Meeting Title: MatterMore | internal Standup Date: 2025-06-25 Meeting participants: Luke Daque, Amber Lin, Annie Yu
WEBVTT
1 00:03:08.650 ⇒ 00:03:09.860 Amber Lin: Hi! There!
2 00:03:14.340 ⇒ 00:03:15.759 Luke Daque: Hi! Embry! How’s it going.
3 00:03:17.000 ⇒ 00:03:22.880 Amber Lin: Very good stuck with work so much work to do.
4 00:03:23.530 ⇒ 00:03:24.430 Luke Daque: Yeah.
5 00:03:25.420 ⇒ 00:03:26.559 Amber Lin: How are you?
6 00:03:27.230 ⇒ 00:03:28.709 Luke Daque: Doing well, how are you.
7 00:03:32.706 ⇒ 00:03:35.589 Amber Lin: How’s the workload feeling? Recently.
8 00:03:37.190 ⇒ 00:03:40.609 Luke Daque: It’s pretty fine. I’m only like doing the matter more stuff.
9 00:03:40.920 ⇒ 00:03:41.660 Amber Lin: Wow!
10 00:03:41.860 ⇒ 00:03:42.640 Luke Daque: So, yeah.
11 00:03:44.280 ⇒ 00:03:54.760 Amber Lin: Okay, okay, oh, Hi, anie I guess.
12 00:03:55.050 ⇒ 00:03:58.379 Amber Lin: Let’s start with Annie, because I think
13 00:03:58.510 ⇒ 00:04:10.600 Amber Lin: probably I need to talk with Luke about how we want to proceed, modeling the different stuff. So let’s any. Let’s talk about if you have anything on your end, and then you can hop off if you need.
14 00:04:12.403 ⇒ 00:04:21.390 Annie Yu: Yeah. One thing is, I guess 2 things. So I realized that even though Luke already refreshed the model.
15 00:04:23.810 ⇒ 00:04:33.240 Annie Yu: But the report itself, I mean, like, even even if the model is refresh, the report won’t
16 00:04:33.680 ⇒ 00:04:35.580 Annie Yu: get those new columns.
17 00:04:38.560 ⇒ 00:04:39.310 Amber Lin: Hmm.
18 00:04:40.470 ⇒ 00:04:42.970 Annie Yu: And I think the only way is to.
19 00:04:45.970 ⇒ 00:04:47.190 Amber Lin: Publish a new one.
20 00:04:49.760 ⇒ 00:04:54.490 Annie Yu: Yeah, with with power bi service, I believe. Or
21 00:04:54.900 ⇒ 00:05:03.750 Annie Yu: I think the other way is to download the the report file and then reconnect to the model again on desktop.
22 00:05:09.079 ⇒ 00:05:15.909 Amber Lin: Let’s let’s see, what have we tried? So let me write this down a ticket. So the issue is
23 00:05:17.370 ⇒ 00:05:21.889 Amber Lin: new models not showing in power. Bi.
24 00:05:30.170 ⇒ 00:05:33.169 Annie Yu: No, it it is showing up. It’s just not
25 00:05:33.820 ⇒ 00:05:50.259 Annie Yu: the the report. We it’s it’s so weird. So the report is using that updated model. But the new columns doesn’t come into the report. So the new columns come into the model file. But it doesn’t come into the report file.
26 00:05:50.500 ⇒ 00:05:58.030 Amber Lin: I see is the report connected to the same same model, or the older one.
27 00:05:58.800 ⇒ 00:06:03.270 Annie Yu: No, the same one. It’s yeah, we’re using the same one
28 00:06:03.390 ⇒ 00:06:07.710 Annie Yu: look, updated it. And I see the columns in the model file.
29 00:06:08.470 ⇒ 00:06:16.140 Annie Yu: But when I refresh the report the columns are not in. But I mean, I
30 00:06:16.450 ⇒ 00:06:27.900 Annie Yu: I have an option to create a new report using that updated model. But there’s no way for me to kind of just copy and paste what I.
31 00:06:28.100 ⇒ 00:06:34.230 Amber Lin: Yeah, totally. I don’t think it makes sense to create a new, a new model. Let’s see.
32 00:06:34.350 ⇒ 00:06:36.729 Amber Lin: did we do a model refresh.
33 00:06:37.250 ⇒ 00:06:39.049 Annie Yu: Yes, that’s already done.
34 00:06:39.050 ⇒ 00:06:39.955 Amber Lin: Okay.
35 00:06:42.010 ⇒ 00:06:43.910 Annie Yu: And the new columns are in there.
36 00:06:46.280 ⇒ 00:06:47.270 Amber Lin: Huh?
37 00:06:49.580 ⇒ 00:06:53.096 Amber Lin: Columns. Oh, okay, is it?
38 00:06:55.580 ⇒ 00:07:00.219 Amber Lin: maybe it’s in the data model, but not use individualizations.
39 00:07:00.370 ⇒ 00:07:05.920 Amber Lin: Maybe we have to go to the fields, pane and power bi and like, make sure they’re listed.
40 00:07:09.024 ⇒ 00:07:15.390 Annie Yu: Yeah, that’s where I went. And I just I can’t see those new new columns.
41 00:07:15.780 ⇒ 00:07:19.030 Annie Yu: So in the fields page using that model.
42 00:07:21.106 ⇒ 00:07:24.190 Amber Lin: The columns are not listed.
43 00:07:24.510 ⇒ 00:07:26.540 Amber Lin: It is in here right.
44 00:07:26.830 ⇒ 00:07:31.619 Luke Daque: Which report are you referring to? I need the matter more import. One right.
45 00:07:36.550 ⇒ 00:07:37.600 Annie Yu: What’s that? Look.
46 00:07:37.930 ⇒ 00:07:40.210 Luke Daque: Is it the matter more import?
47 00:07:42.390 ⇒ 00:07:44.860 Luke Daque: model, or like? What report that.
48 00:07:44.860 ⇒ 00:07:46.690 Annie Yu: Yes, yes, Matchamore.
49 00:07:49.932 ⇒ 00:07:55.349 Annie Yu: No, it’s the matter. It’s the work, activity, synthetic.
50 00:07:55.600 ⇒ 00:07:57.209 Luke Daque: Oh, okay, yeah.
51 00:07:58.690 ⇒ 00:08:01.500 Luke Daque: So let me see, I’m I’m
52 00:08:02.270 ⇒ 00:08:05.570 Luke Daque: I’m gonna try it on my end just to make sure.
53 00:08:05.570 ⇒ 00:08:06.630 Annie Yu: Yeah, yeah.
54 00:08:10.230 ⇒ 00:08:13.280 Luke Daque: Which fields are you looking for the Start date.
55 00:08:13.890 ⇒ 00:08:16.749 Annie Yu: Yeah, start- start date and end date.
56 00:08:17.310 ⇒ 00:08:19.000 Luke Daque: Mine looks like it.
57 00:08:19.560 ⇒ 00:08:24.720 Amber Lin: Showing persona, can you guys share your screen voice or.
58 00:08:26.850 ⇒ 00:08:30.190 Luke Daque: Oh, let me. Yeah, sure. Yeah, give me a second.
59 00:08:38.669 ⇒ 00:08:40.089 Luke Daque: Can you see my screen.
60 00:08:41.090 ⇒ 00:08:41.990 Annie Yu: Yes.
61 00:08:42.559 ⇒ 00:08:47.269 Luke Daque: So yeah, I’m in the work activity, synthetic report.
62 00:08:48.170 ⇒ 00:08:54.239 Luke Daque: And I just looked at. I tried to look at the communication events. So it understand
63 00:08:55.300 ⇒ 00:09:00.980 Luke Daque: MoD source, and it looks like it has the start day.
64 00:09:02.660 ⇒ 00:09:04.020 Annie Yu: Okay. Let me see.
65 00:09:04.590 ⇒ 00:09:05.160 Annie Yu: Okay.
66 00:09:05.160 ⇒ 00:09:06.530 Luke Daque: And and that’s.
67 00:09:07.870 ⇒ 00:09:12.680 Annie Yu: Okay, that’s super weird. I? Okay, let me let me refresh this.
68 00:09:13.090 ⇒ 00:09:17.909 Luke Daque: Maybe it’s a cash thing caching issue. Maybe.
69 00:09:18.830 ⇒ 00:09:20.640 Annie Yu: Yeah, I’m gonna use.
70 00:09:23.930 ⇒ 00:09:26.630 Annie Yu: So you’re in that file. Okay?
71 00:09:29.390 ⇒ 00:09:32.610 Luke Daque: Because I checked the semantic.
72 00:09:33.650 ⇒ 00:09:35.370 Annie Yu: Okay. Now I see it.
73 00:09:36.194 ⇒ 00:09:38.955 Amber Lin: I don’t know if it was me.
74 00:09:39.350 ⇒ 00:09:44.829 Amber Lin: Okay, maybe it took a while, for after Luke refreshed it for you to see it.
75 00:09:45.570 ⇒ 00:09:54.220 Luke Daque: Yeah, maybe. Or maybe it’s a caching issue thing like the browser cached the thing. I don’t know. Yeah.
76 00:09:55.050 ⇒ 00:09:56.790 Luke Daque: okay, yeah.
77 00:09:57.250 ⇒ 00:09:57.900 Amber Lin: Right now.
78 00:09:58.670 ⇒ 00:10:05.119 Annie Yu: Yeah. So that that’s great. But is there a way? Cause I thought.
79 00:10:05.540 ⇒ 00:10:08.259 Annie Yu: so the thing is, okay, let me let me.
80 00:10:09.390 ⇒ 00:10:10.840 Annie Yu: Yeah, I
81 00:10:11.570 ⇒ 00:10:19.850 Annie Yu: I thought the power Bi would let me have options to show, like the state or month, year or
82 00:10:20.080 ⇒ 00:10:31.060 Annie Yu: week start date. But it doesn’t let me. So I’m thinking, like right now, even if I put the date as the X-axis here, it’s gonna be very
83 00:10:32.330 ⇒ 00:10:36.660 Annie Yu: like messy. And so I guess.
84 00:10:36.660 ⇒ 00:10:38.060 Amber Lin: What are you? What are you?
85 00:10:38.060 ⇒ 00:10:38.470 Amber Lin: My name?
86 00:10:38.470 ⇒ 00:10:39.330 Amber Lin: Provisioning.
87 00:10:40.680 ⇒ 00:10:42.850 Annie Yu: No, the x-axis.
88 00:10:43.660 ⇒ 00:10:47.169 Annie Yu: We might wanna show show like month, year.
89 00:10:48.150 ⇒ 00:10:51.030 Luke Daque: So let’s start date.
90 00:10:53.360 ⇒ 00:10:59.990 Annie Yu: Yeah, it only has that like year, quarter month as well. So it’s also not
91 00:11:01.160 ⇒ 00:11:03.360 Annie Yu: showing like the time progression.
92 00:11:11.330 ⇒ 00:11:13.710 Amber Lin: Sorry. What? What was the question here?
93 00:11:16.230 ⇒ 00:11:26.770 Annie Yu: So I’m saying we might need even more columns that has like months, year.
94 00:11:28.990 ⇒ 00:11:37.280 Amber Lin: I think previously. Didn’t Luke show one that was year with the month above it? I just saw one of the the ones flash by.
95 00:11:38.030 ⇒ 00:11:38.700 Amber Lin: yeah, I’m with.
96 00:11:38.700 ⇒ 00:11:39.120 Annie Yu: Had.
97 00:11:39.120 ⇒ 00:11:42.640 Annie Yu: I’m looking at it. Now wait! How how do you do that, Luke?
98 00:11:45.900 ⇒ 00:11:46.780 Amber Lin: Oh!
99 00:11:46.780 ⇒ 00:11:51.290 Luke Daque: I see. Oh, wait, let me let me see. Like, maybe.
100 00:11:52.840 ⇒ 00:12:01.870 Luke Daque: Okay. I’m also not very familiar. But maybe let’s try something like that. Slip.
101 00:12:03.210 ⇒ 00:12:04.119 Amber Lin: That shouldn’t be.
102 00:12:04.120 ⇒ 00:12:04.930 Luke Daque: Where.
103 00:12:05.290 ⇒ 00:12:13.179 Amber Lin: I do think it’s possible. IDI did see a flash by so like I do think that will be possible with the current stuff. We have
104 00:12:14.770 ⇒ 00:12:18.020 Amber Lin: like to just show both year and month.
105 00:12:18.560 ⇒ 00:12:21.689 Luke Daque: This one looks like it shows year and month.
106 00:12:22.330 ⇒ 00:12:22.720 Amber Lin: Yeah.
107 00:12:22.720 ⇒ 00:12:28.640 Luke Daque: But thank you. For instance, just put this
108 00:12:28.640 ⇒ 00:12:30.890 Luke Daque: start date, for example, here, right.
109 00:12:31.270 ⇒ 00:12:37.230 Luke Daque: And then just remove the quarter and the day, so that it’s just year and month. Then this should be
110 00:12:38.350 ⇒ 00:12:41.220 Luke Daque: year, month looks like.
111 00:12:41.850 ⇒ 00:12:45.910 Luke Daque: and and if we filter it, maybe just put this here. I don’t know. How do we.
112 00:12:46.440 ⇒ 00:12:50.650 Annie Yu: No, I think I’ll I’ll have to add that start date as a filter, too.
113 00:12:50.650 ⇒ 00:12:51.510 Amber Lin: Oh, okay.
114 00:12:52.290 ⇒ 00:12:56.950 Luke Daque: You know. Let’s replicate one of these right or something.
115 00:12:59.240 ⇒ 00:13:01.370 Luke Daque: Oh, how do I copy paste?
116 00:13:03.330 ⇒ 00:13:05.200 Luke Daque: Just an example.
117 00:13:06.640 ⇒ 00:13:08.320 Amber Lin: Wow!
118 00:13:10.430 ⇒ 00:13:13.518 Amber Lin: Wow! That’s so fast.
119 00:13:14.290 ⇒ 00:13:15.420 Luke Daque: But it should be.
120 00:13:16.140 ⇒ 00:13:17.920 Luke Daque: I think it should be a big.
121 00:13:17.920 ⇒ 00:13:23.219 Amber Lin: Like if we drag the year into that filter that would work. Yeah.
122 00:13:24.330 ⇒ 00:13:26.939 Amber Lin: instead of start day. We do year.
123 00:13:31.370 ⇒ 00:13:32.800 Amber Lin: Yeah. Yay.
124 00:13:32.800 ⇒ 00:13:34.250 Luke Daque: Let’s verify.
125 00:13:34.750 ⇒ 00:13:35.690 Luke Daque: Yeah, yeah.
126 00:13:35.690 ⇒ 00:13:37.490 Amber Lin: Awesome. Okay.
127 00:13:37.490 ⇒ 00:13:40.200 Annie Yu: Okay, nice. Look, you’re you’re.
128 00:13:44.780 ⇒ 00:13:45.530 Luke Daque: Cool.
129 00:13:45.960 ⇒ 00:13:56.679 Annie Yu: So, yeah, this will. This should can you save this? Actually, I will adjust it further. But I think this is what it should look like
130 00:13:59.060 ⇒ 00:14:00.417 Annie Yu: thanks so much.
131 00:14:00.870 ⇒ 00:14:01.710 Luke Daque: Sure.
132 00:14:02.240 ⇒ 00:14:07.510 Luke Daque: And I think there’s even a different way to show this filter where it’s like
133 00:14:07.620 ⇒ 00:14:09.445 Luke Daque: instead of a list. It’s like
134 00:14:10.410 ⇒ 00:14:11.460 Amber Lin: Arrange.
135 00:14:11.870 ⇒ 00:14:12.989 Luke Daque: Yeah, something like that.
136 00:14:13.010 ⇒ 00:14:15.180 Luke Daque: Oh, okay.
137 00:14:17.490 ⇒ 00:14:21.850 Amber Lin: Luke, was this what you were doing before in your previous show?
138 00:14:23.138 ⇒ 00:14:25.370 Luke Daque: That was like a long time ago, though, like
139 00:14:26.010 ⇒ 00:14:27.599 Luke Daque: 7 years ago, I should say.
140 00:14:27.600 ⇒ 00:14:31.670 Amber Lin: Wow, okay. Well, I guess power Bi hasn’t really changed.
141 00:14:32.485 ⇒ 00:14:32.960 Annie Yu: Okay.
142 00:14:34.350 ⇒ 00:14:35.189 Luke Daque: I don’t know.
143 00:14:35.550 ⇒ 00:14:36.399 Amber Lin: Yeah, anyway.
144 00:14:36.400 ⇒ 00:14:37.140 Luke Daque: It’s never happened.
145 00:14:37.140 ⇒ 00:14:37.890 Amber Lin: Okay.
146 00:14:38.860 ⇒ 00:14:41.205 Amber Lin: Oh, we’ll let any figure it out.
147 00:14:41.540 ⇒ 00:14:41.900 Annie Yu: This is.
148 00:14:41.900 ⇒ 00:14:48.620 Amber Lin: Any other any other items that you’re stuck on, that you need you need us to look at together.
149 00:14:48.620 ⇒ 00:14:49.180 Luke Daque: Okay.
150 00:14:49.809 ⇒ 00:14:57.079 Annie Yu: Not as of now, I I would say, after this is like completed, I think
151 00:14:58.450 ⇒ 00:15:04.859 Annie Yu: I I think the next step is for for you to review amber, and and I’ll see once that
152 00:15:05.926 ⇒ 00:15:07.519 Annie Yu: tool usage
153 00:15:07.920 ⇒ 00:15:13.650 Annie Yu: related datas, and I’ll see if I need to adjust anything on on my end or not.
154 00:15:13.980 ⇒ 00:15:21.250 Amber Lin: Yeah, I think it should be relatively easy, because it’s mostly just another event type. Everything else should be the same.
155 00:15:21.760 ⇒ 00:15:23.440 Annie Yu: Yeah, that’s why I thought so. If that.
156 00:15:23.440 ⇒ 00:15:24.000 Amber Lin: Yeah.
157 00:15:24.000 ⇒ 00:15:24.869 Annie Yu: We just have to.
158 00:15:24.870 ⇒ 00:15:26.199 Amber Lin: Yeah, we’ll aim, yeah.
159 00:15:26.200 ⇒ 00:15:26.730 Annie Yu: Updated.
160 00:15:26.730 ⇒ 00:15:31.730 Amber Lin: We’ll we’ll aim to have it exactly the same for you. So it’s easier.
161 00:15:33.360 ⇒ 00:15:33.830 Annie Yu: Okay.
162 00:15:33.830 ⇒ 00:15:35.010 Annie Yu: All right. Yeah.
163 00:15:35.880 ⇒ 00:15:52.729 Amber Lin: Ping me when you want it reviewed. We can also hop on a call together. I really don’t think there’s much to adjust, but I think the client will have their will, have their stuff that they want to adjust it. But once this is done today, I’ll ping them, and then I’ll let them look at it as well.
164 00:15:53.170 ⇒ 00:15:59.849 Annie Yu: Yeah, yeah, this time. I’m just, I’m just like really sticking to whatever it’s on their dog like.
165 00:15:59.850 ⇒ 00:16:00.710 Amber Lin: Yeah, I know me, too.
166 00:16:00.710 ⇒ 00:16:01.369 Annie Yu: So let’s.
167 00:16:03.940 ⇒ 00:16:05.079 Amber Lin: They learned at the heart.
168 00:16:05.080 ⇒ 00:16:14.009 Annie Yu: I, I think we do have more filters. Just because, like when they say, like team role, I don’t think we have data there, but we do have division and
169 00:16:14.130 ⇒ 00:16:15.880 Annie Yu: departments. So.
170 00:16:16.160 ⇒ 00:16:17.200 Amber Lin: So.
171 00:16:17.320 ⇒ 00:16:18.130 Annie Yu: I think we do have.
172 00:16:18.130 ⇒ 00:16:18.700 Amber Lin: Oh!
173 00:16:18.920 ⇒ 00:16:20.060 Annie Yu: But she.
174 00:16:20.060 ⇒ 00:16:27.630 Amber Lin: Do we have any? Can you also, when you do the documentation? Can you no doubt what fields? We don’t have data for.
175 00:16:29.550 ⇒ 00:16:33.329 Annie Yu: Okay, wait, then.
176 00:16:34.090 ⇒ 00:16:36.110 Annie Yu: So I can do that. But
177 00:16:36.440 ⇒ 00:16:40.210 Annie Yu: some fields that are not in the doc. But we do have.
178 00:16:41.170 ⇒ 00:16:44.859 Annie Yu: Should I include those in the filters.
179 00:16:47.000 ⇒ 00:16:55.830 Amber Lin: I think that’s so. We don’t have to. Can you just list it in the documentation? So we so we can have an idea of
180 00:16:56.240 ⇒ 00:17:03.880 Amber Lin: like what we have and what we don’t have cause. I like, based on what you said. I don’t think we have team more role data right?
181 00:17:05.028 ⇒ 00:17:11.360 Annie Yu: Yeah, there’s no data. But we do have department, which I think
182 00:17:11.460 ⇒ 00:17:16.190 Annie Yu: kind of is equivalent to team. But I could also be wrong.
183 00:17:16.990 ⇒ 00:17:23.529 Amber Lin: I don’t. I think team is the team under each department. Maybe we have it in a different table.
184 00:17:29.542 ⇒ 00:17:30.779 Luke Daque: Which 1 15.
185 00:17:31.060 ⇒ 00:17:35.649 Amber Lin: Yeah, for team enroll. Oh, wow! Look! The the bar is crazy.
186 00:17:36.350 ⇒ 00:17:37.320 Luke Daque: Yeah, this is strange.
187 00:17:39.000 ⇒ 00:17:42.780 Amber Lin: That’s awesome. Do we have team enrolled? Data
188 00:17:43.370 ⇒ 00:17:46.990 Amber Lin: might be in a different table. Oh, yeah, okay.
189 00:17:47.190 ⇒ 00:17:49.679 Annie Yu: We have columns, but I don’t think we have.
190 00:17:50.340 ⇒ 00:17:53.074 Amber Lin: Oh, great! I guess we do.
191 00:17:54.620 ⇒ 00:17:55.810 Annie Yu: Oh, wait!
192 00:17:55.810 ⇒ 00:17:57.469 Luke Daque: We can do something like that.
193 00:18:01.380 ⇒ 00:18:03.559 Amber Lin: Okay, so we do have role data.
194 00:18:03.730 ⇒ 00:18:07.309 Amber Lin: Let’s make sure we have that.
195 00:18:07.310 ⇒ 00:18:08.720 Luke Daque: And we have team as well.
196 00:18:08.940 ⇒ 00:18:09.969 Amber Lin: Oh, great!
197 00:18:11.190 ⇒ 00:18:14.409 Luke Daque: Yeah, and it in the.
198 00:18:14.410 ⇒ 00:18:16.820 Amber Lin: Communication, events, table right.
199 00:18:16.960 ⇒ 00:18:21.870 Luke Daque: Yeah, okay, it’s unknown. Maybe I did something.
200 00:18:23.370 ⇒ 00:18:24.050 Luke Daque: Hang on there.
201 00:18:24.050 ⇒ 00:18:26.890 Amber Lin: Okay, I’ll note that we don’t have.
202 00:18:27.150 ⇒ 00:18:37.095 Amber Lin: Yeah, Annie, I think when I check as well, I’ll check if any of them has any data or don’t have any data, cause I know the clients gonna say, Hey, there’s nothing in there.
203 00:18:40.180 ⇒ 00:18:40.910 Annie Yu: Yeah.
204 00:18:41.220 ⇒ 00:18:47.300 Amber Lin: Yeah. Can you help me check them as well before? You ping me for review?
205 00:18:48.550 ⇒ 00:18:57.570 Annie Yu: Okay? And so, just one more clarifying question. So for the ones that are not in the documents that we have, I should include those right.
206 00:18:57.890 ⇒ 00:19:04.309 Amber Lin: Yeah, we don’t. We don’t need them yet. Let’s just make sure we have everything matched up to their requirements.
207 00:19:05.230 ⇒ 00:19:12.889 Annie Yu: Okay. But then, okay. But then, like time, zone is also not in there. But I know that we have talked about time zone.
208 00:19:14.420 ⇒ 00:19:19.020 Amber Lin: Let’s see anything here that’s not in their initial requirements.
209 00:19:20.260 ⇒ 00:19:21.110 Annie Yu: Division.
210 00:19:22.280 ⇒ 00:19:22.800 Annie Yu: Awesome.
211 00:19:22.800 ⇒ 00:19:25.599 Amber Lin: Huh? What does division mean?
212 00:19:25.900 ⇒ 00:19:36.190 Annie Yu: I think right now we have them as in like kind of like, like Gmail Apla, North America. Emea.
213 00:19:36.190 ⇒ 00:19:39.339 Amber Lin: Oh, can can I see the options under division.
214 00:19:43.430 ⇒ 00:19:43.880 Luke Daque: Yeah.
215 00:19:43.880 ⇒ 00:19:48.699 Amber Lin: Oh, can we say like region for that one?
216 00:19:48.860 ⇒ 00:19:50.770 Amber Lin: Isn’t that geography?
217 00:19:51.010 ⇒ 00:19:52.530 Amber Lin: Wait! How is it?
218 00:19:53.370 ⇒ 00:19:57.140 Amber Lin: How is it different from location? Can I see what’s on location?
219 00:19:58.380 ⇒ 00:20:02.750 Luke Daque: Oh, we already have a team role. We have geography.
220 00:20:03.000 ⇒ 00:20:07.660 Amber Lin: Okay. Okay, okay, division.
221 00:20:09.909 ⇒ 00:20:14.050 Amber Lin: I, okay.
222 00:20:14.050 ⇒ 00:20:14.600 Luke Daque: I am.
223 00:20:14.600 ⇒ 00:20:17.699 Amber Lin: I think we can rename Shia like
224 00:20:17.820 ⇒ 00:20:25.139 Amber Lin: division, as because, since there is not in there, we can rename division as like region.
225 00:20:25.600 ⇒ 00:20:26.220 Luke Daque: Hmm.
226 00:20:27.570 ⇒ 00:20:32.199 Annie Yu: So you’re saying we do want to include the that, even though it’s not in the doc.
227 00:20:32.750 ⇒ 00:20:34.360 Annie Yu: That’s what I’m trying to clarify.
228 00:20:34.360 ⇒ 00:20:55.159 Amber Lin: Oh, I see, I see, I see. I understand. Okay, let’s make sure one that everything in the dock it’s in there and then for the extra stuff. Can you add a note somewhere that it’s not included in a doc? And when I review, I’ll I’ll look at if it actually helps them. But I just wanna make sure that everything in the doc is in here.
229 00:20:55.980 ⇒ 00:20:56.385 Annie Yu: Okay.
230 00:21:01.330 ⇒ 00:21:02.070 Annie Yu: Sure.
231 00:21:02.860 ⇒ 00:21:03.460 Amber Lin: Stuff.
232 00:21:04.680 ⇒ 00:21:13.760 Annie Yu: Yeah. And for the filters, I kind of purposely kept them as drop downs because I read that
233 00:21:15.150 ⇒ 00:21:18.439 Annie Yu: this will help the performance, because if we do like
234 00:21:19.410 ⇒ 00:21:29.559 Annie Yu: Slider, or list that like, I think behind the scene, the power bi will do the query stuff. But then drop down.
235 00:21:29.560 ⇒ 00:21:30.350 Amber Lin: I see.
236 00:21:30.350 ⇒ 00:21:30.850 Annie Yu: For like.
237 00:21:30.850 ⇒ 00:21:33.370 Amber Lin: They like tables and lists better.
238 00:21:33.700 ⇒ 00:21:37.099 Annie Yu: I think so. Not sure.
239 00:21:37.380 ⇒ 00:21:37.735 Amber Lin: Okay.
240 00:21:40.120 ⇒ 00:21:58.080 Amber Lin: yeah, okay, sounds good. Let me know. List down all the different filters we have for each each page. And then maybe list. If this is, this fulfills all the requirements which ones are new, which one doesn’t have data. Let me know.
241 00:21:59.770 ⇒ 00:22:04.910 Luke Daque: Yeah, yeah, it’s also very size doesn’t have data.
242 00:22:11.080 ⇒ 00:22:12.709 Amber Lin: Okay, let’s see.
243 00:22:12.900 ⇒ 00:22:13.860 Amber Lin: So.
244 00:22:13.860 ⇒ 00:22:16.120 Annie Yu: Okay, I’ll document these.
245 00:22:16.840 ⇒ 00:22:17.490 Amber Lin: Okay.
246 00:22:18.900 ⇒ 00:22:20.060 Annie Yu: Okay. Nice.
247 00:22:20.550 ⇒ 00:22:21.599 Amber Lin: Right. Thanks. Annie.
248 00:22:21.600 ⇒ 00:22:22.650 Luke Daque: There you go!
249 00:22:22.650 ⇒ 00:22:23.420 Annie Yu: Thanks.
250 00:22:27.460 ⇒ 00:22:33.510 Amber Lin: Okay, Luke, let’s go look at the synthetic data.
251 00:22:37.810 ⇒ 00:22:41.010 Amber Lin: How is the progress with the audit logs?
252 00:22:42.090 ⇒ 00:22:48.449 Luke Daque: I’m still working on it at the moment, because, like, I don’t know already going for that.
253 00:22:48.620 ⇒ 00:22:53.709 Luke Daque: So yeah, I’ll be. I should be able to get something by the end of the the day.
254 00:22:54.480 ⇒ 00:23:05.430 Amber Lin: Hmm, okay, for I think after that, do you know how we are going to estimate the session times.
255 00:23:08.940 ⇒ 00:23:13.270 Luke Daque: I’ll have to think about it. I’ll have to look into it like.
256 00:23:13.860 ⇒ 00:23:19.920 Luke Daque: Figure out how to do it. I don’t have any idea at the moment like how we can. Potentially.
257 00:23:21.090 ⇒ 00:23:22.360 Luke Daque: I see.
258 00:23:23.573 ⇒ 00:23:34.980 Amber Lin: Last time we I know we looked at the chat Gpt response together. It did say that we can infer clustering of the different of the different times.
259 00:23:37.185 ⇒ 00:23:40.452 Luke Daque: But yeah, but I think that that’s
260 00:23:42.520 ⇒ 00:23:49.439 Luke Daque: not sure if half that’s going to be very accurate in real life, I guess, for now, in synthetic data, we can
261 00:23:49.560 ⇒ 00:23:51.149 Luke Daque: do that. But.
262 00:23:51.460 ⇒ 00:23:59.999 Luke Daque: like I mentioned, if, like an activity was just opening the file and then nothing else, then we wouldn’t know how long it took.
263 00:24:00.820 ⇒ 00:24:02.740 Luke Daque: Then use it.
264 00:24:02.740 ⇒ 00:24:05.219 Luke Daque: Tool, right? Because it’s only one activity.
265 00:24:05.791 ⇒ 00:24:09.580 Luke Daque: It makes sense. If there’s like multiple activities for.
266 00:24:11.630 ⇒ 00:24:18.600 Luke Daque: For a certain event like they open a file, and then 10 min later, they modify the file. Then we would be able to know that
267 00:24:18.830 ⇒ 00:24:19.449 Luke Daque: they use.
268 00:24:19.450 ⇒ 00:24:22.729 Amber Lin: Support for at least 10 min or something, I see.
269 00:24:23.520 ⇒ 00:24:32.550 Amber Lin: Yeah, I agree. I think that’s the is that the only case where we’re less inaccurate is just when they have a single activity.
270 00:24:32.650 ⇒ 00:24:35.209 Amber Lin: And that’s the only event that we see.
271 00:24:36.990 ⇒ 00:24:42.370 Luke Daque: If based on the time duration, most likely or
272 00:24:42.530 ⇒ 00:24:47.569 Luke Daque: like. Even if there are 3 events, we wouldn’t know how long the 3rd event, or the the last event
273 00:24:48.270 ⇒ 00:24:53.070 Luke Daque: took place, like how or how long the duration was for the last event.
274 00:24:54.466 ⇒ 00:25:01.620 Amber Lin: I see, I think I guess, for onedrive there’s a few activities right? They can modify it. They can
275 00:25:01.860 ⇒ 00:25:13.350 Amber Lin: to like anything related to modifying, we probably would be able to estimate pretty accurately. I think anything related to, opened and viewed will have a
276 00:25:13.510 ⇒ 00:25:15.600 Amber Lin: have a bigger problem.
277 00:25:16.301 ⇒ 00:25:18.960 Amber Lin: With S, with estimating right.
278 00:25:19.850 ⇒ 00:25:20.670 Luke Daque: Yeah.
279 00:25:23.860 ⇒ 00:25:24.620 Amber Lin: Oh!
280 00:25:29.243 ⇒ 00:25:34.399 Luke Daque: Try to. I’ll try to create something, and then let’s see what it looks like, and then
281 00:25:34.720 ⇒ 00:25:35.849 Luke Daque: go from there.
282 00:25:36.930 ⇒ 00:25:42.559 Amber Lin: Okay, yeah. Let’s finish up the synthetic data one.
283 00:25:42.760 ⇒ 00:25:48.169 Amber Lin: And then mostly for, like, I, I feel like we’re a bit stuck on the modeling.
284 00:25:50.430 ⇒ 00:25:58.260 Amber Lin: That’s you know what we originally had for the 2 synthetic data sets for co-pilot and like onedrive.
285 00:25:58.650 ⇒ 00:26:02.809 Amber Lin: I don’t think we’re able to do all the filters
286 00:26:02.910 ⇒ 00:26:06.287 Amber Lin: that we currently have with say, like email,
287 00:26:07.350 ⇒ 00:26:09.889 Amber Lin: with the data we currently have right.
288 00:26:11.360 ⇒ 00:26:12.330 Luke Daque: What do you mean?
289 00:26:13.554 ⇒ 00:26:21.810 Amber Lin: So you know, we had. You made the synthetic data sets for copilot and office 3, 65, right.
290 00:26:21.810 ⇒ 00:26:22.810 Luke Daque: Right, yeah.
291 00:26:22.810 ⇒ 00:26:23.949 Amber Lin: Yeah. Well,
292 00:26:24.520 ⇒ 00:26:28.169 Luke Daque: Last. Activity, right? Something like that.
293 00:26:28.170 ⇒ 00:26:28.710 Amber Lin: Oh!
294 00:26:28.710 ⇒ 00:26:31.839 Luke Daque: It wouldn’t also have duration, right? Thing.
295 00:26:32.600 ⇒ 00:26:35.400 Amber Lin: How is that different from the emails?
296 00:26:39.120 ⇒ 00:26:41.830 Luke Daque: The the current one that we have.
297 00:26:42.060 ⇒ 00:26:43.170 Amber Lin: Yeah.
298 00:26:50.000 ⇒ 00:26:52.430 Luke Daque: Wait! Let me check.
299 00:26:53.390 ⇒ 00:26:56.170 Luke Daque: You mean how the duration of the email.
300 00:26:57.468 ⇒ 00:27:10.280 Amber Lin: I guess how the how we were able to apply all those filters on email like, are we able to apply all those filters on these new synthetic data sets as well.
301 00:27:12.240 ⇒ 00:27:16.960 Luke Daque: Like email, you mean the user which user email or something like that.
302 00:27:17.240 ⇒ 00:27:34.599 Amber Lin: Yeah, like, right now, we just looked at power bi, we can look at. Okay, it’s a our weekday, like day hour of day, day a week. Or look at who this person is, what department they’re from, etc. like, are we able to do all of that with the synthetic data, the new syntax data. We have.
303 00:27:35.520 ⇒ 00:27:41.670 Luke Daque: We should still have email, though I mean the the user because of the of the
304 00:27:42.190 ⇒ 00:27:48.820 Luke Daque: yeah. Oh, click, last activity of the user for one running or last activity of the user for outlook.
305 00:27:49.810 ⇒ 00:27:50.460 Amber Lin: Okay.
306 00:27:50.460 ⇒ 00:27:51.100 Luke Daque: Sorry.
307 00:27:51.440 ⇒ 00:27:55.679 Amber Lin: Okay. So looking at this, I’m looking at, let me share my screen.
308 00:27:56.220 ⇒ 00:28:04.340 Amber Lin: Yeah, yeah, I think, based on this anything of user related like all of
309 00:28:04.620 ⇒ 00:28:11.540 Amber Lin: all of these, should be possible. Right? Cause we do have. Okay, we don’t do. We have hour of day when that happened.
310 00:28:12.987 ⇒ 00:28:20.749 Luke Daque: Let me check. I can’t quite remember we’ve got logged out.
311 00:28:24.010 ⇒ 00:28:28.460 Luke Daque: so let me open one of them. Maybe maybe one drive.
312 00:28:29.370 ⇒ 00:28:31.300 Luke Daque: So, onedrive.
313 00:28:31.810 ⇒ 00:28:32.880 Luke Daque: We have a
314 00:28:38.030 ⇒ 00:28:41.450 Luke Daque: no, it’s just it’s just date, not the hour.
315 00:28:41.990 ⇒ 00:28:43.250 Luke Daque: So we we wouldn’t.
316 00:28:43.250 ⇒ 00:28:43.870 Amber Lin: See.
317 00:28:43.870 ⇒ 00:28:49.200 Luke Daque: We only can get day of week and maybe period office mandate. But then.
318 00:28:49.200 ⇒ 00:28:49.730 Amber Lin: I see.
319 00:28:49.730 ⇒ 00:28:50.900 Luke Daque: Won’t have it.
320 00:28:51.602 ⇒ 00:28:59.589 Amber Lin: But we were able to have all the primary and secondary segments right? Cause? That’s only dependent on a user. Id. Okay, that’s awesome.
321 00:28:59.590 ⇒ 00:29:00.690 Amber Lin: Inside the screen.
322 00:29:00.800 ⇒ 00:29:02.050 Luke Daque: Email you.
323 00:29:02.961 ⇒ 00:29:12.869 Amber Lin: I guess then, maybe, for if we get the audit logs we would be able to get the hour of day right? Cause that’s a timestamp.
324 00:29:13.590 ⇒ 00:29:17.349 Luke Daque: Yeah, but it looks like I’m just looking at this at the moment. It doesn’t, doesn’t.
325 00:29:17.680 ⇒ 00:29:20.930 Luke Daque: Show duration. So like
326 00:29:22.330 ⇒ 00:29:30.769 Luke Daque: we shouldn’t be sure. I mean, it doesn’t matter if we show if we calculate the duration of the tool right, because it’s not one of the requirements.
327 00:29:31.820 ⇒ 00:29:38.510 Amber Lin: Yeah, I agree. I I just thought of as your your show. Right? I think we could. Let’s just do account
328 00:29:38.670 ⇒ 00:29:55.949 Amber Lin: by for now. And let’s think I think we still need to get the audit logs right. We need to get the audit logs. And then let’s model this just based on count of activities like, I know, it’s not that accurate, but we can say activity.
329 00:29:55.950 ⇒ 00:30:10.809 Amber Lin: We count one as activity if they opened it, or they made like, however, many modifications. So how many ever the whatever rows in the audit log we have for the different like user events, I guess we can use that as account.
330 00:30:11.740 ⇒ 00:30:12.380 Luke Daque: Yeah.
331 00:30:13.130 ⇒ 00:30:19.120 Amber Lin: Yeah, I think that will make your life a lot easier. And then we can worry about the duration later.
332 00:30:19.920 ⇒ 00:30:21.750 Luke Daque: Yeah, sure, I mean.
333 00:30:21.750 ⇒ 00:30:22.390 Amber Lin: Yes.
334 00:30:23.700 ⇒ 00:30:30.099 Luke Daque: Yeah, if I’m going to create this incentive, Beta for audit logs, I should already also include
335 00:30:30.884 ⇒ 00:30:36.719 Luke Daque: not just onedrive right? Cause I was when I did the research I was just looking at onedrive
336 00:30:37.396 ⇒ 00:30:42.450 Luke Daque: but should I also like add outlook and teams? And what.
337 00:30:42.450 ⇒ 00:30:50.660 Amber Lin: Oh, how? Yeah, maybe. How would that make it take more time for you?
338 00:30:52.340 ⇒ 00:30:52.690 Amber Lin: One time.
339 00:30:52.690 ⇒ 00:30:54.399 Luke Daque: How much more time, what that would take.
340 00:30:55.130 ⇒ 00:31:00.419 Luke Daque: I’ll have to look at all the objects, the event types basically, like
341 00:31:00.580 ⇒ 00:31:07.610 Luke Daque: in in onedrive. It’s we are trying to infer that if a file is open, then it’s probably onedrive, because there’s.
342 00:31:07.610 ⇒ 00:31:07.935 Amber Lin: Hmm.
343 00:31:09.030 ⇒ 00:31:14.010 Luke Daque: Nothing else, but maybe for outlook. I don’t know. Maybe it’s something else.
344 00:31:14.010 ⇒ 00:31:28.160 Amber Lin: Oh, yeah, you’re right. Maybe for outlook. It will have. Okay. User started to write email, user, like, sent email, like between the start and sent. That could be something. But
345 00:31:28.920 ⇒ 00:31:37.030 Amber Lin: all right, let’s you know, what does that? Does the audit logs also have copilot data.
346 00:31:38.190 ⇒ 00:31:41.870 Luke Daque: I’ll have to check as well like it will depend on like what
347 00:31:42.690 ⇒ 00:31:45.620 Luke Daque: what event occurred. Right? I’m not sure like
348 00:31:46.590 ⇒ 00:31:51.010 Luke Daque: what that would look like for co-pilot like. So, yeah.
349 00:31:51.450 ⇒ 00:32:02.760 Amber Lin: Okay, I see. I think it’s real. It’s a great idea to also do it for outlook. That’s outlook will be emails.
350 00:32:02.930 ⇒ 00:32:07.280 Amber Lin: Teams would be. Do they send messages in teams as well.
351 00:32:08.980 ⇒ 00:32:12.089 Luke Daque: Teams is like slack.
352 00:32:13.020 ⇒ 00:32:13.500 Amber Lin: Okay.
353 00:32:13.500 ⇒ 00:32:14.860 Luke Daque: Oh, he has!
354 00:32:15.930 ⇒ 00:32:16.490 Luke Daque: That’s.
355 00:32:16.490 ⇒ 00:32:21.929 Amber Lin: Where? Where do they do meetings? I thought they also do meetings and teams right.
356 00:32:24.820 ⇒ 00:32:25.850 Luke Daque: I guess.
357 00:32:28.370 ⇒ 00:32:30.510 Luke Daque: Guess outlook still, or like.
358 00:32:31.450 ⇒ 00:32:34.929 Amber Lin: I think outlook is just a calendar, and E for emails.
359 00:32:35.660 ⇒ 00:32:38.090 Luke Daque: Outlook is like Gmail, where you can send emails.
360 00:32:39.480 ⇒ 00:32:44.560 Luke Daque: create meetings through the calendar teams is like slack where you can
361 00:32:44.700 ⇒ 00:32:49.290 Luke Daque: chat, and you can also like, do huddles like in slack, which.
362 00:32:49.290 ⇒ 00:32:49.710 Amber Lin: Yeah.
363 00:32:49.710 ⇒ 00:32:51.669 Luke Daque: And also via meeting, I guess.
364 00:32:52.282 ⇒ 00:33:13.800 Amber Lin: I think, for outlook. You don’t do meetings and outlook you. You can book it. It’s just like this, like I can book something in. Add a link, but they probably meet in teams. Okay, let’s do. Let’s do. I’ll put these in the backlog. I’ll put outlook and teams in the backlog. Let’s try to do these 2 1st
365 00:33:15.626 ⇒ 00:33:20.090 Amber Lin: to get the synthetic data, and I’ll make another ticket.
366 00:33:20.090 ⇒ 00:33:20.670 Luke Daque: Yes.
367 00:33:21.454 ⇒ 00:33:30.029 Amber Lin: For like estimating durations of onedrive activities, we probably.
368 00:33:30.210 ⇒ 00:33:34.150 Amber Lin: if we’re successful, we can probably use the same method.
369 00:33:43.640 ⇒ 00:33:45.090 Luke Daque: Is that free?
370 00:33:45.420 ⇒ 00:33:50.130 Luke Daque: I I Hello! I think I’m not sure if I got cut off or you got cut off.
371 00:33:53.080 ⇒ 00:33:54.850 Luke Daque: Are you still there. Amber.
372 00:33:57.770 ⇒ 00:33:58.590 Luke Daque: Hello!
373 00:34:13.870 ⇒ 00:34:15.150 Luke Daque: Hello! Hello! Hello!
374 00:34:15.150 ⇒ 00:34:16.400 Amber Lin: Hello! Sorry I got.
375 00:34:16.400 ⇒ 00:34:16.979 Luke Daque: I feel you.
376 00:34:16.980 ⇒ 00:34:18.149 Amber Lin: Go for coffee.
377 00:34:19.900 ⇒ 00:34:20.460 Luke Daque: Problem.
378 00:34:21.190 ⇒ 00:34:24.489 Amber Lin: Yeah. Sorry. Duration.
379 00:34:27.820 ⇒ 00:34:34.889 Amber Lin: Duration of emails, duration of chat.
380 00:34:35.400 ⇒ 00:34:37.730 Luke Daque: That’s not like part of the requirements, is it?
381 00:34:37.929 ⇒ 00:34:40.756 Amber Lin: Nope, that’s why I’m putting it for later.
382 00:34:41.110 ⇒ 00:34:41.659 Luke Daque: Okay.
383 00:34:43.090 ⇒ 00:34:44.479 Luke Daque: To say, I was thinking.
384 00:34:45.399 ⇒ 00:34:45.939 Amber Lin: Go ahead.
385 00:34:45.940 ⇒ 00:34:50.360 Luke Daque: We don’t need duration at the moment. Then maybe we can utilize the
386 00:34:50.570 ⇒ 00:34:52.940 Luke Daque: synthetic data that I already created.
387 00:34:53.139 ⇒ 00:34:58.970 Luke Daque: although the cons there is that we wouldn’t be able to drill down to the hour.
388 00:34:59.280 ⇒ 00:35:01.120 Luke Daque: But that’s about it.
389 00:35:02.030 ⇒ 00:35:05.376 Amber Lin: Yeah, I, I think,
390 00:35:06.940 ⇒ 00:35:14.750 Luke Daque: Because I’m thinking, maybe it’s going to take way too long to create synthetic data for audit logs when we already have
391 00:35:14.910 ⇒ 00:35:19.780 Luke Daque: the activity user activity data from the others.
392 00:35:20.460 ⇒ 00:35:25.649 Luke Daque: But yeah, that’s the only cons we don’t. We won’t be able to drill it down at the end of the hour.
393 00:35:26.100 ⇒ 00:35:35.459 Amber Lin: I see. My, my thought process is that we’re we’ll we’ll need durations later, especially they if they want to look at focus times.
394 00:35:35.850 ⇒ 00:35:36.600 Luke Daque: Yeah.
395 00:35:36.600 ⇒ 00:35:45.349 Amber Lin: They? They will want to look at. Okay, like, what? What time were they? Let me pull that pull up there
396 00:35:45.820 ⇒ 00:35:47.130 Amber Lin: image.
397 00:35:50.596 ⇒ 00:36:01.079 Amber Lin: Whereas trying to find their focus. Yeah, like, I think they will want to see this and having the audit logs will help us
398 00:36:01.280 ⇒ 00:36:06.690 Amber Lin: like, look at. Okay, during what time were they working? On which one.
399 00:36:07.690 ⇒ 00:36:08.820 Luke Daque: Yeah, that makes sense.
400 00:36:09.090 ⇒ 00:36:25.710 Amber Lin: Yeah. So we will need auto locks eventually. I just think we can survive without having it for emails, chats and meetings for now. But we probably do need it for
401 00:36:25.860 ⇒ 00:36:29.550 Amber Lin: this, or at least one drive.
402 00:36:30.650 ⇒ 00:36:31.400 Luke Daque: Okay.
403 00:36:31.820 ⇒ 00:36:36.030 Amber Lin: Yeah. And hopefully, hopefully, by next, I
404 00:36:37.790 ⇒ 00:36:42.599 Amber Lin: thank you. I really don’t know when we’re gonna get the data set. But probably for
405 00:36:43.210 ⇒ 00:36:48.679 Amber Lin: next week we’ll get the client data. So you don’t have to make the synthetic data sets.
406 00:36:49.260 ⇒ 00:36:53.570 Amber Lin: But at least let’s get. Let’s get like onedrive down.
407 00:36:54.350 ⇒ 00:36:55.260 Luke Daque: Okay.
408 00:36:55.260 ⇒ 00:36:59.515 Amber Lin: Yeah, if you can do copilot, that will be great as well.
409 00:37:01.060 ⇒ 00:37:05.026 Luke Daque: Yeah, maybe I’ll do it one by one, like onedrive, for now and then
410 00:37:05.640 ⇒ 00:37:10.269 Luke Daque: spend another like a few like hours to research on co-pilot.
411 00:37:11.430 ⇒ 00:37:15.539 Luke Daque: Events, because at least for onedrive, we already
412 00:37:15.790 ⇒ 00:37:19.149 Luke Daque: a base of events to inference.
413 00:37:20.400 ⇒ 00:37:24.879 Amber Lin: Okay, let’s finish. Let’s finish up the
414 00:37:25.220 ⇒ 00:37:27.390 Amber Lin: I’ll put this as next cycle.
415 00:37:27.700 ⇒ 00:37:35.980 Amber Lin: Let’s finish. Where is it? Finish this one for audit logs, and then let’s do the modeling for
416 00:37:36.180 ⇒ 00:37:42.350 Amber Lin: for that, for one. Just say onedrive for onedrive.
417 00:37:43.220 ⇒ 00:37:57.560 Amber Lin: That would just be to like, I guess, join the user Id, with all of the other data, I think that would. I don’t know how long that would take it to me. It sounds like it’s just a join. And then Annie can like add it to the power. Bi.
418 00:37:57.990 ⇒ 00:38:02.990 Amber Lin: Yeah, how’s that? Okay, let me scoot this scoot this one later.
419 00:38:03.541 ⇒ 00:38:10.039 Amber Lin: Do you think you can make the send the data for audit logs like, finish that by today.
420 00:38:11.010 ⇒ 00:38:13.469 Luke Daque: Yeah, I can. I can work on that today.
421 00:38:13.470 ⇒ 00:38:17.569 Amber Lin: Okay, okay. Do you think like joining
422 00:38:17.960 ⇒ 00:38:24.370 Amber Lin: joining the your user ids with our current like tables like, how long that would take.
423 00:38:25.651 ⇒ 00:38:38.319 Luke Daque: If I can do the synthetic data earlier today, I can work on the models like the communication events, to add the tool usage for onedrive. If not, then maybe maybe tomorrow, or
424 00:38:38.950 ⇒ 00:38:40.189 Luke Daque: or something like that.
425 00:38:40.890 ⇒ 00:38:58.279 Amber Lin: Yeah, I I’m thinking hopefully, we can like before Annie’s morning tomorrow. So you still have some time to work on it. Like, if this really turn like drags over to not drag over like needs tomorrow as well like, do you? Can we get it done before Annie gets online?
426 00:38:58.770 ⇒ 00:39:01.959 Luke Daque: Yeah. Sure. What time does annual usually go online?
427 00:39:02.568 ⇒ 00:39:06.220 Amber Lin: So we’re probably 8 am. Pst.
428 00:39:06.520 ⇒ 00:39:08.000 Luke Daque: Okay. Gotcha.
429 00:39:08.000 ⇒ 00:39:16.429 Amber Lin: So that would be like probably 3 h before before now. Ish like 2, 3 h before now.
430 00:39:16.970 ⇒ 00:39:18.720 Luke Daque: Okay. Gotcha.
431 00:39:19.030 ⇒ 00:39:19.440 Amber Lin: Yeah.
432 00:39:19.440 ⇒ 00:39:20.000 Luke Daque: Yeah.
433 00:39:21.360 ⇒ 00:39:29.109 Amber Lin: Okay, join log data with.
434 00:39:30.080 ⇒ 00:39:34.060 Amber Lin: But let me.
435 00:39:38.820 ⇒ 00:39:43.030 Amber Lin: Okay. Alright. Let’s focus on these 2. I will
436 00:39:43.160 ⇒ 00:39:46.340 Amber Lin: like, I think, once we do these. We’ll have more
437 00:39:46.670 ⇒ 00:39:52.400 Amber Lin: more time to look at the other ones. I’ll just I put them later. So you don’t have to worry about it now.
438 00:39:53.280 ⇒ 00:39:54.159 Luke Daque: No, it’s good.
439 00:39:54.160 ⇒ 00:39:54.529 Amber Lin: Okay.
440 00:39:55.130 ⇒ 00:39:56.029 Amber Lin: Thank you.
441 00:40:00.520 ⇒ 00:40:01.420 Luke Daque: Bye-bye.
442 00:40:01.650 ⇒ 00:40:03.040 Amber Lin: Alrighty! Bye-bye.