Meeting Title: Internal Omni Dash Sync Date: 2025-11-11 Meeting participants: Mustafa Raja, Henry Zhao, Amber Lin
WEBVTT
1 00:00:55.990 ⇒ 00:00:57.650 Henry Zhao: Hey, Mustafa, how’s it going?
2 00:00:58.720 ⇒ 00:01:00.619 Mustafa Raja: Hey, doing good, how are you?
3 00:01:01.260 ⇒ 00:01:02.170 Henry Zhao: Good, thank you.
4 00:01:02.940 ⇒ 00:01:04.870 Mustafa Raja: How’s the day going so far?
5 00:01:05.290 ⇒ 00:01:06.249 Henry Zhao: Good, how about you?
6 00:01:06.840 ⇒ 00:01:07.470 Mustafa Raja: No.
7 00:01:08.610 ⇒ 00:01:11.570 Mustafa Raja: Working on some income and stuff.
8 00:01:12.980 ⇒ 00:01:16.269 Henry Zhao: Yeah, I saw your Omni case study yesterday, it was good.
9 00:01:16.750 ⇒ 00:01:17.360 Mustafa Raja: Oh.
10 00:01:17.970 ⇒ 00:01:19.119 Henry Zhao: I didn’t know you could talk to…
11 00:01:20.500 ⇒ 00:01:25.710 Mustafa Raja: Oh, it was my first time also working with these voice agents.
12 00:01:27.810 ⇒ 00:01:28.420 Mustafa Raja: Yeah.
13 00:01:29.820 ⇒ 00:01:34.450 Mustafa Raja: Hopefully, we’ll have it, out by the end of the week, and then…
14 00:01:36.000 ⇒ 00:01:38.130 Mustafa Raja: We’ll be taking case studies on this one.
15 00:01:40.440 ⇒ 00:01:41.260 Henry Zhao: Yeah.
16 00:01:57.290 ⇒ 00:01:58.420 Henry Zhao: Hey, Amber.
17 00:02:00.780 ⇒ 00:02:01.880 Amber Lin: Hi there!
18 00:02:03.750 ⇒ 00:02:05.389 Henry Zhao: Ugh, so busy lately.
19 00:02:05.860 ⇒ 00:02:10.349 Amber Lin: I know. I saw your calendar, and I was like, oh… Poor Harry.
20 00:02:10.470 ⇒ 00:02:12.420 Amber Lin: I’m adding another meeting.
21 00:02:16.040 ⇒ 00:02:21.070 Amber Lin: Alright, let me… I think this’ll be pretty quick.
22 00:02:21.210 ⇒ 00:02:30.330 Amber Lin: mostly want to explain to you what the current dashboard looks like, and then Mustafa can help with the data sources.
23 00:02:30.730 ⇒ 00:02:32.669 Henry Zhao: This is about the delivery dashboard, right?
24 00:02:32.670 ⇒ 00:02:40.280 Amber Lin: Yeah. So… Let me… Share screen… Alright.
25 00:02:42.030 ⇒ 00:02:49.769 Amber Lin: So right now… This is used for two instances, so…
26 00:02:50.180 ⇒ 00:02:54.559 Amber Lin: one, the weekly Monday meeting, and two.
27 00:02:54.730 ⇒ 00:02:59.930 Amber Lin: I would say, monthly, an overview of what happened.
28 00:03:00.840 ⇒ 00:03:10.099 Amber Lin: So, for the weekly stuff, this is a… Overview here of per client.
29 00:03:10.300 ⇒ 00:03:12.639 Amber Lin: Let’s say last week.
30 00:03:12.790 ⇒ 00:03:16.949 Amber Lin: What was the total hours based on Clockify entries?
31 00:03:17.060 ⇒ 00:03:20.790 Amber Lin: We do want to switch this goal hours out for…
32 00:03:21.010 ⇒ 00:03:27.949 Amber Lin: operating allocated hours, and then the total cost is calculated based on total hours, and then the
33 00:03:28.520 ⇒ 00:03:31.150 Amber Lin: Per person, what the rate is.
34 00:03:31.370 ⇒ 00:03:42.830 Amber Lin: And then, let’s see… Weekly billable is calculated based on revenue divided by total hours.
35 00:03:42.960 ⇒ 00:03:46.280 Amber Lin: And then, accordingly, like, project margin.
36 00:03:46.280 ⇒ 00:03:48.230 Henry Zhao: Sorry, say that again? Weekly billables how?
37 00:03:48.870 ⇒ 00:03:53.679 Amber Lin: Weekly billable is total weekly revenue divided by weekly hours.
38 00:03:54.460 ⇒ 00:03:56.249 Henry Zhao: Why? I don’t understand.
39 00:03:56.510 ⇒ 00:03:57.459 Henry Zhao: With this billable…
40 00:03:57.460 ⇒ 00:04:03.179 Amber Lin: So, is what UTM uses for… what is our actual billable rate?
41 00:04:06.420 ⇒ 00:04:06.970 Henry Zhao: Okay, gotcha.
42 00:04:07.340 ⇒ 00:04:13.510 Amber Lin: Yeah, so that’s, just divided by this.
43 00:04:14.370 ⇒ 00:04:19.230 Amber Lin: And then, right here, we just have week over week, and then month over month.
44 00:04:19.730 ⇒ 00:04:23.270 Amber Lin: So we can see how we’ve improved throughout.
45 00:04:23.470 ⇒ 00:04:34.160 Amber Lin: Right now, Uten wants this to just be our, clients instead of our internal teams, so we can also filter here to select all the…
46 00:04:34.720 ⇒ 00:04:40.829 Amber Lin: Client projects, and so that… Parts like the AI team won’t show up.
47 00:04:41.600 ⇒ 00:04:42.590 Henry Zhao: Okay, gotcha.
48 00:04:42.590 ⇒ 00:04:46.020 Amber Lin: Yeah. Let’s see…
49 00:04:46.940 ⇒ 00:05:00.510 Amber Lin: Oh, and then this is when we started November, I quickly added one just for the previous month, so, helps us see all projects, the overall revenue, and then…
50 00:05:00.970 ⇒ 00:05:06.730 Amber Lin: The rate margin just for the past month, so this is for the second use case of monthly reviews.
51 00:05:08.090 ⇒ 00:05:12.470 Amber Lin: I think, apart from that, the next most important part is just,
52 00:05:12.700 ⇒ 00:05:18.929 Amber Lin: the hours, so the allocated versus actual hours. Right now, this is based on
53 00:05:19.320 ⇒ 00:05:25.750 Amber Lin: Operating versus clockify. So, operating, we have weekly allocations.
54 00:05:26.190 ⇒ 00:05:28.220 Amber Lin: And then right here, we have…
55 00:05:30.240 ⇒ 00:05:40.979 Amber Lin: the Clockify actual locked hours. And right… right now, I selected to show, I think, the past, 3 or 4 weeks.
56 00:05:42.060 ⇒ 00:05:47.139 Amber Lin: So it will show… It will show the… so for each project.
57 00:05:47.580 ⇒ 00:05:50.759 Amber Lin: For this person, the past weeks.
58 00:05:51.920 ⇒ 00:05:57.110 Amber Lin: And I think this was… You can ignore this, because this is just a…
59 00:05:57.420 ⇒ 00:06:05.340 Amber Lin: like, lesser version of this, then we were able to combine these two charts into one, so I’ll just skip that.
60 00:06:06.950 ⇒ 00:06:24.679 Amber Lin: I think the last thing is the sprint overviews, which we’re not really using now, because our… like, our cycles are not as groomed as before, so our points or estimates or deadlines are not as accurate, so I feel like you can probably skip over these and…
61 00:06:24.810 ⇒ 00:06:29.109 Amber Lin: Until we get the sprint screen, like, these won’t mean anything.
62 00:06:29.250 ⇒ 00:06:32.940 Amber Lin: So, there’s that.
63 00:06:33.720 ⇒ 00:06:36.740 Amber Lin: This is just number of…
64 00:06:36.920 ⇒ 00:06:44.230 Amber Lin: In… in progress or unstarted tickets that don’t have estimates, don’t have due dates without assignees.
65 00:06:44.600 ⇒ 00:06:51.360 Amber Lin: And this is the completed rate, so… For how many was…
66 00:06:51.900 ⇒ 00:06:56.720 Amber Lin: In scope, so this is, like, the changes every single day, so 7 days…
67 00:06:56.900 ⇒ 00:06:59.669 Amber Lin: A week, because sometimes our scope changes, and…
68 00:07:00.440 ⇒ 00:07:02.260 Amber Lin: This is… Oh, I don’t quite understand this.
69 00:07:02.260 ⇒ 00:07:02.880 Henry Zhao: Arts.
70 00:07:02.880 ⇒ 00:07:17.779 Amber Lin: So this is for linear. So, each of these is each single day, right? So Monday, Tuesday, Wednesday, because sometimes we take things out, sometimes we add things in, so the scope changes. So, for example, Eden.
71 00:07:17.780 ⇒ 00:07:19.150 Henry Zhao: What are the… what are these numbers?
72 00:07:19.150 ⇒ 00:07:23.560 Amber Lin: These are linear point estimates, so this is total.
73 00:07:23.560 ⇒ 00:07:24.080 Henry Zhao: Oh, okay.
74 00:07:24.080 ⇒ 00:07:24.840 Amber Lin: points.
75 00:07:25.210 ⇒ 00:07:35.170 Amber Lin: Yeah, and then, for example, Eden, our scope increased, and then we took things out, and so… and then we completed, say, 28 total.
76 00:07:35.870 ⇒ 00:07:39.599 Amber Lin: This was, I think, how many was in progress.
77 00:07:39.950 ⇒ 00:07:41.220 Henry Zhao: Oh, gotcha, gotcha.
78 00:07:41.220 ⇒ 00:07:43.929 Amber Lin: Yeah, that is how many was completed each day.
79 00:07:44.230 ⇒ 00:07:50.600 Henry Zhao: It’s a little bit confusing, and probably why Uten wants this to be optimized, but we’re not really using any of…
80 00:07:50.640 ⇒ 00:07:52.130 Amber Lin: These two charts.
81 00:07:52.230 ⇒ 00:07:55.039 Amber Lin: So, if you want to save time, you can save that.
82 00:07:58.390 ⇒ 00:08:01.100 Henry Zhao: I think once you take 20 seconds to understand that chart, it’s pretty useful.
83 00:08:02.060 ⇒ 00:08:03.000 Amber Lin: Huh, this one?
84 00:08:03.790 ⇒ 00:08:04.899 Henry Zhao: Yeah, the scope one.
85 00:08:14.220 ⇒ 00:08:15.610 Henry Zhao: Yeah, I kinda like it that way.
86 00:08:16.200 ⇒ 00:08:20.349 Amber Lin: And then progress is 15, so divided by 30.
87 00:08:20.350 ⇒ 00:08:21.510 Henry Zhao: That makes sense.
88 00:08:21.510 ⇒ 00:08:22.760 Amber Lin: We’ve completed that much.
89 00:08:22.760 ⇒ 00:08:23.579 Henry Zhao: Makes sense.
90 00:08:23.580 ⇒ 00:08:35.279 Amber Lin: Yeah, and this one is mostly allocations by person. This is mostly for us to see how much do we have allocated, so I can see, okay, for myself.
91 00:08:35.440 ⇒ 00:08:37.939 Amber Lin: How much do I have allocated?
92 00:08:38.390 ⇒ 00:08:40.739 Amber Lin: This week?
93 00:08:40.740 ⇒ 00:08:41.520 Mustafa Raja: Oh.
94 00:08:41.520 ⇒ 00:08:48.000 Amber Lin: One thing about the allocations, This calculates it by day, so today…
95 00:08:48.280 ⇒ 00:08:56.760 Amber Lin: Today is Tuesday, so it only had, like, Monday, Tuesday of this week, so it’s… it’s less.
96 00:08:56.870 ⇒ 00:09:01.430 Amber Lin: Why does my hero… Wait, where?
97 00:09:03.700 ⇒ 00:09:04.979 Henry Zhao: I haven’t been allocated…
98 00:09:04.980 ⇒ 00:09:12.380 Amber Lin: So that’s… Yeah, so probably that’s an error that, we need to look at. I’m not completely sure.
99 00:09:12.980 ⇒ 00:09:14.480 Amber Lin: I haven’t touched this.
100 00:09:14.480 ⇒ 00:09:18.210 Henry Zhao: Otherwise, gotta go, guys, can’t be working any hours today.
101 00:09:22.840 ⇒ 00:09:24.140 Amber Lin: Yeah, so this is…
102 00:09:24.140 ⇒ 00:09:26.379 Henry Zhao: Well, everyone is here for Eden, that’s why.
103 00:09:26.740 ⇒ 00:09:29.319 Henry Zhao: So that’s probably broken. Everyone is zero for Eden.
104 00:09:29.900 ⇒ 00:09:31.340 Amber Lin: I see.
105 00:09:31.340 ⇒ 00:09:34.780 Mustafa Raja: It should be the… from operating right then.
106 00:09:35.490 ⇒ 00:09:45.479 Amber Lin: Yeah, we’ll go check that. So this is just another chart of the allocations just by person, because this one’s, this one’s by project.
107 00:09:45.620 ⇒ 00:09:47.660 Amber Lin: The other one’s by person.
108 00:09:48.620 ⇒ 00:09:52.270 Amber Lin: So, we’ll check. I feel like the November ones are not…
109 00:09:52.710 ⇒ 00:09:56.340 Amber Lin: I’m not syncing up, because this is still showing October.
110 00:09:56.640 ⇒ 00:10:06.120 Amber Lin: Anyways, this has not been polished in a while, and we usually put all the updates in the delivery channel.
111 00:10:06.730 ⇒ 00:10:08.290 Amber Lin: And… Okay.
112 00:10:09.080 ⇒ 00:10:16.059 Amber Lin: From last week, I’ve noted down a few things that was still wrong.
113 00:10:16.570 ⇒ 00:10:19.950 Amber Lin: So, I’ll tag you here, just in case.
114 00:10:21.350 ⇒ 00:10:21.960 Henry Zhao: Okay?
115 00:10:24.800 ⇒ 00:10:32.290 Amber Lin: So, month-over-month metrics… So we wanted…
116 00:10:32.670 ⇒ 00:10:37.070 Amber Lin: Total allocated hours instead of just…
117 00:10:37.450 ⇒ 00:10:40.819 Amber Lin: weekly goal hours. Right now, this is…
118 00:10:41.090 ⇒ 00:10:45.460 Amber Lin: This is based on if we want billable $150,
119 00:10:45.640 ⇒ 00:10:51.899 Amber Lin: How many hours we should do, but we want to add, like, allocated hours so we’re more accurate.
120 00:10:52.650 ⇒ 00:10:55.759 Amber Lin: That we can skip. Even…
121 00:10:56.320 ⇒ 00:10:58.560 Amber Lin: Yeah, some are not showing up.
122 00:10:59.200 ⇒ 00:11:02.660 Amber Lin: Insomnia, we need to update to 10K.
123 00:11:02.910 ⇒ 00:11:08.880 Amber Lin: Yeah. Can I… Mustafa, can I hand over to you to walk through the data sources?
124 00:11:09.310 ⇒ 00:11:09.900 Mustafa Raja: Yes.
125 00:11:10.140 ⇒ 00:11:10.950 Henry Zhao: Okay.
126 00:11:10.950 ⇒ 00:11:12.090 Mustafa Raja: Right.
127 00:11:15.050 ⇒ 00:11:17.620 Mustafa Raja: Okay, so.
128 00:11:17.620 ⇒ 00:11:21.629 Henry Zhao: And Mustafa, do I need to do the Omni, like, certification before I can do any of this?
129 00:11:21.630 ⇒ 00:11:22.310 Mustafa Raja: No, no, no.
130 00:11:22.770 ⇒ 00:11:24.280 Mustafa Raja: They’re pretty silly.
131 00:11:24.280 ⇒ 00:11:31.540 Amber Lin: Well, I think it just helps if we look at the certification, but I did not watch any tutorials, I just played with it.
132 00:11:31.640 ⇒ 00:11:32.390 Amber Lin: And then…
133 00:11:32.390 ⇒ 00:11:34.179 Henry Zhao: They shut so many things on them.
134 00:11:37.240 ⇒ 00:11:45.829 Mustafa Raja: I haven’t got, I haven’t even looked at it yet, but I need… I need to get… get into that. So,
135 00:11:47.280 ⇒ 00:11:51.899 Mustafa Raja: So we have this connection, Brain Forge Snowflake, that is connected to…
136 00:11:52.640 ⇒ 00:11:58.730 Mustafa Raja: So here in Omni, we have this connection, Brainford Snowflake.
137 00:11:58.840 ⇒ 00:12:05.939 Mustafa Raja: And through this connection, we are getting all of the tables from this snowflake. Let me…
138 00:12:06.230 ⇒ 00:12:10.639 Henry Zhao: Okay, so, like, So, like, Clockify data goes into Snowflake.
139 00:12:11.310 ⇒ 00:12:15.409 Mustafa Raja: Yes, all of the data, goes into Snowflake.
140 00:12:16.430 ⇒ 00:12:22.620 Mustafa Raja: I’m forgetting how… Okay, the tables would be…
141 00:12:58.330 ⇒ 00:13:00.880 Mustafa Raja: Do you have access to this snowflake?
142 00:13:01.160 ⇒ 00:13:02.230 Mustafa Raja: instance.
143 00:13:04.430 ⇒ 00:13:09.340 Henry Zhao: I do not. And also, how do I log into OmniApp, also? Are they all in one past? Or do I get my own?
144 00:13:09.340 ⇒ 00:13:11.269 Mustafa Raja: Oh, no, no, you’ll get your own.
145 00:13:11.270 ⇒ 00:13:17.719 Amber Lin: I think they should have sent you an invite, but if not, we can ask, probably.
146 00:13:17.720 ⇒ 00:13:18.910 Mustafa Raja: Yeah, and reason.
147 00:13:18.910 ⇒ 00:13:20.490 Amber Lin: Oh, okay.
148 00:13:21.430 ⇒ 00:13:22.200 Amber Lin: Yes.
149 00:13:22.200 ⇒ 00:13:24.980 Mustafa Raja: Maybe check your inbox for…
150 00:13:24.980 ⇒ 00:13:26.050 Amber Lin: Have an invite?
151 00:13:26.350 ⇒ 00:13:28.489 Amber Lin: Oh, I do, I do, wait, wait, I do.
152 00:13:32.190 ⇒ 00:13:35.010 Henry Zhao: And this snowflake, let’s see…
153 00:13:36.480 ⇒ 00:13:41.669 Mustafa Raja: Snowflake, we can ask Utam to, to get you, access.
154 00:13:41.770 ⇒ 00:13:42.420 Mustafa Raja: Over there.
155 00:13:42.420 ⇒ 00:13:43.800 Henry Zhao: Okay, I’ll ask about some right.
156 00:13:48.350 ⇒ 00:14:00.340 Mustafa Raja: Okay, so yeah, the tables would be in there, and we’re just, getting all of those tables, to this, to the, to this interface.
157 00:14:00.460 ⇒ 00:14:05.819 Mustafa Raja: And once we have the tables in there, we can start modeling them.
158 00:14:05.930 ⇒ 00:14:10.590 Mustafa Raja: So you can see in this delivery topic.
159 00:14:11.060 ⇒ 00:14:17.679 Mustafa Raja: We have the base view as, operating IO raw persons, which is going to be…
160 00:14:20.400 ⇒ 00:14:22.240 Mustafa Raja: This table.
161 00:14:22.410 ⇒ 00:14:24.490 Mustafa Raja: Operating I.O. operations.
162 00:14:24.640 ⇒ 00:14:29.589 Henry Zhao: So, all of the data is actually… Alright, can you show me first, how do I get to…
163 00:14:29.760 ⇒ 00:14:32.999 Henry Zhao: Sorry, I’m in Ami right now, I’m trying to find how to get to the snowflake.
164 00:14:33.880 ⇒ 00:14:39.580 Mustafa Raja: So if you, so if you could, you would look for this, developed.
165 00:14:40.030 ⇒ 00:14:40.840 Henry Zhao: Okay.
166 00:14:41.180 ⇒ 00:14:41.950 Mustafa Raja: And then…
167 00:14:41.950 ⇒ 00:14:42.490 Henry Zhao: Oh, God.
168 00:14:42.490 ⇒ 00:14:45.280 Mustafa Raja: On top, we have this Bring Put Snowflake.
169 00:14:45.420 ⇒ 00:14:52.120 Mustafa Raja: Okay, got it. And then… and then we’ll have all the schemas. This… these… all of these schemas are coming from Snowflake, right?
170 00:14:52.580 ⇒ 00:14:53.180 Henry Zhao: Okay.
171 00:14:53.820 ⇒ 00:14:59.270 Mustafa Raja: And then, we have this topic. This topic is connected to the dashboard.
172 00:14:59.540 ⇒ 00:15:05.099 Mustafa Raja: And then, each topic would have a base view.
173 00:15:05.360 ⇒ 00:15:18.059 Mustafa Raja: So the… the… any table that we want to include in this model, must have, some sort of link with this base view.
174 00:15:19.600 ⇒ 00:15:22.680 Mustafa Raja: and yeah, this is…
175 00:15:23.000 ⇒ 00:15:25.790 Mustafa Raja: This is pretty much it, so…
176 00:15:26.440 ⇒ 00:15:36.950 Mustafa Raja: So operating data would be in operating.io. We have persons, projects, and then, work efforts.
177 00:15:37.230 ⇒ 00:15:48.580 Mustafa Raja: And then, we are using this Clockify, table, and then… We are using…
178 00:15:49.480 ⇒ 00:15:56.889 Mustafa Raja: Yeah, polyatomic linear. We are also using, this, the, all, all these tables.
179 00:15:58.560 ⇒ 00:16:03.650 Mustafa Raja: And then… let me… let me see if we’re using anything else.
180 00:16:07.410 ⇒ 00:16:09.349 Henry Zhao: I mean, this is scary because I’m gonna be, like.
181 00:16:09.930 ⇒ 00:16:11.099 Mustafa Raja: Yeah, yeah, yeah.
182 00:16:11.310 ⇒ 00:16:11.870 Henry Zhao: Yeah.
183 00:16:15.960 ⇒ 00:16:20.509 Mustafa Raja: Yeah, let’s see what else do we have. So, operating…
184 00:16:21.330 ⇒ 00:16:28.339 Mustafa Raja: Yeah, so the, some of these views, I created, to create some… some joins.
185 00:16:28.340 ⇒ 00:16:29.810 Henry Zhao: For the dashboard.
186 00:16:29.810 ⇒ 00:16:35.139 Mustafa Raja: A lot of this work that I did, I did using, AI.
187 00:16:35.270 ⇒ 00:16:41.769 Mustafa Raja: So feel free to change anything if you… if you… if you notice anything buggy, or what…
188 00:16:41.770 ⇒ 00:16:43.389 Henry Zhao: Should I use AI or cursor?
189 00:16:44.940 ⇒ 00:16:52.800 Mustafa Raja: I used ChatGPT, because cursor would be helpful if I had used dbt.
190 00:16:53.210 ⇒ 00:17:03.070 Mustafa Raja: But since I didn’t, use dbt, I went ahead and just, created all of this, all of the models within Omni.
191 00:17:03.180 ⇒ 00:17:06.459 Mustafa Raja: So I used ChatGPT instead.
192 00:17:07.950 ⇒ 00:17:10.880 Henry Zhao: So I actually don’t need to do anything in Snowflake, I can just do everything by…
193 00:17:10.880 ⇒ 00:17:24.499 Mustafa Raja: Yeah, you don’t need to do it in Snowflake, you can do it in Omni. I’d say whatever you feel comfortable in, even if it is, with Snowflake. So, yeah, we have…
194 00:17:24.800 ⇒ 00:17:26.430 Henry Zhao: What language is that in Omni?
195 00:17:28.079 ⇒ 00:17:33.609 Mustafa Raja: It’s, it’s mostly SQL. This, this is YAML, I’ve been…
196 00:17:34.280 ⇒ 00:17:34.890 Henry Zhao: Okay.
197 00:17:35.170 ⇒ 00:17:43.709 Mustafa Raja: And then, these would be SQL and. This is also YAML, but this part only is SQL. Okay.
198 00:17:44.120 ⇒ 00:17:51.779 Mustafa Raja: So this part, I believe, is we are expanding the data. All of the data would be in this raw data field. I can show you that.
199 00:17:52.830 ⇒ 00:17:56.369 Mustafa Raja: So if we go into Blockify…
200 00:18:21.350 ⇒ 00:18:23.770 Mustafa Raja: Yeah, see, there’s only 3 tables.
201 00:18:23.890 ⇒ 00:18:34.589 Mustafa Raja: Sorry, three columns, and one of the columns is raw data, and this raw data will have some sort of schema that we are expanding over here.
202 00:18:36.840 ⇒ 00:18:42.609 Mustafa Raja: See? Created at… so this raw data would have created at, created at all of this stuff.
203 00:18:43.820 ⇒ 00:18:47.900 Mustafa Raja: that, that we would just expand.
204 00:18:49.160 ⇒ 00:18:55.719 Mustafa Raja: So the data would be mostly in broad data, and then we would just expand it in Omni.
205 00:18:56.650 ⇒ 00:18:59.620 Mustafa Raja: So a lot of these tables are structured like that.
206 00:19:01.450 ⇒ 00:19:03.039 Mustafa Raja: I hope it makes sense.
207 00:19:04.530 ⇒ 00:19:08.079 Henry Zhao: Yeah, okay, so now what’s actually feeding the dashboards in that delivery dashboard?
208 00:19:08.880 ⇒ 00:19:14.200 Mustafa Raja: Yeah. So, this topic.
209 00:19:14.200 ⇒ 00:19:15.510 Henry Zhao: Delivery.topic, okay.
210 00:19:15.510 ⇒ 00:19:16.150 Mustafa Raja: Hmm.
211 00:19:16.640 ⇒ 00:19:28.940 Mustafa Raja: So you see that, we decided… we decided this… this table from operating as our base view, so, every other table should have some sort of link with this table.
212 00:19:29.280 ⇒ 00:19:40.340 Mustafa Raja: So, if a table does not have a direct, direct link to it, meaning we cannot directly join with it, so let’s say a table does not have a person ID, right?
213 00:19:40.450 ⇒ 00:19:49.309 Mustafa Raja: So we cannot join it directly. We… we can join it with some other table that is already being joined to this table.
214 00:19:50.020 ⇒ 00:20:00.079 Mustafa Raja: But then we’ll have to define in this, joins that this, this, through indentations, that, we are joining this with this.
215 00:20:00.800 ⇒ 00:20:04.600 Mustafa Raja: And this is joining to the base one. I hope this makes sense. Does this make sense?
216 00:20:04.600 ⇒ 00:20:07.689 Henry Zhao: What are the empty brackets? I don’t understand the empty brackets.
217 00:20:07.690 ⇒ 00:20:14.669 Mustafa Raja: Empty brackets is just saying, okay, this one does not have any child, any children. So, this one does have children.
218 00:20:14.900 ⇒ 00:20:22.699 Mustafa Raja: We can say… we can see this is a child, this is a child. Actually, all of, all of these are children, except…
219 00:20:22.700 ⇒ 00:20:23.259 Henry Zhao: Yeah, but they don’t.
220 00:20:23.260 ⇒ 00:20:27.059 Mustafa Raja: This one has… this one has a further joint, right? Okay.
221 00:20:27.540 ⇒ 00:20:29.170 Mustafa Raja: Does this make sense?
222 00:20:30.510 ⇒ 00:20:33.800 Henry Zhao: Not right now, but when I look into it, I’ll probably… it’ll probably make more sense.
223 00:20:34.180 ⇒ 00:20:41.770 Mustafa Raja: Okay, yeah, yeah. So, yeah, this is pretty much it, and then we have all of the joins of the tables.
224 00:20:41.770 ⇒ 00:20:44.479 Henry Zhao: Oh, okay, that’s where you do the joint statements, okay.
225 00:20:44.480 ⇒ 00:20:49.810 Mustafa Raja: Yeah, yeah, yeah, the statements are here, the statements are here, the structure is here.
226 00:20:50.020 ⇒ 00:20:50.719 Henry Zhao: Got it.
227 00:20:50.910 ⇒ 00:20:51.470 Mustafa Raja: Yeah.
228 00:20:51.810 ⇒ 00:21:06.710 Amber Lin: what Mustafa’s showing is the modeling part. When I did it, I didn’t do any modeling, so I mostly asked Mustafa, can you join this for me? So when you’re in the delivery topic, let me show you what it looks like.
229 00:21:07.360 ⇒ 00:21:11.050 Amber Lin: also share a screen. So, not this one.
230 00:21:12.050 ⇒ 00:21:15.720 Amber Lin: So, let’s say I’m in this dashboard.
231 00:21:16.130 ⇒ 00:21:21.770 Amber Lin: and I want to edit a chart, I would say… Edit, and then take…
232 00:21:21.770 ⇒ 00:21:22.549 Henry Zhao: Me too.
233 00:21:22.550 ⇒ 00:21:23.860 Amber Lin: the workbook?
234 00:21:24.330 ⇒ 00:21:29.829 Amber Lin: and say… So, each chart
235 00:21:30.190 ⇒ 00:21:40.659 Amber Lin: would be… let me create a new chart so you see what it looks like. It can be very, very, very simple, so it… we just start by… I said, new chart.
236 00:21:40.750 ⇒ 00:21:55.249 Amber Lin: in here, and I said I pick Delivery Topic. You can also pick this and write your own SQL based on all the tables and stuff I showed you, but, like, we can just… we can just start here using delivery topic.
237 00:21:56.260 ⇒ 00:22:00.079 Amber Lin: And then, you can see here there’s operating.
238 00:22:00.500 ⇒ 00:22:06.130 Amber Lin: There’s time entries, which is Clockify. Linear, and then…
239 00:22:06.990 ⇒ 00:22:15.769 Amber Lin: this. So, I’ll show you what each of these mean. So, firstly, this one. The REV contract forecast is…
240 00:22:16.360 ⇒ 00:22:23.599 Amber Lin: The… each client, how big their… contract is, so each month…
241 00:22:23.600 ⇒ 00:22:25.779 Henry Zhao: Are they manually maintained by UTM?
242 00:22:26.500 ⇒ 00:22:32.140 Amber Lin: This links to a manual table, yes, so this should link to…
243 00:22:32.330 ⇒ 00:22:35.190 Amber Lin: Like, somewhere… somewhere here. So if…
244 00:22:35.190 ⇒ 00:22:35.700 Henry Zhao: Okay, got it.
245 00:22:35.700 ⇒ 00:22:49.020 Amber Lin: If something’s off, you can always go… where? Let me find it. Something’s, I think… is this? Yeah, if something’s off, you can always go here, edit it, and then add Mustafa to…
246 00:22:49.280 ⇒ 00:22:52.579 Amber Lin: update the sync. Let me send this to you.
247 00:22:52.580 ⇒ 00:22:53.280 Henry Zhao: Okay.
248 00:22:53.720 ⇒ 00:22:55.289 Amber Lin: Let me set this…
249 00:23:01.700 ⇒ 00:23:03.470 Amber Lin: Okay, let me drop it in a chat.
250 00:23:04.830 ⇒ 00:23:14.620 Amber Lin: So, you might need this to look at, okay, water… What are the… So, let’s see… Make the…
251 00:23:14.960 ⇒ 00:23:20.650 Amber Lin: Like, hourly rate, or their fixed Monthly rate.
252 00:23:21.110 ⇒ 00:23:28.199 Amber Lin: Got it. So, I think these two are the most important columns. You can see this is based on, ideally, if we’re gonna do
253 00:23:28.380 ⇒ 00:23:31.869 Amber Lin: 150 per hour, what are the ideal hours we can do?
254 00:23:33.240 ⇒ 00:23:37.780 Amber Lin: Also, Pungle Insights is Robert’s, so this is just how we…
255 00:23:38.090 ⇒ 00:23:41.230 Amber Lin: pay out Robert, but this is… I think this is just Eden.
256 00:23:42.140 ⇒ 00:23:43.740 Henry Zhao: Okay. I believe so.
257 00:23:44.580 ⇒ 00:23:47.780 Amber Lin: Okay… Nope.
258 00:23:49.100 ⇒ 00:23:51.540 Amber Lin: Back… Here.
259 00:23:52.000 ⇒ 00:23:59.670 Amber Lin: Alright, so that’s, I think, depending on… when I did the table, I did, depending on fixed or variable, I timed it.
260 00:23:59.790 ⇒ 00:24:02.199 Amber Lin: I calculate the revenue differently.
261 00:24:02.890 ⇒ 00:24:09.040 Amber Lin: So there’s that one, there is… I think… You know…
262 00:24:10.110 ⇒ 00:24:23.890 Amber Lin: Where is the contract size? Clockified Project is how we, I think how I unify the names, because they’re named differently across the sheet, the Clockify and the operating.
263 00:24:24.060 ⇒ 00:24:29.600 Amber Lin: And let’s see where the… Okay.
264 00:24:30.080 ⇒ 00:24:30.780 Amber Lin: Whoa.
265 00:24:32.540 ⇒ 00:24:34.650 Amber Lin: You might have to go find the…
266 00:24:35.010 ⇒ 00:24:43.059 Mustafa Raja: Yeah, I believe, I believe the, all… so there are 3 different sources coming in. Yeah, yeah,
267 00:24:43.130 ⇒ 00:24:55.740 Mustafa Raja: In the last scene that we were looking at. One is, operating, and then we have linear, and then we have Clockify, and we are joining all of these together.
268 00:24:55.830 ⇒ 00:25:11.680 Mustafa Raja: Using the three columns that we have, linear team here, and then we also have operating ID in this table, and we also have, Blockify, in, in, in that table. So we are joining everything using that.
269 00:25:12.520 ⇒ 00:25:14.409 Amber Lin: Yeah, I just wanted to add that, Ms.
270 00:25:14.900 ⇒ 00:25:15.460 Amber Lin: Yep.
271 00:25:15.850 ⇒ 00:25:24.799 Amber Lin: So, going back to that sheet, there’s two main tabs that we’re using, so there’s this one for contracts, and there’s one for payroll.
272 00:25:24.950 ⇒ 00:25:29.350 Amber Lin: That would be… Any… here…
273 00:25:29.460 ⇒ 00:25:45.200 Amber Lin: Yeah, that’ll be in here, and then we’re mostly using the hourly rates, so if this person is, on salary, we just divide it by, like, 40 hours per week, depending on what the contract is, and we use this column to calculate
274 00:25:45.460 ⇒ 00:25:46.790 Amber Lin: Cost.
275 00:25:46.960 ⇒ 00:25:50.909 Amber Lin: So, going back here, that should be in contract details.
276 00:25:51.420 ⇒ 00:25:54.970 Amber Lin: So we’ll have the email name and the hourly rate.
277 00:25:55.120 ⇒ 00:25:57.400 Amber Lin: And I use that to calculate.
278 00:25:57.580 ⇒ 00:25:58.750 Amber Lin: the cost.
279 00:25:59.890 ⇒ 00:26:06.870 Amber Lin: And there’s… Operating… so for operating, I would say…
280 00:26:07.130 ⇒ 00:26:14.580 Amber Lin: We can start here, so the person and project splits. So this is how much is allocated
281 00:26:14.940 ⇒ 00:26:19.830 Amber Lin: per project, per person, and right now it’s in seconds, but I can… you can…
282 00:26:20.050 ⇒ 00:26:22.840 Amber Lin: Change that to minutes by dividing it by…
283 00:26:22.980 ⇒ 00:26:30.210 Amber Lin: 3,600. So I used that, and I… and then this one has…
284 00:26:30.530 ⇒ 00:26:39.610 Amber Lin: person ID and project ID, and that’s where you would need these persons and projects to get the person and project name.
285 00:26:40.320 ⇒ 00:26:43.419 Amber Lin: I would ignore this, because there’s no entries in…
286 00:26:43.930 ⇒ 00:26:52.999 Amber Lin: Operating yet. So that’s for operating, and then lastly, clockify linear, pretty straightforward. How much was there… oh, not linear.
287 00:26:53.160 ⇒ 00:26:56.350 Amber Lin: How much was the actual duration?
288 00:26:57.020 ⇒ 00:27:01.700 Amber Lin: And then, per project… per user.
289 00:27:03.110 ⇒ 00:27:06.090 Amber Lin: Right. Questions? I know that was a lot.
290 00:27:06.230 ⇒ 00:27:08.849 Amber Lin: Any questions so I can answer and make it better?
291 00:27:08.850 ⇒ 00:27:13.170 Henry Zhao: No, it’s cool that there’s, like, this UI, so I can, like, play around with it and understand the…
292 00:27:13.630 ⇒ 00:27:16.700 Amber Lin: Yeah, you can always just click one, and then…
293 00:27:17.430 ⇒ 00:27:20.170 Amber Lin: See how it turns out.
294 00:27:20.540 ⇒ 00:27:22.870 Amber Lin: Let’s say… If you want.
295 00:27:22.870 ⇒ 00:27:25.850 Henry Zhao: I guess my final question is, what do I need to deliver, and by when?
296 00:27:26.310 ⇒ 00:27:27.540 Henry Zhao: On this dash.
297 00:27:27.650 ⇒ 00:27:29.709 Henry Zhao: Is it just that with Pagnian?
298 00:27:30.750 ⇒ 00:27:36.049 Amber Lin: Y-yeah, so it should be… It should be just this…
299 00:27:36.240 ⇒ 00:27:40.799 Amber Lin: Utam asked you to do, so I don’t know what specific improvements he wants.
300 00:27:40.900 ⇒ 00:27:42.910 Amber Lin: Like, I know that we want to clean.
301 00:27:42.910 ⇒ 00:27:43.500 Henry Zhao: Very vague.
302 00:27:43.500 ⇒ 00:27:44.270 Amber Lin: a little bit.
303 00:27:44.270 ⇒ 00:27:46.460 Henry Zhao: Yeah, it’s very vague sometimes what they want us to do.
304 00:27:47.490 ⇒ 00:28:02.980 Amber Lin: Yeah, so I think we’ll just walk you through a bit. I don’t think it’s not urgent, especially client work, takes priority over this, so maybe just this week, you can scroll through and you can point out a few things that you don’t think are the best.
305 00:28:03.040 ⇒ 00:28:08.160 Amber Lin: And then Wu-Tang will say, oh, great, I’ll go do that. I think that’s maybe the way we can take…
306 00:28:08.160 ⇒ 00:28:08.770 Henry Zhao: Okay.
307 00:28:08.980 ⇒ 00:28:15.890 Amber Lin: And then just be familiar what Annika said, because we’re all taking the certification or whatever, so it’s… I think it’s…
308 00:28:15.890 ⇒ 00:28:16.520 Henry Zhao: I can already…
309 00:28:16.520 ⇒ 00:28:17.800 Amber Lin: Something to do.
310 00:28:20.770 ⇒ 00:28:21.560 Amber Lin: Yeah.
311 00:28:21.780 ⇒ 00:28:33.819 Amber Lin: But if you… if you tell them that you have other stuff that’s very urgent, I would… I… he would probably say de-prioritize this, so I would say, like, depends on your client work.
312 00:28:34.550 ⇒ 00:28:35.220 Henry Zhao: Okay.
313 00:28:35.370 ⇒ 00:28:36.860 Henry Zhao: But, yeah, alright.
314 00:28:37.330 ⇒ 00:28:48.830 Amber Lin: Okay, and I’m always there to answer questions, because I know I did this, and some of them I didn’t really do based on the delivery dashboard, because we didn’t have the joints available then. So if anything’s confusing, please let me know, and I’ll walk you through again.
315 00:28:48.830 ⇒ 00:28:52.860 Henry Zhao: Okay, so should I be doing this to-do list on top, or just provide feedback for now?
316 00:28:54.770 ⇒ 00:28:59.790 Amber Lin: Just feedback for now, because I don’t know, like, this was… Let’s see…
317 00:29:06.960 ⇒ 00:29:17.140 Amber Lin: Yeah, let’s just… let’s just go through, and you can say what you think is… needs to be changed or doable. I think this was the vision that Utum had of what else we wanted.
318 00:29:17.140 ⇒ 00:29:19.570 Henry Zhao: I have no idea what Midway Control Center is, but yeah.
319 00:29:19.880 ⇒ 00:29:32.690 Amber Lin: Yeah, that was just, like, I don’t think that’s something that we need, because this updates real time, so technically it is midweek for this November, like, for each of the November ones.
320 00:29:32.830 ⇒ 00:29:39.069 Amber Lin: It is indeed midweek, so I just, like, I don’t think we need that, because we’re seeing it midweek.
321 00:29:39.960 ⇒ 00:29:41.039 Henry Zhao: So… Yeah, okay.
322 00:29:41.100 ⇒ 00:29:48.129 Amber Lin: but let me know. Shoot me questions in the delivery channel, and then… I can’t answer them.
323 00:29:48.460 ⇒ 00:29:49.970 Henry Zhao: I’ll probably start looking at this tomorrow.
324 00:29:50.320 ⇒ 00:29:51.040 Amber Lin: Okay.
325 00:29:51.450 ⇒ 00:29:52.500 Amber Lin: Yeah. Okay.
326 00:29:52.500 ⇒ 00:29:54.550 Henry Zhao: Thanks, Amber. Thanks, Mustafa.
327 00:29:55.360 ⇒ 00:29:56.140 Mustafa Raja: Thank you.
328 00:29:56.140 ⇒ 00:29:56.520 Henry Zhao: Right.