Meeting Title: Zoom Meeting Date: 2025-04-01 Meeting participants: Annie Yu, Uttam Kumaran
WEBVTT
1 00:00:48.220 ⇒ 00:00:49.479 Annie Yu: Hello, Tom!
2 00:00:49.960 ⇒ 00:00:51.150 Uttam Kumaran: Hey! How are you?
3 00:00:51.510 ⇒ 00:00:53.530 Annie Yu: Good! Good! How are you?
4 00:00:53.530 ⇒ 00:00:56.040 Uttam Kumaran: I’m good. It’s another week
5 00:00:56.040 ⇒ 00:01:00.721 Annie Yu: It’s been. It’s been 20 min since we last talked
6 00:01:01.798 ⇒ 00:01:05.449 Uttam Kumaran: I know. Yeah, I we’re
7 00:01:05.570 ⇒ 00:01:08.180 Uttam Kumaran: we’re doing a lot of sales on like Linkedin.
8 00:01:08.530 ⇒ 00:01:14.769 Uttam Kumaran: And so I just like, haven’t touched it for like a week, and then a bunch of people messaged me. And so I’m like going through messages.
9 00:01:15.190 ⇒ 00:01:27.599 Uttam Kumaran: We So we’re we’re we’re using like a Linkedin automation. So what it does is it will send automatically Linkedin connections to like a certain subset of people. So right now.
10 00:01:28.270 ⇒ 00:01:35.069 Uttam Kumaran: from my account and Robert’s account. We automatically send connections to like Austin executives, or like New York executives.
11 00:01:35.760 ⇒ 00:01:46.169 Uttam Kumaran: Once they accept it automatically, like sends them a message, and then sort of we can start engaging with them. But I’m finally like after like a week, getting some more time for sales.
12 00:01:46.510 ⇒ 00:01:49.139 Uttam Kumaran: So I went through and had to like message everybody, and
13 00:01:49.380 ⇒ 00:01:54.179 Uttam Kumaran: getting like coffees and like meeting with people just to like drum up business.
14 00:01:54.550 ⇒ 00:01:57.660 Uttam Kumaran: So that’s like my day today.
15 00:01:58.020 ⇒ 00:01:59.420 Annie Yu: That’s amazing.
16 00:01:59.710 ⇒ 00:02:01.340 Uttam Kumaran: Yeah, how about you? How’s everything?
17 00:02:02.040 ⇒ 00:02:03.532 Annie Yu: Not too bad.
18 00:02:04.620 ⇒ 00:02:12.479 Annie Yu: not too bad. Still, trying to set it like. Set aside some time just to learn about the data from different different teams.
19 00:02:15.390 ⇒ 00:02:18.949 Annie Yu: And then the real data is a struggle
20 00:02:19.050 ⇒ 00:02:19.810 Uttam Kumaran: Okay.
21 00:02:20.820 ⇒ 00:02:26.509 Annie Yu: But yeah, wait. Are you close to Eden’s data at all?
22 00:02:26.810 ⇒ 00:02:27.144 Uttam Kumaran: Hmm.
23 00:02:30.140 ⇒ 00:02:33.930 Annie Yu: Yeah, I do have some questions around that, too.
24 00:02:35.070 ⇒ 00:02:36.599 Uttam Kumaran: Yeah, tell me where you want to begin?
25 00:02:37.148 ⇒ 00:02:43.180 Annie Yu: I am just looking through the retention dashboards James built.
26 00:02:44.550 ⇒ 00:02:56.079 Annie Yu: And looking through the data, I think one question is just, and I think he’s his data is aligned with the looker studio. So I think it, he’s doing
27 00:02:56.540 ⇒ 00:03:03.349 Annie Yu: the same thing as what the team did before. But I think my question then, is just, why is
28 00:03:04.040 ⇒ 00:03:13.120 Annie Yu: everything counted on a product level? Let’s say, if if we are talking about a repurchase rate for me. That’s
29 00:03:13.500 ⇒ 00:03:26.020 Annie Yu: the unique customers over that 1st order that monthly cohort pool. But I think
30 00:03:26.900 ⇒ 00:03:30.940 Annie Yu: from my understanding now, looking through the data, it looks like
31 00:03:32.730 ⇒ 00:03:45.890 Annie Yu: the numerator, is not unique customers, but the numbers of either orders or products
32 00:03:47.550 ⇒ 00:03:52.550 Uttam Kumaran: Yeah, from my understanding. I think they’re trying to look at retention by like product category
33 00:03:52.790 ⇒ 00:03:53.620 Annie Yu: Okay.
34 00:03:53.620 ⇒ 00:03:56.669 Uttam Kumaran: Meaning they’re trying to see like for what?
35 00:03:56.770 ⇒ 00:04:05.059 Uttam Kumaran: For what? Products like, what products are stickier. But that’s the last. So I do know the underlying data. I don’t know the dashboard side of things as well.
36 00:04:05.430 ⇒ 00:04:14.050 Uttam Kumaran: so that might be a good question for stand up, or to just send in slack, but cause I I know the retention dashboard just got done this week.
37 00:04:14.686 ⇒ 00:04:20.110 Uttam Kumaran: But I’m happy if you, if you want to even share it, I we can go through it and spend some time looking at that.
38 00:04:21.120 ⇒ 00:04:31.300 Annie Yu: Yeah, I I can. I can share some. I’m just looking at the the past data because it’s smaller. And it’s easy to do like easy calculations for validation.
39 00:04:34.150 ⇒ 00:04:39.519 Annie Yu: Okay, so here I’m gonna flash up this one that James built
40 00:04:40.253 ⇒ 00:04:44.800 Annie Yu: so the monthly cohort. And then the months after 1st order
41 00:04:45.720 ⇒ 00:04:51.170 Annie Yu: that here we are trying to calculate the repurchase rate.
42 00:04:52.000 ⇒ 00:04:52.770 Annie Yu: So I’m looking
43 00:04:52.770 ⇒ 00:04:55.538 Uttam Kumaran: So like. So let’s let’s take an example. Right? So like,
44 00:04:56.190 ⇒ 00:04:58.209 Uttam Kumaran: like, what does this number mean?
45 00:04:58.440 ⇒ 00:05:00.110 Uttam Kumaran: This means that
46 00:05:02.820 ⇒ 00:05:07.339 Annie Yu: Yeah, that’s something I’m trying to figure out. Cause I I see here.
47 00:05:08.243 ⇒ 00:05:13.800 Annie Yu: We do. The sum of reorder count over the sum of size.
48 00:05:14.160 ⇒ 00:05:18.310 Annie Yu: And then when I go back to bigquery to this data.
49 00:05:18.620 ⇒ 00:05:23.130 Annie Yu: so I think we already have that fixed amount of
50 00:05:24.690 ⇒ 00:05:29.269 Annie Yu: wait. Actually, it’s this one we already have that fixed amount of
51 00:05:29.891 ⇒ 00:05:33.019 Annie Yu: the cohort size for each month.
52 00:05:35.030 ⇒ 00:05:40.640 Annie Yu: So I think where I’m confused is, why do we sum this up again?
53 00:05:41.770 ⇒ 00:05:44.720 Uttam Kumaran: But what is cohort size? Oh, number of people
54 00:05:45.908 ⇒ 00:05:52.849 Annie Yu: Yeah, the number, the the number of people who placed their 1st order in that month
55 00:05:53.490 ⇒ 00:06:00.059 Uttam Kumaran: Okay, okay, that makes sense. And then if you go back to tableau.
56 00:06:11.390 ⇒ 00:06:12.520 Uttam Kumaran: hmm.
57 00:06:13.930 ⇒ 00:06:21.980 Annie Yu: Here he’s doing like I want each month’s cohort, and then within that.
58 00:06:22.290 ⇒ 00:06:25.660 Annie Yu: within each month after 1st orders
59 00:06:26.260 ⇒ 00:06:32.292 Uttam Kumaran: I guess what we should. So I guess this is almost like, so I guess what I’m seeing is, let’s take
60 00:06:33.750 ⇒ 00:06:37.579 Uttam Kumaran: Let’s take November, December 1, st 2024 right?
61 00:06:42.620 ⇒ 00:06:45.540 Uttam Kumaran: months after 1st order.
62 00:06:46.360 ⇒ 00:06:52.370 Uttam Kumaran: So this is so. What is this is saying that 68% of people
63 00:06:54.100 ⇒ 00:07:05.379 Uttam Kumaran: reorder after the 1st month, and then 45%, and then 36% for all products
64 00:07:07.800 ⇒ 00:07:09.290 Annie Yu: Is that what it means
65 00:07:10.410 ⇒ 00:07:21.469 Uttam Kumaran: That’s what I think it means, because reorder count some of the size this is gonna be the total size of the current pool, right like of the people that ordered X product
66 00:07:21.780 ⇒ 00:07:23.909 Uttam Kumaran: for the 1st time in Jan. First, st
67 00:07:24.230 ⇒ 00:07:27.400 Uttam Kumaran: how many then reordered it in the following month
68 00:07:28.470 ⇒ 00:07:39.000 Annie Yu: Yeah, that’s that’s also how I understood it. But then, after looking at the data, we already have that
69 00:07:46.140 ⇒ 00:07:50.940 Annie Yu: I think where I’m confused is where, how, how? We like some
70 00:07:50.940 ⇒ 00:07:57.439 Uttam Kumaran: Yeah. So I guess let’s let’s just take. Let’s just take like this section. Right? So for this Meta trim.
71 00:07:57.600 ⇒ 00:08:03.929 Uttam Kumaran: the 1st order month, there were 11 people whose 1st order of metatrim was in October.
72 00:08:04.240 ⇒ 00:08:04.940 Annie Yu: Yeah.
73 00:08:06.040 ⇒ 00:08:12.410 Uttam Kumaran: What this is saying is, one month after there were 4 reorders
74 00:08:16.080 ⇒ 00:08:20.289 Uttam Kumaran: This. This one doesn’t make much sense like 0 months after
75 00:08:21.450 ⇒ 00:08:25.630 Uttam Kumaran: this is saying, 3 months after there was 2 reorders.
76 00:08:29.090 ⇒ 00:08:31.230 Uttam Kumaran: so one so
77 00:08:32.360 ⇒ 00:08:33.629 Annie Yu: Oh, wait! Actually
78 00:08:34.120 ⇒ 00:08:36.629 Uttam Kumaran: So one is 4, 2 is 5,
79 00:08:40.240 ⇒ 00:08:42.030 Annie Yu: Ones, earth
80 00:08:42.030 ⇒ 00:08:46.900 Uttam Kumaran: I mean, I guess this is a really good question just to ask in the channel, like, Okay, if I’m looking at.
81 00:08:47.320 ⇒ 00:08:50.700 Uttam Kumaran: If I’m just looking at this this section.
82 00:08:51.660 ⇒ 00:08:55.509 Uttam Kumaran: What is like one of these rows telling me like, is it telling me that
83 00:08:56.200 ⇒ 00:08:59.609 Uttam Kumaran: for of the 11 people who ordered here.
84 00:09:01.340 ⇒ 00:09:05.100 Uttam Kumaran: 14 months later, one reorder happened.
85 00:09:08.090 ⇒ 00:09:10.690 Uttam Kumaran: Or is like. That’s, I guess my question is,
86 00:09:11.660 ⇒ 00:09:19.509 Uttam Kumaran: cause this doesn’t. This may not be custom like, is this customers, or is this cohorts. So one way we can look at this, too, is, if you go into.
87 00:09:19.810 ⇒ 00:09:22.039 Uttam Kumaran: Have you taken a look at the code yet?
88 00:09:23.830 ⇒ 00:09:33.149 Annie Yu: No. And and that’s also one of my questions, because I don’t really know with, speak bigquery. Where do I go to see that?
89 00:09:33.760 ⇒ 00:09:34.450 Annie Yu: Yeah.
90 00:09:34.450 ⇒ 00:09:36.750 Uttam Kumaran: It’s it’s all in. It’s all in Github as well
91 00:09:37.160 ⇒ 00:09:38.410 Annie Yu: Oh, okay.
92 00:09:38.910 ⇒ 00:09:44.889 Uttam Kumaran: So I don’t know. I don’t think I’ve what is your github username
93 00:09:45.010 ⇒ 00:09:46.699 Annie Yu: I’ll make sure you’re here.
94 00:09:46.980 ⇒ 00:09:50.320 Annie Yu: Okay? Cause right now, I think I only have joby
95 00:09:50.520 ⇒ 00:09:53.590 Uttam Kumaran: Okay, yeah, let me let me add you here.
96 00:09:54.160 ⇒ 00:09:54.710 Annie Yu: Hey?
97 00:09:58.206 ⇒ 00:10:00.390 Annie Yu: Just sent you my username
98 00:10:08.200 ⇒ 00:10:10.909 Uttam Kumaran: Okay check. Now, you should have got an invite.
99 00:10:11.200 ⇒ 00:10:14.540 Uttam Kumaran: and then let me just make sure you’re in a bunch of other stuff, too.
100 00:10:30.480 ⇒ 00:10:36.250 Annie Yu: Did I receive? Oh, okay, yeah, that’s my other email.
101 00:11:05.270 ⇒ 00:11:08.010 Uttam Kumaran: Okay, you should have a couple of more repos. Now.
102 00:11:10.430 ⇒ 00:11:13.950 Annie Yu: Yep, going.
103 00:11:21.810 ⇒ 00:11:27.490 Annie Yu: It’s like an added, Okay.
104 00:11:39.000 ⇒ 00:11:41.339 Annie Yu: okay, I think I think these are.
105 00:11:42.640 ⇒ 00:11:44.850 Annie Yu: I think these are the ones
106 00:11:48.610 ⇒ 00:11:52.530 Uttam Kumaran: Yeah. So if you yeah, if you go to Eden.
107 00:11:52.670 ⇒ 00:11:54.739 Uttam Kumaran: well, this is a, this is the team.
108 00:11:55.520 ⇒ 00:12:00.160 Uttam Kumaran: So you actually, it’s not gonna be in our organization.
109 00:12:00.400 ⇒ 00:12:03.670 Uttam Kumaran: If you wanna go click on here, I think
110 00:12:06.073 ⇒ 00:12:08.190 Uttam Kumaran: And you click on search.
111 00:12:09.710 ⇒ 00:12:12.639 Uttam Kumaran: and you go to try Eden. Just type in. Try Eden
112 00:12:14.330 ⇒ 00:12:15.530 Annie Yu: Try.
113 00:12:16.310 ⇒ 00:12:17.090 Uttam Kumaran: Yeah.
114 00:12:17.400 ⇒ 00:12:19.739 Uttam Kumaran: So it’s yeah. So it’s
115 00:12:25.875 ⇒ 00:12:28.709 Annie Yu: Let me see if I’m missing something
116 00:12:38.387 ⇒ 00:12:43.452 Uttam Kumaran: You should have it. So if you it’s not gonna be in our organization, though.
117 00:12:44.080 ⇒ 00:12:45.620 Annie Yu: Isn’t it in
118 00:12:45.620 ⇒ 00:12:51.970 Uttam Kumaran: Can you click on here, on your name here and then click on organizations
119 00:12:54.140 ⇒ 00:12:56.230 Uttam Kumaran: and then, now click on here, try Eden.
120 00:12:58.520 ⇒ 00:13:00.560 Uttam Kumaran: okay, and then click on analytics.
121 00:13:02.150 ⇒ 00:13:02.760 Annie Yu: Oh!
122 00:13:02.760 ⇒ 00:13:10.250 Uttam Kumaran: Yeah. So this is, this is the repo for the Dbt job. So you can go in here. Look in models, and you’ll go find you can find the cohort
123 00:13:10.610 ⇒ 00:13:11.560 Annie Yu: Awesome.
124 00:13:11.830 ⇒ 00:13:12.400 Annie Yu: Okay.
125 00:13:12.400 ⇒ 00:13:13.790 Uttam Kumaran: There should be in March.
126 00:13:14.040 ⇒ 00:13:20.019 Uttam Kumaran: and then should be probably so. Another way you can do it is if you just type it. If you just press T.
127 00:13:21.210 ⇒ 00:13:22.070 Annie Yu: T
128 00:13:22.070 ⇒ 00:13:28.030 Uttam Kumaran: Yeah, it’ll automatically go to search. So then you can just type in like cohort or whatever it is
129 00:13:31.320 ⇒ 00:13:32.910 Uttam Kumaran: Yup, and then you can find
130 00:13:40.650 ⇒ 00:13:44.910 Annie Yu: And how do I deal with this? What’s what’s what’s this mean?
131 00:13:44.910 ⇒ 00:13:50.420 Uttam Kumaran: Yeah, so this is actually like a, this means that it’s a select from this table.
132 00:13:50.830 ⇒ 00:14:03.519 Uttam Kumaran: So the way Dbt works is this is all like what’s called ginger code. Right? It’s all like Python Yaml. When Dbt. Compiles it will replace this with the real table name
133 00:14:06.030 ⇒ 00:14:20.460 Uttam Kumaran: So probably, if this is something again, I think we should. If you run through the Dbt. Intro. To dbt, it’ll give you a little bit more context on, like how this works, but what you can distinct what you can basically take this as is to just a select star from this table.
134 00:14:20.810 ⇒ 00:14:24.340 Uttam Kumaran: and so do you want to go look at legacy cohorts by month.
135 00:14:24.800 ⇒ 00:14:25.470 Annie Yu: Okay.
136 00:14:56.870 ⇒ 00:14:58.980 Annie Yu: order count.
137 00:15:00.430 ⇒ 00:15:04.740 Annie Yu: So it is based on the distinct user. Id?
138 00:15:06.510 ⇒ 00:15:08.030 Annie Yu: Oh.
139 00:15:19.620 ⇒ 00:15:23.560 Annie Yu: okay, I think I I can spend more time on this
140 00:15:24.500 ⇒ 00:15:25.180 Uttam Kumaran: Okay.
141 00:15:25.180 ⇒ 00:15:31.579 Annie Yu: Then. Okay, then I think I I kinda have a more of a, a better, a better sense of
142 00:15:32.060 ⇒ 00:15:36.650 Annie Yu: what that means, like retention rate based on the product
143 00:15:37.800 ⇒ 00:15:46.879 Uttam Kumaran: Yeah. So here you have the cohort size, which is this is the actual number of customers. But look at Reorder. Count is also the number of customers
144 00:15:47.900 ⇒ 00:15:52.369 Uttam Kumaran: So this this isn’t the number like this should say
145 00:15:52.920 ⇒ 00:15:57.420 Uttam Kumaran: like returned use returning users right or returning customers
146 00:15:57.420 ⇒ 00:15:58.000 Annie Yu: Yeah.
147 00:15:58.650 ⇒ 00:16:03.890 Uttam Kumaran: Right, because it’s not actually this is order. So this is where, like a good thing would be
148 00:16:04.300 ⇒ 00:16:10.029 Uttam Kumaran: one to explain that, hey? Right now the retention dashboard is using this legacy table.
149 00:16:10.840 ⇒ 00:16:15.389 Uttam Kumaran: One thing that you can suggest to the team is number one. Some of these columns
150 00:16:15.700 ⇒ 00:16:17.120 Annie Yu: The names, suck.
151 00:16:17.730 ⇒ 00:16:24.289 Uttam Kumaran: Right like you wouldn’t have no idea that this is actually this is reorder Count, but it looks like it’s like it’s a customer.
152 00:16:24.400 ⇒ 00:16:31.210 Uttam Kumaran: Second piece is, if you have any changes to this, you can suggest it, and a waste your demo. A day will go ahead and make those
153 00:16:34.440 ⇒ 00:16:35.190 Annie Yu: Okay.
154 00:16:38.440 ⇒ 00:16:39.260 Annie Yu: okay.
155 00:16:47.366 ⇒ 00:16:48.719 Annie Yu: I see.
156 00:16:51.490 ⇒ 00:17:06.470 Uttam Kumaran: Yeah, like. But I think one thing I’m just trying to share is like you can go. I think it’s helpful for you to go all the way and look at the code itself, because if you go suggest the Ae team to make a change, what you can do here is if you click right here on one of these numbers.
157 00:17:07.190 ⇒ 00:17:09.919 Uttam Kumaran: you can go here, cut and click on that.
158 00:17:10.130 ⇒ 00:17:12.519 Uttam Kumaran: and then you can just copy the permalink.
159 00:17:12.740 ⇒ 00:17:28.429 Uttam Kumaran: and you can literally just say, Hey, go check out this column. It’s like, Can I get help on this column, or can I get us? Can I get help on understanding why. Something is a certain way. It will literally link directly to that line
160 00:17:29.150 ⇒ 00:17:32.070 Uttam Kumaran: So that way, everybody you’re talking about the same thing, you know.
161 00:17:32.600 ⇒ 00:17:34.560 Annie Yu: Oh, that’s cool. That’s cool.
162 00:17:35.590 ⇒ 00:17:42.860 Annie Yu: Okay, yeah, yeah. I don’t wanna spend so much time on this today. But thank you.
163 00:17:42.860 ⇒ 00:17:43.760 Uttam Kumaran: Yeah, yeah.
164 00:17:44.145 ⇒ 00:17:44.530 Annie Yu: Okay.
165 00:17:44.900 ⇒ 00:17:45.680 Uttam Kumaran: Cool.
166 00:17:45.820 ⇒ 00:17:51.410 Uttam Kumaran: Yeah. And and let’s let’s keep going. And then what I’m gonna try to do is also is maybe just like, try to
167 00:17:52.130 ⇒ 00:18:05.030 Uttam Kumaran: block off like an hour of my day, just because I also am doing like one or 2 h of development work every day. Maybe I can just block off. That time is like office hours in case you want to join that and ask any questions for me.
168 00:18:05.410 ⇒ 00:18:07.229 Annie Yu: That would be great.
169 00:18:07.630 ⇒ 00:18:09.310 Uttam Kumaran: Okay. Alright. Cool.
170 00:18:09.870 ⇒ 00:18:10.663 Annie Yu: Good. Just
171 00:18:11.260 ⇒ 00:18:19.079 Annie Yu: follow up question is that I can assume that you’re kind of very familiar with all the data
172 00:18:19.350 ⇒ 00:18:21.480 Uttam Kumaran: Yeah, for the most part, like
173 00:18:22.100 ⇒ 00:18:28.440 Uttam Kumaran: I’m familiar with most most of them. But if you give me a specific problem, at least I can try to guide you in the right direction.
174 00:18:28.880 ⇒ 00:18:29.630 Annie Yu: Okay.
175 00:18:34.700 ⇒ 00:18:35.550 Annie Yu: Cool.
176 00:18:38.160 ⇒ 00:18:39.270 Uttam Kumaran: Okay. Great.
177 00:18:39.840 ⇒ 00:18:42.560 Annie Yu: And you said you wanted to go through my tickets
178 00:18:43.270 ⇒ 00:18:49.720 Uttam Kumaran: Yeah, I guess. Just let me know if there’s any. Yeah, if you want to pull that up and we could just talk through anything in specific would love to just hear like
179 00:18:50.020 ⇒ 00:18:56.220 Uttam Kumaran: what you think about the workload. And there’s anything in particular on your plate that you’re like concerned about.
180 00:18:57.634 ⇒ 00:19:03.280 Annie Yu: Okay, let me see. So these are all my tickets. Okay, I’m gonna
181 00:19:08.430 ⇒ 00:19:12.869 Uttam Kumaran: I think you can. You can hear you can go to display, and you can add the client if you want.
182 00:19:14.430 ⇒ 00:19:16.050 Uttam Kumaran: so you can do grouping
183 00:19:16.460 ⇒ 00:19:17.580 Annie Yu: Grouping.
184 00:19:17.580 ⇒ 00:19:19.409 Uttam Kumaran: By project.
185 00:19:20.137 ⇒ 00:19:23.060 Annie Yu: What’s the difference between team and product
186 00:19:23.060 ⇒ 00:19:27.920 Uttam Kumaran: Oh, actually, team, it’s gonna be team project is like there’s just layers
187 00:19:28.170 ⇒ 00:19:28.540 Annie Yu: Okay.
188 00:19:28.540 ⇒ 00:19:30.760 Uttam Kumaran: Team is, probably, yeah, does this help
189 00:19:31.070 ⇒ 00:19:32.150 Annie Yu: Yeah, yeah.
190 00:19:39.750 ⇒ 00:19:41.040 Annie Yu: for this week.
191 00:19:46.450 ⇒ 00:19:49.850 Uttam Kumaran: Yeah, I guess. Tell me of of, like, I guess
192 00:19:50.410 ⇒ 00:19:55.249 Uttam Kumaran: we can start just from the top, like, how do you feel about the retention dashboard, I guess. Is it
193 00:19:55.920 ⇒ 00:19:56.380 Annie Yu: The other
194 00:19:56.380 ⇒ 00:19:57.250 Uttam Kumaran: Like, what’s what
195 00:19:57.250 ⇒ 00:19:57.830 Annie Yu: And
196 00:19:58.090 ⇒ 00:19:59.639 Uttam Kumaran: What’s part of this ticket
197 00:20:00.810 ⇒ 00:20:01.700 Annie Yu: What’s that?
198 00:20:02.590 ⇒ 00:20:04.089 Uttam Kumaran: I guess what it? So
199 00:20:08.150 ⇒ 00:20:09.110 Uttam Kumaran: okay.
200 00:20:09.390 ⇒ 00:20:09.940 Annie Yu: Yeah.
201 00:20:09.940 ⇒ 00:20:13.730 Uttam Kumaran: Yeah, I guess. Okay.
202 00:20:14.290 ⇒ 00:20:17.620 Uttam Kumaran: cool. So I feel like you’re getting close. I mean, I think the biggest thing is
203 00:20:17.990 ⇒ 00:20:25.160 Uttam Kumaran: this piece like what? What needs, what’s needed to make that in line with Looker. I think you mentioned that it’s basically in line, right
204 00:20:25.160 ⇒ 00:20:32.347 Annie Yu: I I think I think so, but I’m I’m just looking through the metrics, but I think at least the number.
205 00:20:33.070 ⇒ 00:20:38.859 Annie Yu: I was like looking at Looker studio and then the tableau, I think, at least
206 00:20:39.030 ⇒ 00:20:44.449 Annie Yu: from the front end that numbers all align. I’m just trying to understand what’s behind it.
207 00:20:45.380 ⇒ 00:20:46.070 Uttam Kumaran: Okay.
208 00:20:47.643 ⇒ 00:20:54.100 Annie Yu: And and one question for does that mean? Is James still gonna be working on Eden?
209 00:20:54.100 ⇒ 00:20:56.099 Uttam Kumaran: No, so James is rolling off
210 00:20:56.470 ⇒ 00:20:58.160 Annie Yu: Okay. Okay.
211 00:20:58.160 ⇒ 00:21:02.160 Uttam Kumaran: Yeah. James is rolling off so it’ll just be primarily you and Sahana.
212 00:21:03.480 ⇒ 00:21:08.920 Annie Yu: I get a chance to ask him questions, or is is he gone
213 00:21:09.428 ⇒ 00:21:18.540 Uttam Kumaran: I mean, yeah. As of today, he’s technically gone. If you ping him in slack, though, he’ll still answer, or I can give you his number. He’s a really nice guy. He’ll answer anything
214 00:21:19.030 ⇒ 00:21:20.170 Annie Yu: Okay, cool.
215 00:21:23.355 ⇒ 00:21:24.350 Annie Yu: Yeah.
216 00:21:24.540 ⇒ 00:21:30.009 Annie Yu: So that’s for Eden. I think I just saw this new ticket that Akash
217 00:21:32.030 ⇒ 00:21:37.070 Annie Yu: opened earlier. But I haven’t got a chance to go through it
218 00:21:38.190 ⇒ 00:21:38.620 Uttam Kumaran: Okay.
219 00:21:38.620 ⇒ 00:21:41.278 Annie Yu: I think it’s just another
220 00:21:46.340 ⇒ 00:21:47.989 Annie Yu: Another new report
221 00:21:48.680 ⇒ 00:21:52.670 Uttam Kumaran: Okay, yeah. So let’s so one of the things that we look at here.
222 00:21:54.550 ⇒ 00:21:58.129 Uttam Kumaran: new product launch report orders with date range.
223 00:22:02.820 ⇒ 00:22:06.549 Uttam Kumaran: But I guess what I’m confused about is like. Is this a dashboard, or report
224 00:22:07.861 ⇒ 00:22:11.779 Annie Yu: So a report would be a 1 more like a 1 time. Thing
225 00:22:12.130 ⇒ 00:22:13.149 Uttam Kumaran: I don’t know.
226 00:22:13.340 ⇒ 00:22:14.030 Uttam Kumaran: Huh!
227 00:22:14.430 ⇒ 00:22:18.450 Annie Yu: Okay, I’ll I’ll ask this cause. He he just opened this, so I’ll ask
228 00:22:18.450 ⇒ 00:22:18.830 Uttam Kumaran: Okay.
229 00:22:19.330 ⇒ 00:22:24.470 Uttam Kumaran: that’s what. So I think this, these are good, like, you should list the questions in the comments here at the bottom
230 00:22:24.470 ⇒ 00:22:25.030 Annie Yu: Hmm.
231 00:22:25.400 ⇒ 00:22:27.770 Uttam Kumaran: Cause. This is where I’m telling the engineers like
232 00:22:27.930 ⇒ 00:22:33.760 Uttam Kumaran: make sure to push on the Pm. Team to make sure that the requirements are there. So one question is.
233 00:22:34.180 ⇒ 00:22:42.209 Uttam Kumaran: what is this? A dashboard, or like a table like, what is the what is the outcome? Right? So maybe you can even ask. You can leave that as a comment here
234 00:22:42.350 ⇒ 00:22:46.050 Uttam Kumaran: that way tomorrow, during stand up. All you have to say is, I left some comments.
235 00:22:46.360 ⇒ 00:22:50.719 Uttam Kumaran: So one question is, what is the outcome here like?
236 00:22:51.690 ⇒ 00:22:58.669 Uttam Kumaran: It says it. It looks like there’s a report. But then there’s dashboard like, what is this? You know
237 00:23:00.900 ⇒ 00:23:02.150 Annie Yu: So cool.
238 00:23:03.110 ⇒ 00:23:03.740 Uttam Kumaran: Go ahead!
239 00:23:04.030 ⇒ 00:23:14.400 Annie Yu: Is-is. This is, there’s a dashboard, or report
240 00:23:14.400 ⇒ 00:23:16.800 Uttam Kumaran: Or like a or like a data table. Yeah, basically.
241 00:23:16.800 ⇒ 00:23:22.350 Annie Yu: Or that wait. I’m gonna see if okay.
242 00:23:23.570 ⇒ 00:23:30.090 Uttam Kumaran: Yeah, these are no. These are all like, just from the ticket template. So there hasn’t been any
243 00:23:30.570 ⇒ 00:23:31.430 Annie Yu: Okay.
244 00:23:31.600 ⇒ 00:23:34.130 Uttam Kumaran: Like this. The requirements here are super weak.
245 00:23:35.950 ⇒ 00:23:36.990 Uttam Kumaran: You agree
246 00:23:38.260 ⇒ 00:23:44.829 Annie Yu: I I would assume, based on all the comments here. It’s probably a dashboard
247 00:23:45.550 ⇒ 00:23:48.250 Uttam Kumaran: But like this is not enough to build a dashboard. Then
248 00:23:51.020 ⇒ 00:23:51.830 Uttam Kumaran: Right.
249 00:23:52.150 ⇒ 00:23:56.950 Annie Yu: Yeah, okay, I’m gonna ask.
250 00:23:56.950 ⇒ 00:23:59.000 Uttam Kumaran: So I would just ask it. Yeah, don’t worry. Don’t, don’t
251 00:23:59.000 ⇒ 00:24:04.990 Annie Yu: I? No, I think I would want to know, too. I am just making assumptions. But but okay.
252 00:24:04.990 ⇒ 00:24:06.630 Annie Yu: to make assumptions. Yeah.
253 00:24:06.630 ⇒ 00:24:10.959 Uttam Kumaran: Yeah, I would, I agree. Cause otherwise you’re gonna end up somewhere that they don’t want you to. So
254 00:24:11.570 ⇒ 00:24:16.080 Uttam Kumaran: this I would just ask anytime I would just put a comment in the ticket and say, What! What?
255 00:24:16.620 ⇒ 00:24:17.750 Uttam Kumaran: What is this
256 00:24:23.950 ⇒ 00:24:28.600 Annie Yu: Alright! So that’s I think that’s the only 2 from Eden. Now.
257 00:24:29.890 ⇒ 00:24:30.750 Uttam Kumaran: Okay.
258 00:24:31.445 ⇒ 00:24:35.539 Annie Yu: And then I’m gonna can we do Jobby 1st
259 00:24:35.800 ⇒ 00:24:37.020 Uttam Kumaran: Yeah. Sure. Go ahead.
260 00:24:37.020 ⇒ 00:24:41.539 Annie Yu: Because I feel like I’m more comfortable with Joby so far
261 00:24:41.950 ⇒ 00:24:43.280 Uttam Kumaran: Okay. Okay.
262 00:24:43.280 ⇒ 00:24:48.389 Annie Yu: So north beam. I know that this one, I think
263 00:24:50.760 ⇒ 00:24:56.929 Annie Yu: so. I know that the team wants to do north beam within Meta base. But then
264 00:24:57.730 ⇒ 00:25:04.589 Annie Yu: I think even Weish hasn’t been worked with that data yet. So I knew
265 00:25:04.720 ⇒ 00:25:18.290 Annie Yu: that the last time we check he was gonna go through it. But then, if I remember right, robert mentioned that he wanted to kind of ask Aman to just keep using what they have
266 00:25:18.770 ⇒ 00:25:21.600 Annie Yu: without moving everything over
267 00:25:22.880 ⇒ 00:25:26.329 Uttam Kumaran: So what is like? What does this ticket say? Does it say anything
268 00:25:27.860 ⇒ 00:25:33.010 Uttam Kumaran: Oh, replicate northwest. Okay? So it says, replicate this dashboard
269 00:25:34.050 ⇒ 00:25:36.408 Annie Yu: Yeah, which which is fun.
270 00:25:38.160 ⇒ 00:25:47.130 Annie Yu: should be pretty straightforward. But I think Robert’s point is marketing. Data has been changing it.
271 00:25:48.401 ⇒ 00:25:58.700 Annie Yu: Well, there’s a lot. But I think Robert’s point is, marketing. Data changes a lot, and it doesn’t really make sense to put more time and energy
272 00:25:58.900 ⇒ 00:26:03.019 Annie Yu: on like moving everything over to Meta Base
273 00:26:04.120 ⇒ 00:26:06.539 Uttam Kumaran: So then what is what is the last? Comp. So
274 00:26:06.760 ⇒ 00:26:09.826 Annie Yu: Yeah, I think that’s that’s I. I should.
275 00:26:10.210 ⇒ 00:26:11.539 Uttam Kumaran: Yeah, you should ask about this
276 00:26:12.990 ⇒ 00:26:18.005 Uttam Kumaran: like, who? Yeah, I don’t know what. Yeah, if you don’t know, then I don’t know either.
277 00:26:18.980 ⇒ 00:26:22.859 Annie Yu: Cause I I yeah, I think, Robert said. He’s gonna push this back
278 00:26:23.720 ⇒ 00:26:28.359 Uttam Kumaran: Okay? Then I I would just say, like it, am I still? Do I have to do anything here yet?
279 00:26:28.660 ⇒ 00:26:33.169 Annie Yu: Oh, and we’re gonna do
280 00:26:41.510 ⇒ 00:26:42.250 Annie Yu: yep.
281 00:26:42.580 ⇒ 00:26:44.210 Annie Yu: And then this one.
282 00:26:44.330 ⇒ 00:26:58.730 Annie Yu: So we were doing kind of similar things with Javi. But this this is probably like more simple than what Eden’s doing. And this one I’ve I figure out how to do it. But then
283 00:26:58.890 ⇒ 00:27:05.039 Annie Yu: it’s hard to do like cumulative sum, just anything cumulative in Meta base. So
284 00:27:05.040 ⇒ 00:27:05.650 Uttam Kumaran: Okay.
285 00:27:05.650 ⇒ 00:27:14.889 Annie Yu: And I met last Friday, and he’s got a pretty good idea of what he’s gonna build like a summary table. So we should be good. I I just
286 00:27:15.410 ⇒ 00:27:23.200 Annie Yu: need him to do that. And I think this is like lower, probably lower priority compared to all the other. Like crazy tickets. He has
287 00:27:23.200 ⇒ 00:27:27.580 Uttam Kumaran: So. So then, so this is so. Can you scroll down
288 00:27:27.580 ⇒ 00:27:28.210 Annie Yu: Yep.
289 00:27:29.773 ⇒ 00:27:34.930 Uttam Kumaran: Okay, so, is there anything below this?
290 00:27:35.930 ⇒ 00:27:42.379 Uttam Kumaran: Okay? Okay, so is this, is this create? Is this ticket? There?
291 00:27:44.270 ⇒ 00:27:46.200 Annie Yu: I’m not sure.
292 00:27:47.340 ⇒ 00:27:48.939 Annie Yu: How do I check that
293 00:27:49.290 ⇒ 00:27:52.499 Uttam Kumaran: You can go to Javi and then just go to the issues and then
294 00:27:52.610 ⇒ 00:27:56.019 Uttam Kumaran: just check like you can just check, for
295 00:27:56.530 ⇒ 00:28:00.389 Uttam Kumaran: I mean, you could. Just look here, too, if there’s anything assigned to a wish
296 00:28:00.790 ⇒ 00:28:01.470 Annie Yu: Yeah.
297 00:28:01.670 ⇒ 00:28:03.170 Uttam Kumaran: Oh, yeah, okay, that was the one
298 00:28:03.580 ⇒ 00:28:06.170 Annie Yu: But no, I think that’s the one for me.
299 00:28:07.090 ⇒ 00:28:07.800 Uttam Kumaran: Oh!
300 00:28:07.800 ⇒ 00:28:09.450 Annie Yu: Yeah, or maybe
301 00:28:09.450 ⇒ 00:28:12.420 Uttam Kumaran: Okay. So then I. So then my suggestion would be
302 00:28:12.940 ⇒ 00:28:17.329 Uttam Kumaran: like, Yeah, they like, I don’t know whether there’s a ticket created for him.
303 00:28:21.770 ⇒ 00:28:24.749 Uttam Kumaran: Right like I don’t see any ticket related to those things.
304 00:28:24.960 ⇒ 00:28:26.849 Annie Yu: Probably not. Yeah.
305 00:28:27.120 ⇒ 00:28:31.179 Uttam Kumaran: Okay. So then what you should, you should put in the comment. Basically, like.
306 00:28:31.850 ⇒ 00:28:36.750 Uttam Kumaran: you should just put a comment there, that’s like, who’s like.
307 00:28:37.320 ⇒ 00:28:39.629 Uttam Kumaran: who’s gonna create the ticket for this, or
308 00:28:40.310 ⇒ 00:28:47.530 Uttam Kumaran: like a at a cost. Basically like, are you? Are you gonna create a, are you gonna create the follow up ticket and assign it to a wish, or what’s gonna happen here
309 00:28:48.940 ⇒ 00:28:53.959 Uttam Kumaran: So this is also where, like anytime, there’s like, you have any concerns about what is due in a ticket.
310 00:28:54.140 ⇒ 00:28:57.380 Uttam Kumaran: You just have to push back and and and say.
311 00:28:57.930 ⇒ 00:29:03.000 Uttam Kumaran: I need something to work with here. Right? So for this one, it’s pretty clear.
312 00:29:03.860 ⇒ 00:29:07.230 Uttam Kumaran: But then await needs to build something so that needs to go to him
313 00:29:08.130 ⇒ 00:29:10.859 Uttam Kumaran: and get assigned and ticket it out
314 00:29:11.370 ⇒ 00:29:12.170 Annie Yu: Okay.
315 00:29:12.650 ⇒ 00:29:13.330 Annie Yu: Okay.
316 00:29:19.630 ⇒ 00:29:25.180 Annie Yu: I’m gonna while we are on this. I’m just gonna
317 00:29:28.316 ⇒ 00:29:29.589 Annie Yu: okay, cool.
318 00:29:33.630 ⇒ 00:29:39.740 Annie Yu: And then we have that training tomorrow meeting with the new analyst on Joby’s team
319 00:29:45.860 ⇒ 00:29:48.600 Uttam Kumaran: Wait. Say that one more time. Oh, okay. Okay.
320 00:29:49.950 ⇒ 00:29:50.600 Uttam Kumaran: Cool.
321 00:29:51.240 ⇒ 00:29:53.510 Annie Yu: None taking this.
322 00:29:53.960 ⇒ 00:29:56.250 Annie Yu: And Akash is.
323 00:29:56.400 ⇒ 00:30:06.189 Annie Yu: I like how he like. I think he’s really great like telling me to focus on something, and then, like time, box this and Max 1 h. So this one.
324 00:30:06.540 ⇒ 00:30:13.649 Annie Yu: I think, as of now, we don’t have any way to identify Amazon subscribe and save
325 00:30:13.770 ⇒ 00:30:17.360 Annie Yu: based on like either order, id or customer. Id.
326 00:30:19.010 ⇒ 00:30:27.850 Annie Yu: So we got these 4 that’s confirmed subscribe and save. And I’m gonna see if there’s any pattern we can find out to identify
327 00:30:28.782 ⇒ 00:30:31.680 Annie Yu: order ids, even though that sounds
328 00:30:33.600 ⇒ 00:30:37.060 Annie Yu: that’s probably gonna be like challenging. So
329 00:30:37.470 ⇒ 00:30:41.560 Annie Yu: we time box it at 1 h. But I haven’t. I haven’t.
330 00:30:41.850 ⇒ 00:30:45.609 Annie Yu: I just looked through it, Snowflake, but I haven’t started this
331 00:30:46.130 ⇒ 00:30:46.820 Uttam Kumaran: Okay.
332 00:30:46.930 ⇒ 00:30:53.049 Uttam Kumaran: so like, do you do, do you? Do you have a sense of where you would go to look for these
333 00:30:55.730 ⇒ 00:31:00.750 Annie Yu: I think my thinking is, I probably will
334 00:31:01.350 ⇒ 00:31:08.140 Annie Yu: just based on Amazon subscribe and save. I’m probably gonna get like A
335 00:31:10.190 ⇒ 00:31:16.930 Annie Yu: from the repeating orders to get that interval that say, like 30 days or 60 days. So
336 00:31:17.070 ⇒ 00:31:17.979 Annie Yu: to get those
337 00:31:17.980 ⇒ 00:31:18.970 Uttam Kumaran: Hmm.
338 00:31:18.970 ⇒ 00:31:28.040 Annie Yu: People who are like very likely on subscribe and save, and then see if there’s
339 00:31:28.680 ⇒ 00:31:34.630 Annie Yu: any pattern there. Because I I said, I haven’t started this. I looked through these ones.
340 00:31:37.130 ⇒ 00:31:41.430 Annie Yu: And I. I don’t see a lot of information from just these these 4
341 00:31:42.200 ⇒ 00:31:45.040 Uttam Kumaran: Yeah. So so I think I’ll just maybe put a
342 00:31:45.160 ⇒ 00:31:47.579 Uttam Kumaran: I’ll put a couple of comments here. So one
343 00:31:48.030 ⇒ 00:32:02.200 Uttam Kumaran: one like this is, this is all Amazon data. So focus on Amazon orders, but also go into the raw schema, into the rod
344 00:32:02.890 ⇒ 00:32:08.680 Uttam Kumaran: to the raw database and check out.
345 00:32:10.440 ⇒ 00:32:15.070 Uttam Kumaran: Check out the orders directly from the source.
346 00:32:15.400 ⇒ 00:32:18.900 Uttam Kumaran: You may see a row there that helps you figure this out.
347 00:32:19.010 ⇒ 00:32:21.359 Uttam Kumaran: You may see a column there that helps you figure this out
348 00:32:22.010 ⇒ 00:32:26.620 Annie Yu: Okay? And that’s and you said, That’s raw. Do I have access to raw
349 00:32:27.419 ⇒ 00:32:31.610 Uttam Kumaran: Are you in? So this? So this is in Snowflake?
350 00:32:32.420 ⇒ 00:32:32.780 Uttam Kumaran: Oh.
351 00:32:32.780 ⇒ 00:32:37.840 Uttam Kumaran: this is not the data meaning like you should go into the raw is where everything gets dumped into
352 00:32:39.350 ⇒ 00:32:45.680 Uttam Kumaran: So if you open up the Javi Snowflake, you can go, and if you if you go to raw you’ll see the Amazon data in there
353 00:32:46.740 ⇒ 00:32:47.590 Annie Yu: Okay.
354 00:32:53.440 ⇒ 00:32:58.357 Uttam Kumaran: Yep. So if you open up raw here, you will see
355 00:32:59.420 ⇒ 00:33:02.470 Uttam Kumaran: Amazon right here. Yep, Amazon selling partner.
356 00:33:03.740 ⇒ 00:33:10.949 Uttam Kumaran: and these are all the tables. So my suggestion is to work your way back from. So if you go back to Github
357 00:33:12.220 ⇒ 00:33:12.750 Annie Yu: Yep.
358 00:33:12.920 ⇒ 00:33:19.049 Uttam Kumaran: And you go into intermediate, and you try to see if there’s Amazon.
359 00:33:19.670 ⇒ 00:33:27.600 Uttam Kumaran: and just pick on. Pick on one of them like in Amazon order. You can actually see. Okay, cool. I wanna I wanna go find out. Like, where is this data coming from?
360 00:33:27.850 ⇒ 00:33:30.959 Annie Yu: It looks like it’s coming from into Amazon order line
361 00:33:31.350 ⇒ 00:33:34.040 Uttam Kumaran: So you can go back to order line here.
362 00:33:36.740 ⇒ 00:33:40.090 Uttam Kumaran: and then you can see it’s coming from raw source. Amazon orders
363 00:33:41.470 ⇒ 00:33:42.170 Uttam Kumaran: Right.
364 00:33:42.790 ⇒ 00:33:46.910 Uttam Kumaran: So this is what I would. I would go take a look at that table.
365 00:33:47.140 ⇒ 00:33:55.269 Uttam Kumaran: the Amazon raw orders. And so where one thing that you’ll notice here also in in the in these dvts you’ll see the source.
366 00:33:55.810 ⇒ 00:34:03.949 Uttam Kumaran: You’ll see the source like sort of a code, the way you can the way you can identify this if you scroll down here
367 00:34:05.420 ⇒ 00:34:07.540 Uttam Kumaran: and you go to sources
368 00:34:08.520 ⇒ 00:34:09.540 Annie Yu: Sources.
369 00:34:10.320 ⇒ 00:34:18.510 Uttam Kumaran: You will see here there’s an Amazon raw. And then this is basically like a mapping. So Amazon raw maps to Amazon selling partner the schema
370 00:34:18.909 ⇒ 00:34:20.809 Uttam Kumaran: in the in the raw database
371 00:34:22.382 ⇒ 00:34:24.150 Annie Yu: Can you say that
372 00:34:24.159 ⇒ 00:34:24.849 Uttam Kumaran: What I mean
373 00:34:24.850 ⇒ 00:34:25.290 Annie Yu: Okay.
374 00:34:25.290 ⇒ 00:34:29.139 Uttam Kumaran: So go go back. One more. Go back to the Amazon order lines
375 00:34:29.340 ⇒ 00:34:30.150 Annie Yu: Okay.
376 00:34:31.330 ⇒ 00:34:33.480 Uttam Kumaran: So you see here how it says source
377 00:34:33.489 ⇒ 00:34:34.039 Annie Yu: No.
378 00:34:34.239 ⇒ 00:34:39.709 Uttam Kumaran: This is actually just a reference. It’s like a, it’s a, it’s a reference to the source
379 00:34:40.079 ⇒ 00:34:44.899 Uttam Kumaran: in the sources file meaning instead of putting in here
380 00:34:45.219 ⇒ 00:34:50.899 Uttam Kumaran: raw dot Amazon selling partner dot orders. All we have to say is source
381 00:34:51.029 ⇒ 00:34:52.229 Annie Yu: Orders.
382 00:34:52.639 ⇒ 00:34:57.889 Uttam Kumaran: But what this actually resolves into when you compile, is it it brings in the table name.
383 00:34:58.039 ⇒ 00:35:09.909 Uttam Kumaran: And so, in order to find out what is this? You can just go into that sources table, and this will be the same across every Dbt project. Go into the sources table, and then you can just see cool. I’m looking for the Amazon raw source.
384 00:35:10.169 ⇒ 00:35:16.879 Uttam Kumaran: I’m looking for Amazon raw source, and I’m looking for what schema, what database, what schema? And then what tables?
385 00:35:17.029 ⇒ 00:35:20.489 Uttam Kumaran: This is what you should go. What you’ll see in Snowflake
386 00:35:21.880 ⇒ 00:35:22.740 Annie Yu: Hmm.
387 00:35:23.180 ⇒ 00:35:24.900 Uttam Kumaran: You kind of get what I mean a little bit
388 00:35:25.220 ⇒ 00:35:25.955 Annie Yu: Yeah.
389 00:35:26.690 ⇒ 00:35:27.949 Uttam Kumaran: I know it can be a little confusing
390 00:35:28.399 ⇒ 00:35:34.690 Annie Yu: Amazon selling partner. So does that mean here we should see the this one
391 00:35:34.690 ⇒ 00:35:39.809 Uttam Kumaran: Yes, you should see an. And then, basically, if you go back to Github.
392 00:35:40.140 ⇒ 00:35:43.309 Uttam Kumaran: we are referencing these 6 or 7 tables
393 00:35:44.480 ⇒ 00:35:48.689 Annie Yu: Oh, so these are all table table names.
394 00:35:48.690 ⇒ 00:35:49.450 Uttam Kumaran: Yes.
395 00:35:50.860 ⇒ 00:35:56.686 Annie Yu: Oh, okay, alright! There’s so many layers
396 00:35:57.170 ⇒ 00:35:57.865 Uttam Kumaran: Yes.
397 00:36:00.120 ⇒ 00:36:02.369 Annie Yu: Okay, okay, that makes sense.
398 00:36:09.740 ⇒ 00:36:10.730 Annie Yu: okay?
399 00:36:11.590 ⇒ 00:36:20.750 Annie Yu: And also, one question is, what’s
400 00:36:21.060 ⇒ 00:36:24.690 Annie Yu: like order order versus order line.
401 00:36:27.138 ⇒ 00:36:31.460 Uttam Kumaran: So order line is like when you, it’s like your items in your cart.
402 00:36:31.600 ⇒ 00:36:35.499 Uttam Kumaran: So an order can have multiple items. So that’s the line items.
403 00:36:37.730 ⇒ 00:36:41.269 Uttam Kumaran: Do you see what I mean? Like, you can order 5 products in one order
404 00:36:41.730 ⇒ 00:36:42.410 Annie Yu: Yeah.
405 00:36:42.610 ⇒ 00:36:48.759 Uttam Kumaran: So order item will have 5 line items, but then they all have the same order. Id
406 00:36:49.510 ⇒ 00:36:54.970 Annie Yu: Okay, does that mean? It would be 5 rows in borderline
407 00:36:54.970 ⇒ 00:36:55.830 Uttam Kumaran: Correct.
408 00:36:55.960 ⇒ 00:36:56.730 Annie Yu: Okay.
409 00:36:56.950 ⇒ 00:37:03.449 Uttam Kumaran: So that’s why we. That’s why we we have to have 2 different tables, one, that’s order line and one that’s orders.
410 00:37:05.080 ⇒ 00:37:05.770 Annie Yu: And that.
411 00:37:05.770 ⇒ 00:37:09.799 Uttam Kumaran: Because because, for cause, for example, sometimes you have a order level, discount
412 00:37:09.800 ⇒ 00:37:10.310 Annie Yu: Hmm.
413 00:37:10.310 ⇒ 00:37:12.710 Uttam Kumaran: Where, like, your whole order is 20% off
414 00:37:12.710 ⇒ 00:37:13.060 Annie Yu: Yeah.
415 00:37:13.060 ⇒ 00:37:18.270 Uttam Kumaran: But if you were to have that on every order item, it would be duplicated right
416 00:37:18.510 ⇒ 00:37:19.070 Annie Yu: No.
417 00:37:19.070 ⇒ 00:37:23.770 Uttam Kumaran: Because let’s say you have order, order, item, the price and the discount.
418 00:37:24.100 ⇒ 00:37:35.300 Uttam Kumaran: The discount is at the whole order level. Let’s say the discount is 5 to all your 5 order items, it then looks like it’s $25 off.
419 00:37:35.620 ⇒ 00:37:37.980 Uttam Kumaran: So you need to have 2 different granularities
420 00:37:39.106 ⇒ 00:37:40.959 Annie Yu: okay, okay, that’s helpful.
421 00:37:49.650 ⇒ 00:37:57.929 Annie Yu: Okay, yeah, I think this will be enough for me for for 1 h. See.
422 00:37:58.928 ⇒ 00:38:06.659 Annie Yu: see, if there’s anything I feel like. Honestly, I feel like not. No one’s like really hopeful about that. So
423 00:38:06.660 ⇒ 00:38:07.469 Uttam Kumaran: Yeah, why?
424 00:38:07.470 ⇒ 00:38:10.211 Annie Yu: About like finding a pattern
425 00:38:10.760 ⇒ 00:38:14.710 Uttam Kumaran: I mean. So yeah, it’s gonna be tough. So what I would suggest. And again, you can.
426 00:38:14.820 ⇒ 00:38:17.620 Uttam Kumaran: I’ll I’ll I want to put this in the.
427 00:38:17.880 ⇒ 00:38:22.410 Uttam Kumaran: I put this in the notes, but you should go directly to the Amazon
428 00:38:22.620 ⇒ 00:38:31.249 Uttam Kumaran: order. You should go to the Amazon orders raw table and just filter for those 5 orders, and just take a look if you see anything
429 00:38:31.360 ⇒ 00:38:34.749 Uttam Kumaran: right. You’re not what you’re you’re not actually looking for.
430 00:38:34.980 ⇒ 00:38:44.169 Uttam Kumaran: So there’s 2 ways of solving this one. If you find a column there that says this is a subscribe and save, then you’re sort of good. You’re like cool. Just bring that column in. We’re good.
431 00:38:44.390 ⇒ 00:39:00.320 Uttam Kumaran: I don’t think that’s gonna happen. So basically, what what they’re asking is like, just see if there’s any column there that that identifies that this customer is a subscribe and save customer. The second thing is what you doing what you can, what you suggested, which is like.
432 00:39:00.610 ⇒ 00:39:06.769 Uttam Kumaran: maybe we should just look at orders that happened on the same day over a month.
433 00:39:07.000 ⇒ 00:39:09.820 Uttam Kumaran: but, like again, that may take more than an hour for you.
434 00:39:10.140 ⇒ 00:39:20.209 Uttam Kumaran: so I would suggest that as a follow up, I would try the 1st one, which is just like, just look at these, just look for these orders across all the tables and see if you find anything
435 00:39:20.550 ⇒ 00:39:22.480 Annie Yu: Yeah, okay.
436 00:39:22.700 ⇒ 00:39:23.270 Uttam Kumaran: Yeah.
437 00:39:23.450 ⇒ 00:39:24.499 Annie Yu: That sounds good.
438 00:39:24.630 ⇒ 00:39:26.140 Annie Yu: Thank you so much.
439 00:39:26.450 ⇒ 00:39:27.619 Uttam Kumaran: Yeah, of course.
440 00:39:28.380 ⇒ 00:39:29.360 Annie Yu: Cool.
441 00:39:36.270 ⇒ 00:39:40.149 Annie Yu: I’m not sure about this. I haven’t looked through this because it’s.
442 00:39:40.870 ⇒ 00:39:44.970 Annie Yu: I think, a a in the cycle, too. So
443 00:39:45.400 ⇒ 00:39:46.070 Uttam Kumaran: Okay?
444 00:39:46.790 ⇒ 00:39:52.330 Uttam Kumaran: So well, let’s let’s let’s take a look at that one. So this is klaviyo analysis.
445 00:39:52.940 ⇒ 00:39:55.590 Uttam Kumaran: Okay? I mean, these seem.
446 00:39:57.350 ⇒ 00:40:01.430 Uttam Kumaran: how do you feel when you look at when you like. Look at these like requirements.
447 00:40:04.120 ⇒ 00:40:05.710 Annie Yu: I haven’t looked at these
448 00:40:05.710 ⇒ 00:40:09.759 Uttam Kumaran: Okay. But like, let’s let’s say you just like you’re looking at it right now, like, what do you think
449 00:40:10.530 ⇒ 00:40:14.227 Annie Yu: I think it’s pretty comprehensive compared to the
450 00:40:14.690 ⇒ 00:40:15.420 Uttam Kumaran: Yes.
451 00:40:15.820 ⇒ 00:40:16.750 Annie Yu: Yeah.
452 00:40:17.890 ⇒ 00:40:23.180 Uttam Kumaran: If you scroll down like, do you feel like looking at this? You you sort of
453 00:40:23.480 ⇒ 00:40:26.050 Uttam Kumaran: have everything that you need
454 00:40:28.340 ⇒ 00:40:35.249 Annie Yu: I think so. I think for me, I always love that key questions which we have
455 00:40:36.045 ⇒ 00:40:37.560 Annie Yu: and the metrics.
456 00:40:38.380 ⇒ 00:40:41.910 Annie Yu: I think I think these are pretty pretty clear
457 00:40:42.300 ⇒ 00:40:44.450 Uttam Kumaran: Okay, I agree.
458 00:40:46.640 ⇒ 00:40:51.930 Uttam Kumaran: I think my my only suggestion would be, as you see here. It says, data sources
459 00:40:51.930 ⇒ 00:40:52.580 Annie Yu: No.
460 00:40:52.890 ⇒ 00:40:59.099 Uttam Kumaran: What would be helpful is if you if you ask a wish, or someone on the team, hey, what tables are these in
461 00:40:59.290 ⇒ 00:41:00.599 Annie Yu: Yeah, yeah.
462 00:41:00.760 ⇒ 00:41:06.915 Uttam Kumaran: You know. Also, this is a lot. This is like too much for one ticket.
463 00:41:08.066 ⇒ 00:41:11.749 Annie Yu: Right. So one of the things your suggestion should be is like
464 00:41:12.070 ⇒ 00:41:14.659 Uttam Kumaran: Each of these is like a ticket.
465 00:41:15.340 ⇒ 00:41:23.440 Uttam Kumaran: right? Probably like meaning you don’t want to have a task on your on your board. That’s that’s like, gonna take 3 or 4 days
466 00:41:23.830 ⇒ 00:41:27.579 Uttam Kumaran: You always want it to be like around a day or 2 maximum
467 00:41:28.100 ⇒ 00:41:28.480 Annie Yu: Yeah.
468 00:41:28.480 ⇒ 00:41:31.790 Uttam Kumaran: Like for this. You could probably spend 2, 3 weeks on this alone.
469 00:41:32.180 ⇒ 00:41:34.289 Uttam Kumaran: So this is where I would I would go.
470 00:41:34.550 ⇒ 00:41:36.519 Uttam Kumaran: I would go back to the Pm.
471 00:41:36.840 ⇒ 00:41:44.020 Uttam Kumaran: Or I would just put a comment there now and say, Hey, this, this looks like really big. Another way of doing this is
472 00:41:44.670 ⇒ 00:41:46.819 Uttam Kumaran: for this ticket, if you
473 00:41:47.410 ⇒ 00:41:48.700 Uttam Kumaran: if you go ahead and like.
474 00:41:49.150 ⇒ 00:41:52.190 Uttam Kumaran: if you go ahead and do like shift, d.
475 00:41:52.870 ⇒ 00:41:55.649 Uttam Kumaran: or what is it? It’s a command. K. Maybe
476 00:41:56.720 ⇒ 00:41:59.170 Uttam Kumaran: you can add A, you can add an estimation
477 00:42:00.420 ⇒ 00:42:02.049 Uttam Kumaran: you could just type in estimate.
478 00:42:05.034 ⇒ 00:42:08.739 Uttam Kumaran: Is there? No? Oh, there’s no estimates on this. Okay? One second. Hold on.
479 00:42:17.610 ⇒ 00:42:18.440 Uttam Kumaran: Hold on
480 00:42:19.610 ⇒ 00:42:20.360 Annie Yu: Thank you.
481 00:42:27.490 ⇒ 00:42:30.630 Annie Yu: One data, tables.
482 00:42:41.600 ⇒ 00:42:44.340 Uttam Kumaran: Okay. So you see here, and there’s estimate
483 00:42:46.620 ⇒ 00:42:51.649 Uttam Kumaran: See. So this is where we wrote up a document on how to estimate tickets
484 00:42:57.170 ⇒ 00:43:01.469 Uttam Kumaran: So I don’t know. Have you had a chance to take a look at this, Doc? Yet?
485 00:43:02.393 ⇒ 00:43:04.460 Annie Yu: I don’t believe so, but
486 00:43:04.460 ⇒ 00:43:06.284 Uttam Kumaran: Here I will! I will!
487 00:43:06.830 ⇒ 00:43:13.360 Uttam Kumaran: I’ll send it just just poke at it. Take a look at it. There’s a key piece here, which is about how to do estimates.
488 00:43:22.920 ⇒ 00:43:25.839 Uttam Kumaran: So if you look at 3, it’ll be pointing strategy
489 00:43:27.490 ⇒ 00:43:30.260 Uttam Kumaran: So that’s your guide, for how to do points
490 00:43:31.540 ⇒ 00:43:32.360 Annie Yu: Okay.
491 00:43:32.650 ⇒ 00:43:36.840 Uttam Kumaran: So, looking at that task, how would you point it?
492 00:43:38.720 ⇒ 00:43:40.560 Annie Yu: Oh, for the the whole thing!
493 00:43:40.560 ⇒ 00:43:43.080 Uttam Kumaran: Yes, like, let’s say it’s just as written
494 00:43:46.100 ⇒ 00:43:47.380 Annie Yu: Probably
495 00:43:51.770 ⇒ 00:43:53.961 Uttam Kumaran: Probably like this one. Yeah.
496 00:43:54.820 ⇒ 00:43:58.169 Uttam Kumaran: So that’s all you should put. So what you should do is you go to estimate.
497 00:43:58.280 ⇒ 00:44:02.559 Uttam Kumaran: And you can just say so. Okay, right now, it’s hold on, let me
498 00:44:02.810 ⇒ 00:44:04.869 Uttam Kumaran: right now. The Max is 8
499 00:44:07.360 ⇒ 00:44:22.540 Annie Yu: But also Utah, have a question. I I’m not sure, because obviously we haven’t started like looking through Cycle 2. I could it be that Akash just kind of write a big one, and then eventually he’ll do like sub tickets
500 00:44:22.680 ⇒ 00:44:23.350 Annie Yu: when we
501 00:44:23.350 ⇒ 00:44:36.589 Uttam Kumaran: I I think it’s if the ticket is there, you can leave a comment. So this is, this is just what this is just saying or so. If you don’t feel comfortable here, you can leave a comment and just say, Hey, this is like a 21 point ticket. This needs to be split up
502 00:44:36.770 ⇒ 00:44:40.009 Uttam Kumaran: so you can leave that. You can leave that as a comment and move on. That’s it.
503 00:44:40.650 ⇒ 00:44:41.410 Annie Yu: Okay.
504 00:44:42.400 ⇒ 00:44:43.810 Uttam Kumaran: Right. That seems fair
505 00:44:44.370 ⇒ 00:44:45.210 Annie Yu: Yup! Yup!
506 00:44:45.210 ⇒ 00:44:52.050 Uttam Kumaran: So what? So what? Yeah, just do that. And so what I do as like an engineering leaders, I go look at everybody’s board. And I’m like.
507 00:44:52.370 ⇒ 00:44:58.730 Uttam Kumaran: who’s signing up for stuff that like clearly, they’re gonna get screwed on, you know. Cause if you signed up for this.
508 00:44:58.860 ⇒ 00:45:00.310 Uttam Kumaran: this is too much.
509 00:45:00.480 ⇒ 00:45:05.910 Uttam Kumaran: So instead, you can say, Hey, this looks like this is, gonna take more than 3 days. This needs to be broken up.
510 00:45:06.070 ⇒ 00:45:06.950 Uttam Kumaran: Perfect
511 00:45:07.960 ⇒ 00:45:15.729 Annie Yu: Oh, looks like this could take 3 plus days.
512 00:45:17.406 ⇒ 00:45:23.470 Annie Yu: Can we break this sub tickets?
513 00:45:36.220 ⇒ 00:45:39.580 Annie Yu: Yeah, that’s tomorrow’s problem.
514 00:45:40.050 ⇒ 00:45:40.650 Uttam Kumaran: Yeah.
515 00:45:43.430 ⇒ 00:45:51.140 Annie Yu: And this one that’s this is also dependent on. If we find we can find any pattern so
516 00:45:51.140 ⇒ 00:45:51.980 Uttam Kumaran: Okay. Cool.
517 00:45:51.980 ⇒ 00:45:52.699 Annie Yu: Passing on that
518 00:45:52.700 ⇒ 00:45:53.220 Uttam Kumaran: Okay.
519 00:45:58.040 ⇒ 00:46:02.899 Annie Yu: I think I think this one is also
520 00:46:08.380 ⇒ 00:46:11.219 Annie Yu: yeah. Last time I checked Robert said.
521 00:46:11.620 ⇒ 00:46:21.149 Annie Yu: we don’t have the data here. So once we have the data. This should be a pretty straightforward task, but we don’t, and I’m not sure where that
522 00:46:22.140 ⇒ 00:46:23.070 Annie Yu: it’s that
523 00:46:28.870 ⇒ 00:46:35.269 Uttam Kumaran: Okay, I guess I’m not following like, why can’t we do this right now?
524 00:46:37.291 ⇒ 00:46:41.380 Annie Yu: Because we don’t have the data.
525 00:46:41.940 ⇒ 00:46:42.780 Annie Yu: Think
526 00:46:44.140 ⇒ 00:46:45.210 Uttam Kumaran: What data.
527 00:46:46.020 ⇒ 00:46:47.310 Annie Yu: I have no idea.
528 00:46:48.330 ⇒ 00:46:48.920 Annie Yu: Oh, no.
529 00:46:48.920 ⇒ 00:46:51.250 Uttam Kumaran: Okay, then someone has to figure that out
530 00:46:51.620 ⇒ 00:46:52.769 Annie Yu: Okay, I’ll print this.
531 00:46:54.597 ⇒ 00:46:55.369 Annie Yu: I I think
532 00:46:55.370 ⇒ 00:46:55.939 Uttam Kumaran: So what?
533 00:46:55.940 ⇒ 00:46:58.510 Annie Yu: Could pause on this for for a few days, and
534 00:46:58.510 ⇒ 00:47:06.269 Uttam Kumaran: But that’s but that’s fine, because because but the thing is is like, while you’re while it’s still open. Somebody in one week is also gonna ask the same question.
535 00:47:07.020 ⇒ 00:47:11.069 Uttam Kumaran: right? So I would just, I would just in stand up. Just say, Hey.
536 00:47:11.370 ⇒ 00:47:14.219 Uttam Kumaran: like, I’m actually, it’s actually unclear.
537 00:47:16.020 ⇒ 00:47:18.820 Uttam Kumaran: Well, basically, see. So one of the pieces is like
538 00:47:19.320 ⇒ 00:47:22.369 Uttam Kumaran: Akash said, use fact orders to do this.
539 00:47:22.540 ⇒ 00:47:27.380 Uttam Kumaran: Maybe you you should. You should go see? Can I solve all these problems with fact orders.
540 00:47:29.770 ⇒ 00:47:31.850 Uttam Kumaran: Right? That’s the question.
541 00:47:34.090 ⇒ 00:47:34.940 Annie Yu: Okay.
542 00:47:35.170 ⇒ 00:47:39.190 Uttam Kumaran: So one way of taking this is like, if you
543 00:47:39.400 ⇒ 00:47:49.260 Uttam Kumaran: if you say if you go to, if you basically like, take a look at what they’re asking for here, which is, can we create an product specific? P and L report like the top of the sheet
544 00:47:50.300 ⇒ 00:47:52.579 Uttam Kumaran: top of sheet one. If you go back.
545 00:47:53.690 ⇒ 00:48:03.439 Uttam Kumaran: what does it look like? It looks like months, and it looks like ad spend right?
546 00:48:03.970 ⇒ 00:48:11.360 Uttam Kumaran: And it looks like orders sales order sales. I don’t know what ours.
547 00:48:11.490 ⇒ 00:48:16.580 Uttam Kumaran: I don’t know what Rcnc. Is, but these are all the questions that basically they’re trying to replicate this report.
548 00:48:17.770 ⇒ 00:48:21.000 Uttam Kumaran: So I guess the question for you would be, why, why can’t we do that?
549 00:48:24.160 ⇒ 00:48:25.380 Uttam Kumaran: You see what I mean?
550 00:48:25.600 ⇒ 00:48:26.240 Annie Yu: Hmm.
551 00:48:27.230 ⇒ 00:48:34.060 Uttam Kumaran: So I think that’s what you need to work on is basically saying, what are we missing? In fact, orders that we can’t solve this right now?
552 00:48:35.110 ⇒ 00:48:38.347 Annie Yu: Okay, I’m gonna have to add myself.
553 00:48:38.810 ⇒ 00:48:39.400 Uttam Kumaran: Go ahead!
554 00:48:57.300 ⇒ 00:48:58.410 Annie Yu: And
555 00:49:09.520 ⇒ 00:49:15.689 Annie Yu: and does that once backlog me? Is it just means we’re pausing on it
556 00:49:16.920 ⇒ 00:49:23.430 Uttam Kumaran: Yeah. So another piece is like, if you also go reference the documentation again.
557 00:49:23.993 ⇒ 00:49:28.030 Uttam Kumaran: You’ll see at the at the top, under ticket statuses.
558 00:49:28.260 ⇒ 00:49:30.179 Uttam Kumaran: you will see 2.2
559 00:49:30.810 ⇒ 00:49:34.420 Uttam Kumaran: Yup. So, holding area for new unprioritized ideas
560 00:49:34.580 ⇒ 00:49:35.490 Annie Yu: Okay.
561 00:49:35.490 ⇒ 00:49:44.109 Uttam Kumaran: So basically, the way the process should go is that nothing should be put into ready for development until the requirements are done.
562 00:49:44.480 ⇒ 00:49:51.029 Uttam Kumaran: Right now, what we noticed is that there are tickets that are being worked on, and the requirements suck
563 00:49:51.330 ⇒ 00:49:52.539 Uttam Kumaran: is what I see.
564 00:49:53.230 ⇒ 00:50:01.430 Uttam Kumaran: So from my question is, when I talk to the Pm. Team tomorrow, I’m gonna say, Hey, I worked with Annie but 2 of her tickets.
565 00:50:01.540 ⇒ 00:50:03.100 Uttam Kumaran: The requirements sucked.
566 00:50:03.270 ⇒ 00:50:06.580 Uttam Kumaran: So how did that happen because she’s completely blocked.
567 00:50:06.880 ⇒ 00:50:11.190 Uttam Kumaran: That’s what I’m gonna take back right? And so this is also where you as an engineer.
568 00:50:11.670 ⇒ 00:50:15.160 Uttam Kumaran: if you, if there is a ticket that moves to in development.
569 00:50:15.470 ⇒ 00:50:18.930 Uttam Kumaran: You need to. You need to be very clear that you can accomplish that
570 00:50:19.460 ⇒ 00:50:24.040 Uttam Kumaran: right if something is in progress, and then you go back and say, Hey, it’s basically like.
571 00:50:24.450 ⇒ 00:50:40.969 Uttam Kumaran: this is just like a cake through like a bakery, right? So like there’s different stages. But if you don’t know what the icing at the end is going to be. How are you gonna make the cake right? And so that’s all this is saying is that everything needs to have the requirements before being put into ready for development.
572 00:50:41.160 ⇒ 00:50:48.039 Uttam Kumaran: Can I show you another? Maybe I’ll show you one example. That we’re doing on on the AI team. It’ll make it a little bit more clear
573 00:50:50.955 ⇒ 00:50:51.520 Annie Yu: Hello
574 00:50:52.280 ⇒ 00:50:59.209 Uttam Kumaran: I I can. I can. Yeah, I can share this. Okay, cool. So this is the this is the AI team board. Right?
575 00:50:59.450 ⇒ 00:51:02.719 Uttam Kumaran: You’re gonna see that there’s a bunch of stuff in backlog.
576 00:51:02.830 ⇒ 00:51:05.130 Uttam Kumaran: These are just ideas, right?
577 00:51:05.400 ⇒ 00:51:14.639 Uttam Kumaran: So I just throw back ideas. Here, then the requirements get started. And if you open one of these, it looks like Miguel has started working on requirements.
578 00:51:15.830 ⇒ 00:51:17.100 Uttam Kumaran: He hasn’t finished it.
579 00:51:20.440 ⇒ 00:51:29.870 Annie Yu: I’m gonna see that requirements, clarifying scope, context feasibility
580 00:51:31.640 ⇒ 00:51:32.720 Uttam Kumaran: So
581 00:51:33.070 ⇒ 00:51:38.619 Uttam Kumaran: so this is where also it’s like one of the things we’re working on is what are what is good requirements.
582 00:51:38.910 ⇒ 00:51:46.190 Uttam Kumaran: but basically. And maybe this is something I I’ll I’ll add to the I’ll add here, which is
583 00:51:47.242 ⇒ 00:51:50.680 Uttam Kumaran: add, what is a good ticket?
584 00:51:50.880 ⇒ 00:51:53.980 Uttam Kumaran: But basically what I’m looking for is, I need a goal.
585 00:51:54.190 ⇒ 00:51:57.220 Uttam Kumaran: And how do we? How do we all agree that this is done
586 00:51:57.810 ⇒ 00:52:00.120 Annie Yu: And so far in this ticket it looks like
587 00:52:00.380 ⇒ 00:52:05.420 Uttam Kumaran: Data extraction over it looks like they’re working on this. But it’s it’s they haven’t written it out yet.
588 00:52:05.640 ⇒ 00:52:19.180 Uttam Kumaran: Right? So the requirements to start it. This is an example of requirements and review. So this morning they said, Hey, this ticket is up for review. Can you go? Take a look at it. I then went through. I said, cool, if I was brand new. And I’m looking at this.
589 00:52:19.810 ⇒ 00:52:23.330 Uttam Kumaran: does it make sense? If you read this to know. Okay.
590 00:52:23.580 ⇒ 00:52:25.970 Uttam Kumaran: after we do these 10 steps, this is done.
591 00:52:26.460 ⇒ 00:52:32.080 Uttam Kumaran: So I read through that. I then was like, Okay, there are some questions I had. So I left some comments here
592 00:52:33.200 ⇒ 00:52:39.269 Uttam Kumaran: Wonder if we should ditch the client name wonder we should we should move this to a separate ticket. We should add this as a backlog ticket.
593 00:52:40.260 ⇒ 00:52:45.420 Uttam Kumaran: And so I so right now it’s still in review until I approve it. It doesn’t move
594 00:52:45.860 ⇒ 00:52:55.909 Uttam Kumaran: once this is approved right. Once Miguel answers these questions. It will then move to development. But this develop ready for development doesn’t mean it’s in the cycle.
595 00:52:56.170 ⇒ 00:53:00.470 Uttam Kumaran: right? Because the cycle is the work that we’ve agreed upon taking right now.
596 00:53:00.940 ⇒ 00:53:14.759 Uttam Kumaran: right? So ready for develop means. The requirements are created and they were approved. It’s ready to be taken on. Whoever has availability can take it next. But what you’ll see here is these are all things that are in flight right now.
597 00:53:14.940 ⇒ 00:53:19.060 Uttam Kumaran: So these we they’re already being worked on, we can’t take them back.
598 00:53:19.240 ⇒ 00:53:26.409 Uttam Kumaran: And so this is all this stuff right now, that’s being worked on. It’s in cycle. But all this stuff we’re just planning. It’s all planning
599 00:53:27.230 ⇒ 00:53:29.520 Uttam Kumaran: So one example is like
600 00:53:31.780 ⇒ 00:53:35.470 Uttam Kumaran: If if this, if if I was here and then this had like no requirements.
601 00:53:36.010 ⇒ 00:53:39.409 Uttam Kumaran: I would I would ping Miguel and I would say, How did this get into the cycle
602 00:53:39.910 ⇒ 00:53:45.330 Uttam Kumaran: right like? How do we take on this work? And nobody knows how to like what the outcome is.
603 00:53:45.490 ⇒ 00:53:53.090 Uttam Kumaran: because how can you? How can the goal is that anybody should be able to take a look at a ticket and know what it takes to get it done
604 00:53:54.380 ⇒ 00:54:01.909 Uttam Kumaran: right. And so for me. That’s my barometer is that when I look at tickets like, and and Javi and Eden probably the worst
605 00:54:02.070 ⇒ 00:54:08.900 Uttam Kumaran: out of everything where a lot of stuff doesn’t have requirements right? Like ncac marketing model change.
606 00:54:11.180 ⇒ 00:54:18.090 Uttam Kumaran: This doesn’t have anything right? So it’s like embarrassing. I’m like, how can anybody come in and take this
607 00:54:18.400 ⇒ 00:54:35.859 Uttam Kumaran: right? And this is where I’m gonna ask away slowly to say, Hey, anything you work on needs to have requirements, and I’m pushing Robert, and I’m pushing Robert and Akash to do the same thing which any tickets here need to have requirements. At least it needs to have a goal and the acceptance criteria.
608 00:54:36.020 ⇒ 00:54:40.289 Uttam Kumaran: Right? You can write way more if you want to. But this is not enough.
609 00:54:41.560 ⇒ 00:54:42.290 Annie Yu: Okay.
610 00:54:42.520 ⇒ 00:54:48.299 Uttam Kumaran: Because otherwise, if Oasia is gone and you need to work on it, how are you gonna know what to do?
611 00:54:49.110 ⇒ 00:54:51.349 Uttam Kumaran: That’s the thinking, you know.
612 00:54:52.320 ⇒ 00:54:54.220 Uttam Kumaran: So we’re still a long way to go.
613 00:54:54.520 ⇒ 00:55:03.959 Uttam Kumaran: But ideally, what I want to start to see from the team is that anything? Any ticket that’s live has requirements, a due date, an estimate, and a person assigned.
614 00:55:05.620 ⇒ 00:55:10.299 Uttam Kumaran: So you can see for a lot of these. There’s looks like there’s due dates on most of them.
615 00:55:11.350 ⇒ 00:55:15.639 Uttam Kumaran: It looks like there’s not estimates, though, on any of them.
616 00:55:16.890 ⇒ 00:55:20.780 Uttam Kumaran: But there’s this everyone’s assigned. So we’re getting better slowly
617 00:55:23.610 ⇒ 00:55:25.190 Annie Yu: Oh, interesting.
618 00:55:25.470 ⇒ 00:55:28.780 Annie Yu: Yeah, I think it’s also good for for me. And
619 00:55:29.330 ⇒ 00:55:33.656 Uttam Kumaran: Yeah, cause I I had no idea what what those mean.
620 00:55:34.050 ⇒ 00:55:39.590 Uttam Kumaran: So that’s where I think. Taking this, taking a second looking at the the notion will help
621 00:55:39.860 ⇒ 00:55:40.320 Annie Yu: Yeah.
622 00:55:40.320 ⇒ 00:55:44.590 Uttam Kumaran: But ask, feel free to ask these questions out loud, because I’ll answer because we’ll answer them.
623 00:55:45.226 ⇒ 00:55:51.150 Uttam Kumaran: But this is something that should help us make sure that everything is settled before we start working on stuff
624 00:55:53.200 ⇒ 00:55:57.530 Annie Yu: And that means everything before ready for development.
625 00:55:58.010 ⇒ 00:55:58.790 Annie Yu: Right?
626 00:55:59.710 ⇒ 00:56:00.420 Uttam Kumaran: Yes.
627 00:56:01.760 ⇒ 00:56:04.760 Uttam Kumaran: So when something’s in ready for development, it means
628 00:56:05.600 ⇒ 00:56:12.230 Uttam Kumaran: basically the goal should be, it can go from ready, from development to done without any changes to it.
629 00:56:14.270 ⇒ 00:56:14.990 Annie Yu: Huh!
630 00:56:15.150 ⇒ 00:56:16.330 Uttam Kumaran: Does that make sense
631 00:56:17.194 ⇒ 00:56:18.909 Annie Yu: What would? What would be the case?
632 00:56:19.990 ⇒ 00:56:27.540 Uttam Kumaran: Like meaning that it should have the goal, the acceptance criteria, all the necessary information for it to get executed on
633 00:56:28.170 ⇒ 00:56:38.590 Uttam Kumaran: meaning halfway halfway into the sprint. You’re not like adding more stuff in, because that’s what happens. If it’s not an agreed upon amount of work, then what happens halfway in the sprint.
634 00:56:38.700 ⇒ 00:56:42.579 Uttam Kumaran: Then 5 more things get added, and then the ticket stays alive and it never gets done.
635 00:56:42.770 ⇒ 00:56:45.749 Uttam Kumaran: So this is a this is a process of agreeing
636 00:56:46.070 ⇒ 00:56:51.110 Uttam Kumaran: before work gets put on, what the work, what the work includes
637 00:56:51.690 ⇒ 00:56:54.210 Annie Yu: Okay, then.
638 00:56:54.380 ⇒ 00:56:55.380 Annie Yu: Okay.
639 00:56:56.550 ⇒ 00:57:06.115 Annie Yu: then one question will be, let’s say I need to build a dashboard. But before I can do that there will be
640 00:57:07.740 ⇒ 00:57:19.090 Annie Yu: we would require a new model to be built so for me, that would be in requirement, in review, or but then for for the de that would be something
641 00:57:20.310 ⇒ 00:57:22.900 Annie Yu: that that could be another ticket that’s open
642 00:57:23.650 ⇒ 00:57:35.560 Uttam Kumaran: Yeah. So this is a good example is that let’s say you’re trying to create a new dashboard before taking on that work. You should know that, hey? We actually can’t build this without the model
643 00:57:36.380 ⇒ 00:57:39.960 Uttam Kumaran: So it never. So then, so yeah, another ticket has to get created
644 00:57:40.150 ⇒ 00:57:41.800 Uttam Kumaran: for the model to get created
645 00:57:41.960 ⇒ 00:57:42.580 Annie Yu: Yeah.
646 00:57:43.210 ⇒ 00:57:47.240 Uttam Kumaran: So then that way you can say it’s not. If this is delayed, it’s not on me. It’s on the model
647 00:57:47.840 ⇒ 00:57:57.240 Uttam Kumaran: So there’s another ticket to track that, and then that team will take that on and assign it. And then you’ll start to see that. Okay, model has to happen 1st and then the dashboard. And then it sort of happens in order
648 00:57:58.490 ⇒ 00:58:02.869 Annie Yu: Yes, okay, that that makes sense.
649 00:58:03.340 ⇒ 00:58:08.059 Annie Yu: Okay, I I will have to read through the documents
650 00:58:09.370 ⇒ 00:58:14.190 Uttam Kumaran: Yeah, take a look. And then also just ask chat. Gpt, we wrote this with chat gpt.
651 00:58:14.650 ⇒ 00:58:15.250 Annie Yu: Hmm.
652 00:58:16.650 ⇒ 00:58:27.180 Uttam Kumaran: So ask. Ask chat, Gpt, and it’ll explain to you like sort of the history of this, and and any questions you have on there. Did you want to spend any time on real stuff
653 00:58:27.410 ⇒ 00:58:28.519 Annie Yu: Oh, yes, please.
654 00:58:28.780 ⇒ 00:58:29.300 Uttam Kumaran: Okay.
655 00:58:29.686 ⇒ 00:58:30.460 Annie Yu: I guess
656 00:58:30.460 ⇒ 00:58:37.900 Annie Yu: I’m meeting Casey tomorrow. But I don’t wanna use that time for this. So if we can.
657 00:58:42.850 ⇒ 00:58:51.450 Annie Yu: Yeah, yeah, yeah, I I don’t really know the right process. I kind of
658 00:58:54.070 ⇒ 00:58:58.320 Annie Yu: I’m gonna close summer, please.
659 00:59:00.780 ⇒ 00:59:01.680 Annie Yu: Okay.
660 00:59:05.830 ⇒ 00:59:10.770 Annie Yu: yeah. Let me see, I think the last time
661 00:59:11.520 ⇒ 00:59:16.859 Annie Yu: I don’t even understand what’s in it, but I remember.
662 00:59:18.940 ⇒ 00:59:20.339 Annie Yu: Let me find it
663 00:59:36.670 ⇒ 00:59:39.499 Uttam Kumaran: I guess. Tell me, like what the what, the, what the issue is right now
664 00:59:40.350 ⇒ 00:59:43.730 Uttam Kumaran: like. If you go back to the local host, what is the problem
665 00:59:44.800 ⇒ 00:59:48.240 Annie Yu: Yeah, I I mean, I don’t even know what’s the right
666 00:59:48.930 ⇒ 00:59:55.009 Annie Yu: like writing to type in here. So here’s what I did last time
667 00:59:55.690 ⇒ 01:00:00.210 Uttam Kumaran: Okay, that’s fine.
668 01:00:01.620 ⇒ 01:00:07.120 Uttam Kumaran: But like, I guess, what is it? Can you go back to? To real, to local host like, what does it say.
669 01:00:09.710 ⇒ 01:00:14.059 Uttam Kumaran: like, if you click on if you click on like sources, for example, here.
670 01:00:17.130 ⇒ 01:00:19.520 Uttam Kumaran: like, let’s say you just click on one of the sources
671 01:00:22.300 ⇒ 01:00:23.340 Annie Yu: Yeah, I can see.
672 01:00:23.340 ⇒ 01:00:33.609 Uttam Kumaran: Property property. Dsn is required for snowflake. Okay, cool. So this is this, what this is showing is that this is a like you’re missing a
673 01:00:34.350 ⇒ 01:00:39.660 Uttam Kumaran: you’re missing credentials locally. So one thing I would do is if you go back to your terminal
674 01:00:40.160 ⇒ 01:00:40.750 Annie Yu: Okay.
675 01:00:44.820 ⇒ 01:00:46.839 Uttam Kumaran: And then you just do control C,
676 01:00:49.980 ⇒ 01:00:50.820 Annie Yu: Yeah.
677 01:00:52.680 ⇒ 01:00:54.419 Uttam Kumaran: So control C was as exit
678 01:00:56.490 ⇒ 01:00:57.900 Annie Yu: Doesn’t let me
679 01:00:58.350 ⇒ 01:01:02.130 Uttam Kumaran: It’s not working. Yeah, yeah. Just keep doing it great. Just
680 01:01:02.130 ⇒ 01:01:04.140 Uttam Kumaran: do like a bunch of times. It should do it
681 01:01:05.140 ⇒ 01:01:05.620 Annie Yu: No!
682 01:01:07.510 ⇒ 01:01:09.790 Annie Yu: Oh, wait! Count my combo
683 01:01:10.300 ⇒ 01:01:12.869 Uttam Kumaran: You’re actually control, not command. Yeah, yeah.
684 01:01:13.560 ⇒ 01:01:14.220 Annie Yu: Okay.
685 01:01:14.220 ⇒ 01:01:20.119 Uttam Kumaran: Yeah. Yeah. Okay. So now run run real. Emv, pull.
686 01:01:20.310 ⇒ 01:01:23.790 Uttam Kumaran: No, no, no, just real envy. Envy. Poll. No run. Sorry
687 01:01:23.790 ⇒ 01:01:25.050 Annie Yu: Oh, okay.
688 01:01:25.970 ⇒ 01:01:28.170 Annie Yu: Real young people.
689 01:01:28.340 ⇒ 01:01:37.049 Uttam Kumaran: Yeah, unable to infer. Okay, type in Ls
690 01:01:40.010 ⇒ 01:01:40.689 Annie Yu: Just know what
691 01:01:40.690 ⇒ 01:01:47.010 Uttam Kumaran: Center. Yeah, okay? And so have you have, have you done much work with like terminal before
692 01:01:47.370 ⇒ 01:01:50.681 Annie Yu: No, I I do my best to avoid it.
693 01:01:51.050 ⇒ 01:01:51.900 Uttam Kumaran: Okay. Well.
694 01:01:51.900 ⇒ 01:01:53.547 Annie Yu: Escalation with it, but
695 01:01:53.960 ⇒ 01:02:08.550 Uttam Kumaran: You’ll be spending some time in it now from now on. But you should embrace it. It’s a it’s a it’s a good for it’s it’s good to know. So Ls is just says List. It’s basically like listing out. The these are the folders
696 01:02:09.220 ⇒ 01:02:10.000 Annie Yu: Okay, so.
697 01:02:10.000 ⇒ 01:02:17.659 Uttam Kumaran: Right. And so so what? So one of the things you want to do is you want to go? You next want to go to ABC. Home and commercial, so type in CD
698 01:02:19.030 ⇒ 01:02:26.980 Uttam Kumaran: space and then type in A and hit tab great. Now hit enter.
699 01:02:28.030 ⇒ 01:02:30.949 Uttam Kumaran: So now you’re in the ABC. Home and Commercial folder
700 01:02:32.620 ⇒ 01:02:35.809 Annie Yu: Okay, so what does the CD stand for
701 01:02:38.330 ⇒ 01:03:02.039 Uttam Kumaran: I think it’s just a command to like get to the next folder. I actually don’t know what it’s I don’t know what the what it stands for. I just know what it is. I learned it a lot. I learned this a long time ago so sorry I don’t remember exactly what it stands for. I just know that that’s how you navigate and then. Now, type in. Now go user, arrow, key, press up up the 3 times
702 01:03:04.640 ⇒ 01:03:08.640 Uttam Kumaran: great. Now, what what it’s this is just showing previous commands
703 01:03:08.640 ⇒ 01:03:09.473 Annie Yu: Oh, okay.
704 01:03:09.890 ⇒ 01:03:18.740 Uttam Kumaran: So this is like, in case your type of command, you mess up or you don’t want to type it again, and you’re lazy like me. You could just go back and then now hit, enter again
705 01:03:19.150 ⇒ 01:03:19.900 Annie Yu: Okay.
706 01:03:27.170 ⇒ 01:03:28.740 Annie Yu: That’s the same error.
707 01:03:30.590 ⇒ 01:03:37.560 Uttam Kumaran: Okay, can you do? Can you? Can you press up again
708 01:03:38.340 ⇒ 01:03:41.720 Uttam Kumaran: and then just just do space
709 01:03:41.990 ⇒ 01:03:46.800 Uttam Kumaran: and then do dash, dash, project, and then
710 01:03:49.250 ⇒ 01:03:52.459 Uttam Kumaran: type in ABC. Home and commercial like with underscores
711 01:03:53.170 ⇒ 01:03:55.280 Annie Yu: ABC. Home
712 01:03:55.710 ⇒ 01:03:57.829 Uttam Kumaran: And commercial dash, real
713 01:03:59.210 ⇒ 01:04:00.490 Annie Yu: Dash, we go!
714 01:04:04.442 ⇒ 01:04:07.609 Uttam Kumaran: Okay. Great. See? Better. Okay, hold on. One second
715 01:04:11.950 ⇒ 01:04:13.050 Annie Yu: To me
716 01:04:28.480 ⇒ 01:04:29.620 Uttam Kumaran: Okay. Try. Now.
717 01:04:32.020 ⇒ 01:04:32.800 Annie Yu: This one!
718 01:04:35.090 ⇒ 01:04:35.980 Annie Yu: Oh.
719 01:04:36.440 ⇒ 01:04:43.730 Uttam Kumaran: But so what this is doing is it’s pulling the credentials from the cloud to your machine.
720 01:04:44.690 ⇒ 01:04:45.550 Annie Yu: Okay.
721 01:04:45.900 ⇒ 01:04:49.499 Uttam Kumaran: So now you’re using the same credentials as the cloud. So now, type in real start
722 01:04:55.750 ⇒ 01:04:59.029 Annie Yu: Oh, oh, it’s not red anymore
723 01:04:59.240 ⇒ 01:05:00.150 Uttam Kumaran: Yes.
724 01:05:00.980 ⇒ 01:05:03.420 Uttam Kumaran: So now you have. These are the core metrics.
725 01:05:03.950 ⇒ 01:05:07.620 Uttam Kumaran: But to real, real ultimately you have to go.
726 01:05:07.790 ⇒ 01:05:09.639 Uttam Kumaran: You’ll have to go edit some code
727 01:05:10.290 ⇒ 01:05:21.499 Uttam Kumaran: if you need to right. So so part of this is like real is similar to Dbt, and that you have sources, models, and metrics, and then dashboard, sit on top of metrics. So if you click on the dashboard
728 01:05:22.220 ⇒ 01:05:24.139 Uttam Kumaran: and you just click on one of them.
729 01:05:25.170 ⇒ 01:05:29.670 Uttam Kumaran: Yep, and then here’s the dashboard. So this matches. What you’re gonna see in the cloud right now.
730 01:05:29.920 ⇒ 01:05:38.079 Uttam Kumaran: So all we did is you have your credentials. It then pulled, pulled all this onto your local machine. And so now you can test changes.
731 01:05:38.430 ⇒ 01:05:39.360 Uttam Kumaran: So
732 01:05:39.690 ⇒ 01:05:46.830 Uttam Kumaran: this is basically what the dashboard looks like right now, do you have your like local like Vs code, or whatever open
733 01:05:49.023 ⇒ 01:05:50.629 Annie Yu: I can
734 01:05:51.580 ⇒ 01:05:55.080 Uttam Kumaran: So if you go ahead and open this in Bs code.
735 01:05:56.310 ⇒ 01:05:59.370 Uttam Kumaran: this is where you’ll be. You’ll be basically making changes
736 01:06:00.860 ⇒ 01:06:02.779 Annie Yu: Wait. What do you mean? So in the
737 01:06:04.730 ⇒ 01:06:10.000 Uttam Kumaran: So all of this is written as code. There’s there is a Ui
738 01:06:10.480 ⇒ 01:06:14.640 Uttam Kumaran: like to build a canvas. But all of these core metrics
739 01:06:14.860 ⇒ 01:06:18.990 Uttam Kumaran: you’re you’re gonna be writing like code to do that?
740 01:06:20.870 ⇒ 01:06:22.200 Uttam Kumaran: Does that make sense
741 01:06:22.640 ⇒ 01:06:25.710 Annie Yu: I thought you can just do it here. No.
742 01:06:26.430 ⇒ 01:06:29.310 Uttam Kumaran: You, can’t you? You can’t edit any of them here.
743 01:06:30.460 ⇒ 01:06:35.089 Uttam Kumaran: So I think, wait. Maybe if you hit plus I don’t know. Can you see if you hit plus, maybe I don’t know.
744 01:06:36.430 ⇒ 01:06:41.750 Uttam Kumaran: Yeah. So you yeah, I guess you can add, you can create new expressions
745 01:06:42.410 ⇒ 01:06:48.670 Uttam Kumaran: here. But it’s it’ll be on top of your model. So click on model. Here, I think, let’s walk through an example. So click on model.
746 01:06:48.920 ⇒ 01:06:56.879 Uttam Kumaran: So if you see this is a select bunch of stuff from brain trust evals and then click on source and then click on brain trust evals.
747 01:06:57.400 ⇒ 01:06:59.900 Uttam Kumaran: And then this is where the this is where the data is coming from
748 01:07:00.340 ⇒ 01:07:01.000 Annie Yu: Yeah.
749 01:07:01.800 ⇒ 01:07:07.250 Uttam Kumaran: So what? What? What you’re seeing here is you’re saying cool. I want to 1st bring in.
750 01:07:07.730 ⇒ 01:07:16.880 Uttam Kumaran: These are all the brain trust logs that I’m bringing in to the source. We then have a model here that’s just like basically renaming some columns, I guess.
751 01:07:17.560 ⇒ 01:07:22.340 Uttam Kumaran: And then those those columns are then brought into metrics, here
752 01:07:23.720 ⇒ 01:07:24.440 Annie Yu: Yeah.
753 01:07:26.890 ⇒ 01:07:32.709 Uttam Kumaran: But so let’s walk through an example like, if you want to bring in a new table, you have to create a new source
754 01:07:33.000 ⇒ 01:07:33.890 Uttam Kumaran: first, st
755 01:07:34.430 ⇒ 01:07:35.070 Annie Yu: Yeah.
756 01:07:36.120 ⇒ 01:07:37.060 Annie Yu: Good.
757 01:07:37.570 ⇒ 01:07:42.050 Uttam Kumaran: But you can. You have to. You’ll, I think, maybe if you click on data here.
758 01:07:44.000 ⇒ 01:07:48.300 Uttam Kumaran: Yeah, then you can click on source Snowflake. And then you can basically write it here
759 01:07:49.770 ⇒ 01:07:50.450 Annie Yu: Bye.
760 01:07:50.650 ⇒ 01:07:59.719 Annie Yu: I’m gonna see, is there any one cause? We have this one already? And then we have this one. I think we have these 2
761 01:08:00.250 ⇒ 01:08:04.400 Uttam Kumaran: Yes. So this is where it’s like, what? For example, when they give you the phone data
762 01:08:04.530 ⇒ 01:08:06.730 Annie Yu: This is how you’ll have to bring it in.
763 01:08:06.870 ⇒ 01:08:08.870 Annie Yu: But then that’s Csv right
764 01:08:09.340 ⇒ 01:08:17.160 Uttam Kumaran: Yes. So this is where there’s 2 ways to bring in the Csv data. Probably the easiest way is if you go to add data here
765 01:08:17.899 ⇒ 01:08:19.309 Uttam Kumaran: on the left side
766 01:08:22.950 ⇒ 01:08:26.779 Uttam Kumaran: You can then basically just upload a Csv
767 01:08:26.970 ⇒ 01:08:27.670 Annie Yu: Okay.
768 01:08:28.640 ⇒ 01:08:31.090 Annie Yu: And then I can decide where that
769 01:08:31.560 ⇒ 01:08:32.310 Uttam Kumaran: Yes.
770 01:08:32.670 ⇒ 01:08:34.020 Annie Yu: Locate. Okay.
771 01:08:34.020 ⇒ 01:08:38.869 Uttam Kumaran: So they’ll give you that. Csv, so you’ll 1st upload it to Snowflake and then bring it into rail.
772 01:08:39.200 ⇒ 01:08:40.939 Annie Yu: Okay.
773 01:08:45.680 ⇒ 01:08:52.659 Annie Yu: okay, so, but then, is there any one we can try?
774 01:08:54.029 ⇒ 01:08:58.189 Uttam Kumaran: Yeah, why don’t you just try with like, ABC, bot feedback
775 01:09:03.159 ⇒ 01:09:04.799 Annie Yu: Wait. Where is it?
776 01:09:06.069 ⇒ 01:09:07.559 Uttam Kumaran: It’s this one, I think, yeah.
777 01:09:15.200 ⇒ 01:09:16.250 Annie Yu: Oh!
778 01:09:17.620 ⇒ 01:09:20.429 Uttam Kumaran: Yeah. So one thing you can do here is if you click on this table.
779 01:09:21.550 ⇒ 01:09:24.789 Uttam Kumaran: And then, yeah, you could just basically copy this here
780 01:09:26.580 ⇒ 01:09:30.260 Annie Yu: Okay, that’s smarter than what I’ve been doing.
781 01:09:33.380 ⇒ 01:09:34.040 Annie Yu: Yeah.
782 01:09:34.040 ⇒ 01:09:42.870 Uttam Kumaran: And then that’s fine. And then you just want to create a new source. So you can see here, you can just name it something we don’t have like naming conventions. You can just probably use the table name. Yeah.
783 01:09:43.700 ⇒ 01:09:44.460 Uttam Kumaran: yeah.
784 01:09:50.010 ⇒ 01:09:55.129 Uttam Kumaran: And then, now, you can actually hit, generate, dashboard, and it will go ahead and do that for you.
785 01:09:58.120 ⇒ 01:10:07.949 Annie Yu: Yeah, I remember seeing this in their demo. But one thing I probably missed. If someone asked was, if I do need to change something here.
786 01:10:08.640 ⇒ 01:10:09.679 Annie Yu: Do I just
787 01:10:11.240 ⇒ 01:10:18.179 Annie Yu: click on that like I don’t have to do it with I can do it without touching the yaml
788 01:10:18.530 ⇒ 01:10:19.969 Uttam Kumaran: You have to touch the animal
789 01:10:20.180 ⇒ 01:10:22.270 Annie Yu: Oh, for real!
790 01:10:24.430 ⇒ 01:10:39.649 Uttam Kumaran: Yeah. But see, while this is, this, dashboard view is A is A is a view on top of metrics and dimensions from ABC bot feedback metrics. So let’s let me show you an example. Can you put? Can you just put? Can you duplicate this tab and just put it side by side
791 01:10:40.240 ⇒ 01:10:41.580 Annie Yu: Can I? Oh.
792 01:10:42.150 ⇒ 01:10:46.039 Uttam Kumaran: Yeah. Just duplicate the tab, and then put put it side by side. I’ll show you
793 01:10:53.535 ⇒ 01:10:54.090 Annie Yu: Okay.
794 01:10:54.090 ⇒ 01:10:59.410 Uttam Kumaran: Okay? So then, on on this left side, go to go to ABC bot feedback metrics
795 01:11:00.160 ⇒ 01:11:03.190 Annie Yu: Okay. What do you mean? Go to? Oh, here.
796 01:11:03.450 ⇒ 01:11:05.140 Uttam Kumaran: No, no, no, no! Go back to real.
797 01:11:05.930 ⇒ 01:11:14.859 Uttam Kumaran: So you’re going here. Yep, ABC, bot feedback metrics. So this is this is just a these are all the metrics that are in the dashboard.
798 01:11:15.000 ⇒ 01:11:18.810 Uttam Kumaran: right? And so on this on this screen. Let’s open up the dashboard.
799 01:11:20.200 ⇒ 01:11:22.510 Uttam Kumaran: So if you just hit preview.
800 01:11:24.650 ⇒ 01:11:26.319 Uttam Kumaran: So let’s leave this open.
801 01:11:26.730 ⇒ 01:11:39.890 Uttam Kumaran: One thing you you want to do here is, let’s say, like what what an is an example of something you would want to do like. Maybe you want to look at some sort of right now. There’s no, it doesn’t look like there’s any measures like over time.
802 01:11:43.870 ⇒ 01:11:45.080 Annie Yu: Yeah.
803 01:11:45.960 ⇒ 01:11:49.050 Uttam Kumaran: So this is where, like, for example, I guess you you would have to have like a
804 01:11:49.300 ⇒ 01:11:52.749 Uttam Kumaran: example of like what you’re trying to do. But
805 01:11:54.890 ⇒ 01:11:59.599 Uttam Kumaran: This is where, like I don’t know. I spend a lot I just edit directly in Yaml. It’s pretty easy.
806 01:11:59.740 ⇒ 01:12:10.089 Uttam Kumaran: but you can also just edit things directly here, like, if you want to change what a metric is, or change the name of it, you can edit it all here it ends up back in Yaml, though.
807 01:12:17.300 ⇒ 01:12:23.870 Annie Yu: But then, if if we do edit something here, would that re be reflected here
808 01:12:23.870 ⇒ 01:12:24.710 Uttam Kumaran: Yes.
809 01:12:26.780 ⇒ 01:12:27.630 Uttam Kumaran: Correct.
810 01:12:30.310 ⇒ 01:12:32.879 Annie Yu: But what if I just want to change the place?
811 01:12:33.010 ⇒ 01:12:34.460 Annie Yu: Placement
812 01:12:35.630 ⇒ 01:12:37.349 Uttam Kumaran: You have to. You’ll have to do it here
813 01:12:38.340 ⇒ 01:12:41.629 Annie Yu: Oh, okay. Oh, so let’s say,
814 01:12:44.290 ⇒ 01:12:49.530 Annie Yu: Yes, there’s a timestamp
815 01:12:50.270 ⇒ 01:12:56.950 Uttam Kumaran: There is a timestamp, but I don’t know whether real is like recognizing it for some reason.
816 01:12:57.140 ⇒ 01:12:59.329 Uttam Kumaran: So one thing we can do is
817 01:12:59.430 ⇒ 01:13:03.299 Uttam Kumaran: if you go to the dashboard again
818 01:13:03.460 ⇒ 01:13:04.010 Annie Yu: Yep.
819 01:13:05.330 ⇒ 01:13:07.880 Uttam Kumaran: Okay, title metrics, dimensions, measures.
820 01:13:08.150 ⇒ 01:13:13.339 Uttam Kumaran: Okay? And then you click on here like ABC bot feedback and then type in edit in Yaml.
821 01:13:14.010 ⇒ 01:13:15.709 Uttam Kumaran: right here at the bottom, right
822 01:13:16.000 ⇒ 01:13:16.590 Annie Yu: Okay.
823 01:13:17.890 ⇒ 01:13:20.550 Uttam Kumaran: Yep. So then you can edit the yaml directly in here.
824 01:13:20.940 ⇒ 01:13:26.427 Uttam Kumaran: So one of the things is, and if you take a look at the at the real
825 01:13:27.610 ⇒ 01:13:29.319 Uttam Kumaran: at the rail docks.
826 01:13:29.550 ⇒ 01:13:31.930 Uttam Kumaran: You’re basically gonna be able to see
827 01:13:34.460 ⇒ 01:13:35.790 Annie Yu: Which one is, that
828 01:13:39.969 ⇒ 01:13:45.990 Uttam Kumaran: Here, let me just show you real docs time connection.
829 01:13:59.380 ⇒ 01:14:01.120 Uttam Kumaran: Hold on, let me grab it.
830 01:14:03.460 ⇒ 01:14:05.406 Uttam Kumaran: Okay. So
831 01:14:40.830 ⇒ 01:14:44.349 Uttam Kumaran: okay, I just sent you this documentation. So if you open that.
832 01:14:50.130 ⇒ 01:14:53.109 Uttam Kumaran: so you’ll see here that there’s this time series
833 01:14:53.580 ⇒ 01:14:54.170 Annie Yu: Hmm.
834 01:14:54.170 ⇒ 01:14:54.970 Uttam Kumaran: Property
835 01:14:55.090 ⇒ 01:15:02.409 Uttam Kumaran: so refers to the timestamp column that will underline the X axis in the line charts not specified line charts will not appear
836 01:15:02.540 ⇒ 01:15:09.560 Uttam Kumaran: so one thing we can do is if you go and we’re in the metrics view. So go ahead and see. Is there? Is there a time series property.
837 01:15:11.350 ⇒ 01:15:14.759 Uttam Kumaran: If you go back, if you go back to the to our yaml now.
838 01:15:16.560 ⇒ 01:15:24.759 Uttam Kumaran: So it looks like there’s no time series, right? It’s just dimensions and measures. So one thing you can do here is you just write in time series of line 10
839 01:15:25.100 ⇒ 01:15:32.409 Uttam Kumaran: or wherever and then you just literally type in what the column is right, so timestamp
840 01:15:32.940 ⇒ 01:15:33.550 Annie Yu: Oh
841 01:15:38.300 ⇒ 01:15:39.340 Annie Yu: timestamp!
842 01:15:41.024 ⇒ 01:15:44.930 Uttam Kumaran: And then. Now it’s so. So take a look at the error. What does it say?
843 01:15:46.030 ⇒ 01:15:47.480 Annie Yu: Oh, duplicate.
844 01:15:50.800 ⇒ 01:15:55.139 Annie Yu: bound, duplicate, dimension, or measure, name timestamp
845 01:15:55.780 ⇒ 01:15:58.629 Uttam Kumaran: So then take a look. Here. Is there a duplicate
846 01:16:01.770 ⇒ 01:16:05.930 Annie Yu: Would this one? Would that column timestamp, be the one
847 01:16:06.130 ⇒ 01:16:06.850 Uttam Kumaran: Yes.
848 01:16:10.240 ⇒ 01:16:13.410 Uttam Kumaran: so you can go ahead and just delete this dimension.
849 01:16:14.490 ⇒ 01:16:19.029 Uttam Kumaran: because the dimensions always going to be the same. And then you’re gonna want to delete this this one also
850 01:16:19.230 ⇒ 01:16:19.920 Annie Yu: Okay.
851 01:16:23.250 ⇒ 01:16:23.800 Uttam Kumaran: Yep.
852 01:16:25.910 ⇒ 01:16:27.510 Uttam Kumaran: And then if you go back here.
853 01:16:28.060 ⇒ 01:16:31.940 Uttam Kumaran: Oh, so time series. Timestamp is not a timestamp column.
854 01:16:32.300 ⇒ 01:16:38.930 Uttam Kumaran: So this is gonna be like, now we have to work on understanding like, what’s what is the actual column type
855 01:16:39.310 ⇒ 01:16:44.599 Uttam Kumaran: like? Is the timestamp column actually a timestamp? Or is it a string
856 01:16:45.970 ⇒ 01:16:49.770 Annie Yu: Okay, huh?
857 01:16:50.170 ⇒ 01:16:52.549 Annie Yu: Wait. Is that in here? No.
858 01:16:52.550 ⇒ 01:16:53.890 Uttam Kumaran: Just go to columns
859 01:16:55.820 ⇒ 01:16:56.730 Annie Yu: Timestamp.
860 01:16:57.500 ⇒ 01:16:58.400 Uttam Kumaran: Character.
861 01:16:59.180 ⇒ 01:17:00.200 Uttam Kumaran: Bummer
862 01:17:01.880 ⇒ 01:17:02.460 Annie Yu: Oh!
863 01:17:03.810 ⇒ 01:17:04.560 Uttam Kumaran: So.
864 01:17:05.340 ⇒ 01:17:11.939 Annie Yu: So we were not okay. I guess in that case we can build a model in in real
865 01:17:11.940 ⇒ 01:17:14.670 Uttam Kumaran: Yes, correct. You can build a separation
866 01:17:16.410 ⇒ 01:17:17.020 Annie Yu: Huh!
867 01:17:18.010 ⇒ 01:17:26.560 Uttam Kumaran: So go if you go back to the exactly. So we go back here. You can actually create a model. So right now, it’s pulling directly from the source
868 01:17:27.149 ⇒ 01:17:32.849 Annie Yu: Either you could do it either in the source or you can do it in a model. For example, if you go to brain, trust Evals.
869 01:17:33.260 ⇒ 01:17:35.549 Uttam Kumaran: Or if you go to Congress, yeah, you can see how
870 01:17:35.720 ⇒ 01:17:39.360 Uttam Kumaran: conversation logs you can see how it look at. We’re doing a bunch of logic here
871 01:17:40.490 ⇒ 01:17:41.340 Annie Yu: Yeah.
872 01:17:41.760 ⇒ 01:17:45.510 Uttam Kumaran: So that way every time I’m bringing in the data into here.
873 01:17:45.740 ⇒ 01:17:48.859 Uttam Kumaran: I do. I’m doing some. I’m I’m changing a few things.
874 01:17:49.090 ⇒ 01:17:59.230 Uttam Kumaran: so you can also do that. You can change the brain trust evals. You can select each of the different you can. You can do ABC bot feedback. You can select the columns and then just cast
875 01:17:59.720 ⇒ 01:18:02.879 Uttam Kumaran: the right. Cast the column. You want to time series
876 01:18:03.200 ⇒ 01:18:03.900 Annie Yu: No.
877 01:18:07.030 ⇒ 01:18:08.340 Annie Yu: So
878 01:18:13.390 ⇒ 01:18:15.250 Annie Yu: what’s the right way to do this?
879 01:18:17.473 ⇒ 01:18:20.900 Uttam Kumaran: I would just do select. And then you just have each column name
880 01:18:21.000 ⇒ 01:18:22.720 Uttam Kumaran: right? So all of these ones here
881 01:18:24.610 ⇒ 01:18:30.810 Annie Yu: Okay, is there any way? Okay, do I type them manually?
882 01:18:39.270 ⇒ 01:18:41.670 Uttam Kumaran: Yes, you’re just writing a normal select statement.
883 01:18:43.130 ⇒ 01:18:43.890 Annie Yu: Okay.
884 01:19:02.700 ⇒ 01:19:06.919 Annie Yu: no will want to.
885 01:19:08.380 ⇒ 01:19:10.240 Annie Yu: Was that everything, I think.
886 01:19:17.240 ⇒ 01:19:20.950 Annie Yu: how do you do it? With 2 column?
887 01:19:29.190 ⇒ 01:19:30.680 Annie Yu: Is this right?
888 01:19:31.080 ⇒ 01:19:31.700 Uttam Kumaran: Yeah.
889 01:19:32.184 ⇒ 01:19:39.300 Uttam Kumaran: yes, that’s it. And then you can just but again, this is something that you should probably just ask Chatgpt. That’s what I would suggest.
890 01:19:40.100 ⇒ 01:19:44.614 Annie Yu: Yeah, I wanna try it now, though.
891 01:19:44.930 ⇒ 01:19:46.644 Uttam Kumaran: No try it, try it. But
892 01:19:47.342 ⇒ 01:19:51.470 Annie Yu: Would you do? Spring or time? What’s that time?
893 01:19:51.470 ⇒ 01:19:54.041 Uttam Kumaran: You could just do timestamp timestamp
894 01:19:55.000 ⇒ 01:20:01.060 Uttam Kumaran: So so the one way, if you just Google, timestamp format Snowflake, it’ll give you what the versions are
895 01:20:08.510 ⇒ 01:20:11.660 Annie Yu: So it should be this one
896 01:20:16.290 ⇒ 01:20:19.890 Uttam Kumaran: Yeah, if you could just do times, basically, you could just do
897 01:20:21.330 ⇒ 01:20:28.930 Uttam Kumaran: timestamp tz, probably you’ll, you’ll have to look at if the timestamp has a has a
898 01:20:29.060 ⇒ 01:20:30.590 Uttam Kumaran: time zone in it or not.
899 01:20:31.940 ⇒ 01:20:32.970 Annie Yu: Oh.
900 01:20:48.160 ⇒ 01:20:52.029 Annie Yu: what does this mean is that that’s the time zone
901 01:20:54.700 ⇒ 01:20:58.030 Uttam Kumaran: Yes.
902 01:21:01.990 ⇒ 01:21:03.019 Annie Yu: Okay, I’m gonna try
903 01:21:03.020 ⇒ 01:21:06.370 Uttam Kumaran: Actually it. Look, it doesn’t look like there is a time zone on that one.
904 01:21:07.430 ⇒ 01:21:11.600 Uttam Kumaran: But you can just do. You can just do time. I think you can just do timestamp.
905 01:21:12.590 ⇒ 01:21:19.490 Uttam Kumaran: Ntz, yeah, or date or date time. It’s the same thing you can see here. It’s like alias
906 01:21:19.490 ⇒ 01:21:20.120 Annie Yu: Yeah.
907 01:21:20.970 ⇒ 01:21:23.000 Annie Yu: Okay, daytime.
908 01:21:26.420 ⇒ 01:21:29.940 Annie Yu: And do I hit? Enter, where is it? Safe?
909 01:21:32.150 ⇒ 01:21:33.070 Annie Yu: Cool.
910 01:21:34.950 ⇒ 01:21:37.770 Annie Yu: Detail, of course. Hey.
911 01:21:43.650 ⇒ 01:21:44.570 Annie Yu: nice.
912 01:21:59.300 ⇒ 01:22:01.670 Annie Yu: Is this a name? Okay?
913 01:22:07.260 ⇒ 01:22:08.310 Annie Yu: Daytime.
914 01:22:11.610 ⇒ 01:22:13.990 Annie Yu: Okay, think.
915 01:22:16.920 ⇒ 01:22:20.960 Annie Yu: And then do we in the world?
916 01:22:27.680 ⇒ 01:22:29.220 Annie Yu: Is it updating?
917 01:22:29.420 ⇒ 01:22:30.220 Annie Yu: Okay?
918 01:22:35.610 ⇒ 01:22:37.409 Annie Yu: So that’s the time series.
919 01:22:37.830 ⇒ 01:22:38.670 Annie Yu: Right?
920 01:22:41.670 ⇒ 01:22:42.410 Uttam Kumaran: Yes.
921 01:22:42.990 ⇒ 01:22:44.600 Uttam Kumaran: Yep. Okay. Cool. You got it.
922 01:22:45.440 ⇒ 01:22:51.660 Annie Yu: Oh, nice. But okay.
923 01:22:56.460 ⇒ 01:23:02.787 Annie Yu: okay, that’s gonna be. Now, I kinda like Meta base
924 01:23:04.330 ⇒ 01:23:10.979 Uttam Kumaran: The the I mean this is was just because analysts have a fear of code. Don’t worry. This is so easy.
925 01:23:12.480 ⇒ 01:23:15.333 Uttam Kumaran: What what you’ll find is that we can.
926 01:23:16.230 ⇒ 01:23:35.650 Uttam Kumaran: we’ll be able to actually build like what you’ll you’ll what you’ll enjoy about the dashboarding is that you can quickly. You can quickly like click on different things, and you’ll see parameterized filters so it’ll quickly filter. You can do comparisons. So have you had a chance to look at the other. ABC. Home Dashboard by chance.
927 01:23:35.961 ⇒ 01:23:39.389 Annie Yu: I look at it, but not not play around with it.
928 01:23:39.590 ⇒ 01:23:45.410 Uttam Kumaran: Okay, so this is where I think it’s really helpful to understand. Like, you kind of get what we’re doing with this pro whole project, right?
929 01:23:45.410 ⇒ 01:23:46.140 Annie Yu: Hmm.
930 01:23:47.611 ⇒ 01:24:00.049 Annie Yu: so I know the goal is, one is, I think we’re already doing it, tracking the AI product performance. And then we want to see how our AI product performance affect
931 01:24:00.370 ⇒ 01:24:09.960 Annie Yu: or could affect the whatever like metrics like time, resolution, time.
932 01:24:09.960 ⇒ 01:24:15.679 Uttam Kumaran: Yeah. But but even bait, do you understand? Like, like, very simply like what the product is
933 01:24:17.882 ⇒ 01:24:19.769 Annie Yu: I mean our product
934 01:24:19.770 ⇒ 01:24:20.450 Uttam Kumaran: Yeah.
935 01:24:21.170 ⇒ 01:24:24.210 Annie Yu: Is it kind of like a virtual assistant
936 01:24:24.730 ⇒ 01:24:31.669 Uttam Kumaran: It’s basically we built an AI agent so that the people so so do you, have you? You’ve have you Googled, ABC home
937 01:24:32.020 ⇒ 01:24:32.320 Annie Yu: Yeah.
938 01:24:32.940 ⇒ 01:24:35.239 Uttam Kumaran: Okay. So you know, it’s like a home service company
939 01:24:35.240 ⇒ 01:24:35.840 Annie Yu: Yeah.
940 01:24:35.840 ⇒ 01:24:42.209 Uttam Kumaran: They have about 25 people sitting here in Austin picking up phone calls from clients that are calling them
941 01:24:42.630 ⇒ 01:24:43.100 Annie Yu: Hmm.
942 01:24:43.100 ⇒ 01:24:47.090 Uttam Kumaran: And they’re calling them about all sorts of issues about their house and things like that.
943 01:24:47.470 ⇒ 01:24:51.339 Uttam Kumaran: And the the customer service reps have to then answer that question on the fly.
944 01:24:51.470 ⇒ 01:24:57.720 Uttam Kumaran: Commonly they can’t answer some of those questions, because there’s so much information they need to know. So they put them on hold
945 01:24:57.970 ⇒ 01:24:58.300 Annie Yu: Yeah.
946 01:24:58.300 ⇒ 01:25:02.019 Uttam Kumaran: What happens when you get put on hold, you hang up. What happens when you hang up?
947 01:25:02.150 ⇒ 01:25:03.929 Uttam Kumaran: Then you you could churn.
948 01:25:04.100 ⇒ 01:25:10.320 Uttam Kumaran: So that’s the problem we’re trying to help the customer service reps solve the problems on the 1st call
949 01:25:10.930 ⇒ 01:25:23.910 Uttam Kumaran: and what our our product that we built for them is a chat bot. So you can within Google Chat, you could just send a message saying, Hey, a customer just asked about this, what should I say? And it will respond to you with the answer.
950 01:25:25.250 ⇒ 01:25:31.410 Uttam Kumaran: What would have taken them? Probably 30 min to a few hours to go find we now made possible.
951 01:25:31.650 ⇒ 01:25:34.279 Uttam Kumaran: So what we want to do is show them that
952 01:25:34.990 ⇒ 01:25:40.519 Uttam Kumaran: the for the calls where the Csrs are using the agent. They’re solving them way faster.
953 01:25:41.010 ⇒ 01:25:43.279 Uttam Kumaran: That’s it. That’s all we’re trying to show
954 01:25:44.370 ⇒ 01:25:52.749 Annie Yu: So does that. Csv file from from past month will be the samples that are already using it.
955 01:25:53.390 ⇒ 01:26:06.159 Uttam Kumaran: No. So so people. So currently, I only think there’s a couple of one that where they they used it. But going forward after this week there will be more and more people using it. So Amber has this sort of the launch plan. Basically
956 01:26:07.248 ⇒ 01:26:11.469 Annie Yu: and one more question is, so this product is
957 01:26:11.970 ⇒ 01:26:17.599 Annie Yu: for the support agent to use not the customers
958 01:26:19.150 ⇒ 01:26:21.359 Uttam Kumaran: No, yeah. It’s for the support agent to use
959 01:26:21.360 ⇒ 01:26:22.630 Annie Yu: Okay. Okay?
960 01:26:22.750 ⇒ 01:26:27.720 Annie Yu: Oh, okay. Cause I. Okay. For some reason, I also thought, it’s it’s for customers.
961 01:26:27.850 ⇒ 01:26:33.070 Annie Yu: Okay, that that all makes sense.
962 01:26:33.714 ⇒ 01:26:40.760 Annie Yu: And then I know that while we’re on it I know that there’s Kpis we talk about.
963 01:26:41.260 ⇒ 01:26:52.499 Annie Yu: but I’m not sure where to find that I did read through the ABC documentation, and I think there are some kpis, but I think they were
964 01:26:52.920 ⇒ 01:26:57.020 Annie Yu: put there before it was
965 01:26:57.394 ⇒ 01:27:02.573 Uttam Kumaran: Did you check in? So there is a let’s see
966 01:27:12.060 ⇒ 01:27:14.010 Uttam Kumaran: that’s probably a good.
967 01:27:14.620 ⇒ 01:27:16.799 Uttam Kumaran: Okay. Yeah. Here, this is. Probably it.
968 01:27:33.270 ⇒ 01:27:34.779 Uttam Kumaran: Okay, I just sent it to you
969 01:27:35.690 ⇒ 01:27:37.780 Annie Yu: Okay, thank you.
970 01:27:43.840 ⇒ 01:27:45.030 Annie Yu: Wait. Did you?
971 01:27:45.580 ⇒ 01:27:46.480 Uttam Kumaran: And maybe
972 01:28:00.620 ⇒ 01:28:02.330 Annie Yu: April. 4, th
973 01:28:09.530 ⇒ 01:28:10.440 Annie Yu: okay.
974 01:28:25.210 ⇒ 01:28:31.270 Annie Yu: okay, for our save this
975 01:28:33.800 ⇒ 01:28:40.869 Uttam Kumaran: Yeah, I think fundamentally, it’s just trying to get a good understanding of like, actually, what? Even for all of your clients.
976 01:28:41.060 ⇒ 01:28:48.490 Uttam Kumaran: like the the real reliability for you on the analyst side is that you have a really good understanding of what each business does
977 01:28:48.600 ⇒ 01:28:51.790 Uttam Kumaran: right? So beyond the dashboards, beyond the metrics.
978 01:28:52.190 ⇒ 01:28:55.900 Uttam Kumaran: Fundamentally, I think it’s helpful just to know, like, what does Javi do?
979 01:28:56.000 ⇒ 01:29:13.779 Uttam Kumaran: They sell coffee? Right? So what are they interested in selling more coffee, selling new coffee right? So start with the business context always, because otherwise you’re gonna you’ll be on a call with the ABC. Folks and we’ll be talking the dashboard, the metrics. And they’re gonna ask simply.
980 01:29:13.900 ⇒ 01:29:17.180 Uttam Kumaran: did we solve more? Could we solve it? Customer problems?
981 01:29:17.460 ⇒ 01:29:19.899 Uttam Kumaran: And then we’re gonna say, well, we have 50 dashboards.
982 01:29:20.130 ⇒ 01:29:22.649 Uttam Kumaran: But then you’re gonna ask very simply.
983 01:29:22.900 ⇒ 01:29:30.869 Uttam Kumaran: did our bot help them solve more problems faster? That’s everything. This is why I’m trying to get the AI team to think about is like
984 01:29:31.330 ⇒ 01:29:35.769 Uttam Kumaran: we need to be able to answer very simple questions. Very precisely.
985 01:29:36.000 ⇒ 01:29:38.759 Uttam Kumaran: So that’s, I think, the biggest thing to sort of
986 01:29:38.970 ⇒ 01:29:50.589 Uttam Kumaran: continue to focus on is, how can how can one of their executives open the dashboard and see very clearly if that we’re making a effect? Right?
987 01:29:50.900 ⇒ 01:30:06.650 Uttam Kumaran: That’s that’s the goal. So I think part of it is understanding from amber what the product we’re building understanding from Casey how the product works. And then it for you. It’s gonna be okay. Now, I have data. How can I show through the dashboard that the the product is effective?
988 01:30:09.830 ⇒ 01:30:10.810 Annie Yu: Okay?
989 01:30:13.410 ⇒ 01:30:16.950 Annie Yu: And I think there’s at least for this project. There’s
990 01:30:18.130 ⇒ 01:30:22.360 Annie Yu: like enough space for us to to define things
991 01:30:23.080 ⇒ 01:30:23.690 Uttam Kumaran: Yeah.
992 01:30:23.690 ⇒ 01:30:29.660 Annie Yu: But okay. But then that’s a separate question. I feel like
993 01:30:30.600 ⇒ 01:30:33.650 Annie Yu: for Joby and Eden. It’s more like
994 01:30:34.480 ⇒ 01:30:40.179 Annie Yu: when we’re building dashboards. They know what they want, and they
995 01:30:40.480 ⇒ 01:30:49.680 Annie Yu: they know they don’t like line charts and all that like they. They will tell you like, I just want this. And in that case, we just do what they want right?
996 01:30:49.680 ⇒ 01:30:54.350 Uttam Kumaran: It depends. But this is where, like, you can push back. But then you have to have confidence.
997 01:30:55.930 ⇒ 01:31:01.810 Uttam Kumaran: So ultimately, like as data people, we can, we can say
998 01:31:02.250 ⇒ 01:31:05.519 Uttam Kumaran: we can push back and say, Hey.
999 01:31:05.910 ⇒ 01:31:10.309 Uttam Kumaran: I don’t think that’s what you need, but then they’re gonna ask us for what they need.
1000 01:31:10.670 ⇒ 01:31:20.000 Uttam Kumaran: So on those clients. We’re expecting you to sort of get ramped up and then start to be able to understand from their business, like what what they need to. You know what they need to succeed really.
1001 01:31:21.470 ⇒ 01:31:27.910 Annie Yu: and okay, and then I know that people have like different
1002 01:31:28.310 ⇒ 01:31:37.049 Annie Yu: sentiment toward different, clients, so what’s what’s the tea about eden, I I haven’t like really interacted with with them.
1003 01:31:37.220 ⇒ 01:31:40.330 Uttam Kumaran: But is there anything you should pay attention to?
1004 01:31:40.990 ⇒ 01:31:42.360 Uttam Kumaran: They’re just stressful.
1005 01:31:42.770 ⇒ 01:31:45.829 Uttam Kumaran: So everything for them is a stressful engagement.
1006 01:31:46.780 ⇒ 01:31:47.530 Annie Yu: Okay.
1007 01:31:47.890 ⇒ 01:31:52.570 Uttam Kumaran: That’s it. So I but I would push that on. Akash that should not come to the engineers.
1008 01:31:53.650 ⇒ 01:31:59.420 Uttam Kumaran: That’s their job. Their job is to manage the client expectations. It’s not for engineers.
1009 01:32:00.160 ⇒ 01:32:04.619 Uttam Kumaran: Our job is to execute the best data work possible
1010 01:32:06.310 ⇒ 01:32:12.359 Uttam Kumaran: Determining whether that’s the right work or whether we should have been working on something else. That’s on project management.
1011 01:32:13.020 ⇒ 01:32:16.749 Uttam Kumaran: right like. You can also take that burden on. It’ll it’ll be tough.
1012 01:32:18.840 ⇒ 01:32:20.519 Uttam Kumaran: right? Do you kind of see what I mean?
1013 01:32:20.770 ⇒ 01:32:21.350 Annie Yu: Yeah.
1014 01:32:21.520 ⇒ 01:32:26.159 Uttam Kumaran: But to give you to be really clear. One of the things that both of those clients really need
1015 01:32:26.310 ⇒ 01:32:30.469 Uttam Kumaran: is just a good understanding of the business, like from the data side.
1016 01:32:30.780 ⇒ 01:32:31.590 Uttam Kumaran: Right?
1017 01:32:31.940 ⇒ 01:32:38.039 Uttam Kumaran: So that’s what I think for you is to build up like, how do I wake up and become the best expert for Eden data.
1018 01:32:38.350 ⇒ 01:32:41.640 Uttam Kumaran: Right? So that way you’re you can help them set the roadmap.
1019 01:32:43.040 ⇒ 01:32:49.189 Uttam Kumaran: So it’s not just, hey, go do this dashboard. It’s like. Okay, I understand that this is an e-commerce
1020 01:32:49.850 ⇒ 01:32:53.370 Uttam Kumaran: glp, one subscription company.
1021 01:32:53.560 ⇒ 01:33:11.120 Uttam Kumaran: What do they want to look at? Of course they want to look at how many people are ordering multiple times. They want to look at people who are ordering multiple products. They want to look at. The product is effective at doing the weight loss. They want to know where their customers right? So that’s where you can go. Suggest, hey? There’s no view right now. Looking at this, we should go do that
1022 01:33:13.020 ⇒ 01:33:13.530 Annie Yu: Okay.
1023 01:33:16.030 ⇒ 01:33:17.720 Uttam Kumaran: So that’s what that would be.
1024 01:33:17.990 ⇒ 01:33:41.600 Uttam Kumaran: That’s the sort of I think the path that you should look to take is basically, how do I go from like sort of this reactive breaking, basically taking in and then be having a seat at the table, as like the analyst, you know, being able to run the meeting, being able to say, Okay, I I analyze the data. And this is what I saw right, and finding the insights that nobody else, because you’re the only person in the position to find those things.
1025 01:33:41.800 ⇒ 01:33:48.540 Uttam Kumaran: Nobody else has the da, the data modeled in a in a dashboard with all the context.
1026 01:33:48.780 ⇒ 01:33:58.110 Uttam Kumaran: So for us to move from, just here’s a dashboard to like, here’s the actual decision. Here’s the change. That’s where I really feel like you can be really powerful like in this, in this spot
1027 01:33:59.120 ⇒ 01:34:01.290 Annie Yu: Okay, that’s
1028 01:34:01.910 ⇒ 01:34:10.370 Annie Yu: good to know. Yeah, I feel like, at least so far, there’s less work around like insights. Input
1029 01:34:11.710 ⇒ 01:34:18.940 Annie Yu: but I think it also makes sense, because we still have a lot like, yeah, data
1030 01:34:18.940 ⇒ 01:34:19.610 Uttam Kumaran: Yeah.
1031 01:34:20.040 ⇒ 01:34:22.020 Annie Yu: Reports. Okay?
1032 01:34:28.889 ⇒ 01:34:29.959 Annie Yu: Alright.
1033 01:34:31.540 ⇒ 01:34:34.400 Annie Yu: I learned a lot today. So thank you.
1034 01:34:34.400 ⇒ 01:34:34.930 Uttam Kumaran: Okay.
1035 01:34:34.930 ⇒ 01:34:36.799 Annie Yu: Thanks for your time.
1036 01:34:36.800 ⇒ 01:34:43.319 Uttam Kumaran: Of course. So what? So what I’m gonna do is I actually, I I like this, I’m happy to go through this. So I’m gonna try to at least have an hour
1037 01:34:44.150 ⇒ 01:34:49.489 Uttam Kumaran: hour or 30, 45 min every day where I’m just gonna keep his office hours.
1038 01:34:49.700 ⇒ 01:34:54.589 Uttam Kumaran: I’m just it’s my schedule is all over. So I’m gonna find hours where I can do that.
1039 01:34:54.720 ⇒ 01:34:56.559 Uttam Kumaran: where I can just come on and help.
1040 01:34:56.760 ⇒ 01:35:07.790 Uttam Kumaran: But I also I also think that a lot of these questions definitely spend some time with a wish with them, a lot with Casey like, call them and and ask them for help. They’ll be very, very helpful.
1041 01:35:08.470 ⇒ 01:35:13.150 Uttam Kumaran: But I’ll do my best to to still sort of be around
1042 01:35:13.690 ⇒ 01:35:15.430 Annie Yu: Okay, okay? And then
1043 01:35:15.430 ⇒ 01:35:17.240 Uttam Kumaran: Yeah, go ahead. Go ahead.
1044 01:35:17.240 ⇒ 01:35:21.280 Annie Yu: No, no? Well, you mentioned a wish. Is he?
1045 01:35:21.500 ⇒ 01:35:23.629 Annie Yu: Gonna stay in the same role or not?
1046 01:35:25.890 ⇒ 01:35:27.060 Annie Yu: Okay. Okay.
1047 01:35:27.360 ⇒ 01:35:31.429 Uttam Kumaran: Yeah on Eden. He’s on Javi, and then he’s gonna start working on more stuff
1048 01:35:31.610 ⇒ 01:35:35.331 Annie Yu: Yeah, I think he’s magical. Honestly, I think he’s really good
1049 01:35:35.670 ⇒ 01:35:37.670 Uttam Kumaran: Good. Yeah, I know he’s great. He’s great.
1050 01:35:38.640 ⇒ 01:35:44.080 Uttam Kumaran: I’m like so happy. That’s why. But I also. This is why I want. I want everybody to lean on each other like
1051 01:35:44.290 ⇒ 01:35:47.110 Uttam Kumaran: no question is dumb. Just ask for help.
1052 01:35:47.240 ⇒ 01:35:47.630 Annie Yu: Yes.
1053 01:35:47.974 ⇒ 01:35:51.760 Uttam Kumaran: And also push back on requirements if there’s not enough requirements.
1054 01:35:52.020 ⇒ 01:35:53.799 Uttam Kumaran: Otherwise you’re gonna get jammed.
1055 01:35:54.199 ⇒ 01:36:02.319 Uttam Kumaran: If you take a ticket, and there’s like 100 things, and you say, cool, I can do it. And you didn’t look at it. You’re gonna get jammed, and then I can’t do anything about that.
1056 01:36:02.550 ⇒ 01:36:06.269 Uttam Kumaran: It’s gonna come back. It’s gonna come back to me. And then I’m gonna I’m gonna ask.
1057 01:36:07.000 ⇒ 01:36:11.660 Uttam Kumaran: I’m gonna ask, like, Who’s who’s who? Whose expectation was not right
1058 01:36:11.826 ⇒ 01:36:12.160 Annie Yu: I see.
1059 01:36:12.160 ⇒ 01:36:17.809 Uttam Kumaran: Similar thing happened today where someone was like, Hey, this thing is taking too long. And then I said, Well, the due date is Friday.
1060 01:36:18.040 ⇒ 01:36:18.960 Uttam Kumaran: So
1061 01:36:19.130 ⇒ 01:36:24.919 Uttam Kumaran: why are we bring? Why are you telling me that this is taking too long? There’s 3 more days. And they’re like, Oh, yeah, you’re right. I’m like.
1062 01:36:25.430 ⇒ 01:36:34.480 Uttam Kumaran: okay, so was the due date wrong? Then whose fault is that like, Go, that’s so. The tickets really, really are important.
1063 01:36:35.750 ⇒ 01:36:43.139 Annie Yu: Yeah, yeah. Yeah, that’s something I I need to learn about, too. Cause remember that ticket
1064 01:36:43.140 ⇒ 01:36:46.309 Uttam Kumaran: How was it like at? How was it like at Microsoft like? What do they do?
1065 01:36:47.359 ⇒ 01:36:56.639 Annie Yu: I feel like my role in in Microsoft was very different than this one, but I would say, like Roll back at Nike would be more similar to this. So back
1066 01:36:56.700 ⇒ 01:37:23.817 Annie Yu: back in Nike, we did have to kind of go through Snowflake and build our own model for each project and then build the visualization and then to the final presentation. But then things move definitely, move quicker here, back in it, things were so slowly and data was such a mess, so I think most projects were like quarterly long, or unless it’s like ad hoc report
1067 01:37:24.610 ⇒ 01:37:38.539 Annie Yu: But at Microsoft I I was in a very different team. We really just utilize like quantitative side. We only look at like Csat Mps, as well as some of like
1068 01:37:38.920 ⇒ 01:37:46.229 Annie Yu: ratings, reviews, data but then we also look at a lot of quantitative data. There
1069 01:37:48.430 ⇒ 01:37:53.130 Uttam Kumaran: Yeah, I I mean, I but I think this is, we’re in a spot where we’re gonna get to that stuff later.
1070 01:37:53.250 ⇒ 01:37:56.169 Uttam Kumaran: like, it’ll take some time like this is very, very basic.
1071 01:37:56.690 ⇒ 01:38:01.840 Uttam Kumaran: And also it’s like again, the the better we can execute the more space we have like.
1072 01:38:02.000 ⇒ 01:38:06.950 Uttam Kumaran: If we can start to get some of the basics done. Then we can start taking a week to do things longer.
1073 01:38:07.210 ⇒ 01:38:15.950 Uttam Kumaran: So this is like what it’s like engineering. And Pm, both have to work together to say like, does this really need to come out tomorrow like, can this come out
1074 01:38:16.140 ⇒ 01:38:31.350 Uttam Kumaran: Wednesday? Can this come out on Friday, right like pushing back a little bit. There things will slow down like, but also things will slow down. But you’ll be will be working on cooler stuff. So you know, that’s sort of what my thinking is.
1075 01:38:31.980 ⇒ 01:38:39.715 Annie Yu: Sounds good. Yeah, that’s awesome. And also is my contract ready?
1076 01:38:40.360 ⇒ 01:38:41.320 Uttam Kumaran: Yes.
1077 01:38:41.630 ⇒ 01:38:46.490 Uttam Kumaran: yeah. So I have a version of it. I’m getting it approved by Robert, and then I’ll send it over
1078 01:38:46.490 ⇒ 01:38:47.280 Annie Yu: Okay.
1079 01:38:47.830 ⇒ 01:38:55.030 Uttam Kumaran: Yeah, so just, but just keep tracking your hours. But you can basically con, you could just we’ll backdate it. So you’ll be okay.
1080 01:38:55.620 ⇒ 01:38:56.690 Annie Yu: Yeah. And
1081 01:38:56.960 ⇒ 01:39:08.610 Annie Yu: for the hours I also reach out to Marion earlier today. But I think she was off already. I only see joby as my project option on
1082 01:39:08.610 ⇒ 01:39:12.150 Uttam Kumaran: Oh, okay, let me let me change that
1083 01:39:12.390 ⇒ 01:39:13.020 Annie Yu: Okay.
1084 01:39:31.940 ⇒ 01:39:34.250 Uttam Kumaran: Oh, all my lights turned off.
1085 01:39:34.860 ⇒ 01:39:36.279 Uttam Kumaran: It’s already 8 o’clock
1086 01:39:37.730 ⇒ 01:39:40.970 Annie Yu: Wait. What do you mean? So is that like a smart light
1087 01:39:41.200 ⇒ 01:39:46.660 Uttam Kumaran: Yeah, that’s what I know. It’s like, it’s getting too late.
1088 01:39:49.830 ⇒ 01:39:53.490 Uttam Kumaran: Okay, hold on. ABC,
1089 01:40:03.580 ⇒ 01:40:05.190 Uttam Kumaran: okay, can you check now?
1090 01:40:06.720 ⇒ 01:40:07.550 Annie Yu: Okay.
1091 01:40:08.080 ⇒ 01:40:08.950 Annie Yu: See?
1092 01:40:20.950 ⇒ 01:40:21.660 Annie Yu: Yep.
1093 01:40:22.220 ⇒ 01:40:23.620 Uttam Kumaran: Okay. Cool.
1094 01:40:24.030 ⇒ 01:40:25.090 Annie Yu: Everything.
1095 01:40:25.730 ⇒ 01:40:27.020 Annie Yu: Okay.
1096 01:40:33.670 ⇒ 01:40:34.390 Annie Yu: all right.
1097 01:40:34.390 ⇒ 01:40:34.920 Uttam Kumaran: Okay?
1098 01:40:35.590 ⇒ 01:40:37.819 Uttam Kumaran: Great. Yeah. Ping me, if you need anything else.
1099 01:40:38.740 ⇒ 01:40:39.330 Annie Yu: Yeah.
1100 01:40:39.590 ⇒ 01:40:41.630 Annie Yu: Okay. Thank you. Tom.
1101 01:40:42.140 ⇒ 01:40:42.810 Uttam Kumaran: Bye.
1102 01:40:42.970 ⇒ 01:40:43.520 Annie Yu: Bye.