Meeting Title: MatterMore | internal Standup Date: 2025-07-08 Meeting participants: Amber Lin, Ryan Phillips, Mathew
WEBVTT
1 00:03:18.290 ⇒ 00:03:19.400 Amber Lin: Hi! Ryan.
2 00:03:19.940 ⇒ 00:03:21.150 Ryan Phillips: Hello! How’s it going.
3 00:03:21.360 ⇒ 00:03:23.240 Amber Lin: Very good, nice to meet you.
4 00:03:23.450 ⇒ 00:03:24.391 Ryan Phillips: Nice to meet you, too.
5 00:03:25.080 ⇒ 00:03:27.120 Amber Lin: Are you also based in New York.
6 00:03:28.163 ⇒ 00:03:31.059 Ryan Phillips: Yes, mostly. Yeah. I’m in Oakland.
7 00:03:31.590 ⇒ 00:03:34.500 Amber Lin: Oh, I see I didn’t.
8 00:03:34.500 ⇒ 00:03:35.110 Ryan Phillips: How are you?
9 00:03:35.110 ⇒ 00:03:37.690 Amber Lin: I’ll go visit Oakland when I was in New York.
10 00:03:37.690 ⇒ 00:03:42.759 Ryan Phillips: Okay. Yeah. Yeah. I’m sorry if I said, we work which is where I am. I don’t know if you recognize.
11 00:03:42.760 ⇒ 00:03:43.719 Amber Lin: Oh, I see!
12 00:03:43.720 ⇒ 00:03:44.489 Ryan Phillips: See you later.
13 00:03:44.490 ⇒ 00:03:53.640 Amber Lin: I couldn’t tell, but it was a little bit familiar to me, because I I feel like I’ve seen seen them a few times on people’s calls, so I didn’t know what they were.
14 00:03:53.640 ⇒ 00:03:58.984 Ryan Phillips: Yeah, yeah, yeah. No. I’m out in Oakland. Yeah, it’s
15 00:03:59.680 ⇒ 00:04:05.370 Ryan Phillips: working pretty well. So I just started a couple of weeks ago. So I’m still getting used to things. But but yeah, it’s been good.
16 00:04:06.280 ⇒ 00:04:28.019 Amber Lin: That’s good. I mean this call. I don’t know if Matthew would join. I would just love to walk you through all the stuff that we did help answer any questions and help direct you to know where everything is. Cause I know that’s always a problem. We have everything pretty well documented, so it would be relatively easy for you to navigate and
17 00:04:28.670 ⇒ 00:04:32.290 Amber Lin: let’s see, let me share this one.
18 00:04:32.510 ⇒ 00:04:34.829 Amber Lin: This documentation with you.
19 00:04:35.130 ⇒ 00:04:36.070 Amber Lin: Good.
20 00:04:41.920 ⇒ 00:04:43.030 Amber Lin: hmm!
21 00:04:47.690 ⇒ 00:04:48.930 Amber Lin: Let’s see.
22 00:04:49.080 ⇒ 00:04:55.730 Amber Lin: Okay, so I cannot publish. We have it on notion. I’m gonna move it to a Google doc. So I can share
23 00:04:58.440 ⇒ 00:04:59.930 Amber Lin: here.
24 00:05:01.160 ⇒ 00:05:06.170 Amber Lin: So do you have an idea of what this project was about.
25 00:05:06.690 ⇒ 00:05:08.720 Ryan Phillips: I have a basic idea. But I
26 00:05:09.000 ⇒ 00:05:12.860 Ryan Phillips: and I’ve like poked around in the data that’s available. But
27 00:05:13.790 ⇒ 00:05:19.539 Ryan Phillips: honestly, I’d love to hear how you phrase it, and just like walk me through it. Exactly what you said sounds great.
28 00:05:20.010 ⇒ 00:05:21.460 Amber Lin: Okay. Sounds good.
29 00:05:22.190 ⇒ 00:05:22.960 Ryan Phillips: Hello!
30 00:05:24.870 ⇒ 00:05:26.170 Ryan Phillips: Oh, no sound.
31 00:05:29.860 ⇒ 00:05:30.610 Mathew: Alright!
32 00:05:30.980 ⇒ 00:05:31.670 Ryan Phillips: Nope.
33 00:05:31.820 ⇒ 00:05:33.126 Amber Lin: No sound.
34 00:05:33.920 ⇒ 00:05:34.810 Mathew: No sound.
35 00:05:35.300 ⇒ 00:05:36.065 Ryan Phillips: There we go,
36 00:05:36.320 ⇒ 00:05:37.530 Amber Lin: Anyway, there we go.
37 00:05:38.670 ⇒ 00:05:39.540 Mathew: Hi! Ryan.
38 00:05:39.860 ⇒ 00:05:40.640 Ryan Phillips: Good morning!
39 00:05:41.340 ⇒ 00:05:42.670 Mathew: Happy. Tuesday.
40 00:05:43.360 ⇒ 00:05:44.560 Ryan Phillips: Happy. Tuesday.
41 00:05:44.560 ⇒ 00:05:51.469 Mathew: Good morning, all right. Amber your ball! You’re you’re take us away.
42 00:05:52.030 ⇒ 00:06:09.300 Amber Lin: Okay. So mostly, I just wanna walk, Ryan. My purpose of this call just to walk Ryan through. What? What we did, where everything is, and if he has any questions and what we think still needs to get done, so we can hand off all our thinking and our work.
43 00:06:09.300 ⇒ 00:06:09.870 Mathew: Okay.
44 00:06:09.870 ⇒ 00:06:20.399 Amber Lin: Through Ryan, so he feels prepared. We have a we have prepared a off boarding document, and I think I’ll share that
45 00:06:23.150 ⇒ 00:06:27.830 Amber Lin: or let’s see, it was originally a notion, so I.
46 00:06:28.090 ⇒ 00:06:34.849 Mathew: Amber if we can, if we can. Sorry if we can record this just for Trevor, because he might need some, he might any technical insight.
47 00:06:35.375 ⇒ 00:06:39.864 Mathew: Cause he’ll be. He’s effectively. He’ll be like managing the Dbt pipelines and
48 00:06:40.210 ⇒ 00:06:44.339 Amber Lin: Yeah, totally it’s recorded on our side. Feel free to record it for you.
49 00:06:44.340 ⇒ 00:06:47.489 Mathew: Oh, cool, cool. It says it’s recording. But I’ll yeah. I’ll do that.
50 00:06:47.800 ⇒ 00:06:48.245 Amber Lin: Okay.
51 00:06:50.670 ⇒ 00:06:51.970 Mathew: I just sent you a request.
52 00:06:54.760 ⇒ 00:06:55.930 Amber Lin: Yeah, there we go.
53 00:07:03.490 ⇒ 00:07:10.980 Amber Lin: Okay, let me share this document with you.
54 00:07:14.070 ⇒ 00:07:15.370 Amber Lin: So
55 00:07:37.242 ⇒ 00:07:47.230 Amber Lin: I showed you guys the handoff document. And since this is recorded, I’ll do a quick walk through. I’ll share my screen. I’ll walk you through all the items that’s there.
56 00:07:47.230 ⇒ 00:07:48.000 Mathew: Awesome.
57 00:07:48.000 ⇒ 00:08:04.619 Amber Lin: So, Ryan said, you are not completely familiar with what we did, and I can give you understanding what we think this project was, and how we approach this project. So here let me share my screen to start off with a document that.
58 00:08:04.950 ⇒ 00:08:15.099 Amber Lin: oh, Matthew originally shared. So our approach is based off of.
59 00:08:15.570 ⇒ 00:08:16.660 Mathew: It’s awesome.
60 00:08:18.260 ⇒ 00:08:19.280 Amber Lin: This one?
61 00:08:19.970 ⇒ 00:08:21.780 Amber Lin: No, not that one.
62 00:08:24.830 ⇒ 00:08:53.559 Amber Lin: this one. So this document is what we base it off of. I hope I think you have a bit of context of what this project is, what we want to do is enable the granular and modular analysis that we want to do over, say, these data sources, and we want to analyze them based on these segments and categories.
63 00:08:54.180 ⇒ 00:08:55.380 Mathew: And.
64 00:08:55.540 ⇒ 00:09:06.899 Amber Lin: I think it will give you a pretty quick understanding to see this dashboard, which I I think you have already looked around in. So
65 00:09:07.500 ⇒ 00:09:08.630 Amber Lin: God.
66 00:09:09.140 ⇒ 00:09:12.129 Ryan Phillips: Essentially what we want to enable is.
67 00:09:12.290 ⇒ 00:09:24.860 Amber Lin: Say, we look at a specific timeframe, and we have the analysis of different event types.
68 00:09:25.740 ⇒ 00:09:48.490 Amber Lin: and we can choose what we want and look at it at different segments, so we can choose the other segments as needed. So this way, we can enable the user to analyze their their employees. Productivity in a granular fashion in a modular fashion. So whatever, however, they want to analyze it.
69 00:09:48.650 ⇒ 00:09:51.610 Amber Lin: So that’s the context of this project. And
70 00:09:51.760 ⇒ 00:10:03.260 Amber Lin: what we did is, since we don’t have client data. Yet everything is based on synthetic data. We generated a synthetic data based on the api documentation.
71 00:10:03.360 ⇒ 00:10:10.709 Mathew: From the 2 sources, Microsoft Graph Api, and also the success success factors.
72 00:10:11.350 ⇒ 00:10:14.900 Ryan Phillips: So I’m having my team write up a.
73 00:10:15.467 ⇒ 00:10:38.369 Amber Lin: A quick documentation on how? What these tabs mean? But we have all the documentation on how we did things, what metrics were used how each, what each of them mean. So you would be able to find everything here that about all the work that we did.
74 00:10:39.560 ⇒ 00:10:53.179 Amber Lin: So after we created synthetic data. We did some modeling so that we can analyze it in a modular fashion. And so that if it’s power bi, it’s also documented
75 00:10:53.520 ⇒ 00:10:56.130 Amber Lin: in this, in this spreadsheet.
76 00:10:56.310 ⇒ 00:11:09.199 Amber Lin: And lastly, we went from synthetic data to modeling. A synthetic data is stored in bigquery. Then we use Dbt to do the do the modeling. And lastly, we fed it into power bi.
77 00:11:09.850 ⇒ 00:11:25.080 Amber Lin: and we also have those documented of the different charts. We have the different slicers as defined in the phase one requirements, and if there is any extra fields that we felt was necessary, that we added them in.
78 00:11:28.890 ⇒ 00:11:35.800 Amber Lin: so I’ll open the floor for any questions if you want to. If you want me to walk you through anything more specific.
79 00:11:35.940 ⇒ 00:11:47.310 Amber Lin: if you have more technical questions, just feel free to ask, and if there’s anything you need I can put it as a to do for my team to do a walkthrough of, so that you feel more prepared.
80 00:11:48.150 ⇒ 00:11:55.890 Ryan Phillips: Yeah, that all sounds good. I think that I’ve seen the sort of the Api docs that are used for
81 00:11:57.018 ⇒ 00:12:08.860 Ryan Phillips: and I’ve seen the power bi. It’s really the piece in between the Dbt that I’m the least clear on is there something that I should look into for that in particular.
82 00:12:09.580 ⇒ 00:12:11.680 Amber Lin: Do you have access to? Dbt.
83 00:12:12.120 ⇒ 00:12:16.740 Ryan Phillips: I don’t know. Let’s see this. Oh, there we go! The the Github.
84 00:12:17.470 ⇒ 00:12:18.719 Amber Lin: I believe. Yes.
85 00:12:19.180 ⇒ 00:12:20.810 Ryan Phillips: Yeah, I do not.
86 00:12:21.390 ⇒ 00:12:24.730 Amber Lin: We won’t be able to give you access to that.
87 00:12:24.730 ⇒ 00:12:25.780 Ryan Phillips: Okay.
88 00:12:25.780 ⇒ 00:12:26.800 Mathew: From Trevor.
89 00:12:29.600 ⇒ 00:12:34.300 Amber Lin: Yes, that is your repository. I can write it down as a to do.
90 00:12:35.240 ⇒ 00:12:41.653 Ryan Phillips: Okay. Let me see, I’m actually let me see if I if I sign in on my
91 00:12:41.860 ⇒ 00:12:42.220 Mathew: Okay.
92 00:12:42.220 ⇒ 00:12:47.204 Ryan Phillips: That one didn’t work, though. Let me see if I sign in on matter more account.
93 00:12:51.700 ⇒ 00:12:53.750 Mathew: I thought it could be access. Your bigquery.
94 00:12:55.358 ⇒ 00:13:00.460 Amber Lin: Do I think? Dvt, I think Github and Bigquery are separate.
95 00:13:06.690 ⇒ 00:13:08.880 Mathew: I’ll I’ll also ask Trevor.
96 00:13:09.450 ⇒ 00:13:10.020 Ryan Phillips: Like that.
97 00:13:10.470 ⇒ 00:13:16.499 Amber Lin: Yeah, access issues shouldn’t be a problem. If you want extra documentation, I can ask the team to
98 00:13:16.930 ⇒ 00:13:34.020 Amber Lin: walk you through what they did through Dbt. Trevor also has contacts. I know Ari also has contacts on what it what it is. Is there anything specific you would like to know about Dbt, I can ask the team to give you a more specific walkthrough.
99 00:13:34.692 ⇒ 00:13:41.039 Ryan Phillips: Kind of just looking for an overview of what? The different steps of processing were. Yeah.
100 00:13:41.410 ⇒ 00:13:47.310 Amber Lin: Okay, steps of transformation.
101 00:13:47.500 ⇒ 00:13:54.950 Amber Lin: Let’s see, I know they’ve documented the different models that they that they’ve made.
102 00:13:55.667 ⇒ 00:13:59.569 Amber Lin: I’ll ask them to call it out and
103 00:14:01.930 ⇒ 00:14:11.659 Amber Lin: and give you a video walkthrough, or at least a documentation of how these came about and how they’re used, or they’re still, they’re not used
104 00:14:11.880 ⇒ 00:14:12.410 Amber Lin: so.
105 00:14:12.410 ⇒ 00:14:13.120 Ryan Phillips: Sounds good. Yeah.
106 00:14:13.120 ⇒ 00:14:14.279 Amber Lin: Take note of that.
107 00:14:15.730 ⇒ 00:14:16.630 Amber Lin: Oh, shit.
108 00:14:16.660 ⇒ 00:14:18.840 Mathew: Oh, yeah, I have the Github link. Yeah.
109 00:14:19.357 ⇒ 00:14:20.392 Amber Lin: Sounds, good.
110 00:14:24.190 ⇒ 00:14:26.539 Mathew: Let me see if I can actually see it.
111 00:14:28.770 ⇒ 00:14:31.240 Mathew: Yeah, I can’t. I can’t access it. Well.
112 00:14:39.770 ⇒ 00:14:43.899 Amber Lin: Okay. We can ask Trevor to do that.
113 00:14:44.540 ⇒ 00:14:54.910 Amber Lin: Would you like a more detailed walkthrough of the dashboard, or where would where would we be most helpful in making sure that you feel prepared, and that you.
114 00:14:54.910 ⇒ 00:14:58.060 Ryan Phillips: Yeah, while I’ve got you here, walk through the dashboard, would be helpful.
115 00:14:58.780 ⇒ 00:15:02.389 Amber Lin: Okay, anything other than that, we would need.
116 00:15:03.650 ⇒ 00:15:04.780 Ryan Phillips: Oh, awesome!
117 00:15:07.770 ⇒ 00:15:23.330 Mathew: I mean to Ryan’s Point. I think a lot of the work that we ended up doing was taking the logic and building it into Dvt. So I think he actually just to get in there, you could ask you could have somebody amber on your team like record a screen share from their side, talking through. What’s in there?
118 00:15:23.590 ⇒ 00:15:26.269 Amber Lin: That was part of the plan. So.
119 00:15:26.270 ⇒ 00:15:27.130 Mathew: Okay. Thanks.
120 00:15:27.290 ⇒ 00:15:35.450 Amber Lin: Yeah, okay, so let me start off by here.
121 00:15:36.590 ⇒ 00:15:45.760 Amber Lin: Hmm, so to start you off, this is the requirements that we started our analysis on.
122 00:15:45.880 ⇒ 00:15:47.960 Amber Lin: So we want to have
123 00:15:48.570 ⇒ 00:15:58.409 Amber Lin: to have these basic activities. And then we want to have it by different time grains. And then the different segments.
124 00:15:59.020 ⇒ 00:16:11.360 Amber Lin: And so based on this, which is also documented in the original requirements. We made the dashboard. And so we can see, for example, here
125 00:16:11.990 ⇒ 00:16:15.209 Amber Lin: we’ll have the day of week.
126 00:16:15.820 ⇒ 00:16:17.830 Amber Lin: So we go on this page.
127 00:16:18.120 ⇒ 00:16:21.289 Amber Lin: And here we can select
128 00:16:21.500 ⇒ 00:16:30.789 Amber Lin: the different activities that you would like to see. And then you can select the different time granularities.
129 00:16:31.230 ⇒ 00:16:34.750 Amber Lin: and also the other segments that we want.
130 00:16:35.320 ⇒ 00:16:39.170 Amber Lin: And I think what’s most important here is to look at
131 00:16:39.930 ⇒ 00:16:48.670 Amber Lin: these few selectors here. So this one dictates what what goes into the X-axis?
132 00:16:48.890 ⇒ 00:16:52.609 Amber Lin: So we could say, we want day of week.
133 00:16:52.880 ⇒ 00:17:08.379 Amber Lin: and then we want it measured by so on the y-axis. Is it measured by average minute? Or is it measured by average hour, or just the count of events?
134 00:17:08.710 ⇒ 00:17:31.290 Amber Lin: And lastly, I made we got a feedback that we wanted to be able to compare it across these dimensions. So here we have an option of, if you want to compare it across, say, department, we can select here department. And then here will be a clustered bar graph
135 00:17:31.290 ⇒ 00:17:43.860 Amber Lin: to see how it differs across each department, and how it differs across the day of week. So I think that would be really helpful for someone to look at, does it? What is the trends that changes.
136 00:17:43.900 ⇒ 00:17:47.269 Mathew: Between the days. And how does it compare
137 00:17:47.270 ⇒ 00:17:53.940 Mathew: Amber? This is useful, I mean, Ryan. Ryan’s used to looking at these all day, so I think he gets. I think I think he gets the gist. Yeah.
138 00:17:53.940 ⇒ 00:17:58.040 Amber Lin: Okay, so is there anything other specific that you want me to walk through.
139 00:18:01.700 ⇒ 00:18:05.520 Mathew: Maybe click or maybe click through each of the pages. Just so you can see at a high level.
140 00:18:06.060 ⇒ 00:18:07.519 Ryan Phillips: And then give me a second. Yeah.
141 00:18:09.220 ⇒ 00:18:14.289 Amber Lin: Yeah, totally. And, Ryan, you will also. You also have access to this dashboard. So once.
142 00:18:14.290 ⇒ 00:18:14.860 Ryan Phillips: Yes.
143 00:18:14.860 ⇒ 00:18:30.799 Amber Lin: Around more feel free to ask any questions you have. If you have questions about how these are these are done, or how. What was the logic? It’s unclear. Just feel free to shoot me an email, or in the slack channel, and we’ll answer them.
144 00:18:31.670 ⇒ 00:18:32.470 Ryan Phillips: Handsome.
145 00:18:32.470 ⇒ 00:18:33.050 Ryan Phillips: Yeah.
146 00:18:33.050 ⇒ 00:18:38.680 Amber Lin: Yeah, quick overview. This one is very similar to day of week. So it’s a
147 00:18:38.870 ⇒ 00:18:43.790 Amber Lin: time comparison across time. But this is just by hour. If each day
148 00:18:43.890 ⇒ 00:18:53.129 Amber Lin: and weekend load and weekday load are just very similar looks at specific periods of time, and you.
149 00:18:53.130 ⇒ 00:18:54.840 Ryan Phillips: They’re down to. Yeah.
150 00:18:54.840 ⇒ 00:18:59.650 Amber Lin: Yeah. So they’re segmented by essentially, for the day of week
151 00:18:59.820 ⇒ 00:19:11.389 Amber Lin: and after hours load is a similar segmentation. But for the hour of day. So we look at what happens after a certain day. So after 6 Pm. Is what we selected here.
152 00:19:11.390 ⇒ 00:19:11.815 Ryan Phillips: Gotcha.
153 00:19:12.550 ⇒ 00:19:29.330 Amber Lin: These 2 are more specific. So these are is it in office versus remote? So we selected one of the segments here, and so that we can compare the these, this dimension. And lastly, this is
154 00:19:29.440 ⇒ 00:19:49.169 Amber Lin: this is a Pre post office mandate event, and we assumed a specific date of when that happened. And then this helps us compare before that happened. After that happened, what changed, and how does it compare across these dimensions.
155 00:19:49.960 ⇒ 00:19:57.143 Ryan Phillips: Cool. Yeah, no, I think that that all makes sense with the documentation. I’ve read but yeah, overall, I think.
156 00:19:57.650 ⇒ 00:20:09.780 Ryan Phillips: I think that makes sense. I think the one piece that I do want to get is access to the github. But we’ll work that out with Trevor. And then, yeah, Amber, you’re the person I should email if I do have any further questions on this kind of stuff.
157 00:20:10.170 ⇒ 00:20:15.400 Amber Lin: Yeah, totally you can. I can add you to our slack channel if you would like. But.
158 00:20:15.400 ⇒ 00:20:16.230 Ryan Phillips: Sure sounds good.
159 00:20:16.230 ⇒ 00:20:17.249 Amber Lin: You email me.
160 00:20:18.910 ⇒ 00:20:25.389 Mathew: Yeah, I think for now, if you if you want to, just if we have anything we can, we can. I can route it and loop you in.
161 00:20:25.550 ⇒ 00:20:26.340 Mathew: Yeah,
162 00:20:27.670 ⇒ 00:20:28.320 Ryan Phillips: Please.
163 00:20:29.290 ⇒ 00:20:38.080 Mathew: Or you can. Yeah, you can add Ryan. The the one thing Amber that I would say is like there was some. I think the latest things you were working on. There was some interesting logic.
164 00:20:38.501 ⇒ 00:20:43.649 Mathew: In the in, the, in, the, in the slack, like you. The last questions that you would ask me.
165 00:20:45.490 ⇒ 00:20:49.489 Mathew: For example, da da da.
166 00:20:49.810 ⇒ 00:20:58.439 Amber Lin: Yeah, there was one on duration of the logic we want to implement. And there’s the other questions with about the further segments that we could do, but I know.
167 00:20:58.440 ⇒ 00:20:58.990 Mathew: Yeah, they’re.
168 00:20:59.230 ⇒ 00:20:59.840 Amber Lin: Oh!
169 00:20:59.840 ⇒ 00:21:10.350 Mathew: Yeah, there is session, duration modeling. To unlock focus time context, switching and experience analysis by looking at timestamps event, timestamps
170 00:21:10.470 ⇒ 00:21:33.259 Mathew: and combining timestamps across a single tool. I think that kind of logic. Just like if you could just like grab all the latest thinking about basically whatever you were thinking through or planning for the next sprint. If you could just like get a snapshot of that of like these are the these are the features, and these were the things we were considering. Then, at least, Ryan has your thought process.
171 00:21:33.260 ⇒ 00:21:38.260 Amber Lin: Okay. Totally does Ryan have all the access to the original documents we had? If not.
172 00:21:38.260 ⇒ 00:21:48.839 Mathew: Yeah, yeah, he has. He has all the original docs. I think I also shared the metrics spreadsheet. But you can, as part of the handoff. You can just put like, put any references to critical docs.
173 00:21:49.400 ⇒ 00:21:55.699 Amber Lin: Yeah, we have that in there. So you would I. That’s good to hear. So I’ll also add a section about
174 00:21:57.630 ⇒ 00:22:02.479 Amber Lin: are for what we originally planned for the next cycle. So I’ll add that.
175 00:22:02.480 ⇒ 00:22:13.250 Mathew: Yeah, yeah. Yeah. Cause then he could pick up from where you left off, and then if he has questions, you can ask, etc, and then in the future, we can also see, like, if and how it makes sense to integrate you guys or anything like that.
176 00:22:13.560 ⇒ 00:22:14.660 Amber Lin: Totally.
177 00:22:15.880 ⇒ 00:22:44.150 Mathew: Thanks again for everything. I I think, amber. It was like, this is a crazy project, because we didn’t have any of the live data, but just having it was almost like y’all got us ahead, but that when we got the client data it’s like Ryan’s grabbing the football like much further down the field than if he was completely starting from scratch on this and so and and by like with this handoff, he knows, like, you know, to use another football metaphor, but he sees like where where y’all were going. So.
178 00:22:44.150 ⇒ 00:22:44.480 Amber Lin: Yeah.
179 00:22:44.480 ⇒ 00:22:48.760 Mathew: And and ultimately with Dbt. What he would, what he would be inheriting, and how to leverage it.
180 00:22:48.760 ⇒ 00:22:49.085 Ryan Phillips: Yeah.
181 00:22:49.410 ⇒ 00:22:52.760 Amber Lin: Yeah, Ryan, would you be working with Dvt as well.
182 00:22:53.730 ⇒ 00:22:58.520 Ryan Phillips: But I think it could come up like I think primarily I’m past that. But you know.
183 00:22:58.520 ⇒ 00:22:59.300 Amber Lin: Oh!
184 00:22:59.300 ⇒ 00:23:04.220 Ryan Phillips: When stuff goes wrong. I always want to know, like what could be happening, and and see the whole thing. So.
185 00:23:04.220 ⇒ 00:23:04.830 Amber Lin: I see.
186 00:23:04.830 ⇒ 00:23:05.649 Mathew: Kind of mind. Yeah.
187 00:23:05.650 ⇒ 00:23:16.189 Amber Lin: That the team points you to where the items are, because usually that’s what we do for our analyst is that we make sure that they they know all the endpoints that we need.
188 00:23:16.190 ⇒ 00:23:17.290 Mathew: Exactly.
189 00:23:17.290 ⇒ 00:23:17.840 Amber Lin: And.
190 00:23:17.840 ⇒ 00:23:27.660 Mathew: Exactly. Trev Trevor would likely be doing more of the Dvt lifting to productionize and operationalize further. But then, yeah, Ryan and Siffold and
191 00:23:29.700 ⇒ 00:23:31.090 Mathew: I’m working with that. So.
192 00:23:31.270 ⇒ 00:23:31.740 Amber Lin: Okay.
193 00:23:31.740 ⇒ 00:23:39.879 Mathew: Yeah, amber. Thank thank you for everything for rolling with the punches. And also, you know, setting Ryan up for success. It feels like a you know a win win all around.
194 00:23:41.050 ⇒ 00:23:57.089 Amber Lin: Yeah, I appreciate you. And it’s it’s a very interesting project for us to work on. And it it really tells us, well, it teaches us what how to look at our own performance as well. It’s like never looked at our my own performance that way.
195 00:23:57.929 ⇒ 00:24:02.579 Ryan Phillips: Yeah, it’s a, it’s a fascinating data set. So yeah, cool. I think that’s
196 00:24:03.750 ⇒ 00:24:06.717 Ryan Phillips: I think that’s all I’m I’m looking for. So yeah, nice to meet you
197 00:24:06.930 ⇒ 00:24:07.400 Mathew: Awesome.
198 00:24:07.400 ⇒ 00:24:10.428 Ryan Phillips: And yeah, I’m sure we’ll be in a little bit of touch here and there.
199 00:24:10.630 ⇒ 00:24:11.359 Amber Lin: Of course.
200 00:24:11.670 ⇒ 00:24:12.159 Ryan Phillips: Anything else.
201 00:24:12.474 ⇒ 00:24:24.440 Amber Lin: Thank you, Ryan. I think one last question, Matthew, is for you. I know you’ve been talking to Utah most want. I think our our company wants to ask you if you’re open to a case study
202 00:24:25.540 ⇒ 00:24:26.590 Amber Lin: organized.
203 00:24:27.590 ⇒ 00:24:33.550 Mathew: Yeah, I mean, I want to be mindful of, like the trade secrets of like the type of analysis.
204 00:24:33.650 ⇒ 00:24:40.839 Mathew: the type of analysis that we’re doing, but maybe like something higher level. But I will say, no matter what, I’ll serve as a reference.
205 00:24:41.310 ⇒ 00:24:42.150 Amber Lin: Okay.
206 00:24:42.470 ⇒ 00:24:43.110 Mathew: Yeah, yeah, so.
207 00:24:43.110 ⇒ 00:24:53.989 Amber Lin: Oh, probably will get in touch with you further on. What? You’re comfortable sharing in the case. Study if you’re comfortable sharing, say a review for our company.
208 00:24:53.990 ⇒ 00:25:06.279 Mathew: Yeah, I would. I’ll do. I’ll do reviews. I’ll be a reference. I don’t like. These are pretty much our trade secrets, so I don’t wanna publish them. Publish a case study about how we’re doing those hiring analysis or anything like that.
209 00:25:06.740 ⇒ 00:25:13.480 Amber Lin: Yeah, I’m glad I asked, because that’s a really important to tell us what we can, what we can do with this project and what we can’t.
210 00:25:14.290 ⇒ 00:25:23.280 Amber Lin: Yeah, I think sales might send you a delivery acceptance form just to close off this project. I’ll be in touch with that.
211 00:25:23.610 ⇒ 00:25:24.520 Mathew: Awesome.
212 00:25:24.520 ⇒ 00:25:28.880 Amber Lin: Okay, thank you so much for this journey. I’ll talk to you soon.
213 00:25:28.880 ⇒ 00:25:29.470 Mathew: Thank you, Andrew.
214 00:25:30.050 ⇒ 00:25:31.330 Amber Lin: Bye, bye.
215 00:25:31.330 ⇒ 00:25:31.870 Mathew: Bye.