Meeting Title: MatterMore | internal Standup Date: 2025-06-18 Meeting participants: Amber Lin, Luke Daque, Annie Yu, Awaish Kumar
WEBVTT
1 00:01:19.630 ⇒ 00:01:20.970 Amber Lin: Hello!
2 00:01:22.750 ⇒ 00:01:23.280 Luke Daque: Thank you.
3 00:01:23.280 ⇒ 00:01:23.620 Luke Daque: Nice.
4 00:01:25.460 ⇒ 00:01:32.669 Amber Lin: Hi, so we have someone from the matter. More team that should be joining us.
5 00:01:34.480 ⇒ 00:01:35.780 Amber Lin: So
6 00:01:36.600 ⇒ 00:01:43.009 Amber Lin: we can start our updates. And once he joins. I want to introduce you guys to him, and then
7 00:01:43.457 ⇒ 00:01:52.590 Amber Lin: see how he can help with that. Let me check if he’s oh.
8 00:01:55.680 ⇒ 00:02:01.810 Amber Lin: one Pm. Oh, my God, I’m so silly one! Pm, pst, anyways.
9 00:02:02.510 ⇒ 00:02:09.669 Amber Lin: silly me! He’s not joining at this time. Let me. I’ll share my screen, and then we’ll talk through some things.
10 00:02:13.580 ⇒ 00:02:16.420 Amber Lin: Here.
11 00:02:20.810 ⇒ 00:02:21.500 Amber Lin: okay.
12 00:02:25.180 ⇒ 00:02:30.770 Amber Lin: anyways, here.
13 00:02:35.480 ⇒ 00:02:36.270 Amber Lin: So
14 00:02:47.110 ⇒ 00:02:52.189 Amber Lin: I was looking at looking at these. And
15 00:02:53.606 ⇒ 00:02:58.123 Amber Lin: I guess, firstly, we just wanna check. If these are all in
16 00:02:59.210 ⇒ 00:03:01.760 Amber Lin: like, are these all in bigquery?
17 00:03:07.560 ⇒ 00:03:10.269 Luke Daque: Yeah, those should be in bigquery. But I think there are like
18 00:03:10.470 ⇒ 00:03:14.639 Luke Daque: like, Annie sent a message. I think you already created tickets for those as well.
19 00:03:15.230 ⇒ 00:03:25.680 Luke Daque: Or yeah, adding, like primary and secondary dimensions as well as the other stuff. But yeah, those should be
20 00:03:26.390 ⇒ 00:03:27.020 Luke Daque: good.
21 00:03:27.020 ⇒ 00:03:33.430 Amber Lin: Okay, okay, I will mark these as done. I will.
22 00:03:34.000 ⇒ 00:03:35.909 Amber Lin: Great. Huh!
23 00:03:37.310 ⇒ 00:03:38.470 Amber Lin: Status.
24 00:03:38.610 ⇒ 00:03:44.700 Amber Lin: Alright. Let me create another ticket to add a wish to.
25 00:03:44.920 ⇒ 00:03:54.190 Amber Lin: It’s hub me to do high priority.
26 00:03:54.930 ⇒ 00:03:57.850 Amber Lin: I’ll do that, and then.
27 00:03:57.850 ⇒ 00:04:01.940 Luke Daque: Yeah, maybe Harry has access, so maybe he can add a wish.
28 00:04:01.940 ⇒ 00:04:05.050 Amber Lin: Yeah, yeah, yeah, I’ll go check with him.
29 00:04:06.150 ⇒ 00:04:17.760 Amber Lin: And well, this is still going through. Annie. Are you blocked until Luke helps change the modeling.
30 00:04:18.542 ⇒ 00:04:28.670 Annie Yu: No, not really. But I think right now to set up those visuals in between. I have to set up the metrics using Dax.
31 00:04:28.780 ⇒ 00:04:33.750 Annie Yu: So maybe be another ticket before the visuals.
32 00:04:34.130 ⇒ 00:04:47.380 Amber Lin: I see. So set up tax and power bi to calculate needed fields.
33 00:04:48.000 ⇒ 00:04:50.510 Amber Lin: Yeah, I was reading on.
34 00:04:51.110 ⇒ 00:04:52.360 Luke Daque: On this.
35 00:04:53.180 ⇒ 00:04:56.190 Amber Lin: I think they’re like, instead of
36 00:04:56.470 ⇒ 00:05:03.780 Amber Lin: like, instead of just directly using our average duration, we’re gonna calculate it in in Dax.
37 00:05:05.720 ⇒ 00:05:06.150 Annie Yu: Yeah.
38 00:05:06.150 ⇒ 00:05:12.460 Amber Lin: So I think that will take you some time, and then that allows Luke to set up the stuff we need.
39 00:05:12.840 ⇒ 00:05:23.080 Annie Yu: Yeah, I’m also wondering it. I’ll leave this up to you. But should I? Should someone review my decks
40 00:05:23.440 ⇒ 00:05:31.519 Annie Yu: or not like it’s gonna be my 1st time setting up. I think it’s hopefully straightforward enough, but just in case
41 00:05:33.899 ⇒ 00:05:41.240 Amber Lin: I can see if I can review it, and also, like Luke, or a wish, can help review it as well.
42 00:05:45.050 ⇒ 00:05:50.230 Awaish Kumar: Are you able any to dude?
43 00:05:50.400 ⇒ 00:05:55.680 Awaish Kumar: We will like write the Dax in web version of power. Bi.
44 00:05:56.270 ⇒ 00:05:58.970 Annie Yu: Yeah, I haven’t, but I I will have to.
45 00:06:00.740 ⇒ 00:06:01.450 Awaish Kumar: Okay.
46 00:06:02.540 ⇒ 00:06:03.140 Amber Lin: And but we.
47 00:06:03.140 ⇒ 00:06:03.630 Awaish Kumar: We can’t.
48 00:06:03.630 ⇒ 00:06:06.909 Amber Lin: Write. We can write docs in the web version, right?
49 00:06:07.915 ⇒ 00:06:11.010 Annie Yu: No, yeah. I’ll use the desktop.
50 00:06:12.440 ⇒ 00:06:13.650 Awaish Kumar: Yeah, like.
51 00:06:18.030 ⇒ 00:06:21.000 Amber Lin: Okay, sounds good.
52 00:06:21.330 ⇒ 00:06:26.169 Amber Lin: So I know, Annie, you’re going on vacation in 10 days, right.
53 00:06:28.112 ⇒ 00:06:29.380 Annie Yu: Yeah. Next. Friday.
54 00:06:29.932 ⇒ 00:06:35.149 Amber Lin: I see. So we’ll we’ll try to get these done before next Friday.
55 00:06:37.130 ⇒ 00:06:40.999 Annie Yu: Yeah, I also, yeah.
56 00:06:41.280 ⇒ 00:06:47.499 Annie Yu: yeah. I also don’t know how that will look like. But I I will carve out some time to do the decks today.
57 00:06:47.710 ⇒ 00:07:02.120 Amber Lin: Okay, for these. Which of which ones actually need to access? Some of them are pretty straightforward, but some of them, I think, does need it. I I just at the current time. I don’t know what you need, Dax, for.
58 00:07:02.120 ⇒ 00:07:02.850 Annie Yu: So.
59 00:07:03.490 ⇒ 00:07:06.890 Annie Yu: Let’s say, like, if we wanna see average count.
60 00:07:07.730 ⇒ 00:07:12.459 Annie Yu: then we want the denominator to be person right.
61 00:07:12.460 ⇒ 00:07:13.000 Amber Lin: Hmm.
62 00:07:13.250 ⇒ 00:07:17.289 Annie Yu: But then we don’t have that right now.
63 00:07:17.580 ⇒ 00:07:27.840 Annie Yu: Oh, have to get all sum up all the event, count, and then divide it by the discount users.
64 00:07:28.520 ⇒ 00:07:29.230 Amber Lin: Hmm.
65 00:07:29.230 ⇒ 00:07:32.110 Annie Yu: And then same thing for duration.
66 00:07:32.740 ⇒ 00:07:40.899 Amber Lin: I see. So actually, it reduces the modeling we need to do in Dbt, and we have to do it in power. Bi.
67 00:07:41.250 ⇒ 00:07:50.033 Annie Yu: Yeah, I think that’s the. But I don’t know. What does that mean? Like another model? And then I I don’t really know. But
68 00:07:52.930 ⇒ 00:08:00.870 Annie Yu: I’ll see as I go. But I imagine with what we need now, the 2
69 00:08:01.370 ⇒ 00:08:05.579 Annie Yu: metrics to set up is average count and average duration.
70 00:08:05.580 ⇒ 00:08:08.190 Amber Lin: Okay.
71 00:08:08.520 ⇒ 00:08:17.920 Amber Lin: okay, sounds good. And I think that should be all of it. Right cause these are all denominators. I believe.
72 00:08:18.080 ⇒ 00:08:24.810 Amber Lin: like these are all denominators, and I don’t think you need to calculate any of them.
73 00:08:26.700 ⇒ 00:08:27.740 Amber Lin: Is that correct?
74 00:08:27.740 ⇒ 00:08:30.310 Annie Yu: Would be filters.
75 00:08:30.600 ⇒ 00:08:37.919 Amber Lin: Right. So like when you we I don’t think you need to write specific ones to calculate. Oh, the the level tenure is.
76 00:08:37.929 ⇒ 00:08:39.659 Annie Yu: Oh, yeah, no, I don’t think so.
77 00:08:39.659 ⇒ 00:08:41.659 Amber Lin: Right? So I think this wouldn’t.
78 00:08:41.949 ⇒ 00:08:50.029 Amber Lin: It’s probably smaller than we think it is, because it’s probably we only need to calculate for email chat meetings.
79 00:08:51.330 ⇒ 00:09:01.409 Annie Yu: And I will imagine, actually, it will be just 2 metrics, because we do have a field that says, this is email, this is chat. This is this meeting, so that could be another.
80 00:09:01.410 ⇒ 00:09:03.510 Amber Lin: The count, so that could be the Count.
81 00:09:04.850 ⇒ 00:09:05.740 Amber Lin: Kind of.
82 00:09:07.650 ⇒ 00:09:11.440 Annie Yu: That could be the filter to decide what we’re counting.
83 00:09:12.396 ⇒ 00:09:14.049 Amber Lin: Yeah. Okay.
84 00:09:14.320 ⇒ 00:09:15.380 Amber Lin: Sounds good.
85 00:09:20.720 ⇒ 00:09:22.820 Amber Lin: no.
86 00:09:25.970 ⇒ 00:09:28.440 Amber Lin: Let me create another one
87 00:09:32.210 ⇒ 00:09:33.920 Amber Lin: for meeting.
88 00:09:40.280 ⇒ 00:09:46.269 Annie Yu: Yeah, I’ll give an update on this Dax thing today. Hopefully, I can
89 00:09:46.430 ⇒ 00:09:50.420 Annie Yu: get it done. But I’ll update regardless.
90 00:09:57.850 ⇒ 00:10:01.860 Amber Lin: Yeah. And honestly, if you can’t do for
91 00:10:01.960 ⇒ 00:10:06.850 Amber Lin: probably for email and chat, you can save the duration, for
92 00:10:07.060 ⇒ 00:10:15.690 Amber Lin: you know what? Disregard that. Okay, just let let us know I’m gonna break this. Put this into
93 00:10:16.060 ⇒ 00:10:23.950 Amber Lin: this cycle. Sign it to you that. Okay?
94 00:10:26.250 ⇒ 00:10:36.243 Amber Lin: So okay, let’s let. Next, let’s look at Luke’s task. Let’s look at the 1st one.
95 00:10:37.110 ⇒ 00:10:39.030 Amber Lin: the few that Annie added.
96 00:10:39.180 ⇒ 00:10:46.259 Amber Lin: So this and you would need this to do the power bi. So right now.
97 00:10:46.370 ⇒ 00:11:03.360 Amber Lin: I think they’re in separate models. But the problem with power bi is that we, if we have it in separate tables, currently, the secondary segments, such as tenure, etc. They’re in a separate model, and then Annie can’t select them as a filter for the same graph.
98 00:11:04.180 ⇒ 00:11:07.570 Amber Lin: So I think we do need to join those tables together.
99 00:11:08.960 ⇒ 00:11:11.860 Luke Daque: I think that can be done in power Bi as well. They have.
100 00:11:12.230 ⇒ 00:11:14.940 Luke Daque: like in the data sources you can actually like.
101 00:11:15.350 ⇒ 00:11:15.810 Amber Lin: Really.
102 00:11:15.810 ⇒ 00:11:22.829 Luke Daque: As well. But yeah, we can. I can also create a model that’s already joined. If that’s better.
103 00:11:24.210 ⇒ 00:11:32.060 Amber Lin: I mean, if if we join it in power, Bi, Luke, would you know where that is? Or can you help
104 00:11:32.540 ⇒ 00:11:36.960 Amber Lin: Annie do that like, how are we.
105 00:11:36.960 ⇒ 00:11:41.409 Luke Daque: Sure it should be somewhere in the data sources. Section
106 00:11:42.340 ⇒ 00:11:43.460 Awaish Kumar: But why?
107 00:11:43.800 ⇒ 00:11:44.390 Luke Daque: Okay.
108 00:11:44.390 ⇒ 00:11:47.499 Awaish Kumar: Environment, how to move behind these in power. Bi.
109 00:11:47.950 ⇒ 00:11:49.950 Luke Daque: Yeah, that’s also a good point, like,
110 00:11:51.260 ⇒ 00:11:55.489 Awaish Kumar: Like, let’s let’s like, let’s try to like
111 00:11:55.610 ⇒ 00:12:00.889 Awaish Kumar: like any is new to power. Bi. Let’s try to move all the complexity into Dbt.
112 00:12:00.890 ⇒ 00:12:01.550 Luke Daque: Yeah.
113 00:12:01.550 ⇒ 00:12:05.160 Amber Lin: Okay, let’s do it and just keep the simple things in the Powerpi.
114 00:12:05.860 ⇒ 00:12:06.840 Luke Daque: Yeah, sounds good.
115 00:12:06.840 ⇒ 00:12:07.380 Amber Lin: Okay.
116 00:12:07.700 ⇒ 00:12:14.420 Luke Daque: Maybe. Can you add that to the ticket? Maybe like, which models need to be joined and stuff? So yeah, cool.
117 00:12:14.420 ⇒ 00:12:25.430 Amber Lin: Yeah, I think it’s this one. In the event. Engagement, join communication events.
118 00:12:25.850 ⇒ 00:12:29.229 Amber Lin: Annie. Can you confirm right now which ones you need?
119 00:12:42.700 ⇒ 00:12:47.240 Amber Lin: I guess, Luke, when you look at it, it’s everything that includes.
120 00:12:48.620 ⇒ 00:12:53.300 Amber Lin: for whatever fields that includes. Like all these.
121 00:12:53.730 ⇒ 00:12:59.710 Amber Lin: all these granularities, we should join them into one table and Dbt.
122 00:13:13.030 ⇒ 00:13:22.440 Luke Daque: like I in the it depends on the model, right? Like like, we can’t join the team collaboration
123 00:13:22.910 ⇒ 00:13:27.799 Luke Daque: model with the with a like.
124 00:13:28.435 ⇒ 00:13:31.909 Luke Daque: What’s the other model that we have sample here?
125 00:13:34.400 ⇒ 00:13:43.261 Amber Lin: I don’t think the team collaboration model is included here. I I know what you’re saying. I think I remember the 2 models. One is just a
126 00:13:43.990 ⇒ 00:13:53.039 Amber Lin: The events and the other one was engagement that has like tenure level span of control like that type.
127 00:13:53.720 ⇒ 00:13:57.780 Amber Lin: I think the collaboration one.
128 00:13:58.730 ⇒ 00:14:04.360 Amber Lin: We don’t need it. What does the collaboration table have? Do you want to share your screen? And we can look at that?
129 00:14:05.720 ⇒ 00:14:07.740 Amber Lin: Yeah, sure. Give me a second. Here.
130 00:14:26.400 ⇒ 00:14:27.650 Luke Daque: Can you see my screen?
131 00:14:34.430 ⇒ 00:14:35.770 Luke Daque: Can you share my screen?
132 00:14:41.660 ⇒ 00:14:43.050 Luke Daque: So
133 00:14:49.660 ⇒ 00:14:53.950 Luke Daque: like, we have communication events which has all the.
134 00:14:56.880 ⇒ 00:15:02.939 Amber Lin: Yes, that would be the main table. Can we look at engagement.
135 00:15:05.010 ⇒ 00:15:06.069 Luke Daque: Yeah, this should be like.
136 00:15:06.070 ⇒ 00:15:06.890 Amber Lin: Yeah.
137 00:15:07.170 ⇒ 00:15:09.240 Luke Daque: Just a user table with all the.
138 00:15:09.240 ⇒ 00:15:10.010 Amber Lin: Yeah.
139 00:15:10.330 ⇒ 00:15:11.669 Luke Daque: Yeah, I think.
140 00:15:12.100 ⇒ 00:15:16.370 Amber Lin: I think we would need that.
141 00:15:18.670 ⇒ 00:15:19.560 Amber Lin: Huh?
142 00:15:21.080 ⇒ 00:15:26.609 Luke Daque: I guess we can you join them using user id, but then.
143 00:15:27.040 ⇒ 00:15:29.050 Amber Lin: I think we there.
144 00:15:29.050 ⇒ 00:15:29.690 Luke Daque: Yeah.
145 00:15:30.080 ⇒ 00:15:37.379 Amber Lin: Yeah, I think we at least, I guess Annie can tell you at least which ones she needs.
146 00:15:37.550 ⇒ 00:15:42.750 Amber Lin: such as like, maybe we need all of maybe we don’t. Annie. What do you say?
147 00:15:43.673 ⇒ 00:16:04.969 Annie Yu: I’m thinking I wouldn’t even join communication events with employee engagement. I like by communication events. We see like user, we see department. And I just need more information for each user so department and like location level. So I think you did this join using just the
148 00:16:05.230 ⇒ 00:16:05.810 Annie Yu: source.
149 00:16:06.270 ⇒ 00:16:12.350 Annie Yu: So I think that’s the way to go, just just to bring in more information.
150 00:16:12.350 ⇒ 00:16:12.720 Luke Daque: Yeah.
151 00:16:13.150 ⇒ 00:16:15.449 Luke Daque: Each year more dimensions for each user.
152 00:16:15.450 ⇒ 00:16:16.210 Annie Yu: Basically right.
153 00:16:16.210 ⇒ 00:16:19.429 Luke Daque: Like which teams they are coming from and stuff like that. Okay.
154 00:16:19.430 ⇒ 00:16:22.589 Annie Yu: Yeah. So all the filter fields that we need.
155 00:16:23.470 ⇒ 00:16:24.180 Luke Daque: Okay.
156 00:16:24.660 ⇒ 00:16:25.930 Luke Daque: Great copy. That one.
157 00:16:26.470 ⇒ 00:16:27.100 Amber Lin: Yay.
158 00:16:28.172 ⇒ 00:16:30.009 Amber Lin: So we would.
159 00:16:30.630 ⇒ 00:16:37.550 Annie Yu: But yeah, I wouldn’t be blocked by this. I I have what I need to to work on my tickets now.
160 00:16:38.780 ⇒ 00:16:40.570 Amber Lin: So as long as we.
161 00:16:41.790 ⇒ 00:16:43.599 Luke Daque: As so we’re trying to
162 00:16:43.600 ⇒ 00:16:49.623 Luke Daque: which the the communication events table. Because that’s the main table that you’re using. Right
163 00:16:50.000 ⇒ 00:16:51.550 Annie Yu: Yeah. Yes.
164 00:16:51.550 ⇒ 00:16:54.100 Luke Daque: Yeah, so we have, yeah.
165 00:16:54.100 ⇒ 00:16:54.650 Annie Yu: M.
166 00:16:55.660 ⇒ 00:17:01.789 Luke Daque: Yeah, if we can enrich that with the user, just data dimensions, should be good. I guess.
167 00:17:02.220 ⇒ 00:17:05.160 Annie Yu: Yeah, I think it’s gonna be good for for the
168 00:17:05.948 ⇒ 00:17:09.830 Annie Yu: the key deliverables that they they want for this phase.
169 00:17:10.500 ⇒ 00:17:10.880 Luke Daque: Okay.
170 00:17:16.240 ⇒ 00:17:18.299 Amber Lin: Is this a better ticket, Luke?
171 00:17:19.109 ⇒ 00:17:25.520 Amber Lin: It does this make sense? If we just make sure we include all of these doesn’t matter which one it is.
172 00:17:26.680 ⇒ 00:17:28.519 Luke Daque: Yeah. That should be fine.
173 00:17:28.820 ⇒ 00:17:35.559 Amber Lin: Okay. I think after we do that, we’ll need to publish as publish a power bi. Again.
174 00:17:38.220 ⇒ 00:17:38.960 Luke Daque: Yeah.
175 00:17:40.000 ⇒ 00:17:49.450 Amber Lin: Oh, but on that also do we publish the new models that you just did? You know the worker, location worker.
176 00:17:49.760 ⇒ 00:17:53.880 Amber Lin: type and in office mandate was that published.
177 00:17:54.850 ⇒ 00:18:00.320 Luke Daque: Yeah. But yeah, like, if we still need, we just basically need to just enrich the
178 00:18:00.750 ⇒ 00:18:04.100 Luke Daque: communication events with all those state. I guess that’s already
179 00:18:04.460 ⇒ 00:18:08.090 Luke Daque: there. It’s just not shown in the communication events.
180 00:18:08.955 ⇒ 00:18:19.840 Amber Lin: Okay, okay, so do you think this is something doable? To today, or at least before Friday.
181 00:18:19.840 ⇒ 00:18:21.800 Luke Daque: Prioritize that.
182 00:18:21.800 ⇒ 00:18:23.300 Amber Lin: Okay, sounds good.
183 00:18:23.510 ⇒ 00:18:35.000 Amber Lin: because Annie is also off tomorrow. So as long as she can get this by Friday we can us. We can be pretty close to what they wanted from us.
184 00:18:36.050 ⇒ 00:18:38.629 Amber Lin: So I’m gonna I’m just gonna say, Thursday.
185 00:18:40.182 ⇒ 00:18:44.910 Amber Lin: looking at the other one other 2 that Annie added.
186 00:18:45.460 ⇒ 00:18:51.949 Amber Lin: So this one is an error. There’s null values for this. Why do you think that is.
187 00:18:52.610 ⇒ 00:19:00.550 Luke Daque: I have to check, I’ll have to investigate. Why, there’s nose there, and like also the missing id that Annie mentioned. So yeah.
188 00:19:02.440 ⇒ 00:19:06.339 Amber Lin: How long? Oh, my bad! How long do you think this will take.
189 00:19:10.930 ⇒ 00:19:18.550 Luke Daque: I guess I can start working on that after I add, enrich the communication events. Or would you rather me in
190 00:19:18.700 ⇒ 00:19:20.720 Luke Daque: prioritize that first.st
191 00:19:22.607 ⇒ 00:19:24.510 Amber Lin: A wish. What do you think?
192 00:19:28.500 ⇒ 00:19:30.279 Amber Lin: Sorry. What was the
193 00:19:31.027 ⇒ 00:19:40.479 Amber Lin: we’ve been debating between a few tickets or what to prioritize? First, st because we only have one, Luke. So we’re debating. If we want to do.
194 00:19:41.020 ⇒ 00:19:53.669 Amber Lin: they’ll have everything in one table so that Annie can create filters in power bi or to work on the error that there’s no values in department and division.
195 00:19:55.550 ⇒ 00:19:56.990 Awaish Kumar: Which one is blocking any.
196 00:19:58.120 ⇒ 00:20:00.190 Amber Lin: This somewhat blocking.
197 00:20:00.190 ⇒ 00:20:00.950 Awaish Kumar: Annie.
198 00:20:01.260 ⇒ 00:20:08.410 Amber Lin: No, no, both of them, I don’t think, are directly blocking Annie, but it’s I think this is more.
199 00:20:08.410 ⇒ 00:20:10.790 Amber Lin: I’m sure, to our final result.
200 00:20:11.720 ⇒ 00:20:13.500 Awaish Kumar: Maybe by fixing this we’ll fix this.
201 00:20:13.500 ⇒ 00:20:16.120 Amber Lin: Other one sorry.
202 00:20:16.120 ⇒ 00:20:19.929 Awaish Kumar: We should prioritize the 6th 1. First.st
203 00:20:20.270 ⇒ 00:20:25.010 Amber Lin: Okay, sounds good. Yeah, I’ll say, this is for Friday.
204 00:20:25.950 ⇒ 00:20:34.850 Amber Lin: And then this one probably needs additional modeling.
205 00:20:36.280 ⇒ 00:20:44.360 Amber Lin: look, I think this this ticket means just say for email, right for email, sorry for meetings. We have
206 00:20:45.271 ⇒ 00:20:54.090 Amber Lin: and he said, usually, if there’s multiple participants, currently, we only record like one participant per meeting.
207 00:20:54.520 ⇒ 00:20:59.749 Amber Lin: But we need all 4 as their separate rows like, does that make sense?
208 00:21:00.000 ⇒ 00:21:05.280 Amber Lin: It was that the clear explanation, okay, what do you think needs to get done to make this happen.
209 00:21:07.092 ⇒ 00:21:08.460 Luke Daque: Yeah, I’ll have to.
210 00:21:08.460 ⇒ 00:21:20.460 Awaish Kumar: That’s something like proof of Api is like, yeah, generating synthetic data. So I, if there are 4 participants.
211 00:21:20.460 ⇒ 00:21:20.820 Amber Lin: Oh!
212 00:21:20.820 ⇒ 00:21:21.420 Awaish Kumar: Greeting.
213 00:21:22.410 ⇒ 00:21:26.749 Awaish Kumar: So what the Api sends like is the Api sending
214 00:21:27.040 ⇒ 00:21:30.439 Awaish Kumar: 4 records? Or is it going to send only one record?
215 00:21:30.620 ⇒ 00:21:32.590 Awaish Kumar: That’s the question. Right.
216 00:21:39.650 ⇒ 00:21:42.450 Awaish Kumar: For the event. There are 4 participants.
217 00:21:43.970 ⇒ 00:21:46.230 Awaish Kumar: So Microsoft Graph Api works.
218 00:21:50.190 ⇒ 00:21:53.019 Awaish Kumar: what data we are going to get from the Api.
219 00:21:53.230 ⇒ 00:21:54.880 Awaish Kumar: and we are going to just
220 00:21:55.290 ⇒ 00:21:57.790 Awaish Kumar: generate synthetic data according to that.
221 00:21:58.240 ⇒ 00:22:00.230 Awaish Kumar: Okay, okay, yep.
222 00:22:00.980 ⇒ 00:22:12.589 Annie Yu: I think we already have that so in the source table there’s 1 column that’s organizer, and for each meeting, and there’s 1 column. That’s an object with multiple
223 00:22:12.820 ⇒ 00:22:23.420 Annie Yu: attendees. So I think we need to flatten those attendees out and then to have per person per oh.
224 00:22:23.420 ⇒ 00:22:34.639 Luke Daque: May. Maybe I’m only using the organ organizer in the communication event. Yeah. So I’ll have to convert that using the attendees name something.
225 00:22:35.770 ⇒ 00:22:36.750 Annie Yu: Thanks.
226 00:22:37.240 ⇒ 00:22:47.010 Amber Lin: Okay, let’s see, I think, having this will let any
227 00:22:48.080 ⇒ 00:22:51.120 Amber Lin: make a power bi, and then that one.
228 00:22:51.220 ⇒ 00:22:58.580 Amber Lin: it will just give us more data. We do need to do this and then publish.
229 00:23:04.510 ⇒ 00:23:07.279 Amber Lin: but I would say, this is.
230 00:23:08.160 ⇒ 00:23:11.340 Amber Lin: this is still pretty important. Anyways.
231 00:23:12.780 ⇒ 00:23:15.480 Amber Lin: maybe this is also by Friday.
232 00:23:16.270 ⇒ 00:23:17.339 Amber Lin: I don’t know.
233 00:23:19.280 ⇒ 00:23:24.530 Awaish Kumar: What is the target date for 6th one.
234 00:23:25.810 ⇒ 00:23:36.029 Amber Lin: 6 1. So we’re aiming for that by tomorrow, so that on Friday Annie can look at the different tasks. And then this is
235 00:23:36.370 ⇒ 00:23:46.540 Amber Lin: this is right after 6 3 is, gonna be right after this one. I do think we have space to complete 6, 2 this week as well.
236 00:23:47.890 ⇒ 00:23:48.499 Luke Daque: Yeah, that should be.
237 00:23:48.500 ⇒ 00:23:49.859 Awaish Kumar: It’s not like this.
238 00:23:50.620 ⇒ 00:23:51.280 Amber Lin: Hmm.
239 00:23:52.020 ⇒ 00:23:58.030 Awaish Kumar: Yeah, this should be fine like this. This should not take longer just to flatten the list right.
240 00:24:00.800 ⇒ 00:24:02.620 Amber Lin: That’s my assumption as well.
241 00:24:02.910 ⇒ 00:24:06.130 Amber Lin: How long should this take if we time box this?
242 00:24:08.940 ⇒ 00:24:10.340 Amber Lin: I wish. What do you think.
243 00:24:14.360 ⇒ 00:24:15.830 Awaish Kumar: It’s for me.
244 00:24:15.990 ⇒ 00:24:18.580 Awaish Kumar: If I just have to flatten like like
245 00:24:19.070 ⇒ 00:24:22.150 Awaish Kumar: an hour would be enough. I don’t know about.
246 00:24:22.310 ⇒ 00:24:23.980 Awaish Kumar: Look like. What do you think.
247 00:24:25.600 ⇒ 00:24:29.969 Luke Daque: And maybe an hour or 2. But yeah, including the investigation.
248 00:24:30.550 ⇒ 00:24:36.500 Amber Lin: Okay, the investigation, I’ll say, like 2 points.
249 00:24:39.880 ⇒ 00:24:44.709 Amber Lin: and that joining the tables, how long would that take
250 00:24:51.010 ⇒ 00:24:53.200 Amber Lin: a wish? What do you think this one would take.
251 00:24:53.200 ⇒ 00:24:56.470 Luke Daque: That should be pretty straightforward, maybe an hour as well.
252 00:24:56.470 ⇒ 00:24:57.130 Amber Lin: Okay?
253 00:24:58.648 ⇒ 00:25:02.060 Amber Lin: If we say an an hour.
254 00:25:07.230 ⇒ 00:25:15.449 Amber Lin: Okay, if these all this takes like an hour, an hour or 2 h.
255 00:25:15.450 ⇒ 00:25:16.280 Awaish Kumar: Oh, gosh! Like.
256 00:25:16.280 ⇒ 00:25:24.279 Amber Lin: That takes an hour. Then we do have space to do the synthetic data sets like at least one of them this week as well
257 00:25:27.960 ⇒ 00:25:30.539 Amber Lin: as we have today. Tomorrow and Friday.
258 00:25:31.590 ⇒ 00:25:32.190 Luke Daque: Yeah.
259 00:25:32.450 ⇒ 00:25:39.740 Amber Lin: Okay, do you feel like you have everything for creating the synthetic data sets.
260 00:25:41.802 ⇒ 00:25:43.990 Luke Daque: Yeah, I think we should be good.
261 00:25:44.460 ⇒ 00:25:45.050 Amber Lin: Okay.
262 00:25:48.925 ⇒ 00:25:54.540 Amber Lin: Alright. So I’m gonna say, okay, let’s do Friday
263 00:25:56.380 ⇒ 00:26:05.899 Amber Lin: for the co-pilot. I clarified. We’re not using any non microsoft non Microsoft sources. So we only need to use the Microsoft usage. Api.
264 00:26:08.230 ⇒ 00:26:09.190 Luke Daque: Copy that.
265 00:26:09.190 ⇒ 00:26:10.900 Amber Lin: Yeah, okay,
266 00:26:13.090 ⇒ 00:26:20.069 Amber Lin: once, we have a synthetic data set, what needs to be done between that and having it in power. Bi.
267 00:26:25.540 ⇒ 00:26:29.860 Luke Daque: I guess we’ll have to create Dbt models again. For tool usage, and then.
268 00:26:30.530 ⇒ 00:26:32.370 Luke Daque: Then we should be able to see it in power. Bi.
269 00:26:33.240 ⇒ 00:26:36.070 Amber Lin: Okay, okay? So I have.
270 00:26:36.820 ⇒ 00:26:56.109 Amber Lin: So for this week, we’re gonna address these error errors, which are pretty small, make the synthetic data sets. And then next week we’ll have the models for them, and then add them to power Bi as well. And after that we should have all of this, all of the main sources that we need.
271 00:26:57.840 ⇒ 00:27:00.910 Amber Lin: I’m gonna say, cancel this one.
272 00:27:03.350 ⇒ 00:27:06.890 Amber Lin: So I’m gonna scoot this for next cycle.
273 00:27:10.580 ⇒ 00:27:19.549 Amber Lin: Okay, okay, this sounds good to me. Anything else that we have questions on.
274 00:27:24.870 ⇒ 00:27:25.670 Amber Lin: Okay.
275 00:27:25.940 ⇒ 00:27:32.870 Amber Lin: let me know how it goes. Keep me posted, and I’ll check in at the end of day, today, on progress.
276 00:27:35.030 ⇒ 00:27:36.280 Luke Daque: Yeah. Sounds. Good. Okay.
277 00:27:36.280 ⇒ 00:27:38.019 Amber Lin: Okay, thank you. All.