Meeting Title: Mattermore: Modeling Breakdown Date: 2025-06-10 Meeting participants: Amber Lin, Annie Yu, Awaish Kumar, Luke Daque
WEBVTT
1 00:03:12.680 ⇒ 00:03:16.089 Amber Lin: Hi! Annie! Waiting for a wish.
2 00:03:18.210 ⇒ 00:03:25.700 Annie Yu: Yeah, this morning at Eden. Stand up. A wish didn’t show up, so I’m not sure so.
3 00:03:26.880 ⇒ 00:03:29.149 Amber Lin: Oh, dear, okay, let me see.
4 00:03:29.150 ⇒ 00:03:31.309 Annie Yu: I think he’s online now.
5 00:03:31.310 ⇒ 00:03:35.879 Amber Lin: Oh, okay, that’s good check. If there was a out of office.
6 00:03:35.880 ⇒ 00:03:36.849 Annie Yu: No worries, here.
7 00:03:37.420 ⇒ 00:03:40.210 Amber Lin: Hi, Elish, how you been doing.
8 00:03:41.090 ⇒ 00:03:42.640 Awaish Kumar: I’m good. How about you?
9 00:03:44.500 ⇒ 00:03:50.200 Amber Lin: I’m finally back home. I’m in la right now, so happy about that.
10 00:03:52.160 ⇒ 00:03:53.750 Awaish Kumar: You’re finally back home.
11 00:03:53.750 ⇒ 00:04:00.820 Amber Lin: Yeah, I I was traveling for the past 2 weeks, and I am finally back in La.
12 00:04:04.220 ⇒ 00:04:05.099 Awaish Kumar: Good to hear.
13 00:04:05.280 ⇒ 00:04:06.240 Amber Lin: Yeah,
14 00:04:08.350 ⇒ 00:04:09.560 Awaish Kumar: How was your traveling.
15 00:04:11.646 ⇒ 00:04:13.583 Amber Lin: It was long.
16 00:04:15.660 ⇒ 00:04:20.339 Amber Lin: it was fun, but it it went on for a while, so I got really really tired.
17 00:04:26.260 ⇒ 00:04:31.650 Awaish Kumar: Okay? And so what is the like solution?
18 00:04:32.250 ⇒ 00:04:36.400 Awaish Kumar: Share this status with the client? And like, what’s
19 00:04:36.530 ⇒ 00:04:39.069 Awaish Kumar: was the agenda for the today’s meeting?
20 00:04:39.070 ⇒ 00:04:46.799 Amber Lin: Okay. Sounds good. 1st thing, Luke, I wanna check. How’s the progress of meeting moving stuff to Dbt.
21 00:04:52.690 ⇒ 00:04:54.170 Luke Daque: Hello! Hello! Can you hear me?
22 00:04:54.170 ⇒ 00:04:55.349 Amber Lin: Hi! I can hear you.
23 00:04:55.610 ⇒ 00:04:57.189 Awaish Kumar: Your voice is very low.
24 00:04:58.510 ⇒ 00:05:00.830 Luke Daque: Oh, how about now?
25 00:05:00.830 ⇒ 00:05:01.240 Amber Lin: Yeah.
26 00:05:01.240 ⇒ 00:05:01.940 Awaish Kumar: Yeah, it’s good. Now.
27 00:05:01.940 ⇒ 00:05:02.770 Amber Lin: Sounds good.
28 00:05:03.250 ⇒ 00:05:10.279 Luke Daque: Yeah, cool. So yeah, I was able. Well, I I wasn’t able to complete the setup because we don’t have
29 00:05:11.160 ⇒ 00:05:14.650 Luke Daque: that. The access what do you call this?
30 00:05:15.190 ⇒ 00:05:18.690 Luke Daque: We’re missing access levels, basically for bigquery.
31 00:05:18.690 ⇒ 00:05:19.110 Amber Lin: Oh!
32 00:05:19.110 ⇒ 00:05:24.749 Luke Daque: So yeah, but I was able to set it up. Well, I’m just waiting, basically for
33 00:05:25.150 ⇒ 00:05:28.049 Luke Daque: Trevor to give us the correct access.
34 00:05:28.570 ⇒ 00:05:29.080 Amber Lin: Okay.
35 00:05:29.080 ⇒ 00:05:31.220 Luke Daque: Like permissions basically.
36 00:05:32.060 ⇒ 00:05:32.990 Awaish Kumar: Don’t speak.
37 00:05:33.270 ⇒ 00:05:35.849 Awaish Kumar: Okay. So now we have the access right.
38 00:05:37.180 ⇒ 00:05:45.860 Luke Daque: Yes, but we don’t have permissions to create anything in the project, so we can’t do any like Dbt. Run, or whatever commands.
39 00:05:45.860 ⇒ 00:05:49.490 Awaish Kumar: Okay, so so, like, we are still blocked.
40 00:05:50.180 ⇒ 00:05:51.510 Luke Daque: Yes, that’s correct.
41 00:05:51.510 ⇒ 00:05:52.180 Awaish Kumar: Okay.
42 00:05:58.170 ⇒ 00:06:02.570 Amber Lin: Okay, I noted that down. Let me share my screen. And wow.
43 00:06:03.900 ⇒ 00:06:13.610 Amber Lin: and today, essentially, wish I wanted you here to talk about this one
44 00:06:13.990 ⇒ 00:06:19.280 Amber Lin: this ticket because we’re waiting on Trevor for power. Bi. I think they’re good. I think they already set it up.
45 00:06:20.130 ⇒ 00:06:26.009 Awaish Kumar: Yes, like I see that in the I I just read the messages in the client channel, and I see both
46 00:06:26.140 ⇒ 00:06:30.070 Awaish Kumar: Annie and Luke was having problems signing into these.
47 00:06:30.420 ⇒ 00:06:31.020 Amber Lin: Oh!
48 00:06:31.230 ⇒ 00:06:32.230 Awaish Kumar: Instances, like.
49 00:06:32.730 ⇒ 00:06:33.160 Amber Lin: Okay.
50 00:06:33.160 ⇒ 00:06:35.039 Awaish Kumar: Is that right? Right, Annie?
51 00:06:35.930 ⇒ 00:06:40.940 Annie Yu: Yeah, he provided a link where? So I was able to kind of
52 00:06:41.080 ⇒ 00:06:50.010 Annie Yu: enter their metamor workplace on windows. But we can’t. I can’t access the power bi
53 00:06:50.360 ⇒ 00:06:53.670 Annie Yu: or yeah, I’m not sure what that is. But
54 00:06:54.613 ⇒ 00:06:58.939 Annie Yu: I went through all the kind of security check. And then
55 00:06:59.310 ⇒ 00:07:00.939 Annie Yu: I just ended up with that
56 00:07:03.560 ⇒ 00:07:09.019 Annie Yu: that screen that I share. It says, like your organization doesn’t currently allow its users to purchase.
57 00:07:11.490 ⇒ 00:07:13.120 Amber Lin: Let me see.
58 00:07:15.680 ⇒ 00:07:16.799 Annie Yu: That’s in that thread.
59 00:07:16.800 ⇒ 00:07:19.760 Awaish Kumar: Did you get the email invite, or what.
60 00:07:19.910 ⇒ 00:07:24.469 Annie Yu: Yeah, yeah, got an email invite. And I accepted.
61 00:07:25.370 ⇒ 00:07:30.380 Annie Yu: And I was able to go into that. There’s kind of like a blank workspace.
62 00:07:30.740 ⇒ 00:07:34.800 Annie Yu: But then I can’t access this link that he shared.
63 00:07:36.490 ⇒ 00:07:37.730 Awaish Kumar: Okay, hang on
64 00:07:42.060 ⇒ 00:07:46.699 Awaish Kumar: okay, and look like you must have got it as well. Did you set up.
65 00:07:47.320 ⇒ 00:07:49.820 Luke Daque: I did not try it for power Bi.
66 00:07:51.610 ⇒ 00:07:52.400 Awaish Kumar: Okay.
67 00:07:52.770 ⇒ 00:08:00.274 Awaish Kumar: Say, can you also try like, just like, except in white, so we can see. Verify it. And then
68 00:08:00.900 ⇒ 00:08:04.819 Awaish Kumar: escalated to the client. If it’s not working for both of you.
69 00:08:07.960 ⇒ 00:08:08.590 Luke Daque: Sure.
70 00:08:10.690 ⇒ 00:08:15.719 Amber Lin: Great. I think another another thing is that
71 00:08:16.050 ⇒ 00:08:19.680 Amber Lin: I think, last meeting we discussed with Annie, that she.
72 00:08:20.350 ⇒ 00:08:21.209 Awaish Kumar: I think.
73 00:08:21.210 ⇒ 00:08:28.099 Amber Lin: Mostly on a Mac. So what are your I wish? What do you think about how we’re gonna deal with this.
74 00:08:28.670 ⇒ 00:08:29.600 Awaish Kumar: Sorry.
75 00:08:30.211 ⇒ 00:08:39.219 Amber Lin: So Annie has a Mac and power Bi desktop doesn’t run on Mac OS. So we were trying to find a workaround to.
76 00:08:39.690 ⇒ 00:08:43.999 Awaish Kumar: Yeah, like Uttam. Already, I think, mentioned that Powerpi, we can
77 00:08:44.169 ⇒ 00:08:49.540 Awaish Kumar: like create the stars in power Bi cloud as well, like just open it in the browser.
78 00:08:49.960 ⇒ 00:08:50.740 Amber Lin: Okay.
79 00:08:52.480 ⇒ 00:08:55.289 Amber Lin: I mean, if we’re sure that we’ll have all the
80 00:08:55.980 ⇒ 00:09:00.189 Amber Lin: features and abilities, then we’ll be fine. But I just wanna make sure that.
81 00:09:00.190 ⇒ 00:09:04.700 Awaish Kumar: Yeah, like, that’s when I need to. Just like once she get an
82 00:09:05.020 ⇒ 00:09:12.899 Awaish Kumar: access, she can go into the log, into the cloud and see all the charts, and like she can create so
83 00:09:14.530 ⇒ 00:09:18.479 Awaish Kumar: like she can verify. Once she has the access.
84 00:09:20.580 ⇒ 00:09:24.600 Luke Daque: Yeah. And I haven’t received any email actually, for.
85 00:09:24.600 ⇒ 00:09:25.280 Amber Lin: Oh!
86 00:09:25.510 ⇒ 00:09:27.370 Luke Daque: R. Bi, so I won’t be able to do that.
87 00:09:27.370 ⇒ 00:09:30.779 Awaish Kumar: I don’t know like he says that like 4 of you
88 00:09:36.030 ⇒ 00:09:36.780 Awaish Kumar: should have that.
89 00:09:38.565 ⇒ 00:09:40.350 Amber Lin: Instance.
90 00:09:44.350 ⇒ 00:09:49.710 Annie Yu: Screenshot of the email invite that I got from him. That’s that looks like it.
91 00:09:50.030 ⇒ 00:09:50.650 Annie Yu: The.
92 00:09:50.650 ⇒ 00:09:56.050 Luke Daque: Yeah, I didn’t get anything it looks like.
93 00:09:56.670 ⇒ 00:10:01.819 Luke Daque: But let me just try to go to Power Bi and see if I can log in.
94 00:10:02.240 ⇒ 00:10:04.700 Awaish Kumar: The way organization travel.
95 00:10:06.770 ⇒ 00:10:09.329 Awaish Kumar: So did you click on accept invitation.
96 00:10:10.640 ⇒ 00:10:11.630 Annie Yu: Yeah, yeah.
97 00:10:12.370 ⇒ 00:10:13.390 Awaish Kumar: Okay, so.
98 00:10:16.090 ⇒ 00:10:18.990 Annie Yu: So once I went through it.
99 00:10:19.250 ⇒ 00:10:26.979 Annie Yu: I this is why I got which it looks like a just a blank workspace. So no problem accessing this.
100 00:10:27.650 ⇒ 00:10:33.810 Annie Yu: but have trouble accessing power. Bi specifically.
101 00:10:37.620 ⇒ 00:10:40.510 Awaish Kumar: But this is like power, Bi, or what.
102 00:10:40.730 ⇒ 00:10:43.160 Annie Yu: No, this looks like a just a blank
103 00:10:43.380 ⇒ 00:10:46.360 Annie Yu: workspace. I don’t even know what this is called, but.
104 00:10:46.605 ⇒ 00:10:48.570 Amber Lin: Do you want to share your screen, Annie?
105 00:10:49.052 ⇒ 00:10:52.040 Annie Yu: No! I put them in the chat of zoom.
106 00:10:52.600 ⇒ 00:10:54.490 Amber Lin: Oh, let me go! Check!
107 00:10:56.400 ⇒ 00:11:03.650 Awaish Kumar: So like Jake, can can we go through it now like? Do you have access to laptop.
108 00:11:05.250 ⇒ 00:11:12.490 Annie Yu: I do, but there’s nothing like nothing to show, really. So this is why I got that’s right.
109 00:11:12.490 ⇒ 00:11:20.170 Awaish Kumar: Can we try entering this link and maybe see where it takes you? It’s okay. And in the
110 00:11:20.430 ⇒ 00:11:21.650 Awaish Kumar: my apps.
111 00:11:23.510 ⇒ 00:11:25.959 Amber Lin: Yeah. So we add up.
112 00:11:25.960 ⇒ 00:11:27.959 Awaish Kumar: On the left. Yeah, here.
113 00:11:28.080 ⇒ 00:11:31.339 Awaish Kumar: Can you search for power, Bi, or something? Can you see anything.
114 00:11:33.125 ⇒ 00:11:35.240 Amber Lin: Okay, this is a site. Hmm.
115 00:11:36.000 ⇒ 00:11:46.839 Awaish Kumar: Name URL can can you name it? Something power, Bi or something, and copy the link from your email? I see there’s 1 link
116 00:11:47.830 ⇒ 00:11:51.629 Awaish Kumar: domain matter more.microsoft.com.
117 00:12:00.130 ⇒ 00:12:00.670 Amber Lin: Hmm.
118 00:12:00.670 ⇒ 00:12:01.230 Awaish Kumar: Okay.
119 00:12:02.080 ⇒ 00:12:03.839 Awaish Kumar: It doesn’t take us anywhere to.
120 00:12:09.310 ⇒ 00:12:14.590 Amber Lin: What about Luke? Do you have that email by any chance, or do you wanna use Linkedin.
121 00:12:15.030 ⇒ 00:12:17.080 Luke Daque: Nope! I don’t have. I didn’t.
122 00:12:17.080 ⇒ 00:12:22.040 Awaish Kumar: Can we click on on the top left from these squares?
123 00:12:22.170 ⇒ 00:12:24.140 Awaish Kumar: Can you click on that one with
124 00:12:24.520 ⇒ 00:12:28.840 Awaish Kumar: explore like, can you click on, explore all your apps.
125 00:12:30.060 ⇒ 00:12:31.670 Awaish Kumar: Okay. Now.
126 00:12:41.230 ⇒ 00:12:42.220 Annie Yu: We actually.
127 00:12:42.960 ⇒ 00:12:44.020 Annie Yu: With this one.
128 00:12:46.150 ⇒ 00:12:48.070 Awaish Kumar: But this is okay.
129 00:12:48.330 ⇒ 00:12:54.960 Amber Lin: Yeah, I also got the link. I’m gonna try and go in now, except.
130 00:12:54.960 ⇒ 00:12:55.590 Awaish Kumar: Let’s cool.
131 00:12:56.820 ⇒ 00:12:59.399 Amber Lin: Wish? Did you get an invite by any chance.
132 00:12:59.650 ⇒ 00:13:04.249 Awaish Kumar: It’s no I didn’t, so I don’t know who, for, like maybe Utam and you.
133 00:13:06.620 ⇒ 00:13:10.250 Awaish Kumar: so can we now see install apps.
134 00:13:11.250 ⇒ 00:13:15.420 Awaish Kumar: What is that? Get cookie?
135 00:13:17.330 ⇒ 00:13:20.890 Annie Yu: Yeah, this, it’s like, just m, 3, 65.
136 00:13:28.320 ⇒ 00:13:31.300 Awaish Kumar: And okay, can you search
137 00:13:32.080 ⇒ 00:13:34.869 Awaish Kumar: like, when you click on admin what it says.
138 00:13:38.420 ⇒ 00:13:39.270 Awaish Kumar: oh.
139 00:13:52.960 ⇒ 00:13:53.970 Awaish Kumar: take care.
140 00:13:54.450 ⇒ 00:13:59.389 Awaish Kumar: So okay, hey, what is add-ins?
141 00:14:06.580 ⇒ 00:14:09.200 Awaish Kumar: Okay, this is power. Bi here.
142 00:14:12.430 ⇒ 00:14:13.220 Awaish Kumar: No.
143 00:14:15.490 ⇒ 00:14:16.879 Annie Yu: Yeah. No.
144 00:14:23.040 ⇒ 00:14:24.010 Awaish Kumar: Oh, God!
145 00:14:26.900 ⇒ 00:14:34.680 Awaish Kumar: Can you just search app, source on the top, like on the
146 00:14:34.820 ⇒ 00:14:37.109 Awaish Kumar: in the middle, it says, search access.
147 00:14:40.420 ⇒ 00:14:42.999 Awaish Kumar: Okay, let maybe go from here also.
148 00:14:48.990 ⇒ 00:14:51.490 Awaish Kumar: like on the top, it says, such app source.
149 00:14:52.420 ⇒ 00:14:53.120 Annie Yu: Oh, okay.
150 00:14:53.580 ⇒ 00:14:54.250 Awaish Kumar: Sure
151 00:15:00.970 ⇒ 00:15:04.809 Awaish Kumar: no, like these are not. This is not something.
152 00:15:05.070 ⇒ 00:15:09.940 Awaish Kumar: These are external apps. We need to install like, okay.
153 00:15:14.250 ⇒ 00:15:16.560 Awaish Kumar: I don’t see Priya.
154 00:15:26.470 ⇒ 00:15:29.249 Annie Yu: Yeah, it. It looks like he wants us
155 00:15:29.450 ⇒ 00:15:34.580 Annie Yu: to go through that link that he shared.
156 00:15:35.860 ⇒ 00:15:41.480 Annie Yu: And I’m not sure how this kind of space is related to to this.
157 00:15:43.240 ⇒ 00:15:44.510 Awaish Kumar: One side.
158 00:15:53.480 ⇒ 00:16:01.030 Awaish Kumar: Okay, like they, he said. He already created a power bi instance.
159 00:16:02.040 ⇒ 00:16:03.000 Awaish Kumar: Wow!
160 00:16:03.910 ⇒ 00:16:12.719 Awaish Kumar: If if that is, then we shouldn’t not have to sign up. Because if there’s already an and you are invited as a user, you should be able to
161 00:16:13.600 ⇒ 00:16:16.560 Awaish Kumar: this excess, darling, why we create another one.
162 00:16:16.860 ⇒ 00:16:25.800 Annie Yu: Yeah. So this is what it looks like when I just go through the link that he shared.
163 00:16:26.290 ⇒ 00:16:29.069 Annie Yu: So I will have to input my email
164 00:16:29.690 ⇒ 00:16:34.520 Annie Yu: and then go through like a human verification.
165 00:16:38.950 ⇒ 00:16:39.530 Amber Lin: Yeah, I.
166 00:16:39.530 ⇒ 00:16:39.950 Awaish Kumar: And just.
167 00:16:39.950 ⇒ 00:16:44.880 Amber Lin: Logged in through to his email through the same through through the same problem.
168 00:16:44.880 ⇒ 00:16:54.599 Amber Lin: And I see the same screen as Annie sees. I also checked the members in our group. And, Luke, you should have gotten a email.
169 00:16:55.318 ⇒ 00:16:58.900 Amber Lin: Can you try and search Trevor’s email and your.
170 00:16:58.900 ⇒ 00:16:59.240 Awaish Kumar: You know.
171 00:16:59.240 ⇒ 00:16:59.690 Amber Lin: Box.
172 00:16:59.690 ⇒ 00:17:03.720 Luke Daque: Hmm, yeah, that’s interesting. I do. You know what the
173 00:17:04.160 ⇒ 00:17:06.359 Luke Daque: titleways or something? Because I don’t.
174 00:17:06.790 ⇒ 00:17:07.250 Amber Lin: Think, maybe.
175 00:17:07.250 ⇒ 00:17:09.139 Luke Daque: If I search for power. Bi, I don’t.
176 00:17:09.567 ⇒ 00:17:17.099 Amber Lin: Search for Trevor, and just go to his email. Trevor, Trevor, M. Attlemore.
177 00:17:17.349 ⇒ 00:17:19.069 Luke Daque: Yeah. I don’t think I have.
178 00:17:19.069 ⇒ 00:17:20.849 Amber Lin: You would have sent you something.
179 00:17:23.460 ⇒ 00:17:25.960 Luke Daque: Okay, let me search for it.
180 00:17:34.810 ⇒ 00:17:44.880 Luke Daque: I see it now. Yeah, it doesn’t mention anything related to power. Bi. That’s why sign in.
181 00:17:46.000 ⇒ 00:17:55.209 Amber Lin: We probably will have to escalate this, because I also checked, and it’s the same thing for me. So we’ll go. We’ll go check in with Trevor to get help with that.
182 00:17:57.005 ⇒ 00:18:04.059 Amber Lin: I I wanted to for us to talk about how we’re going to do the modeling.
183 00:18:04.410 ⇒ 00:18:06.770 Amber Lin: so I’m going to go back here.
184 00:18:09.370 ⇒ 00:18:16.310 Amber Lin: Oh, that’s blocked. Okay, so this one and I think of which I kind of want
185 00:18:16.460 ⇒ 00:18:21.259 Amber Lin: help from you to define this together, of how.
186 00:18:21.480 ⇒ 00:18:25.500 Amber Lin: how we are going to achieve this, this level of.
187 00:18:25.500 ⇒ 00:18:26.060 Awaish Kumar: Yeah.
188 00:18:27.146 ⇒ 00:18:29.380 Awaish Kumar: So 1st of all, like
189 00:18:29.510 ⇒ 00:18:37.427 Awaish Kumar: we have the models. I just want to understand from Luke like
190 00:18:39.280 ⇒ 00:18:43.849 Awaish Kumar: like, for like at what granularity we have. These models.
191 00:18:46.180 ⇒ 00:18:46.780 Luke Daque: We like.
192 00:18:46.780 ⇒ 00:18:47.100 Awaish Kumar: Don’t!
193 00:18:47.100 ⇒ 00:18:53.709 Luke Daque: Already have the views, which will be what we will be like
194 00:18:54.180 ⇒ 00:19:02.417 Luke Daque: migrating to Dbt. But then we’ll have to add, like additional granularity like, what we have in that like the
195 00:19:03.800 ⇒ 00:19:06.540 Luke Daque: yeah, like, blend.
196 00:19:06.660 ⇒ 00:19:07.230 Luke Daque: Yep.
197 00:19:07.230 ⇒ 00:19:12.799 Awaish Kumar: My just, my question is like you already have some views which which should be basically
198 00:19:13.690 ⇒ 00:19:20.260 Awaish Kumar: having all of these fields into it is that right? Or we are missing some fields.
199 00:19:22.530 ⇒ 00:19:27.990 Luke Daque: We should have most of it, except for the granularity, I would say.
200 00:19:28.130 ⇒ 00:19:30.450 Luke Daque: But yeah, I can always add the missing ones.
201 00:19:30.450 ⇒ 00:19:30.960 Amber Lin: Let’s.
202 00:19:30.960 ⇒ 00:19:34.660 Awaish Kumar: In terms of granularity. Do you mean time drain, or something else?
203 00:19:35.190 ⇒ 00:19:39.419 Luke Daque: Yeah, like time drain. Well, I have to check it because I can’t remember everything.
204 00:19:40.210 ⇒ 00:19:50.030 Awaish Kumar: Okay, let’s let’s let’s verify, because I I know that you have been working with autumn on this data platform sheet. So let’s go
205 00:19:50.846 ⇒ 00:19:59.000 Awaish Kumar: like 1st step like, look verify that we we have the models
206 00:20:01.370 ⇒ 00:20:29.069 Awaish Kumar: like this is an aggregated version, like when it says from atomic metrics. We want to go subcategory or category, or we want to aggregate somehow or filter somehow. That’s basically going to happen power Bi, but we should have some tables which support this like right? So so it has all the fields where and it can select to filter. It has all these time range which can then be used to aggregate things.
207 00:20:29.170 ⇒ 00:20:35.180 Awaish Kumar: So what we need to verify is that we have all our
208 00:20:35.610 ⇒ 00:20:39.900 Awaish Kumar: raw intermediate and the large tables
209 00:20:40.590 ⇒ 00:20:43.389 Awaish Kumar: on this level of granularity.
210 00:20:43.540 ⇒ 00:20:49.400 Awaish Kumar: which means, like, maybe have a timestamp column from where you can get hour of the day day of week
211 00:20:50.159 ⇒ 00:20:54.059 Awaish Kumar: and then all these filters like we post policy and all of that.
212 00:20:54.430 ⇒ 00:20:58.439 Awaish Kumar: and maybe how many models we have created? I don’t know.
213 00:20:59.230 ⇒ 00:21:00.350 Awaish Kumar: So let’s
214 00:21:02.250 ⇒ 00:21:02.740 Amber Lin: So
215 00:21:02.740 ⇒ 00:21:10.339 Amber Lin: so from my understanding, these all have to be their own separate columns. Right? Each of these say, say, we have a basic
216 00:21:10.440 ⇒ 00:21:18.489 Amber Lin: table. And then we need to add a column of Okay, who this is which team they’re from, what functions they perform. What location did they perform.
217 00:21:18.490 ⇒ 00:21:19.850 Awaish Kumar: So from the
218 00:21:20.480 ⇒ 00:21:38.280 Awaish Kumar: so from the data like it’s, I don’t know, like in from Microsoft Graph, I don’t know how, in how, in which format we are going to get that. But we basically from that structure, we are going to create another structure where we have a
219 00:21:38.390 ⇒ 00:21:42.289 Awaish Kumar: a flat table inside of it. We have all these columns.
220 00:21:42.880 ⇒ 00:21:50.839 Awaish Kumar: And which basically then can be used in power bi to aggregate filter segment things.
221 00:21:52.840 ⇒ 00:22:15.660 Amber Lin: okay. Luke, how far do you think we’re from that so let’s list out all the all the steps that we need and then let’s see how far we are, because I know we have some models, but we probably should. There’s a lot more work we need to do, and the more we know what we haven’t done, the better we can tell them what the estimate of time should be, because I don’t want to
222 00:22:16.130 ⇒ 00:22:16.740 Amber Lin: time.
223 00:22:17.430 ⇒ 00:22:18.300 Awaish Kumar: Soup.
224 00:22:18.850 ⇒ 00:22:21.979 Awaish Kumar: Okay, let’s some plan out how we are going to do that.
225 00:22:21.980 ⇒ 00:22:24.059 Amber Lin: Yeah, let’s let’s write that out.
226 00:22:26.400 ⇒ 00:22:28.330 Awaish Kumar: So like this has this
227 00:22:28.970 ⇒ 00:22:38.959 Awaish Kumar: like, we have this sheet from them. And then I think, Luke, you have a data platform sheet as well, but I think you’re not sure of if we have all the fields
228 00:22:39.100 ⇒ 00:22:41.130 Awaish Kumar: which are mentioned here in our
229 00:22:41.870 ⇒ 00:22:46.870 Awaish Kumar: in our in our table. So I think 1st step is to verify
230 00:22:48.560 ⇒ 00:22:56.070 Awaish Kumar: that our existing models which we have already created have all these fields.
231 00:22:56.700 ⇒ 00:23:02.929 Awaish Kumar: and even one step further to verify. Even if if the raw data
232 00:23:03.768 ⇒ 00:23:10.339 Awaish Kumar: which, which which is like our source, has the all the fields we need.
233 00:23:14.060 ⇒ 00:23:15.910 Awaish Kumar: Right, keep clicking on.
234 00:23:19.600 ⇒ 00:23:25.824 Awaish Kumar: And if if answer is the second and second thing is that we
235 00:23:26.860 ⇒ 00:23:30.920 Awaish Kumar: have like from our existing views. We just say
236 00:23:31.040 ⇒ 00:23:51.260 Awaish Kumar: that how much from the we, how far we are from this sheet. That means like, maybe, like 30% of the work is already done. And we need maybe 20% more to do to to bring in all the fees, or something like that. So we need to verify how far we are from the
237 00:23:51.550 ⇒ 00:23:58.530 Awaish Kumar: find them sheet, and then how
238 00:23:59.140 ⇒ 00:24:03.240 Awaish Kumar: how much effort is required to move. These views, I know, like
239 00:24:03.570 ⇒ 00:24:11.689 Awaish Kumar: these mode views are essentially like slack carries, you can easily move. But but, like still, just just
240 00:24:12.240 ⇒ 00:24:19.259 Awaish Kumar: so, we just estimate the effort. We need to move this into a DVD project, maybe a few hours, or whatever it is.
241 00:24:19.430 ⇒ 00:24:21.530 Awaish Kumar: And that’s.
242 00:24:26.650 ⇒ 00:24:27.210 Awaish Kumar: Okay.
243 00:24:27.650 ⇒ 00:24:37.910 Amber Lin: Yeah, I think, just 1st off, I think from my understanding, we do not have 2 usage. We have email activity. We have chat activity and meetings. We do not have this
244 00:24:38.500 ⇒ 00:24:42.020 Amber Lin: tool activity, as in, say, when they use
245 00:24:44.062 ⇒ 00:24:46.539 Amber Lin: 3, 60, like other other tools.
246 00:24:46.540 ⇒ 00:24:53.589 Awaish Kumar: So like from the synthetic data, what we are basically generating like, is it data for email or chat? Or
247 00:24:58.290 ⇒ 00:24:59.830 Awaish Kumar: it’s a question for Luke.
248 00:25:03.346 ⇒ 00:25:16.069 Luke Daque: I believe we have list messages, which is like email, wait, let me, check and then
249 00:25:28.420 ⇒ 00:25:32.700 Luke Daque: let me just open up the bigquery that should be struggling.
250 00:26:15.850 ⇒ 00:26:18.779 Amber Lin: Should we pull up the does she?
251 00:26:20.790 ⇒ 00:26:22.299 Amber Lin: Is this the right one?
252 00:26:24.870 ⇒ 00:26:27.410 Luke Daque: So get all messages. Is chat.
253 00:26:28.630 ⇒ 00:26:32.570 Luke Daque: That’s like the raw data that’s like coming from our synthetic data.
254 00:26:32.840 ⇒ 00:26:38.290 Luke Daque: And then list call records, would B calls.
255 00:26:38.780 ⇒ 00:26:40.290 Amber Lin: Oh, calls! We don’t.
256 00:26:40.290 ⇒ 00:26:41.179 Luke Daque: So we have.
257 00:26:41.180 ⇒ 00:26:43.530 Amber Lin: Different ones for calls and meetings.
258 00:26:44.290 ⇒ 00:26:47.530 Luke Daque: List events is or
259 00:26:57.200 ⇒ 00:26:57.800 Awaish Kumar: Bye.
260 00:27:00.360 ⇒ 00:27:05.140 Luke Daque: This would most probably be. But these are just events they’re not really like.
261 00:27:05.380 ⇒ 00:27:07.370 Luke Daque: But there’s like we, we can
262 00:27:07.889 ⇒ 00:27:14.910 Luke Daque: these are meetings, basically because, like, we have meeting provider like Webex, whether it’s Google meet teams or whatever.
263 00:27:15.200 ⇒ 00:27:18.659 Luke Daque: So this should be, meetings listed.
264 00:27:18.660 ⇒ 00:27:19.660 Amber Lin: Oh! This!
265 00:27:19.660 ⇒ 00:27:25.519 Luke Daque: And and then message message list. Events would be
266 00:27:29.600 ⇒ 00:27:32.679 Luke Daque: email, message list, events.
267 00:27:32.920 ⇒ 00:27:33.550 Amber Lin: Oh!
268 00:27:33.550 ⇒ 00:27:34.379 Luke Daque: Yep. So we have.
269 00:27:34.380 ⇒ 00:27:36.899 Amber Lin: Where can I see all of it? Is it in.
270 00:27:37.430 ⇒ 00:27:41.049 Luke Daque: You should be able to see it in the in bigquery already, because we already loaded.
271 00:27:41.050 ⇒ 00:27:41.550 Amber Lin: Oh!
272 00:27:41.550 ⇒ 00:27:43.900 Luke Daque: And be great also the views and the
273 00:27:44.670 ⇒ 00:27:47.579 Luke Daque: models that I already created initially.
274 00:27:48.080 ⇒ 00:27:55.000 Amber Lin: Oh, I mean is I I think my question was, is there somewhere in this data platform sheet that I can see it?
275 00:27:57.500 ⇒ 00:27:58.460 Amber Lin: Oh, there we go.
276 00:27:58.460 ⇒ 00:27:59.640 Awaish Kumar: Let me go to this.
277 00:28:00.410 ⇒ 00:28:00.990 Luke Daque: Yeah.
278 00:28:04.070 ⇒ 00:28:06.950 Awaish Kumar: Message, calendar, messaging, contact, person.
279 00:28:10.430 ⇒ 00:28:13.850 Amber Lin: So calendar list list events.
280 00:28:14.300 ⇒ 00:28:14.850 Amber Lin: Okay.
281 00:28:14.850 ⇒ 00:28:23.490 Awaish Kumar: So from so basically from the list event, we are going to get the meetings information quite
282 00:28:24.960 ⇒ 00:28:27.770 Awaish Kumar: like who has joined the meeting or not right.
283 00:28:28.510 ⇒ 00:28:28.784 Luke Daque: Yep.
284 00:28:29.950 ⇒ 00:28:33.699 Amber Lin: How is it is that, please? Like there’s there’s 2.
285 00:28:42.360 ⇒ 00:28:45.139 Luke Daque: Like I mentioned list events is meetings.
286 00:28:46.480 ⇒ 00:28:53.930 Luke Daque: And then messages is emails.
287 00:28:54.640 ⇒ 00:28:56.779 Luke Daque: I get all messages, is chat.
288 00:28:58.640 ⇒ 00:28:59.380 Amber Lin: Oh!
289 00:28:59.380 ⇒ 00:29:04.029 Luke Daque: And then call records, is calls.
290 00:29:04.770 ⇒ 00:29:11.000 Amber Lin: Okay, sounds like we might need some renaming.
291 00:29:12.180 ⇒ 00:29:13.550 Amber Lin: Just the emails.
292 00:29:13.550 ⇒ 00:29:14.419 Luke Daque: The raw data.
293 00:29:14.420 ⇒ 00:29:15.219 Amber Lin: The same.
294 00:29:15.620 ⇒ 00:29:17.880 Luke Daque: But that’s what it’s called in the Api.
295 00:29:17.880 ⇒ 00:29:19.000 Amber Lin: I see, I see.
296 00:29:20.340 ⇒ 00:29:23.199 Luke Daque: We can name it in our staging models, or whatever or whatnot.
297 00:29:23.200 ⇒ 00:29:23.530 Amber Lin: Cool.
298 00:29:24.069 ⇒ 00:29:33.780 Awaish Kumar: Final march table. And then, so we have the data for emails and the chat and the meetings.
299 00:29:33.940 ⇒ 00:29:40.760 Awaish Kumar: What we are missing is tool usage. I think we have like, I see, the Microsoft Graph says that
300 00:29:40.940 ⇒ 00:29:48.540 Awaish Kumar: basically we can have to usage. But on the Microsoft apps, right? So.
301 00:29:48.540 ⇒ 00:29:51.730 Amber Lin: Yes, I think that’s all they need. Anyways.
302 00:29:52.270 ⇒ 00:29:54.810 Awaish Kumar: So on the Microsoft platform.
303 00:29:55.040 ⇒ 00:30:04.799 Awaish Kumar: I don’t know what like, how, which Api endpoint. We are going to use it. But using Microsoft Api, maybe if you research a little bit maybe there’s some
304 00:30:05.240 ⇒ 00:30:07.869 Awaish Kumar: we we can somehow find like.
305 00:30:09.690 ⇒ 00:30:14.070 Awaish Kumar: like, we need the information of cruisers.
306 00:30:14.240 ⇒ 00:30:19.590 Awaish Kumar: what tool they use like what Microsoft tools they are using
307 00:30:19.710 ⇒ 00:30:25.340 Awaish Kumar: and how like. It’s even better if we get a little more granular information like
308 00:30:25.550 ⇒ 00:30:33.690 Awaish Kumar: how frequent they use, how much time they spend on it like something like that on on each tool.
309 00:30:34.010 ⇒ 00:30:36.819 Awaish Kumar: So, for for example, user, a spent like
310 00:30:37.180 ⇒ 00:30:40.400 Awaish Kumar: 30 min every day on power pi.
311 00:30:40.780 ⇒ 00:30:49.670 Awaish Kumar: something like that we know, react. We monitor in user activity for each tool on the
312 00:30:50.030 ⇒ 00:30:51.809 Awaish Kumar: like. The Microsoft platform.
313 00:30:56.030 ⇒ 00:31:01.869 Awaish Kumar: right? Like, you know, do you know, like, do you know, like, if we can get it somewhere from our.
314 00:31:01.870 ⇒ 00:31:13.729 Amber Lin: Searched it up. There’s something in the Api documents that says, yes, we can get that. So it’s in the Api. We just need to make some synthetic data sets based on that.
315 00:31:14.380 ⇒ 00:31:20.840 Awaish Kumar: B, do we wanna like, do we want to create synthetic data like, how tunnel.
316 00:31:20.970 ⇒ 00:31:26.090 Awaish Kumar: how long it’s going to take to generate that data.
317 00:31:26.090 ⇒ 00:31:27.989 Amber Lin: I guess that’s the question for Luke.
318 00:31:29.630 ⇒ 00:31:30.560 Amber Lin: This.
319 00:31:35.510 ⇒ 00:31:41.200 Luke Daque: I don’t know. I’ll have to look at the Api like what fields there would be, and stuff like that, so I I can’t.
320 00:31:41.200 ⇒ 00:31:41.750 Awaish Kumar: Thank you.
321 00:31:41.750 ⇒ 00:31:42.739 Luke Daque: Maybe a day.
322 00:31:42.740 ⇒ 00:31:43.899 Awaish Kumar: I like, I don’t.
323 00:31:44.270 ⇒ 00:31:47.244 Awaish Kumar: Yeah, I I don’t mean like exact
324 00:31:48.090 ⇒ 00:31:54.130 Awaish Kumar: timing, but an estimate with like, if you have already generated some synthetic data for other endpoints. So like.
325 00:31:54.130 ⇒ 00:31:54.830 Luke Daque: Day or 2.
326 00:31:54.830 ⇒ 00:31:56.279 Awaish Kumar: Some case or something.
327 00:32:00.360 ⇒ 00:32:03.499 Amber Lin: Okay, so this would take about a day.
328 00:32:05.560 ⇒ 00:32:06.490 Amber Lin: Okay.
329 00:32:06.490 ⇒ 00:32:10.220 Awaish Kumar: Okay, so that means, but how? How?
330 00:32:10.220 ⇒ 00:32:15.739 Luke Daque: The actual api endpoint because we don’t. I don’t want to create synthetic data that’s not like.
331 00:32:16.690 ⇒ 00:32:17.090 Amber Lin: Hmm.
332 00:32:17.090 ⇒ 00:32:19.750 Luke Daque: It’s the same cause like like I mentioned, like.
333 00:32:19.750 ⇒ 00:32:20.220 Amber Lin: He’s just.
334 00:32:20.220 ⇒ 00:32:24.339 Luke Daque: Synthetic data. And once we get the real data, it might be very different. So it’s
335 00:32:25.250 ⇒ 00:32:27.780 Luke Daque: feel like it’s like so much effort for
336 00:32:27.910 ⇒ 00:32:30.510 Luke Daque: not real data and stuff like that. So.
337 00:32:30.510 ⇒ 00:32:34.450 Amber Lin: I know, but I mean that’s the client wants to do this.
338 00:32:34.790 ⇒ 00:32:38.579 Amber Lin: So that’s what that’s what we should do.
339 00:32:38.580 ⇒ 00:32:45.499 Awaish Kumar: Okay. So like amber like, yes, I don’t know how far we are from getting the real data.
340 00:32:45.770 ⇒ 00:32:46.860 Awaish Kumar: Are you still a week.
341 00:32:47.321 ⇒ 00:32:56.549 Amber Lin: I think at least a week or 2 weeks. They just signed the contract last week, say, last Thursday ish.
342 00:32:56.670 ⇒ 00:33:03.609 Amber Lin: and so they’re gonna need a bit of time to get the data set up and give that to us. So I think we should
343 00:33:03.970 ⇒ 00:33:08.130 Amber Lin: if we have 2 weeks, and we definitely should get this so.
344 00:33:08.130 ⇒ 00:33:18.059 Awaish Kumar: So let’s let’s prioritize it at the end. I think like, let’s get everything to dbt. Have all the models
345 00:33:18.230 ⇒ 00:33:22.680 Awaish Kumar: and set any up for Microsoft power bi.
346 00:33:23.060 ⇒ 00:33:23.580 Amber Lin: Yeah.
347 00:33:23.580 ⇒ 00:33:24.430 Awaish Kumar: Then we.
348 00:33:24.430 ⇒ 00:33:25.370 Amber Lin: And then we can look at.
349 00:33:26.440 ⇒ 00:33:30.559 Amber Lin: Okay, I I agree. Let’s talk about what we’re missing here.
350 00:33:30.890 ⇒ 00:33:35.769 Amber Lin: And then, so that Luke knows what kind of modeling we still need to do.
351 00:33:37.580 ⇒ 00:33:38.280 Awaish Kumar: Hello!
352 00:33:38.820 ⇒ 00:33:40.409 Amber Lin: Okay, sounds good.
353 00:33:41.200 ⇒ 00:33:41.910 Amber Lin: I think we like.
354 00:33:41.910 ⇒ 00:33:42.319 Awaish Kumar: Do we want.
355 00:33:42.320 ⇒ 00:33:43.080 Amber Lin: To.
356 00:33:43.530 ⇒ 00:33:47.430 Awaish Kumar: So do we want to ticket, create ticket for these things, or.
357 00:33:47.430 ⇒ 00:34:08.120 Amber Lin: Yeah, I will. I will. I just want us to talk about it first, st I think, for now we’re gonna stop at time. We’re gonna stop at the second segment filters. We don’t need to worry about the 3rd and the 4th yet. So just these boxes. And I listed out everything down here. So let’s just talk about what we have. I think we have.
358 00:34:08.765 ⇒ 00:34:12.474 Amber Lin: I’m not sure if this is in python, or if this is in
359 00:34:13.370 ⇒ 00:34:16.350 Awaish Kumar: Yeah, this is not like this is, this will be just one.
360 00:34:17.060 ⇒ 00:34:17.780 Amber Lin: Oh, column. Yeah.
361 00:34:18.250 ⇒ 00:34:20.910 Awaish Kumar: Time column. It should be there. But
362 00:34:21.620 ⇒ 00:34:25.959 Awaish Kumar: so like I just want to give. Maybe I just want to give a
363 00:34:26.440 ⇒ 00:34:30.500 Awaish Kumar: have a ticket for investigation and assign it to Lou, for
364 00:34:30.830 ⇒ 00:34:35.730 Awaish Kumar: maybe give him give him some time to figure out if we have all the things there.
365 00:34:36.409 ⇒ 00:34:40.319 Awaish Kumar: And if not, how long it’s going to take him to
366 00:34:40.749 ⇒ 00:34:43.529 Awaish Kumar: adjust the models, to bring in those fields
367 00:34:44.019 ⇒ 00:34:47.979 Awaish Kumar: just an investigation ticket, not a implementation. One.
368 00:34:47.980 ⇒ 00:34:48.830 Amber Lin: Don’t know.
369 00:34:48.830 ⇒ 00:34:53.709 Awaish Kumar: So maybe spend just just a few hours on it, like maybe one or 2
370 00:34:53.989 ⇒ 00:35:00.189 Awaish Kumar: figure out if all the fields are there, if not how long it’s going to take to bring those in.
371 00:35:00.992 ⇒ 00:35:02.560 Amber Lin: That’s all right.
372 00:35:02.560 ⇒ 00:35:17.670 Amber Lin: Have some sort of idea to begin with, and I and I just think that since Annie is here, any can talk about what she has, what she has to do in python. Essentially, I guess that’s what that’s why I wanted to see if any, if you can input really quickly.
373 00:35:17.910 ⇒ 00:35:22.868 Amber Lin: What are these ones that you had to do in python? And I think that could make
374 00:35:23.320 ⇒ 00:35:27.370 Amber Lin: make Luke’s life easier if we give him a
375 00:35:27.550 ⇒ 00:35:29.649 Amber Lin: if we have something to start off with.
376 00:35:30.956 ⇒ 00:35:47.629 Annie Yu: I think day of week, hour of day are pretty self exploratory, and in python what I did was actually not accurate, because I use the universal time zone. But in reality we want these columns to be based on localized time.
377 00:35:47.630 ⇒ 00:35:50.879 Amber Lin: Were you able to find it in the bigquery models.
378 00:35:51.210 ⇒ 00:35:54.719 Annie Yu: No, I just assumed they are all in their local time.
379 00:35:55.710 ⇒ 00:35:57.440 Annie Yu: but I don’t think they are.
380 00:35:57.780 ⇒ 00:35:58.850 Amber Lin: Oh!
381 00:35:59.940 ⇒ 00:36:00.280 Awaish Kumar: Sorry.
382 00:36:00.280 ⇒ 00:36:02.030 Amber Lin: To add local time.
383 00:36:03.200 ⇒ 00:36:05.290 Awaish Kumar: Yeah, but it’s like.
384 00:36:06.200 ⇒ 00:36:12.760 Awaish Kumar: but when like, whenever we get a real data like it’s, it’s it’s whatever time zone it is. We just use that.
385 00:36:17.090 ⇒ 00:36:24.770 Annie Yu: Yeah, Luke can confirm this, but I think when we look at the documentation.
386 00:36:25.660 ⇒ 00:36:34.439 Annie Yu: they kind of just standardize everything into one time zone, so Luke would have to convert the time zone.
387 00:36:34.440 ⇒ 00:36:39.719 Awaish Kumar: That is, is that. But is that a real requirement from like
388 00:36:40.190 ⇒ 00:36:49.470 Awaish Kumar: we don’t need to like, we just have to verify, that is that a requirement to move it to a local time zone for a client? Or is that the standard works for them? Maybe.
389 00:36:49.470 ⇒ 00:36:59.750 Annie Yu: I think it makes sense to have local time, because we want to identify the behavior by like Monday, Tuesday, Wednesday. So we want to make sure that’s based on the local time.
390 00:36:59.750 ⇒ 00:37:08.790 Amber Lin: Yes, cause we want to look at after hours as well. So it depends on the client. If they work in one unified time zone, or if it, if they work.
391 00:37:08.790 ⇒ 00:37:18.819 Awaish Kumar: The problem is, what if? But the thing is, what if, like every employee has a different time zone, then we have to do that for each employee of the client.
392 00:37:22.070 ⇒ 00:37:25.310 Awaish Kumar: So if Monday for each employee will be different, then.
393 00:37:37.450 ⇒ 00:37:45.920 Amber Lin: I mean great, that’s that’s a problem. We need to verify that itself would be.
394 00:37:52.100 ⇒ 00:38:02.080 Amber Lin: so I think we don’t have this as we didn’t. I think this is essentially a specific date. And then in the columns we’ll just say.
395 00:38:02.691 ⇒ 00:38:07.109 Amber Lin: this one was depending on the date. It will be before
396 00:38:07.480 ⇒ 00:38:10.930 Amber Lin: in office, mandate after an office mandate.
397 00:38:11.270 ⇒ 00:38:13.080 Amber Lin: but I don’t think we have that yet.
398 00:38:14.090 ⇒ 00:38:14.900 Awaish Kumar: What is that?
399 00:38:14.900 ⇒ 00:38:18.980 Annie Yu: This they will need to give us a date, so Luke can.
400 00:38:19.790 ⇒ 00:38:20.950 Annie Yu: The label.
401 00:38:20.950 ⇒ 00:38:29.180 Amber Lin: Let’s just make up a date because we have synthetic data, and then we can tag anything before that as as Pre and tag anything
402 00:38:29.350 ⇒ 00:38:31.579 Amber Lin: after as post. How’s that.
403 00:38:32.820 ⇒ 00:38:34.929 Awaish Kumar: Yeah, just making make up a.
404 00:38:53.930 ⇒ 00:38:59.569 Awaish Kumar: So technically, we should not have to do any of these.
405 00:39:00.120 ⇒ 00:39:04.310 Awaish Kumar: And like ideally, we should not have to do any of these in the pythons. Right?
406 00:39:04.430 ⇒ 00:39:05.590 Awaish Kumar: All of this.
407 00:39:05.590 ⇒ 00:39:07.090 Amber Lin: Yes, all everything should be.
408 00:39:07.874 ⇒ 00:39:11.010 Awaish Kumar: I don’t see any.
409 00:39:14.800 ⇒ 00:39:23.279 Awaish Kumar: Yeah, except this like day of week, hour of day. Like, if we just have a timestamp, it can be done in power Bi. So
410 00:39:23.820 ⇒ 00:39:29.729 Awaish Kumar: maybe we created these individual columns or not, it’s it’s okay. But
411 00:39:29.950 ⇒ 00:39:34.336 Awaish Kumar: and all of this should like it should be the the DVD model. And
412 00:39:35.850 ⇒ 00:39:39.860 Awaish Kumar: and I think like, that’s what I want to just investigate it
413 00:39:40.250 ⇒ 00:39:43.360 Awaish Kumar: look like. So if he has everything or not.
414 00:39:56.500 ⇒ 00:39:57.040 Amber Lin: Hmm!
415 00:40:04.550 ⇒ 00:40:08.470 Amber Lin: I mean, I think we have.
416 00:40:13.091 ⇒ 00:40:21.449 Awaish Kumar: But like these are filters, and then we need like, what are the metrics like we are like in this list. We are missing the metrics.
417 00:40:21.840 ⇒ 00:40:23.819 Amber Lin: What do you mean? The metrics.
418 00:40:23.820 ⇒ 00:40:26.550 Awaish Kumar: What we are marrying right? But we are may like
419 00:40:26.720 ⇒ 00:40:33.000 Awaish Kumar: these are filters like who, like amber amber, did what right amber attended like.
420 00:40:33.529 ⇒ 00:40:36.809 Amber Lin: Sent 30 emails per day, or what like.
421 00:40:38.010 ⇒ 00:40:39.440 Awaish Kumar: What is the metric? Right?
422 00:40:40.520 ⇒ 00:40:41.360 Amber Lin: It.
423 00:40:42.210 ⇒ 00:40:46.609 Awaish Kumar: So we have it on that. If you see see the top level screenshot.
424 00:40:47.280 ⇒ 00:40:50.510 Awaish Kumar: We have this kind of like
425 00:40:50.770 ⇒ 00:40:55.784 Awaish Kumar: atomic metrics like email check tool usage. So we need to include that
426 00:40:56.310 ⇒ 00:40:57.900 Amber Lin: Yeah in our tables.
427 00:40:58.350 ⇒ 00:40:59.679 Amber Lin: So I think
428 00:40:59.950 ⇒ 00:41:06.360 Amber Lin: this is how much of this? Yeah, let me grab this. I think this makes a lot of
429 00:41:06.650 ⇒ 00:41:13.380 Amber Lin: a sense is, how does activity vary by segments?
430 00:41:13.880 ⇒ 00:41:19.260 Luke Daque: We already have that in the in the configuration sheet, or what whatever we call that that data.
431 00:41:19.390 ⇒ 00:41:23.830 Luke Daque: But data sheet or matter more.
432 00:41:24.330 ⇒ 00:41:27.300 Luke Daque: we should have that for each of the metrics already.
433 00:41:28.480 ⇒ 00:41:29.160 Awaish Kumar: Okay.
434 00:41:29.490 ⇒ 00:41:34.260 Annie Yu: And one thing about the data platform documentation
435 00:41:34.480 ⇒ 00:41:42.170 Annie Yu: that those metrics, some of the formula there are not going to be accurate in real life. I don’t think
436 00:41:42.330 ⇒ 00:41:48.349 Annie Yu: like I don’t know if things are confirmed by the client or so, but.
437 00:41:48.570 ⇒ 00:41:48.980 Amber Lin: Okay.
438 00:41:48.980 ⇒ 00:41:50.810 Annie Yu: Formulas here are just.
439 00:41:51.710 ⇒ 00:41:53.660 Amber Lin: Assumptions.
440 00:41:54.250 ⇒ 00:41:56.520 Awaish Kumar: It should be on the in the 1st time.
441 00:41:57.190 ⇒ 00:41:57.670 Amber Lin: Oh!
442 00:41:58.590 ⇒ 00:42:07.570 Awaish Kumar: Yeah, this one. Yes. Okay, we have this metric family and atomic metrics email meetings.
443 00:42:07.900 ⇒ 00:42:09.040 Awaish Kumar: Kong
444 00:42:12.040 ⇒ 00:42:14.330 Awaish Kumar: question when it’s meeting duration.
445 00:42:21.030 ⇒ 00:42:25.399 Annie Yu: And amber. I think those formulas have to be kind of
446 00:42:26.320 ⇒ 00:42:35.799 Annie Yu: defined by the client. So the current formulas that we have here are just whatever we used in Python, which I don’t think it’s gonna be
447 00:42:36.400 ⇒ 00:42:37.630 Annie Yu: realistic.
448 00:42:37.630 ⇒ 00:42:44.109 Amber Lin: I see. So I think a few things. First.st I think this is still in python. This is not in Gpt.
449 00:42:44.330 ⇒ 00:42:47.999 Amber Lin: 2, I think for
450 00:42:48.260 ⇒ 00:42:56.210 Amber Lin: these durations. I don’t think we’ll have to worry that much, because when we get the real data we’ll have a start time and end time.
451 00:42:57.213 ⇒ 00:43:00.390 Annie Yu: Not for not for email and message.
452 00:43:00.870 ⇒ 00:43:04.429 Annie Yu: And Luke can correct me if I’m wrong. But I don’t think.
453 00:43:04.430 ⇒ 00:43:04.910 Awaish Kumar: But.
454 00:43:04.910 ⇒ 00:43:07.049 Annie Yu: Start time for email or a message.
455 00:43:08.660 ⇒ 00:43:17.029 Awaish Kumar: Yeah. But for email and messages, we we do, we should not measure the duration. Right? We just see them account of emails per user or something like that.
456 00:43:17.200 ⇒ 00:43:23.479 Annie Yu: Yeah, if that’s the case, I I think that totally makes sense. But then client needs to have that expectations.
457 00:43:25.260 ⇒ 00:43:25.740 Awaish Kumar: Yes, yes.
458 00:43:25.740 ⇒ 00:43:26.570 Amber Lin: I think they want to.
459 00:43:26.570 ⇒ 00:43:28.480 Awaish Kumar: I think this sheet is shared with the
460 00:43:29.140 ⇒ 00:43:36.210 Awaish Kumar: I think, like amber. This sheet is shared with the client, and we what we need is just like, maybe
461 00:43:36.400 ⇒ 00:43:42.130 Awaish Kumar: ask them that we have have provided the description for the formula.
462 00:43:42.430 ⇒ 00:43:46.170 Awaish Kumar: please just let just confirm like, if that is.
463 00:43:47.040 ⇒ 00:43:49.450 Awaish Kumar: if that’s what? What is their expectation?
464 00:43:58.230 ⇒ 00:44:04.260 Amber Lin: I think I can do that if we can confirm this is the data that we will be getting cause. Then.
465 00:44:04.690 ⇒ 00:44:05.899 Amber Lin: like, maybe we could.
466 00:44:05.900 ⇒ 00:44:08.590 Awaish Kumar: This sheet is already shared with the client. Right?
467 00:44:08.770 ⇒ 00:44:21.319 Awaish Kumar: Utham has already shared it right? They have reviewed it. We just need. They just just verify to us as well that they are okay. They they like this sheet, or they. They are okay with the metric definition.
468 00:44:28.350 ⇒ 00:44:33.940 Annie Yu: Yeah. One thing I called out yesterday was the focus time like that focus time. Definition was just
469 00:44:34.070 ⇒ 00:44:36.450 Annie Yu: like a assumption by me, which is.
470 00:44:36.890 ⇒ 00:44:37.370 Awaish Kumar: Yeah, yeah.
471 00:44:37.370 ⇒ 00:44:43.119 Annie Yu: Pretty ridiculous. So I think that that kind of like needs to be defined.
472 00:44:43.120 ⇒ 00:44:49.740 Awaish Kumar: So these are the things like focus time, which you defined as some, some.
473 00:44:50.100 ⇒ 00:45:01.129 Awaish Kumar: some guesses you you took. You guessed some some of the numbers and to, and kept it here also, like the the one thing Amber mentioned like. Just take a date, and then
474 00:45:01.570 ⇒ 00:45:08.180 Awaish Kumar: and then the in for the in office and after like, Oh, no!
475 00:45:08.460 ⇒ 00:45:10.760 Awaish Kumar: After we have this remote work like.
476 00:45:11.440 ⇒ 00:45:20.949 Awaish Kumar: So now we are guessing that date right, we should be getting that from client. So these are things we we will be getting from. We should be getting from the client.
477 00:45:21.537 ⇒ 00:45:27.880 Awaish Kumar: We are just like right now, we’re just just guessing to implement something.
478 00:45:27.880 ⇒ 00:45:28.610 Amber Lin: Yes.
479 00:45:28.610 ⇒ 00:45:32.990 Awaish Kumar: But at the end we are. We are going to get these things. We should be getting these things from the cloud.
480 00:45:32.990 ⇒ 00:45:39.219 Amber Lin: Yeah. And also one other thing I think for now, all they want to focus on is
481 00:45:39.700 ⇒ 00:45:47.849 Amber Lin: is these things. So I think we can take focus time out of the equation for now, so that will alleviate some of our guest work as well.
482 00:45:48.170 ⇒ 00:45:52.410 Awaish Kumar: But that’s okay. Like just one column, they they can verify.
483 00:45:53.370 ⇒ 00:45:58.988 Awaish Kumar: When we ask them to verify the formulas, they will have some some feedback, at least
484 00:46:04.010 ⇒ 00:46:05.280 Awaish Kumar: apart from.
485 00:46:09.560 ⇒ 00:46:21.049 Amber Lin: I I still feel a little bit stuck. To be very honest, I maybe it’s my perception of how we’re at. I don’t. I don’t really see a clear path forward.
486 00:46:21.740 ⇒ 00:46:26.260 Awaish Kumar: Yeah. So like, I, I define the role like the
487 00:46:26.370 ⇒ 00:46:29.310 Awaish Kumar: the task we need to do like on. If on the
488 00:46:31.320 ⇒ 00:46:36.010 Awaish Kumar: if you scroll up. I, you wrote something right? Like what we need to do as a steps.
489 00:46:36.660 ⇒ 00:46:38.200 Awaish Kumar: Yeah, verify
490 00:46:38.833 ⇒ 00:46:46.310 Awaish Kumar: these, like we have should have this investigation ticket just scroll up there. There is something you already wrote.
491 00:46:46.740 ⇒ 00:46:56.427 Amber Lin: I see, I think it’s just because we already wanted to investigate a week ago. And we still don’t really have anything. So I just.
492 00:46:58.510 ⇒ 00:47:03.319 Awaish Kumar: Like, but that’s like like, whatever we have is
493 00:47:04.490 ⇒ 00:47:07.579 Awaish Kumar: is like like wherever we we are right now.
494 00:47:08.050 ⇒ 00:47:14.100 Awaish Kumar: Like, that’s okay. Like, do we not need to find now the path forward? So.
495 00:47:14.280 ⇒ 00:47:14.750 Amber Lin: Hmm.
496 00:47:14.750 ⇒ 00:47:18.084 Awaish Kumar: Right. So if you scroll up
497 00:47:19.183 ⇒ 00:47:26.849 Awaish Kumar: like, that’s where we want to start. I want to start at least from here that I want to like. 1st of all, we want to verify
498 00:47:27.630 ⇒ 00:47:30.469 Awaish Kumar: that existing model have the
499 00:47:30.650 ⇒ 00:47:37.940 Awaish Kumar: all the fields, all the metric fields, or the filtered fields required, and if if not.
500 00:47:39.030 ⇒ 00:47:41.320 Awaish Kumar: it’s just, it’s just there.
501 00:47:42.920 ⇒ 00:47:45.000 Amber Lin: Great. Let me.
502 00:47:46.550 ⇒ 00:47:53.320 Amber Lin: I’ll create a ticket, and then maybe we can have a spreadsheet that maps each of these will have like maybe
503 00:47:53.480 ⇒ 00:47:58.530 Amber Lin: we can say what model it is in, or what kind of filter. I don’t know.
504 00:47:58.530 ⇒ 00:48:00.379 Awaish Kumar: No, no, but we already have a
505 00:48:01.670 ⇒ 00:48:07.111 Awaish Kumar: we already have a sheet where we have to get out the model.
506 00:48:07.530 ⇒ 00:48:14.720 Amber Lin: Client what these are right like. They gave us a list. We need to check off each of those ones to tell them. Hey, we have these things.
507 00:48:22.650 ⇒ 00:48:23.360 Awaish Kumar: Like.
508 00:48:23.600 ⇒ 00:48:30.999 Awaish Kumar: That’s what we are going to like with this investigation ticket like that’s where find out.
509 00:48:31.000 ⇒ 00:48:31.969 Amber Lin: Yeah, that’s.
510 00:48:31.970 ⇒ 00:48:46.079 Awaish Kumar: What is there and what is possible. So we we don’t have to tell the client if something we have not modeled out. So something is, is in the raw data. We is there, and we have not model it out. We have to do it, implement it.
511 00:48:46.980 ⇒ 00:48:53.539 Awaish Kumar: Only we, we escalate it to client. Only if it’s not possible to do it right from the raw data.
512 00:48:54.370 ⇒ 00:48:55.140 Amber Lin: Yes.
513 00:48:56.760 ⇒ 00:49:00.289 Awaish Kumar: So for that, we need to 1st go through these steps.
514 00:49:00.430 ⇒ 00:49:03.849 Awaish Kumar: figure out what is possible and what’s not.
515 00:49:08.020 ⇒ 00:49:18.909 Amber Lin: I I understand what you’re saying. So like, this is a investigation. This is investigation ticket. I think I’m trying to define what the output of this would be.
516 00:49:23.840 ⇒ 00:49:24.860 Amber Lin: Is this gonna be?
517 00:49:24.860 ⇒ 00:49:28.799 Amber Lin: Is this gonna what? What would be done?
518 00:49:28.910 ⇒ 00:49:43.459 Amber Lin: What would define done for this ticket? Is this going to be a spreadsheet that we can see. Is this going to be a list of explanations on where to find these links to where things are like what what is done for this investigation ticket.
519 00:49:43.460 ⇒ 00:49:49.360 Awaish Kumar: I think I think the easiest way is that we have a data platform sheet.
520 00:49:49.760 ⇒ 00:49:58.830 Awaish Kumar: It’s like it. It has the metric different metric like the name it has. The filters needs to be applied on that metric
521 00:49:59.180 ⇒ 00:50:06.329 Awaish Kumar: just on the right. We just have one more column which basically says, like.
522 00:50:06.600 ⇒ 00:50:18.960 Awaish Kumar: if it has a like, it’s like, if it has everything, or modeled out or available in source, but not modeled out, or, what is missing, something, just
523 00:50:19.080 ⇒ 00:50:22.270 Awaish Kumar: one more column on the bright side.
524 00:50:24.220 ⇒ 00:50:25.870 Awaish Kumar: Sounds good
525 00:50:25.870 ⇒ 00:50:35.889 Awaish Kumar: feedback from the loop, which we can say, loop will either say, like, Okay, it’s possible it’s at source, but not modeled out, and the second comment will be.
526 00:50:36.030 ⇒ 00:50:38.699 Awaish Kumar: it’s modeled out already. Right, Doug.
527 00:50:38.850 ⇒ 00:50:42.220 Awaish Kumar: or it’s it’s not possible, like 3 of these things.
528 00:50:42.420 ⇒ 00:50:49.980 Amber Lin: Okay, so we’ll, I think we need one comment per each, like for each row. And for each of these like
529 00:50:50.640 ⇒ 00:50:53.510 Amber Lin: these specific things. And I don’t.
530 00:50:53.510 ⇒ 00:50:55.250 Amber Lin: Yeah, like, don’t think we should
531 00:50:55.250 ⇒ 00:51:03.140 Amber Lin: this yet. I think we’re just focusing on ignore column. I okay.
532 00:51:03.360 ⇒ 00:51:10.360 Awaish Kumar: Yes, just maybe not create us like so much columns. But just a comments column.
533 00:51:10.750 ⇒ 00:51:12.960 Awaish Kumar: And he has some feedback. We can read it.
534 00:51:13.310 ⇒ 00:51:23.379 Awaish Kumar: and while doing that he can also estimate how long it’s going to take him to add everything which is missing in the models.
535 00:51:26.480 ⇒ 00:51:32.339 Luke Daque: Should we add them, though, even if they’re not in the actual Api, if they’re missing.
536 00:51:32.340 ⇒ 00:51:37.289 Awaish Kumar: So if it’s not possible to get it from actual Api, then we say not possible right?
537 00:51:38.000 ⇒ 00:51:45.109 Awaish Kumar: Otherwise we are setting the wrong expectation, like, you will generate the synthetic data. But when it’s real client and real data, and we.
538 00:51:45.110 ⇒ 00:51:51.500 Luke Daque: Yeah. But we already created the synthetic data based on the Api fields. Yes.
539 00:51:51.840 ⇒ 00:51:54.640 Luke Daque: So if it’s not in the raw data, then
540 00:51:54.750 ⇒ 00:51:57.100 Luke Daque: we can say it’s not. It’s not.
541 00:51:57.100 ⇒ 00:51:57.480 Awaish Kumar: Yeah, yeah.
542 00:51:57.480 ⇒ 00:51:57.810 Luke Daque: Even.
543 00:51:57.810 ⇒ 00:51:59.199 Awaish Kumar: That’s okay. That’s okay.
544 00:51:59.710 ⇒ 00:52:01.450 Awaish Kumar: But that’s okay. To look like
545 00:52:01.750 ⇒ 00:52:13.149 Awaish Kumar: you. You know that you have created the data based on the Api. If it’s not possible. You can just say that. Okay, it’s not possible to get it. It’s not available from the Api.
546 00:52:18.220 ⇒ 00:52:20.600 Awaish Kumar: That’s what we need for this ticket.
547 00:52:26.620 ⇒ 00:52:27.680 Awaish Kumar: Is that okay?
548 00:52:29.570 ⇒ 00:52:36.099 Amber Lin: That for me, I’m happy. I’m happy with this. If this is okay with Luke, then we can go forward with this.
549 00:52:42.640 ⇒ 00:52:43.390 Amber Lin: Okay.
550 00:52:43.390 ⇒ 00:52:46.569 Awaish Kumar: So after this, after this.
551 00:52:46.570 ⇒ 00:52:47.160 Amber Lin: Priority.
552 00:52:47.790 ⇒ 00:52:54.610 Awaish Kumar: Yeah. And after this, like as if, like, we should also add the points to this.
553 00:52:56.180 ⇒ 00:53:00.220 Amber Lin: How point estimate and a due date, please.
554 00:53:00.790 ⇒ 00:53:06.820 Amber Lin: So where is oh, they haven’t enabled, give me a second.
555 00:53:07.040 ⇒ 00:53:12.180 Amber Lin: when do you think that would be done. Can someone tell me how how long that would be?
556 00:53:25.720 ⇒ 00:53:29.990 Amber Lin: How many points would this be a wish.
557 00:53:31.500 ⇒ 00:53:36.699 Awaish Kumar: Like I I’m assuming spanning like maybe one to 2 h.
558 00:53:39.040 ⇒ 00:53:39.560 Amber Lin: Okay.
559 00:53:44.670 ⇒ 00:53:46.939 Amber Lin: one to 2 h. That’s good.
560 00:53:47.290 ⇒ 00:53:48.390 Amber Lin: 2 h.
561 00:53:51.550 ⇒ 00:54:00.719 Annie Yu: Can I clarify one thing? So so is Luke gonna be modeling still with the synthetic data, not the actual data.
562 00:54:00.970 ⇒ 00:54:03.869 Amber Lin: Yeah, we don’t have actual data yet. Very unfortunately.
563 00:54:04.400 ⇒ 00:54:05.870 Awaish Kumar: Yes, Henry.
564 00:54:09.250 ⇒ 00:54:10.100 Annie Yu: Okay.
565 00:54:11.370 ⇒ 00:54:13.530 Awaish Kumar: Like, we don’t have actual data. So.
566 00:54:13.530 ⇒ 00:54:14.801 Amber Lin: Yeah, so we can’t.
567 00:54:16.160 ⇒ 00:54:17.920 Awaish Kumar: So, okay, so just
568 00:54:18.130 ⇒ 00:54:27.519 Awaish Kumar: with this ticket, we are just going to have some clarity on how what to do. And then the next tickets like will be implementation tickets.
569 00:54:27.520 ⇒ 00:54:47.340 Amber Lin: Yes, so I would say, Luke, is this possible today? Or at least that we can. We can review this with a wish tomorrow, like I cause we’re already on Tuesday, and we we don’t really have much left. I don’t know how long you have in your day, but it will be great if we can have this today.
570 00:54:47.610 ⇒ 00:54:50.079 Amber Lin: If it’s take. If it can take 2 h.
571 00:54:53.280 ⇒ 00:54:58.930 Luke Daque: Yeah, sure I can. I’ll see what I can do. I’ll let you know if I finish it every everything in
572 00:54:59.240 ⇒ 00:55:02.259 Luke Daque: within today, or if I need more time.
573 00:55:07.770 ⇒ 00:55:12.489 Amber Lin: I mean, let’s let’s look at what else is on your plate, and then we can talk about
574 00:55:12.750 ⇒ 00:55:17.650 Amber Lin: cause this is just an investigation ticket. I wish that it was. Gonna take 2 h.
575 00:55:18.274 ⇒ 00:55:18.740 Amber Lin: Let’s
576 00:55:20.700 ⇒ 00:55:22.990 Awaish Kumar: But yeah, because, like.
577 00:55:23.620 ⇒ 00:55:24.030 Luke Daque: Yogurt.
578 00:55:24.030 ⇒ 00:55:24.550 Awaish Kumar: He has.
579 00:55:24.550 ⇒ 00:55:30.439 Luke Daque: It’s faster just to create the models based on what they have. And then just think, just
580 00:55:31.415 ⇒ 00:55:34.699 Luke Daque: leave what is not there like. It’s not available
581 00:55:34.970 ⇒ 00:55:39.739 Luke Daque: compared to like investigating each and every single raw data source here and then.
582 00:55:40.860 ⇒ 00:55:44.664 Awaish Kumar: Yeah, like. But like you, you have already created models. And
583 00:55:45.150 ⇒ 00:55:47.600 Awaish Kumar: we want to figure out what
584 00:55:48.340 ⇒ 00:55:59.089 Awaish Kumar: isn’t there like. So I I don’t see this a lot of fields, right? So maybe 10 in total, I think there’s maybe 10 to 15 fields which we have to figure out.
585 00:56:07.870 ⇒ 00:56:12.039 Amber Lin: Can you guys tell me, is it better to investigate? Is it better to just.
586 00:56:12.040 ⇒ 00:56:22.240 Awaish Kumar: Yeah, it’s better to investigate, because, like in the in the role, like, if I just go in and see such in my raw date table. If if Field is there or not.
587 00:56:22.968 ⇒ 00:56:26.720 Awaish Kumar: Then to implement like obviously implements not be
588 00:56:27.510 ⇒ 00:56:31.799 Awaish Kumar: done in 2 h, it’s it’s going to take a lot of time.
589 00:56:33.100 ⇒ 00:56:38.609 Awaish Kumar: So until then we don’t have clarity until it is implemented. So I just want that we are not
590 00:56:38.770 ⇒ 00:56:41.559 Awaish Kumar: working in Blind Spot. Right? We have
591 00:56:42.020 ⇒ 00:56:44.579 Awaish Kumar: investigated, and we know we are what we are doing.
592 00:56:47.491 ⇒ 00:56:55.460 Annie Yu: One reminder, though. So, even if some columns that look identifies that we don’t have
593 00:56:55.570 ⇒ 00:57:05.199 Annie Yu: in the Kpi. To my knowledge, the client would still provide some kind of Csv. For us to use in the future. So just like, know.
594 00:57:05.200 ⇒ 00:57:06.429 Amber Lin: That’s I think that’s fine
595 00:57:06.430 ⇒ 00:57:15.239 Amber Lin: only for the badge swipe data. So I don’t think that relates. I think everything else is coming from the Api. So I don’t think that we we need to worry about that.
596 00:57:15.770 ⇒ 00:57:24.300 Annie Yu: Okay, yeah. But just know any extra columns will require Luke to take more time, for, like, just like setting expectation.
597 00:57:25.620 ⇒ 00:57:31.020 Awaish Kumar: Yeah. But that’s that’s not this like the point here, like the when we have the new Csv.
598 00:57:31.280 ⇒ 00:57:34.020 Awaish Kumar: we’ll have new ticket for that. We’ll have new
599 00:57:34.820 ⇒ 00:57:39.170 Awaish Kumar: story points for that, like. Luke will have his time to work on that.
600 00:57:39.600 ⇒ 00:57:40.310 Annie Yu: Yeah.
601 00:57:42.570 ⇒ 00:57:46.080 Amber Lin: Louis, is this? Is this comfortable with you like? Is this good with you?
602 00:57:46.970 ⇒ 00:57:51.859 Luke Daque: Yeah, I’ll I’ll do that. I’ll let you know if I complete anything today, or if I
603 00:57:52.310 ⇒ 00:57:54.070 Luke Daque: need to spill it for tomorrow.
604 00:57:56.406 ⇒ 00:57:56.980 Awaish Kumar: Yeah, it’s
605 00:57:59.960 ⇒ 00:58:06.919 Awaish Kumar: Okay, hold on like, once we have that, the second task would be to migrate
606 00:58:08.140 ⇒ 00:58:14.459 Awaish Kumar: to Gbt and then we start. Once we’ve migrated we’ll start
607 00:58:16.440 ⇒ 00:58:17.070 Amber Lin: Yeah.
608 00:58:17.892 ⇒ 00:58:27.779 Awaish Kumar: Start adding the missing fields. So what about credentials? Thing like Luke? Do you want to migrate, Michael?
609 00:58:28.050 ⇒ 00:58:33.180 Awaish Kumar: Only after we get the like the authentication issue, resolved or.
610 00:58:34.020 ⇒ 00:58:36.659 Luke Daque: I can always create the limiting models, and
611 00:58:37.720 ⇒ 00:58:42.620 Luke Daque: even if it’s not resolved. But I’m not sure if we can run that in Dev.
612 00:58:44.800 ⇒ 00:58:51.829 Luke Daque: I mean, we can always create the Pr and like, what if it works? It works? If we won’t be able to test it in there, though.
613 00:58:52.730 ⇒ 00:58:59.320 Awaish Kumar: Okay, so so 2 things here for one for Amber to escalate
614 00:58:59.610 ⇒ 00:59:02.659 Awaish Kumar: that Luke is still stuck with authentication issue.
615 00:59:02.880 ⇒ 00:59:03.900 Awaish Kumar: Oh, well.
616 00:59:03.900 ⇒ 00:59:06.379 Amber Lin: Of both power. Bi. And this I will ask.
617 00:59:06.380 ⇒ 00:59:12.440 Awaish Kumar: Yes, yes, and in the meantime Luke can create a Yay. But if
618 00:59:12.440 ⇒ 00:59:16.970 Awaish Kumar: he can decide whatever you like, he could want to work on investigation 1st
619 00:59:17.220 ⇒ 00:59:19.260 Awaish Kumar: or no. I want to migrate first.st
620 00:59:23.320 ⇒ 00:59:37.899 Annie Yu: Amber. I do have one thing that I want you to clarify with the client. That’s okay. I’m looking at the document that they put together the the Google Doc. And I know that there is listed
621 00:59:38.190 ⇒ 00:59:40.880 Annie Yu: kind of charts as deliverables
622 00:59:41.050 ⇒ 00:59:46.799 Annie Yu: for us. But then, when they say work, activity, what does that mean is that.
623 00:59:46.800 ⇒ 00:59:48.920 Amber Lin: Where do you? Where? Where is that?
624 00:59:49.465 ⇒ 00:59:52.390 Annie Yu: In the matter more, doc. That they share.
625 00:59:54.800 ⇒ 00:59:56.799 Awaish Kumar: But sorry.
626 00:59:56.950 ⇒ 01:00:02.910 Annie Yu: Yeah, yeah. Go down to the the 3rd section. Yeah.
627 01:00:02.910 ⇒ 01:00:03.370 Amber Lin: One.
628 01:00:03.370 ⇒ 01:00:05.990 Annie Yu: Yeah, like, I don’t know what that means. Like, we’re.
629 01:00:05.990 ⇒ 01:00:07.090 Amber Lin: Oh, I think thank you.
630 01:00:07.090 ⇒ 01:00:10.379 Amber Lin: Means work, activity, like a certain
631 01:00:10.870 ⇒ 01:00:19.609 Amber Lin: weekly say, example, weekly email activity, blah, blah, or it like.
632 01:00:20.310 ⇒ 01:00:23.240 Amber Lin: So that would be activity.
633 01:00:23.240 ⇒ 01:00:27.270 Annie Yu: Average, count not the duration or.
634 01:00:31.390 ⇒ 01:00:32.230 Amber Lin: Hmm!
635 01:00:32.420 ⇒ 01:00:38.049 Annie Yu: That’s why, like I, yeah, that’s why I needed to know if we are gonna go with like
636 01:00:38.240 ⇒ 01:00:43.639 Annie Yu: 5 min, 10 min duration. For, like the things that only have Timestamps.
637 01:00:45.230 ⇒ 01:00:46.060 Amber Lin: See?
638 01:00:48.270 ⇒ 01:00:53.470 Amber Lin: Okay, so let me go here in.
639 01:00:53.830 ⇒ 01:00:55.030 Amber Lin: Where is this?
640 01:00:56.040 ⇒ 01:00:57.190 Amber Lin: The.
641 01:00:58.730 ⇒ 01:00:59.065 Awaish Kumar: I.
642 01:00:59.830 ⇒ 01:01:03.320 Amber Lin: Needs. Let me write down need to verify.
643 01:01:07.570 ⇒ 01:01:08.230 Awaish Kumar: Sure.
644 01:01:12.990 ⇒ 01:01:21.140 Amber Lin: Yes, emails and chat are by duration or by count. Is that correct?
645 01:01:22.465 ⇒ 01:01:31.329 Annie Yu: Yeah? Or, yeah, I think that I think that’s for everything. But you know and chat, yeah, specifically, because we don’t have a.
646 01:01:31.340 ⇒ 01:01:38.220 Amber Lin: Yeah, for meetings and stuff. We do have their integrations, and those are easy to get. But I think these are hard.
647 01:01:38.220 ⇒ 01:01:42.519 Awaish Kumar: But the email, like, I don’t know like then maybe.
648 01:01:42.520 ⇒ 01:01:43.140 Amber Lin: They open.
649 01:01:43.140 ⇒ 01:01:46.259 Amber Lin: Oh, we understand email versus when they closed it.
650 01:01:47.600 ⇒ 01:01:53.950 Annie Yu: Yeah. But yeah. But then, like these, things have to be defined before.
651 01:01:54.770 ⇒ 01:01:57.720 Annie Yu: Can do the like correct modeling. I think.
652 01:01:58.530 ⇒ 01:01:59.050 Amber Lin: Great.
653 01:01:59.456 ⇒ 01:02:07.169 Annie Yu: And one more thing. Sorry. One more thing I just remember that is the meeting blocks like, sometimes
654 01:02:07.310 ⇒ 01:02:11.880 Annie Yu: we we like block our calendars for our.
655 01:02:11.880 ⇒ 01:02:17.929 Amber Lin: Yeah, that was, that was also my, that was also my thought of like
656 01:02:18.510 ⇒ 01:02:21.409 Amber Lin: when we look at this. And it’s
657 01:02:21.860 ⇒ 01:02:25.779 Amber Lin: activity, you know. Here, where is it?
658 01:02:29.560 ⇒ 01:02:34.200 Annie Yu: Yeah, so those things have to be defined as well. So if there’s any.
659 01:02:34.320 ⇒ 01:02:34.850 Amber Lin: Right like.
660 01:02:35.240 ⇒ 01:02:43.850 Amber Lin: if it’s just it blocked off on the calendar, and it’s a working block that I blocked off 5 h to just work. It’s not a call.
661 01:02:45.810 ⇒ 01:02:53.239 Annie Yu: Yeah, I don’t think there is a way that we’re using now to identify those to exclude those.
662 01:02:53.640 ⇒ 01:02:56.669 Amber Lin: Just calling that out in case that matters.
663 01:02:56.670 ⇒ 01:03:02.809 Amber Lin: Hmm, so just in case there’s only one person in, say the meeting, then it’s not a meeting.
664 01:03:03.260 ⇒ 01:03:10.749 Annie Yu: Yeah. But then we have to have a clear definition there. So Luke can have a guideline to follow as well.
665 01:03:13.750 ⇒ 01:03:16.869 Awaish Kumar: But what those fields say like
666 01:03:17.636 ⇒ 01:03:21.800 Awaish Kumar: when when it says meeting like, are we getting the fields from
667 01:03:22.593 ⇒ 01:03:24.126 Awaish Kumar: meeting that it says
668 01:03:26.052 ⇒ 01:03:29.669 Awaish Kumar: like that. We are just getting the blocked
669 01:03:30.930 ⇒ 01:03:33.499 Awaish Kumar: timings from the calendar. Are we
670 01:03:33.690 ⇒ 01:03:38.890 Awaish Kumar: actually getting the names from like, who attended the meeting, or
671 01:03:40.250 ⇒ 01:03:42.880 Awaish Kumar: how long it ran, or things like that
672 01:03:48.630 ⇒ 01:03:54.450 Awaish Kumar: from the Api, like which which data are we able to get?
673 01:03:59.800 ⇒ 01:04:01.200 Awaish Kumar: Luke? Are you there.
674 01:04:04.080 ⇒ 01:04:05.580 Luke Daque: What was the question? Again.
675 01:04:06.660 ⇒ 01:04:11.679 Awaish Kumar: Yeah. My question was that like, when we are getting the meeting data from the Apis.
676 01:04:11.800 ⇒ 01:04:16.479 Awaish Kumar: are we just like getting the slots from calendar.
677 01:04:16.650 ⇒ 01:04:18.170 Awaish Kumar: The book slots
678 01:04:18.930 ⇒ 01:04:25.729 Awaish Kumar: like the Api is returning the book slots of the calendar as meetings, or we are actually getting the
679 01:04:25.980 ⇒ 01:04:34.579 Awaish Kumar: the like. The time spent in the meeting the participant list of participants or start and end time. Things like that.
680 01:04:43.320 ⇒ 01:04:44.380 Awaish Kumar: You’re on mute.
681 01:04:46.870 ⇒ 01:04:54.600 Luke Daque: Oh, sorry about that. So in the raw data, we have created date time, we have original start time, zone, original end time. Zone.
682 01:04:57.540 ⇒ 01:05:01.440 Luke Daque: Yeah, I think those are the only timestamps or dates that we have.
683 01:05:02.890 ⇒ 01:05:04.989 Awaish Kumar: Yeah. But what I meant is more like.
684 01:05:04.990 ⇒ 01:05:09.680 Luke Daque: And then, so we we can get the duration because there’s a start and end.
685 01:05:10.670 ⇒ 01:05:11.230 Awaish Kumar: Okay.
686 01:05:11.230 ⇒ 01:05:21.270 Awaish Kumar: like, it’s the real meeting which started right. For example, I book like my question is, I book a meeting from 9 to 9 30, but I don’t start it like
687 01:05:21.710 ⇒ 01:05:23.600 Awaish Kumar: is it still counted or not?
688 01:05:24.160 ⇒ 01:05:27.590 Luke Daque: I’m not sure that’s a good question. We’ll have to check their documentation.
689 01:05:28.460 ⇒ 01:05:29.130 Awaish Kumar: Okay.
690 01:05:40.510 ⇒ 01:05:49.269 Amber Lin: Okay, sounds like, we need a few other meetings to go through each of them to make sure that we’re clearly defined and know what questions we need to ask the client.
691 01:05:50.490 ⇒ 01:05:51.650 Amber Lin: So I think
692 01:05:51.760 ⇒ 01:06:04.690 Amber Lin: right now, since migrating to Dbt is blocked, I think this is the only ticket that you have your on your plate, I’ll go confirm with these 2 clients, and I think we need to meet tomorrow.
693 01:06:07.300 ⇒ 01:06:11.600 Awaish Kumar: And okay, apart from okay.
694 01:06:14.520 ⇒ 01:06:19.760 Amber Lin: Yeah, cause we need to talk about how we’re gonna build them, which we didn’t weren’t able to get to today.
695 01:06:21.450 ⇒ 01:06:26.790 Amber Lin: Maybe a wish you can. You and I can have a meeting on how to build them.
696 01:06:27.470 ⇒ 01:06:28.730 Amber Lin: Latest day.
697 01:06:30.530 ⇒ 01:06:35.080 Luke Daque: So it looks like the start. I’m looking at their documentations. By the way, right now.
698 01:06:35.390 ⇒ 01:06:41.140 Luke Daque: doesn’t really say if it’s the books meeting or the actual start end to end.
699 01:06:42.260 ⇒ 01:06:47.480 Luke Daque: But the the definition is basically the start date time and time zone of the event.
700 01:06:48.702 ⇒ 01:06:50.749 Luke Daque: In Utc, so.
701 01:06:51.320 ⇒ 01:06:58.650 Amber Lin: Maybe instead of for meetings, maybe instead of getting it from the calendar, we should get it from Google meets Api. So then.
702 01:06:58.650 ⇒ 01:06:59.250 Awaish Kumar: Tough.
703 01:06:59.520 ⇒ 01:07:03.730 Amber Lin: Be an actual meeting where people are in it versus just a time block on the calendar.
704 01:07:03.730 ⇒ 01:07:07.919 Awaish Kumar: No. But like, we are not using Google meet right?
705 01:07:08.620 ⇒ 01:07:12.940 Amber Lin: Oh, so sorry! There was 2 people talking. I didn’t hear any of that.
706 01:07:13.730 ⇒ 01:07:18.090 Awaish Kumar: Yeah. But we are not using Google meets like, we are using it from Microsoft.
707 01:07:18.870 ⇒ 01:07:22.640 Amber Lin: Oh, sorry, my bad the teams, meetings.
708 01:07:23.190 ⇒ 01:07:23.650 Awaish Kumar: No.
709 01:07:27.530 ⇒ 01:07:33.360 Luke Daque: But this is already the event. Meet Api.
710 01:07:33.680 ⇒ 01:07:34.430 Amber Lin: Oh!
711 01:07:36.310 ⇒ 01:07:37.009 Awaish Kumar: But the
712 01:07:37.150 ⇒ 01:07:45.190 Awaish Kumar: okay. But embers like in the Google Sheet, you have 2 things like call recordings which also mention meetings.
713 01:07:45.460 ⇒ 01:07:47.150 Awaish Kumar: And then the calendar.
714 01:07:47.410 ⇒ 01:07:48.080 Amber Lin: Yeah.
715 01:07:48.550 ⇒ 01:07:50.169 Awaish Kumar: Which also return meetings.
716 01:07:51.980 ⇒ 01:07:55.289 Amber Lin: Calendar might have more than just meetings. That’s that’s my point.
717 01:07:57.760 ⇒ 01:07:58.320 Luke Daque: Yeah.
718 01:08:02.530 ⇒ 01:08:04.159 Awaish Kumar: If you bring up the calendar
719 01:08:04.880 ⇒ 01:08:07.529 Awaish Kumar: number or the Google Sheet, maybe.
720 01:08:07.670 ⇒ 01:08:08.969 Amber Lin: Yeah, let me do that.
721 01:08:10.020 ⇒ 01:08:16.009 Awaish Kumar: Yeah, so this call and online meetings. So does that only include calls or also the meetings.
722 01:08:16.399 ⇒ 01:08:20.670 Awaish Kumar: Do you know, Luke, or you need to investigate that.
723 01:08:22.930 ⇒ 01:08:23.819 Luke Daque: What do you mean?
724 01:08:24.779 ⇒ 01:08:27.789 Awaish Kumar: Like this, that row number 7
725 01:08:28.069 ⇒ 01:08:35.399 Awaish Kumar: calls and online meetings. If, like list call records, is this, Api only return the phone calls, or
726 01:08:35.659 ⇒ 01:08:37.399 Awaish Kumar: also the meetings.
727 01:08:41.640 ⇒ 01:08:46.490 Luke Daque: Let me check phone records.
728 01:08:47.010 ⇒ 01:08:54.990 Annie Yu: I think this calls just include the costs with teams.
729 01:09:01.029 ⇒ 01:09:04.149 Awaish Kumar: Okay. But these are then these are the meetings right?
730 01:09:05.270 ⇒ 01:09:09.449 Annie Yu: No, because people put things on
731 01:09:09.569 ⇒ 01:09:14.800 Annie Yu: the calendar, they might be meeting in person, and then they might be using zoom
732 01:09:15.600 ⇒ 01:09:17.269 Annie Yu: that wouldn’t be fall under.
733 01:09:17.270 ⇒ 01:09:19.509 Awaish Kumar: Okay, then we wouldn’t be able to.
734 01:09:19.750 ⇒ 01:09:34.210 Awaish Kumar: We will never be able to figure that out. Then the question was that in the calendar we find a meeting, but we don’t don’t know if if people join that meeting right, if they just meet in person, we are never, never going to figure that out right
735 01:09:36.080 ⇒ 01:09:39.690 Awaish Kumar: if they met only or not, if they met or not.
736 01:09:40.410 ⇒ 01:09:52.630 Annie Yu: Oh, yeah, I don’t think we can figure that out. But if we are just trying to identify like a personal block, maybe we can use a proxy like if there’s only one organizer in that meeting without any attendees
737 01:09:53.399 ⇒ 01:09:55.089 Annie Yu: that could be a proxy.
738 01:09:57.960 ⇒ 01:10:04.130 Awaish Kumar: Yeah, but okay, and then. But then for all those meetings we are not going.
739 01:10:04.900 ⇒ 01:10:10.140 Awaish Kumar: We don’t know. Like, if if somebody meets on zoom. Somebody moves on Google meet.
740 01:10:12.470 ⇒ 01:10:15.350 Awaish Kumar: Yeah, that that just makes it complicated.
741 01:10:28.290 ⇒ 01:10:33.930 Awaish Kumar: So like. We then maybe have this question for client to clarify.
742 01:10:34.680 ⇒ 01:10:35.100 Amber Lin: Okay.
743 01:10:35.100 ⇒ 01:10:45.059 Awaish Kumar: Which act when they say meeting is they didn’t just want to like grab the data from calendar slots.
744 01:10:45.390 ⇒ 01:10:48.199 Awaish Kumar: or they they want the real meetings.
745 01:10:51.510 ⇒ 01:10:55.920 Awaish Kumar: But but that’s that like.
746 01:10:56.110 ⇒ 01:11:01.430 Awaish Kumar: For after the remote work, like, we know that mostly people have this
747 01:11:01.950 ⇒ 01:11:09.160 Awaish Kumar: online links, right? I mean, if some people are in person, some people join from
748 01:11:10.540 ⇒ 01:11:13.340 Awaish Kumar: from home, or whatever places. But
749 01:11:13.540 ⇒ 01:11:18.670 Awaish Kumar: before the remote work, if everybody is joining in the in person we
750 01:11:19.590 ⇒ 01:11:24.000 Awaish Kumar: like. Don’t have. We just have to rely on calendar slots.
751 01:11:24.000 ⇒ 01:11:30.359 Amber Lin: Sounds good great! I think I think we should have a
752 01:11:30.720 ⇒ 01:11:35.840 Amber Lin: clear list of things we want to go through to list any questions that I want, that we
753 01:11:36.120 ⇒ 01:12:03.270 Amber Lin: ask the client for each of these metrics, because it seems like every single one of them, we have a specific question like for messages. We want to ask at this duration, or just counts for meetings. We want to ask where we’re really getting it from, like what counts as a meeting what doesn’t count as a meeting. So I think once we let’s do the investigation 1st to figure out what we have, and if any questions come up through the process. Just document that, and then we’ll need a we’ll have a meeting where we
754 01:12:04.190 ⇒ 01:12:19.850 Amber Lin: list all those questions, because if we don’t define them, or if we don’t ask the client, the clients will blame blame us when they find out there’s a problem. So I think it’s very necessary for us to book another meeting to just walk through all the questions we might have, and then we can clarify them.
755 01:12:19.960 ⇒ 01:12:20.940 Amber Lin: How’s that.
756 01:12:24.250 ⇒ 01:12:24.940 Awaish Kumar: Okay.
757 01:12:25.340 ⇒ 01:12:36.760 Amber Lin: Yeah, okay. Oh, I will book a meeting for us tomorrow. And Luke, thank you. For working on the investigation tickets. We will see you tomorrow. Hopefully, we have
758 01:12:36.970 ⇒ 01:12:39.070 Amber Lin: more clarity tomorrow.
759 01:12:46.280 ⇒ 01:12:47.230 Amber Lin: Okay.
760 01:12:50.590 ⇒ 01:13:04.940 Amber Lin: if you’re able to work on. Oh, sorry you’re muted. If you’re able to work on these things, please let please let us know in the Channel. We would love to have this investigation ticket done by by the time we meet tomorrow.
761 01:13:07.570 ⇒ 01:13:08.549 Luke Daque: Sounds good.
762 01:13:08.550 ⇒ 01:13:10.839 Amber Lin: Okay, thank you. All.
763 01:13:11.280 ⇒ 01:13:12.300 Luke Daque: Makes relaxing.
764 01:13:12.300 ⇒ 01:13:13.710 Amber Lin: Yeah, bye-bye.