Meeting Title: Mattermore x BF Date: 2025-06-03 Meeting participants: Mathew’s Notetaker (Otter.ai), Fireflies.ai Notetaker Awaish, Amber Lin, Mathew, Awaish Kumar
WEBVTT
1 00:01:46.390 ⇒ 00:01:51.909 Mathew: Hey, team, or hey? Amber looks like a wife is not in yet. Okay, amber.
2 00:01:52.990 ⇒ 00:01:54.800 Mathew: Hi, Hi!
3 00:01:54.960 ⇒ 00:01:57.509 Amber Lin: Hello! Good to see you guys together again.
4 00:01:57.690 ⇒ 00:01:59.940 Mathew: We’re back the the gang back.
5 00:02:01.530 ⇒ 00:02:02.640 Mathew: Here we are.
6 00:02:04.360 ⇒ 00:02:09.510 Amber Lin: Awesome I am, I think, the closest to you guys I’ve ever been.
7 00:02:10.150 ⇒ 00:02:10.810 Mathew: Where are you?
8 00:02:10.810 ⇒ 00:02:13.570 Amber Lin: Graphically. I am in New York.
9 00:02:13.770 ⇒ 00:02:15.110 Mathew: Nice.
10 00:02:15.110 ⇒ 00:02:16.380 Amber Lin: Yeah.
11 00:02:16.380 ⇒ 00:02:16.949 Mathew: But you have seen.
12 00:02:16.950 ⇒ 00:02:21.580 Amber Lin: I’m flying. I’m flying Aldo tonight, though, so I’m flying to Chicago later.
13 00:02:22.060 ⇒ 00:02:23.710 Mathew: Sweet. Where are you staying in New York?
14 00:02:24.237 ⇒ 00:02:29.810 Amber Lin: Stay at a hostel in, I think, closer to Queens and Long Island, so on the East
15 00:02:30.170 ⇒ 00:02:33.079 Amber Lin: Side didn’t stay in Manhattan.
16 00:02:33.370 ⇒ 00:02:34.160 Mathew: Nice.
17 00:02:34.430 ⇒ 00:02:36.810 Amber Lin: Yeah, where are you? Whereabouts? Are you guys.
18 00:02:37.900 ⇒ 00:02:39.435 Mathew: I I
19 00:02:40.940 ⇒ 00:02:42.369 Mathew: How’s the hostel?
20 00:02:42.800 ⇒ 00:02:48.900 Amber Lin: No, it’s pretty good. It’s really big. So we have a big co-working space and a lot of people doing work around here.
21 00:02:50.210 ⇒ 00:02:51.329 Mathew: Do you make any friends.
22 00:02:52.260 ⇒ 00:02:58.910 Amber Lin: This is not as a sociable hostel as I expected it to be. I made friends while I was cooking.
23 00:02:59.080 ⇒ 00:03:02.870 Amber Lin: but everybody else is kind of working. So it’s a little bit hard.
24 00:03:02.870 ⇒ 00:03:07.929 Mathew: I get that? I it’s funny. The hostel system in the Us. Is so different from everywhere else.
25 00:03:07.930 ⇒ 00:03:08.810 Amber Lin: No.
26 00:03:08.810 ⇒ 00:03:13.659 Mathew: Not that common. They’re so expensive, but I’ve still had like experiences at some.
27 00:03:13.660 ⇒ 00:03:14.530 Amber Lin: Yeah.
28 00:03:14.530 ⇒ 00:03:17.519 Mathew: Like. I stayed a really good one in Tennessee. It was like
29 00:03:18.514 ⇒ 00:03:21.690 Mathew: like it was a climbing like a lot of climbing, and like outdoor.
30 00:03:21.690 ⇒ 00:03:23.240 Amber Lin: Oh, wow!
31 00:03:23.240 ⇒ 00:03:24.390 Mathew: Lot of friends there.
32 00:03:24.390 ⇒ 00:03:34.169 Amber Lin: Yeah, I usually just I try to stay hostels, even though I have Airbnb options just because I get to meet people. But it’s very different than when I was in Europe.
33 00:03:34.460 ⇒ 00:03:35.250 Mathew: Yeah.
34 00:03:35.250 ⇒ 00:03:36.040 Amber Lin: Yeah.
35 00:03:36.040 ⇒ 00:03:37.940 Mathew: You’ve done. You’ve done the Europe hostile thing.
36 00:03:38.450 ⇒ 00:03:40.529 Amber Lin: Yeah, I I went from
37 00:03:40.660 ⇒ 00:03:48.239 Amber Lin: north to south. I started from closer to sweet. I started in Sweden and went all the way down to Southern Italy.
38 00:03:48.240 ⇒ 00:03:49.090 Mathew: Whoa!
39 00:03:49.090 ⇒ 00:03:50.420 Amber Lin: Yeah.
40 00:03:50.820 ⇒ 00:03:51.220 Mathew: Trip.
41 00:03:52.232 ⇒ 00:03:58.539 Amber Lin: It’s like a few months. I’d say it was before when I was in Europe it was 2023.
42 00:03:59.230 ⇒ 00:04:00.189 Mathew: Nice. That’s awesome.
43 00:04:01.460 ⇒ 00:04:02.290 Mathew: Solo.
44 00:04:03.196 ⇒ 00:04:07.759 Amber Lin: So long. I think I think if you travel with someone, it’s harder to
45 00:04:07.950 ⇒ 00:04:11.210 Amber Lin: go talk to new people because you’re just a lot more comfortable.
46 00:04:11.330 ⇒ 00:04:15.240 Amber Lin: and people are more comfortable to talk to you. If you’re just one person.
47 00:04:15.240 ⇒ 00:04:19.200 Mathew: Yeah, totally. And then and then you’re like, Oh, yeah, like, what are you doing? Oh, nothing like, let’s let’s go.
48 00:04:19.200 ⇒ 00:04:20.135 Amber Lin: Yeah.
49 00:04:21.129 ⇒ 00:04:26.569 Mathew: That’s best. Yeah, I love I I didn’t solo travel until yeah. For me, it was like 2019. But.
50 00:04:26.570 ⇒ 00:04:27.190 Amber Lin: Oh!
51 00:04:27.190 ⇒ 00:04:31.139 Mathew: I’m older than you, I think so. You’re you’re ahead of me.
52 00:04:32.000 ⇒ 00:04:34.530 Amber Lin: No, I tried stole when I was
53 00:04:34.740 ⇒ 00:04:40.430 Amber Lin: when I I was around like 19 or 20, I think, yeah.
54 00:04:40.430 ⇒ 00:04:40.990 Mathew: So.
55 00:04:41.380 ⇒ 00:04:43.149 Amber Lin: Yeah. Doesn’t make a difference.
56 00:04:43.150 ⇒ 00:04:43.670 Mathew: Yeah.
57 00:04:43.670 ⇒ 00:04:50.860 Amber Lin: No worries. We’re both in the we’re all stuck in New York now, duck, how dare you?
58 00:04:51.632 ⇒ 00:05:01.540 Amber Lin: No, I might move here later, depending on how my finances allow me, but if I if I don’t become broke, I’ll move here.
59 00:05:01.540 ⇒ 00:05:03.659 Mathew: Deal. It’s it’s expensive.
60 00:05:03.790 ⇒ 00:05:10.179 Mathew: Yeah, I felt it. I did my June finances. It’s like, Oh, no, no.
61 00:05:10.440 ⇒ 00:05:11.120 Amber Lin: Yeah.
62 00:05:11.800 ⇒ 00:05:13.950 Mathew: Yeah, I know. I know all about that.
63 00:05:14.270 ⇒ 00:05:18.749 Amber Lin: I’m glad you guys get to share the space. The the prices are insane.
64 00:05:19.220 ⇒ 00:05:20.620 Mathew: Yeah, it’s true, but.
65 00:05:20.980 ⇒ 00:05:23.600 Amber Lin: Okay.
66 00:05:24.070 ⇒ 00:05:31.909 Amber Lin: let’s get to let’s get to work. I haven’t heard back from you guys in a while and you guys are super busy excited that the the contracts getting signed.
67 00:05:32.150 ⇒ 00:05:33.439 Mathew: Yes, we are too.
68 00:05:33.440 ⇒ 00:05:34.580 Amber Lin: Yeah.
69 00:05:34.580 ⇒ 00:05:35.270 Mathew: Cross.
70 00:05:36.443 ⇒ 00:05:38.050 Mathew: Okay, so.
71 00:05:38.750 ⇒ 00:05:42.159 Mathew: I think the the thing that I’m like
72 00:05:43.010 ⇒ 00:05:50.680 Mathew: struggling with, but also excited by and I think our opportunity is is that I think, to the degree that you can just mirror
73 00:05:51.040 ⇒ 00:05:54.380 Mathew: what the slides are that I shared in the deck.
74 00:05:54.750 ⇒ 00:05:59.649 Mathew: and we just see that that is that we’re there to like that. That’s our foundation.
75 00:06:00.680 ⇒ 00:06:06.420 Mathew: Then I’ll know and feel good that what we sold the client on.
76 00:06:07.080 ⇒ 00:06:09.079 Mathew: That we have that capability.
77 00:06:09.980 ⇒ 00:06:18.150 Mathew: So I would literally, and then like cause, every time we add a new slide or a new thing, we are then wrap trying to wrap our heads around it.
78 00:06:18.570 ⇒ 00:06:20.399 Amber Lin: If it was actually what you sold.
79 00:06:20.400 ⇒ 00:06:26.440 Mathew: Well not? Or even if it’s like, is it useful? Does it make sense? Because just because we can track something doesn’t mean we should.
80 00:06:27.360 ⇒ 00:06:28.590 Mathew: Or that it’s valuable.
81 00:06:29.520 ⇒ 00:06:37.010 Mathew: So like as I get later into the deck, I start wondering. And I’m like, do I care about
82 00:06:41.440 ⇒ 00:06:42.380 Mathew: like
83 00:06:42.870 ⇒ 00:06:55.979 Mathew: how much someone is communicating during a day by the day of the week, like, I’m just like, what is this? I don’t know if it’s valuable or not, so we have to be very careful before introducing new metrics or new things, and we have to be.
84 00:06:55.980 ⇒ 00:06:56.400 Amber Lin: We have.
85 00:06:56.400 ⇒ 00:06:57.909 Mathew: Start with the why behind it.
86 00:06:58.732 ⇒ 00:07:01.180 Amber Lin: Gotcha. That’s very important.
87 00:07:01.360 ⇒ 00:07:04.630 Mathew: Yeah. So that’s why I would start with like
88 00:07:04.880 ⇒ 00:07:11.430 Mathew: I would start with, what is the deck that we sold them on? Does the deck does like what’s in here.
89 00:07:12.380 ⇒ 00:07:18.870 Mathew: Do that? And then, before you got before Annie goes off and starts to like, actually build stuff, let’s have a roadmap
90 00:07:19.220 ⇒ 00:07:22.290 Mathew: for what we think is valuable. Why or why not?
91 00:07:22.420 ⇒ 00:07:24.229 Mathew: And like, let’s have a case for that.
92 00:07:24.770 ⇒ 00:07:25.530 Amber Lin: -
93 00:07:26.230 ⇒ 00:07:29.200 Mathew: Cause we could go. And and so it’s almost like bringing the
94 00:07:29.806 ⇒ 00:07:37.229 Mathew: and I know this is on me to like help, to like, steer, steer the project, but like I’ll just screen, share for a second.
95 00:07:45.940 ⇒ 00:07:46.780 Mathew: Hold on.
96 00:07:53.430 ⇒ 00:07:55.060 Mathew: alright! Let me know if you can see this.
97 00:07:55.340 ⇒ 00:07:56.530 Amber Lin: I can see it.
98 00:07:56.530 ⇒ 00:08:00.330 Mathew: Like this is the scope right meeting emails and obviously chat
99 00:08:01.172 ⇒ 00:08:07.650 Mathew: how that compares in office. Then, also getting like productivity based on workplace tools.
100 00:08:08.520 ⇒ 00:08:11.800 Mathew: Time of day. Historical look backs is important.
101 00:08:12.690 ⇒ 00:08:15.489 Mathew: So we need the capability to look back in time.
102 00:08:16.390 ⇒ 00:08:25.030 Mathew: Comparison of like or show things like what happened over a course of course of a period. And this this would just honestly, this would be it.
103 00:08:25.460 ⇒ 00:08:26.130 Amber Lin: Who knows?
104 00:08:26.130 ⇒ 00:08:30.410 Mathew: So I think we have a lot of this in there, but it gets noisy, and it’s hard for me to just be.
105 00:08:30.410 ⇒ 00:08:31.520 Amber Lin: Yeah, yeah.
106 00:08:31.800 ⇒ 00:08:36.870 Mathew: To just know? Like, do we have this? How is this accounted for? Check or no right.
107 00:08:37.289 ⇒ 00:08:40.899 Amber Lin: Totally makes sense. What we have in the deck is, is
108 00:08:41.149 ⇒ 00:09:07.839 Amber Lin: I included. I agree I included too much. I wanted to show you everything we did. But I agree that ultimately we should only show those that that are the end results. Right now, there’s a lot of processes to get to the end. Results is very specific breakdowns. Let me go ahead and I’ll highlight those that are the end results. And I will consolidate them. So we can say, okay, what this is, how it relates, how it matches this deck, and why it’s in there.
109 00:09:07.840 ⇒ 00:09:13.410 Mathew: Yeah, because thinking about this is good for you to hear, too, like, in terms of our process, I think what we’re gonna do is this.
110 00:09:13.520 ⇒ 00:09:24.809 Mathew: we’re gonna schedule a meeting with our champion. He’s out here in New Jersey. We’re gonna go drive to him. We’re gonna say, we just signed the contract. We’re really excited. Here’s what we sold you and your team on.
111 00:09:26.010 ⇒ 00:09:26.710 Mathew: Right.
112 00:09:26.820 ⇒ 00:09:33.290 Mathew: Then we’re going to open up a box Pandora’s box, and we’re going to say, here’s what we could do on top of this.
113 00:09:34.180 ⇒ 00:09:41.190 Mathew: Here’s after we deliver these. Here’s the next back of bag of tricks we can turn up, and so
114 00:09:41.420 ⇒ 00:09:42.510 Mathew: like, and then.
115 00:09:42.510 ⇒ 00:09:43.999 Amber Lin: All the other parts.
116 00:09:44.400 ⇒ 00:09:45.960 Mathew: Well, that is all. The other parts.
117 00:09:46.620 ⇒ 00:09:50.610 Mathew: Here’s here’s what we could do next, or we can build on these.
118 00:09:50.780 ⇒ 00:09:56.329 Mathew: And so that means with you. And like, I thought a wash was gonna join the call today? Is he still joining.
119 00:09:56.587 ⇒ 00:10:01.740 Amber Lin: I thought he is. I talked to him earlier. Let me ping him, and he should be able to join.
120 00:10:02.150 ⇒ 00:10:14.119 Mathew: So I want to be able to turn around. I want to. I want to be able to like sync with y’all and like, whoever is the is the internal lead data scientist, whether it’s a wash, or Robert, or whoever is helping to say.
121 00:10:14.560 ⇒ 00:10:18.440 Mathew: How do we from a like a process standpoint, unpack.
122 00:10:19.090 ⇒ 00:10:21.589 Mathew: Different core metrics we’re working with.
123 00:10:21.820 ⇒ 00:10:31.519 Mathew: How do we like? And then me and Trevor can prioritize what we think we there’s like, what must. This is what we must like. Have ready.
124 00:10:31.520 ⇒ 00:10:32.000 Amber Lin: I love.
125 00:10:32.000 ⇒ 00:10:36.239 Mathew: The must haves the should. Haves bless you.
126 00:10:38.980 ⇒ 00:10:48.189 Mathew: bless you! The should have the should. Haves are going to be the things that we think would be valuable to them. The could haves are all the rest, and then the won’t. Haves are just the things that don’t make sense right.
127 00:10:48.800 ⇒ 00:10:52.924 Mathew: I put this little Doc together. I don’t think you should look at it. But
128 00:10:53.810 ⇒ 00:11:03.509 Mathew: Something like this, I think, is something that we want to get to, which is like, what are the base raw, like metrics? This is like, this is a metric, a data dictionary.
129 00:11:05.360 ⇒ 00:11:07.110 Mathew: What’s our data dictionary?
130 00:11:07.680 ⇒ 00:11:13.250 Mathew: These are things that you’re gonna have like location. To meet.
131 00:11:13.250 ⇒ 00:11:14.000 Amber Lin: Same time.
132 00:11:14.000 ⇒ 00:11:20.369 Mathew: And then there’s going to be things that are derivatives which are down here drive scores that we’re that we can get more creative.
133 00:11:21.360 ⇒ 00:11:22.190 Mathew: About.
134 00:11:22.980 ⇒ 00:11:28.790 Mathew: I’m not expecting you to like. I don’t want you to run with this or start working with it. We’re working on this because this is like, obviously our core. IP, this is our core
135 00:11:29.560 ⇒ 00:11:35.040 Mathew: logic or whatever. But like, I think, we need to get to the point where it’s like this is the base, just like
136 00:11:36.660 ⇒ 00:11:39.319 Mathew: base base, like volume
137 00:11:39.970 ⇒ 00:11:46.369 Mathew: activity stuff. And then we can build on top of that and say, and then it gets more interesting, which is like.
138 00:11:46.590 ⇒ 00:11:51.770 Mathew: you know, like, what would a score be? That’s like a burden index of when people are.
139 00:11:52.290 ⇒ 00:12:00.220 Mathew: Hours or not. What inputs do we need from the client? Because they might actually say that it’s not a burden for certain teams to work after hours, so we might have to configure that
140 00:12:01.170 ⇒ 00:12:02.480 Mathew: certain assumptions.
141 00:12:02.590 ⇒ 00:12:11.319 Mathew: So there’s gonna be like some push and pull. But I don’t want to overwhelm you, I’d say for next meeting. If you can just get this deck ready to go
142 00:12:11.430 ⇒ 00:12:12.120 Mathew: of like.
143 00:12:12.890 ⇒ 00:12:26.870 Mathew: What we committed to client, and then we will work on what the next layer of things that we want to have to present to the customer are, and say, Here’s our hey, hey? You know we sold you on the things that you thought you wanted. Here’s the full capability of the set.
144 00:12:27.590 ⇒ 00:12:34.659 Mathew: Possible. We’ll obviously cross check, cross, check that with you beforehand, and then I think it’s important that you, Annie.
145 00:12:34.950 ⇒ 00:12:50.969 Mathew: and whoever’s like the authority on like the lead on your side is thinking through, how do we turn this from like Annie, building one chart at a time to what’s a. And this is also for you. What’s a foundational approach where she’s just building each metric that would be then easy to like, slot into.
146 00:12:50.970 ⇒ 00:12:52.463 Amber Lin: Yes, yes.
147 00:12:53.310 ⇒ 00:12:56.129 Mathew: That’s the value. That’s what we’re paying you for as well.
148 00:12:56.570 ⇒ 00:12:57.870 Amber Lin: Totally. And
149 00:12:58.030 ⇒ 00:13:11.049 Amber Lin: actually it will be great if you share I I’m trying to think of a way how she can share the processes so it can be tied to each metric. So it doesn’t. It doesn’t get bottled up in Annie’s say notebook
150 00:13:11.652 ⇒ 00:13:19.780 Amber Lin: because you have all these individual metrics that ultimately you should be able to just slot them together, combine them, and I want them to be.
151 00:13:19.780 ⇒ 00:13:20.600 Mathew: Exactly.
152 00:13:20.600 ⇒ 00:13:22.740 Amber Lin: Document is somewhere. So it’s modular.
153 00:13:22.740 ⇒ 00:13:29.649 Mathew: Exactly. That’s how we want her to be built. Start. That’s what how we’re gonna want her to build. At least these metrics right.
154 00:13:30.530 ⇒ 00:13:53.950 Mathew: But like, if you can help us right like, if if we have that capability, don’t we need that capability which, like, have the underlying metrics built out versus like, it’s almost like getting the bottom up pieces. Like, yeah, I know absolutely that. I mean, that’s that’s the idea of having those incremental tables that we have like we have like pre-joined table. I have sorry, Amber. I haven’t looked at like the Dbt stuff, or like the
155 00:13:54.310 ⇒ 00:14:08.239 Mathew: and I’m planning on it. But yeah, I’m assuming that like, we have incremental models that like to pre join stuff, and then ones that like, do those? Yeah, if we don’t already having tables that have these like base underlying metrics, that we then do. The derive metrics based on.
156 00:14:08.730 ⇒ 00:14:17.250 Mathew: Because, yeah, like, last said, that means that we can then like, mix and match them in different patterns. That’s what we want. Right? Yeah, of course, to be flexible, right? Modular or whatever.
157 00:14:17.750 ⇒ 00:14:23.370 Mathew: Tell our champ be like, Hey, you know, we can analyze this and and for him to be like, Hey, can we analyze this and that. And we just look at our dictionary, and we’re like.
158 00:14:23.370 ⇒ 00:14:24.670 Amber Lin: Oh!
159 00:14:24.670 ⇒ 00:14:30.609 Mathew: That’s what we want. Yeah, that’s that’s how this becomes like, really, really fun. Right?
160 00:14:30.610 ⇒ 00:14:35.890 Amber Lin: Awesome, awesome. This helps me clarify what we want in my mind. A lot.
161 00:14:36.180 ⇒ 00:14:53.480 Mathew: Like we want him to come in with ideas of like like, it’s imagine we’re in a Dj booth, right? And he or like, we’re building a song. And he’s like, I want this beat with that. Can we? Sorry like, could we do that? And we like I want Trevor to actually be empowered
162 00:14:53.550 ⇒ 00:15:06.470 Mathew: to one talk, to Gpt and be like, how do I do that? And then turn to the turn to the model that you guys have built out for us and spin it up because, like the best companies in the world that are selling a type of service like this should be able to literally show him stuff in real time.
163 00:15:08.380 ⇒ 00:15:09.640 Mathew: Right. Yes.
164 00:15:09.640 ⇒ 00:15:20.379 Mathew: Gotcha. So the more modular and more clearly defined these things are, the more you will be able to do that, and faster you’ll be able to do that. So I think a core thing and.
165 00:15:20.851 ⇒ 00:15:35.330 Amber Lin: I’m gonna ask my tech lead on. This is how we make that more flexible and make it so that you can do the modular things that you want, so that they’re not completely too complex, and to weave together that you can’t separate them apart.
166 00:15:35.330 ⇒ 00:15:40.779 Mathew: And I’ll work with Trevor to like actually iron this out because we could, as an input and say, like.
167 00:15:41.660 ⇒ 00:15:49.529 Mathew: cause this, this is a graph right number of chat messages sent via teams per person per day, which can obviously be filtered by.
168 00:15:49.530 ⇒ 00:15:50.170 Amber Lin: Yeah.
169 00:15:50.170 ⇒ 00:15:54.379 Mathew: By remote or whatever like that covers that graph like check
170 00:15:54.500 ⇒ 00:15:56.650 Mathew: when we get to like. How
171 00:15:56.770 ⇒ 00:16:08.339 Mathew: much of this, how how focused is this team, meaning, like how many uninterrupted work blocks do they have? How much time are they not in a meeting like I would love to be able to point to a team and say, This team is constantly in meetings.
172 00:16:08.630 ⇒ 00:16:10.200 Amber Lin: Bigger, scattered.
173 00:16:10.470 ⇒ 00:16:11.790 Mathew: They’re fragmented.
174 00:16:13.210 ⇒ 00:16:15.440 Mathew: They’re broken inside.
175 00:16:15.680 ⇒ 00:16:30.169 Mathew: They have. They have nothing, nothing left, nothing. This team has like like they have like cadences in the week where they’re like they can work. They’re not on meetings all day, like it’s okay to send a little chat here or there like that’s part of working like people get.
176 00:16:30.750 ⇒ 00:16:38.930 Mathew: Like, and we should set thresholds for that. But like we’re in, we’re in some like new territory. So we have to start to get really smart about how we’re continuing to work together.
177 00:16:42.940 ⇒ 00:16:55.249 Amber Lin: Totally so. I think what I hear from now 1st steps is just to clarify that we have everything in the phase one which I think, which we do, I will clarify that. So it’s much more easy to read.
178 00:16:55.877 ⇒ 00:16:59.989 Amber Lin: I’ll consolidate that, and I know that somewhere here
179 00:17:02.410 ⇒ 00:17:07.919 Amber Lin: I’ll go off of the Insights column and not the examples column, because I think there’s
180 00:17:08.079 ⇒ 00:17:10.839 Amber Lin: there’s a few more things that say
181 00:17:10.960 ⇒ 00:17:14.199 Amber Lin: more than just Hr partners. So we’ll see if we can do.
182 00:17:14.200 ⇒ 00:17:16.410 Mathew: This is an example. Yeah.
183 00:17:16.410 ⇒ 00:17:25.759 Amber Lin: Yeah, yeah, totally. So I will list it out. I’ll make a I think I’ll make a checklist so that you can know that each one of them is is completed.
184 00:17:25.890 ⇒ 00:17:27.550 Mathew: Yup, and then.
185 00:17:27.980 ⇒ 00:17:39.680 Amber Lin: I will. I think this part is very important. I’ll work with the tech lead to make sure that we have. We can do everything modularly, because that’s also how our deliverables should be shaped, because that’s how you want to use them.
186 00:17:40.122 ⇒ 00:18:01.020 Amber Lin: It will be great if we can have those metrics. So we can also have an internal checklist, and it’ll help me create the tickets of okay. These are the things that need to be done. Because right now I’m riffing off of this document, which might not be that complete this document, the slides. But I I want to have a checklist that’ll be really helpful.
187 00:18:01.020 ⇒ 00:18:05.519 Mathew: Well, so yeah, you’ll update the doc to have the must haves of what we’ve delivered like. Even this one.
188 00:18:05.520 ⇒ 00:18:05.900 Amber Lin: I know.
189 00:18:06.120 ⇒ 00:18:09.959 Mathew: It’s been fleshed out like digital activity. That’s a huge component of productivity.
190 00:18:11.140 ⇒ 00:18:11.850 Mathew: Right.
191 00:18:13.660 ⇒ 00:18:18.560 Mathew: The none of this stuff actually is like
192 00:18:19.020 ⇒ 00:18:21.940 Mathew: none of these are derived scores. This is just like, what is the truth.
193 00:18:21.940 ⇒ 00:18:22.500 Amber Lin: Yes.
194 00:18:22.710 ⇒ 00:18:24.710 Mathew: Of what is happening.
195 00:18:25.680 ⇒ 00:18:26.970 Amber Lin: Totally.
196 00:18:27.220 ⇒ 00:18:31.989 Mathew: It gets more complicated when we get into this stuff that I showed you here.
197 00:18:31.990 ⇒ 00:18:36.990 Amber Lin: Yes, example of that would be, say, focus time, right focus. Time is a derived score from all of these.
198 00:18:36.990 ⇒ 00:18:40.470 Mathew: Yeah, well, well, even the way we define focus time, like even using this as an example.
199 00:18:42.210 ⇒ 00:18:49.420 Mathew: It’s it’s not even a thing like it gets more opinionated when you get to these composite scores, which is like, like.
200 00:18:51.670 ⇒ 00:18:54.267 Mathew: actually, all of this is cool.
201 00:18:55.940 ⇒ 00:19:01.069 Mathew: all of these are just like the truth. It’s when you start having an opinion about it of like. Well.
202 00:19:01.919 ⇒ 00:19:08.940 Mathew: like, if we were to give it an arbitrary or or subjective score of like. This team is more burnt, more burnt out, or less burnt out. But again, that can be.
203 00:19:09.510 ⇒ 00:19:13.840 Mathew: Standard deviation. So I, wanna, yeah, okay, cool ignore that last thing, cool.
204 00:19:13.840 ⇒ 00:19:14.410 Amber Lin: Yeah.
205 00:19:15.480 ⇒ 00:19:25.709 Amber Lin: Well, if these all are descriptive statistics, and I think it’s very possible for us to do without interfering with like subjective measurements that needs to be defined by your client.
206 00:19:25.710 ⇒ 00:19:29.139 Mathew: Yeah. And then we added a bunch of comments that I think are exposing some of the things that.
207 00:19:29.850 ⇒ 00:19:34.060 Mathew: Team might be, might overlook that we’ve just been exposed to, which is like.
208 00:19:34.585 ⇒ 00:19:35.110 Amber Lin: Lovely.
209 00:19:35.110 ⇒ 00:19:40.019 Mathew: Might have calendar holds. They might have a meeting that they didn’t join.
210 00:19:41.182 ⇒ 00:19:43.439 Mathew: Can you determine that or not like?
211 00:19:43.570 ⇒ 00:19:48.179 Mathew: Are they getting invited to too many meetings? Do they have multiple meetings at the same time.
212 00:19:49.040 ⇒ 00:19:53.289 Mathew: Have to be like we gotta. We gotta normalize that in order to.
213 00:19:54.770 ⇒ 00:19:56.979 Mathew: You know, just know that we’re doing this right.
214 00:19:57.750 ⇒ 00:20:01.290 Amber Lin: That sounds great Oish is here. Hi! Awaish!
215 00:20:01.780 ⇒ 00:20:04.059 Amber Lin: This is Matthew and Trevor.
216 00:20:06.340 ⇒ 00:20:07.749 Awaish Kumar: Hello! How are you?
217 00:20:08.180 ⇒ 00:20:09.500 Mathew: Hi! Hello!
218 00:20:10.340 ⇒ 00:20:25.690 Amber Lin: Wishes our tech lead on this team. He’s a very, very talented and senior data engineer, and he has been leading most of our junior folks, so he manages Luke and Annie, so I think he will be very, very helpful here.
219 00:20:25.980 ⇒ 00:20:27.149 Mathew: Cool. Nice to meet you.
220 00:20:27.500 ⇒ 00:20:28.030 Amber Lin: Yeah.
221 00:20:28.030 ⇒ 00:20:34.860 Awaish Kumar: Yeah, thank you so much. Matthew and Trevor. I am sorry for late joining, and it’s nice to meet you.
222 00:20:35.390 ⇒ 00:20:36.260 Mathew: Good to meet you.
223 00:20:37.420 ⇒ 00:20:52.627 Amber Lin: Yeah. And I wish a quick, just quickly recap on this. We have all our analysis currently in Annie’s notebook, right? And we have module models that Luke has done. Ultimately, what we want to do is we want to have
224 00:20:53.140 ⇒ 00:20:57.790 Amber Lin: these models work modularly. So, if you can see on the
225 00:20:58.110 ⇒ 00:21:15.859 Amber Lin: on the screen right here is that we want to enable the clients to okay, pull these 2 specific metrics that we that we create a model score and analyze them together. So that really impacts how we think about our deliverables.
226 00:21:16.120 ⇒ 00:21:20.399 Amber Lin: And so I think this is a great thing that we can think about
227 00:21:20.730 ⇒ 00:21:26.139 Amber Lin: offline and give give Matthew and Trevor a roadmap on what we want to do.
228 00:21:28.590 ⇒ 00:21:35.570 Awaish Kumar: Okay, fine like do have, do we have like priorities on these metrics like.
229 00:21:36.890 ⇒ 00:21:39.110 Awaish Kumar: how like in terms of
230 00:21:43.580 ⇒ 00:21:54.419 Awaish Kumar: like in like, what Metro and the travel are thinking about. How should we move forward with like? If you have any priority on which things should we build 1st and
231 00:21:54.740 ⇒ 00:21:57.159 Awaish Kumar: and then, which one afterwards.
232 00:22:00.080 ⇒ 00:22:01.249 Mathew: Is that a question for me?
233 00:22:02.360 ⇒ 00:22:06.850 Mathew: Yeah, yeah. So this is, this is what the team’s been working on right here.
234 00:22:07.420 ⇒ 00:22:14.669 Mathew: We want to make sure that we have these locked and loaded, and as as like modular and reusable as possible.
235 00:22:16.780 ⇒ 00:22:17.960 Awaish Kumar: Okay. Yeah.
236 00:22:17.960 ⇒ 00:22:28.099 Amber Lin: Yeah. So which I think this is something that we’ll work very closely with Luke on and we’ll see how how best is the way that we can present this
237 00:22:28.838 ⇒ 00:22:31.980 Amber Lin: I think. Oh, another thing crossed my
238 00:22:34.580 ⇒ 00:22:53.010 Amber Lin: Oh, I have a question on if we want to use power. Bi, because I remember when we 1st started this engagement, we mentioned that the client wants to use power Bi. And right now, since you said, we have 2 more weeks. This is a period of time that we can start building it also in power. Bi, would you prefer that we do that.
239 00:22:53.811 ⇒ 00:22:56.789 Mathew: Right. Now all this is being done in Python.
240 00:22:56.990 ⇒ 00:22:57.640 Amber Lin: Yes.
241 00:22:57.910 ⇒ 00:23:02.970 Mathew: And then from python, would we pipe that into power? Bi like, what is.
242 00:23:03.210 ⇒ 00:23:09.779 Mathew: what’s the connection between python and power? Bi? Yeah, it it. It’s so. It’s like, it’s in python.
243 00:23:09.900 ⇒ 00:23:23.731 Mathew: Well, I guess it. Okay, that’s a good question. Right? So so we like, obviously, the Dbt stuff that creates the the tables like that we can pump into power bi. And and they want us to, because they want like sort of like underlying intermediate models. Yep,
244 00:23:24.720 ⇒ 00:23:33.090 Mathew: for. And so I think that in general, like the the most as much as we can do in sequel, the better, because that just means that like we have a database
245 00:23:33.900 ⇒ 00:23:53.220 Mathew: we can like build on top and do layers and stuff, you know, at a certain point, right? Like we need to actually do the visualization part. And so I’ve never used power. Bi, I don’t know. Can you write? Can you add python scripts to power bi to do that stuff? Or do you have to like use use? They have like their own language. Do you have to use the Ui like, how does that work? Yeah.
246 00:23:58.480 ⇒ 00:23:59.950 Mathew: Especially for you guys. Yeah.
247 00:24:01.230 ⇒ 00:24:02.703 Amber Lin: I believe
248 00:24:04.410 ⇒ 00:24:06.580 Mathew: And if you don’t know, that’s okay, we.
249 00:24:06.580 ⇒ 00:24:08.110 Amber Lin: Yeah, I.
250 00:24:08.110 ⇒ 00:24:08.640 Awaish Kumar: So what?
251 00:24:08.640 ⇒ 00:24:13.469 Amber Lin: I don’t know. My quick search says you can run python scripts directly in power bi desktop.
252 00:24:14.240 ⇒ 00:24:16.680 Amber Lin: That’s what the search tells me. I don’t know.
253 00:24:17.500 ⇒ 00:24:18.060 Mathew: Great.
254 00:24:18.960 ⇒ 00:24:21.670 Mathew: I don’t know what that means, so we have to figure that out because.
255 00:24:21.670 ⇒ 00:24:22.890 Amber Lin: Okay, great.
256 00:24:23.010 ⇒ 00:24:23.956 Amber Lin: So is,
257 00:24:24.430 ⇒ 00:24:30.030 Mathew: We’re gonna we’re gonna be on the hook when this starts to go to client. And then, like I.
258 00:24:30.030 ⇒ 00:24:31.039 Awaish Kumar: So is it.
259 00:24:31.040 ⇒ 00:24:31.590 Mathew: Yeah.
260 00:24:32.770 ⇒ 00:24:37.160 Awaish Kumar: I have one quick question here, so is it a requirement to use power? Bi.
261 00:24:37.320 ⇒ 00:24:37.990 Mathew: Yes.
262 00:24:38.827 ⇒ 00:24:47.169 Mathew: the 1st requirement is that we deliver the insights, and as quickly like, that’s why we’re doing these reports and pi out of the being generated out of python
263 00:24:49.240 ⇒ 00:25:00.760 Mathew: but eventually, like right at shortly thereafter. We need the capability to to for this to pipe directly into their power. Bi.
264 00:25:01.270 ⇒ 00:25:04.569 Mathew: we’re piping all of this data and into their azure
265 00:25:06.026 ⇒ 00:25:19.089 Mathew: sequel server into a SQL server. And we’re they’re gonna give us access to do stuff to then be able to work with them to in their power. Bi. So you’re gonna we’re gonna want your team to hand us.
266 00:25:19.470 ⇒ 00:25:28.159 Mathew: presumably, whatever setup or handbooks, or whatever work you would do in power Bi, so that we could implement it there. Right?
267 00:25:30.960 ⇒ 00:25:31.610 Mathew: That makes sense.
268 00:25:31.660 ⇒ 00:25:37.180 Awaish Kumar: So basically, we will. We be working directly on the on your clients.
269 00:25:37.340 ⇒ 00:25:37.670 Mathew: No.
270 00:25:39.210 ⇒ 00:25:54.969 Mathew: no, we have. We’re very. We have to be very mindful of like security and access. So ideally, you’re building some. You’re doing some something on your side handing it to us, and then, Trevor, assume Trevor has direct access. He’ll just like ideally what copy and paste it, or like. We quickly replicate
271 00:25:55.390 ⇒ 00:25:57.030 Mathew: in there. Yeah.
272 00:25:57.600 ⇒ 00:25:58.230 Awaish Kumar: Thank you.
273 00:26:00.270 ⇒ 00:26:14.909 Amber Lin: So when we do build it in power, Bi, which I assume is soon we can use probably either your your madam, or instance, or say our own instance, because ultimately it doesn’t, it will get transferred to the client site. Anyways.
274 00:26:14.910 ⇒ 00:26:15.600 Mathew: Yes.
275 00:26:15.780 ⇒ 00:26:17.669 Amber Lin: Okay, sounds good. That’s great to hear.
276 00:26:18.150 ⇒ 00:26:26.589 Mathew: Probably use ours. But, Tom, about getting us in, and it’s a pain in the ass. It’s been a just a pain. Setting up a Microsoft account. He said he was gonna take a look at that. So.
277 00:26:27.340 ⇒ 00:26:29.079 Mathew: Depending on. When we prioritize that.
278 00:26:30.470 ⇒ 00:26:41.720 Mathew: What I don’t want to happen is is like, we deliver all these insights, and then client turns around and says, Okay, are you ready to pipe this into power Bi, and then we’re like, Oh, yeah, let’s like start figuring that out. I want to be like.
279 00:26:41.720 ⇒ 00:26:42.310 Amber Lin: Yeah.
280 00:26:42.310 ⇒ 00:26:45.950 Mathew: And like flip a switch. And it’s we’re already able to do that within a, you know.
281 00:26:46.200 ⇒ 00:26:48.500 Mathew: within hours and days as opposed to weeks and months.
282 00:26:48.500 ⇒ 00:27:04.929 Amber Lin: Okay, if that’s something once we want to do, I suggest that we at least get it set up now. So we actually get into the environment, because it seems like both Matthew and our team. It’s not that haven’t been consistently working in power. Bi. So it it’s best at least to start.
283 00:27:04.930 ⇒ 00:27:08.010 Mathew: What we talk, and he’s worked in it so she hasn’t. Then we have.
284 00:27:08.010 ⇒ 00:27:10.360 Amber Lin: Okay. Annie has. Okay. Sounds good.
285 00:27:12.660 ⇒ 00:27:15.190 Awaish Kumar: Team mostly has worked on tableau stuff.
286 00:27:15.190 ⇒ 00:27:15.750 Mathew: Yeah.
287 00:27:16.732 ⇒ 00:27:19.329 Awaish Kumar: But power bi like she will
288 00:27:20.020 ⇒ 00:27:26.670 Awaish Kumar: like. If if we get get instance, it would be really nice. So we can start developing on on top of it.
289 00:27:26.670 ⇒ 00:27:36.880 Mathew: That if you guys don’t have power bi experience, I’d rather you say you don’t. You’re not. That’s not your sweet spot, or it’s gonna be a a major learning curve. And we’ll just go find somebody who’s a power bi
290 00:27:37.200 ⇒ 00:27:44.610 Mathew: yeah supplement. Yeah, like, I don’t. Wanna. I don’t want us like I don’t want. I don’t I? We don’t have time for waste or inefficiency.
291 00:27:48.140 ⇒ 00:27:58.950 Awaish Kumar: Like we, I, I, what we, what we are saying is that like we do have team members who can work on power bi with the like. We are just saying like, because we wanted to.
292 00:27:59.250 ⇒ 00:28:10.919 Awaish Kumar: After you acquire the client, and we immediately want to show something. They will just get some time to build dashboards like now they are doing in repeater notebook. Instead, they will just directly do on power bi.
293 00:28:11.510 ⇒ 00:28:17.419 Mathew: Well, yeah, the workflow, I think, will be like, we’ll continue to work in python and then trail into power. Bi.
294 00:28:18.920 ⇒ 00:28:19.690 Awaish Kumar: Okay.
295 00:28:20.880 ⇒ 00:28:21.500 Mathew: Right?
296 00:28:21.630 ⇒ 00:28:22.799 Mathew: Yeah, that’s right.
297 00:28:24.270 ⇒ 00:28:38.430 Mathew: So if you have team members that are that, you feel really confident that that isn’t Annie, or if it’s going to be, Annie, please let us know, and if you have that capability or have to find it, let us know, or we’ll find it. I I don’t want us to be in this like.
298 00:28:38.770 ⇒ 00:28:48.120 Mathew: yeah, we don’t really have experience power Bi, but maybe we’ll figure it out. And then when we’re in there, we’re then because we have to be effectively experts in working in power. Bi.
299 00:28:53.230 ⇒ 00:28:55.039 Amber Lin: And totally makes sense.
300 00:28:56.570 ⇒ 00:28:57.390 Amber Lin: I
301 00:28:58.360 ⇒ 00:29:07.189 Amber Lin: we haven’t. I think one of our clients is using power. Bi, I’ll go check in who’s actually doing that work, I believe, is Annie. I’ll get back to you.
302 00:29:07.190 ⇒ 00:29:10.499 Mathew: Yeah, let me know the confidence level. If that’s
303 00:29:11.040 ⇒ 00:29:19.740 Mathew: guys feel confident about, or if you think we should go resource that, or if you want to resource that like I, it doesn’t matter. I just wanna what I don’t want is could. And then you can.
304 00:29:19.920 ⇒ 00:29:26.309 Amber Lin: Yeah, we’ll make sure that it’s an expert on it. So if it’s not Annie, then it’s not Annie, but we’ll make sure there’s an expert.
305 00:29:27.010 ⇒ 00:29:32.239 Amber Lin: Yeah, will we get? Will we be able to have that instance of power bi set up soon?
306 00:29:33.470 ⇒ 00:29:39.340 Mathew: I guess. Yeah, we could try. It’s a it’s been a pain in the, in pain in the ass. But we’ll get back in there and give it a shot.
307 00:29:42.330 ⇒ 00:29:51.230 Amber Lin: Is, it’s I believe it’s best that you guys set it up. So just in case anything happens in future, you can still have that instance, I think that will be the best best call.
308 00:29:51.770 ⇒ 00:29:57.320 Amber Lin: Okay, alright. Let us know when it gets set up, and we can go there and start building
309 00:29:57.490 ⇒ 00:30:11.050 Amber Lin: and me and wish this. This is a really helpful call me and wish will go look at the roadmaps. Give you guys a clear idea of how we’re gonna make that modular and how what our handoff deliverables are gonna look like.
310 00:30:11.220 ⇒ 00:30:14.209 Amber Lin: And I think we can meet from there.
311 00:30:14.470 ⇒ 00:30:19.420 Mathew: Cool. You work on that. We’ll work on like what we think the next wave of
312 00:30:19.880 ⇒ 00:30:26.019 Mathew: like could like should haves and could haves be once we’ve once we’re aligned on the must haves, which again should.
313 00:30:26.800 ⇒ 00:30:30.826 Mathew: 1st time, and then and then
314 00:30:32.040 ⇒ 00:30:34.830 Mathew: and then we could sync again on Thursday with those inputs.
315 00:30:35.290 ⇒ 00:30:40.960 Amber Lin: Sure. I don’t know if you want, or would be able to share, that spreadsheet with me that you just shared.
316 00:30:40.960 ⇒ 00:30:43.830 Mathew: I’m not gonna share yet, because it’ll confuse you. I’d rather send.
317 00:30:43.830 ⇒ 00:30:44.580 Amber Lin: Okay.
318 00:30:44.580 ⇒ 00:30:45.710 Mathew: Feel really good about.
319 00:30:45.980 ⇒ 00:30:50.110 Amber Lin: Okay. Sounds good. Ultimately, I would like a checklist for my team.
320 00:30:50.110 ⇒ 00:30:51.320 Mathew: Yeah, we yeah.
321 00:30:51.480 ⇒ 00:30:52.010 Amber Lin: Yeah.
322 00:30:52.220 ⇒ 00:30:54.570 Mathew: That we’re gonna no, no, that’s what we’re gonna work on. We’re gonna update.
323 00:30:54.570 ⇒ 00:30:55.080 Amber Lin: Okay.
324 00:30:55.800 ⇒ 00:30:56.800 Amber Lin: Sounds good.
325 00:30:58.650 ⇒ 00:31:00.730 Amber Lin: Alrighty. Thank you all for the meeting.
326 00:31:01.440 ⇒ 00:31:06.480 Mathew: Thanks, Amber. Thank you, Amber, good to meet you. Awash bye.
327 00:31:06.480 ⇒ 00:31:07.299 Awaish Kumar: You, bye.
328 00:31:07.300 ⇒ 00:31:08.070 Amber Lin: Bye.