Meeting Title: MatterMore | internal Standup Date: 2025-06-26 Meeting participants: Amber Lin, Luke Daque
WEBVTT
1 00:02:25.100 ⇒ 00:02:26.140 Amber Lin: Hi Luke.
2 00:02:28.160 ⇒ 00:02:29.650 Luke Daque: Hi! Amber! How’s it going.
3 00:02:30.532 ⇒ 00:02:38.749 Amber Lin: Stressed. I have a meeting with ABC. The clients. It’s soon, and I have not made the slides. So I’m making the slides.
4 00:02:38.970 ⇒ 00:02:39.760 Luke Daque: Oh no!
5 00:02:39.760 ⇒ 00:02:43.574 Amber Lin: So I’ll make this quick.
6 00:02:44.440 ⇒ 00:02:51.230 Amber Lin: Let’s see let me pull up, madam, more.
7 00:02:52.190 ⇒ 00:02:53.100 Luke Daque: Sure.
8 00:02:54.260 ⇒ 00:03:00.830 Amber Lin: You said you let me see. Oh, it’s in the events model. Okay?
9 00:03:01.300 ⇒ 00:03:08.820 Amber Lin: So we should ask Annie to include that.
10 00:03:10.890 ⇒ 00:03:15.769 Luke Daque: Yeah, I think she already started working on that. The v 1
11 00:03:16.890 ⇒ 00:03:19.089 Luke Daque: report. I’m not sure if
12 00:03:19.640 ⇒ 00:03:22.399 Luke Daque: yeah let we can check. Let me check.
13 00:03:30.670 ⇒ 00:03:31.960 Luke Daque: Don’t type.
14 00:03:32.430 ⇒ 00:03:37.579 Luke Daque: Yeah, it looks like it should be there in the v, 1 report.
15 00:03:39.675 ⇒ 00:03:40.340 Luke Daque: Yeah.
16 00:03:45.980 ⇒ 00:03:49.829 Luke Daque: I also like notice. There’s a lot of new tickets and.
17 00:03:50.520 ⇒ 00:03:51.140 Amber Lin: Hmm.
18 00:03:51.140 ⇒ 00:03:52.350 Luke Daque: Another more.
19 00:03:53.850 ⇒ 00:04:03.869 Luke Daque: What do you call this linear? But they’re like in the next cycle category, like the build SQL, models.
20 00:04:06.470 ⇒ 00:04:10.649 Luke Daque: Buffer connection permissions. What would you know like what these are?
21 00:04:11.390 ⇒ 00:04:22.039 Amber Lin: Oh, those are for next cycle. So in case we are able to work on the you know the tertiary and the other metrics.
22 00:04:22.360 ⇒ 00:04:27.820 Amber Lin: Those are about tickets for those. Yeah. We don’t need to care about them, for now.
23 00:04:28.000 ⇒ 00:04:28.940 Luke Daque: Okay.
24 00:04:29.110 ⇒ 00:04:38.360 Amber Lin: Do you know if any has incorporated the the off, the onedrive usage.
25 00:04:40.270 ⇒ 00:04:47.670 Luke Daque: I don’t know, but it looks like it’s showing in the event type in the power Bi, so probably.
26 00:04:48.480 ⇒ 00:04:48.880 Amber Lin: Okay.
27 00:05:02.620 ⇒ 00:05:09.099 Amber Lin: Alright. Let me check the lot of more than year.
28 00:05:09.910 ⇒ 00:05:15.039 Amber Lin: Okay, so.
29 00:05:17.560 ⇒ 00:05:18.260 Luke Daque: Yeah.
30 00:05:19.450 ⇒ 00:05:20.920 Amber Lin: Oh.
31 00:05:26.860 ⇒ 00:05:34.400 Amber Lin: look at the current cycle. So we added the one drive.
32 00:05:35.680 ⇒ 00:05:38.540 Amber Lin: We have the onedrive data right?
33 00:05:39.250 ⇒ 00:05:44.579 Luke Daque: Yeah. I also already added, like of the other proposals as well.
34 00:05:44.860 ⇒ 00:05:51.369 Luke Daque: Onedrive is there co-pilot outlook and teams? Basically.
35 00:05:52.410 ⇒ 00:05:54.089 Amber Lin: Oh, for audit log.
36 00:05:55.430 ⇒ 00:05:56.210 Luke Daque: Yeah.
37 00:05:56.510 ⇒ 00:05:57.950 Amber Lin: Oh, that’s awesome.
38 00:05:58.090 ⇒ 00:06:04.060 Amber Lin: Okay? So every all of those tools are in power. Bi, right?
39 00:06:05.690 ⇒ 00:06:06.660 Luke Daque: Yeah, yeah.
40 00:06:06.660 ⇒ 00:06:15.509 Amber Lin: Okay, great. Let me do. We still need to do any modeling on those? Or are we able to filter by all those metrics.
41 00:06:18.790 ⇒ 00:06:26.140 Luke Daque: It should be filterable. If you look at the power Bi that Annie created, it’s should be under the event type.
42 00:06:26.530 ⇒ 00:06:29.969 Amber Lin: Do you want to share your screen? Mine’s not loading.
43 00:06:31.020 ⇒ 00:06:31.810 Luke Daque: Sure.
44 00:06:38.260 ⇒ 00:06:40.169 Luke Daque: There you go. Can you see my screen?
45 00:06:41.540 ⇒ 00:06:42.050 Amber Lin: Yeah.
46 00:06:42.560 ⇒ 00:06:50.480 Luke Daque: Yeah. So like, there’s an event type here filter like onedrive to a usage.
47 00:06:50.480 ⇒ 00:06:53.910 Amber Lin: Let’s see if it’s just one drive.
48 00:06:55.300 ⇒ 00:06:58.970 Luke Daque: This looked like this is how it looks like there.
49 00:06:59.860 ⇒ 00:07:01.400 Amber Lin: Oh!
50 00:07:01.790 ⇒ 00:07:02.840 Luke Daque: Looks like.
51 00:07:03.360 ⇒ 00:07:04.060 Amber Lin: Oh!
52 00:07:04.060 ⇒ 00:07:04.700 Luke Daque: Yeah.
53 00:07:06.330 ⇒ 00:07:08.389 Amber Lin: Maybe if we select all.
54 00:07:09.960 ⇒ 00:07:12.769 Luke Daque: Let me select all it look like this.
55 00:07:15.170 ⇒ 00:07:18.840 Amber Lin: Like is this, oh, this is events.
56 00:07:19.590 ⇒ 00:07:20.980 Amber Lin: Okay.
57 00:07:22.060 ⇒ 00:07:26.709 Luke Daque: Yeah. So we have all the email, the email events call events meeting events. And they they.
58 00:07:26.710 ⇒ 00:07:27.499 Amber Lin: So this is.
59 00:07:27.500 ⇒ 00:07:28.230 Luke Daque: Events.
60 00:07:28.230 ⇒ 00:07:32.360 Amber Lin: Oh, this is count right! This is count of events.
61 00:07:32.950 ⇒ 00:07:35.980 Luke Daque: It looks like it’s average events per user.
62 00:07:36.480 ⇒ 00:07:37.680 Amber Lin: Okay.
63 00:07:38.580 ⇒ 00:07:39.680 Luke Daque: And not count.
64 00:07:43.010 ⇒ 00:07:46.739 Amber Lin: So it’s I think it doesn’t look like it’s duration.
65 00:07:48.620 ⇒ 00:07:57.260 Luke Daque: Yeah. Oh, well, I guess let’s check out of day. I guess.
66 00:08:03.860 ⇒ 00:08:10.070 Luke Daque: Yeah, this looks like average events per hour.
67 00:08:12.260 ⇒ 00:08:13.500 Luke Daque: Then.
68 00:08:16.780 ⇒ 00:08:23.840 Luke Daque: Yeah, I don’t think that we have a duration dashboard here. Looks like just based on this.
69 00:08:24.290 ⇒ 00:08:25.240 Amber Lin: Okay.
70 00:08:25.860 ⇒ 00:08:30.310 Luke Daque: It’s all average events by subcontact.
71 00:08:31.280 ⇒ 00:08:34.230 Luke Daque: Oh, wait, I think, yeah.
72 00:08:34.360 ⇒ 00:08:39.620 Luke Daque: there’s average minutes and hours. So maybe you can do this. You could use this.
73 00:08:43.250 ⇒ 00:08:44.550 Luke Daque: Yeah, here you go.
74 00:08:45.080 ⇒ 00:08:47.229 Luke Daque: So if it’s select onedrive.
75 00:08:47.520 ⇒ 00:08:52.929 Luke Daque: Yeah, this would be like the average minutes producer. By Dave Lynch.
76 00:08:55.110 ⇒ 00:09:00.100 Amber Lin: Oh, wait! How do we even get duration for that.
77 00:09:01.997 ⇒ 00:09:07.269 Luke Daque: I did some what do you call this?
78 00:09:10.450 ⇒ 00:09:16.429 Luke Daque: At the moment I just inferred, or like used some random
79 00:09:16.820 ⇒ 00:09:22.050 Luke Daque: average duration based on what Chat Gpt provided, so.
80 00:09:22.050 ⇒ 00:09:28.050 Amber Lin: Oh, okay, okay. So all of our durations right now are like a set number estimate.
81 00:09:28.050 ⇒ 00:09:38.740 Luke Daque: Yeah, estimate. So like for co-pilot, it’s like depending on the use. The token count times us, number.
82 00:09:38.740 ⇒ 00:09:42.930 Luke Daque: Okay, like, yeah. 2 seconds. Weird, you know, or like sure.
83 00:09:42.930 ⇒ 00:09:43.810 Amber Lin: Really, quickly.
84 00:09:44.620 ⇒ 00:09:49.599 Luke Daque: Onedrive 5 min or file modify something like that. So it’s all
85 00:09:51.327 ⇒ 00:09:54.190 Luke Daque: assumed values for now, just like.
86 00:09:54.590 ⇒ 00:09:58.069 Luke Daque: yeah, it’s it’s pretty difficult to get the actual.
87 00:09:58.070 ⇒ 00:10:17.809 Amber Lin: That’s that’s a really good start like, that’s all. Like, I didn’t expect that we would be able to do this, so I really thank you for doing that. I think all of them are modeling and cause we got. I didn’t know that you were able to get all the audit locks like that’s awesome. I think that really allows us to
88 00:10:19.090 ⇒ 00:10:23.139 Amber Lin: do more modeling for duration.
89 00:10:23.330 ⇒ 00:10:27.889 Amber Lin: And like we could, we could talk about
90 00:10:27.990 ⇒ 00:10:47.620 Amber Lin: cause we already like. We already have our current modeling for duration, which is, assume it based on a set period of time, right? And then the next part, like we, we satisfied all the existing requirements, which is, which is great. And I was thinking for the next.
91 00:10:48.810 ⇒ 00:10:53.150 Amber Lin: for what’s up next, like, we can start thinking about how to
92 00:10:53.755 ⇒ 00:11:00.699 Amber Lin: inferred duration based on all the audit logs, because you were able to get audit logs for all of the different tools.
93 00:11:01.880 ⇒ 00:11:15.807 Amber Lin: And let me share my screen so like, where is it?
94 00:11:18.190 ⇒ 00:11:19.310 Amber Lin: So?
95 00:11:19.640 ⇒ 00:11:21.230 Amber Lin: I think we have
96 00:11:22.933 ⇒ 00:11:32.899 Amber Lin: like, if we have put all of those audit logs together, do you think this is like this stuff. This is possible.
97 00:11:37.070 ⇒ 00:11:42.820 Amber Lin: like if we group for every single person, we group all of their
98 00:11:43.110 ⇒ 00:11:46.860 Amber Lin: audit log activities in that day.
99 00:11:47.170 ⇒ 00:11:52.040 Amber Lin: and then we can infer when they switch tools. They’ve switched activities.
100 00:12:00.750 ⇒ 00:12:05.949 Luke Daque: yeah, I can take a look and see if that if that works or something.
101 00:12:05.950 ⇒ 00:12:12.989 Amber Lin: Yeah, sure, everything here. I’ll assign this to you so you can see where where this is
102 00:12:13.450 ⇒ 00:12:19.770 Amber Lin: like. It’s the second approach. 2
103 00:12:22.160 ⇒ 00:12:26.310 Amber Lin: can maybe. Can you work with Chatgvt to get a quick
104 00:12:28.570 ⇒ 00:12:31.249 Amber Lin: quick test on if this would work.
105 00:12:31.910 ⇒ 00:12:39.270 Amber Lin: or if it doesn’t work can you also note down like why it might not be possible.
106 00:12:42.380 ⇒ 00:12:45.139 Luke Daque: Yeah, I’ll I’ll see what I can do.
107 00:12:45.660 ⇒ 00:12:47.500 Amber Lin: Yeah, okay, no.
108 00:12:47.960 ⇒ 00:12:59.739 Amber Lin: no pressure on this. I think we’re pretty good with the 1st phase 1st phase modeling the duration stuff is just a good to have
109 00:13:00.604 ⇒ 00:13:10.859 Amber Lin: let’s see. Oh, were we able, I know, probably didn’t have time for that to does the worker type and team still doesn’t have data.
110 00:13:13.300 ⇒ 00:13:14.069 Luke Daque: Which one.
111 00:13:14.935 ⇒ 00:13:23.320 Amber Lin: No, yes. Today, when we’re looking at it together, the looker type and the worker type. And then
112 00:13:25.370 ⇒ 00:13:29.780 Amber Lin: the team fields didn’t have data.
113 00:13:31.330 ⇒ 00:13:34.989 Luke Daque: Oh, yeah, I haven’t looked into that. I’ll have to check again.
114 00:13:36.070 ⇒ 00:13:41.929 Amber Lin: Okay, I think we can do that today. For the stuff.
115 00:13:42.140 ⇒ 00:13:49.630 Amber Lin: I will. I think we can spike on how to do it. But we don’t have to do it yet.
116 00:13:50.540 ⇒ 00:13:52.119 Luke Daque: Okay. Sounds good.
117 00:13:52.690 ⇒ 00:13:55.350 Amber Lin: Okay, awesome.
118 00:13:56.420 ⇒ 00:13:57.829 Amber Lin: Thank you so much.
119 00:13:59.740 ⇒ 00:14:00.390 Luke Daque: Sure.
120 00:14:01.010 ⇒ 00:14:04.809 Amber Lin: Alrighty. I am. Gonna go make my slides talk to you later.
121 00:14:04.810 ⇒ 00:14:06.440 Luke Daque: That’s a good thanks. Thanks.
122 00:14:06.440 ⇒ 00:14:07.540 Amber Lin: Okay. Bye-bye.