Meeting Title: ABC Data Warehouse Evaluation Sync Date: 2025-12-15 Meeting participants: Uttam Kumaran, Awaish Kumar
WEBVTT
1 00:00:31.350 ⇒ 00:00:32.170 Awaish Kumar: Hello.
2 00:00:34.910 ⇒ 00:00:38.959 Uttam Kumaran: Sorry, dude, the… something broke on ABC, I had to go.
3 00:00:39.740 ⇒ 00:00:43.780 Uttam Kumaran: Trying to figure it out, so… Got delayed.
4 00:00:46.130 ⇒ 00:00:46.650 Awaish Kumar: Never.
5 00:00:46.650 ⇒ 00:00:48.859 Uttam Kumaran: Never a… never a dull moment.
6 00:00:51.200 ⇒ 00:00:52.440 Uttam Kumaran: You’re up late.
7 00:00:56.000 ⇒ 00:00:56.760 Awaish Kumar: Sorry.
8 00:00:56.760 ⇒ 00:00:57.859 Uttam Kumaran: Said you’re up late.
9 00:00:58.700 ⇒ 00:01:00.269 Awaish Kumar: Yes.
10 00:01:11.940 ⇒ 00:01:20.969 Uttam Kumaran: Okay, maybe I’ll… I’ll… I’ll review… let me… let me review this with you also, so you can kind of see, like, how I’m thinking about things. I’m sorry, I don’t have, like, an exact science…
11 00:01:21.580 ⇒ 00:01:24.439 Uttam Kumaran: But I’m kind of balancing…
12 00:01:24.960 ⇒ 00:01:28.759 Uttam Kumaran: Making sure that it’s super clear, but also removing fluff.
13 00:01:29.000 ⇒ 00:01:35.129 Uttam Kumaran: of, you know, so the nice thing about AI is at least it’ll make it everything there. The problem is, there will be a lot of fluff.
14 00:01:35.310 ⇒ 00:01:41.150 Uttam Kumaran: So, I do want to make sure that, like, as we put things out, it’s clear. And I know, sorry about, like.
15 00:01:41.350 ⇒ 00:01:45.579 Uttam Kumaran: They’re just so… she’s just so specific about, kind of, these small things, and…
16 00:01:46.000 ⇒ 00:01:49.069 Uttam Kumaran: It’s just the way it’s gonna be, dude, for a sec here, so…
17 00:01:49.480 ⇒ 00:01:53.590 Uttam Kumaran: It’s okay, we just continued to try, so… .
18 00:01:55.870 ⇒ 00:01:57.759 Awaish Kumar: Yeah, okay, no worries.
19 00:02:06.170 ⇒ 00:02:11.990 Uttam Kumaran: So one thing that I also thought we could, you know, begin to put here…
20 00:02:12.880 ⇒ 00:02:20.880 Uttam Kumaran: Is also information just about… learning about You know, data warehouse.
21 00:02:21.170 ⇒ 00:02:26.369 Uttam Kumaran: And so… I kind of think it is helpful to…
22 00:02:26.490 ⇒ 00:02:30.359 Uttam Kumaran: Maybe even link to, like, what a… what is a data warehouse.
23 00:02:30.660 ⇒ 00:02:32.170 Uttam Kumaran: Type documents.
24 00:02:32.810 ⇒ 00:02:39.590 Uttam Kumaran: Like, I was even thinking, I’m like, okay, if we were to explain, like, what a data warehouse is, like, what… how would me and you go learn?
25 00:02:40.170 ⇒ 00:02:43.280 Uttam Kumaran: You know, again, and so I think this is a good one, so…
26 00:02:55.680 ⇒ 00:03:00.979 Uttam Kumaran: So I’m just gonna start to put in, like, links like this. This is where ETL pipeline, land raw data.
27 00:03:01.430 ⇒ 00:03:04.190 Uttam Kumaran: dbt and SQL Transformation BI Tools.
28 00:03:04.960 ⇒ 00:03:09.480 Uttam Kumaran: So I’m gonna delete this one, because kind of… redundant.
29 00:03:09.790 ⇒ 00:03:14.670 Uttam Kumaran: why this matters out. As we lay down the evaluations, three important issues are right tools and systems.
30 00:03:15.980 ⇒ 00:03:22.040 Uttam Kumaran: The other thing I’m gonna do is remove the periods here, just something that AI does, and…
31 00:03:22.670 ⇒ 00:03:25.039 Uttam Kumaran: I don’t know, just feel like it’s kind of annoying.
32 00:03:25.220 ⇒ 00:03:30.789 Uttam Kumaran: Laying the data foundations, LMNT, Walmart today, more retailers spent tomorrow.
33 00:03:59.370 ⇒ 00:04:09.579 Uttam Kumaran: And then the other thing I’m gonna do here is… Ken… Leverage… Month? Two months?
34 00:04:09.760 ⇒ 00:04:12.930 Uttam Kumaran: Without long-term contract.
35 00:04:13.400 ⇒ 00:04:19.179 Uttam Kumaran: plus… Free trial with just, CC.
36 00:04:29.760 ⇒ 00:04:33.690 Uttam Kumaran: Yeah, so I’m interested in kind of, like, how you looked at the AI piece.
37 00:06:04.490 ⇒ 00:06:06.390 Uttam Kumaran: It seems like a repeat.
38 00:06:08.730 ⇒ 00:06:13.270 Uttam Kumaran: The other thing here is I want to talk about, BI tool.
39 00:06:14.210 ⇒ 00:06:15.719 Uttam Kumaran: Oh, you did? Okay.
40 00:06:16.100 ⇒ 00:06:16.930 Uttam Kumaran: Alright.
41 00:06:18.980 ⇒ 00:06:25.310 Uttam Kumaran: I think the other thing is, like, I want to just put in very clearly, like, supports…
42 00:06:25.620 ⇒ 00:06:42.240 Uttam Kumaran: Being the warehouse to power the most common BI tools… Sigma, Looker… Omni, Tableau… Thoughts for… etc.
43 00:06:53.830 ⇒ 00:07:03.429 Uttam Kumaran: The other thing I may ask Awayish here is to also indicate, like, number of years in business…
44 00:07:03.650 ⇒ 00:07:10.880 Uttam Kumaran: Total revenue… And then, like, basically category leader.
45 00:07:16.070 ⇒ 00:07:19.490 Uttam Kumaran: And then similar here… I think…
46 00:07:19.600 ⇒ 00:07:36.810 Uttam Kumaran: I want to put that, like, under Google Cloud… But… Pop of class, but not… category… Leader.
47 00:07:52.010 ⇒ 00:08:01.870 Uttam Kumaran: And the other thing I want us to put here in evaluation criteria is… Basically, like, the cloud… ownership.
48 00:08:02.460 ⇒ 00:08:07.240 Uttam Kumaran: So, I wanna put, like… AWS versus…
49 00:08:07.510 ⇒ 00:08:11.569 Uttam Kumaran: Google versus Snowflake, which is, like, hybrid.
50 00:08:12.970 ⇒ 00:08:14.640 Uttam Kumaran: And then, etc.
51 00:08:16.140 ⇒ 00:08:18.780 Uttam Kumaran: Because they don’t have, like, a…
52 00:08:19.100 ⇒ 00:08:21.460 Uttam Kumaran: They’re basically… they said they’re on DigitalOcean.
53 00:08:21.830 ⇒ 00:08:26.449 Uttam Kumaran: Which, I guess, doesn’t really matter, but I think it may just be important to put that down.
54 00:08:29.780 ⇒ 00:08:31.150 Awaish Kumar: Hmm, okay.
55 00:08:42.860 ⇒ 00:08:50.160 Uttam Kumaran: And then the other thing is, like, can we link to the… Home page… for each.
56 00:08:51.130 ⇒ 00:08:54.500 Uttam Kumaran: Fervous… Or a walkthrough.
57 00:08:55.490 ⇒ 00:08:56.580 Uttam Kumaran: Video.
58 00:08:57.500 ⇒ 00:09:00.710 Uttam Kumaran: Just small things that may help. Redshift is AWS.
59 00:09:01.990 ⇒ 00:09:09.379 Uttam Kumaran: Okay, if it’s naturally… so good performance. I would put cost, right? You think this is the cheapest option?
60 00:09:12.570 ⇒ 00:09:17.739 Awaish Kumar: It doesn’t… yeah, the cost is the… is there, like, at the bottom.
61 00:09:21.400 ⇒ 00:09:23.279 Awaish Kumar: Yeah, it’s a separate section.
62 00:09:23.410 ⇒ 00:09:25.029 Uttam Kumaran: Okay, oh, okay, okay, okay.
63 00:09:27.880 ⇒ 00:09:40.640 Uttam Kumaran: Even for this, I would put Redshift would require… Redshift… And its main tenant’s upkeep.
64 00:09:41.050 ⇒ 00:09:54.320 Uttam Kumaran: would require… 10-25%… I would probably say, like, 25 to 50% of… One engineer’s… time to manage.
65 00:10:04.950 ⇒ 00:10:09.279 Uttam Kumaran: The other thing I would put here is ClickHouse is typically used for power
66 00:10:09.570 ⇒ 00:10:14.959 Uttam Kumaran: Also used to power customer-facing analytics.
67 00:10:19.930 ⇒ 00:10:30.399 Uttam Kumaran: And then I would additionally… wait, let’s put in the total revenue… Date started… Clads, etc.
68 00:10:32.440 ⇒ 00:10:33.950 Uttam Kumaran: Postgres…
69 00:10:38.280 ⇒ 00:10:47.430 Uttam Kumaran: Yeah, if we can put… if we can also have something on, like, read… analytical… Phoebe versus…
70 00:10:47.880 ⇒ 00:10:51.399 Uttam Kumaran: what would you call it? Like, transactional DB?
71 00:10:52.490 ⇒ 00:10:53.070 Awaish Kumar: And maybe, huh.
72 00:10:53.070 ⇒ 00:10:54.669 Uttam Kumaran: Helpful for people to know.
73 00:10:58.170 ⇒ 00:11:01.299 Uttam Kumaran: Just so they understand, like, what the true difference is.
74 00:11:02.670 ⇒ 00:11:03.690 Awaish Kumar: Okay, yep.
75 00:11:10.300 ⇒ 00:11:14.239 Awaish Kumar: So, how do you want this data to be, like, data created, revenue.
76 00:11:14.240 ⇒ 00:11:15.830 Uttam Kumaran: No, I would just put in, like.
77 00:11:16.250 ⇒ 00:11:18.910 Uttam Kumaran: Oh, under overview, you can just put in, like.
78 00:11:19.560 ⇒ 00:11:22.280 Uttam Kumaran: When they started, how much revenue they have.
79 00:11:23.700 ⇒ 00:11:28.499 Uttam Kumaran: Just so people understand, because they may have the… they may have never heard of some of these companies.
80 00:11:30.600 ⇒ 00:11:31.290 Awaish Kumar: Bye.
81 00:11:41.660 ⇒ 00:11:44.169 Awaish Kumar: And then one thing I want to put in here is, like.
82 00:11:45.270 ⇒ 00:11:47.759 Uttam Kumaran: Let’s do another column that is…
83 00:11:48.530 ⇒ 00:11:51.329 Uttam Kumaran: Oh, this is storage cost, compute costs.
84 00:11:51.870 ⇒ 00:11:54.619 Uttam Kumaran: And then let’s do… well…
85 00:11:54.920 ⇒ 00:12:00.009 Uttam Kumaran: Let’s do storage costs, and then let’s also put in the… into the right, the compute costs.
86 00:12:01.650 ⇒ 00:12:03.040 Awaish Kumar: Hmm, okay.
87 00:12:04.010 ⇒ 00:12:05.080 Awaish Kumar: I just didn’t…
88 00:12:05.080 ⇒ 00:12:06.280 Uttam Kumaran: And total cost.
89 00:12:08.230 ⇒ 00:12:09.349 Uttam Kumaran: What do you think?
90 00:12:10.880 ⇒ 00:12:19.659 Awaish Kumar: Yeah, we can, but I just, like, because it is based on a lot of assumptions, so I prefer to write down, like.
91 00:12:21.470 ⇒ 00:12:22.010 Awaish Kumar: Cool.
92 00:12:22.010 ⇒ 00:12:28.210 Uttam Kumaran: You can also do, like, total cost, and then, like,
93 00:12:31.570 ⇒ 00:12:33.229 Uttam Kumaran: Like, high, low.
94 00:12:35.070 ⇒ 00:12:36.159 Uttam Kumaran: You know what I mean?
95 00:12:37.760 ⇒ 00:12:39.160 Awaish Kumar: If you want.
96 00:12:55.830 ⇒ 00:13:01.880 Uttam Kumaran: Well, honestly, I think this is kinda high. I feel like it’ll be less than this. I don’t know, actually. Depends.
97 00:13:02.610 ⇒ 00:13:03.699 Awaish Kumar: But, like, these are the…
98 00:13:03.700 ⇒ 00:13:07.049 Uttam Kumaran: Depends. Yeah, I think that this is actually probably right.
99 00:13:15.070 ⇒ 00:13:16.999 Uttam Kumaran: Really? This seems pretty high.
100 00:13:19.910 ⇒ 00:13:24.069 Awaish Kumar: Yeah, like, for urban steps, like, I saw that…
101 00:13:24.410 ⇒ 00:13:33.979 Uttam Kumaran: 1300, yeah. But they’re running… they’re running… like, a… XL3 or something like that.
102 00:13:35.900 ⇒ 00:13:36.880 Awaish Kumar: Open.
103 00:13:37.050 ⇒ 00:13:41.100 Awaish Kumar: But they had, like, smallest cluster, like, with only two machines.
104 00:13:41.370 ⇒ 00:13:43.080 Awaish Kumar: Still was, like…
105 00:13:44.740 ⇒ 00:13:45.590 Uttam Kumaran: Yeah…
106 00:13:48.930 ⇒ 00:13:51.719 Awaish Kumar: Brickcare is, like, cheaper.
107 00:13:51.720 ⇒ 00:13:52.500 Uttam Kumaran: Oh, really?
108 00:13:53.610 ⇒ 00:13:58.619 Awaish Kumar: But it is completely… it completely depends on the usage.
109 00:13:59.740 ⇒ 00:14:05.169 Awaish Kumar: And, and the, like, the proper architecture.
110 00:14:05.450 ⇒ 00:14:09.820 Awaish Kumar: But… If, ideally, if, like, the…
111 00:14:11.660 ⇒ 00:14:18.080 Awaish Kumar: It is configured properly and used properly, there is the… The cheapest option.
112 00:14:18.770 ⇒ 00:14:19.460 Uttam Kumaran: Okay.
113 00:14:21.260 ⇒ 00:14:31.540 Uttam Kumaran: The other thing I’m gonna put here for Snowflake is if we can also… highlight, highlight. Snowflake.
114 00:14:32.370 ⇒ 00:14:36.880 Uttam Kumaran: Marketplace…
115 00:14:41.910 ⇒ 00:14:47.549 Uttam Kumaran: Snowflake, direct share…
116 00:15:00.650 ⇒ 00:15:02.380 Awaish Kumar: Correct, like, the private share.
117 00:15:04.580 ⇒ 00:15:10.719 Uttam Kumaran: This is sort of, Snowflake Direct, like, Snowflake Direct ETL, basically, or Zero ETL.
118 00:15:12.010 ⇒ 00:15:17.040 Awaish Kumar: Yeah, but I added this, like, native support for Snowflake Private Share.
119 00:15:17.870 ⇒ 00:15:22.570 Uttam Kumaran: Yeah, I guess this is more of, like, show, like…
120 00:15:22.820 ⇒ 00:15:23.790 Awaish Kumar: Oh, okay.
121 00:15:23.790 ⇒ 00:15:25.160 Uttam Kumaran: You’re set, basically.
122 00:15:25.400 ⇒ 00:15:29.100 Uttam Kumaran: Basically, I want to show that they actually invested in these, like, larger features.
123 00:15:53.640 ⇒ 00:15:55.469 Uttam Kumaran: Okay, that’s pretty good.
124 00:15:55.760 ⇒ 00:16:00.360 Uttam Kumaran: I think they’re… I think they just want to see probably more about the AI piece.
125 00:16:01.700 ⇒ 00:16:06.519 Uttam Kumaran: So… Wondering if we could, like, link a demo or something.
126 00:16:08.860 ⇒ 00:16:09.570 Awaish Kumar: Okay.
127 00:16:22.700 ⇒ 00:16:29.999 Awaish Kumar: Yeah, on this tool itself, there’s nothing much, like, I can maybe, if I figure out… find out some videos, but…
128 00:16:32.900 ⇒ 00:16:35.559 Awaish Kumar: But I have tried, like, I’ve tried both.
129 00:16:35.720 ⇒ 00:16:40.320 Awaish Kumar: Tools… They are not, like,
130 00:16:40.980 ⇒ 00:16:49.920 Awaish Kumar: like, what we saw in bond.ai, like, a chat GPT, like… Private conversation.
131 00:16:50.110 ⇒ 00:16:55.649 Awaish Kumar: chat box where you are talking about data, getting concerned. It’s not personalized, like…
132 00:16:56.040 ⇒ 00:16:56.510 Uttam Kumaran: Yeah.
133 00:16:56.660 ⇒ 00:16:57.990 Awaish Kumar: theater.
134 00:16:58.340 ⇒ 00:17:02.229 Awaish Kumar: tab, write a prompt, get a SQL, that’s all.
135 00:17:02.470 ⇒ 00:17:03.180 Uttam Kumaran: Okay.
136 00:17:05.550 ⇒ 00:17:09.570 Uttam Kumaran: So you’re saying that, like, I mean, maybe one thing we can talk about
137 00:17:13.810 ⇒ 00:17:17.039 Uttam Kumaran: Maybe one thing we can talk about here is AI at the top.
138 00:17:19.030 ⇒ 00:17:19.940 Uttam Kumaran: like…
139 00:17:24.380 ⇒ 00:17:30.590 Uttam Kumaran: how AI… Changes the… Beta warehouse landscape.
140 00:17:31.470 ⇒ 00:17:40.579 Uttam Kumaran: And I think one thing that I want to basically write here is, how the heck?
141 00:17:46.210 ⇒ 00:17:59.570 Uttam Kumaran: I kind of want to write something about, how, like… Data… warehouses…
142 00:17:59.850 ⇒ 00:18:03.919 Uttam Kumaran: May end up owning the compute.
143 00:18:04.580 ⇒ 00:18:10.649 Uttam Kumaran: But… There is a huge missing context problem.
144 00:18:11.370 ⇒ 00:18:18.640 Uttam Kumaran: we see both the warehouses and BI tools go after this.
145 00:18:19.000 ⇒ 00:18:25.250 Uttam Kumaran: But so far, the BI tools… Seem to be winning.
146 00:18:25.400 ⇒ 00:18:31.169 Uttam Kumaran: RE, Omni… Text, QL, or just, like, Omni.
147 00:18:31.930 ⇒ 00:18:40.120 Uttam Kumaran: It’s… Hard to say… What?
148 00:18:40.400 ⇒ 00:18:42.090 Uttam Kumaran: What will happen?
149 00:18:42.440 ⇒ 00:18:50.669 Uttam Kumaran: In the future… As Snowflake, to get that business.
150 00:18:51.020 ⇒ 00:19:05.459 Uttam Kumaran: But… All in all… Customers are getting more AI opportunities across different… Parts of the stack.
151 00:19:07.680 ⇒ 00:19:09.780 Uttam Kumaran: Okay, maybe you can,
152 00:19:12.290 ⇒ 00:19:17.839 Uttam Kumaran: let me know what you think, and add some of your commentary as well, but that’s roughly how I feel.
153 00:19:18.200 ⇒ 00:19:19.580 Uttam Kumaran: What do you feel like?
154 00:19:20.250 ⇒ 00:19:26.570 Awaish Kumar: Yeah, I… I also feel the same. That’s why I… at this…
155 00:19:27.260 ⇒ 00:19:34.440 Awaish Kumar: At this stage, like, when deciding the data warehouse, we want to more focus on If the native
156 00:19:35.440 ⇒ 00:19:41.980 Awaish Kumar: AI… tools they have, like, AI capabilities they have, are they…
157 00:19:42.570 ⇒ 00:19:46.409 Awaish Kumar: Are they strong enough or mature enough? So, they are not.
158 00:19:46.740 ⇒ 00:19:52.030 Awaish Kumar: But, like, a more detailed discussion, maybe we want to do it later, like, in a…
159 00:19:52.500 ⇒ 00:19:54.879 Awaish Kumar: When we are talking about BI tool.
160 00:20:06.000 ⇒ 00:20:08.089 Uttam Kumaran: Yeah, I do feel like…
161 00:20:09.880 ⇒ 00:20:17.219 Uttam Kumaran: I kind of just want to share that it’s not clear whether it’s gonna be a warehouse or not, so I don’t want them to focus too much on that right now.
162 00:20:18.310 ⇒ 00:20:19.000 Awaish Kumar: Yeah.
163 00:20:21.180 ⇒ 00:20:28.060 Awaish Kumar: But for now, like, the… PI tools and other… the external wrappers are… are the ones.
164 00:20:28.060 ⇒ 00:20:28.400 Uttam Kumaran: Better.
165 00:20:28.400 ⇒ 00:20:29.010 Awaish Kumar: turn.
166 00:20:29.150 ⇒ 00:20:30.339 Awaish Kumar: Right over there.
167 00:20:30.770 ⇒ 00:20:33.270 Uttam Kumaran: Well, because they’re just putting way more context, you know?
168 00:20:41.120 ⇒ 00:20:48.620 Awaish Kumar: Yeah, even the Snowflake, like, they have… they try to create a chat box, like… What?
169 00:20:49.520 ⇒ 00:20:52.990 Awaish Kumar: That’s, like, really naive.
170 00:20:58.320 ⇒ 00:21:03.349 Awaish Kumar: Even though it has all the… Schemas that…
171 00:21:04.510 ⇒ 00:21:07.490 Awaish Kumar: Like, like, all the databases came up.
172 00:21:09.180 ⇒ 00:21:14.029 Awaish Kumar: table fields, and that, like, forever, like, it had everything.
173 00:21:14.670 ⇒ 00:21:18.920 Awaish Kumar: And it is, like, very rich, standard…
174 00:21:18.920 ⇒ 00:21:20.069 Uttam Kumaran: Comments and stuff?
175 00:21:23.020 ⇒ 00:21:32.949 Awaish Kumar: No, I mean, like, the… like, the source, like, immersion, already had very, like, the defined documentation around it.
176 00:21:33.480 ⇒ 00:21:40.910 Awaish Kumar: like, if they explode LLMs, right, obviously, it can read from Webb. And then,
177 00:21:41.650 ⇒ 00:21:55.300 Awaish Kumar: it has all the information with tables and schemas and the data, and it should figure out, like, the complex queries, but so naive. I tried it, and it can’t create, like, a…
178 00:21:55.890 ⇒ 00:21:59.210 Uttam Kumaran: Yeah, I think that would be helpful, dude, if you can show that in here.
179 00:22:02.160 ⇒ 00:22:04.660 Uttam Kumaran: Can we show, like, a Loom demo?
180 00:22:05.910 ⇒ 00:22:13.000 Uttam Kumaran: Of the… of the, like… Snowflake, AI on Emerson data.
181 00:22:16.170 ⇒ 00:22:21.069 Uttam Kumaran: I just want people to… I want the team to be able to just see that, you know?
182 00:22:30.070 ⇒ 00:22:30.980 Awaish Kumar: Okay.
183 00:22:32.600 ⇒ 00:22:33.200 Uttam Kumaran: Okay.
184 00:22:34.940 ⇒ 00:22:40.510 Uttam Kumaran: Cool. And then maybe, like, maybe we can briefly, while I have you, can just… we can talk about the,
185 00:22:42.160 ⇒ 00:22:48.859 Uttam Kumaran: This one? You can walk me through what you thought for the metrics.
186 00:22:55.940 ⇒ 00:22:59.140 Uttam Kumaran: Or… Where is that one?
187 00:23:01.170 ⇒ 00:23:02.239 Uttam Kumaran: This one, right?
188 00:23:02.970 ⇒ 00:23:03.690 Awaish Kumar: Yes.
189 00:23:04.300 ⇒ 00:23:04.960 Uttam Kumaran: Cool.
190 00:23:05.940 ⇒ 00:23:11.150 Awaish Kumar: So… What I’m, like, I’m getting these metrics from Carlo’s sheet.
191 00:23:11.150 ⇒ 00:23:12.219 Uttam Kumaran: Yeah. Pretty comfortable.
192 00:23:14.030 ⇒ 00:23:16.610 Awaish Kumar: And, like… The…
193 00:23:17.460 ⇒ 00:23:25.670 Awaish Kumar: things, like, number one is metric name and the domain, and, like, these are the fields which I…
194 00:23:26.320 ⇒ 00:23:31.229 Awaish Kumar: Priority, like, this makes sense, like, for which domain we are.
195 00:23:31.350 ⇒ 00:23:34.210 Awaish Kumar: Calculate… calculating this matrix for…
196 00:23:34.210 ⇒ 00:23:34.550 Uttam Kumaran: Yeah.
197 00:23:34.550 ⇒ 00:23:38.579 Awaish Kumar: As a priority, we are focusing for the implementation part.
198 00:23:38.790 ⇒ 00:23:44.490 Awaish Kumar: Okay. But that rain is, like, how, like…
199 00:23:45.880 ⇒ 00:23:51.109 Awaish Kumar: how this metric is to be calculated? Like, if it is for an order, or if it’s for a…
200 00:23:51.430 ⇒ 00:23:58.300 Awaish Kumar: for a customer, like, for example, average LTV is for a customer.
201 00:23:58.850 ⇒ 00:24:02.970 Awaish Kumar: It can’t be by a day, like, lifetime value.
202 00:24:03.280 ⇒ 00:24:12.900 Awaish Kumar: But the revenue can be, like, by day, like, at a granular level, we can get it by day, but then we can obviously aggregate by week, months, and whatever.
203 00:24:13.240 ⇒ 00:24:13.910 Uttam Kumaran: Okay.
204 00:24:14.300 ⇒ 00:24:19.050 Awaish Kumar: So… so the formula is… is basically what…
205 00:24:20.280 ⇒ 00:24:23.169 Awaish Kumar: What is going to show us, basically…
206 00:24:23.440 ⇒ 00:24:30.179 Awaish Kumar: For a day, if we want to calculate gross revenue for all products, what is the formula for that?
207 00:24:30.700 ⇒ 00:24:31.570 Awaish Kumar: That’s what…
208 00:24:31.570 ⇒ 00:24:32.470 Uttam Kumaran: I see.
209 00:24:34.840 ⇒ 00:24:39.980 Awaish Kumar: Calculating here, and, like, yeah, this… this is still, like.
210 00:24:41.990 ⇒ 00:24:47.619 Uttam Kumaran: So, a couple things I think I want to add here is, like, we have primary source systems.
211 00:24:54.910 ⇒ 00:24:58.170 Uttam Kumaran: Wish there was a better formatting, but it’s fine.
212 00:24:58.650 ⇒ 00:25:05.900 Uttam Kumaran: a couple things. So, one is, I’m gonna freeze… This…
213 00:25:06.380 ⇒ 00:25:08.920 Uttam Kumaran: Oh, look! They have a Gemini column here.
214 00:25:12.140 ⇒ 00:25:13.040 Uttam Kumaran: Interesting.
215 00:25:13.800 ⇒ 00:25:17.779 Uttam Kumaran: I’m gonna freeze, up to the current column.
216 00:25:18.810 ⇒ 00:25:23.469 Uttam Kumaran: And then… what I’m gonna do here is we have metric type.
217 00:25:25.640 ⇒ 00:25:30.659 Uttam Kumaran: We have grain… I’m gonna make all of these…
218 00:25:31.150 ⇒ 00:25:32.170 Awaish Kumar: And…
219 00:25:32.170 ⇒ 00:25:33.950 Uttam Kumaran: These are the same size.
220 00:25:41.490 ⇒ 00:25:45.780 Awaish Kumar: only thing I’m concerned about is, like, having…
221 00:25:46.400 ⇒ 00:25:53.430 Awaish Kumar: metric and a channel, that is… Kind of making metrics duplicated.
222 00:25:53.430 ⇒ 00:25:57.259 Uttam Kumaran: No, exactly. Like, that’s what… that’s what I want to highlight to them, basically.
223 00:25:58.900 ⇒ 00:26:05.450 Uttam Kumaran: So, for example, here, I want to put, like… This is, like, notes.
224 00:26:06.610 ⇒ 00:26:09.340 Awaish Kumar: Yeah, it’s if we wanna add any…
225 00:26:09.340 ⇒ 00:26:10.170 Uttam Kumaran: Okay.
226 00:26:10.470 ⇒ 00:26:11.810 Uttam Kumaran: I guess, like…
227 00:26:13.950 ⇒ 00:26:17.090 Awaish Kumar: Which is different from description, like, if there’s anything.
228 00:26:23.220 ⇒ 00:26:27.339 Uttam Kumaran: Hmm… Well, I don’t really want to have, like…
229 00:26:35.980 ⇒ 00:26:38.679 Uttam Kumaran: Okay, we should think about what to call this.
230 00:26:39.680 ⇒ 00:26:41.829 Uttam Kumaran: Not exactly sure yet.
231 00:26:42.600 ⇒ 00:26:44.259 Uttam Kumaran: What to call this?
232 00:26:47.280 ⇒ 00:26:54.440 Uttam Kumaran: Refresh frequency, daily, source model, analytics… open source model.
233 00:26:54.600 ⇒ 00:27:02.309 Awaish Kumar: We can rename it, basically, Mart’s model, like, from where this… we are going to, yeah, from DVT.
234 00:27:03.880 ⇒ 00:27:08.729 Awaish Kumar: Like, it’s a sales… Under sales mods, it may be… Okay.
235 00:27:10.110 ⇒ 00:27:14.049 Uttam Kumaran: So, the other thing, I’m not gonna do, calculation verified, I’m gonna put.
236 00:27:15.290 ⇒ 00:27:18.360 Awaish Kumar: Yeah, it’s Mart’s model, basically. We are going to…
237 00:27:20.230 ⇒ 00:27:23.100 Awaish Kumar: Table name, we’re basically going to write table names.
238 00:27:24.430 ⇒ 00:27:25.270 Uttam Kumaran: Okay.
239 00:27:26.810 ⇒ 00:27:29.399 Uttam Kumaran: So I’m gonna put here, like…
240 00:27:33.200 ⇒ 00:27:41.630 Uttam Kumaran: Brain Forge… Recommendation… And then I’m gonna put,
241 00:27:45.400 ⇒ 00:27:46.940 Uttam Kumaran: LMNT…
242 00:27:50.910 ⇒ 00:27:57.830 Uttam Kumaran: Notes… And then I’m gonna put… Calculation verified.
243 00:27:58.100 ⇒ 00:28:02.549 Uttam Kumaran: I’m actually gonna say, like, KPI sign-off…
244 00:28:06.170 ⇒ 00:28:07.400 Uttam Kumaran: And…
245 00:28:17.710 ⇒ 00:28:19.609 Uttam Kumaran: Let’s just do that for now.
246 00:28:21.080 ⇒ 00:28:22.460 Awaish Kumar: Where is the decision?
247 00:28:23.060 ⇒ 00:28:34.149 Uttam Kumaran: I’m gonna put here, like, decide to keep, consolidate… Or remove…
248 00:28:44.100 ⇒ 00:28:45.520 Awaish Kumar: Okay, good.
249 00:28:45.720 ⇒ 00:28:50.800 Awaish Kumar: Yeah, this logic is going to come after we have the data, so right now…
250 00:28:50.800 ⇒ 00:28:54.249 Uttam Kumaran: Well, I think we should put some, like, as another thing, right?
251 00:28:56.010 ⇒ 00:28:56.830 Awaish Kumar: Hmm.
252 00:28:58.610 ⇒ 00:29:00.660 Uttam Kumaran: Aggregation, like, some.
253 00:29:04.520 ⇒ 00:29:05.710 Uttam Kumaran: What do you think?
254 00:29:07.110 ⇒ 00:29:09.519 Awaish Kumar: That is it.
255 00:29:11.150 ⇒ 00:29:13.060 Awaish Kumar: There’s the same thing in…
256 00:29:13.060 ⇒ 00:29:15.010 Uttam Kumaran: But then you could do a count, right?
257 00:29:18.760 ⇒ 00:29:20.439 Uttam Kumaran: We could have sum and count.
258 00:29:24.480 ⇒ 00:29:27.650 Awaish Kumar: Yeah, but the gross revenue is always some, and the…
259 00:29:28.940 ⇒ 00:29:31.019 Awaish Kumar: The formula will just show that.
260 00:29:34.590 ⇒ 00:29:36.400 Uttam Kumaran: Yeah, but it could be some.
261 00:29:36.710 ⇒ 00:29:41.389 Uttam Kumaran: And then there’s also, like… Metric sum, right?
262 00:29:42.350 ⇒ 00:29:44.609 Uttam Kumaran: Where you’re, like, combining metrics together.
263 00:29:45.000 ⇒ 00:29:47.989 Uttam Kumaran: You can do aggregation, or it’s basically, like.
264 00:29:48.280 ⇒ 00:29:51.210 Uttam Kumaran: Some, or is it a derivative? Is it a derivative?
265 00:29:54.320 ⇒ 00:29:55.160 Uttam Kumaran: Right.
266 00:29:56.080 ⇒ 00:29:58.340 Uttam Kumaran: So I want to put, like, is derivative.
267 00:30:03.020 ⇒ 00:30:10.550 Uttam Kumaran: The other thing we can do here is we can put, like, a… definition…
268 00:30:21.420 ⇒ 00:30:26.549 Uttam Kumaran: I was just gonna literally put, like, what the definition is of the thing, but whatever, it’s…
269 00:30:27.410 ⇒ 00:30:29.650 Uttam Kumaran: Is derivative metric.
270 00:30:31.800 ⇒ 00:30:40.960 Uttam Kumaran: Right? Like, is a metric calculated by other metrics? For example, gross revenue, discounts, refunds, and shipping are all metrics.
271 00:30:41.650 ⇒ 00:30:42.240 Awaish Kumar: Yep.
272 00:30:49.270 ⇒ 00:30:51.910 Uttam Kumaran: So this is a Shopify gross revenue.
273 00:31:02.350 ⇒ 00:31:07.180 Uttam Kumaran: Shopify gross revenue… Okay.
274 00:31:07.940 ⇒ 00:31:12.090 Uttam Kumaran: And what’s in this one? Oh, this is all… .
275 00:31:12.090 ⇒ 00:31:12.820 Awaish Kumar: Yeah, there’s…
276 00:31:13.950 ⇒ 00:31:15.059 Uttam Kumaran: They’re adding ease.
277 00:31:15.260 ⇒ 00:31:19.209 Awaish Kumar: I haven’t touched it, it just comes from Sheets, so… but I haven’t looked.
278 00:31:20.620 ⇒ 00:31:21.600 Uttam Kumaran: Okay, okay.
279 00:31:33.950 ⇒ 00:31:36.659 Uttam Kumaran: So the other thing I want to do is,
280 00:31:38.340 ⇒ 00:31:40.690 Uttam Kumaran: I want to put it in the context here.
281 00:31:40.970 ⇒ 00:31:43.570 Uttam Kumaran: The, like, business domain, channel…
282 00:31:47.170 ⇒ 00:31:49.639 Uttam Kumaran: Like, we should just create them all here.
283 00:31:50.880 ⇒ 00:31:56.140 Uttam Kumaran: So it’s, like, domain… And then… description.
284 00:31:56.630 ⇒ 00:32:03.060 Uttam Kumaran: Channel… description… See what I mean?
285 00:32:04.240 ⇒ 00:32:06.600 Awaish Kumar: And then you can just pull all the fields from here.
286 00:32:07.110 ⇒ 00:32:13.110 Uttam Kumaran: The business domain channel… We also want to do… product.
287 00:32:14.970 ⇒ 00:32:17.480 Uttam Kumaran: Product… categories…
288 00:32:23.080 ⇒ 00:32:27.450 Uttam Kumaran: And then these should be coming from… Okay.
289 00:32:27.760 ⇒ 00:32:32.879 Uttam Kumaran: Well, these technically should be coming from… the data sources.
290 00:32:33.720 ⇒ 00:32:34.530 Uttam Kumaran: Right.
291 00:32:35.700 ⇒ 00:32:36.380 Awaish Kumar: Preview.
292 00:32:37.880 ⇒ 00:32:40.649 Uttam Kumaran: So, maybe I’ll comment you in there if you want to do that.
293 00:32:41.770 ⇒ 00:32:46.919 Uttam Kumaran: Let’s pull these from data sources sheet…
294 00:32:52.580 ⇒ 00:32:57.550 Awaish Kumar: But… Can this come, like, as a drop-down from there?
295 00:32:57.550 ⇒ 00:32:58.549 Uttam Kumaran: Yes, you can.
296 00:32:58.750 ⇒ 00:33:00.190 Uttam Kumaran: To pull it from a range.
297 00:33:04.580 ⇒ 00:33:09.020 Uttam Kumaran: Like, what you can do here is you can go, edit.
298 00:33:09.530 ⇒ 00:33:10.790 Uttam Kumaran: Advanced.
299 00:33:11.280 ⇒ 00:33:12.310 Uttam Kumaran: Or, sorry.
300 00:33:12.730 ⇒ 00:33:15.570 Uttam Kumaran: You could actually go here and do drop-down from a range.
301 00:33:15.770 ⇒ 00:33:16.940 Uttam Kumaran: Fuck the range.
302 00:33:19.450 ⇒ 00:33:26.440 Uttam Kumaran: Martz models… And then here, I’m gonna put a big line.
303 00:33:34.630 ⇒ 00:33:35.430 Uttam Kumaran: Oops.
304 00:33:37.210 ⇒ 00:33:38.240 Uttam Kumaran: Okay…
305 00:34:12.780 ⇒ 00:34:13.929 Uttam Kumaran: What is this?
306 00:34:14.350 ⇒ 00:34:15.159 Uttam Kumaran: Oh.
307 00:34:16.610 ⇒ 00:34:19.080 Awaish Kumar: Yeah, I already asked Greg and about it.
308 00:34:19.080 ⇒ 00:34:22.359 Uttam Kumaran: Oh, nice. Okay. I was like, who’s G?
309 00:34:49.100 ⇒ 00:34:56.000 Uttam Kumaran: Yeah, I feel like we should just put the… we need to really probably put the definitions to… for these somewhere.
310 00:34:58.120 ⇒ 00:35:00.400 Awaish Kumar: The definition for column?
311 00:35:00.400 ⇒ 00:35:02.949 Uttam Kumaran: Like, yeah, for the columns.
312 00:35:04.350 ⇒ 00:35:07.340 Uttam Kumaran: I’m wondering if they’re gonna want that, so…
313 00:35:09.220 ⇒ 00:35:11.510 Uttam Kumaran: Maybe I’m gonna put it here, like…
314 00:35:13.800 ⇒ 00:35:16.910 Uttam Kumaran: Like, we need to put, like, a how to use this, basically.
315 00:35:20.390 ⇒ 00:35:21.550 Awaish Kumar: It’s fine.
316 00:35:58.470 ⇒ 00:36:03.119 Awaish Kumar: Okay, we need column descriptions, right? Because vetted descriptions are already there.
317 00:36:27.700 ⇒ 00:36:29.290 Uttam Kumaran: Let’s ask Gemini.
318 00:36:29.870 ⇒ 00:36:37.179 Uttam Kumaran: What else are we missing from this core metrics and… core metric sheets?
319 00:36:38.560 ⇒ 00:36:42.399 Uttam Kumaran: I’m just gonna remove AND lineage for now, because I don’t feel like…
320 00:36:43.600 ⇒ 00:36:46.470 Uttam Kumaran: This makes a ton of sense in this context.
321 00:36:53.140 ⇒ 00:36:56.510 Uttam Kumaran: Great, this is so stupid. Didn’t tell me anything.
322 00:37:02.160 ⇒ 00:37:05.139 Awaish Kumar: They added an AI column as well.
323 00:37:05.750 ⇒ 00:37:07.030 Uttam Kumaran: Yeah, I saw that.
324 00:37:07.940 ⇒ 00:37:10.859 Uttam Kumaran: Well, I’m just gonna put source systems here.
325 00:37:12.260 ⇒ 00:37:14.539 Uttam Kumaran: Mart’s model, owner…
326 00:37:20.450 ⇒ 00:37:22.909 Uttam Kumaran: Well, let’s do owner…
327 00:37:23.190 ⇒ 00:37:31.530 Uttam Kumaran: I’m trying to think about… I did this a few companies ago, we had, like… data owner…
328 00:37:33.610 ⇒ 00:37:36.210 Uttam Kumaran: And we had, like, business owner.
329 00:37:37.300 ⇒ 00:37:38.600 Uttam Kumaran: Something like that.
330 00:37:40.170 ⇒ 00:37:42.170 Awaish Kumar: Yeah, maybe a stakeholder.
331 00:37:43.350 ⇒ 00:37:49.040 Uttam Kumaran: Yeah… like… Primary stakeholder.
332 00:38:03.670 ⇒ 00:38:05.819 Uttam Kumaran: Okay, this is looking better.
333 00:38:08.130 ⇒ 00:38:10.189 Uttam Kumaran: Let’s try to just,
334 00:38:37.720 ⇒ 00:38:41.640 Uttam Kumaran: Yeah, let’s try to pull this category, too, from the other category.
335 00:38:42.770 ⇒ 00:38:44.610 Uttam Kumaran: Just, like, business domain?
336 00:38:49.480 ⇒ 00:38:55.589 Uttam Kumaran: Pull from biz domain… And then I’m gonna go ahead and…
337 00:38:56.140 ⇒ 00:39:00.860 Uttam Kumaran: But she says she didn’t like the, the colors here.
338 00:39:04.190 ⇒ 00:39:05.020 Uttam Kumaran: a dot.
339 00:39:11.050 ⇒ 00:39:12.339 Awaish Kumar: Just all gray.
340 00:39:13.060 ⇒ 00:39:13.660 Uttam Kumaran: Hmph.
341 00:39:35.040 ⇒ 00:39:36.650 Uttam Kumaran: What is demo needs?
342 00:39:37.720 ⇒ 00:39:40.460 Awaish Kumar: Yeah, she wondered if we need to… Hmm.
343 00:39:40.920 ⇒ 00:39:43.349 Awaish Kumar: Someone to show us the platform.
344 00:39:45.760 ⇒ 00:39:46.620 Uttam Kumaran: Okay.
345 00:40:15.010 ⇒ 00:40:16.120 Uttam Kumaran: Oops.
346 00:40:59.750 ⇒ 00:41:03.600 Uttam Kumaran: Okay. Alright, this looks getting better, too.
347 00:41:04.230 ⇒ 00:41:08.110 Uttam Kumaran: I’m just gonna… these ones, I don’t want it to be red, I just want it to be, like…
348 00:41:08.890 ⇒ 00:41:09.810 Uttam Kumaran: gray.
349 00:41:46.670 ⇒ 00:41:47.450 Uttam Kumaran: Okay.
350 00:42:22.610 ⇒ 00:42:23.390 Uttam Kumaran: Cool.
351 00:42:25.010 ⇒ 00:42:26.760 Uttam Kumaran: Alright,
352 00:42:29.830 ⇒ 00:42:30.990 Uttam Kumaran: What?
353 00:42:31.110 ⇒ 00:42:32.560 Uttam Kumaran: Else, dude.
354 00:42:32.870 ⇒ 00:42:34.229 Uttam Kumaran: What else can I look at?
355 00:42:34.650 ⇒ 00:42:35.520 Uttam Kumaran: That’s it.
356 00:42:37.140 ⇒ 00:42:38.630 Awaish Kumar: Yep, that’s it.
357 00:42:39.430 ⇒ 00:42:39.900 Uttam Kumaran: Okay.
358 00:42:40.430 ⇒ 00:42:43.960 Awaish Kumar: So, did you, did you lift… Converse or universal?
359 00:42:44.520 ⇒ 00:42:45.330 Uttam Kumaran: I did.
360 00:42:45.880 ⇒ 00:42:46.610 Awaish Kumar: Okay.
361 00:42:48.060 ⇒ 00:42:49.089 Uttam Kumaran: It’s almost done.
362 00:42:49.760 ⇒ 00:42:54.350 Uttam Kumaran: Yeah, almost ready. I basically said, like, That’s also,
363 00:42:55.860 ⇒ 00:43:02.669 Uttam Kumaran: if we’re gonna write about Walmart and Target, let’s just write both of them, like, I guess is the Target data coming the same way?
364 00:43:05.050 ⇒ 00:43:06.870 Awaish Kumar: Nope.
365 00:43:07.960 ⇒ 00:43:09.170 Uttam Kumaran: Oh, okay.
366 00:43:10.100 ⇒ 00:43:12.009 Awaish Kumar: Like, different table structures.
367 00:43:13.270 ⇒ 00:43:13.920 Uttam Kumaran: Okay.
368 00:43:16.030 ⇒ 00:43:20.030 Awaish Kumar: It only has one table, which gives us the daily sales.
369 00:43:20.570 ⇒ 00:43:25.070 Awaish Kumar: Nothing else. And one table which gives us the daily inventory.
370 00:43:25.330 ⇒ 00:43:26.210 Awaish Kumar: That’s all.
371 00:43:27.350 ⇒ 00:43:29.059 Uttam Kumaran: Okay, I think we should,
372 00:43:31.040 ⇒ 00:43:33.230 Uttam Kumaran: I think we should put that in there, because…
373 00:43:33.850 ⇒ 00:43:42.009 Uttam Kumaran: I don’t think we were clear about, like, what we found in terms of Target. Like, we should just highlight what we found in terms of Walmart, what we found in terms of Target.
374 00:43:43.280 ⇒ 00:43:46.560 Uttam Kumaran: And basically, this is, like, the whole view of Emerson.
375 00:43:47.970 ⇒ 00:43:51.729 Awaish Kumar: Okay, yeah, for Target, I… yeah.
376 00:43:53.130 ⇒ 00:43:57.760 Awaish Kumar: what she was asking for, doing kind of analysis, so I just did,
377 00:43:57.900 ⇒ 00:44:04.309 Awaish Kumar: I didn’t, like, put the, basically, schema metadata, what I did for Walmart.
378 00:44:05.680 ⇒ 00:44:09.049 Awaish Kumar: For target, I just put the numbers, like, how’s the sales?
379 00:44:09.940 ⇒ 00:44:16.429 Awaish Kumar: And, it doesn’t… it does not include, like, profiling of the tables.
380 00:44:17.120 ⇒ 00:44:17.980 Awaish Kumar: Right now.
381 00:44:21.750 ⇒ 00:44:22.090 Uttam Kumaran: Yeah.
382 00:44:22.090 ⇒ 00:44:31.310 Awaish Kumar: include sales, how… how it, how sales were in October versus November, and… Yeah.
383 00:44:31.310 ⇒ 00:44:36.190 Uttam Kumaran: I think we should put the, the…
384 00:44:38.090 ⇒ 00:44:41.140 Uttam Kumaran: Like, basically an overview of the target tables, too.
385 00:44:42.700 ⇒ 00:44:44.520 Uttam Kumaran: It’s gonna be the question they ask.
386 00:44:45.710 ⇒ 00:44:47.220 Awaish Kumar: Okay, yeah.
387 00:44:52.340 ⇒ 00:44:53.380 Awaish Kumar: Okay.
388 00:44:54.030 ⇒ 00:44:55.909 Awaish Kumar: I’ll add that.
389 00:44:55.910 ⇒ 00:44:56.440 Uttam Kumaran: Okay.
390 00:45:03.780 ⇒ 00:45:04.710 Awaish Kumar: Oh, boy.
391 00:45:05.120 ⇒ 00:45:06.540 Awaish Kumar: 40 meter.
392 00:45:07.270 ⇒ 00:45:08.980 Awaish Kumar: Add example, thanks.
393 00:45:09.780 ⇒ 00:45:10.679 Uttam Kumaran: Where is that?
394 00:45:11.510 ⇒ 00:45:13.970 Awaish Kumar: In the document you commented, like.
395 00:45:15.570 ⇒ 00:45:18.189 Awaish Kumar: Add example, let me show.
396 00:45:20.650 ⇒ 00:45:22.530 Awaish Kumar: It’s just example word, or…
397 00:45:24.450 ⇒ 00:45:26.460 Uttam Kumaran: Oh, I wrote that, yeah.
398 00:45:26.690 ⇒ 00:45:30.770 Uttam Kumaran: Because basically, if you scroll down.
399 00:45:31.180 ⇒ 00:45:33.049 Uttam Kumaran: We didn’t put all the states.
400 00:45:33.170 ⇒ 00:45:38.869 Uttam Kumaran: So, I just wanted to make sure that they’re, they’re, like, this is an example, like, analysis that we did.
401 00:45:39.080 ⇒ 00:45:40.169 Uttam Kumaran: You know what I mean?
402 00:45:40.990 ⇒ 00:45:41.790 Awaish Kumar: Okay.
403 00:45:58.260 ⇒ 00:46:02.900 Uttam Kumaran: So, that’s why I put that comment there, like, this mentions Target as a separate data source.
404 00:46:03.360 ⇒ 00:46:07.409 Uttam Kumaran: But it does not include enough information about, like.
405 00:46:07.790 ⇒ 00:46:09.810 Uttam Kumaran: We should just do a section on target.
406 00:46:17.710 ⇒ 00:46:23.150 Awaish Kumar: Yeah, like, the document I added, it has the separate target analysis.
407 00:46:23.690 ⇒ 00:46:30.930 Awaish Kumar: But… Here, it just said competitive analysis, I think it’s… Yeah, I’m up…
408 00:46:30.930 ⇒ 00:46:38.490 Uttam Kumaran: Yeah, I think, like, this… this just may be, like, what I thought was best, but looking at it now, we should definitely look at
409 00:46:38.600 ⇒ 00:46:40.360 Uttam Kumaran: The target data source.
410 00:46:41.370 ⇒ 00:46:44.520 Uttam Kumaran: I mean, we should… I think we should keep the comparison if you want, but…
411 00:46:44.670 ⇒ 00:46:48.430 Uttam Kumaran: I want to also outline what tables we found from Target.
412 00:46:48.880 ⇒ 00:46:50.410 Uttam Kumaran: What they have in them.
413 00:46:56.720 ⇒ 00:46:57.550 Awaish Kumar: Okay.
414 00:47:00.270 ⇒ 00:47:02.890 Awaish Kumar: Prince Matrician’s bad.
415 00:47:04.170 ⇒ 00:47:08.370 Awaish Kumar: Okay, yeah, so I will just add, maybe…
416 00:47:09.650 ⇒ 00:47:13.429 Awaish Kumar: like, I will add that here, under this…
417 00:47:17.390 ⇒ 00:47:20.360 Uttam Kumaran: That’d be great, yeah, just like an overview of Target.
418 00:47:25.830 ⇒ 00:47:29.370 Awaish Kumar: Yeah, in the profile, we have Walmart.
419 00:47:29.970 ⇒ 00:47:34.880 Awaish Kumar: After it finishes, I will just add all the tables from… Got it, good.
420 00:47:36.570 ⇒ 00:47:37.210 Uttam Kumaran: Okay.
421 00:47:38.390 ⇒ 00:47:41.720 Awaish Kumar: And then we will keep the document as it is.
422 00:47:45.520 ⇒ 00:47:48.120 Awaish Kumar: Like, the comparative part and everything.
423 00:47:48.120 ⇒ 00:47:50.669 Uttam Kumaran: Yeah, I would just keep the… yeah, exactly.
424 00:47:51.540 ⇒ 00:47:53.999 Awaish Kumar: I will just add a few tables here.
425 00:47:54.110 ⇒ 00:47:55.180 Awaish Kumar: For target.
426 00:47:55.820 ⇒ 00:47:56.420 Uttam Kumaran: Okay.
427 00:48:03.130 ⇒ 00:48:03.750 Awaish Kumar: Okay.
428 00:48:09.410 ⇒ 00:48:11.229 Uttam Kumaran: Okay, anything else?
429 00:48:12.450 ⇒ 00:48:18.909 Uttam Kumaran: I’m gonna be spending some time on ABC, and then I’m gonna go look at, a few other things, so…
430 00:48:20.790 ⇒ 00:48:21.640 Uttam Kumaran: Cool.
431 00:48:22.240 ⇒ 00:48:25.620 Uttam Kumaran: Okay. Alright, dude, thank you. Sorry for the late call.
432 00:48:26.370 ⇒ 00:48:28.299 Awaish Kumar: Otherwise, thank you. Bye.
433 00:48:28.300 ⇒ 00:48:29.919 Uttam Kumaran: Okay. Thanks, dude. Bye.