour… I forgot what term it is.
91 00:10:30.180 ⇒ 00:10:32.950 Hannah Wang: Allocations and stuff like that, and…
92 00:10:33.170 ⇒ 00:10:36.480 Hannah Wang: Anyway, yeah, just trying to truck.
93 00:10:36.590 ⇒ 00:10:48.189 Hannah Wang: how we’re doing as a company, so that makes sense. Okay, I guess that kind of already answered the next section, which is, like, challenge. Like, basically what.
94 00:10:49.000 ⇒ 00:10:49.370 Awaish Kumar: -
95 00:10:49.370 ⇒ 00:10:54.000 Hannah Wang: I guess in this case, like, what they were trying to solve for, because usually for case studies, like.
96 00:10:54.430 ⇒ 00:11:00.970 Hannah Wang: usually the client tries something, they fail, or it’s hard, and then they, like, yes, but…
97 00:11:00.970 ⇒ 00:11:12.670 Awaish Kumar: Yeah, challenge… I don’t know what… when… what can we… in terms of… in terms of product, like, they are selling, right, that was a challenge, to figure… view the employee productivity.
98 00:11:12.670 ⇒ 00:11:34.889 Awaish Kumar: And there’s no plug-and-play solutions right now in the market. Second thing, from the task perspective, if I be a technical person, and then want to… as a lead developer, if I want to talk about challenges, I would say, like, I didn’t have access to data, so I’m not sure how exactly data looks like, right? And what… that’s why I use
99 00:11:34.890 ⇒ 00:11:40.360 Awaish Kumar: for example, ChatGPD, to help me figure out, with a…
100 00:11:40.560 ⇒ 00:11:56.260 Awaish Kumar: generating the data. So what I would actually do is, basically, what I would say, I… as a challenge, there’s no data. Without having data, I need to, to solve, like, it was… it is hard to basically see what’s…
101 00:11:56.260 ⇒ 00:12:10.470 Awaish Kumar: going to, come, and then how, basically, what kind of chart I can build on top of it. So what I did is, basically, I, synthetically generated the data, and to generate the data, what I did, basically, I,
102 00:12:10.470 ⇒ 00:12:28.380 Awaish Kumar: went into the… like, basically, for them, we only worked on Microsoft, right? So, I went… I used Microsoft Graph API to basically collect the data, for all the different users who spend time
103 00:12:28.380 ⇒ 00:12:33.690 Awaish Kumar: on using multiple apps available on Microsoft 365.
104 00:12:34.150 ⇒ 00:12:46.399 Awaish Kumar: 365, yeah? So, basically, and I… when we don’t have that data, but we don’t have access to Microsoft 365, we don’t have any instance, what I would do… what I did was, basically, I went in.
105 00:12:46.450 ⇒ 00:12:58.999 Awaish Kumar: to study the documentation for Microsoft 365 API, and there are multiple APIs available, and multiple endpoints, and then there’s multiple
106 00:12:59.070 ⇒ 00:13:12.540 Awaish Kumar: the kind of different, like, different kind of data available there, and I studied that. I used ChatGPT, basically, to basically, like.
107 00:13:12.910 ⇒ 00:13:27.790 Awaish Kumar: copy-paste a lot of different schemas or endpoints into the… and fill it to the GPT, and then ask it to, like, help me writing some scripts to generate that data.
108 00:13:28.050 ⇒ 00:13:40.770 Awaish Kumar: So, there’s an API endpoint, which basically just gets me some table with 10 fields. I will copy the schema, and the context, and what it is, what it is about, to ChetGPD, and ask him.
109 00:13:40.770 ⇒ 00:13:54.289 Awaish Kumar: asked ChatGP to write a Python script to help me generate this kind of data with this schema, and it basically… and using, like, I’m using AI in my work that speeds up my,
110 00:13:54.350 ⇒ 00:14:09.340 Awaish Kumar: development work, in generating that data, and I have these few Python scripts to generate different, like, different, schemas which I can get from Microsoft Graph API.
111 00:14:09.650 ⇒ 00:14:16.400 Awaish Kumar: And that’s how we basically generated the data, and then we,
112 00:14:17.230 ⇒ 00:14:25.010 Awaish Kumar: Where did we loaded that? Okay, so then, from there, it basically… I think it went to some database.
113 00:14:25.480 ⇒ 00:14:30.519 Awaish Kumar: When we… Maybe to some Mac…
114 00:14:31.830 ⇒ 00:14:34.959 Awaish Kumar: was, I don’t know, Microsoft SQL server, I think.
115 00:14:36.330 ⇒ 00:14:37.050 Hannah Wang: Okay.
116 00:14:38.040 ⇒ 00:14:45.970 Awaish Kumar: Yeah, that’s the first part, right? That’s the challenge, to generate the synthetic data without having access to real data.
117 00:14:46.660 ⇒ 00:14:47.950 Awaish Kumar: Yeah, that’s…
118 00:14:50.390 ⇒ 00:14:59.500 Hannah Wang: That sounds hard. Like, I don’t know what you do without real data, so that’s… that’s crazy. Okay.
119 00:14:59.660 ⇒ 00:15:07.420 Hannah Wang: I guess, while you were talking about that, you also kind of went into the solution part, which is the next section, so…
120 00:15:07.560 ⇒ 00:15:11.599 Hannah Wang: Yeah, you did all this fancy backend stuff that…
121 00:15:11.600 ⇒ 00:15:14.670 Awaish Kumar: That was just essential. Like, that was one, like…
122 00:15:15.550 ⇒ 00:15:18.740 Awaish Kumar: I can divide these things in, like.
123 00:15:19.100 ⇒ 00:15:31.030 Awaish Kumar: two sections. Number one was generating synthetic data, which I just talked about, right? Second thing is, second thing, which is part of the project, was to basically
124 00:15:31.380 ⇒ 00:15:44.809 Awaish Kumar: like, when we have generated the data, then putting it into the database, and then building a dbt project. dbt project, we have to…
125 00:15:44.810 ⇒ 00:15:47.540 Hannah Wang: Transform the data, and using…
126 00:15:47.540 ⇒ 00:15:49.119 Awaish Kumar: That, basically.
127 00:15:49.400 ⇒ 00:16:04.480 Awaish Kumar: Now we have the data in kind of raw data, which is coming as it is from Microsoft Graph API. Then we have DVD project, where we want to transform the data in a way that can be useful for
128 00:16:04.620 ⇒ 00:16:08.930 Awaish Kumar: For the end users and the reporting tools. So…
129 00:16:09.190 ⇒ 00:16:16.609 Awaish Kumar: Using, for example, that dbt project, I built some mods, and one of the mods is called, like.
130 00:16:16.780 ⇒ 00:16:20.710 Awaish Kumar: Employer productivity.
131 00:16:21.540 ⇒ 00:16:30.769 Awaish Kumar: where I have information about employees, and their interactions with different tools, and then I build a final
132 00:16:30.920 ⇒ 00:16:37.239 Awaish Kumar: VAC table, which basically have the entry for each interaction of the employee.
133 00:16:38.090 ⇒ 00:16:41.300 Awaish Kumar: And, using that, we can see how much
134 00:16:41.640 ⇒ 00:16:45.820 Awaish Kumar: How many meetings he joined in a…
135 00:16:46.040 ⇒ 00:17:03.950 Awaish Kumar: in a day, and how many hours you spend in the meetings, things like that. And then the third part is, basically, when you have those marks, we can connect that data to Power BI, and using Power BI, our data analysts… analysts, basically.
136 00:17:04.540 ⇒ 00:17:20.000 Awaish Kumar: investigated the data, right, and figure out, okay, what different kind of metrics we want to build. We can basically, make, because the customer basically wanted us to figure out what different metrics we should measure
137 00:17:20.160 ⇒ 00:17:30.149 Awaish Kumar: basically to answer… to… to figure out the employee productivity. We didn’t get the list of metrics that, okay, I want to measure this. It was also big.
138 00:17:30.510 ⇒ 00:17:46.320 Awaish Kumar: question for us, like, what we should measure to figure out the employee productivity. And then, basically, using, we, our data analysts, basically investigated what metrics should be
139 00:17:46.500 ⇒ 00:17:56.189 Awaish Kumar: calculated, and then based on that, he… she figured out what different charts we are gonna build, and then basically she implemented those in Power BI.
140 00:17:56.690 ⇒ 00:17:57.580 Hannah Wang: I see.
141 00:17:57.720 ⇒ 00:17:58.640 Hannah Wang: Got it.
142 00:17:59.860 ⇒ 00:18:06.699 Hannah Wang: Okay, so the tools that I heard you… because I had, like, a tools section in the case study, or I guess, like, the tech stack.
143 00:18:06.700 ⇒ 00:18:12.940 Awaish Kumar: And I hope, yeah, I hope with my conversation, like, ChatGBT can… Figure that out.
144 00:18:12.940 ⇒ 00:18:23.639 Hannah Wang: Yeah, hopefully. You’d be surprised, I feel like sometimes it’s not that great, but I know there’s, like, dbt, Power BI, like, TrashGPT, Microsoft endpoints and APIs and stuff, so hopefully…
145 00:18:23.640 ⇒ 00:18:25.090 Awaish Kumar: So, Graph API?
146 00:18:25.090 ⇒ 00:18:26.410 Hannah Wang: FAPI, right, okay.
147 00:18:26.410 ⇒ 00:18:34.119 Awaish Kumar: Microsoft Graph API, Python scripts, dbt Project, Power BI?
148 00:18:35.060 ⇒ 00:18:36.849 Hannah Wang: Jet GPT? Yeah.
149 00:18:38.260 ⇒ 00:18:41.690 Hannah Wang: That should be good. Okay, so…
150 00:18:41.800 ⇒ 00:18:55.429 Hannah Wang: I guess I also personally want to know, like, were they able to go to market with this? Like, what was the result of us, like, doing all this with the synthetic data and trying to build, like, a mock-up and stuff? Like, what… yeah, what…
151 00:18:55.430 ⇒ 00:19:03.650 Awaish Kumar: Like, we were able to deliver that. We basically delivered everything, in the Power BI.
152 00:19:03.810 ⇒ 00:19:14.699 Awaish Kumar: And that… that was the final deliverable for us. But then… so what they would have… they’ve… they did at their end is that they basically just,
153 00:19:15.340 ⇒ 00:19:22.800 Awaish Kumar: Like, brought in the customer’s data to their… Database, and then…
154 00:19:22.970 ⇒ 00:19:30.129 Awaish Kumar: use our dbt project to basically run the transformations on top of it, and then
155 00:19:30.210 ⇒ 00:19:45.159 Awaish Kumar: run, Power Up, like, from Power BI, you also can copy-paste it. So they… they downloaded, like, kind of chart schema kind of thing from Power BI from our work, and they… they uploaded it in…
156 00:19:45.320 ⇒ 00:19:53.699 Awaish Kumar: Like, they basically replicated that work in their customer’s, system, like.
157 00:19:57.500 ⇒ 00:19:58.390 Hannah Wang: Right.
158 00:19:58.980 ⇒ 00:20:12.899 Hannah Wang: And do you know, like, did they give any verbal feedback? Like, I guess, what was the general consensus? Like, oh, was it… was it good? Like, oh, thank you for building this, or was it like, oh… I don’t know, like, what feedback did the client…
159 00:20:13.120 ⇒ 00:20:17.220 Hannah Wang: Give about, what we… the work we did for them.
160 00:20:19.200 ⇒ 00:20:37.350 Awaish Kumar: I think they were happy. They said, like, it’s good delivery, but I didn’t see them really very excited that, like, oh, like, what you’ve done is, like, outclass, or something like that. I don’t know if, like, maybe, like, I was not the account manager.
161 00:20:37.370 ⇒ 00:20:44.349 Awaish Kumar: Right? So maybe you, Otam, or Robert have much more feedback on that, like, what exactly the client said to them, but…
162 00:20:44.470 ⇒ 00:20:49.439 Awaish Kumar: Overall, what I see is they were… didn’t… Don’t plan much, so…
163 00:20:49.440 ⇒ 00:20:50.290 Hannah Wang: Great.
164 00:20:53.730 ⇒ 00:21:07.730 Hannah Wang: Cool. Let me see if there’s any other questions… I think… That’s…
165 00:21:07.920 ⇒ 00:21:21.339 Hannah Wang: Good. I mean, like, what I’d like to do in the… I’m talking about the results section now of the case study. What I like to do, obviously, case studies, you want numbers and metrics and stuff. I don’t know if you have any, like.
166 00:21:21.780 ⇒ 00:21:24.669 Hannah Wang: numbers you can give me, like, oh…
167 00:21:24.670 ⇒ 00:21:25.029 Awaish Kumar: Oh, moon.
168 00:21:25.030 ⇒ 00:21:28.549 Hannah Wang: 90%, like, blah, or something.
169 00:21:29.990 ⇒ 00:21:38.150 Awaish Kumar: Yeah, I like… I don’t know, like, with this case study, we didn’t… basically optimized.
170 00:21:38.150 ⇒ 00:21:41.209 Hannah Wang: Right. Or anything, like, where I could say numbers.
171 00:21:41.260 ⇒ 00:21:46.079 Awaish Kumar: We can say we help clients speed up their process to go to market, right?
172 00:21:46.080 ⇒ 00:21:46.770 Hannah Wang: Right.
173 00:21:47.090 ⇒ 00:21:51.220 Awaish Kumar: That’s… Yeah. But how… yeah, how much? I don’t know, like, they…
174 00:21:51.700 ⇒ 00:21:55.030 Awaish Kumar: Even if they ended up going to market or not, I don’t know.
175 00:21:55.670 ⇒ 00:21:58.790 Awaish Kumar: Maybe they got something else to do, or I don’t know, yeah, so…
176 00:21:58.790 ⇒ 00:22:02.390 Hannah Wang: Yeah. That’s okay. Do you have, like, a…
177 00:22:02.580 ⇒ 00:22:14.479 Hannah Wang: Can you sh… because I… another option is to screenshot, like, the deliverable and put it in the case study. Is there a dashboard? Can you show me the Power BI dashboard with the fake data?
178 00:22:14.480 ⇒ 00:22:24.649 Awaish Kumar: I think we can… Yeah, like, I can’t do it. We did all of our work in customers’ Power B instance. We will no longer have access to that.
179 00:22:24.650 ⇒ 00:22:25.030 Hannah Wang: Okay.
180 00:22:25.030 ⇒ 00:22:32.340 Awaish Kumar: as a quotation, like, that’s what I can do, like… like, from generating synthetic data to…
181 00:22:32.510 ⇒ 00:22:38.410 Awaish Kumar: Transforming the data, and then finally building some
182 00:22:38.540 ⇒ 00:22:44.430 Awaish Kumar: Dashboards, we help our clients speed up their…
183 00:22:44.930 ⇒ 00:22:50.659 Awaish Kumar: Like, speed up the go-to-market thing, those kind of things, right?
184 00:22:50.940 ⇒ 00:22:56.699 Awaish Kumar: We… yeah, but that’s… that’s not, like, variable in numbers, but we can do something like that.
185 00:22:57.040 ⇒ 00:22:57.900 Hannah Wang: Okay.
186 00:22:58.390 ⇒ 00:23:07.849 Hannah Wang: Yeah, I just… I feel like when people skim through case studies, they like to look for numbers, so ideally, like, we’d have it, but that’s okay if we don’t.
187 00:23:07.850 ⇒ 00:23:19.530 Awaish Kumar: But how, like, I don’t know, like, how… I can figure… get the numbers if you have any, like, approach, like, if for case studies like this, where we are not exactly.
188 00:23:19.530 ⇒ 00:23:22.230 Hannah Wang: working on any numbers, right? So, but… Yeah.
189 00:23:22.230 ⇒ 00:23:25.570 Awaish Kumar: How… how would I… Get the number.
190 00:23:25.790 ⇒ 00:23:29.270 Hannah Wang: Yeah, I mean, sometimes I feel like some of these numbers are kind of…
191 00:23:29.910 ⇒ 00:23:32.100 Hannah Wang: Bad, but, like, we just put.
192 00:23:32.100 ⇒ 00:23:36.099 Awaish Kumar: Yeah, like, some… some… sometimes, like, I know, like, if I…
193 00:23:36.490 ⇒ 00:23:53.800 Awaish Kumar: I am optimizing a project where I say, okay, we are spending 7,000 per month, and now, after my optimization, we are spending $3,000. I saved 50% of my client’s money. But here, I… I don’t know, like…
194 00:23:53.800 ⇒ 00:23:54.460 Hannah Wang: Yeah.
195 00:23:54.870 ⇒ 00:23:57.700 Awaish Kumar: That’s not measurable in that way.
196 00:23:57.700 ⇒ 00:24:12.899 Hannah Wang: Yeah. Do you know… can we say, like, 100%, like, adoption? Like, I don’t… I don’t know if the client, like, you know, like you said, adopted it, or if they, like, had to work on something else. I don’t know. But that… that’s okay. I think…
197 00:24:12.900 ⇒ 00:24:16.029 Awaish Kumar: They did adopt it. They had client…
198 00:24:16.030 ⇒ 00:24:16.370 Hannah Wang: Okay.
199 00:24:16.370 ⇒ 00:24:28.420 Awaish Kumar: And, like, they basically had the client, right? They only had one client, so I don’t know what 100% means, but they only had one client, and for them, they use our
200 00:24:28.770 ⇒ 00:24:32.649 Awaish Kumar: Like, that was our deliverable, like, we want this…
201 00:24:33.050 ⇒ 00:24:41.990 Awaish Kumar: thing in Power BI, and we did, and they was… then it was their task to replicate, because they didn’t want us to be the face
202 00:24:42.340 ⇒ 00:24:45.050 Awaish Kumar: In front of the headline, right? So they just…
203 00:24:46.700 ⇒ 00:24:53.290 Awaish Kumar: All our work, and then… and did it, like, replicated that for their own client, and that’s… that’s where it closed.
204 00:24:53.750 ⇒ 00:24:55.340 Awaish Kumar: Okay. There’s only one parent.
205 00:24:55.340 ⇒ 00:24:57.450 Hannah Wang: Yeah, then 100% adoption.
206 00:24:57.450 ⇒ 00:24:57.920 Awaish Kumar: That’s accurate.
207 00:24:57.920 ⇒ 00:25:03.590 Hannah Wang: I feel like… yeah, okay, I can run with that.
208 00:25:03.590 ⇒ 00:25:04.290 Awaish Kumar: Okay.
209 00:25:04.290 ⇒ 00:25:11.700 Hannah Wang: Cool. And then, I mean, I also asked Utam to look at the case studies before I publish them, so he might have an opinion as well.
210 00:25:11.900 ⇒ 00:25:19.020 Hannah Wang: Utang was the… quote-unquote PM, right? Basically, for this project? Or… He was, like, the overt…
211 00:25:19.290 ⇒ 00:25:21.769 Hannah Wang: Who was the PM for this case study?
212 00:25:24.420 ⇒ 00:25:25.960 Awaish Kumar: on Metamorph?
213 00:25:27.150 ⇒ 00:25:29.120 Awaish Kumar: Bye.
214 00:25:29.800 ⇒ 00:25:31.760 Awaish Kumar: I think Amber…
215 00:25:31.940 ⇒ 00:25:32.750 Hannah Wang: Okay.
216 00:25:33.620 ⇒ 00:25:39.370 Awaish Kumar: Amber and Utam were involved, and then Utam… Basically, I was…
217 00:25:39.610 ⇒ 00:25:46.479 Awaish Kumar: I came in, and Utam left, and then Amber was beaming, I think, and I was depleting it, and…
218 00:25:47.070 ⇒ 00:25:48.709 Awaish Kumar: Yeah, Luke and any…
219 00:25:49.010 ⇒ 00:25:49.340 Hannah Wang: Okay.
220 00:25:49.340 ⇒ 00:25:50.170 Awaish Kumar: our developer.
221 00:25:52.210 ⇒ 00:25:56.690 Hannah Wang: Alright, I think this is good. I think after I…
222 00:25:56.790 ⇒ 00:26:13.729 Hannah Wang: design it, I’ll probably ask you to take a look at it, just to see, that it’s all correct, and then… yeah, and then we can go from there. So, appreciate your time, and, I know I sent you a bunch of messages in… in Slack, so sorry.
223 00:26:14.260 ⇒ 00:26:21.860 Hannah Wang: And I know also Anne is asking you for stuff, too, because we’re trying to do the website, so sorry for the spam, but…
224 00:26:21.860 ⇒ 00:26:26.389 Awaish Kumar: Yeah, I was not able to reply to her, like, she sent a long message.
225 00:26:26.390 ⇒ 00:26:27.030 Hannah Wang: Yeah.
226 00:26:27.030 ⇒ 00:26:35.570 Awaish Kumar: So… Yeah, I have a lot of meetings today, maybe I meet… Tomorrow’s… Hotel, something like that.
227 00:26:35.750 ⇒ 00:26:43.620 Hannah Wang: That’s okay. Yeah, like, just… essentially, we’re just trying to put the services Like, on the website, and…
228 00:26:44.030 ⇒ 00:26:47.879 Hannah Wang: I don’t know if you looked at the Services Notion page, but you were, like, the…
229 00:26:48.590 ⇒ 00:26:54.700 Hannah Wang: lead, I guess, of, like, certain sub-services in the catalog, like.
230 00:26:56.130 ⇒ 00:27:01.089 Hannah Wang: warehouse and DB… warehousing and modeling, like, you’re… I, I guess.
231 00:27:01.090 ⇒ 00:27:01.550 Awaish Kumar: So…
232 00:27:01.550 ⇒ 00:27:10.499 Hannah Wang: U.S. Solutions Architect, so that’s why I told her to reach out to you, because I didn’t want Utam to be the bottleneck. So, I don’t think it’s, like, a huge
233 00:27:10.880 ⇒ 00:27:21.330 Hannah Wang: rush, like, but it’d be nice to get this website stuff done. So yeah, whenever you get time to… to do it, that’d be helpful.
234 00:27:21.950 ⇒ 00:27:25.919 Hannah Wang: Okay. And you could just, like, record, like, yourself talking about
235 00:27:26.310 ⇒ 00:27:36.689 Hannah Wang: What, like, warehousing and modeling is, like, what observability and trust is, and you can just, like, talk about it, and she can take the transcript and…
236 00:27:36.810 ⇒ 00:27:38.710 Hannah Wang: use ChatGPT, so…
237 00:27:38.930 ⇒ 00:27:39.670 Awaish Kumar: Hmm.
238 00:27:39.880 ⇒ 00:27:40.490 Hannah Wang: Yeah.
239 00:27:41.930 ⇒ 00:27:48.480 Hannah Wang: Okay. Well… Good luck today with all your meetings, and I’ll talk to you on Slack. Thanks, Awash.
240 00:27:48.600 ⇒ 00:27:49.710 Awaish Kumar: Okay, thank you.
241 00:27:49.710 ⇒ 00:27:50.300 Hannah Wang: Hey, bike.