Meeting Title: MatterMore | internal Standup Date: 2025-06-23 Meeting participants: Amber Lin, Luke Daque, Awaish Kumar, Annie Yu
WEBVTT
1 00:00:52.190 ⇒ 00:00:53.180 Awaish Kumar: Hello!
2 00:00:54.750 ⇒ 00:00:55.820 Amber Lin: Hi!
3 00:00:57.020 ⇒ 00:00:59.049 Amber Lin: Think Annie will be here soon.
4 00:01:09.160 ⇒ 00:01:12.920 Awaish Kumar: Correct no.
5 00:01:12.920 ⇒ 00:01:13.760 Amber Lin: Everyone.
6 00:01:14.065 ⇒ 00:01:14.370 Awaish Kumar: No!
7 00:01:15.570 ⇒ 00:01:17.169 Amber Lin: Sorry. I wish you were saying.
8 00:01:17.910 ⇒ 00:01:21.079 Awaish Kumar: I have a question for Luke. Actually, I
9 00:01:21.520 ⇒ 00:01:26.710 Awaish Kumar: I see, Luke, that you replied about the calm.
10 00:01:27.730 ⇒ 00:01:31.219 Awaish Kumar: the time zone field for the employees.
11 00:01:31.630 ⇒ 00:01:38.020 Awaish Kumar: But there’s 1 more question here about the date field in this
12 00:01:38.140 ⇒ 00:01:43.099 Awaish Kumar: communication image table. Is that possible to add that as well? Or
13 00:01:44.120 ⇒ 00:01:46.340 Awaish Kumar: are we getting that from Endpoint?
14 00:01:46.340 ⇒ 00:01:47.010 Luke Daque: Good.
15 00:01:48.180 ⇒ 00:01:49.989 Luke Daque: Which field are you referring to?
16 00:01:52.030 ⇒ 00:01:56.039 Awaish Kumar: Like, I’m referring to the slack message we were just talking in.
17 00:01:56.540 ⇒ 00:01:57.240 Luke Daque: Yeah.
18 00:01:58.650 ⇒ 00:01:59.560 Awaish Kumar: So like.
19 00:02:00.510 ⇒ 00:02:07.049 Awaish Kumar: So in the communication events table like. And he says it would be nice if we can have a date field
20 00:02:07.520 ⇒ 00:02:10.210 Awaish Kumar: instead of like a month of the year
21 00:02:10.430 ⇒ 00:02:14.930 Awaish Kumar: quarter of the year. If we have, we can have that feel.
22 00:02:15.270 ⇒ 00:02:19.640 Awaish Kumar: if it, if that is possible, to get from endpoint. That’s my question.
23 00:02:20.300 ⇒ 00:02:25.590 Luke Daque: Yep, we can do that mostly already. Have that August. It’s broken down by
24 00:02:26.941 ⇒ 00:02:31.580 Luke Daque: yeah, they agree. Yeah, we can. We can use the date.
25 00:02:32.620 ⇒ 00:02:35.740 Luke Daque: Wait! Let me just double check.
26 00:02:35.960 ⇒ 00:02:41.039 Awaish Kumar: Yeah, I can see in your screenshot there is a field called Create a daytime.
27 00:02:42.380 ⇒ 00:02:44.440 Luke Daque: Yeah, you can use that.
28 00:02:47.950 ⇒ 00:02:51.840 Awaish Kumar: So that what is that created of like is that.
29 00:02:51.840 ⇒ 00:02:59.249 Luke Daque: I can use this start time there in the start date or start time.
30 00:02:59.250 ⇒ 00:02:59.860 Awaish Kumar: Okay.
31 00:03:00.810 ⇒ 00:03:05.839 Luke Daque: You can use that as the Dave instantaneous, because that’s the date when the
32 00:03:05.950 ⇒ 00:03:09.679 Luke Daque: email or like when the meeting or the call happened.
33 00:03:10.200 ⇒ 00:03:11.510 Luke Daque: The start time.
34 00:03:13.830 ⇒ 00:03:14.600 Awaish Kumar: Okay.
35 00:03:22.950 ⇒ 00:03:26.960 Luke Daque: We’re getting so.
36 00:03:29.130 ⇒ 00:03:34.129 Amber Lin: Do I need to make another a ticket for that? And if so, what would that look like.
37 00:03:34.710 ⇒ 00:03:40.675 Awaish Kumar: But, like the what? What are where are these coming from? Month of year, quarter of year, like so.
38 00:03:40.990 ⇒ 00:03:43.710 Luke Daque: Bye, start plan.
39 00:03:43.710 ⇒ 00:03:44.350 Awaish Kumar: Okay.
40 00:03:44.810 ⇒ 00:03:49.484 Luke Daque: Yeah, that’s just a breakdown of the start time. I did it that way, because that was the
41 00:03:50.490 ⇒ 00:03:51.100 Awaish Kumar: Requirement.
42 00:03:51.100 ⇒ 00:03:57.339 Luke Daque: Requirement right from the core metrics. So we wanted, yeah, okay, so.
43 00:03:57.340 ⇒ 00:03:58.100 Awaish Kumar: So.
44 00:03:58.460 ⇒ 00:03:59.180 Luke Daque: There you go!
45 00:03:59.180 ⇒ 00:04:05.059 Awaish Kumar: So I think it would be nice if you can also just put the start.
46 00:04:06.110 ⇒ 00:04:08.169 Awaish Kumar: Yeah, it’s already there, and best appreciate you.
47 00:04:08.170 ⇒ 00:04:08.600 Luke Daque: Okay.
48 00:04:08.600 ⇒ 00:04:09.500 Awaish Kumar: The model.
49 00:04:09.840 ⇒ 00:04:11.570 Awaish Kumar: Okay? So provision.
50 00:04:12.160 ⇒ 00:04:12.720 Luke Daque: Yeah.
51 00:04:14.400 ⇒ 00:04:19.719 Awaish Kumar: Okay. Then it’s just just to let any know that it’s there. And she can.
52 00:04:19.720 ⇒ 00:04:20.060 Awaish Kumar: Yeah.
53 00:04:21.091 ⇒ 00:04:30.589 Annie Yu: That one is a timestamp and in power bi the the granular I can get with the timestamp would be month.
54 00:04:30.770 ⇒ 00:04:35.100 Annie Yu: so I think, to show the accurate trend
55 00:04:35.210 ⇒ 00:04:38.230 Annie Yu: we will need the date. Does that make sense.
56 00:04:39.096 ⇒ 00:04:42.030 Luke Daque: So we need I can.
57 00:04:42.030 ⇒ 00:04:46.850 Luke Daque: I just need to just like casted as date. Then, yeah, yeah.
58 00:04:47.340 ⇒ 00:04:48.320 Amber Lin: Oh, awesome!
59 00:04:48.320 ⇒ 00:04:50.089 Luke Daque: Start date, maybe. Do it.
60 00:04:50.090 ⇒ 00:04:54.419 Annie Yu: Okay, so is that start time, local time.
61 00:04:55.610 ⇒ 00:04:57.890 Luke Daque: It is.
62 00:04:59.250 ⇒ 00:05:03.440 Annie Yu: Cause. I think we probably need local time.
63 00:05:04.020 ⇒ 00:05:11.349 Annie Yu: and I guess what I’m asking is for months of year and months. All that are they based on local time.
64 00:05:13.850 ⇒ 00:05:16.369 Luke Daque: Let me check.
65 00:05:17.070 ⇒ 00:05:21.259 Luke Daque: It’s like it. It’s it’s coming from each of the
66 00:05:22.200 ⇒ 00:05:31.280 Luke Daque: event table, right? For example, emails. The start time would be the sent at and I don’t think.
67 00:05:32.240 ⇒ 00:05:33.780 Annie Yu: Think it’s all you?
68 00:05:33.780 ⇒ 00:05:34.839 Annie Yu: PC, right?
69 00:05:35.150 ⇒ 00:05:38.980 Luke Daque: Yeah, I think it’s Utc like, if it’s less than that.
70 00:05:39.270 ⇒ 00:05:41.289 Luke Daque: And then if it’s the.
71 00:05:41.780 ⇒ 00:05:48.659 Luke Daque: if it’s message, then start. Time is that created objects also looks like it’s also.
72 00:05:53.140 ⇒ 00:05:54.140 Luke Daque: yeah.
73 00:05:55.300 ⇒ 00:06:01.750 Amber Lin: Okay. I made a ticket to one cast. Start time timestamp as date.
74 00:06:01.860 ⇒ 00:06:08.690 Amber Lin: and then I’ll make another ticket to convert all of the current time logs as a local time.
75 00:06:09.890 ⇒ 00:06:13.899 Awaish Kumar: But like do we like? Have we confirmed that with the matter? Mode.
76 00:06:18.740 ⇒ 00:06:20.059 Awaish Kumar: great question.
77 00:06:20.060 ⇒ 00:06:22.189 Amber Lin: They have not gotten back to me.
78 00:06:23.530 ⇒ 00:06:30.090 Awaish Kumar: Yeah. But like, so I I like, I’m not sure like, if if that really matters. Because, for example.
79 00:06:30.360 ⇒ 00:06:34.010 Awaish Kumar: right now for me, it’s it’s around 10 pm.
80 00:06:34.300 ⇒ 00:06:42.310 Awaish Kumar: But if Brain Forge wants to analyze that, if I’m in what time I’m more active or
81 00:06:42.730 ⇒ 00:06:48.619 Awaish Kumar: part of our like productive like, if they see in the
82 00:06:49.330 ⇒ 00:06:55.810 Awaish Kumar: est time zone like it’s it’s for them. It’s 1 Pm. So they can just get it like in the est time zone
83 00:06:55.930 ⇒ 00:06:59.050 Awaish Kumar: which is productive between 7 Pm. To 9 Pm.
84 00:06:59.520 ⇒ 00:07:00.210 Awaish Kumar: So.
85 00:07:01.600 ⇒ 00:07:04.209 Awaish Kumar: Why local time zone matters that much.
86 00:07:04.400 ⇒ 00:07:04.930 Awaish Kumar: I’m.
87 00:07:04.930 ⇒ 00:07:05.280 Amber Lin: True.
88 00:07:05.855 ⇒ 00:07:06.430 Awaish Kumar: Sure!
89 00:07:06.430 ⇒ 00:07:23.569 Amber Lin: That’s a true point. I’m gonna put that into the backlog, I think, for now it should like Annie. What would change if how much effort do you need after it’s cast in local time? It wouldn’t change much in the visualizations right.
90 00:07:23.570 ⇒ 00:07:28.419 Annie Yu: I don’t think so. Yeah, I think we just a refresh, and then I.
91 00:07:28.420 ⇒ 00:07:40.930 Amber Lin: I see, I see. So this wouldn’t be a too big of a task after, even if we’ve got the client data. Luke, how much would this be on your end if we needed to convert everything to local time?
92 00:07:41.390 ⇒ 00:07:44.390 Luke Daque: What should? Oh, you mean local time.
93 00:07:46.308 ⇒ 00:07:48.191 Luke Daque: Let me see.
94 00:07:50.170 ⇒ 00:07:55.389 Luke Daque: Yeah, I can probably do that today on the day I should be able to do go by.
95 00:07:55.390 ⇒ 00:07:57.180 Awaish Kumar: But this but.
96 00:07:57.180 ⇒ 00:07:58.359 Amber Lin: Oh, no, I just I just.
97 00:07:58.360 ⇒ 00:07:58.700 Awaish Kumar: Check.
98 00:07:59.790 ⇒ 00:08:00.480 Amber Lin: I’m sorry.
99 00:08:00.480 ⇒ 00:08:06.490 Awaish Kumar: Question, is this original start time zone field, which is coming from the Api endpoint.
100 00:08:06.860 ⇒ 00:08:09.699 Awaish Kumar: So this gives us the
101 00:08:10.300 ⇒ 00:08:18.690 Awaish Kumar: time zone of like this gives us the time in each employee’s time zone. Or is this going to give us the standard time zone result.
102 00:08:19.810 ⇒ 00:08:22.230 Amber Lin: I think that’s a utc right, as Luke said.
103 00:08:25.550 ⇒ 00:08:26.540 Awaish Kumar: It’s question for Luke.
104 00:08:26.540 ⇒ 00:08:31.900 Luke Daque: We also have a I think we also have a time zone field from the users table.
105 00:08:32.179 ⇒ 00:08:36.230 Luke Daque: so we can use that to convert whatever
106 00:08:37.743 ⇒ 00:08:38.770 Amber Lin: I see.
107 00:08:38.950 ⇒ 00:08:42.719 Luke Daque: Okay, from like emails to whatever their time zone is based on.
108 00:08:42.720 ⇒ 00:08:44.461 Amber Lin: I see, I see. Okay,
109 00:08:44.910 ⇒ 00:08:49.060 Amber Lin: current. Currently, I’m gonna leave it out
110 00:08:49.220 ⇒ 00:08:55.060 Amber Lin: to convert all time to local time. I’m gonna leave it out because it sounds like we just need to use the time zone.
111 00:08:56.150 ⇒ 00:08:58.630 Amber Lin: Column to convert.
112 00:08:59.278 ⇒ 00:09:02.210 Amber Lin: I think currently, if you guys can.
113 00:09:02.210 ⇒ 00:09:03.320 Awaish Kumar: Bye, bye.
114 00:09:03.320 ⇒ 00:09:05.940 Amber Lin: To just sorry.
115 00:09:05.940 ⇒ 00:09:08.900 Awaish Kumar: Right now. We don’t have a clarity on
116 00:09:09.130 ⇒ 00:09:12.060 Awaish Kumar: what time these fields are showing.
117 00:09:12.200 ⇒ 00:09:14.469 Awaish Kumar: and I would like to confirm that.
118 00:09:20.670 ⇒ 00:09:25.160 Amber Lin: Okay. I thought they were in Utc. But we can confirm that.
119 00:09:26.920 ⇒ 00:09:31.100 Awaish Kumar: Because that’s going to change what we are going to say to client.
120 00:09:39.280 ⇒ 00:09:47.460 Amber Lin: Alright, I’m gonna make a ticket for that. Just a quick confirmation. What are the areas you want to confirm? So for all
121 00:09:48.210 ⇒ 00:09:52.000 Amber Lin: for email, chat.
122 00:09:52.000 ⇒ 00:09:54.940 Awaish Kumar: Yeah, all the date fields which we are using
123 00:09:55.080 ⇒ 00:09:58.760 Awaish Kumar: month, year, or any time zone field which is coming in
124 00:09:58.950 ⇒ 00:10:04.759 Awaish Kumar: for all our models like are they coming in in the standardized format, like.
125 00:10:05.460 ⇒ 00:10:06.620 Awaish Kumar: Or
126 00:10:07.490 ⇒ 00:10:15.940 Awaish Kumar: or all the date fields are in Utc or est, or they are like, based on the employee time, zone.
127 00:10:21.590 ⇒ 00:10:28.240 Luke Daque: It’s in Utc. The event is in Utc, which is the email I’m checking there
128 00:10:29.850 ⇒ 00:10:34.370 Luke Daque: documentation. I’ll have to check. The others can’t remember everything.
129 00:10:35.260 ⇒ 00:10:39.460 Amber Lin: Okay, sounds good. I made a ticket. We can document everything there.
130 00:10:40.830 ⇒ 00:10:44.149 Luke Daque: Let me check this before, and then all right.
131 00:10:44.150 ⇒ 00:10:47.080 Amber Lin: Yeah. I remember, we checked sometime.
132 00:10:49.110 ⇒ 00:10:54.639 Luke Daque: Yeah, I think everything’s in Utc, and that’s why Annie wants the time zone
133 00:10:55.265 ⇒ 00:10:57.649 Luke Daque: of the user, because we also have the time
134 00:10:58.630 ⇒ 00:11:05.179 Luke Daque: in the users table. So we can convert all these Utc time zones. And whatever the time zone of the user is.
135 00:11:05.470 ⇒ 00:11:09.810 Luke Daque: I can add a field there in the communication events like us
136 00:11:10.030 ⇒ 00:11:14.789 Luke Daque: start date in user time zone, basically just converting the Upc
137 00:11:15.270 ⇒ 00:11:23.789 Luke Daque: start date to whatever the time zone. The user is that shouldn’t be a think of a.
138 00:11:24.990 ⇒ 00:11:30.960 Amber Lin: I see. I made a ticket to convert that. We can convert that
139 00:11:30.990 ⇒ 00:11:56.789 Amber Lin: later. It wouldn’t change too much in the visualizations. It will just do a refresh, and it’ll help us convert things over. Let’s just double check. Let’s for madame this ticket. Let’s double check. If every single date field is in Utc. I know we checked before. Let’s just we can maybe take a screenshot or just confirm that all of the date fields are in either local time or Utc.
140 00:11:57.050 ⇒ 00:12:02.370 Amber Lin: and we can talk about converting them to local time. Later.
141 00:12:03.370 ⇒ 00:12:04.040 Awaish Kumar: And at.
142 00:12:04.040 ⇒ 00:12:07.040 Awaish Kumar: So what I mean by confirming is.
143 00:12:07.200 ⇒ 00:12:13.060 Awaish Kumar: I know, like it’s a synthetic data. So we maybe have created all in you to see. But what.
144 00:12:13.160 ⇒ 00:12:16.349 Awaish Kumar: like all the Api endpoints, are going to send the
145 00:12:16.850 ⇒ 00:12:19.610 Awaish Kumar: the time zone in Utc like. That’s what we have to
146 00:12:19.760 ⇒ 00:12:22.029 Awaish Kumar: confirm. If you have already done that.
147 00:12:22.150 ⇒ 00:12:25.200 Awaish Kumar: That’s okay. I I was not there then.
148 00:12:25.540 ⇒ 00:12:28.280 Awaish Kumar: But yeah, that’s at least my concern.
149 00:12:31.330 ⇒ 00:12:32.330 Amber Lin: Totally.
150 00:12:34.620 ⇒ 00:12:39.310 Amber Lin: Let me add all of that to current cycle.
151 00:12:39.880 ⇒ 00:12:45.850 Amber Lin: Right? And I see that a few of these, sorry Annie, you you had a point earlier.
152 00:12:46.200 ⇒ 00:12:51.730 Annie Yu: Oh, yeah, I was gonna ask. So in the meantime, will we still add a date field.
153 00:12:53.327 ⇒ 00:12:56.729 Amber Lin: Yes, yes, I put it here.
154 00:12:57.170 ⇒ 00:12:57.780 Annie Yu: Okay.
155 00:12:59.390 ⇒ 00:13:03.080 Amber Lin: So we can do that today.
156 00:13:05.650 ⇒ 00:13:13.679 Amber Lin: looking at this current cycle, right? We have a few things in internal review. Do we know who’s reviewing them.
157 00:13:16.950 ⇒ 00:13:19.340 Amber Lin: or if any of them have been reviewed.
158 00:13:25.770 ⇒ 00:13:32.119 Amber Lin: I guess we’ll start with Luke’s tickets. Any of these you want the wish, or Annie to help you review.
159 00:13:35.680 ⇒ 00:13:39.370 Luke Daque: Which tickets are these with the synthetic data? I think.
160 00:13:39.370 ⇒ 00:13:40.030 Amber Lin: Yeah.
161 00:13:40.030 ⇒ 00:13:43.820 Luke Daque: Open that hasn’t renewed yet, which is the duty models.
162 00:13:44.270 ⇒ 00:13:46.199 Luke Daque: Okay, let me double check.
163 00:13:51.460 ⇒ 00:13:55.340 Luke Daque: Yeah, I think there’s 2. Now, one is for the synthetic.
164 00:13:56.870 ⇒ 00:13:59.679 Luke Daque: Data for the
165 00:14:01.069 ⇒ 00:14:14.360 Luke Daque: Microsoft tools usage, and then the other is for the Dbt models. I’m not sure if it’s should be like coming from us. That’s doing the review, because I can’t merge these Prs. If nobody reviews
166 00:14:14.840 ⇒ 00:14:15.860 Luke Daque: unless we.
167 00:14:15.860 ⇒ 00:14:16.560 Amber Lin: Oh!
168 00:14:16.560 ⇒ 00:14:18.609 Luke Daque: Like Trevor, to review this.
169 00:14:18.610 ⇒ 00:14:21.790 Amber Lin: I think a wish can help review them.
170 00:14:22.210 ⇒ 00:14:23.159 Luke Daque: Yeah, but he.
171 00:14:23.160 ⇒ 00:14:27.330 Awaish Kumar: Yesterday. I got a message, I think, from Trevor like, he said. I have.
172 00:14:27.550 ⇒ 00:14:31.129 Amber Lin: Yeah, I’ve added, I’ve asked them to add, you guys, so you should be in there.
173 00:14:31.130 ⇒ 00:14:34.430 Awaish Kumar: But I can’t. I can’t see the repository.
174 00:14:34.650 ⇒ 00:14:38.080 Awaish Kumar: Can anyone send me the link so I can see if I can access?
175 00:14:38.280 ⇒ 00:14:41.770 Awaish Kumar: I’m not able to search it in my profile.
176 00:14:45.820 ⇒ 00:14:49.820 Luke Daque: I sent them in in the Zoom chat, or the Pr.
177 00:14:52.760 ⇒ 00:14:53.930 Awaish Kumar: Let me.
178 00:14:58.520 ⇒ 00:15:00.220 Awaish Kumar: Yeah, I can’t get in.
179 00:15:03.260 ⇒ 00:15:07.390 Amber Lin: Wait. What? But, he added, your username.
180 00:15:08.690 ⇒ 00:15:13.240 Amber Lin: Lou, can you see if oasis in the in the repo.
181 00:15:13.240 ⇒ 00:15:15.270 Luke Daque: Yeah, I can.
182 00:15:16.090 ⇒ 00:15:16.860 Amber Lin: Hmm.
183 00:15:20.100 ⇒ 00:15:22.530 Amber Lin: okay, how do we move forward here?
184 00:15:23.150 ⇒ 00:15:25.820 Luke Daque: We need Trevor to provider.
185 00:15:25.820 ⇒ 00:15:29.339 Awaish Kumar: This is my, yeah, we can maybe bump him again.
186 00:15:31.050 ⇒ 00:15:32.280 Amber Lin: I mean? I asked.
187 00:15:32.570 ⇒ 00:15:35.310 Amber Lin: We asked him to include you. No.
188 00:15:35.550 ⇒ 00:15:37.510 Amber Lin: he said, you guys are included.
189 00:15:37.510 ⇒ 00:15:40.320 Awaish Kumar: He said. He has. He has added me.
190 00:15:40.440 ⇒ 00:15:42.710 Awaish Kumar: but I am not able to access it.
191 00:15:43.020 ⇒ 00:15:46.240 Awaish Kumar: So that means like something is not working.
192 00:15:48.700 ⇒ 00:15:50.419 Luke Daque: Maybe in the meantime we can have
193 00:15:51.240 ⇒ 00:15:54.569 Luke Daque: pull the Prs because she has access.
194 00:15:57.220 ⇒ 00:16:02.300 Amber Lin: Do we? Should we review them before we publish them?
195 00:16:03.330 ⇒ 00:16:05.760 Luke Daque: Yeah, I need to review with team.
196 00:16:05.960 ⇒ 00:16:08.179 Luke Daque: So she, she’s okay to review it.
197 00:16:08.850 ⇒ 00:16:10.320 Luke Daque: The models and stuff.
198 00:16:11.870 ⇒ 00:16:15.069 Luke Daque: That’s what we did in for the synthetic data. Like we just.
199 00:16:15.070 ⇒ 00:16:15.430 Amber Lin: Huh!
200 00:16:15.430 ⇒ 00:16:17.699 Luke Daque: Each other’s and prs.
201 00:16:23.256 ⇒ 00:16:24.719 Amber Lin: What do you think?
202 00:16:29.120 ⇒ 00:16:32.750 Awaish Kumar: But I we can still create these tables from
203 00:16:33.060 ⇒ 00:16:36.750 Awaish Kumar: without, even without merging them, merging them right.
204 00:16:37.190 ⇒ 00:16:39.010 Luke Daque: Yeah. That’s what I did.
205 00:16:40.030 ⇒ 00:16:42.710 Awaish Kumar: Yeah, like, we can just just do that.
206 00:16:43.420 ⇒ 00:16:45.270 Amber Lin: So we’ll move on with the modeling.
207 00:16:46.950 ⇒ 00:16:50.140 Awaish Kumar: Yeah, we move on with modeling. We create Prs, and
208 00:16:50.950 ⇒ 00:16:54.289 Awaish Kumar: we’ll just create the tables as as Luke has already done.
209 00:16:55.220 ⇒ 00:16:59.340 Awaish Kumar: And yeah, I will just review in March. Once I have a.
210 00:16:59.810 ⇒ 00:17:00.140 Amber Lin: Okay.
211 00:17:00.140 ⇒ 00:17:01.600 Awaish Kumar: Right, yeah.
212 00:17:01.700 ⇒ 00:17:02.220 Amber Lin: Yeah.
213 00:17:02.220 ⇒ 00:17:09.310 Amber Lin: Sounds good I wish. Can you reply to Trevor’s comment that you still don’t have access.
214 00:17:10.339 ⇒ 00:17:12.239 Awaish Kumar: Yeah, I’m just on it.
215 00:17:12.619 ⇒ 00:17:23.419 Amber Lin: Okay, sounds good. So I think we’ll wait for those review items. For a wish. And then for now have
216 00:17:23.999 ⇒ 00:17:32.889 Amber Lin: these items to do to do the modeling, and then cast them as dates.
217 00:17:33.319 ⇒ 00:17:36.059 Amber Lin: I think, for the models. We should
218 00:17:36.179 ⇒ 00:17:40.179 Amber Lin: should aim to have them earlier, so that Annie can finish up.
219 00:17:41.799 ⇒ 00:17:46.829 Amber Lin: Her dashboards, Luke, how do you? How long do you think that modeling would take.
220 00:17:48.840 ⇒ 00:17:50.600 Luke Daque: Which one the tools usage.
221 00:17:50.600 ⇒ 00:17:51.400 Amber Lin: Yeah.
222 00:17:53.390 ⇒ 00:17:57.200 Luke Daque: I can focus on that today. So you’ll have it by tomorrow.
223 00:17:57.580 ⇒ 00:18:03.140 Amber Lin: Oh, okay, how long? In terms of hours, do you think that will take.
224 00:18:03.730 ⇒ 00:18:07.690 Luke Daque: I don’t know. Maybe 2 HI don’t know.
225 00:18:08.580 ⇒ 00:18:15.290 Amber Lin: Okay, so I’m gonna give them each 2 points I’ll save by.
226 00:18:18.080 ⇒ 00:18:24.060 Amber Lin: So you’re gonna do office 3, 65 1st and then co-pilot right?
227 00:18:25.330 ⇒ 00:18:28.619 Luke Daque: We already have both, so maybe I can just do both at the same.
228 00:18:28.620 ⇒ 00:18:38.809 Amber Lin: Okay. Okay, sounds good. I don’t know if you’ll get to finish them if we still need to cast a timestamp as a date, but that probably won’t take long.
229 00:18:39.480 ⇒ 00:18:43.150 Luke Daque: Yeah, I think I already did it. We should have it in the.
230 00:18:43.770 ⇒ 00:18:44.830 Amber Lin: Wow!
231 00:18:45.070 ⇒ 00:18:48.090 Luke Daque: So that was just casting.
232 00:18:48.390 ⇒ 00:18:48.950 Amber Lin: Hmm.
233 00:18:51.210 ⇒ 00:18:56.870 Luke Daque: Yeah, there’s a start date and end date that we can use. That’s already dates.
234 00:18:57.290 ⇒ 00:18:58.290 Amber Lin: Yay!
235 00:18:58.880 ⇒ 00:19:05.449 Annie Yu: And to confirm for the co-pilot and office 3, 65. They will just have the same fields, but
236 00:19:05.610 ⇒ 00:19:09.380 Annie Yu: they’ll be added into that event type right?
237 00:19:11.840 ⇒ 00:19:15.520 Luke Daque: Is that what we want.
238 00:19:15.660 ⇒ 00:19:18.969 Annie Yu: Yeah, that’s where I’m confused because I don’t know.
239 00:19:19.180 ⇒ 00:19:20.689 Amber Lin: What would this look like.
240 00:19:23.090 ⇒ 00:19:27.939 Luke Daque: I’m not sure as well. I thought it was just like counting how many times a user has
241 00:19:29.740 ⇒ 00:19:31.770 Luke Daque: accessed a tool.
242 00:19:33.590 ⇒ 00:19:34.100 Annie Yu: So that will also.
243 00:19:34.100 ⇒ 00:19:34.660 Luke Daque: It.
244 00:19:34.660 ⇒ 00:19:39.010 Annie Yu: One row, one event, one person, right.
245 00:19:41.470 ⇒ 00:19:43.730 Luke Daque: Oh, go ahead! Can you say that again.
246 00:19:43.730 ⇒ 00:19:49.229 Annie Yu: Each role will represent per person per event is that it.
247 00:19:50.530 ⇒ 00:19:51.000 Amber Lin: Okay, really.
248 00:19:51.000 ⇒ 00:19:51.540 Luke Daque: Okay, this is.
249 00:19:51.540 ⇒ 00:19:53.660 Amber Lin: Synthetic data set by any chance.
250 00:19:54.400 ⇒ 00:19:59.769 Amber Lin: Would you mind sharing your screen? We can? If we look at it, I think we’ll understand what’s going on.
251 00:20:04.270 ⇒ 00:20:05.429 Luke Daque: Can you see my screen.
252 00:20:06.390 ⇒ 00:20:07.050 Amber Lin: Yes.
253 00:20:08.140 ⇒ 00:20:16.010 Luke Daque: So we’re going to file that user activity. For instance, it’s looks like this.
254 00:20:26.510 ⇒ 00:20:27.740 Amber Lin: Oh!
255 00:20:27.740 ⇒ 00:20:29.339 Luke Daque: And there are like dates.
256 00:20:29.620 ⇒ 00:20:30.320 Luke Daque: Okay.
257 00:20:30.320 ⇒ 00:20:31.019 Amber Lin: By a way, she.
258 00:20:31.020 ⇒ 00:20:32.460 Luke Daque: User has.
259 00:20:34.090 ⇒ 00:20:39.129 Luke Daque: Nice like what’s last happened. For example.
260 00:20:41.250 ⇒ 00:20:44.830 Amber Lin: How does this, huh?
261 00:20:45.200 ⇒ 00:20:51.920 Amber Lin: Wait? So is this by event, or is it like an aggregate account of how much they’ve used it?
262 00:20:52.300 ⇒ 00:20:52.790 Luke Daque: Looks like it.
263 00:20:52.790 ⇒ 00:20:53.730 Amber Lin: Oh, okay.
264 00:20:53.730 ⇒ 00:20:55.969 Luke Daque: Day, my own user.
265 00:20:56.180 ⇒ 00:20:58.220 Amber Lin: Oh!
266 00:20:59.020 ⇒ 00:21:04.999 Annie Yu: So what? Only the last kind of a last time stand right. The last.
267 00:21:05.000 ⇒ 00:21:10.950 Luke Daque: Yeah, it looks like they really have like, last activity for a specific co-pilot in.
268 00:21:11.290 ⇒ 00:21:11.900 Amber Lin: Huh!
269 00:21:11.900 ⇒ 00:21:17.259 Luke Daque: Microsoft teams. For example, yeah, yeah, something like that.
270 00:21:17.840 ⇒ 00:21:18.440 Luke Daque: Wait.
271 00:21:18.440 ⇒ 00:21:22.290 Amber Lin: Annie, can you use this for your visualizations?
272 00:21:22.290 ⇒ 00:21:25.309 Annie Yu: That’s what I’m thinking. That’s why I’m curious, which I I.
273 00:21:25.310 ⇒ 00:21:26.610 Amber Lin: Yeah.
274 00:21:27.300 ⇒ 00:21:29.530 Amber Lin: Hmm, wait. Look, how does it?
275 00:21:29.880 ⇒ 00:21:32.549 Amber Lin: How do the Api look like?
276 00:21:33.680 ⇒ 00:21:34.630 Amber Lin: Oh.
277 00:21:35.650 ⇒ 00:21:41.319 Annie Yu: But with the but we kind of had this with like message and email, right? We only.
278 00:21:41.320 ⇒ 00:21:42.950 Amber Lin: Oh!
279 00:21:43.100 ⇒ 00:21:50.259 Annie Yu: We assume the start time. But then this is activity instead of like a send time. So I don’t know.
280 00:21:51.980 ⇒ 00:21:57.699 Luke Daque: Looks it looks like same for like onedrive like it’s also like last.
281 00:21:58.490 ⇒ 00:21:59.400 Luke Daque: And then.
282 00:22:03.820 ⇒ 00:22:04.180 Amber Lin: Well.
283 00:22:04.180 ⇒ 00:22:07.000 Luke Daque: Other stuff like common files.
284 00:22:07.314 ⇒ 00:22:08.570 Amber Lin: I see, I see.
285 00:22:08.570 ⇒ 00:22:10.149 Luke Daque: I don’t know. Let me do this.
286 00:22:10.150 ⇒ 00:22:15.340 Amber Lin: When you looked at the Apis, is there a way to have
287 00:22:15.860 ⇒ 00:22:26.019 Amber Lin: information that’s kind of like what we have for our current? Say, emails or chat. So it’s like this, specific activity started here and ended here
288 00:22:26.370 ⇒ 00:22:28.759 Amber Lin: like, is there a way to get that.
289 00:22:32.425 ⇒ 00:22:41.180 Luke Daque: This is the only wait. That’s why I added that link to that more linear.
290 00:22:41.420 ⇒ 00:22:44.200 Luke Daque: But this is the only fields that they had.
291 00:22:44.960 ⇒ 00:22:50.280 Luke Daque: So I mean, we can’t force adding fluids. If it’s not, part of
292 00:22:51.420 ⇒ 00:22:56.710 Luke Daque: the Api is like, once we get the real thing, it’s just going to be that right.
293 00:22:56.710 ⇒ 00:22:58.460 Amber Lin: I see, I see.
294 00:22:59.570 ⇒ 00:23:07.320 Amber Lin: Is there other Apis that also has information on tool usage? Or is that the only one.
295 00:23:08.220 ⇒ 00:23:12.160 Luke Daque: This is the only one, I added, because this is the only one that was.
296 00:23:12.550 ⇒ 00:23:18.510 Luke Daque: Let’s going copilot and then office.
297 00:23:20.000 ⇒ 00:23:21.580 Luke Daque: So why do you see that
298 00:23:24.720 ⇒ 00:23:26.140 Luke Daque: close? Let’s see.
299 00:23:29.790 ⇒ 00:23:30.460 Luke Daque: you know.
300 00:23:48.400 ⇒ 00:23:54.579 Luke Daque: So so it’s active users
301 00:24:00.720 ⇒ 00:24:04.129 Luke Daque: us, or something like this. So it’s just get active
302 00:24:04.850 ⇒ 00:24:13.620 Luke Daque: counts. So it’s basically, just the way that a dates for last activity, and then.
303 00:24:23.560 ⇒ 00:24:24.779 Amber Lin: how to.
304 00:24:26.130 ⇒ 00:24:31.539 Amber Lin: So is this different from like, how we got email or chat data.
305 00:24:35.450 ⇒ 00:24:36.220 Luke Daque: What do you mean?
306 00:24:39.320 ⇒ 00:24:44.259 Amber Lin: I was wondering why that those have Timestamps, and this one doesn’t.
307 00:24:50.010 ⇒ 00:24:52.350 Luke Daque: Like list events, for example.
308 00:24:52.550 ⇒ 00:24:58.150 Amber Lin: Yeah. Yeah. So when it’s and it
309 00:24:58.450 ⇒ 00:25:05.020 Amber Lin: cause if they check the tools, it saw an event. I was wondering if any we can find anything there.
310 00:25:11.120 ⇒ 00:25:16.430 Annie Yu: No, I think it makes sense that those events have timestamps, but not these ones. Because
311 00:25:16.620 ⇒ 00:25:26.660 Annie Yu: people like leave a trace. Right? We sent an email. And we like start a meeting. But then this is like usage. And it’s gonna be different.
312 00:25:31.280 ⇒ 00:25:33.810 Amber Lin: I mean, as long as we can
313 00:25:33.940 ⇒ 00:25:49.039 Amber Lin: confidently say that we’ve looked at all the apis there’s none. There, then we can just use the original one, but I’m just afraid that if we say there’s none, and then they come up with something that hey, this has a start and end time we’ll get in trouble.
314 00:25:59.550 ⇒ 00:26:08.589 Annie Yu: I think we actually talk about this when Utam was still here with Trevor. They talk about something called audit audit log
315 00:26:10.440 ⇒ 00:26:21.450 Annie Yu: and but it’s it’s very, very. I forgot what that was. And then I think they talk about this. And this was like related to usage.
316 00:26:21.690 ⇒ 00:26:26.110 Luke Daque: But maybe this one the audit log. Query, I think.
317 00:26:26.860 ⇒ 00:26:27.590 Amber Lin: Hmm.
318 00:26:30.300 ⇒ 00:26:32.490 Annie Yu: But yeah, I don’t really know what they mean.
319 00:26:33.180 ⇒ 00:26:34.990 Annie Yu: or if that’s the one we should.
320 00:26:48.290 ⇒ 00:26:55.709 Amber Lin: maybe look at the different tabs under audit lock query, or maybe at overview. It’ll give us an idea what it’s about.
321 00:26:57.020 ⇒ 00:26:59.060 Amber Lin: What is audit logs.
322 00:27:03.970 ⇒ 00:27:05.620 Annie Yu: Odd roadblocks.
323 00:27:06.610 ⇒ 00:27:09.079 Amber Lin: Can you click into overview? Luke.
324 00:27:11.130 ⇒ 00:27:21.039 Annie Yu: Provides an audit trail of all user and app activity in your tenant. But I think we talk about this because this requires
325 00:27:21.450 ⇒ 00:27:27.009 Annie Yu: some kind of payment, right? So the other Apis we use are free. But this is like.
326 00:27:27.400 ⇒ 00:27:30.480 Amber Lin: Hmm! This is still Microsoft graph, though.
327 00:28:00.680 ⇒ 00:28:01.560 Amber Lin: Hmm!
328 00:28:08.690 ⇒ 00:28:12.440 Amber Lin: Let me search and ask Gpt real quick.
329 00:28:20.375 ⇒ 00:28:27.949 Amber Lin: Annie, I guess, for you, for the current synthetic data set that you saw for tool usage. Can you use that for any visualizations.
330 00:28:32.600 ⇒ 00:28:36.480 Annie Yu: You mean to show the last use time.
331 00:28:36.890 ⇒ 00:28:51.320 Amber Lin: No, for the current cause. It’s ultimately it’s another type of event type. Right? So I was wondering if that if the do, if the documentation we have can help you do any of those visualizations.
332 00:28:52.240 ⇒ 00:28:56.320 Annie Yu: I actually don’t see those things in the required
333 00:28:56.530 ⇒ 00:28:58.970 Annie Yu: and the requirements for the charts.
334 00:28:59.680 ⇒ 00:29:00.210 Annie Yu: So that’s.
335 00:29:00.210 ⇒ 00:29:04.627 Amber Lin: Oh, it’s part, it’s it’s tool usage. So
336 00:29:05.771 ⇒ 00:29:10.010 Amber Lin: if you go to the spreadsheet that we have.
337 00:29:10.120 ⇒ 00:29:12.519 Amber Lin: I think they just want to see it as
338 00:29:12.720 ⇒ 00:29:17.260 Amber Lin: similarly, similarly, of how you would see email or see chat.
339 00:29:17.620 ⇒ 00:29:26.379 Amber Lin: They want to see tool usage, say, by day of week, hour of day and Pre. Post event. They want to see it by team, and with all those filters.
340 00:29:29.887 ⇒ 00:29:32.950 Annie Yu: Then, with the current fields we can.
341 00:29:33.530 ⇒ 00:29:36.210 Annie Yu: If we have those in.
342 00:29:38.680 ⇒ 00:29:47.780 Annie Yu: let me think about this, I think we can probably do. Average event counts, but just not anything. Duration.
343 00:29:56.420 ⇒ 00:30:00.769 Annie Yu: Yeah. But then we have to have the same feels.
344 00:30:01.030 ⇒ 00:30:02.780 Annie Yu: The other stuff have.
345 00:30:04.350 ⇒ 00:30:05.160 Amber Lin: Hmm.
346 00:30:05.370 ⇒ 00:30:09.930 Annie Yu: That’s why, like I, I was thinking it would be best to have those stuff.
347 00:30:09.930 ⇒ 00:30:12.440 Amber Lin: Yeah, but that’s interesting.
348 00:30:12.650 ⇒ 00:30:18.140 Luke Daque: To usage, actually meaning like. For if we, for example, outlook the email phone.
349 00:30:18.140 ⇒ 00:30:18.520 Amber Lin: Microsoft.
350 00:30:18.520 ⇒ 00:30:19.330 Luke Daque: Right?
351 00:30:19.540 ⇒ 00:30:22.250 Luke Daque: Like, what does tool usage mean? Is it like
352 00:30:23.120 ⇒ 00:30:29.089 Luke Daque: a user sending an email or a user just opening outlook is already considered usage.
353 00:30:29.090 ⇒ 00:30:38.029 Amber Lin: I think. Yes, so, for I guess, for onedrive. For example, I’m looking at.
354 00:30:41.730 ⇒ 00:30:49.869 Amber Lin: I’m sharing my screen. I’m looking at Chat Gpt response. So I think, for, for example, for onedrive, it would mean like
355 00:30:50.120 ⇒ 00:31:00.990 Amber Lin: when the fall I don’t know like when when we start modifying something. And we huh!
356 00:31:03.620 ⇒ 00:31:05.090 Amber Lin: Confused
357 00:31:09.320 ⇒ 00:31:14.929 Annie Yu: Yeah, I don’t think we have the start modifying time. Don’t think we will have that.
358 00:31:16.060 ⇒ 00:31:17.870 Luke Daque: Yeah, I don’t think so, too, for.
359 00:31:18.810 ⇒ 00:31:20.469 Annie Yu: Update the Api out.
360 00:31:21.870 ⇒ 00:31:24.410 Luke Daque: We only have, like last activity, or something.
361 00:31:28.160 ⇒ 00:31:32.930 Luke Daque: Unless we can find it from a different Api endpoint, which I don’t know.
362 00:31:33.670 ⇒ 00:31:37.189 Luke Daque: Employment if we find something like that
363 00:32:07.180 ⇒ 00:32:16.329 Luke Daque: But for like onedrive, it makes sense if they like, uploaded something or downloaded something, but like for outlook, for example, is it just
364 00:32:17.150 ⇒ 00:32:24.669 Luke Daque: when they open the tool? Or is it when they send an email because it? It’s just sending an email? We already have that right.
365 00:32:27.910 ⇒ 00:32:35.659 Amber Lin: Maybe they would check. Maybe they were checking emails. Yes, we could focus on onedrive for now and then.
366 00:32:36.520 ⇒ 00:32:44.719 Amber Lin: Gosh, for currently for office? 365. What does it? What did you include when you were doing synthetic data?
367 00:32:46.760 ⇒ 00:32:49.136 Amber Lin: What office I have?
368 00:32:49.730 ⇒ 00:32:51.650 Amber Lin: Oh, oh, I see teams.
369 00:32:51.650 ⇒ 00:33:01.220 Luke Daque: Teams, outlook and onedrive user. And they are all like user activity. Basically. So like one guy, for instance.
370 00:33:02.840 ⇒ 00:33:04.740 Luke Daque: notes live.
371 00:33:07.010 ⇒ 00:33:09.559 Luke Daque: Yeah, it’s vast.
372 00:33:10.030 ⇒ 00:33:13.380 Luke Daque: It’s not event related. Then.
373 00:33:15.560 ⇒ 00:33:20.110 Amber Lin: I see. So I I was looking at this.
374 00:33:21.443 ⇒ 00:33:26.770 Amber Lin: It says, option one is, if you guys can see my screen
375 00:33:27.140 ⇒ 00:33:37.660 Amber Lin: option. One is, we infer it, which is not very accurate. Option 2 is that we get it from the Microsoft 3, 65 audit log.
376 00:33:38.390 ⇒ 00:33:47.120 Amber Lin: and so like, I guess each one has fall access.
377 00:33:48.440 ⇒ 00:33:49.120 Amber Lin: Oh!
378 00:33:52.670 ⇒ 00:33:59.449 Amber Lin: And then we group them into time windows to infer when sessions started and ended, or
379 00:34:05.160 ⇒ 00:34:06.400 Amber Lin: confusing.
380 00:34:17.650 ⇒ 00:34:23.999 Luke Daque: I don’t see those like what Chat Gpt mentioned regarding like file access or something. I don’t
381 00:34:25.000 ⇒ 00:34:27.720 Luke Daque: see those prints in the audit log.
382 00:34:28.679 ⇒ 00:34:29.569 Amber Lin: Oh, really.
383 00:34:33.590 ⇒ 00:34:40.049 Luke Daque: Yeah, I want I don’t know where in depth stop it. But.
384 00:35:06.776 ⇒ 00:35:15.040 Amber Lin: Okay, I think we need to get some help here, let’s focus on, go back to linear.
385 00:35:15.830 ⇒ 00:35:23.900 Amber Lin: Think this one also, I from my Google search. It says, everything is in Utc for, yeah.
386 00:35:25.630 ⇒ 00:35:32.789 Luke Daque: Kind of remember, we just didn’t like put it in writing or something, but I I think I kind of remember us discussing that before.
387 00:35:34.150 ⇒ 00:35:37.810 Amber Lin: Yeah. Alright
388 00:35:38.800 ⇒ 00:35:44.429 Amber Lin: think we are a bit blocked. We need to go find out how we can get those
389 00:35:45.652 ⇒ 00:35:48.750 Amber Lin: tool usage when they start and end.
390 00:35:49.822 ⇒ 00:36:02.000 Amber Lin: Luke, can you work with AI to suggest a few possible options, and then we should escalate to a wish? Or, if a wish doesn’t know. We can ask Utam.
391 00:36:03.050 ⇒ 00:36:05.759 Amber Lin: Can you send what you find in the Channel?
392 00:36:06.740 ⇒ 00:36:07.460 Luke Daque: Okay.
393 00:36:07.750 ⇒ 00:36:08.350 Amber Lin: Yeah.
394 00:36:08.740 ⇒ 00:36:19.000 Amber Lin: So essentially, if we can see, say, the use, say, amber logged in at 10. She opened onedrive at 1030 and then
395 00:36:19.350 ⇒ 00:36:25.779 Amber Lin: closed it at maybe like 11. So amber use onedrive for 30 min.
396 00:36:25.930 ⇒ 00:36:28.519 Amber Lin: Like, if we can get that type of data.
397 00:36:35.950 ⇒ 00:36:36.960 Amber Lin: okay,
398 00:36:40.266 ⇒ 00:36:49.020 Annie Yu: And amber. I did create those charts with the filters. I also put them in documents.
399 00:36:49.380 ⇒ 00:36:49.980 Annie Yu: Oh, my!
400 00:36:51.680 ⇒ 00:36:53.729 Annie Yu: All the updates in my ticket and.
401 00:36:53.730 ⇒ 00:36:56.329 Amber Lin: Yeah, I I was looking at them earlier.
402 00:36:56.330 ⇒ 00:36:59.059 Annie Yu: I think you can start reviewing them.
403 00:36:59.060 ⇒ 00:37:02.249 Amber Lin: Oh, okay, I can do that.
404 00:37:04.140 ⇒ 00:37:09.330 Annie Yu: Yeah, I’m I’m assuming you’ll be the one to review those. But yeah, okay.
405 00:37:09.330 ⇒ 00:37:15.699 Amber Lin: Sounds good. I’ll do that, and I’ll also look at the lineage documentation as well.
406 00:37:17.240 ⇒ 00:37:17.920 Annie Yu: Yeah.
407 00:37:20.868 ⇒ 00:37:29.079 Amber Lin: Thanks, everyone. Good work. I know we’re a little bit stuck on the tool usage. But let’s involve a way, for if we’re not clear.
408 00:37:31.680 ⇒ 00:37:32.210 Annie Yu: Okay.
409 00:37:32.620 ⇒ 00:37:33.660 Amber Lin: Okay. Thanks.
410 00:37:34.050 ⇒ 00:37:34.730 Amber Lin: Thanks.