Meeting Title: Brainforge x Mattermore Sync Date: 2025-05-21 Meeting participants: Annie Yu, Amber Lin, Mathew
WEBVTT
1 00:01:40.930 ⇒ 00:01:42.270 Amber Lin: Hi.
2 00:01:43.100 ⇒ 00:01:44.510 Annie Yu: Hello, Amber.
3 00:01:50.510 ⇒ 00:01:52.540 Annie Yu: are you? At Utam’s home?
4 00:01:54.410 ⇒ 00:01:55.180 Annie Yu: Nice.
5 00:01:55.180 ⇒ 00:01:59.780 Amber Lin: Can’t tell. Things are a bit different, too.
6 00:02:00.270 ⇒ 00:02:00.810 Mathew: 18.
7 00:02:03.586 ⇒ 00:02:04.760 Annie Yu: Hello, Matthew.
8 00:02:07.370 ⇒ 00:02:11.639 Amber Lin: Matthew. I’m in Utam’s house right now, and he is on vacation.
9 00:02:12.180 ⇒ 00:02:13.970 Mathew: Your house, sitting, your dog sitting.
10 00:02:14.281 ⇒ 00:02:29.218 Amber Lin: No there was an event here in Austin that we were hosting, so I flew in to photography. Oh, you have a nice background and then going on vacation. So it was like, Yo can I? Can. I just use your house? He was like, Yeah, sure.
11 00:02:29.530 ⇒ 00:02:30.430 Mathew: Funny.
12 00:02:31.570 ⇒ 00:02:32.469 Mathew: Good news.
13 00:02:33.810 ⇒ 00:02:37.129 Amber Lin: Where are you? Where are you at right now, or what city are you in.
14 00:02:37.130 ⇒ 00:02:42.153 Mathew: I’m in New York. I’m in Brooklyn. My local Bradham cava were
15 00:02:43.124 ⇒ 00:02:45.429 Mathew: like a work bar setup. One.
16 00:02:45.430 ⇒ 00:02:51.889 Amber Lin: Wow! I’m going to New York soon. I’m going to New York the weekend, like end of May.
17 00:02:52.150 ⇒ 00:02:52.900 Mathew: Let me know!
18 00:02:52.900 ⇒ 00:02:53.640 Amber Lin: Love, to meet up.
19 00:02:53.640 ⇒ 00:02:55.490 Mathew: Yeah. Yeah. Let me know.
20 00:02:56.540 ⇒ 00:02:58.899 Amber Lin: I’ll make all the recommendations I can
21 00:02:58.900 ⇒ 00:03:06.760 Amber Lin: exciting. Yeah. Luton was telling me about what what kind of stuff you were doing before you started this. And I was like, Wow, that’s very exciting.
22 00:03:10.842 ⇒ 00:03:17.357 Amber Lin: yeah, yeah, I’ll get in touch with you. Don’t wanna take your time, because I know you’re super busy.
23 00:03:17.700 ⇒ 00:03:21.599 Mathew: I mean this, this is this is super important to me. So I’m not gonna.
24 00:03:21.600 ⇒ 00:03:43.870 Amber Lin: I agree, cause this is your 1st impression to the client. When the project starts pressure every every single time we start a new project so hopefully. What I have is aligned with what you were thinking and then based on there, we can make adjustments. Well, did you have the chance to look at the loom video I sent. If not, I can just walk you through right now.
25 00:03:44.100 ⇒ 00:03:46.169 Mathew: Can’t see the loom video, but I have the doc.
26 00:03:46.570 ⇒ 00:03:54.789 Amber Lin: Okay, so quickly to walk you through what I talked about in the loom video, before I get into the details here, I want to talk about.
27 00:03:54.790 ⇒ 00:03:57.719 Mathew: No, we’re good. No, no, we’re good. We’re good.
28 00:03:59.020 ⇒ 00:04:20.589 Amber Lin: Yeah, before I go into details, I kind of want to talk about my approach of why I structured it this way. So 1st of all, the deck you sent me was really great, and I extracted from there the structure, and then the visual elements that they were using, and I liked their approach when they 1st of all
29 00:04:21.890 ⇒ 00:04:31.660 Amber Lin: had a first, st a wake-up call of establishing. Establishing the problem. They introduced their new mindset, which I think.
30 00:04:31.860 ⇒ 00:04:36.710 Mathew: Yeah. So so that was that was that was from that was like better upstack right?
31 00:04:37.320 ⇒ 00:04:37.930 Amber Lin: Yeah.
32 00:04:38.390 ⇒ 00:04:39.779 Mathew: Are you following up.
33 00:04:41.120 ⇒ 00:04:41.710 Amber Lin: Hmm.
34 00:04:42.190 ⇒ 00:04:46.409 Mathew: So that definitely was from better apps. Yeah.
35 00:04:46.410 ⇒ 00:04:46.840 Amber Lin: Totally.
36 00:04:46.840 ⇒ 00:04:55.920 Mathew: Their in their annual conference, and our and our decision Maker had just sent it to us, and he was like, Oh, I’m like, I like this. And then I shared it with you guys just from a design perspective not to like.
37 00:04:55.920 ⇒ 00:05:19.100 Amber Lin: Yeah, yeah, I see. And I like their design. And I like how they approach the problem. I don’t think we will not be able to replicate it because we’re in a different stage with a problem. But I do want to approach it as an okay, we want to establish a problem. We want to tell them this is our new model, because, Madam More is doing something very novel and very important.
38 00:05:19.100 ⇒ 00:05:25.150 Mathew: I think the thing is that I don’t. Wanna I don’t want to like resell them on what I we kind of already did. This.
39 00:05:25.150 ⇒ 00:05:26.760 Amber Lin: Yeah, yeah, I remember.
40 00:05:26.940 ⇒ 00:05:37.740 Mathew: Yeah, when we sold them on the original deck. So I think, like, before we introduce our own concepts or try and like mirror. What better up has.
41 00:05:38.010 ⇒ 00:05:38.400 Amber Lin: I’ve seen.
42 00:05:38.800 ⇒ 00:05:44.070 Mathew: I think that we really want to lean on what they what they’re currently expecting.
43 00:05:44.440 ⇒ 00:05:44.850 Amber Lin: And then.
44 00:05:44.850 ⇒ 00:05:48.459 Mathew: Like we we work to get that very much like.
45 00:05:49.090 ⇒ 00:05:58.820 Mathew: just like feeling really confident about what those outputs are gonna look like, and then, and then work on the story before that, and after that.
46 00:05:59.290 ⇒ 00:06:00.990 Amber Lin: Yeah, totally. So yeah.
47 00:06:00.990 ⇒ 00:06:05.140 Amber Lin: I didn’t want to derail you too far, I think, based on what you said. I do.
48 00:06:05.140 ⇒ 00:06:09.949 Mathew: No, no, you didn’t, you didn’t you? Didn’t. You didn’t derail. I I like, like, for example, I think Act
49 00:06:10.480 ⇒ 00:06:13.579 Mathew: Act 3 is the I like in.
50 00:06:13.580 ⇒ 00:06:14.360 Amber Lin: Up, to.
51 00:06:14.360 ⇒ 00:06:17.090 Mathew: Slide 5. I like.
52 00:06:17.560 ⇒ 00:06:30.640 Mathew: I like how you’ve like broken that. That’s that I that stuff I love. I love at here like I could. Where I’m like looking at on myself. Yeah. So I love. Act 2. What you did with like slide 5,
53 00:06:30.790 ⇒ 00:06:35.049 Mathew: maybe I should share. Can I share my screen because it’ll be harder.
54 00:06:37.020 ⇒ 00:06:41.499 Mathew: And this is very useful like, and I think this is exactly what we need to be iterating on so.
55 00:06:43.420 ⇒ 00:06:44.900 Mathew: Let me know if you can see those.
56 00:06:45.270 ⇒ 00:06:49.359 Mathew: Yeah, I can see it saying, like, start with, Yeah, go ahead.
57 00:06:50.102 ⇒ 00:06:53.649 Amber Lin: Just a quick like 30 seconds before we get into this.
58 00:06:53.650 ⇒ 00:06:54.140 Mathew: Yeah.
59 00:06:54.140 ⇒ 00:07:15.599 Amber Lin: Look at the other tab. What I? What I approached it from is essentially they hired you already, and right now what our role is is to make the client feel cared for and feel confident in in that. You’re gonna lead this in a way, address your problems because we wanna make sure that psychologically, emotionally, they feel safe with you guys.
60 00:07:15.600 ⇒ 00:07:17.449 Mathew: Exactly exact, exactly exactly.
61 00:07:17.450 ⇒ 00:07:33.419 Amber Lin: And so there’s a lot of questions that we want to answer. They they have doubts about, and that’s why they hired you like emotionally, they feel very exposed and lost. They don’t know where to make decisions from. And kind of that core emotion is how I built this around. We’re gonna answer.
62 00:07:33.420 ⇒ 00:07:55.160 Mathew: Yeah. Well, the thing is, the thing is is like there. I wouldn’t say they feel lost. They’re they’re very capable stakeholders, right? This guy needs a 30 person. People analytics team. He’s very confident. I want him to feel like we’re his best resource on his people analytics team. That’s using a new, a new. This new resource very confidently, and, matter of factly.
63 00:07:55.910 ⇒ 00:08:01.299 Amber Lin: I see. So to comment on that. So
64 00:08:01.640 ⇒ 00:08:12.660 Amber Lin: what you’re trying to make them feel is they’re not that lost per se. They they’re not that doubtful of their how people are performing. I think what you’re trying to say.
65 00:08:12.660 ⇒ 00:08:14.560 Mathew: I didn’t say that. I didn’t say that. I said there.
66 00:08:14.560 ⇒ 00:08:15.340 Amber Lin: Okay.
67 00:08:16.120 ⇒ 00:08:23.739 Mathew: They’re not. If we’re being really, really specific, they are. They know what they don’t know. They know what gaps exist in their data.
68 00:08:23.740 ⇒ 00:08:24.150 Amber Lin: Oh!
69 00:08:24.150 ⇒ 00:08:41.589 Mathew: And they want. And they want to use basically passive collaboration data analysis to gain that insight. So they have defensible data, driven reports, outputs, etc, that they can use to align stakeholder perceptions.
70 00:08:42.100 ⇒ 00:08:46.479 Mathew: And then B use that for strategic workforce planning decisions.
71 00:08:46.830 ⇒ 00:08:51.569 Amber Lin: Yeah, I get you so essentially, this meeting.
72 00:08:51.570 ⇒ 00:08:55.080 Mathew: Even say what I just said. We can. Even we can say that upfront. That’s like.
73 00:08:55.080 ⇒ 00:09:22.529 Amber Lin: Yeah, yeah, that’s what I that’s what I wanted to outline. If you scroll up a little bit just a little bit above Act 2. Those are the questions that essentially I want us to have an answer for, or have our synthetic data know how to answer for. So I want us to, to iterate it, reiterate what you said to be able to answer, how and what like are people working? And then.
74 00:09:22.530 ⇒ 00:09:31.989 Mathew: And I and I. I like this. But for my process, I think for for me to think this through, I would love to start with like, what outputs do we have.
75 00:09:32.670 ⇒ 00:09:33.070 Amber Lin: Hmm.
76 00:09:33.070 ⇒ 00:09:46.569 Mathew: We feel really confident about. And then what questions can we back into that? We feel confident that we can answer. So my mind doesn’t go here first.st My mind goes to act 2 and act 3 really act 3. Act 3 is the meet
77 00:09:47.960 ⇒ 00:09:49.340 Mathew: of where we go.
78 00:09:49.490 ⇒ 00:09:54.120 Mathew: Get that? Let’s make sure we’re aligned with what that’s gonna be. And then we can work backwards intact
79 00:09:54.340 ⇒ 00:10:11.899 Mathew: 2, and like making that more like a framework, and then eventually get back to act one. Which is like, what are we answering? But like things like like today’s leaders are flying blind. That’s like, I don’t need to re-educate them about how they’re making decisions. That’s more salesy.
80 00:10:12.100 ⇒ 00:10:24.921 Mathew: And for, like new people that don’t understand this for stakeholders. Like, that’s that that 1 4 questions you want to answer. I can. I can think this through and translate
81 00:10:26.280 ⇒ 00:10:27.549 Mathew: that makes sense.
82 00:10:28.030 ⇒ 00:10:30.419 Amber Lin: Yeah, I I understand. So
83 00:10:30.670 ⇒ 00:10:50.929 Amber Lin: I agree, that’s what we need to start from what we can do. So we don’t extend ourselves to too far and promise what we can’t deliver. I just wanted to hear what these questions would be, because essentially, we can make a lot of these analysis happen in Act 3. But we
84 00:10:51.020 ⇒ 00:11:06.769 Amber Lin: want to align it to a story right? Because if we have a lot of scatter analysis, we might not use a lot of them. I don’t, and I and I want to guide the stakeholders through their thinking, and not just scatter.
85 00:11:06.770 ⇒ 00:11:15.399 Mathew: Well, yeah, I mean, so so ultimately, I know, I like that. We’re like mocking this out. But ultimately, like, even, I’m looking at a slide, a.
86 00:11:15.400 ⇒ 00:11:15.880 Amber Lin: 3.
87 00:11:15.880 ⇒ 00:11:17.030 Mathew: Slide a 1.
88 00:11:18.270 ⇒ 00:11:26.740 Mathew: This claim like, ultimately, we’re just gonna need to show like this is how emails are happening. This is what chat looks like. This is what meetings look like.
89 00:11:27.360 ⇒ 00:11:28.380 Mathew: Right?
90 00:11:28.380 ⇒ 00:11:29.030 Amber Lin: Hmm.
91 00:11:29.030 ⇒ 00:11:32.750 Mathew: This is what focus. And then the reverse is like, this is what focus time looks like.
92 00:11:36.490 ⇒ 00:11:51.540 Mathew: And then, and then we’re and then we slice it into like what you have here, different departments, different rhythms, different variables, remote, etc. So yeah, I I think it’s I think we’re saying the same thing here. But I’m just. I’m hesitant to to be like
93 00:11:53.190 ⇒ 00:11:55.150 Mathew: to say, like.
94 00:11:56.507 ⇒ 00:12:06.529 Mathew: I want to believe our teams are engaged. But I have no way of seeing. Like, yeah, I mean, I’m gonna need to think that through. I know I my eyes blaze over because I assume a lot of some of this is AI generated right.
95 00:12:08.200 ⇒ 00:12:21.049 Mathew: That’s where my eyes glaze over. I’m like AI can say a lot, but like I want to be careful before, like buying into it and taking it for granted. And then I. And then I want to like, I want to know, like, for example, like
96 00:12:21.743 ⇒ 00:12:35.480 Mathew: slide a 1, it’s like, yeah, we’re gonna look at emails. We’re gonna look at like like you have emails, chat meetings. Probably something like, maybe combine ace like normalize Async
97 00:12:35.870 ⇒ 00:12:50.640 Mathew: Comms, maybe like map meetings and emails to and and Comms together. You know what I’m saying again and then and then let’s let’s look at this by teams, you know. Then let’s, this is Globe. This is like on average right total. Then let’s go by teams.
98 00:12:50.760 ⇒ 00:12:54.230 Mathew: Then let’s do this remote. You know. You see what I’m typing.
99 00:12:56.750 ⇒ 00:13:02.180 Mathew: Which is like what you have here. It’s it’s like that. Yeah. So I think this is, I think this is solid.
100 00:13:05.920 ⇒ 00:13:09.370 Mathew: Are we losing time or efficiency?
101 00:13:15.760 ⇒ 00:13:21.649 Amber Lin: So this is more of a insight of okay. We mapped all of that. What does that mean?
102 00:13:22.420 ⇒ 00:13:22.990 Mathew: Yeah.
103 00:13:22.990 ⇒ 00:13:25.060 Amber Lin: Relate to output. So we’ve got.
104 00:13:25.060 ⇒ 00:13:25.889 Mathew: Yeah, yeah.
105 00:13:25.890 ⇒ 00:13:27.709 Amber Lin: Get it to something impact, impactful.
106 00:13:27.710 ⇒ 00:13:32.411 Mathew: Yeah, we’re talking more, creating less.
107 00:13:34.010 ⇒ 00:13:36.680 Mathew: well, the missing piece here is gonna be
108 00:13:37.744 ⇒ 00:13:41.779 Mathew: sorry, like, actual productivity, time and productivity tools.
109 00:13:46.740 ⇒ 00:13:51.419 Mathew: Like, yeah, that what we’re trying to establish like productivity so old.
110 00:13:53.050 ⇒ 00:13:56.670 Mathew: And then I’d like to then see these like overlaid against each other.
111 00:13:59.700 ⇒ 00:14:06.580 Amber Lin: Would we ha eventually have data against their company productivity, like output.
112 00:14:06.580 ⇒ 00:14:09.590 Mathew: Sorry this is. This is Microsoft 3, 65, basically.
113 00:14:14.227 ⇒ 00:14:18.760 Mathew: I I don’t. I don’t think we’ll have raw productivity outputs. No.
114 00:14:18.910 ⇒ 00:14:21.340 Amber Lin: I see. Okay, so that’s sort of
115 00:14:21.510 ⇒ 00:14:27.029 Amber Lin: we’ll give them all these data, and they can decide. So we we have to also help them.
116 00:14:27.150 ⇒ 00:14:29.249 Amber Lin: help them. Learn how to use these.
117 00:14:29.660 ⇒ 00:14:30.330 Mathew: Yeah.
118 00:14:30.630 ⇒ 00:14:31.310 Amber Lin: Okay.
119 00:14:34.290 ⇒ 00:14:37.480 Mathew: Are we losing time? Does that all make sense so far before I keep going.
120 00:14:37.480 ⇒ 00:14:45.280 Amber Lin: Yeah, totally. These parts are like pretty foundational. I I think both Annie me, Luke, have good understanding of what that is.
121 00:14:47.210 ⇒ 00:14:47.585 Mathew: Wow.
122 00:14:48.360 ⇒ 00:14:55.000 Mathew: Section 2, are we losing time or efficiency?
123 00:14:58.240 ⇒ 00:14:58.940 Mathew: Let’s
124 00:15:02.650 ⇒ 00:15:08.339 Mathew: content generation. We have to define. I I think it’s actually more like what I just did, productivity, tools.
125 00:15:08.480 ⇒ 00:15:15.709 Amber Lin: That’s why this is. This makes a lot of it’s load. It’s loaded with a lot of assumptions. We have to be careful before jumping with questions like these.
126 00:15:16.270 ⇒ 00:15:16.869 Amber Lin: I see.
127 00:15:17.141 ⇒ 00:15:25.279 Mathew: We haven’t defined focus time yet. So we have to actually like up here. I think there’s a bridge to actually like. Then we can start getting into focus time.
128 00:15:25.860 ⇒ 00:15:33.160 Mathew: which is basically the absence of like, it was basically like the like longer periods of uninterrupted.
129 00:15:38.580 ⇒ 00:15:41.298 Mathew: Tool switching. I don’t know how we would capture
130 00:15:45.440 ⇒ 00:15:50.269 Mathew: this would. This is basically this needs to be answered by
131 00:15:56.960 ⇒ 00:16:00.570 Mathew: this needs to just be answered by like productivity.
132 00:16:00.870 ⇒ 00:16:05.616 Mathew: slash meetings by days, like just showing that there’s baseline activity.
133 00:16:15.400 ⇒ 00:16:17.610 Mathew: this is cool. I like this. This is cool.
134 00:16:18.738 ⇒ 00:16:22.620 Mathew: Some like. The our risk is that we’re kind of making this up.
135 00:16:22.880 ⇒ 00:16:23.650 Mathew: It’s like we’re not.
136 00:16:23.650 ⇒ 00:16:25.120 Amber Lin: Yeah, a lot of a lot of.
137 00:16:25.120 ⇒ 00:16:27.310 Mathew: So we have to be. We have to be very careful.
138 00:16:27.310 ⇒ 00:16:27.690 Amber Lin: Yeah.
139 00:16:27.690 ⇒ 00:16:49.590 Mathew: I really think we have to like almost like unpack. What’s the 1st principle? Claim that you’re making rather than you like trying to build out all these slides, and then, because it’s just, my eyes will glaze over. It has to be like, what’s the 1st principle assumption here, for example, you know, like this is kind of like it feels like an insight. But there’s actually there’s a there’s a 1st principle here that, like calendar
140 00:16:50.482 ⇒ 00:17:05.160 Mathew: like not just calendar, like lots of meetings will do this, or lots of calendar will do that. So and we have to build that case. I’m not saying, you have to do this alone. I probably need to sit with this and give you more direction, because we have to be the experts in this. If you know what I mean.
141 00:17:05.160 ⇒ 00:17:15.329 Amber Lin: Yeah. Yeah. And what I came across when I was trying to do this is that all of these are floating, and especially because we’re also making up the data.
142 00:17:15.540 ⇒ 00:17:22.760 Amber Lin: None of this can. Sometimes maybe all this would be wrong. So I was trying to do a lot of research on actually.
143 00:17:22.760 ⇒ 00:17:23.170 Mathew: We just said.
144 00:17:23.619 ⇒ 00:17:27.659 Mathew: Yeah, we just we yeah, we have to know what levers we can pull. We have to know, like.
145 00:17:28.099 ⇒ 00:17:32.349 Mathew: if there’s after hours work. What’s the implications of that? Right?
146 00:17:32.659 ⇒ 00:17:41.899 Mathew: So instead of you jumping to the inside of like trying to create this, the narrative, instead of jumping to the net, the insight, like the mock, the fake insight of.
147 00:17:41.900 ⇒ 00:17:42.920 Amber Lin: It would be after.
148 00:17:42.920 ⇒ 00:17:44.190 Mathew: Work hours
149 00:17:45.140 ⇒ 00:17:52.510 Mathew: like in. Just like, just let’s let’s list out the variables, right? Like, after yeah, after work, hours.
150 00:17:52.510 ⇒ 00:17:54.560 Amber Lin: Happened then. It means this, but.
151 00:17:54.560 ⇒ 00:17:56.900 Mathew: Yeah, could mean that. Like, yeah.
152 00:17:59.750 ⇒ 00:18:01.439 Amber Lin: So that will help also.
153 00:18:01.440 ⇒ 00:18:03.899 Mathew: It’s kind of this. But
154 00:18:04.960 ⇒ 00:18:10.179 Mathew: this, this I I care less about what it suggests. And I it’s more about like what the
155 00:18:10.983 ⇒ 00:18:15.590 Mathew: that some of this, there’s some, some of basically some of this.
156 00:18:22.200 ⇒ 00:18:27.790 Mathew: let me ask you, what’s your what’s your next step of when you’re planning to like work through this stuff.
157 00:18:28.040 ⇒ 00:18:28.590 Mathew: Because I.
158 00:18:29.470 ⇒ 00:18:30.020 Amber Lin: Sure.
159 00:18:30.180 ⇒ 00:18:32.919 Mathew: Yeah, like, if I had, if I had like a
160 00:18:33.240 ⇒ 00:18:38.929 Mathew: a good work block of time, I could probably do some like significant damage on this dock.
161 00:18:39.210 ⇒ 00:18:43.520 Amber Lin: Yeah, this is totally up for change. I wanted to.
162 00:18:43.680 ⇒ 00:18:49.240 Amber Lin: I I gave you a framework that you can comment on, and then you can fully just destroy this and make something new.
163 00:18:49.240 ⇒ 00:18:50.299 Mathew: Yeah, yeah, yeah.
164 00:18:50.300 ⇒ 00:18:53.030 Amber Lin: Something that you can disagree on so.
165 00:18:53.030 ⇒ 00:19:01.919 Mathew: No, no, you did. You did. Mission mission no, no mission accomplished you. You’ve gotten me like, I realize, like, where our gaps are and how I need to, how I need to get ahead of this.
166 00:19:01.920 ⇒ 00:19:04.055 Amber Lin: Yeah, cool.
167 00:19:05.680 ⇒ 00:19:18.869 Amber Lin: continue to doing the analysis, especially on the foundational blocks, that we already are very clear on the 1st part that we define for Act 3. You know the meetings, chat, etc, and once you have a
168 00:19:19.130 ⇒ 00:19:26.999 Amber Lin: new comments on the outline, and I can polish it. You do it last pass, and I can just pull out a slide deck.
169 00:19:27.000 ⇒ 00:19:41.560 Mathew: Ask you any. Do you need anything for me from us to continue your work, or you go through like the next week on, like, just like starting to get. I assume you’re basically creating like the the ways of analyzing and the functions and the outputs like, you’re just like working through those.
170 00:19:42.623 ⇒ 00:19:45.447 Annie Yu: I actually don’t know the next step, but
171 00:19:45.800 ⇒ 00:19:48.129 Mathew: Okay, that’s not good. So what do we? What do.
172 00:19:48.320 ⇒ 00:19:52.240 Annie Yu: No, I mean, can I walk you through what I did so far.
173 00:19:52.240 ⇒ 00:19:52.990 Mathew: Yes.
174 00:19:54.270 ⇒ 00:19:57.790 Annie Yu: And then so.
175 00:19:58.090 ⇒ 00:20:01.169 Mathew: And how long have you not known what to what to do?
176 00:20:02.072 ⇒ 00:20:03.520 Annie Yu: No, I mean
177 00:20:03.780 ⇒ 00:20:12.839 Annie Yu: for this analysis. I I think I shipped it where I commented. That’s where I’m focusing on. Only like
178 00:20:13.532 ⇒ 00:20:15.070 Annie Yu: I’m I’m gonna
179 00:20:16.850 ⇒ 00:20:32.930 Annie Yu: so first, st I explore some of the data using the synthetic data. And I realized, okay, due to the random setup. I couldn’t find any like patterns or relationships. So I had to download it.
180 00:20:34.680 ⇒ 00:20:45.770 Annie Yu: So that’s where I like. I decided, I’m not gonna do like all the data sets. So I only focus on 2. I downloaded the Sync success factors, users
181 00:20:45.930 ⇒ 00:20:46.560 Annie Yu: as well.
182 00:20:46.560 ⇒ 00:20:50.640 Annie Yu: You need to have the you have. You need to know who’s who and what team they’re on
183 00:20:50.640 ⇒ 00:21:04.530 Annie Yu: as well as the user the emails activity. So for this kind of small study, I only focus on those 2 because I had the data and then adjusted the event setup the event sent
184 00:21:05.050 ⇒ 00:21:08.959 Annie Yu: timestamp. So we can make sure. Okay, there is a pattern
185 00:21:11.080 ⇒ 00:21:15.509 Annie Yu: compare like Friday, remote days relative to Monday to Thursday.
186 00:21:16.280 ⇒ 00:21:21.220 Annie Yu: And then that’s where I okay. So we got to get to the
187 00:21:21.750 ⇒ 00:21:26.750 Annie Yu: some of the exploratory visuals. So here we are just
188 00:21:27.260 ⇒ 00:21:41.029 Annie Yu: describing. Okay, we see like that difference between Monday, Thursday versus Friday. Back up with some more visuals. But then I run I ran like a
189 00:21:41.280 ⇒ 00:21:48.609 Annie Yu: T test to see pretty messy. But we actually, we can look at this.
190 00:21:48.970 ⇒ 00:21:52.490 Annie Yu: So with all these visuals, I then run
191 00:21:52.720 ⇒ 00:21:57.260 Annie Yu: a test, so we can see that, based on statistics, there is a.
192 00:21:57.260 ⇒ 00:21:58.989 Mathew: Yeah, yeah, okay.
193 00:21:58.990 ⇒ 00:22:10.360 Annie Yu: Relation between email sent versus remote day or
194 00:22:10.930 ⇒ 00:22:22.360 Annie Yu: on Site Day. And I added, like a final chart where we can see 95% confidence intervals. So I’m so for this one, I’m just really focused on these 2. Just because we.
195 00:22:22.500 ⇒ 00:22:32.659 Annie Yu: I had to do like manual tweaks. For to the data. But I was thinking, if we do want more like added onto this, we can
196 00:22:33.568 ⇒ 00:22:39.100 Annie Yu: definitely like we’ll have to spend more time to adjust the data. But I was
197 00:22:40.453 ⇒ 00:22:49.649 Annie Yu: thinking if for the next step we do one more to this analysis we can look at if like meeting time is a confounder.
198 00:22:50.040 ⇒ 00:22:50.920 Annie Yu: If
199 00:22:52.960 ⇒ 00:22:58.349 Mathew: Let’s not get fancy here, here’s what I really. Here’s what I like about this. Go up, scroll up a little.
200 00:22:58.350 ⇒ 00:22:58.980 Annie Yu: Yeah.
201 00:22:59.350 ⇒ 00:23:00.389 Mathew: To the 1st chart.
202 00:23:01.280 ⇒ 00:23:10.429 Mathew: This is basically the most important thing for us to figure out like, what are our chart, what are our charts, and how how much work do we need to do to be able to deliver what’s in
203 00:23:13.590 ⇒ 00:23:17.559 Mathew: in here? Right I’ll share my screen for one sec.
204 00:23:19.160 ⇒ 00:23:23.329 Amber Lin: Like looking at this. It seems like you’ve started to build out these right.
205 00:23:23.520 ⇒ 00:23:28.170 Mathew: This capability email sent by day of the week by person.
206 00:23:30.990 ⇒ 00:23:36.740 Mathew: That’s great right. And if we had a backlog of like tasks that needed needed to be delivered on on the front end.
207 00:23:39.940 ⇒ 00:23:41.320 Mathew: We would need
208 00:23:41.490 ⇒ 00:23:47.609 Mathew: to be able to show email like this for emails. We need to show this for chats. We need to do this by teams. Right?
209 00:23:47.990 ⇒ 00:23:51.139 Mathew: Excuse me by by meetings we need to do. We need to be able to show that.
210 00:23:53.030 ⇒ 00:23:53.720 Annie Yu: Yeah.
211 00:23:53.960 ⇒ 00:24:14.523 Annie Yu: And if that’s the case, Amber, I would say, we have to go back to Luke to do the modeling just because right now, we don’t have the right granularity. So I had to do like manual wrangling there. That’s why I only focus on 2 data sets. But if we want more comprehensive views, I I would say we need
212 00:24:15.250 ⇒ 00:24:16.000 Annie Yu: like a.
213 00:24:16.000 ⇒ 00:24:22.109 Mathew: But like like, what do you need in terms of additional granularity?
214 00:24:24.038 ⇒ 00:24:27.621 Annie Yu: We now have them in 6 different data sets.
215 00:24:28.630 ⇒ 00:24:37.990 Annie Yu: and look also build 2 models. But they are showing like, I think, the past they are showing
216 00:24:38.190 ⇒ 00:24:46.579 Annie Yu: I could be wrong. But they’re showing like, based on the last 30 days how many emails there are. But we don’t have any timestamp in those models.
217 00:24:47.180 ⇒ 00:24:50.609 Mathew: Yeah. So you’re then there’s no way to know like what’s per day. Right?
218 00:24:50.610 ⇒ 00:24:56.379 Annie Yu: Yeah, that’s why we needed time. Time stamps in those models.
219 00:24:56.790 ⇒ 00:25:01.439 Amber Lin: I see. I think that’s something we can coordinate with, Luke, I think.
220 00:25:01.760 ⇒ 00:25:19.419 Amber Lin: from what I hear, there’s 2 things that sounds like our team still needs to do. One is adding granularity. So we can actually do these graphs. And 2, it seems that when we’re doing these graphs, we’re kind of having to tweak the data. So it shows a certain pattern. Is that right? Like.
221 00:25:19.420 ⇒ 00:25:26.609 Mathew: No, I don’t. There’s no, there’s no, there’s no point in tweaking the data to show patterns that we’re going to be making up anyway.
222 00:25:27.380 ⇒ 00:25:47.359 Mathew: if it if it helps you from like an output perspective, if it helps you like, just do it. That’s fine. We basically need this capability right here be able to show meetings per day of the week for all, then for all departments, and then for sub roles within departments, then do the same thing for meetings, and then do do the same thing for chat
223 00:25:48.040 ⇒ 00:25:59.860 Mathew: by day of the week, and then do it by time of day. If any, if you could figure out and reverse engineer what it takes, so that when we get the live data from the client very soon to be able to just do this section right here
224 00:26:00.230 ⇒ 00:26:03.130 Mathew: I will be. I will be like so happy.
225 00:26:03.820 ⇒ 00:26:13.459 Annie Yu: So I I think I do have a question. I think last time, because we were trying to show like statistics correlation. That’s why I needed to tweak the data.
226 00:26:13.460 ⇒ 00:26:18.139 Mathew: That that comes next. The 1st foundation is just what what is happening.
227 00:26:18.140 ⇒ 00:26:22.369 Annie Yu: Then, if we are really just showing the visuals, I would say, we don’t need python.
228 00:26:23.740 ⇒ 00:26:24.639 Mathew: What do you mean?
229 00:26:24.850 ⇒ 00:26:25.500 Mathew: Well.
230 00:26:25.500 ⇒ 00:26:46.479 Mathew: well, well, sorry. That’s going to be the foundation right whatever whatever gets us the foundation. But then we’re going to have to analyze it. So you tell me, like, is it better to just do the basic raw visualization stuff and whatever’s easiest out of the box. But then, in order to then like, analyze it, would you have had to just do it in python, anyway? What do you think.
231 00:26:48.145 ⇒ 00:26:53.420 Annie Yu: So the final output you want it in power. Bi. Is that correct?
232 00:26:53.910 ⇒ 00:27:01.700 Mathew: Final output needs to be to start. There’s 2 final outputs right? There’s like the report like this.
233 00:27:02.590 ⇒ 00:27:07.519 Mathew: just a visual that gets them. The answers as quickly as possible. And then power Bi follows.
234 00:27:08.430 ⇒ 00:27:15.320 Mathew: Remember, like we talked about last time, we want to lead with the quick reports that are facts, and then we want to make sure that they could. They could have it in power. Bi.
235 00:27:17.160 ⇒ 00:27:24.510 Annie Yu: So I can get you more visuals. But I can’t draw any statistical relation correlation, if that’s
236 00:27:24.780 ⇒ 00:27:27.199 Annie Yu: that’s what we should prioritize.
237 00:27:28.620 ⇒ 00:27:33.969 Mathew: I’m I’m struggling to I’m trying to. I’m trying to like, I wanna get this right.
238 00:27:35.290 ⇒ 00:27:38.699 Mathew: What is the most efficient way for us
239 00:27:38.860 ⇒ 00:27:43.240 Mathew: to balance that we need to turn around quick insight.
240 00:27:44.170 ⇒ 00:27:50.899 Mathew: and that you’ll also have to analyze it right? Because we’re gonna once we show them this, the next set of slides is gonna be
241 00:27:51.030 ⇒ 00:28:01.520 Mathew: trying to show like smart correlations like, would you have had to build it in python, anyway? Or is it a different like what? And and I’m not a data analyst. So I don’t know.
242 00:28:03.443 ⇒ 00:28:05.259 Annie Yu: If the next step is
243 00:28:06.000 ⇒ 00:28:10.680 Annie Yu: like stats. Yes, I would say python is the easiest way.
244 00:28:10.680 ⇒ 00:28:14.370 Mathew: So should you do the 1st step in python 2. Or should that just be done? And
245 00:28:14.630 ⇒ 00:28:17.370 Mathew: what would that be done in sequel?
246 00:28:20.090 ⇒ 00:28:22.050 Annie Yu: You mean other visuals? Python.
247 00:28:22.870 ⇒ 00:28:28.059 Mathew: Okay. So let me. So what? I’m what you’re what I’m showing you on my screen right now, what would you build these in
248 00:28:34.990 ⇒ 00:28:39.209 Mathew: like, what would you need to do to spit out an output that then we could create these charts.
249 00:28:40.616 ⇒ 00:28:42.120 Annie Yu: Are you screen sharing.
250 00:28:42.810 ⇒ 00:28:45.539 Amber Lin: Yeah, I am. Yeah. I’ve been screen sharing.
251 00:28:46.790 ⇒ 00:28:49.340 Amber Lin: You probably have to hit the tab on the top of your zoom.
252 00:28:50.870 ⇒ 00:28:52.240 Mathew: To go to my screen.
253 00:28:54.790 ⇒ 00:29:00.830 Annie Yu: Yeah, this, this I can do it in python. And I think we did do this.
254 00:29:03.050 ⇒ 00:29:05.120 Mathew: You’ve done it for emails, Seth.
255 00:29:05.310 ⇒ 00:29:06.140 Annie Yu: Yeah.
256 00:29:06.140 ⇒ 00:29:26.730 Amber Lin: Yeah, I think based on what I’ve done in the past for data analysis. I think we can spit out these type of charts we already have for email. We can do it really quickly for meetings and chats. We’ll have all the basic graphs that we can put on a slide to show. Hey, we have all of this. And then, as I think.
257 00:29:26.730 ⇒ 00:29:30.150 Mathew: And how and how are you doing that, amber? How are you doing that?
258 00:29:30.781 ⇒ 00:29:37.460 Amber Lin: We what we Annie, showed you earlier about the meeting graphs. They were done in Python. So any.
259 00:29:37.460 ⇒ 00:29:38.199 Mathew: So let.
260 00:29:38.200 ⇒ 00:29:39.789 Amber Lin: Updates in Python that we can.
261 00:29:39.790 ⇒ 00:29:43.190 Mathew: Would you be doing for the rest of these? Would you be doing those in Python.
262 00:29:45.090 ⇒ 00:29:49.680 Amber Lin: I would assume. That’s the fastest way to replicate what we have already done for meetings. Right, Annie.
263 00:29:50.550 ⇒ 00:29:54.519 Annie Yu: Yeah. But the okay. But the hard part is the modeling.
264 00:29:57.620 ⇒ 00:30:01.989 Annie Yu: I think. Wait. Actually, if we don’t need them to have correlations, I guess.
265 00:30:02.453 ⇒ 00:30:03.380 Mathew: Will they?
266 00:30:03.380 ⇒ 00:30:04.980 Annie Yu: Going in python.
267 00:30:05.340 ⇒ 00:30:05.804 Mathew: Okay.
268 00:30:07.450 ⇒ 00:30:13.390 Annie Yu: And then I did this for the meetings, too. So so
269 00:30:14.820 ⇒ 00:30:24.760 Annie Yu: if we’re, we are just trying to show bunch of graphs definitely doable with python.
270 00:30:25.070 ⇒ 00:30:27.020 Mathew: Okay, is that the best way to do it.
271 00:30:31.660 ⇒ 00:30:37.799 Mathew: or, for example, is the best way to like export what the data is. And then we just build it in a, we have a designer, build it.
272 00:30:40.220 ⇒ 00:30:47.960 Amber Lin: Matthew, I think my question here is, what do you mean by the best way? Do you mean the best way in that? We can replicate it. Once the client sends us data.
273 00:30:50.320 ⇒ 00:30:52.720 Mathew: I’m trying to balance a few things.
274 00:30:52.900 ⇒ 00:31:14.650 Mathew: One, we want these graphs to look good. Right? That’s why we were even talking about the storytelling, though. So it’s and then 2. We want to do that. I assume we want to work in a way where you’re not just doing it one way, and then, when we need to do correlation thing, you have to rebuild everything, or it’s another way. But then I also want to optimize, for like if there’s a faster way of getting these reports done
275 00:31:15.180 ⇒ 00:31:22.049 Mathew: that wouldn’t be duplicative if you didn’t do them in python like. So it’s like it’s moving. There’s like a there’s a few moving pieces.
276 00:31:22.050 ⇒ 00:31:28.570 Amber Lin: I see. I see. Okay, there’s a few things you want to do. There’s 1 we want this report to look good to the clients going forward.
277 00:31:28.570 ⇒ 00:31:34.690 Mathew: Yes, and that’s why python, I know it’s just spitting this out. So it’s like, Okay, but like, I don’t want you to try to.
278 00:31:34.690 ⇒ 00:31:36.050 Amber Lin: Professional looking
279 00:31:36.730 ⇒ 00:31:48.148 Mathew: It’s professional looking. I just don’t want you to have to become a I don’t want you to have to try to design this with python like. If there’s a way, if there’s a way where it spits out, then the the
280 00:31:48.800 ⇒ 00:31:54.549 Mathew: the values that we then hand to a designer that designs the matter more sick looking graphs.
281 00:31:54.650 ⇒ 00:31:56.400 Mathew: Maybe we do that.
282 00:31:56.400 ⇒ 00:31:57.599 Amber Lin: Yeah, if you.
283 00:31:57.600 ⇒ 00:32:09.409 Mathew: Then, from Annie’s perspective, from a workflow perspective. If you were, gonna have to do all of your analysts and correlation and everything in python, anyway, it probably makes sense for you to continue to work in Python for everything.
284 00:32:09.410 ⇒ 00:32:13.419 Annie Yu: I I’m aligned, and I think
285 00:32:13.640 ⇒ 00:32:21.459 Annie Yu: for me where that we spend so much time will be the data wrangling. So I will. I still wanna stand by my
286 00:32:22.000 ⇒ 00:32:27.000 Annie Yu: stance like we do need like a comprehensive model. So I don’t have.
287 00:32:27.000 ⇒ 00:32:27.510 Amber Lin: Yeah.
288 00:32:27.510 ⇒ 00:32:29.850 Mathew: Manual wrangling every time. What does that mean?
289 00:32:29.850 ⇒ 00:32:32.189 Mathew: Mean? Educate me! What do you mean by that?
290 00:32:33.737 ⇒ 00:32:38.480 Annie Yu: So I did my join in the
291 00:32:38.730 ⇒ 00:32:41.050 Annie Yu: I I can screen share, too.
292 00:32:41.710 ⇒ 00:32:47.857 Annie Yu: I mean, if you can explain it like, I’m a 5 year old. That’ll be the best way for me to just wrap my head around it and then give you like valuable
293 00:32:48.230 ⇒ 00:32:48.990 Mathew: Pull it up.
294 00:32:48.990 ⇒ 00:32:49.550 Mathew: You should. Yeah.
295 00:32:49.550 ⇒ 00:32:54.122 Annie Yu: With the model I want to see. There’s already
296 00:32:57.570 ⇒ 00:33:10.840 Annie Yu: There’s already a flat for remote day versus on Site Day. And there’s already average email sent calculated.
297 00:33:10.980 ⇒ 00:33:15.090 Annie Yu: So I don’t have to calculate everything or flag everything.
298 00:33:15.500 ⇒ 00:33:18.259 Mathew: Where does that? That’s that’s spit out. By what.
299 00:33:20.490 ⇒ 00:33:22.160 Amber Lin: I see, I think, based on my understanding.
300 00:33:22.160 ⇒ 00:33:24.760 Amber Lin: Hold on! Hold on! What is that? Any? What are you saying?
301 00:33:25.270 ⇒ 00:33:34.370 Annie Yu: So for that small case study, I had to split the data into remote and on site.
302 00:33:34.370 ⇒ 00:33:40.920 Mathew: Well, can you just can you can. You just tag people that were remote and on site, so that it’s maintained within that database.
303 00:33:41.260 ⇒ 00:33:43.570 Annie Yu: Yeah, that’s what I’m saying. So.
304 00:33:43.570 ⇒ 00:33:44.200 Mathew: Cool.
305 00:33:44.200 ⇒ 00:33:50.680 Annie Yu: Typically, if that’s done with the model, I just have to grab the right data to do the visuals.
306 00:33:51.400 ⇒ 00:33:56.130 Annie Yu: But now I have to prep those each metrics and each flag
307 00:33:56.920 ⇒ 00:34:00.099 Annie Yu: to be able to build the visuals. Does that make sense.
308 00:34:01.900 ⇒ 00:34:05.290 Mathew: I think. If let me make sure I have it right?
309 00:34:05.430 ⇒ 00:34:07.080 Mathew: You’re saying that.
310 00:34:07.390 ⇒ 00:34:09.077 Mathew: Basically, it’s it’s
311 00:34:09.880 ⇒ 00:34:19.889 Mathew: when you’re building visuals, you’re basically making like, almost like a new call where you’re like, all right, I’m gonna need all the remote people that and their emails. And I’m gonna need all the non remote people in their emails. I’m gonna
312 00:34:20.120 ⇒ 00:34:23.999 Mathew: create individuals. For in for each of those individual graphs, for each of those.
313 00:34:24.000 ⇒ 00:34:29.100 Annie Yu: Yeah. So almost like, I’m building bills for for different cases.
314 00:34:29.100 ⇒ 00:34:31.290 Mathew: Okay, that’s good.
315 00:34:31.560 ⇒ 00:34:32.600 Annie Yu: So.
316 00:34:34.820 ⇒ 00:34:41.120 Annie Yu: so I think, yeah, that’s why that’s why I said, like, if there is a comprehensive model, I don’t.
317 00:34:41.120 ⇒ 00:34:43.669 Mathew: When you say comprehensive model, what does that mean?
318 00:34:43.670 ⇒ 00:34:50.680 Annie Yu: So a model that have all of the data set joined together. But there’s also timestamp.
319 00:34:50.900 ⇒ 00:34:54.320 Mathew: Yeah, I assume we should have that. Yes, for sure.
320 00:34:55.840 ⇒ 00:34:58.590 Annie Yu: So right now with the synthetic data, we don’t have that.
321 00:35:01.994 ⇒ 00:35:04.590 Mathew: So that’s that’s a that’s a problem. Right?
322 00:35:06.870 ⇒ 00:35:09.900 Amber Lin: Yeah, I don’t think that’s a problem for you, Matthew. That’s a problem.
323 00:35:09.900 ⇒ 00:35:15.010 Mathew: No, no, I know I know exactly, but if it’s if it’s getting in your way, it’s then it’s then that’s where my head goes
324 00:35:15.950 ⇒ 00:35:17.499 Mathew: because we need to solve that.
325 00:35:20.760 ⇒ 00:35:24.049 Mathew: Okay, so, Annie, what do you? What do you need then? Is that is that all? From Luke.
326 00:35:26.049 ⇒ 00:35:26.709 Annie Yu: I do.
327 00:35:28.440 ⇒ 00:35:39.170 Annie Yu: I doubt. I think I will have to sync with him, because I doubt he’ll be able to tell, like what columns to make.
328 00:35:40.290 ⇒ 00:35:41.070 Mathew: Okay.
329 00:35:41.650 ⇒ 00:35:44.429 Annie Yu: By just looking at the deck. So
330 00:35:47.109 ⇒ 00:35:51.809 Annie Yu: so I my short answer is, yes, I do want
331 00:35:53.706 ⇒ 00:35:56.830 Annie Yu: like a model built by an engineer.
332 00:35:57.560 ⇒ 00:36:02.519 Annie Yu: Wait, wait! I don’t get that. Wait! I don’t get what what is? What does an engineer have to do with this?
333 00:36:02.960 ⇒ 00:36:04.909 Annie Yu: No, Luke is the engineer.
334 00:36:04.910 ⇒ 00:36:06.399 Mathew: Oh, okay, okay, see?
335 00:36:07.100 ⇒ 00:36:12.150 Mathew: So are you basically saying that your inputs are not up to snuff in order for you to
336 00:36:12.300 ⇒ 00:36:14.620 Mathew: work on what you need to do.
337 00:36:15.250 ⇒ 00:36:19.559 Annie Yu: Yeah, that’s why I only focus on 2 data sets. When I did my thing.
338 00:36:20.060 ⇒ 00:36:30.419 Mathew: Okay, so amber. How do we get Luke to like? What do we need? How does Annie communicate what she needs in order for Luke to then update the data set in order for Annie to be able to
339 00:36:31.030 ⇒ 00:36:33.850 Mathew: start to produce the outputs that we’re talking about.
340 00:36:36.310 ⇒ 00:36:40.559 Amber Lin: Nanny, I believe we have done this in the past of.
341 00:36:40.560 ⇒ 00:36:49.250 Annie Yu: Yeah. But remember my message last week, I said, there’s no timestamp, so I couldn’t use those models to build visuals. That’s why I had to export
342 00:36:49.450 ⇒ 00:36:52.949 Annie Yu: the data sets. And then there’s some manual thing.
343 00:36:52.950 ⇒ 00:36:53.340 Amber Lin: Hmm.
344 00:36:53.340 ⇒ 00:36:55.340 Annie Yu: On on the iphone side.
345 00:36:55.660 ⇒ 00:37:02.060 Amber Lin: Okay, so were you able, did Luke respond to your message to meet up and sync about that.
346 00:37:02.640 ⇒ 00:37:07.630 Annie Yu: No, because I because he did ask that would take
347 00:37:07.920 ⇒ 00:37:10.379 Annie Yu: some time if that makes sense
348 00:37:10.800 ⇒ 00:37:18.890 Annie Yu: to do it. And I told him for this use case. I can just do the wrangling, using python.
349 00:37:18.890 ⇒ 00:37:25.330 Amber Lin: Okay, I see. Do you need my help to tell him that this is something that we need to do together?
350 00:37:25.500 ⇒ 00:37:28.710 Amber Lin: He’s he will be willing to do that if you want.
351 00:37:28.710 ⇒ 00:37:34.070 Annie Yu: I’m no, I’m sure I’m sure. So I think we just have to figure out the right next steps.
352 00:37:34.190 ⇒ 00:37:44.799 Annie Yu: and then I do need you if there’s like a need to create tickets for him or but I also do think I can.
353 00:37:45.590 ⇒ 00:37:53.510 Annie Yu: I’m gonna need to provide him some columns that I want to see in the final model.
354 00:37:54.280 ⇒ 00:38:20.609 Amber Lin: Okay. So we do know that the so from what you just said, I think the next step would be to define what columns you need, because Matthew provided the charts that we need. So we have the final outputs. And as you look at as you think about how to build them in python, you will see. Okay, this is what I’m missing, and I think that will make it very clear to Luke of this is the exact output that we need.
355 00:38:20.610 ⇒ 00:38:21.230 Annie Yu: Yeah.
356 00:38:21.800 ⇒ 00:38:28.439 Amber Lin: Okay, seems like we’re clear on the next steps. Is there? Is there anything else that we need.
357 00:38:28.440 ⇒ 00:38:47.619 Mathew: I. So I I need. I need you before you and I get on to be on top of making sure that Annie has no issues like this internally, because I don’t have. I don’t work with Luke. I don’t have any visibility into that, and it sounds like there’s some. There was something hanging whereby he where that Annie doesn’t have what she needs in order to produce this output.
358 00:38:51.300 ⇒ 00:38:59.023 Mathew: So if there’s any way that you could just like stay on top of him. And and just this this like internal loop to make sure that
359 00:39:00.232 ⇒ 00:39:03.499 Mathew: that Annie’s able to produce that kind of stuff.
360 00:39:04.250 ⇒ 00:39:09.010 Mathew: It sounds like there was like an internal lapse that that’s that’s holding Annie back.
361 00:39:12.260 ⇒ 00:39:16.284 Amber Lin: Totally. I meant for this meeting to only be about the deck.
362 00:39:16.770 ⇒ 00:39:19.890 Amber Lin: I think, and then surface some issues internally that should be receiving.
363 00:39:19.890 ⇒ 00:39:44.519 Mathew: Which is crying, yeah, yeah, which is which is which is great. No, I I think it’s like we’re trying to work with synthetic data right? If we were just working with the if the actual data that was coming out of the system, we would know what we’re dealing with, and we would just be working it right because we don’t. We have an external. We have an internal dependency on Luke, and I’m I’m asking you to be like aggressive and proactive with him, and and to understand, like what’s getting in his way to make that happen.
364 00:39:44.650 ⇒ 00:39:56.179 Mathew: So cause you have the visibility there. So whatever you can do to push that forward like ideally in the night, like, when do you? What do you guys, Annie? Is this a full blocker for you like is this holding back? You being able to move forward.
365 00:39:56.911 ⇒ 00:40:05.650 Annie Yu: No. So I was able to join those 2 data sets. So I think if anything, I can still do it on my side. It just takes more time.
366 00:40:06.830 ⇒ 00:40:18.860 Mathew: Well, it would it be more because I don’t wanna also burn extra hours on your side, is it? Would it be more efficient for Luke to do it, or for you to push forward? Or is it? Are we at the point where it makes sense for you to just keep trying to push that forward.
367 00:40:19.531 ⇒ 00:40:23.470 Annie Yu: I don’t have the right answer to that, because I don’t really know how.
368 00:40:23.470 ⇒ 00:40:24.220 Mathew: No Luke’s.
369 00:40:24.220 ⇒ 00:40:26.049 Annie Yu: Or tape for a look.
370 00:40:27.000 ⇒ 00:40:32.539 Mathew: Yeah is is, what are Luke’s hours? Is he like nights? Is he like the reverse hours, or is he work the same time zone.
371 00:40:35.410 ⇒ 00:40:43.579 Amber Lin: So I. This team has been collaborating pretty well. I think we just need to get a meeting on, and then we’ll be working together.
372 00:40:43.880 ⇒ 00:40:56.290 Amber Lin: and we will give you the estimates of when we need and how long this would take to produce those graphs. And if I can get from you what you want for the slide deck also that would be great. So we have both going on the same time.
373 00:40:56.290 ⇒ 00:41:03.600 Mathew: Honestly for the slide deck. The only thing that I’ll need is like, basically.
374 00:41:05.560 ⇒ 00:41:13.479 Mathew: if if Annie has the Cape, the the capability to make sure that you guys see my screen.
375 00:41:15.420 ⇒ 00:41:18.859 Mathew: So and you’re able to see it.
376 00:41:19.380 ⇒ 00:41:19.715 Annie Yu: Yeah.
377 00:41:20.890 ⇒ 00:41:28.540 Mathew: If if I have the Ca, if you have the capability of producing meetings, emails, and chat
378 00:41:29.029 ⇒ 00:41:38.140 Mathew: excuse me. Well, chat’s not in here. But if you basically have the ability to do these screens and then plus teams, data,
379 00:41:40.350 ⇒ 00:41:49.310 Mathew: by day of week, and then obviously, and then by time of day that will be
380 00:41:49.860 ⇒ 00:41:51.468 Mathew: the heart of
381 00:41:52.720 ⇒ 00:42:08.199 Mathew: This deck I can work on like the narrative before and after that, because truthfully, this is a lot of these insights are just gonna be like, whatever the truth of those slides ultimately end up being I can. I can make that happen. The most important thing for me would be
382 00:42:08.360 ⇒ 00:42:13.049 Mathew: making sure that by the time we get the actual data that Annie’s that we’ve already kind of like
383 00:42:13.574 ⇒ 00:42:19.540 Mathew: battle tested our ability to produce these graphs because these are the 1st things we’re gonna need to deliver to customer.
384 00:42:20.134 ⇒ 00:42:26.099 Annie Yu: Matthew? So one follow up. I just wanna clarify on. Do we need breakdown by a department because.
385 00:42:26.100 ⇒ 00:42:26.550 Mathew: Yes.
386 00:42:26.550 ⇒ 00:42:29.079 Annie Yu: No, I can just use separate tables.
387 00:42:29.080 ⇒ 00:42:46.340 Mathew: No. 100. So when I say we need this, it’s like the aggregate of the whole group of 4,500. But then you’re gonna need to filter it by department, you’re gonna need to filter it by sub department, like groups within departments, so that capability a hundred percent will need. Then, on top of that, if I were to build almost like a like a
388 00:42:46.480 ⇒ 00:42:51.099 Mathew: like a backlog here, not even a backlog. It’s like what the prior. The tickets or priorities would be. It’s like
389 00:42:51.260 ⇒ 00:43:00.440 Mathew: email meeting chat capability for everyone just at large, filter by teams, by role, by roles within teams
390 00:43:01.780 ⇒ 00:43:13.279 Mathew: obviously, location which includes remote or in person, and then ultimately and then and then I would like to see like where it gets not. It’s not complex. But I’d like us to then combine
391 00:43:13.550 ⇒ 00:43:29.109 Mathew: email and chat for Async communications patterns, because that’s going to be more interesting. They’re going to be like, all right. You have. You have chats, you have meetings like Excuse me, you have chats, you have email. How does that look when you just take Async asynchronous communication at Hold. You know what I’m saying.
392 00:43:29.610 ⇒ 00:43:30.280 Annie Yu: Yeah.
393 00:43:30.750 ⇒ 00:43:44.599 Mathew: And then do that for time of day, if you can, if you can get that and I can. I could put this in the doc if you could get, and then we could talk about like ticketing, whatever you need, and I’m happy to roll up my sleeves as much as possible. To making this happen.
394 00:43:44.910 ⇒ 00:43:53.270 Mathew: That would be if we could have. If you could get. If you can make meaningful progress in those areas asap, we will be set up for success.
395 00:43:58.410 ⇒ 00:44:03.759 Annie Yu: Yeah, I’m just thinking through if it makes sense to build a model. But.
396 00:44:05.480 ⇒ 00:44:26.483 Mathew: You tell me I can’t. I, personally don’t know what the answer to that question is. You’d you’d have to say like, what’s gonna work best for you. Because I assume the I assume when we’re working out of the graph Api, and we have the unique identifier. We’re gonna know, like that. This person is gonna correspond, this their meetings, their chats, and their what’s the call is gonna correspond to all of to
397 00:44:27.110 ⇒ 00:44:28.350 Mathew: that same person.
398 00:44:31.490 ⇒ 00:44:34.779 Annie Yu: Yeah, yeah, we’ll. We’ll discuss internally.
399 00:44:35.010 ⇒ 00:44:35.740 Mathew: Yeah.
400 00:44:36.680 ⇒ 00:44:37.340 Annie Yu: Yeah.
401 00:44:38.390 ⇒ 00:44:43.909 Mathew: I’m assuming that the goal with Luke is to try and emulate as close to possible what we’re expecting from the graph Api
402 00:44:45.460 ⇒ 00:44:46.330 Mathew: right.
403 00:44:46.940 ⇒ 00:44:47.840 Annie Yu: Say that again.
404 00:44:48.220 ⇒ 00:45:03.620 Mathew: I assume the goal with Luke, and the whole reason that we created any synthetic data. Well, the 2 correct me if I’m wrong. The 2 reasons we created synthetic data were to get you to start, to be able to like, build out what kind of analysis templates you’re building.
405 00:45:03.620 ⇒ 00:45:18.489 Mathew: And then 2 is also to try and emulate what the graph Api, what we’re getting from the graph Api, so that you, when we actually point to the live. Api, and you’re getting data outputs from Trevor’s system. You’re able to do what you need to do, which are they’re related. But like, you know what I mean.
406 00:45:18.770 ⇒ 00:45:39.940 Annie Yu: Yeah, yeah. So my my thinking now. But we will still discuss internally amber. But my thinking now is, if we want to get things done as soon as possible. I can. We can skip like Luke’s part, but when we get the actual data Luke will have to build a model that could be an option too.
407 00:45:41.030 ⇒ 00:45:44.729 Mathew: Well, why, if we have the actual data, can you just work with the actual data.
408 00:45:45.810 ⇒ 00:45:50.159 Annie Yu: Oh, I mean there will still be multiple tables.
409 00:45:51.180 ⇒ 00:45:54.339 Mathew: I imagine Trevor will be your guy then?
410 00:45:55.420 ⇒ 00:45:56.310 Mathew: Right.
411 00:45:59.520 ⇒ 00:46:03.270 Annie Yu: I don’t know who will be the the one to do the data modeling.
412 00:46:03.910 ⇒ 00:46:11.729 Mathew: I think Trevor’s job is to build the data warehouse and the pipelines in a way that you get. You have everything that you need to be able to do what you need to do
413 00:46:19.310 ⇒ 00:46:22.939 Mathew: when I’m talking about when we’re live in production with the client.
414 00:46:26.070 ⇒ 00:46:27.430 Mathew: Are you following.
415 00:46:27.430 ⇒ 00:46:28.150 Annie Yu: Yeah.
416 00:46:29.810 ⇒ 00:46:32.580 Mathew: Does that make sense? Or is that like, what?
417 00:46:35.040 ⇒ 00:46:36.509 Mathew: Yeah, it does.
418 00:46:36.810 ⇒ 00:46:45.839 Annie Yu: No, I I think I’ll just sync with amber and look and see if it makes sense for him to make a model, because I don’t know how much time take him, either.
419 00:46:45.840 ⇒ 00:46:52.839 Mathew: Yeah. And and you did just say, like, there’s a world where maybe that’s not worth it right now, is that are you? Do you think that’s a possibility?
420 00:46:53.860 ⇒ 00:46:59.120 Annie Yu: If if he says that could take a good while.
421 00:47:00.360 ⇒ 00:47:05.010 Annie Yu: then, if it’s not faster than I do it in python.
422 00:47:05.480 ⇒ 00:47:07.460 Annie Yu: then I should do it in python.
423 00:47:08.020 ⇒ 00:47:08.630 Mathew: Okay?
424 00:47:11.550 ⇒ 00:47:14.520 Mathew: Yeah. Cause if his model isn’t gonna reflect.
425 00:47:15.180 ⇒ 00:47:40.949 Mathew: if his model doesn’t get up to the granularity or fidelity of like what we’re gonna ultimately need either we have to work with him to get it as close as possible to what we’re ultimately expecting or you have to like. You’ll have to just make some assumptions and build some flexibility into how you’re working, such that when you do get those things from from our from like the core dates from the actual data set that it won’t, that it’s like that we’re continuing to like
426 00:47:41.190 ⇒ 00:47:43.239 Mathew: that. You’re getting ahead of as much as possible.
427 00:47:47.220 ⇒ 00:47:48.020 Annie Yu: Yes.
428 00:47:48.700 ⇒ 00:47:51.380 Mathew: Okay, okay.
429 00:47:52.347 ⇒ 00:48:16.772 Mathew: what would be what? What’s what do you think? What do you all think our next steps and timeframe to like make these decisions and calls. And then when can I? When should we then sync again on what? The or like? What? Yeah. What do you think, because this is this is this is really, I think this is really critical, and the amount of the amount of blockers that we can clear now for you to be able to build those chart. The charts that I did that I pulled up.
430 00:48:17.190 ⇒ 00:48:20.539 Mathew: is like. That’s the whole reason why we’re getting ahead of all this.
431 00:48:29.900 ⇒ 00:48:30.700 Mathew: Hello!
432 00:48:35.661 ⇒ 00:48:40.999 Annie Yu: Well, next Tuesday work, because next Monday is off. I can work on Monday.
433 00:48:41.708 ⇒ 00:48:45.789 Annie Yu: Because I I do have some other deadlines this week.
434 00:48:47.100 ⇒ 00:48:48.320 Mathew: Amber.
435 00:48:49.960 ⇒ 00:48:59.270 Amber Lin: We will need to coordinate internally to get that to you. Based on what we how we deliver the initial parts. I think it would just be a quick
436 00:48:59.540 ⇒ 00:49:01.359 Amber Lin: multiplier of that.
437 00:49:01.500 ⇒ 00:49:02.620 Amber Lin: So.
438 00:49:05.120 ⇒ 00:49:12.080 Mathew: I guess my question is, when are you guys gonna and when are you guys gonna discuss internally? Or when can you discuss internally what the best
439 00:49:12.280 ⇒ 00:49:27.609 Mathew: path is for Annie to figure out if she’s gonna continue on with python, or if she’s gonna bank on loop, when can you let me know that by. And then, when could, based on that, we should have a plan of when we’re gonna expect when we could? Yeah, when we could.
440 00:49:27.610 ⇒ 00:49:35.990 Amber Lin: Tomorrow we can give you a result of the internal discussion, and then we’ll give you estimate of when this initial part will be done.
441 00:49:35.990 ⇒ 00:49:37.980 Mathew: Okay, cool. That sounds great.
442 00:49:41.880 ⇒ 00:49:43.010 Mathew: Okay, awesome.
443 00:49:48.340 ⇒ 00:49:51.670 Mathew: Cool anything else. Y’all need.
444 00:49:54.270 ⇒ 00:50:02.159 Amber Lin: This is not related to the analysis clarifying question on the deck. Do you want me to create the final deck? Or do you have designers that can do that for you.
445 00:50:02.778 ⇒ 00:50:28.099 Mathew: Do you have an example of like, of like a design, like of a design like paradigm or thing? Because before we like start jumping to designing it. I think it would be cool to align on the design direction, and, like any examples that you have of designs you made would be great one. But then, secondly, because then that could also help me figure out what what design direction we want to go with. Secondly, what are you creating it in? Would it be in? Will be Google slides
446 00:50:28.416 ⇒ 00:50:32.949 Mathew: power. What is it? Photoshop like? How do you usually build? Build your decks in.
447 00:50:33.790 ⇒ 00:50:44.370 Amber Lin: We either do it in Google slides. Or if figma, if we use if we are using designers, if it’s a regular update slide. We do it in Google slides.
448 00:50:44.370 ⇒ 00:50:47.276 Mathew: I like Google, I like Google slides personally.
449 00:50:48.360 ⇒ 00:50:57.630 Mathew: I, what do you? What do you like? What do you think would be a good way of like helping me figure out like are the options that we have in terms of like design, direction.
450 00:50:58.490 ⇒ 00:51:17.370 Amber Lin: You shared the better upside with us thematic direction. I can give you a few examples. I think once you give me how you want the outline to go at it, how it should be approached, and then I can give you a few layouts of.
451 00:51:17.370 ⇒ 00:51:18.090 Mathew: Yeah, I think.
452 00:51:18.090 ⇒ 00:51:22.179 Amber Lin: Header and recommendation at the end and graph in the middle, side by side.
453 00:51:22.500 ⇒ 00:51:28.200 Mathew: Take a take a crack at attempt, regardless of, like the the exact outline. We’re gonna need a slide that’s like
454 00:51:28.630 ⇒ 00:51:43.970 Mathew: just like bold statements. We’re gonna need a slide. That’s with the graph. We’re gonna need, like, probably some side by side stuff. So do you want to do you want to like. Show me an example, or or like. You can either show me examples how you’ve done it, or you could take a crack at it. Whatever is most efficient.
455 00:51:44.190 ⇒ 00:51:50.369 Amber Lin: Yeah, let me show you a quick example essentially consulting decks. I did for.
456 00:51:50.740 ⇒ 00:51:51.090 Mathew: So.
457 00:51:51.686 ⇒ 00:51:52.319 Amber Lin: Our other.
458 00:51:52.320 ⇒ 00:51:55.619 Mathew: And I could see cool, and I can tell you like what I like, or whatever.
459 00:51:56.040 ⇒ 00:52:04.420 Amber Lin: Yeah. So if you see here, this is one of the sliders that we did so, we were doing analysis task core question. We want to answer
460 00:52:05.600 ⇒ 00:52:14.560 Amber Lin: information, call outs with a final statement each one of these, the goal is that each one of these slides are individually usable, so they can. They tell.
461 00:52:14.560 ⇒ 00:52:15.500 Mathew: Yeah, yeah.
462 00:52:15.500 ⇒ 00:52:15.890 Amber Lin: Okay.
463 00:52:15.890 ⇒ 00:52:16.410 Mathew: Yeah.
464 00:52:16.840 ⇒ 00:52:27.889 Amber Lin: And then we also have say we can have recommendations. We have assumptions of how these are calculated, call outs of specific insights, and then we have next steps.
465 00:52:28.460 ⇒ 00:52:37.430 Amber Lin: appendix, so typical, very typical consulting slide formatting all centered around key insights and answering key questions.
466 00:52:38.732 ⇒ 00:52:40.780 Mathew: Let me think, because.
467 00:52:41.540 ⇒ 00:52:46.210 Mathew: I I sold them on, even if we were like.
468 00:52:47.110 ⇒ 00:52:50.560 Mathew: like, basically, if I sold them on this.
469 00:52:50.760 ⇒ 00:52:51.490 Amber Lin: Yeah.
470 00:52:51.880 ⇒ 00:52:55.729 Mathew: Then we don’t have to deviate very far from from this.
471 00:52:55.940 ⇒ 00:53:01.799 Mathew: But what better up did which I really like? I just, I like the better up, look and feel, and the graphs are actually like.
472 00:53:02.090 ⇒ 00:53:05.505 Mathew: I don’t want to do dark theme graphs instead of light ones.
473 00:53:06.380 ⇒ 00:53:09.070 Mathew: What was the name? What was the name of that deck? Again.
474 00:53:09.070 ⇒ 00:53:15.259 Amber Lin: Better up. Ai! In the next phase. You shared it in our slack so we can go look at it. There.
475 00:53:15.730 ⇒ 00:53:18.159 Mathew: Let me let me look at it, and I’ll tell you like
476 00:53:18.450 ⇒ 00:53:30.879 Mathew: cause the slides that you just showed those examples. That’s like to your point. It’s standard consulting slides, and it’s not so much of a big lift or risk to be able to like. There’s not too much design built into them, which is fine.
477 00:53:31.720 ⇒ 00:53:34.860 Mathew: let me pull up. What do I send you? Or, okay.
478 00:53:35.290 ⇒ 00:53:38.229 Mathew: so winning in the age of AI, I’m just looking at it.
479 00:53:44.310 ⇒ 00:53:45.659 Mathew: okay, hold on.
480 00:53:51.480 ⇒ 00:53:54.489 Mathew: Okay, let me switch my screen share.
481 00:53:55.640 ⇒ 00:53:59.459 Mathew: Yeah, like, in order, like, these are kind of.
482 00:53:59.630 ⇒ 00:54:08.389 Mathew: you’re just. I think the graphs are really nice here. So I think that was a question I was kind of alluding to when we were talking. When we were asking Annie those questions. Do we still need Annie anymore? Or should we?
483 00:54:08.740 ⇒ 00:54:09.789 Mathew: What do you think.
484 00:54:10.990 ⇒ 00:54:16.313 Amber Lin: And if you have something that you you have to do, you can hop off. I I can relay these.
485 00:54:16.580 ⇒ 00:54:18.110 Annie Yu: Yeah, I’m gonna drop.
486 00:54:18.560 ⇒ 00:54:19.400 Mathew: Thanks. Annie.
487 00:54:19.590 ⇒ 00:54:20.650 Amber Lin: Yeah, thank, you.
488 00:54:23.240 ⇒ 00:54:24.349 Mathew: I mean, basically.
489 00:54:24.350 ⇒ 00:54:26.279 Amber Lin: Yeah, I know, yeah.
490 00:54:26.280 ⇒ 00:54:31.102 Mathew: Yeah, yeah, it will. Exactly. I think we’ll we’re gonna have to figure out if we end up.
491 00:54:32.380 ⇒ 00:54:38.030 Mathew: we’re gonna have to figure out if we end up like, how we’re gonna basically, I guess that’s my question for you is is like.
492 00:54:38.030 ⇒ 00:54:38.420 Amber Lin: Yeah.
493 00:54:38.420 ⇒ 00:54:40.669 Mathew: How do you? How do you think we’re gonna produce.
494 00:54:40.800 ⇒ 00:54:42.819 Mathew: or wait? Where to go? Where am I? Where am I?
495 00:54:43.233 ⇒ 00:54:48.190 Amber Lin: Matthew, my my thinking is that you already have all the graphs
496 00:54:48.290 ⇒ 00:55:16.010 Amber Lin: that you want, Annie to produce. My opinion is that Annie is really figuring out how to go from the data to those graphs. But the graphs are going to look the same as yours, and like the ones the examples you gave us, because all of it is based on synthetic data that can be changed. So if you want to get your designers started to just draw graphs like, it’s okay. Annie’s work is preparing for when the clients data.
497 00:55:16.010 ⇒ 00:55:17.860 Mathew: Yeah, yeah, yeah, yeah, agreed. Agreed.
498 00:55:18.230 ⇒ 00:55:20.559 Amber Lin: She doesn’t have to worry about design.
499 00:55:20.560 ⇒ 00:55:39.523 Mathew: Yeah, so I don’t know. I don’t know then what you would, what what deck you would create right now, other than almost like staging out if you were to like almost like if we were to wireframe the deck and show like what like the like without, we’re trying not trying to design, and not trying to come up with content per se. But if you were, if we were to just like
500 00:55:40.050 ⇒ 00:55:50.539 Mathew: come up with what the overall flow is then that might be useful. The ultimate design. I’ll probably to your point. We’ll probably have to get a designer to either.
501 00:55:50.800 ⇒ 00:55:51.980 Mathew: Yeah.
502 00:55:52.340 ⇒ 00:55:54.989 Amber Lin: Yeah, okay, that’s good cause.
503 00:55:55.280 ⇒ 00:56:00.002 Amber Lin: When I was creating this, I was like, you already, have all the graphs.
504 00:56:00.340 ⇒ 00:56:01.860 Amber Lin: Right? Right? Right? Yeah.
505 00:56:01.860 ⇒ 00:56:03.130 Amber Lin: What a beautiful day.
506 00:56:03.130 ⇒ 00:56:04.479 Mathew: Yeah, I, I.
507 00:56:04.590 ⇒ 00:56:06.099 Amber Lin: For the flow.
508 00:56:06.100 ⇒ 00:56:17.400 Mathew: Yeah, where we had left off with with with Tom about about about doing that, I think, was more to just like Orient us towards the towards the narrative.
509 00:56:18.110 ⇒ 00:56:18.900 Amber Lin: Yeah.
510 00:56:18.900 ⇒ 00:56:26.510 Mathew: So here’s what I can do. My homework coming out of this call one I can.
511 00:56:27.620 ⇒ 00:56:39.870 Mathew: I can write up like almost like the this like I could list out the slides kind of like what I rattled off on this call. I could list out the slides that I think we’re gonna need into in terms in order of priority.
512 00:56:40.298 ⇒ 00:56:48.660 Mathew: And then and then I could take what you did in the storytelling deck. And I could think through like how we’re gonna position this for them.
513 00:56:49.340 ⇒ 00:56:59.429 Amber Lin: Okay, I. I appreciate that. So there’s not much I can do. If you you just want me to organize the graphs in a different way.
514 00:56:59.430 ⇒ 00:57:02.249 Amber Lin: Yeah, I don’t think I but I want to score it.
515 00:57:02.550 ⇒ 00:57:08.360 Mathew: I think the value I think I think the the most valuable thing you could do is just make sure that Annie’s not blocked.
516 00:57:08.680 ⇒ 00:57:13.629 Amber Lin: Okay, we I really shouldn’t have let that happen in the meeting. I’m so sorry.
517 00:57:13.630 ⇒ 00:57:23.020 Mathew: No, no, it doesn’t matter. You don’t know Amber, it’s amber, it’s all good. There’s no issue. I I don’t. I like honestly, I’d rather I’d rather it comes up. And we’re like, Okay, what do we need to do? That’s all good. I don’t mind that.
518 00:57:23.020 ⇒ 00:57:39.909 Amber Lin: Yeah, cause for me. I’m like, your time is much more valuable, and I don’t want you to get stuck on execution and unblocking project execution. Same with Uton like these things are my responsibilities. If it doesn’t go, I’m responsible.
519 00:57:40.100 ⇒ 00:57:50.940 Mathew: Yeah. Yeah. So so I think it’s actually really valuable that we work. We’re working through this together. And I’m just, I’m like, whenever I see anything like that? I just, I’m like, all right. What do we need to do to put that in to put it back.
520 00:57:50.940 ⇒ 00:57:57.449 Amber Lin: I know every time I see you at Wutum do that, I was like, well, that’s so impressive to just like
521 00:57:57.910 ⇒ 00:58:01.009 Amber Lin: you’re not gonna take a no for answer, and it just get.
522 00:58:01.010 ⇒ 00:58:06.880 Mathew: No, no, you can’t. You can’t be festive. You have to be extremely aggressive and proactive.
523 00:58:07.010 ⇒ 00:58:09.859 Mathew: I don’t, you know. I’m telling. I’m telling you. This is like on your.
524 00:58:10.171 ⇒ 00:58:11.418 Amber Lin: Like. Oh, my gosh!
525 00:58:11.730 ⇒ 00:58:21.239 Mathew: Yeah, yeah, no, no, no, no. You have to be aggressive. You can’t just be like, yeah, okay, cool. You gotta be like, what are, what do you have to ask the questions like, what is getting in the way of this? What do we need to do.
526 00:58:21.430 ⇒ 00:58:28.169 Mathew: and what are the implications of either of these pathways. And let’s make a decision. And then let’s ask Matthew what he thinks based on these trade-offs
527 00:58:29.420 ⇒ 00:58:54.259 Mathew: like. That’s it. That’s it like. It’s very simple. You have to. You all will have to talk to, and and then it’s like talk to like. If you could, you could call him. Now, I would be on the phone and be like, What’s it like? How long would it take you to to? Well, you’d have to get the inputs from Annie of like like what she thinks he needs to do in order for basically like to build the model that she’s talking about. Figure out how long it would take him to update those things
528 00:58:54.620 ⇒ 00:58:59.239 Mathew: and then say, like, All right, based on this, let’s make the call. That’s it, and just be aggressive about it.
529 00:58:59.650 ⇒ 00:59:08.543 Amber Lin: And honestly, I think both of them are so capable. Both of them have already done it. And I I think it’s just. She’s probably really, really tired today.
530 00:59:08.850 ⇒ 00:59:10.219 Amber Lin: okay, it’s okay.
531 00:59:10.220 ⇒ 00:59:19.409 Amber Lin: Probably just didn’t turn around for her. But I I don’t think it’s a big issue. But I’ll get them together hopefully. Luke, still awake and.
532 00:59:19.410 ⇒ 00:59:40.840 Mathew: Yeah, yeah, you got it, yeah. And then and then, like, cause I I would love the next time we meet that she’s like, yeah, I there were no blockers for me. I created the template readouts of spinning out meetings, chats, emails, teams, for just like the whole, the whole company. And okay, the next step is, how do we do that by team? How do we do that by remote versus on site? And then we’re good.
533 00:59:42.150 ⇒ 00:59:42.890 Amber Lin: Cool.
534 00:59:43.880 ⇒ 00:59:45.160 Amber Lin: Thank you.
535 00:59:45.160 ⇒ 00:59:46.509 Mathew: Yeah, yeah, you got this.
536 00:59:46.510 ⇒ 00:59:49.050 Amber Lin: And thank you for taking so long today.
537 00:59:49.050 ⇒ 00:59:54.610 Mathew: This is nothing. This is what this is, nothing. This is, this is the most important. This is literally the most important project.
538 00:59:54.800 ⇒ 00:59:55.480 Mathew: like.
539 00:59:55.480 ⇒ 00:59:57.090 Amber Lin: Is this your biggest client right now?
540 00:59:57.090 ⇒ 00:59:59.189 Mathew: The biggest client of my life.
541 00:59:59.190 ⇒ 01:00:00.630 Amber Lin: Oh, my, gosh, okay.
542 01:00:00.630 ⇒ 01:00:04.190 Mathew: So like. That’s why I’m like I like, let’s go like we got this.
543 01:00:04.630 ⇒ 01:00:05.480 Amber Lin: Okay.
544 01:00:06.360 ⇒ 01:00:18.309 Mathew: Alright. Yeah. Yeah. Text, Ping, me, DM, me like anything. That’s why I sometimes do things outside of the channel, just because it’s like, I don’t wanna. I don’t wanna distract the team from like our back and forth, but like you can always hit me up.
545 01:00:18.530 ⇒ 01:00:21.249 Mathew: You could literally call me any time of the day. I don’t care.
546 01:00:21.990 ⇒ 01:00:26.369 Amber Lin: Okay, that’s that’s really helpful, because I’m always scared to distract you from your time. Because
547 01:00:26.370 ⇒ 01:00:29.160 Amber Lin: no, no, this is the this gotta work.
548 01:00:29.500 ⇒ 01:00:34.300 Mathew: This is the thing. This is the most important thing. This is my, this is my work. Yeah.
549 01:00:34.300 ⇒ 01:00:35.900 Amber Lin: Okay. Okay. Sounds good.
550 01:00:35.900 ⇒ 01:00:37.269 Mathew: Nothing else matters.
551 01:00:37.270 ⇒ 01:00:39.370 Amber Lin: Sleeve matters.
552 01:00:39.690 ⇒ 01:00:43.122 Amber Lin: Oh, than that reading? Well, matters. Okay.
553 01:00:43.620 ⇒ 01:00:46.000 Mathew: Work wise. This is the most important thing.
554 01:00:46.430 ⇒ 01:00:49.241 Amber Lin: No, all right, thank you so much.
555 01:00:49.590 ⇒ 01:00:50.139 Mathew: Of course.
556 01:00:50.140 ⇒ 01:00:53.090 Amber Lin: The coaching, because Utah’s not here this week.
557 01:00:53.580 ⇒ 01:00:59.839 Mathew: I got you be aggressive. Assume that things are. Assume that things are not working out until you force it to be
558 01:01:00.160 ⇒ 01:01:08.960 Mathew: right, but then. But don’t let your team know that you’re thinking that way. Just you. You always have to assume there’s things are not working out in there until people are like, yeah, I got this.
559 01:01:09.270 ⇒ 01:01:17.179 Amber Lin: Does it impact the personal relationship because I don’t wanna press her and and make her feel bad like, does it really.
560 01:01:17.180 ⇒ 01:01:18.809 Mathew: Make people feel bad, but but.
561 01:01:18.810 ⇒ 01:01:19.229 Amber Lin: Like, you know.
562 01:01:19.230 ⇒ 01:01:28.209 Mathew: Notice when when I was going back and forth. I don’t think she was feeling bad. I think it was just like she like we’re just answering questions. We’re just going. I think you’re I think you were. You were starting to feel bad and like.
563 01:01:28.210 ⇒ 01:01:30.060 Amber Lin: No, it’s like, Oh.
564 01:01:30.060 ⇒ 01:01:36.629 Mathew: No, no, but this is your job. Your job is to get to answers. Your job is to be like is to ask the questions.
565 01:01:37.490 ⇒ 01:01:42.609 Mathew: You have to be. You have to be bold people. You want to be respected more than you want to be liked.
566 01:01:42.910 ⇒ 01:01:51.809 Mathew: because get that’ll make you more liked in the long run. People like. If you’re afraid of asking this or hurting feelings, it’s you’re actually gonna not. Then people won’t trust you.
567 01:01:52.330 ⇒ 01:01:54.080 Amber Lin: Okay. Okay.
568 01:01:54.760 ⇒ 01:01:56.580 Amber Lin: Good to know. Oh, implemented.
569 01:01:56.890 ⇒ 01:02:18.640 Mathew: And with with engineers or with analysts, you’re just asking like very specific questions about you’re not. I didn’t say like, why haven’t you done this or what I’m asking, what’s in the way of that. And then I’m asking questions to understand what is what? What would unblock her, that’s all. She doesn’t mind that. You could ask her. Be like we could ask her, I’m sure, like I don’t think I offended her, but obviously like if I did like, you know, work through that.
570 01:02:18.640 ⇒ 01:02:19.240 Amber Lin: Yeah.
571 01:02:19.780 ⇒ 01:02:22.100 Amber Lin: Ultimately, it’s gonna be all good like, you’re gonna work.
572 01:02:22.100 ⇒ 01:02:22.780 Mathew: Yeah.
573 01:02:23.570 ⇒ 01:02:26.019 Amber Lin: Okay, thank you so much.
574 01:02:26.020 ⇒ 01:02:35.709 Mathew: Yeah. Text call anytime. Let’s push this through the most important thing. Get, get, get her what she needs to be able to turn around those graphs, and we’ll be good.
575 01:02:35.950 ⇒ 01:02:36.650 Amber Lin: Okay.
576 01:02:37.510 ⇒ 01:02:38.610 Mathew: You got this?
577 01:02:41.380 ⇒ 01:02:43.719 Mathew: Take care, text, call anytime.
578 01:02:44.060 ⇒ 01:02:46.239 Amber Lin: Okay, thank you so much.
579 01:02:46.380 ⇒ 01:02:47.060 Mathew: Thank you.
580 01:02:47.460 ⇒ 01:02:48.273 Amber Lin: Bye, bye.