Meeting Title: New Team Member Introduction and Project Overview Date: 2025-06-18 Meeting participants: Ari S, Amber Lin
WEBVTT
1 00:01:19.880 ⇒ 00:01:20.880 Ari S: Hello!
2 00:01:20.880 ⇒ 00:01:23.530 Amber Lin: Hi! Pretty good!
3 00:01:23.820 ⇒ 00:01:25.339 Ari S: Nice to meet you.
4 00:01:25.560 ⇒ 00:01:26.140 Ari S: Yeah.
5 00:01:28.020 ⇒ 00:01:31.710 Amber Lin: How long have you been on the team? Did you just join like 2 days ago?
6 00:01:32.410 ⇒ 00:01:39.169 Ari S: Yeah, I am actually friends with Matthew from high school.
7 00:01:39.990 ⇒ 00:01:43.571 Ari S: and I knew Utam and I I set them up.
8 00:01:43.930 ⇒ 00:01:44.950 Amber Lin: One.
9 00:01:45.470 ⇒ 00:01:46.000 Amber Lin: It’s cool.
10 00:01:46.950 ⇒ 00:01:52.740 Amber Lin: So how come you weren’t working for, madam? Or when they 1st started working together? How come you’re just joining now?
11 00:01:53.863 ⇒ 00:02:05.576 Ari S: I am just coming in to just like help out with data stuff. Part time. So they have like a go to person to kind of
12 00:02:06.580 ⇒ 00:02:13.819 Ari S: understand what’s going on. And they could focus on, you know, other day to day things they they have.
13 00:02:14.050 ⇒ 00:02:18.439 Amber Lin: Yeah, that’s great cause, I know for the past few
14 00:02:19.040 ⇒ 00:02:22.239 Amber Lin: few sprints. It’s been really difficult to
15 00:02:22.520 ⇒ 00:02:33.730 Amber Lin: stay aligned because they’re so busy and because requirements are changing so much. So it’s really nice that new join. And so I had. I can have someone to go to to check with things.
16 00:02:34.620 ⇒ 00:02:35.660 Ari S: Yeah, for sure.
17 00:02:36.320 ⇒ 00:02:36.860 Amber Lin: No.
18 00:02:36.970 ⇒ 00:02:42.029 Ari S: How long I should ask Matthew and Trevor this. But how long have
19 00:02:42.410 ⇒ 00:02:44.430 Ari S: you’ve been working on this project?
20 00:02:45.810 ⇒ 00:02:51.080 Amber Lin: So it’s been, and I say a bit more than a month. We started early may.
21 00:02:51.460 ⇒ 00:02:52.430 Ari S: So.
22 00:02:52.590 ⇒ 00:02:53.980 Amber Lin: It’s been.
23 00:02:54.180 ⇒ 00:02:59.089 Amber Lin: I think we’re on the 3rd or 4th sprint right now.
24 00:02:59.570 ⇒ 00:03:01.350 Amber Lin: and we do 2 week sprints.
25 00:03:01.930 ⇒ 00:03:02.950 Ari S: Okay. Nice.
26 00:03:04.050 ⇒ 00:03:06.730 Amber Lin: I think. 3rd sprint, 3rd or 4.th
27 00:03:07.080 ⇒ 00:03:07.870 Amber Lin: Yeah.
28 00:03:08.510 ⇒ 00:03:16.520 Amber Lin: how much do you know about what we’re doing and how that relates to the project? And what questions do you have? And I can fill you in.
29 00:03:17.720 ⇒ 00:03:19.769 Ari S: Yeah. I think I’m like.
30 00:03:21.300 ⇒ 00:03:28.750 Ari S: Pretty up to date, but you can tell me what I’m missing. I I mean. Matthew shared like.
31 00:03:28.750 ⇒ 00:03:29.330 Amber Lin: Hmm.
32 00:03:30.699 ⇒ 00:03:37.410 Ari S: The measurement framework with me that has the phase one.
33 00:03:38.860 ⇒ 00:03:42.740 Ari S: you know, 6 kind of pillars that they’re trying to.
34 00:03:43.450 ⇒ 00:03:51.079 Ari S: Together, and the segmentation they want to do like the attributes in the tables they need. And
35 00:03:51.670 ⇒ 00:04:00.790 Ari S: I have access to bigquery. So I’ve seen all the like synthetic data that you guys have generated. I know it’s from like teams. And
36 00:04:01.010 ⇒ 00:04:11.650 Ari S: Microsoft graph. And you have the events, data calls data emails, calendar events, teams, messages,
37 00:04:13.890 ⇒ 00:04:17.950 Ari S: and I’ve seen the views in bigquery that you put together.
38 00:04:18.842 ⇒ 00:04:21.740 Ari S: Looked at the sequel for them.
39 00:04:22.170 ⇒ 00:04:26.210 Ari S: I had a couple of questions that we can get into about like
40 00:04:26.390 ⇒ 00:04:31.459 Ari S: some of the tables that exist there. That aren’t the synthetic data. And I just wanted to like.
41 00:04:32.320 ⇒ 00:04:37.525 Ari S: See the sequel for those and
42 00:04:39.210 ⇒ 00:04:41.390 Amber Lin: Do you have access to? Dbt.
43 00:04:43.960 ⇒ 00:04:45.730 Ari S: Not that I know of. No.
44 00:04:46.119 ⇒ 00:04:51.570 Amber Lin: I don’t know if I can add you to there. Maybe if you hmm
45 00:04:51.800 ⇒ 00:04:57.550 Amber Lin: cause maybe Trevor can add you to Dbt. Cause in my knowledge, some of our some of the sequel is
46 00:04:57.810 ⇒ 00:05:00.130 Amber Lin: N. Dbt.
47 00:05:00.230 ⇒ 00:05:08.000 Ari S: Okay, I’m not sure. I’m not totally sure. Let me confirm just tell me which ones you’re missing, and I’ll write that down, and I’ll get you access.
48 00:05:08.250 ⇒ 00:05:14.590 Amber Lin: Okay, I can send over a message to after. But it was communication events and team collaboration.
49 00:05:15.204 ⇒ 00:05:28.389 Amber Lin: yeah, those are models, we so we have the basic synthetic data once, and then we created additional models so that we can use them for power bi.
50 00:05:28.610 ⇒ 00:05:30.120 Ari S: Yeah, that makes sense.
51 00:05:30.120 ⇒ 00:05:47.040 Amber Lin: Yeah, I can have you. If you have a list of questions. I ha! I can have our analyst engineer write up how he did things. What’s what’s the logic, and where the logic can be found? I can have him write that up, and I think that will help you sort of look at how things are.
52 00:05:47.440 ⇒ 00:05:48.940 Ari S: Okay, yeah, that would be
53 00:05:50.755 ⇒ 00:05:59.920 Ari S: my list of questions. But yeah, that would be a good 1st start. I think I don’t necessarily need access to dbt, right now, either.
54 00:06:00.270 ⇒ 00:06:03.249 Ari S: And just like sort of see the sequel. But
55 00:06:04.470 ⇒ 00:06:08.530 Ari S: yeah, are, are we using Dbt cloud, or.
56 00:06:08.580 ⇒ 00:06:10.499 Amber Lin: I think we’re using DVD core.
57 00:06:11.660 ⇒ 00:06:28.929 Amber Lin: that’s based on my understanding. And okay, let me show you our. I invited you to our linear board, just so that you can have a sense of what we’re working on. Whenever you want to just check, I can share my screen. I can.
58 00:06:29.110 ⇒ 00:06:31.569 Amber Lin: So I’m going to walk you through the process.
59 00:06:32.201 ⇒ 00:06:36.800 Amber Lin: That we’re doing with now. So currently.
60 00:06:37.380 ⇒ 00:06:58.129 Amber Lin: currently, we’re at the point we’re creating power bi creating the power bi and finishing up one last thing on the data side, which is the tool usage which initially, we didn’t generate any synthetic data, says, for so that’s underway to generate synthetic data for that. And so for this sprint, which is
61 00:06:58.980 ⇒ 00:07:18.139 Amber Lin: sorry, this this sprint just started this week, and by the end of the sprint. I think we should have power Bi ready to deliver. That has all of the data and filters, including tool usage. So I’m asking my engineers to finish generating the synthetic data sets
62 00:07:19.430 ⇒ 00:07:33.139 Amber Lin: for tool usage this week and then next week, do any modeling that’s needed and publish it to power Bi, so that Annie, who is our analyst, can go into power bi and make sure everything is set up.
63 00:07:36.051 ⇒ 00:07:42.920 Amber Lin: just before today we were doing some adjustments to modeling so that it fits power bi, because usually
64 00:07:43.210 ⇒ 00:07:50.039 Amber Lin: most of our clients, we use a different visualization tool and power. Bi is a little bit different. So we’re just making sure that it
65 00:07:50.825 ⇒ 00:07:54.709 Amber Lin: if it’s power bi, and that is our.
66 00:07:55.370 ⇒ 00:07:57.339 Amber Lin: yeah, that is our current progress.
67 00:07:57.590 ⇒ 00:08:01.609 Amber Lin: And here, do you have access to this sheet?
68 00:08:02.290 ⇒ 00:08:03.020 Ari S: Yes.
69 00:08:03.380 ⇒ 00:08:15.229 Amber Lin: Okay, that’s awesome. So I think everything should be documented here. And if there’s anything else you need, I’ll ask Luke to also put the documentation here. So we have one place to look at them.
70 00:08:15.870 ⇒ 00:08:23.940 Amber Lin: and in terms of these are all the requirements for phase one. I think those these few are for later.
71 00:08:25.170 ⇒ 00:08:34.649 Amber Lin: I also had some specific metric definition questions I have for Matthew. But maybe now that you’re on board.
72 00:08:34.789 ⇒ 00:08:42.380 Amber Lin: perhaps you would help us answer them. Or maybe this is something that Matthew will have to work with the client to define.
73 00:08:42.990 ⇒ 00:08:48.460 Amber Lin: because this sort of relates to their business logic of how they define different things, and how we want to show them.
74 00:08:49.780 ⇒ 00:08:51.460 Ari S: Yeah, that makes sense.
75 00:08:52.160 ⇒ 00:08:58.190 Ari S: I had some questions about those metrics as well, and I think that, like
76 00:08:58.760 ⇒ 00:09:03.315 Ari S: Matthew will give an update. But we should be getting real data soon. And I think.
77 00:09:03.600 ⇒ 00:09:05.310 Amber Lin: Okay, that will be lovely.
78 00:09:05.310 ⇒ 00:09:07.639 Ari S: I think that can help clear up like
79 00:09:08.040 ⇒ 00:09:09.739 Ari S: some of the metrics, and we can go back.
80 00:09:10.541 ⇒ 00:09:14.548 Amber Lin: Okay. Awesome for wait.
81 00:09:17.630 ⇒ 00:09:21.899 Amber Lin: Yeah, I don’t think I have specific questions immediately on the top of mind.
82 00:09:22.060 ⇒ 00:09:28.640 Amber Lin: And also I apologize. I’ve been in meetings for 6 h. That was. My brain is a little bit scattered all over the place.
83 00:09:28.840 ⇒ 00:09:30.269 Ari S: No worries, no worries.
84 00:09:34.370 ⇒ 00:09:36.754 Ari S: Cool, I think.
85 00:09:38.850 ⇒ 00:09:42.089 Ari S: Let me see, I’m just taking a look at the.
86 00:09:42.510 ⇒ 00:09:44.810 Amber Lin: My main question was like.
87 00:09:46.640 ⇒ 00:09:48.859 Ari S: The tables as I see them like.
88 00:09:53.180 ⇒ 00:09:55.821 Ari S: The main like sort of
89 00:09:57.010 ⇒ 00:10:04.710 Ari S: more denormalized ones, or like not staging tables, you know, like marks, data where, like focus time.
90 00:10:05.810 ⇒ 00:10:06.770 Ari S: Which
91 00:10:08.870 ⇒ 00:10:15.769 Ari S: it’s just aggregated on the last 30 days, and and we really want to be able to cut by like hour of day.
92 00:10:15.860 ⇒ 00:10:20.129 Amber Lin: Yeah, yeah, that’s I got an update that’s adjusted. I think
93 00:10:20.230 ⇒ 00:10:44.079 Amber Lin: the reason why that’s still there is because we migrated all the initial views and dB, in bigquery to Dbt, because we want it in Dbt, and not all in bigquery. So let me confirm if this is up to date, and then I’ll make sure that you get access to the up to date version, because I do remember in the stand up they were talking about this and that should be fixed.
94 00:10:44.460 ⇒ 00:10:45.420 Ari S: Okay. Cool.
95 00:10:45.420 ⇒ 00:10:45.990 Amber Lin: Hmm.
96 00:10:46.737 ⇒ 00:10:53.805 Ari S: Yeah, I think it’ll help to have access to dbt, this table looks looks really good.
97 00:10:54.740 ⇒ 00:11:02.929 Ari S: but I wanted how this was kind of generated. But I think this is the kind of thing we need.
98 00:11:03.320 ⇒ 00:11:03.890 Amber Lin: Okay.
99 00:11:05.190 ⇒ 00:11:07.860 Ari S: I think longer term.
100 00:11:08.820 ⇒ 00:11:14.519 Ari S: It’s not really a phase, one thing, and maybe I can help like scope. Oh, there’s new tables, and say.
101 00:11:15.440 ⇒ 00:11:16.110 Amber Lin: Oh!
102 00:11:16.110 ⇒ 00:11:19.435 Ari S: Oh, is there no anyway?
103 00:11:22.670 ⇒ 00:11:42.380 Amber Lin: yeah, I would love your help to think about how it’s gonna be, when we connect to the client data, it’s a little bit unclear how that’s gonna be. And I and I want our team to succeed. And I think your insight will be really helpful there, and also with any risks on the project that you see. Anything that
104 00:11:42.380 ⇒ 00:11:54.740 Amber Lin: you think might break apart, even not just in terms of data, or maybe in terms of communication and alignment like anything you see. Please please tell me, and then we can have the team like, make sure we have a mitigation plan.
105 00:11:55.230 ⇒ 00:11:57.070 Ari S: Okay, that makes sense.
106 00:11:57.741 ⇒ 00:12:02.399 Ari S: Let me confirm with Matthew on like when we’re gonna get real data. Cause I think.
107 00:12:03.780 ⇒ 00:12:07.400 Ari S: yeah, I think what we have so far is pretty good.
108 00:12:08.510 ⇒ 00:12:15.710 Ari S: I was a little concerned about like building out something in the future to like show, like focus time, for example.
109 00:12:17.260 ⇒ 00:12:24.009 Ari S: and like, basically like, it’s sort of the negative time between events.
110 00:12:24.280 ⇒ 00:12:24.940 Amber Lin: Yeah.
111 00:12:26.140 ⇒ 00:12:28.050 Ari S: And I thought like we would.
112 00:12:29.300 ⇒ 00:12:40.040 Ari S: Possibly it’s not really a phase, one thing, but I want to start thinking about, like how to build out a table to support that. And it’s like, sort of a user, our level.
113 00:12:40.850 ⇒ 00:12:43.170 Ari S: like table. And then just like
114 00:12:43.570 ⇒ 00:12:46.039 Ari S: flags. For if there was like
115 00:12:46.430 ⇒ 00:12:52.160 Ari S: a call, or how many calls, or how many emails, how many messages like meetings, etc.
116 00:12:52.160 ⇒ 00:12:52.940 Amber Lin: Yeah.
117 00:12:53.340 ⇒ 00:12:55.449 Ari S: So I wanted to start thinking about that. But.
118 00:12:55.450 ⇒ 00:13:25.329 Amber Lin: Of course. Actually, we’ve done something related to that before before we got clearly defined. Okay, actually, we want modular metrics. So before then we were trying to do these things in Python and make sure that we are able to do this analysis. And our people was able to look at focus time. And we did produce something in python with a definition of, Okay, this is a uninterrupted 1 h period between events per person. And
119 00:13:25.500 ⇒ 00:13:37.129 Amber Lin: I believe we did do the logic in python, and so it’s probably would be possible in sequel as well. And so we can. We just didn’t
120 00:13:37.660 ⇒ 00:13:53.330 Amber Lin: put that into the phase one after we got the new requirements from Matthew. So it’s just not in anywhere except for the python fault. But once we say okay for the next phase, we’re gonna work on focus time. We do have some work on that already.
121 00:13:53.690 ⇒ 00:13:54.599 Ari S: Okay. Sweet.
122 00:13:54.600 ⇒ 00:13:55.000 Amber Lin: Yeah.
123 00:13:55.530 ⇒ 00:13:56.560 Ari S: That sounds good.
124 00:13:57.500 ⇒ 00:14:04.650 Ari S: Yeah, my main call out to Matthew was like before, when I was just looking at the staging tables like I thought it made sense to have
125 00:14:04.890 ⇒ 00:14:13.799 Ari S: one table that just had like sort of an activity stream of like user of that timestamp.
126 00:14:14.690 ⇒ 00:14:15.989 Ari S: And then, like
127 00:14:16.100 ⇒ 00:14:29.681 Ari S: your other tables that are like dimension tables that? Yeah, you can like slice and dice filters by. But it seems like that team collaboration or communication events. Channel. Sorry table.
128 00:14:30.340 ⇒ 00:14:36.940 Ari S: is. Is that? So yeah, I think next step is like, Figure out.
129 00:14:37.150 ⇒ 00:14:41.629 Ari S: dbt, access. And I can review that. And.
130 00:14:42.810 ⇒ 00:14:44.830 Ari S: If you have any information about like.
131 00:14:45.720 ⇒ 00:14:53.190 Ari S: here are the power bi charts we plan to build and like. Here’s the underlying data source for them.
132 00:14:53.340 ⇒ 00:14:59.360 Ari S: I still have to log into power. Bi. I mistakenly just thought it was in azure.
133 00:14:59.550 ⇒ 00:15:01.775 Amber Lin: No, I thought that too.
134 00:15:03.070 ⇒ 00:15:07.420 Ari S: But if yeah, if you have any information about like, here’s what’s gonna be in power. Bi, here’s.
135 00:15:08.070 ⇒ 00:15:11.489 Ari S: In the sequel, or as much detail as you can provide. Then.
136 00:15:11.490 ⇒ 00:15:12.210 Amber Lin: Okay.
137 00:15:12.810 ⇒ 00:15:15.580 Ari S: I can review that for them, so they
138 00:15:16.550 ⇒ 00:15:20.009 Ari S: don’t have to, and can understand. A little bit more like is.
139 00:15:20.010 ⇒ 00:15:20.870 Amber Lin: Awesome.
140 00:15:22.610 ⇒ 00:15:27.249 Amber Lin: Yeah. Well, I’ll do that. I’ll make sure that Annie adds it to the sheet as well.
141 00:15:27.702 ⇒ 00:15:54.099 Amber Lin: I think one comment on what you brought up having it in the in the different tables. That’s what we initially thought as well to have one table that just has the basic metrics and for other ones for the filters. What we discovered in power Bi was that if it’s not in the same table per se, you can’t add filters for those attributes. So what we decided to do today is that we’re gonna do
142 00:15:54.414 ⇒ 00:16:07.940 Amber Lin: some modeling and dbt, so that it doesn’t affect the original. The original data tables, and so that Annie will be able to see all the attributes for a certain table in power Bi, and then she can add the different filters in.
143 00:16:08.900 ⇒ 00:16:11.189 Amber Lin: Okay, so it’ll be like.
144 00:16:11.620 ⇒ 00:16:20.950 Ari S: Denormalize like we’ll have all the dimension columns on the fact kind of tables. Basically.
145 00:16:22.740 ⇒ 00:16:30.059 Amber Lin: yeah, I’ll get you access to dbt, it probably is more, clear once, you see it in in numbers.
146 00:16:30.960 ⇒ 00:16:31.840 Ari S: Okay. Cool.
147 00:16:31.840 ⇒ 00:16:32.570 Amber Lin: Yeah.
148 00:16:32.780 ⇒ 00:16:35.739 Amber Lin: Okay, thanks for the meeting.
149 00:16:35.880 ⇒ 00:16:37.730 Ari S: Yeah, thanks. Amber. Appreciate it.
150 00:16:37.730 ⇒ 00:16:44.469 Amber Lin: Okay, of course, I’ll probably ask you to join future meetings, maybe maybe for sprint planning
151 00:16:44.949 ⇒ 00:16:51.769 Amber Lin: or say grooming sessions. Would you be down to join that and make sure, like we’re doing the right task. That’s aligned.
152 00:16:51.990 ⇒ 00:16:59.094 Ari S: Yeah, I might need to work around my schedule just to like, make sure I can join them. But
153 00:16:59.850 ⇒ 00:17:02.530 Ari S: yeah, that would like that would be great.
154 00:17:02.530 ⇒ 00:17:03.170 Amber Lin: Okay.
155 00:17:03.540 ⇒ 00:17:04.880 Ari S: We’ll talk soon, then.
156 00:17:05.440 ⇒ 00:17:06.259 Ari S: See ya.
157 00:17:06.260 ⇒ 00:17:06.859 Amber Lin: Bye.