Meeting Title: MatterMore | internal Standup Date: 2025-05-27 Meeting participants: Annie Yu, Ryan Luke Daque, Amber Lin
WEBVTT
1 00:00:23.050 ⇒ 00:00:27.109 Amber Lin: Waiting for Annie to join, and then we get started.
2 00:00:50.230 ⇒ 00:00:52.780 Amber Lin: Have you had a chance to look at?
3 00:00:53.910 ⇒ 00:00:59.210 Amber Lin: Are current visualizations and also
4 00:00:59.972 ⇒ 00:01:03.209 Amber Lin: the document that has our modeling needs.
5 00:01:04.510 ⇒ 00:01:06.401 Ryan Luke Daque: Yeah, I checked it out.
6 00:01:07.520 ⇒ 00:01:09.370 Ryan Luke Daque: but yeah, I haven’t done any
7 00:01:09.570 ⇒ 00:01:15.180 Ryan Luke Daque: any modeling yet. At the moment, just like we yeah, we haven’t like discussed about it. But yeah.
8 00:01:15.380 ⇒ 00:01:23.550 Amber Lin: Yeah, I think this meeting will mostly be. Annie will tell you what she needs, because I think we I went through with her
9 00:01:23.650 ⇒ 00:01:28.650 Amber Lin: to figure out what we actually need, and she’ll be able to communicate that to you.
10 00:01:29.350 ⇒ 00:01:34.367 Amber Lin: Okay, see, you have the
11 00:01:35.150 ⇒ 00:01:36.210 Amber Lin: Me check
12 00:01:40.880 ⇒ 00:01:43.800 Amber Lin: the matter. More tables.
13 00:01:47.670 ⇒ 00:01:57.100 Amber Lin: You have the spreadsheet for sample data. And also, if you want to take a look at the deck, that will be, maybe that will also give you some context.
14 00:01:58.390 ⇒ 00:01:58.950 Ryan Luke Daque: Yeah.
15 00:02:23.680 ⇒ 00:02:30.320 Amber Lin: I sent the presentation and slack as well. So let me show you.
16 00:02:30.885 ⇒ 00:02:32.580 Ryan Luke Daque: Thanks, okay.
17 00:02:33.510 ⇒ 00:02:35.610 Amber Lin: Show you what we did. So far.
18 00:02:36.510 ⇒ 00:02:43.530 Amber Lin: So right now, we have the basic ones that we did the average emails.
19 00:02:44.320 ⇒ 00:02:51.530 Amber Lin: So 1, st 1, st one is by day of week, right? So for day of week, for emails, for
20 00:02:51.930 ⇒ 00:03:02.590 Amber Lin: meeting, duration, for messages, and then the total of that, and then total, like percentage wise
21 00:03:02.730 ⇒ 00:03:06.959 Amber Lin: and like time per each of these by day of week.
22 00:03:07.070 ⇒ 00:03:15.360 Amber Lin: Then we also did by hour of day. So again, the same way of emails, meetings, messages.
23 00:03:15.870 ⇒ 00:03:23.140 Amber Lin: and then I don’t think we have this data right, Luke, do we have.
24 00:03:23.140 ⇒ 00:03:24.320 Ryan Luke Daque: They don’t.
25 00:03:24.320 ⇒ 00:03:26.029 Amber Lin: Other than meetings.
26 00:03:26.340 ⇒ 00:03:26.890 Amber Lin: Oh.
27 00:03:26.890 ⇒ 00:03:27.750 Ryan Luke Daque: No.
28 00:03:27.750 ⇒ 00:03:30.730 Amber Lin: Do they want it? Initially? I thought they.
29 00:03:30.730 ⇒ 00:03:31.230 Ryan Luke Daque: Oh!
30 00:03:31.230 ⇒ 00:03:33.100 Amber Lin: Let me see, I thought I wanted.
31 00:03:33.620 ⇒ 00:03:36.179 Amber Lin: Let’s have those data.
32 00:03:37.400 ⇒ 00:03:40.959 Amber Lin: Oh, do you remember where this was for?
33 00:03:45.530 ⇒ 00:03:49.049 Amber Lin: Are we using this at all? Success factors.
34 00:03:50.370 ⇒ 00:03:56.009 Ryan Luke Daque: We did create some success factors, synthetic data that was based on like ants.
35 00:03:59.270 ⇒ 00:04:03.949 Ryan Luke Daque: There was like, there was a Google sheet that we were following.
36 00:04:04.280 ⇒ 00:04:08.590 Amber Lin: Like, for whatever the success data, a success factors and.
37 00:04:08.840 ⇒ 00:04:11.689 Ryan Luke Daque: Like the Microsoft team. Msf team.
38 00:04:12.950 ⇒ 00:04:19.179 Ryan Luke Daque: So yeah, like that one, for example. So we have users, employment employee, John.
39 00:04:19.670 ⇒ 00:04:21.930 Amber Lin: I see. I see
40 00:04:25.150 ⇒ 00:04:29.370 Amber Lin: Cora Corey deployment.
41 00:04:32.700 ⇒ 00:04:39.939 Amber Lin: I see. So there’s a lot more exploration we can do related to the success factors.
42 00:04:40.330 ⇒ 00:04:43.039 Amber Lin: I guess we will finish up.
43 00:04:43.730 ⇒ 00:04:47.880 Amber Lin: Here’s like a sample thing that I think we also wanted.
44 00:04:48.130 ⇒ 00:04:50.489 Amber Lin: But let me come back to this.
45 00:04:51.210 ⇒ 00:05:00.480 Amber Lin: So we have by hour of day, and then
46 00:05:01.010 ⇒ 00:05:05.089 Amber Lin: we also have it remote versus not remote.
47 00:05:05.450 ⇒ 00:05:18.780 Amber Lin: So for each of the tools, and then remote versus in person, for emails by department.
48 00:05:20.758 ⇒ 00:05:27.979 Amber Lin: This is by a time of day. Think what we’re lacking right now is, actually, we can’t really do this by department
49 00:05:28.250 ⇒ 00:05:31.159 Amber Lin: analysis, because a lot of it is.
50 00:05:31.747 ⇒ 00:05:36.529 Amber Lin: requires a lot more modeling. So that that’s where you would be really.
51 00:05:36.530 ⇒ 00:05:36.940 Ryan Luke Daque: Okay.
52 00:05:36.940 ⇒ 00:05:40.895 Amber Lin: Is helping us create a table that
53 00:05:42.740 ⇒ 00:05:51.680 Amber Lin: that has all that data ingrained. For example, here is I was creating a mock table, and I was thinking
54 00:05:52.070 ⇒ 00:05:56.170 Amber Lin: for each of them. We have Communication type department.
55 00:05:56.570 ⇒ 00:06:02.269 Amber Lin: then time of day. I I think these just need to be broken down a lot more.
56 00:06:02.840 ⇒ 00:06:06.609 Amber Lin: And then I essentially, I did a pivot.
57 00:06:07.460 ⇒ 00:06:14.049 Amber Lin: And then we were able to get, say, like average communication time per tool and per department.
58 00:06:14.320 ⇒ 00:06:18.170 Amber Lin: So Annie was saying that she wants.
59 00:06:19.670 ⇒ 00:06:24.370 Ryan Luke Daque: I I think she also listed the models that she might need in the.
60 00:06:24.370 ⇒ 00:06:25.459 Amber Lin: Yeah, but.
61 00:06:25.460 ⇒ 00:06:25.780 Ryan Luke Daque: Yeah.
62 00:06:25.780 ⇒ 00:06:37.240 Amber Lin: I don’t think it was the most straightforward for you unless you you think otherwise. So I was asking, okay, can we have Sp, more specific tables, and we have
63 00:06:37.880 ⇒ 00:06:40.360 Amber Lin: lot of them, I think. Here.
64 00:06:44.310 ⇒ 00:06:48.560 Amber Lin: let me check like this.
65 00:06:52.720 ⇒ 00:06:56.280 Amber Lin: So if we have start time and time.
66 00:07:05.440 ⇒ 00:07:11.930 Amber Lin: I think, mostly see these
67 00:07:15.680 ⇒ 00:07:21.629 Amber Lin: if you can still see my screen. So if we have, we add those
68 00:07:22.580 ⇒ 00:07:30.929 Amber Lin: specific things, and I think she wanted it to be from department and to department.
69 00:07:31.800 ⇒ 00:07:34.040 Amber Lin: So let me add that in
70 00:07:55.670 ⇒ 00:08:00.810 Amber Lin: think she wants it in are in local time.
71 00:08:14.170 ⇒ 00:08:14.900 Ryan Luke Daque: Okay.
72 00:08:15.440 ⇒ 00:08:18.990 Amber Lin: Does that make sense, I wonder?
73 00:08:19.990 ⇒ 00:08:20.930 Amber Lin: Hmm!
74 00:08:33.549 ⇒ 00:08:38.039 Amber Lin: Does that make sense to you, or what questions.
75 00:08:39.190 ⇒ 00:08:39.870 Ryan Luke Daque: Yeah, I think.
76 00:08:41.370 ⇒ 00:08:47.160 Ryan Luke Daque: Yeah, I think that makes sense. I’ll have to make, maybe just take a stab at it and see
77 00:08:48.050 ⇒ 00:08:57.389 Ryan Luke Daque: like I don’t have questions at the moment. But maybe once I start working on the modeling and like, get stuck on something. Maybe that’s when I have questions to Annie or something.
78 00:08:57.390 ⇒ 00:08:57.950 Amber Lin: Okay.
79 00:08:58.140 ⇒ 00:09:05.770 Ryan Luke Daque: Something. Maybe we have. We don’t have the data ready from the synthetic data that we currently have or stuff like that. So
80 00:09:06.270 ⇒ 00:09:08.380 Ryan Luke Daque: yeah, but here.
81 00:09:08.728 ⇒ 00:09:17.430 Amber Lin: If it’s not ready, can we just use AI to generate it? How long would the iteration cycle be if we want to change something.
82 00:09:18.060 ⇒ 00:09:21.030 Ryan Luke Daque: We’ll have to. Yeah, we’ll have to update the
83 00:09:21.540 ⇒ 00:09:27.109 Ryan Luke Daque: the script that generates the synthetic data to add the the missing ones.
84 00:09:27.300 ⇒ 00:09:30.379 Ryan Luke Daque: But yeah, well, I’ll have to take a look.
85 00:09:31.850 ⇒ 00:09:33.000 Ryan Luke Daque: Yeah, I don’t.
86 00:09:33.950 ⇒ 00:09:40.000 Ryan Luke Daque: I don’t know. Do you know, like, when we need this data, or like the models.
87 00:09:40.000 ⇒ 00:09:49.260 Amber Lin: Hi, Annie! Ideally, the models as soon as possible. I don’t think it needs to be super super complete like I like, maybe even
88 00:09:49.610 ⇒ 00:10:19.599 Amber Lin: if we have in spreadsheets, and we have it maneuvered quickly. I just want Annie to be able to do the modeling. And right now she’s having to do a lot of the adding the timestamps, adding this, adding that, and that takes up a lot of the analysis time. So it’d be great if you guys can work together to speed that up and doesn’t need to look that polished. We can polish it. Moving forward. But the clients it’s starting. They’re starting on June first, st and I really want some of this to
89 00:10:20.540 ⇒ 00:10:27.920 Amber Lin: get some more stuff out of the door. So just even basics adding it in an excel sheet that would be great.
90 00:10:30.880 ⇒ 00:10:31.500 Amber Lin: Yeah.
91 00:10:31.500 ⇒ 00:10:33.040 Ryan Luke Daque: Okay. I’ll.
92 00:10:33.040 ⇒ 00:10:36.680 Amber Lin: Do you wanna tell Luke exactly what you need?
93 00:10:37.910 ⇒ 00:10:40.630 Amber Lin: Didn’t you have a document yet?
94 00:10:40.630 ⇒ 00:10:51.830 Amber Lin: Yeah, I I shared just any specifics or any any priorities to deliver, first, st that you need immediately.
95 00:10:54.195 ⇒ 00:11:00.140 Annie Yu: We are talking about the level. 1.2 started.
96 00:11:01.520 ⇒ 00:11:09.240 Ryan Luke Daque: I would think so, because, like, this is the one that needs modeling right? Like the the one level, one doesn’t need modeling. It looks like.
97 00:11:10.080 ⇒ 00:11:11.850 Ryan Luke Daque: And $1.2.
98 00:11:11.850 ⇒ 00:11:15.169 Amber Lin: 1.2. I think one is finished.
99 00:11:15.843 ⇒ 00:11:30.400 Amber Lin: We need. We’re looking at 1.2. We actually do already have the collaboration time. So that’s done. What we need actually is for the focus time. So want to do it by focus time. And actually, once we do that, we also want
100 00:11:31.003 ⇒ 00:11:43.669 Amber Lin: level 2, which is by department we already have by remote or in person. So we want to be able to model focus time, which means we really really need those timestamps.
101 00:11:44.430 ⇒ 00:11:49.490 Amber Lin: So that we can calculate when things happened for this person.
102 00:11:49.970 ⇒ 00:11:52.960 Amber Lin: And like.
103 00:11:52.960 ⇒ 00:11:53.650 Ryan Luke Daque: Okay.
104 00:11:53.650 ⇒ 00:12:02.350 Amber Lin: Yeah. Does that make like? Does the recommended table schema below, Annie like? Is that everything you’ll need.
105 00:12:03.996 ⇒ 00:12:11.500 Annie Yu: If we don’t talk about department, we are just talking about individual focus time. I would say so.
106 00:12:12.876 ⇒ 00:12:16.209 Annie Yu: One thing I do. Wanna note is that
107 00:12:18.600 ⇒ 00:12:22.009 Annie Yu: Each time derived columns
108 00:12:22.180 ⇒ 00:12:31.450 Annie Yu: things like day of day wait time of hour of day, day of week. Everything should be based on each person’s local time.
109 00:12:31.929 ⇒ 00:12:35.039 Amber Lin: Does that apply to the start time and end time as well.
110 00:12:36.530 ⇒ 00:12:38.700 Amber Lin: Or will that make it too complicated?
111 00:12:38.910 ⇒ 00:12:43.429 Annie Yu: I I would say so, because if we just are curious
112 00:12:43.840 ⇒ 00:12:49.990 Annie Yu: people’s behavior by day by hour, everything should be based on local time.
113 00:12:52.310 ⇒ 00:12:58.639 Annie Yu: Cause. It doesn’t make sense to to mix everyone’s time zone into one time zone. Does that make sense.
114 00:12:58.640 ⇒ 00:13:06.490 Amber Lin: Yes, then it will introduce a lot of like time zone calculations.
115 00:13:06.710 ⇒ 00:13:12.130 Annie Yu: Yeah, that that’s something I I’m not comfortable doing on my end.
116 00:13:13.010 ⇒ 00:13:14.030 Amber Lin: Totally.
117 00:13:14.030 ⇒ 00:13:18.039 Amber Lin: But I also, and the start and end time to be in local time.
118 00:13:19.450 ⇒ 00:13:20.120 Ryan Luke Daque: Okay.
119 00:13:20.370 ⇒ 00:13:22.639 Annie Yu: I I think honestly, I
120 00:13:22.760 ⇒ 00:13:29.360 Annie Yu: don’t really need start and end time. We, I guess, for focus time. Let me see.
121 00:13:29.360 ⇒ 00:13:31.359 Amber Lin: I think we’re focused on. You might have to.
122 00:13:31.490 ⇒ 00:13:36.720 Annie Yu: Yeah, yeah, so everything should be in local time.
123 00:13:36.930 ⇒ 00:13:37.460 Amber Lin: It.
124 00:13:38.020 ⇒ 00:13:45.959 Annie Yu: But with with synthetic data, I guess we can just make assumption that everything is in local time. I don’t know what’s
125 00:13:47.690 ⇒ 00:13:52.176 Annie Yu: yeah, but just like just to know that in in reality everything.
126 00:13:52.550 ⇒ 00:13:58.660 Amber Lin: Yeah, yeah, that’s really helpful that we can say this, because then we can help tell them, hey, we need.
127 00:13:59.070 ⇒ 00:14:02.680 Amber Lin: we need this type of data. Or this is a transformation we need to do
128 00:14:03.693 ⇒ 00:14:14.979 Amber Lin: so based on the current tables, what modifications needs to be done, like what’s not, what’s there, and what’s not there, Annie? I think you’ve worked with it
129 00:14:15.290 ⇒ 00:14:24.849 Amber Lin: a lot like, where should Luke start? Because there’s a lot for him to think about, and I want I want a
130 00:14:26.077 ⇒ 00:14:33.509 Amber Lin: I want this to start as soon as possible. So any, if you can tell Luke we’re still what should where should he start first? st
131 00:14:34.074 ⇒ 00:14:41.880 Annie Yu: I’m just following a document. If we are just focusing on 1.2, I think your recommended table schema will work.
132 00:14:42.260 ⇒ 00:14:46.130 Annie Yu: That doesn’t include any collaboration across department.
133 00:14:48.095 ⇒ 00:14:55.299 Amber Lin: I added, 2 more rows, one from department to department.
134 00:14:55.550 ⇒ 00:14:56.180 Annie Yu: Yeah, as.
135 00:14:56.180 ⇒ 00:15:00.370 Amber Lin: Will that include? Will that help you do everything like cross collaboration, wise.
136 00:15:00.777 ⇒ 00:15:04.040 Annie Yu: I don’t think so. Just because the department.
137 00:15:04.280 ⇒ 00:15:12.089 Annie Yu: So for each email message and meeting there, I’m assuming there would be multiple to departments.
138 00:15:14.390 ⇒ 00:15:32.540 Annie Yu: So that that’s that’s that’s gonna introduce the same kind of the same issue that I show you about the meetings, you know, like there are multiple meeting attendees that I have to flatten those out. But so I think
139 00:15:33.330 ⇒ 00:15:35.409 Annie Yu: I actually think.
140 00:15:35.410 ⇒ 00:15:36.250 Amber Lin: Event, table.
141 00:15:36.250 ⇒ 00:15:41.129 Annie Yu: The yeah, there, there should be a separate table that’s aggregated by team level.
142 00:15:41.130 ⇒ 00:15:43.385 Amber Lin: Okay. Okay. Sounds good.
143 00:15:43.950 ⇒ 00:15:59.260 Annie Yu: And and I actually think Luke already have something similar. Remember bigquery. He did provide 2 kind of model example that he did. He did have one. That’s collaboration, but there’s 1,
144 00:15:59.720 ⇒ 00:16:02.639 Annie Yu: but that is just. There’s no time time stamp in that.
145 00:16:02.640 ⇒ 00:16:03.489 Amber Lin: But I think.
146 00:16:03.490 ⇒ 00:16:06.550 Annie Yu: The idea is similar to what he did there.
147 00:16:06.550 ⇒ 00:16:07.629 Amber Lin: Okay. Okay.
148 00:16:08.810 ⇒ 00:16:10.010 Amber Lin: Sounds good.
149 00:16:11.340 ⇒ 00:16:12.570 Annie Yu: Wait? Did he leave.
150 00:16:14.030 ⇒ 00:16:15.990 Amber Lin: Maybe he dropped off.
151 00:16:15.990 ⇒ 00:16:16.840 Annie Yu: Okay.
152 00:16:16.840 ⇒ 00:16:17.940 Amber Lin: Interesting.
153 00:16:18.220 ⇒ 00:16:22.959 Annie Yu: Yeah, but with with one, yeah, that’s why I was trying to say.
154 00:16:22.960 ⇒ 00:16:37.990 Amber Lin: I see. I see. Yeah, let me try. I’m gonna pass it through tragedy really, quickly, because that’s what that’s what gave me the table. Schema. I didn’t calculate anything, but let me pass it through real quick, and let me confirm with you right here of what?
155 00:16:38.190 ⇒ 00:16:40.939 Amber Lin: What it, what it is that we
156 00:16:41.740 ⇒ 00:16:44.550 Amber Lin: 8? 0, give me a sec.
157 00:17:18.960 ⇒ 00:17:19.980 Amber Lin: Oh.
158 00:18:35.388 ⇒ 00:18:42.340 Amber Lin: I’m gonna put it at the end of the document, let me know, if if that works.
159 00:19:05.840 ⇒ 00:19:07.310 Annie Yu: Is that a
160 00:19:08.370 ⇒ 00:19:10.049 Annie Yu: Did you already put it in.
161 00:19:11.080 ⇒ 00:19:17.700 Amber Lin: Yes, it should be at the end of the document I’m getting.
162 00:19:17.950 ⇒ 00:19:19.960 Amber Lin: Let me get the.
163 00:19:19.960 ⇒ 00:19:23.460 Annie Yu: So, page 11.
164 00:19:23.460 ⇒ 00:19:26.120 Amber Lin: Yeah, down.
165 00:19:27.470 ⇒ 00:19:29.040 Amber Lin: Yeah. Let me ask you.
166 00:19:44.490 ⇒ 00:19:48.679 Annie Yu: Do we need individual level for this table?
167 00:19:50.762 ⇒ 00:19:52.410 Amber Lin: What do you mean?
168 00:19:52.410 ⇒ 00:19:58.629 Annie Yu: Because I see that you have column here from user id to user. Id, I’m thinking.
169 00:19:58.630 ⇒ 00:20:06.330 Amber Lin: Yeah, I think that only that because everything we do is based on per user.
170 00:20:06.800 ⇒ 00:20:18.499 Amber Lin: Right? So if we look at a email, we actually start from the fact that okay, user, a emailed it to user B and C, and then we’ll be able to see. Okay, A is from Department
171 00:20:19.150 ⇒ 00:20:20.180 Amber Lin: something.
172 00:20:21.470 ⇒ 00:20:25.070 Amber Lin: So I think it the user Id would be there from the start.
173 00:20:26.560 ⇒ 00:20:27.210 Annie Yu: Hmm.
174 00:20:28.660 ⇒ 00:20:33.619 Amber Lin: Because we have to do a find their department, anyway. So might as well.
175 00:21:19.630 ⇒ 00:21:22.340 Amber Lin: were you able to see it in the document.
176 00:21:23.910 ⇒ 00:21:24.730 Annie Yu: Yeah.
177 00:21:42.470 ⇒ 00:21:43.770 Annie Yu: think about this.
178 00:21:44.100 ⇒ 00:21:44.630 Amber Lin: Hmm!
179 00:22:05.500 ⇒ 00:22:09.179 Amber Lin: The 2 modeling approach is for loop.
180 00:22:09.961 ⇒ 00:22:17.289 Amber Lin: Probably. What you look at is number one and number 3 and 4, which is pretty short.
181 00:22:32.040 ⇒ 00:22:32.960 Amber Lin: Oh.
182 00:22:41.260 ⇒ 00:22:44.558 Annie Yu: Yeah, I think my my point is,
183 00:22:48.490 ⇒ 00:22:54.950 Annie Yu: Can I? Okay, I’m gonna screen, share this. And I’m looking at that table. He billed before.
184 00:22:55.520 ⇒ 00:23:01.100 Annie Yu: So see this table, and I don’t have like a preview here. But this table.
185 00:23:01.100 ⇒ 00:23:01.929 Amber Lin: Wrong department.
186 00:23:01.930 ⇒ 00:23:02.480 Annie Yu: Level.
187 00:23:02.480 ⇒ 00:23:02.990 Amber Lin: Okay.
188 00:23:02.990 ⇒ 00:23:09.829 Annie Yu: Yeah, this one is collaboration, but the the most granular. It’s by department. So we don’t have to include
189 00:23:10.870 ⇒ 00:23:18.040 Annie Yu: individual level. So with this, I can avoid doing doing something like this here.
190 00:23:18.300 ⇒ 00:23:28.190 Annie Yu: So here we just have team aggregated level, even though I I don’t think we we need the exact same columns here, but this is just what he did.
191 00:23:28.440 ⇒ 00:23:30.769 Annie Yu: So this table just has.
192 00:23:30.770 ⇒ 00:23:31.600 Amber Lin: Okay.
193 00:23:32.260 ⇒ 00:23:40.499 Annie Yu: Yeah, team level. And I think this team level should be enough for showing team collab team collaboration.
194 00:23:40.500 ⇒ 00:23:59.950 Amber Lin: Okay? Sure. I mean, if you think that’s all you need, just tell Luke or edit, or just screenshot that send it to Chatgvt and send the existing table that we have in a document. And just let him know, like what what else you need. It seems like this is quite a this is
195 00:24:00.430 ⇒ 00:24:03.829 Amber Lin: this allows you to quite do quite a bit already. So
196 00:24:04.350 ⇒ 00:24:06.520 Amber Lin: like, what do you need to get started.
197 00:24:12.330 ⇒ 00:24:16.060 Amber Lin: Let me clarify. Let me ask this.
198 00:24:16.410 ⇒ 00:24:23.590 Annie Yu: So for this one we do want to show. Is it? Is it collaboration between teams.
199 00:24:26.840 ⇒ 00:24:30.819 Annie Yu: Or or we just want to show this one.
200 00:24:30.820 ⇒ 00:25:00.379 Amber Lin: For collaboration. Let’s let’s do more straightforward. I think you you already did the collaboration time right for the meetings, chats and emails. Right? We already have that I think the next part would be to find out the focus time first, st I think that’s a big thing we need to tackle. There’s a bit of a calculation involved of at least an hour of uninterrupted time. That’s focus time by their definition. And then we can say, compare by departments.
201 00:25:01.040 ⇒ 00:25:08.000 Amber Lin: and we can do a lot of things. When we compare by departments, we can compare by tools. We can compare by focus time by collaboration time.
202 00:25:08.210 ⇒ 00:25:09.120 Amber Lin: So
203 00:25:09.570 ⇒ 00:25:20.669 Amber Lin: I think there’ll there’ll be a lot more to do with the department. And that’s kinda I wanted us to do the focus time 1st and get the basic modeling needs down. But let me know what you think.
204 00:25:22.740 ⇒ 00:25:25.810 Annie Yu: Okay, let me let me think about this. So we do wanna
205 00:25:26.120 ⇒ 00:25:30.500 Annie Yu: be able to dive in. Let’s say, within one department.
206 00:25:30.900 ⇒ 00:25:37.680 Annie Yu: what’s the average focus time. And what’s the average focus time by day and by by time, by hour.
207 00:25:37.680 ⇒ 00:25:41.679 Amber Lin: Yeah. Yeah. Or by say, by month, like that.
208 00:25:42.250 ⇒ 00:25:43.449 Annie Yu: Oh, that works, too.
209 00:25:43.700 ⇒ 00:25:48.959 Annie Yu: Time, average focus time. Okay by month.
210 00:25:50.590 ⇒ 00:25:53.380 Amber Lin: Focus. Time looks more like a
211 00:25:53.800 ⇒ 00:25:59.649 Amber Lin: probably we have to do it individually first, st and then aggregate it
212 00:25:59.780 ⇒ 00:26:03.229 Amber Lin: because I would. I don’t know how else we would calculate.
213 00:26:03.500 ⇒ 00:26:05.920 Amber Lin: This whole department had 1 h.
214 00:26:05.920 ⇒ 00:26:09.619 Annie Yu: Yeah, that’s that’s 1 thing I’m thinking of. Okay.
215 00:26:17.960 ⇒ 00:26:27.680 Annie Yu: so we do it by individual level. And then aggregate those on the team level.
216 00:26:29.348 ⇒ 00:26:34.440 Amber Lin: We don’t even have to do it by by team level. First, st we can just say across the company.
217 00:26:34.870 ⇒ 00:26:35.970 Annie Yu: Okay. Yeah.
218 00:26:35.970 ⇒ 00:26:37.079 Amber Lin: Let’s do that first.st
219 00:26:37.390 ⇒ 00:26:42.480 Annie Yu: Okay, across the company average focus time by.
220 00:26:42.480 ⇒ 00:26:44.240 Amber Lin: Month, date.
221 00:26:44.450 ⇒ 00:26:46.160 Annie Yu: Time.
222 00:26:49.830 ⇒ 00:26:50.900 Annie Yu: Yeah. Okay.
223 00:26:50.900 ⇒ 00:26:54.090 Amber Lin: And then we can worry about departments later.
224 00:26:56.957 ⇒ 00:27:01.879 Amber Lin: Or if you wanna if you wanna tell Luke what you need. That would be great.
225 00:27:10.350 ⇒ 00:27:15.939 Annie Yu: And and did he react to this part that you you already shared this part.
226 00:27:17.056 ⇒ 00:27:25.369 Amber Lin: He’ll say, Yeah, okay, I’ll I’ll take a I’ll take a look at it, which means he’s gonna take a long time. So I I would want us to
227 00:27:26.400 ⇒ 00:27:28.650 Amber Lin: get him started sooner.
228 00:27:33.810 ⇒ 00:27:36.740 Annie Yu: Yeah, if if he can get to this.
229 00:27:37.050 ⇒ 00:27:41.530 Annie Yu: this one, I think, will be great. Without worrying
230 00:27:41.630 ⇒ 00:27:47.280 Annie Yu: about like across department yet. But this will be great, because this has
231 00:27:48.160 ⇒ 00:28:01.540 Annie Yu: all the chats, meetings, emails. I that’s the idea. That’s something that I I will struggle to have them on one place. So the I think this one is
232 00:28:01.540 ⇒ 00:28:02.489 Annie Yu: sounds good.
233 00:28:02.490 ⇒ 00:28:07.519 Annie Yu: We yeah should be prioritized over the team level.
234 00:28:07.520 ⇒ 00:28:12.450 Amber Lin: Okay, okay, this is great. So I will add him.
235 00:28:12.920 ⇒ 00:28:15.930 Amber Lin: And so.
236 00:28:20.460 ⇒ 00:28:27.419 Amber Lin: okay, I’m gonna say, prioritize this, and I’ll let him like.
237 00:28:27.530 ⇒ 00:28:33.490 Amber Lin: when do you want this? By ideally? It’s probably not going to be today is my feeling.
238 00:28:34.773 ⇒ 00:28:47.810 Annie Yu: I would let him to set the timeline cause. I I don’t really know how much time that would take him. I think he would know better than I do. But then.
239 00:28:47.970 ⇒ 00:28:48.690 Annie Yu: when.
240 00:28:48.690 ⇒ 00:28:50.779 Amber Lin: Awful. Pressed by the client.
241 00:28:51.700 ⇒ 00:28:56.930 Annie Yu: Yeah, when do we wanna deliver this this part.
242 00:28:57.199 ⇒ 00:29:10.119 Amber Lin: It’s already Tuesday. I don’t know how long. Model. Like ideal ideally in the perfect world. He works on this while you’re asleep you wake up and you do this, and by end of day tomorrow we have something for the client. But that’s in the ideal world.
243 00:29:10.460 ⇒ 00:29:18.129 Annie Yu: Yeah. And I I yeah, if you just give me like one daily time. That’s like risky. I wanna say.
244 00:29:18.130 ⇒ 00:29:22.096 Amber Lin: So that I don’t. I’m not gonna say that it would say like Thursday.
245 00:29:22.690 ⇒ 00:29:29.250 Amber Lin: But is there anything else you can work on while you’re blocked by that.
246 00:29:33.450 ⇒ 00:29:39.209 Annie Yu: I mean, I can always try to work with that focus time. But I don’t think that’s a.
247 00:29:39.500 ⇒ 00:29:40.690 Amber Lin: No, because you’ll be.
248 00:29:40.690 ⇒ 00:29:42.169 Annie Yu: Good Use of Time.
249 00:29:42.170 ⇒ 00:29:42.990 Amber Lin: Yeah.
250 00:29:43.320 ⇒ 00:29:43.830 Annie Yu: But.
251 00:29:45.425 ⇒ 00:29:46.510 Amber Lin: May.
252 00:29:46.510 ⇒ 00:29:53.889 Annie Yu: Do. I have to have something to do in the meantime, for matter more because I do have something to do for our clients. But yeah, I.
253 00:29:53.890 ⇒ 00:29:55.530 Amber Lin: Wow. Okay.
254 00:29:55.530 ⇒ 00:30:00.639 Annie Yu: Like is that is that the idea, like everyone, has something to do at the same time.
255 00:30:01.190 ⇒ 00:30:01.920 Amber Lin: Yeah.
256 00:30:03.900 ⇒ 00:30:06.940 Amber Lin: That way we can push it a little faster.
257 00:30:07.120 ⇒ 00:30:14.340 Annie Yu: Yeah, I can figure out that team team aggregate level for the next step. But
258 00:30:18.980 ⇒ 00:30:29.639 Annie Yu: yeah, and and for for the kind of the fake data. There’s like more columns that we’re not using, which I think in reality, we probably want to use.
259 00:30:29.880 ⇒ 00:30:30.570 Amber Lin: Okay.
260 00:30:36.570 ⇒ 00:30:55.700 Amber Lin: I mean you. There’s if you scroll down to correlations, there’s a few easy correlations we can explore versus like, for example, meeting duration versus messaging volume. Right? We already have all that data. Maybe that’s an interesting correlation. We can explore. Maybe email volume versus meeting count.
261 00:30:56.210 ⇒ 00:31:02.829 Amber Lin: And like, we can explore some of those correlations.
262 00:31:04.290 ⇒ 00:31:06.240 Amber Lin: That will be a nice to add.
263 00:31:07.230 ⇒ 00:31:09.400 Annie Yu: Yeah, with the current.
264 00:31:09.730 ⇒ 00:31:12.859 Amber Lin: Yeah, with the current stuff we have. I think that’s pretty possible.
265 00:31:16.390 ⇒ 00:31:21.999 Annie Yu: Yeah, I can. I can work on that. But like, not by department.
266 00:31:22.190 ⇒ 00:31:23.110 Amber Lin: No, don’t need.
267 00:31:23.110 ⇒ 00:31:28.820 Annie Yu: Across company. Yeah, I think that can that can be done.
268 00:31:31.360 ⇒ 00:31:46.200 Annie Yu: Yeah, but I’m not. I think in general, I’m not gonna promise. I can like, get one thing done within one day, like I usually like. I also talk about this with Robert like on Eden, like usually like I would want to have at least 2 days.
269 00:31:46.706 ⇒ 00:31:51.860 Annie Yu: So I I mean if I can. I can deliver earlier. But I just I don’t like that.
270 00:31:51.860 ⇒ 00:31:52.969 Amber Lin: Yeah, that’s okay. That’s okay.
271 00:31:52.970 ⇒ 00:31:54.640 Annie Yu: Promise, but under deliver.
272 00:31:54.640 ⇒ 00:32:15.640 Amber Lin: Okay, that’s all good. I actually prefer that. All I need to know is that okay? When we actually show Matthew say on Thursday, right that we don’t just have one single thing after 2, 2 to 3 days of work, because we have Tuesday, Wednesday, Thursday. I don’t want us to show one thing after 3 days, so I just wanted to squeeze something in there. That’s all.
273 00:32:15.810 ⇒ 00:32:28.760 Annie Yu: Yeah, yeah. Then the correlation. Let me think about this. Yes, I can do. If there’s correlation between meeting average, I guess meeting, yeah, meeting and emails.
274 00:32:30.140 ⇒ 00:32:39.409 Annie Yu: I think that will work and then I think we already have the correlation between remote versus on on site, in office.
275 00:32:40.010 ⇒ 00:32:44.910 Amber Lin: Hmm! Do we have correlation, or do we have like a.
276 00:32:44.910 ⇒ 00:32:50.479 Annie Yu: Did that but there’s nothing that we can show in visuals with that statistics.
277 00:32:50.480 ⇒ 00:32:53.500 Annie Yu: There’s like numbers. Yeah, that’s.
278 00:32:53.520 ⇒ 00:32:54.160 Amber Lin: See.
279 00:32:54.160 ⇒ 00:32:57.499 Annie Yu: That’s presentable. But it’s not gonna be.
280 00:32:57.500 ⇒ 00:32:58.390 Amber Lin: I see.
281 00:32:58.390 ⇒ 00:32:59.780 Annie Yu: Visual. Yeah.
282 00:33:01.440 ⇒ 00:33:04.742 Amber Lin: I’ll I’ll put like a big number on the.
283 00:33:05.110 ⇒ 00:33:14.599 Annie Yu: Yeah, so yeah, so with correlations, there’s not just one approach for each data. There are different types of correlate. But I can do
284 00:33:15.340 ⇒ 00:33:15.850 Annie Yu: on like.
285 00:33:15.850 ⇒ 00:33:20.782 Amber Lin: Oh, great! Yeah, I’ll squeeze that into the.
286 00:33:21.270 ⇒ 00:33:31.639 Annie Yu: Okay, yeah. But I do believe, yeah, I will let you know. I do believe some things can be presented in some type of charge, but not everything can.
287 00:33:31.640 ⇒ 00:33:33.560 Amber Lin: Yeah, yeah, that’s okay.
288 00:33:36.070 ⇒ 00:33:36.810 Amber Lin: And.
289 00:33:37.540 ⇒ 00:33:40.296 Annie Yu: Okay? So I, yeah, that let’s say,
290 00:33:41.890 ⇒ 00:33:47.280 Annie Yu: okay, I I can try to make like correlations by tomorrow.
291 00:33:48.050 ⇒ 00:33:50.530 Annie Yu: but probably not a lot of
292 00:33:50.740 ⇒ 00:33:59.370 Annie Yu: a lot of topics, but I think meetings and will be worth worth doing.
293 00:34:04.630 ⇒ 00:34:16.379 Amber Lin: great, sounds good. I’m gonna message Luke. I’ll let him explore the stuff, we have build that as soon as possible, and he probably will. Might have some questions for you, too.
294 00:34:17.770 ⇒ 00:34:18.370 Amber Lin: Yeah.
295 00:34:18.370 ⇒ 00:34:29.639 Annie Yu: Okay, yeah, I think, I think, yeah, I’m I’m just curious how how much time it will take for him. Cause there’s like different type of things. So I, yeah, I don’t have an answer, but.
296 00:34:34.330 ⇒ 00:34:35.020 Annie Yu: Okay?
297 00:34:39.270 ⇒ 00:34:47.019 Annie Yu: Okay, then, will you do? We still have tickets for metamor. Is that.
298 00:34:47.880 ⇒ 00:35:01.489 Amber Lin: I can create them. I mean, right now is Luke’s modeling your correlation stuff and after Luke does the modeling the focus time. Right? Those are the 3 main tickets that we’re gonna do.
299 00:35:01.490 ⇒ 00:35:02.250 Annie Yu: Yes, yes.
300 00:35:02.250 ⇒ 00:35:08.019 Amber Lin: Yeah, I can. I can help you create them. Once I get some time, I just want some time off to get coffee.
301 00:35:08.110 ⇒ 00:35:09.920 Annie Yu: Are you back in LA.
302 00:35:09.920 ⇒ 00:35:14.340 Amber Lin: Yeah, back in La. And I feel claustrophobic working in my room.
303 00:35:16.240 ⇒ 00:35:16.820 Amber Lin: Oh.
304 00:35:17.286 ⇒ 00:35:22.420 Annie Yu: Okay, no, I. Okay. I think this direction is good. Okay.
305 00:35:22.420 ⇒ 00:35:23.190 Amber Lin: Great.
306 00:35:23.400 ⇒ 00:35:23.810 Annie Yu: Cool.
307 00:35:23.810 ⇒ 00:35:24.730 Amber Lin: Sounds good.
308 00:35:25.010 ⇒ 00:35:26.350 Annie Yu: Thank you. Amber.
309 00:35:26.350 ⇒ 00:35:29.449 Amber Lin: Yeah, thank you. And we’ll talk soon.
310 00:35:29.450 ⇒ 00:35:30.599 Annie Yu: Yeah. Okay, bye.
311 00:35:30.780 ⇒ 00:35:31.710 Amber Lin: Bye.