Meeting Title: Amber Lin’s Personal Meeting Room Date: 2025-06-02 Meeting participants: Amber Lin, Awaish Kumar
WEBVTT
1 00:00:20.040 ⇒ 00:00:23.170 Amber Lin: Testing the zoom microphone.
2 00:00:38.480 ⇒ 00:00:39.270 Awaish Kumar: Hello!
3 00:00:40.820 ⇒ 00:00:42.060 Amber Lin: Hi.
4 00:00:43.210 ⇒ 00:00:43.910 Awaish Kumar: Hi.
5 00:00:44.456 ⇒ 00:00:52.953 Amber Lin: Let me let me catch you up on speed with matter more. I’m gonna need some serious help with this client, because,
6 00:00:54.246 ⇒ 00:01:02.809 Amber Lin: there’s quite a few technical requirements, and then our loop of Luke and Annie is pretty junior. So a lot of times they are
7 00:01:03.840 ⇒ 00:01:10.660 Amber Lin: very lost on what needs to be done, and I feel the pressure of I don’t think I can
8 00:01:11.590 ⇒ 00:01:17.020 Amber Lin: drag this okay along with my technical abilities.
9 00:01:19.900 ⇒ 00:01:21.030 Awaish Kumar: Okay. Yeah.
10 00:01:21.220 ⇒ 00:01:28.330 Amber Lin: Yeah, so this client we started, I think, in early May.
11 00:01:28.460 ⇒ 00:01:31.710 Amber Lin: And they do
12 00:01:32.288 ⇒ 00:01:53.849 Amber Lin: productivity analytics. So what they do is that they analyze the productivity of their clients employees. So they use all this different data, say, on your activity, on different tools. If you communicate across departments, how you are geographically located, and they want to see, say.
13 00:01:54.982 ⇒ 00:02:04.279 Amber Lin: remote versus in person. So all these different factors and they want to show to their clients in data of how
14 00:02:04.970 ⇒ 00:02:12.619 Amber Lin: their current company is doing in terms of employee productivity. Right? And that’s their premise. So that’s what they’re doing.
15 00:02:14.250 ⇒ 00:02:16.190 Awaish Kumar: Okay, that’s their product.
16 00:02:17.370 ⇒ 00:02:32.109 Amber Lin: Yeah, that’s that’s their service as their product, essentially. And what they came to us for is, I think they want they have. They’re building like the platform, their product. They want us to figure out the analytic
17 00:02:32.510 ⇒ 00:02:41.839 Amber Lin: capabilities. And so the problem is, the current situation is that they need to sign the contract with their client.
18 00:02:42.000 ⇒ 00:02:42.760 Amber Lin: either pretty.
19 00:02:42.760 ⇒ 00:02:43.269 Awaish Kumar: It is.
20 00:02:43.270 ⇒ 00:02:50.449 Amber Lin: Set on signing the contract. It’s probably going to happen, but we don’t have the client data
21 00:02:51.870 ⇒ 00:02:57.500 Amber Lin: which is to me is really silly, but we are trying to figure out
22 00:02:58.280 ⇒ 00:03:03.639 Amber Lin: the analytic capabilities, so that when we do get the client data
23 00:03:03.910 ⇒ 00:03:15.340 Amber Lin: we already know what to do. So what we’re doing now is that we’re using the synthetic data to figure out, okay, all these analysis needs to go this way. And
24 00:03:15.980 ⇒ 00:03:26.289 Amber Lin: so that when the client data comes. We already know what models we need to build, we already know, like, what needs to be done to do the analysis. So it can be done really, really, quickly.
25 00:03:27.280 ⇒ 00:03:27.970 Awaish Kumar: Okay.
26 00:03:28.430 ⇒ 00:03:30.000 Awaish Kumar: Yeah.
27 00:03:30.000 ⇒ 00:03:30.460 Amber Lin: Yeah.
28 00:03:30.460 ⇒ 00:03:34.999 Awaish Kumar: We basically have to figure out the like, what is the
29 00:03:35.310 ⇒ 00:03:40.860 Awaish Kumar: what is the thing we want to give them like dashboard when they’re looking for the dashboard work
30 00:03:41.060 ⇒ 00:03:41.990 Awaish Kumar: from us.
31 00:03:43.575 ⇒ 00:03:47.024 Amber Lin: So can you still hear me? I made an adjustment to my audio.
32 00:03:48.780 ⇒ 00:03:51.750 Awaish Kumar: So I said, like, what are they looking like?
33 00:03:51.810 ⇒ 00:03:53.230 Amber Lin: Yeah, yeah, so.
34 00:03:54.235 ⇒ 00:03:55.240 Awaish Kumar: Dashboard!
35 00:03:56.116 ⇒ 00:03:59.100 Amber Lin: So ideally they would want.
36 00:03:59.310 ⇒ 00:04:02.630 Amber Lin: They would want a power bi dashboard.
37 00:04:02.810 ⇒ 00:04:14.409 Amber Lin: That’s what they they would. The that’s the deliverable. But ultimately they want us to know how to do the analysis. So to catch you up on currently, what we’re doing is that
38 00:04:14.890 ⇒ 00:04:23.450 Amber Lin: one we’re using synthetic data that we generated. And 2, we’re doing our analysis in a
39 00:04:24.140 ⇒ 00:04:26.770 Amber Lin: notebook and with python.
40 00:04:26.770 ⇒ 00:04:30.310 Awaish Kumar: I would like you would like to understand.
41 00:04:30.930 ⇒ 00:04:36.149 Awaish Kumar: I this dashboard which we are going to build? Is it for them, or is it for their client?
42 00:04:36.600 ⇒ 00:04:37.660 Amber Lin: For their client.
43 00:04:38.220 ⇒ 00:04:39.670 Awaish Kumar: Other plan basically.
44 00:04:39.670 ⇒ 00:04:42.329 Amber Lin: Yeah, their client wants to use power Bi.
45 00:04:42.960 ⇒ 00:04:50.295 Awaish Kumar: Yeah, so basically, we are like, if we just say, and if we just
46 00:04:50.880 ⇒ 00:04:53.379 Awaish Kumar: hide matter mode, we are, we are kind of.
47 00:04:53.490 ⇒ 00:04:59.940 Awaish Kumar: we kind of have a client which which wants to mayor their employees.
48 00:05:02.160 ⇒ 00:05:07.780 Awaish Kumar: Productivity. And we want to build a dash shows the employees productivity right?
49 00:05:10.004 ⇒ 00:05:17.650 Amber Lin: Essentially, yes. And we want to explore, like, maybe by by day of week, by hour of day. The correlation.
50 00:05:17.650 ⇒ 00:05:19.609 Amber Lin: enjoy your afternoon different things, etc.
51 00:05:19.610 ⇒ 00:05:24.750 Awaish Kumar: So we that that like that, we need to figure out like in that dashboard
52 00:05:25.000 ⇒ 00:05:34.990 Awaish Kumar: what we will be showing maybe creating multiple tabs, multiple charts, multiple things, filters and
53 00:05:35.160 ⇒ 00:05:39.909 Awaish Kumar: essentially help get them to so that they can answer.
54 00:05:41.050 ⇒ 00:05:43.390 Awaish Kumar: They get the answers they need right.
55 00:05:43.600 ⇒ 00:05:53.330 Amber Lin: Yeah, you’re you’re so right, you’ve just enlightened me. So what we need to. Essentially, we need to figure out what we’re showing and how we’re gonna do it. Essentially.
56 00:05:53.650 ⇒ 00:05:57.549 Awaish Kumar: So so 1st of all, maybe it would be nice if we start from
57 00:05:57.670 ⇒ 00:06:00.500 Awaish Kumar: instead of instead of starting from
58 00:06:00.680 ⇒ 00:06:04.870 Awaish Kumar: the synthetic data. If you start from dashboard
59 00:06:05.670 ⇒ 00:06:11.499 Awaish Kumar: like like not building the dashboard, but figuring out what needs to be in the dashboard.
60 00:06:12.529 ⇒ 00:06:19.080 Amber Lin: So they actually, here’s why I got a little bit confused on this client, because they
61 00:06:19.640 ⇒ 00:06:31.820 Amber Lin: have a deck of all the different visualizations, essentially, all of them of the visualizations that they want to show that they kind of want their client on right. So they had a deck where they showed.
62 00:06:32.470 ⇒ 00:06:37.689 Amber Lin: Show them everything. Let me see if I can. If you can open that.
63 00:06:38.590 ⇒ 00:06:42.620 Amber Lin: Yeah, I’m sending the link for you. Let me know what you if you can see it.
64 00:06:44.510 ⇒ 00:06:52.430 Awaish Kumar: So we have the dashboard where basically, we are able to see what the.
65 00:06:52.800 ⇒ 00:06:56.200 Awaish Kumar: what, the what they need right?
66 00:06:56.300 ⇒ 00:07:04.660 Awaish Kumar: And so after we have all the visualization and the filters and the
67 00:07:05.730 ⇒ 00:07:11.910 Awaish Kumar: like in all this stuff they need. Then, yeah, second thing would be my
68 00:07:12.040 ⇒ 00:07:22.249 Awaish Kumar: to figure out from where that data is going to come. So right now, we are using synthetic data. But when we have a client.
69 00:07:22.808 ⇒ 00:07:30.139 Awaish Kumar: real client, so how we are going to get those data from real tools, right?
70 00:07:30.570 ⇒ 00:07:32.540 Amber Lin: Yeah, totally. And I think.
71 00:07:32.540 ⇒ 00:07:34.310 Awaish Kumar: How we are that yes, that’s.
72 00:07:34.310 ⇒ 00:07:40.290 Amber Lin: Yeah, yeah. And totally, that’s something that is making me not so confident on this is because
73 00:07:40.520 ⇒ 00:08:02.530 Amber Lin: I don’t know how close our synthetic data is from the real data. And that makes me uncomfortable because I don’t know if we’re gonna succeed when we actually get the real data or how transferable or current. All the stuff we’re doing now is going to be. They said they were gonna sign the contract today. And I before we started, we thought, we’re gonna get a client data today.
74 00:08:02.690 ⇒ 00:08:17.000 Amber Lin: But I just asked Matthew. And it’s gonna be a 2 other weeks. So if we go on this trail for 2 other weeks, and nothing like if it doesn’t transfer. Well, I’m scared that we’re gonna.
75 00:08:17.000 ⇒ 00:08:18.299 Awaish Kumar: So where is this client.
76 00:08:18.510 ⇒ 00:08:19.350 Amber Lin: So.
77 00:08:19.350 ⇒ 00:08:23.410 Awaish Kumar: Matter more is going to send us this client data, or we will be
78 00:08:23.570 ⇒ 00:08:27.209 Awaish Kumar: directly connected with the client’s tool to get this data.
79 00:08:28.560 ⇒ 00:08:30.210 Amber Lin: I that’s.
80 00:08:30.210 ⇒ 00:08:45.119 Awaish Kumar: So like. For example, if if I give an example, we have a client called Eden. So when, if I need data for Eden from. I have to figure out like, okay, the Eden data is in some tool A and I have to
81 00:08:45.620 ⇒ 00:08:56.820 Awaish Kumar: do like some. I have to utilize some tools, or maybe write something to get data from their tool A to our warehouse, where I can run some modeling and build a dashboard
82 00:08:57.488 ⇒ 00:09:08.900 Awaish Kumar: in here, I want to know, like, because we are not directly working with client, we are working with matter more so. Metamor might already have something to bring in the data. So I’m not sure like.
83 00:09:08.900 ⇒ 00:09:10.670 Amber Lin: Yeah, so, item or question.
84 00:09:10.670 ⇒ 00:09:29.959 Awaish Kumar: Wants us. Yeah, so matter more wants us to directly connect with clients tools to get the data, or they would do that and just transfer that to some warehouse where we can just read it. So I want to understand this ingestion part. Who is going to handle that us.
85 00:09:29.960 ⇒ 00:09:41.629 Amber Lin: I have a sense that it’s probably gonna be in bigquery cause they were setting up some stuff in bigger. But that’s a great question that I wrote it down. We’re gonna confirm with the client.
86 00:09:43.350 ⇒ 00:09:43.890 Awaish Kumar: So because.
87 00:09:43.890 ⇒ 00:09:44.400 Amber Lin: But what.
88 00:09:44.400 ⇒ 00:09:57.718 Awaish Kumar: I want to hear the profitability dashboard for my like for my my employees, profitability what I will like. And we are working with linear. So number one thing, I would go in and
89 00:09:58.310 ⇒ 00:10:02.909 Awaish Kumar: get the linear tickets and get the story points. And all these things
90 00:10:03.110 ⇒ 00:10:17.359 Awaish Kumar: right. Secondly, if I want to see if they have. If I have given someone account to what Figma, for example, would see how many times he logged in, or how many times
91 00:10:17.810 ⇒ 00:10:20.009 Awaish Kumar: he did something on the pig map, so.
92 00:10:20.420 ⇒ 00:10:22.830 Awaish Kumar: Things like that right? So how, Polly.
93 00:10:22.830 ⇒ 00:10:26.990 Awaish Kumar: if if they want to, if they they are kind of.
94 00:10:27.480 ⇒ 00:10:27.860 Amber Lin: The most.
95 00:10:27.860 ⇒ 00:10:30.929 Awaish Kumar: Something similar, as as I understand. So if.
96 00:10:31.580 ⇒ 00:10:37.189 Awaish Kumar: So they must. We must have to get, though, that data from the clients tools, right?
97 00:10:37.790 ⇒ 00:10:52.680 Awaish Kumar: So if the client are like engaging with some tools. If the client, if their employees are maybe they are using jira or linear whatever. So how the tickets are going out. The story, perhaps points are going and.
98 00:10:52.680 ⇒ 00:10:53.030 Amber Lin: Yeah.
99 00:10:53.670 ⇒ 00:10:59.610 Awaish Kumar: Then, if they want to make it also select communication, then they give them that they are using it.
100 00:10:59.610 ⇒ 00:11:04.289 Amber Lin: Most of totally. I think that’s really important. Most of the
101 00:11:04.560 ⇒ 00:11:07.670 Amber Lin: here’s another piece of information is that most of their
102 00:11:07.940 ⇒ 00:11:15.199 Amber Lin: tools and sources is Microsoft. So they use teams. They use outlook hence. Why, they also use power.
103 00:11:17.010 ⇒ 00:11:18.659 Amber Lin: Clients, metamor’s client.
104 00:11:19.430 ⇒ 00:11:26.039 Awaish Kumar: Okay, matterables client. Okay? So like, if they are using whatever they’re using. The question is.
105 00:11:27.080 ⇒ 00:11:29.109 Awaish Kumar: does we have to.
106 00:11:29.110 ⇒ 00:11:29.790 Amber Lin: Yeah, yeah.
107 00:11:29.790 ⇒ 00:11:30.350 Awaish Kumar: Have the data.
108 00:11:30.350 ⇒ 00:11:31.690 Amber Lin: We have to get from.
109 00:11:31.690 ⇒ 00:11:39.299 Awaish Kumar: Microsoft services, or like or Meta mode, will handle that part, and we will just work on analytics.
110 00:11:42.680 ⇒ 00:11:43.370 Amber Lin: Alright, great.
111 00:11:43.370 ⇒ 00:11:48.857 Awaish Kumar: So once that’s answered like, we kind of have solved 25% of the problem.
112 00:11:49.650 ⇒ 00:11:51.500 Amber Lin: I see that is great.
113 00:11:52.270 ⇒ 00:11:58.720 Amber Lin: that is really, really helpful. I wrote that down, and then another part is another part. Is that
114 00:11:59.830 ⇒ 00:12:17.319 Awaish Kumar: And are these sources like, well defined, so like because metamor already shared the charts with client and already shared visualization like what we are going to show you. That means matter more already knows like what kind of source.
115 00:12:17.320 ⇒ 00:12:17.990 Amber Lin: Yes.
116 00:12:18.100 ⇒ 00:12:21.499 Awaish Kumar: Data will be coming from. So do we also know that.
117 00:12:25.147 ⇒ 00:12:32.240 Amber Lin: I think Lattimore knows that I think we somewhat knows know that. But it’s not very clear. I think we lack
118 00:12:32.630 ⇒ 00:12:33.350 Amber Lin: because this was.
119 00:12:33.722 ⇒ 00:12:40.050 Awaish Kumar: That’s the thing right. We we must know that right. If, because the if the dashboard is.
120 00:12:40.320 ⇒ 00:12:45.600 Awaish Kumar: if finalized like, if so, they must have a planned like
121 00:12:45.700 ⇒ 00:12:57.669 Awaish Kumar: like. From which source this data is going to come from. So they know the sources. And if they know the sources, if we get that even if even if they like, even if they
122 00:12:59.390 ⇒ 00:13:12.370 Awaish Kumar: ingest the data. But we will know that, like what what we are expecting. If, for example, source is linear. We are using linear like, we can just use our linear data to build something great instead of creating synthetic data.
123 00:13:12.370 ⇒ 00:13:14.010 Amber Lin: Let me also, that’s true.
124 00:13:14.010 ⇒ 00:13:15.760 Amber Lin: This spreadsheet.
125 00:13:15.910 ⇒ 00:13:21.730 Amber Lin: That’s our data platform spreadsheet. I looked at it. It’s not very comprehensive.
126 00:13:22.110 ⇒ 00:13:23.410 Amber Lin: So I.
127 00:13:23.860 ⇒ 00:13:28.370 Awaish Kumar: I see I saw it, but it basically was empty.
128 00:13:29.607 ⇒ 00:13:36.029 Amber Lin: Yes, yes, so we I think they know we need to get that information from them. So.
129 00:13:36.030 ⇒ 00:13:37.820 Awaish Kumar: Basically have to figure out.
130 00:13:38.040 ⇒ 00:13:49.949 Awaish Kumar: We maybe just ask them, like, if all these charts where the data is coming from. And so this this, says Microsoft, graph and success factors here. There are only 2
131 00:13:52.580 ⇒ 00:13:53.089 Amber Lin: I think so.
132 00:13:53.090 ⇒ 00:13:54.389 Awaish Kumar: 2 sources mentioned.
133 00:13:54.670 ⇒ 00:14:07.443 Amber Lin: I think that question we should ask our internal team first.st I think Luke and Annie will be be able to answer some of those questions, and then let’s escalate to the client for anything else that we don’t need, because right now we have, say
134 00:14:08.980 ⇒ 00:14:12.210 Amber Lin: find. On which item of the sheet.
135 00:14:14.160 ⇒ 00:14:21.710 Amber Lin: I think there’s many, many different tabs, which I think is horrible organization.
136 00:14:21.920 ⇒ 00:14:25.799 Amber Lin: but if you go look at them, they we do have.
137 00:14:25.800 ⇒ 00:14:31.420 Awaish Kumar: Basically, they have created all the tables. The data is going to come from in the tabs.
138 00:14:31.770 ⇒ 00:14:34.520 Awaish Kumar: It should not be like that.
139 00:14:35.120 ⇒ 00:14:41.130 Amber Lin: I know a lot of things. I think we created most of these things so, but but, like I, I haven’t dug into.
140 00:14:41.130 ⇒ 00:14:44.460 Awaish Kumar: The sheet is created by us. This sheet is created by Horti.
141 00:14:44.910 ⇒ 00:14:48.299 Amber Lin: Yes, so I’m a little bit confused on what’s going on.
142 00:14:48.300 ⇒ 00:15:00.079 Awaish Kumar: So so like what I understand from this sheet is, we have 2 sources, Microsoft draft and success factors, and the data is coming from there to go, it goes to bigquery. So so what?
143 00:15:00.851 ⇒ 00:15:10.600 Awaish Kumar: So like? 1st of all, just get clarification. Who is going to move this data to bigquery us, or they number one. Question number 2 is that
144 00:15:10.840 ⇒ 00:15:15.250 Awaish Kumar: if these are the only sources confirm that for this client, at least
145 00:15:15.350 ⇒ 00:15:18.080 Awaish Kumar: for this, the client which is going to come soon?
146 00:15:18.270 ⇒ 00:15:21.809 Awaish Kumar: Are they only looking to get data from one of these 2 sources?
147 00:15:21.930 ⇒ 00:15:25.450 Awaish Kumar: And then if it’s that like, then
148 00:15:26.478 ⇒ 00:15:32.200 Awaish Kumar: yeah, synthetic like I can. And I can then maybe meet with internal team.
149 00:15:32.825 ⇒ 00:15:40.510 Awaish Kumar: Look like how he built the synthetic data is, it was like, somewhat similar to real data like, is he.
150 00:15:40.510 ⇒ 00:15:41.560 Amber Lin: Yeah, as usual.
151 00:15:41.560 ⇒ 00:15:41.990 Amber Lin: Oh.
152 00:15:41.990 ⇒ 00:15:45.160 Awaish Kumar: Microsoft Graphs data or something.
153 00:15:45.610 ⇒ 00:15:54.119 Amber Lin: Which, based on that. There’s another thing I wanted to ask you. So there is sample data packs that Microsoft graph Microsoft
154 00:15:55.010 ⇒ 00:16:01.120 Amber Lin: has. So another option. Instead of building synthetic data, we would set up
155 00:16:02.220 ⇒ 00:16:13.650 Amber Lin: that account and then get that since the well, like their sample data from Microsoft Graphs, which might be more closer to what we’re doing.
156 00:16:14.240 ⇒ 00:16:19.219 Amber Lin: But then the initial setup might take some time. Do you think it’s worth it to
157 00:16:19.560 ⇒ 00:16:26.339 Amber Lin: dish synthetic data and try to use what Microsoft provides that sample data.
158 00:16:27.110 ⇒ 00:16:30.349 Awaish Kumar: Yeah, like, I don’t know how synthetic data is built. I
159 00:16:30.470 ⇒ 00:16:37.230 Awaish Kumar: maybe Luke is already using that or whatever I don’t know. So I we have to ask him first, st
160 00:16:37.360 ⇒ 00:16:47.239 Awaish Kumar: how is doing that? And then otherwise we maybe just go in and set up that account. If it’s not that if that that doesn’t cost us. We can. We can use that.
161 00:16:47.670 ⇒ 00:16:57.439 Amber Lin: Okay. I think I would need some help to look at how that’s gonna get set up. I do think you need some like maybe a Paid Microsoft account, or maybe the client needs to get the account.
162 00:16:58.760 ⇒ 00:17:04.230 Amber Lin: I asked Luke to do it last time. It’s didn’t like nothing about
163 00:17:04.710 ⇒ 00:17:09.589 Amber Lin: that. He was just like, Oh, it needs the account, or it needs to be paid. And that was that.
164 00:17:10.440 ⇒ 00:17:13.339 Awaish Kumar: Okay, it needs to be paid. Then, like
165 00:17:13.920 ⇒ 00:17:19.800 Awaish Kumar: I I like, I think we would be. We would want to do that.
166 00:17:20.480 ⇒ 00:17:24.390 Amber Lin: So if we have to pay something for that, we’ll build a client
167 00:17:24.390 ⇒ 00:17:29.910 Amber Lin: like don’t worry. If we need to pay like it will be built to the client. We’ll build to matter more.
168 00:17:30.480 ⇒ 00:17:34.330 Awaish Kumar: Yeah, like, then we need their approval as well. Right?
169 00:17:35.072 ⇒ 00:17:39.439 Amber Lin: They were the one that sent us the pack. They were like, you can- can you use this.
170 00:17:39.440 ⇒ 00:17:40.630 Awaish Kumar: Okay. Then it’s okay.
171 00:17:40.630 ⇒ 00:17:41.300 Amber Lin: Okay.
172 00:17:42.520 ⇒ 00:17:55.550 Amber Lin: yeah. Last time I said, no, we’re going to continue with synthetic data, because we were very, very pressed to give them some visualizations, but I think at this point we did buy some time. We had some pretty good progress, and so I think we have a little bit of time
173 00:17:55.650 ⇒ 00:18:01.050 Amber Lin: to get that set up, and then get it moving with more.
174 00:18:01.050 ⇒ 00:18:01.840 Awaish Kumar: Have the 2.
175 00:18:01.840 ⇒ 00:18:02.630 Amber Lin: Microsoft, agent.
176 00:18:02.630 ⇒ 00:18:07.999 Awaish Kumar: With the. So with the synthetic data, we have built everything the dashboards needed, the charts needed.
177 00:18:08.000 ⇒ 00:18:13.499 Amber Lin: We didn’t. We didn’t build the dashboard, but we were able to produce A lot of.
178 00:18:13.500 ⇒ 00:18:14.170 Awaish Kumar: Because.
179 00:18:14.350 ⇒ 00:18:20.110 Amber Lin: A lot of the models and a lot of the visualizations that was needed, I think, especially.
180 00:18:20.110 ⇒ 00:18:22.089 Awaish Kumar: Utilizations are the charge right?
181 00:18:22.720 ⇒ 00:18:24.659 Amber Lin: Yeah, let me show you.
182 00:18:26.330 ⇒ 00:18:32.290 Amber Lin: Here here is the might not be able.
183 00:18:32.290 ⇒ 00:18:32.740 Awaish Kumar: Okay.
184 00:18:32.740 ⇒ 00:18:36.099 Amber Lin: Open that document, but you should be able to open this presentation.
185 00:18:42.020 ⇒ 00:18:46.939 Awaish Kumar: So like we, we built it. We built it in Jupyter notebook. You are saying that.
186 00:18:47.390 ⇒ 00:18:47.930 Amber Lin: Yeah.
187 00:18:48.090 ⇒ 00:18:53.890 Amber Lin: And then if you open the slide deck, that is pretty much what we have
188 00:18:54.190 ⇒ 00:19:00.320 Amber Lin: delivered so far. Let me see if I can share this document with you.
189 00:19:01.450 ⇒ 00:19:04.098 Awaish Kumar: Okay, I’m I’m able to see it. And
190 00:19:05.510 ⇒ 00:19:12.910 Awaish Kumar: okay, I can see that we have built this somewhere. And this infrastructure. And we haven’t worked on our bi right.
191 00:19:12.910 ⇒ 00:19:17.169 Amber Lin: No. So we also need to set up power Bi in these 2 weeks.
192 00:19:17.970 ⇒ 00:19:23.250 Awaish Kumar: So we have a power. And who is going to basically.
193 00:19:23.250 ⇒ 00:19:27.069 Amber Lin: I think they’re gonna set up the instance. And then we’re gonna take on the rest.
194 00:19:33.320 ⇒ 00:19:43.590 Amber Lin: So Pepper, which is on the technical person on their team is going to set up the power bi instance. And then I guess the next week, these 2 weeks, we’re gonna work on
195 00:19:43.710 ⇒ 00:19:45.220 Amber Lin: power Bi.
196 00:19:46.500 ⇒ 00:19:47.469 Amber Lin: So I guess.
197 00:19:47.470 ⇒ 00:19:52.230 Awaish Kumar: So maybe we can ask them to set up the power. Bi stands for us.
198 00:19:52.400 ⇒ 00:19:52.840 Amber Lin: Okay.
199 00:19:52.860 ⇒ 00:19:59.009 Awaish Kumar: And and we can. And like, we can. Secondly, we can
200 00:19:59.210 ⇒ 00:20:02.680 Awaish Kumar: see if we can get the Microsoft graph data.
201 00:20:02.680 ⇒ 00:20:03.070 Amber Lin: I’m sorry.
202 00:20:03.070 ⇒ 00:20:04.010 Awaish Kumar: Sample data.
203 00:20:04.850 ⇒ 00:20:07.780 Awaish Kumar: And the 3rd week, like
204 00:20:08.050 ⇒ 00:20:12.949 Awaish Kumar: may also maybe sync up with the bloke. How he’s generating the synthetic data.
205 00:20:13.681 ⇒ 00:20:23.309 Amber Lin: I think that would be that would be a task for you to investigate a sample synthetic data, I don’t think the client has time or cares, or it will look at that.
206 00:20:25.750 ⇒ 00:20:26.430 Awaish Kumar: Sorry.
207 00:20:27.320 ⇒ 00:20:33.150 Amber Lin: Do you? I feel like the I like what you said of one.
208 00:20:34.010 ⇒ 00:20:39.360 Amber Lin: Microsoft has Microsoft sample data to power bi, and
209 00:20:42.150 ⇒ 00:20:48.569 Amber Lin: and I guess 3 confirm the sources and 4 investigate. The sample data was that the 3 things.
210 00:20:49.140 ⇒ 00:20:53.219 Awaish Kumar: Yeah, yeah, I said, I, I think I will meet with Luke to.
211 00:20:53.900 ⇒ 00:20:59.829 Awaish Kumar: Investigate synthetic data, and we can see if we can if we want to move to get the Microsoft Graph
212 00:21:00.000 ⇒ 00:21:12.810 Awaish Kumar: account set up and get some sample data from there. So this is point number one. We confirm the sources and also confirm they are going to handle the ingestion. That’s number 2.
213 00:21:14.500 ⇒ 00:21:22.460 Awaish Kumar: So like. So they will be will will they be responsible for moving the data from sources to the bigquery?
214 00:21:22.900 ⇒ 00:21:24.730 Awaish Kumar: So also confirm that
215 00:21:26.290 ⇒ 00:21:32.580 Awaish Kumar: And if there are going to be more sources, or just these 2 3rd thing, no?
216 00:21:33.030 ⇒ 00:21:37.990 Awaish Kumar: And then, oh, yeah, it’s like initializing the power behind starts.
217 00:21:39.640 ⇒ 00:21:40.100 Awaish Kumar: Yep.
218 00:21:40.936 ⇒ 00:21:42.609 Amber Lin: Yeah. Confirm.
219 00:21:42.610 ⇒ 00:21:48.220 Awaish Kumar: So then, number one, I will do that, and other 3 you have to confirm with the client.
220 00:21:48.620 ⇒ 00:21:53.630 Amber Lin: Sounds good. Yeah, let me. This is wish this is so helpful.
221 00:21:55.420 ⇒ 00:22:00.210 Amber Lin: Why haven’t I met with you earlier? This flight has been so stressful for me.
222 00:22:05.261 ⇒ 00:22:07.369 Amber Lin: Okay, thank you so much.
223 00:22:07.820 ⇒ 00:22:09.410 Awaish Kumar: And other inputs. Right?
224 00:22:09.740 ⇒ 00:22:10.970 Amber Lin: Hmm sorry.
225 00:22:10.970 ⇒ 00:22:13.359 Awaish Kumar: Also, please just let me know, like what else
226 00:22:13.723 ⇒ 00:22:16.360 Awaish Kumar: we are planning. If you arrange a meeting with client.
227 00:22:16.460 ⇒ 00:22:27.849 Awaish Kumar: just let me know, like, what? What is the agenda or what we are looking to meet for? And like, if we want to plan something, maybe we do it first, st internally, like.
228 00:22:27.850 ⇒ 00:22:34.820 Awaish Kumar: yeah, yeah, totally plan out some tickets or like roadmap for 2 weeks, like or how how long
229 00:22:34.950 ⇒ 00:22:37.030 Awaish Kumar: we have a conflict
230 00:22:37.160 ⇒ 00:22:42.900 Awaish Kumar: contract with this client like, we have to finish this project, and how how much time like, you know.
231 00:22:43.520 ⇒ 00:23:08.240 Amber Lin: I see, I think on that side, we’re currently just hourly. So we just build them as we work. And we definitely have until they sign a contract with their client, which is 2 weeks. And we, they, I asked, should I assume that we’re just gonna keep working with you? Once we get client data. And Matthew said, yes. So currently, we assume this is a longer term project. But we don’t have
232 00:23:08.650 ⇒ 00:23:13.070 Amber Lin: like exact, exact confirmation of dates.
233 00:23:14.220 ⇒ 00:23:23.743 Awaish Kumar: Okay, okay, but but we already know the scope of the project. So we should just try to
234 00:23:24.610 ⇒ 00:23:31.270 Amber Lin: Like, ideally, I want, yeah, I want us to have a clear plan. And I wasn’t really able to do that.
235 00:23:32.520 ⇒ 00:23:33.860 Awaish Kumar: Okay, so
236 00:23:34.961 ⇒ 00:23:44.110 Awaish Kumar: so that dashboard you were saying, Where is it like you? Should? We have shared like what? Exactly what visualization they they need? Right?
237 00:23:44.470 ⇒ 00:23:46.919 Amber Lin: Where the links that I sent you.
238 00:23:47.100 ⇒ 00:23:48.350 Awaish Kumar: The deck, or.
239 00:23:48.750 ⇒ 00:23:52.939 Amber Lin: The yeah, the deck I’ll download. Let me try and download this.
240 00:23:53.390 ⇒ 00:23:54.210 Amber Lin: Please send it.
241 00:23:54.210 ⇒ 00:23:59.369 Awaish Kumar: Is it in the Collab, or that Docs link? Which link is that.
242 00:24:00.957 ⇒ 00:24:05.550 Amber Lin: Sorry. Give me a quick second. Let me download this one
243 00:24:05.730 ⇒ 00:24:09.610 Amber Lin: because they only shared it to some links, and
244 00:24:09.980 ⇒ 00:24:13.319 Amber Lin: maybe if you were able to log into this.
245 00:24:16.500 ⇒ 00:24:20.620 Amber Lin: Give me one second download.
246 00:24:22.730 ⇒ 00:24:26.920 Awaish Kumar: Okay call. I might not be able to log in because it needs credentials.
247 00:24:27.570 ⇒ 00:24:33.520 Amber Lin: Yeah, dear, let me also share the credentials for the collab notebook.
248 00:24:33.930 ⇒ 00:24:38.019 Amber Lin: That is just it should be on one pass.
249 00:24:38.530 ⇒ 00:24:40.480 Amber Lin: It’s just our matter more.
250 00:24:41.550 ⇒ 00:24:42.290 Awaish Kumar: Okay.
251 00:24:43.240 ⇒ 00:24:45.279 Amber Lin: They made an account for us.
252 00:24:46.420 ⇒ 00:24:48.410 Awaish Kumar: What it’s called like. What’s the name?
253 00:24:50.250 ⇒ 00:24:55.200 Amber Lin: This is just matter more. Gcp, I mean copy link
254 00:24:56.060 ⇒ 00:25:03.940 Amber Lin: also sent to you send it to Rdm, okay and there.
255 00:25:03.940 ⇒ 00:25:04.470 Awaish Kumar: So.
256 00:25:04.470 ⇒ 00:25:07.310 Amber Lin: This, I’ll try.
257 00:25:12.906 ⇒ 00:25:15.820 Amber Lin: How do I share this with you?
258 00:25:44.490 ⇒ 00:25:45.970 Amber Lin: Yeah. And and I’m going to.
259 00:25:45.970 ⇒ 00:25:48.759 Amber Lin: I also please add me to.
260 00:25:49.170 ⇒ 00:25:51.989 Awaish Kumar: Do we have linear project for Matamo.
261 00:25:53.060 ⇒ 00:25:58.299 Amber Lin: Yes, let me let me add you.
262 00:25:58.850 ⇒ 00:25:59.510 Awaish Kumar: Okay.
263 00:26:03.100 ⇒ 00:26:07.430 Amber Lin: And I’ll ask them if they can share with your email as well.
264 00:26:09.640 ⇒ 00:26:10.540 Amber Lin: So.
265 00:26:12.320 ⇒ 00:26:18.160 Amber Lin: Coffee, and I will add you to
266 00:26:26.080 ⇒ 00:26:29.640 Amber Lin: were you able to log into the Google account like the matter, more account.
267 00:26:32.105 ⇒ 00:26:34.690 Awaish Kumar: I I haven’t tried actually.
268 00:26:35.150 ⇒ 00:26:35.740 Amber Lin: Hmm.
269 00:26:36.580 ⇒ 00:26:37.449 Awaish Kumar: But we’re here.
270 00:26:39.260 ⇒ 00:26:42.800 Awaish Kumar: Okay, I have Automo.
271 00:26:47.770 ⇒ 00:26:48.660 Awaish Kumar: what is?
272 00:27:02.770 ⇒ 00:27:06.090 Awaish Kumar: So you haven’t shared it
273 00:27:07.830 ⇒ 00:27:15.069 Awaish Kumar: like I I’m not able to see it in one pass. If I searched for it, I can only reach it with your link.
274 00:27:15.390 ⇒ 00:27:19.130 Amber Lin: Oh, that’s so interesting! Give me a quick second.
275 00:27:19.880 ⇒ 00:27:29.120 Amber Lin: This should be I don’t really know how to share this
276 00:27:32.580 ⇒ 00:27:38.780 Amber Lin: it should be. It should be in, though let me see if I need to add you to
277 00:27:42.980 ⇒ 00:27:47.820 Amber Lin: oh, I see, let me add you here.
278 00:28:22.940 ⇒ 00:28:24.012 Awaish Kumar: Okay. I’m in.
279 00:28:25.150 ⇒ 00:28:27.150 Amber Lin: Oh, you’re in okay, awesome.
280 00:28:33.110 ⇒ 00:28:36.730 Amber Lin: Let me add you as well.
281 00:28:48.150 ⇒ 00:28:49.530 Awaish Kumar: Good girl.
282 00:28:50.130 ⇒ 00:28:52.639 Awaish Kumar: Let me come back on.
283 00:28:52.640 ⇒ 00:28:53.110 Amber Lin: Okay.
284 00:28:53.110 ⇒ 00:29:00.669 Awaish Kumar: After meeting with Luke on this on Point Number One, which discussed and.
285 00:29:00.670 ⇒ 00:29:07.090 Amber Lin: Well, I will meet with clients, and we can meet again to discuss if we can have another roadmap.
286 00:29:08.750 ⇒ 00:29:09.890 Awaish Kumar: Okay. Sure.
287 00:29:10.460 ⇒ 00:29:12.900 Amber Lin: Yeah, awesome. Thank you so much.
288 00:29:13.490 ⇒ 00:29:14.730 Awaish Kumar: Thank you. Bye.
289 00:29:14.730 ⇒ 00:29:15.940 Amber Lin: Thank you. Bye.