Meeting Title: Omni Data Platform Walkthrough Date: 2026-03-19 Meeting participants: Amber Lin, Advait Nandakumar Menon
WEBVTT
1 00:00:04.310 ⇒ 00:00:05.470 Amber Lin: Hello.
2 00:00:06.680 ⇒ 00:00:08.160 Advait Nandakumar Menon: Yeah, I’m over money.
3 00:00:08.700 ⇒ 00:00:09.779 Amber Lin: Good morning.
4 00:00:10.630 ⇒ 00:00:15.610 Amber Lin: I, let’s get started. I think Greg is not coming.
5 00:00:18.600 ⇒ 00:00:28.519 Amber Lin: Let’s see… Okay, so a quick walkthrough of what we have in Omni and what we have built.
6 00:00:31.250 ⇒ 00:00:35.050 Amber Lin: Let me show you, so… First of all…
7 00:00:36.430 ⇒ 00:00:41.460 Amber Lin: This is the data platform pic documentation.
8 00:00:41.950 ⇒ 00:00:42.380 Advait Nandakumar Menon: -
9 00:00:42.380 ⇒ 00:00:56.500 Amber Lin: It says mostly updated, but not completely updated. So, for example, this will have the sources of where things originate, our data, where that comes from.
10 00:00:56.850 ⇒ 00:01:03.209 Amber Lin: For core metrics, look at this one. This is the somewhat newly updated one.
11 00:01:04.180 ⇒ 00:01:04.680 Advait Nandakumar Menon: Okay.
12 00:01:04.680 ⇒ 00:01:11.579 Amber Lin: And then… I think the other one would be… Here, on dashboards.
13 00:01:11.760 ⇒ 00:01:13.329 Amber Lin: So this is…
14 00:01:14.300 ⇒ 00:01:21.870 Amber Lin: Just a quick reference of where things are, and I can keep updating these so we have record of where things are.
15 00:01:22.890 ⇒ 00:01:23.500 Advait Nandakumar Menon: Perfect.
16 00:01:24.180 ⇒ 00:01:32.979 Amber Lin: Right now, what we have is wholesale and retail, which is going to be, these two.
17 00:01:33.140 ⇒ 00:01:43.330 Amber Lin: Where we originally made spreadsheet reporting for them. And of course, right now, we’re starting to build them in Office.
18 00:01:43.540 ⇒ 00:01:44.290 Amber Lin: So…
19 00:01:45.000 ⇒ 00:01:51.630 Amber Lin: You can click on these links to see what they originally looked like. So, for wholesale…
20 00:01:51.630 ⇒ 00:01:56.990 Advait Nandakumar Menon: So this was the… this was what they were relying on before Omni came into the picture, right?
21 00:01:56.990 ⇒ 00:02:00.030 Amber Lin: Yes, so this is what we built before we did Omni.
22 00:02:00.550 ⇒ 00:02:01.830 Advait Nandakumar Menon: Okay, okay.
23 00:02:01.830 ⇒ 00:02:18.059 Amber Lin: Yeah, so in these spreadsheets, in the wholesale one, so there’s two main reports. This is a summary report, so we have the top parts about partner status of, okay, how many wholesale customers they have.
24 00:02:18.230 ⇒ 00:02:22.099 Amber Lin: Partner just mean they’re wholesale customers.
25 00:02:22.220 ⇒ 00:02:30.809 Amber Lin: And then their customers, how they cut the data is they like to look at it by wholesale partner segments.
26 00:02:31.060 ⇒ 00:02:36.829 Amber Lin: And then, of course, by product category, so when we look at sales and orders. So…
27 00:02:36.830 ⇒ 00:02:37.770 Advait Nandakumar Menon: Right.
28 00:02:38.480 ⇒ 00:02:47.420 Amber Lin: What we have here is we have the wholesale customer data.
29 00:02:47.930 ⇒ 00:02:54.489 Amber Lin: I think it’ll be nice if you can take a look at this, and this is also in Omni, so we…
30 00:02:54.490 ⇒ 00:02:54.820 Advait Nandakumar Menon: Okay.
31 00:02:54.820 ⇒ 00:03:03.040 Amber Lin: We added these tables, synced them to Google Sheet, but they’re all within Omni.
32 00:03:03.320 ⇒ 00:03:04.080 Amber Lin: So…
33 00:03:04.080 ⇒ 00:03:08.169 Advait Nandakumar Menon: Okay, so Omni is getting its data from this Google Sheet, is what you’re saying?
34 00:03:08.170 ⇒ 00:03:11.789 Amber Lin: No. All of this data comes from.
35 00:03:11.910 ⇒ 00:03:12.430 Advait Nandakumar Menon: Snowflake?
36 00:03:12.430 ⇒ 00:03:15.690 Amber Lin: Snowflake, so this is just synced from Snowflake.
37 00:03:16.280 ⇒ 00:03:16.940 Advait Nandakumar Menon: Okay.
38 00:03:16.940 ⇒ 00:03:33.989 Amber Lin: This is before we had Omni. And this one is a report, I believe. This is for finance, but this is essentially product sales by SKU by price, because they have the same SKU in
39 00:03:34.260 ⇒ 00:03:40.299 Amber Lin: D2C and wholesale that have different prices, so it’s important for us to do
40 00:03:40.530 ⇒ 00:03:46.919 Amber Lin: Product, by price, and then what the gross sales, discounts, and all that is about.
41 00:03:48.650 ⇒ 00:03:56.560 Amber Lin: That’s wholesale. Let’s go to Omni and see what the wholesale dashboards currently look like, and then we can go on to…
42 00:03:57.020 ⇒ 00:03:59.020 Amber Lin: Retail. So…
43 00:03:59.020 ⇒ 00:03:59.600 Advait Nandakumar Menon: Huh?
44 00:03:59.980 ⇒ 00:04:06.960 Amber Lin: Yeah, so let’s… if you come to Omni, and you’re at home, you can go into Hub right here.
45 00:04:07.670 ⇒ 00:04:10.450 Amber Lin: and then see this V1 dashboards.
46 00:04:10.730 ⇒ 00:04:17.180 Amber Lin: And then we have two wholesale reports, and then the finance is the one I just showed you.
47 00:04:17.769 ⇒ 00:04:23.279 Amber Lin: Okay. So… Take a quick look at the finance one. So this is essentially…
48 00:04:24.040 ⇒ 00:04:26.839 Amber Lin: The same thing of, okay, what…
49 00:04:27.130 ⇒ 00:04:32.660 Amber Lin: The month is, and what is the breakdown.
50 00:04:32.980 ⇒ 00:04:34.430 Amber Lin: Bye.
51 00:04:34.970 ⇒ 00:04:37.860 Amber Lin: Channel, by product category, and then…
52 00:04:37.970 ⇒ 00:04:44.409 Amber Lin: SKU, price, what these things are. So, pretty straightforward, this is just currently just a table.
53 00:04:44.990 ⇒ 00:04:52.289 Amber Lin: And then… We have… also, this one’s pretty straightforward. This is the Wholesale Partners.
54 00:04:54.060 ⇒ 00:05:03.180 Amber Lin: This was, like, an ad hoc table that they requested, but essentially this is just their address versus their current shipping address.
55 00:05:03.390 ⇒ 00:05:09.579 Amber Lin: So, a toddler table, but I think the main wholesale one is going to be here.
56 00:05:11.230 ⇒ 00:05:17.150 Amber Lin: So this is… is still… Sort of a replica of what they currently have.
57 00:05:17.300 ⇒ 00:05:21.340 Amber Lin: So we have sales current month.
58 00:05:21.670 ⇒ 00:05:32.340 Amber Lin: And then this is essentially the replica of the monthly view we have here, and then these are some visualizations of the same metrics.
59 00:05:33.230 ⇒ 00:05:33.890 Advait Nandakumar Menon: Okay.
60 00:05:34.260 ⇒ 00:05:40.750 Amber Lin: Yeah, so that’s wholesale, and then let’s take a quick look at retail. We have… Oh.
61 00:05:41.310 ⇒ 00:05:43.250 Amber Lin: 7 minutes.
62 00:05:43.790 ⇒ 00:05:52.729 Amber Lin: So, in retail, We have also two types of reports. Similarly, a…
63 00:05:53.450 ⇒ 00:05:58.039 Amber Lin: Summary report, where they look at stores, and then look at sales.
64 00:05:58.170 ⇒ 00:06:09.650 Amber Lin: So, stores, similarly, they have Target and Walmart, so we have total stores, active stores, churned stores, you can look up the directions here.
65 00:06:09.830 ⇒ 00:06:13.150 Amber Lin: And then we have… POS revenue.
66 00:06:13.660 ⇒ 00:06:16.129 Amber Lin: And of course, we have weekly…
67 00:06:16.250 ⇒ 00:06:24.460 Amber Lin: Monthly, and then quarterly, weekly averages, and then in our database, we have daily grades.
68 00:06:25.400 ⇒ 00:06:25.980 Advait Nandakumar Menon: Okay.
69 00:06:25.980 ⇒ 00:06:38.880 Amber Lin: And then here is a report for one of their executives, which is, he requested that we have the latest date, what the sales were like, if units and
70 00:06:39.100 ⇒ 00:06:47.599 Amber Lin: Dollar amount sales, and look at it based on same day last week, same day last month, all the definitions you can find here.
71 00:06:48.140 ⇒ 00:06:56.179 Amber Lin: And similarly, this is a latest week, very similar to what this is just on the weekly grain.
72 00:06:56.180 ⇒ 00:06:57.410 Advait Nandakumar Menon: Vehicle baseline.
73 00:06:58.640 ⇒ 00:07:11.420 Amber Lin: Yeah, and for inventory, we have more data than this, but this is just what they requested, a very simple on-hand, on-order, in-transit inventory, and then what it looks like before.
74 00:07:12.800 ⇒ 00:07:16.090 Amber Lin: So, going into Omni, we have…
75 00:07:16.200 ⇒ 00:07:21.050 Amber Lin: these three things. So, let’s look at summary report.
76 00:07:21.710 ⇒ 00:07:31.170 Amber Lin: This is very similar to the structure of the wholesale summary report, so we have the current month, what it was like, so they can track progress.
77 00:07:31.280 ⇒ 00:07:34.900 Amber Lin: And then they have… we have the monthly section.
78 00:07:35.040 ⇒ 00:07:38.640 Amber Lin: The store status, and the sales.
79 00:07:38.880 ⇒ 00:07:42.569 Amber Lin: And of course, these are just some visualizations that we added.
80 00:07:42.730 ⇒ 00:07:47.069 Amber Lin: and weekly… You know the view.
81 00:07:48.040 ⇒ 00:07:48.660 Advait Nandakumar Menon: No.
82 00:07:48.660 ⇒ 00:07:52.420 Amber Lin: And… This is the report for Phil.
83 00:07:52.640 ⇒ 00:07:57.180 Amber Lin: Essentially just a replica, so not gonna do too much here.
84 00:07:57.500 ⇒ 00:08:03.060 Amber Lin: And… This is a by store.
85 00:08:03.170 ⇒ 00:08:06.879 Amber Lin: Drill down of, okay, what is the…
86 00:08:07.780 ⇒ 00:08:14.889 Amber Lin: Inspi… by day, by store, by product name. What is the sales, and…
87 00:08:15.060 ⇒ 00:08:19.810 Amber Lin: inventory. So this is kind of still in progress.
88 00:08:20.020 ⇒ 00:08:24.879 Amber Lin: We haven’t really built this for them before, so this was more of an exploration.
89 00:08:25.450 ⇒ 00:08:29.439 Amber Lin: So, that’s all of the… I mean, that’s all the dashboards we have.
90 00:08:29.690 ⇒ 00:08:33.140 Amber Lin: Currently, when you come into Omni.
91 00:08:33.860 ⇒ 00:08:39.280 Amber Lin: Here are… if you click on Develop, Here is the bottle.
92 00:08:41.150 ⇒ 00:08:42.920 Advait Nandakumar Menon: Snowflake? Yes, yeah.
93 00:08:42.929 ⇒ 00:08:47.169 Amber Lin: So these are our prod marts. You can click on these.
94 00:08:47.449 ⇒ 00:08:56.739 Amber Lin: And then these are the topics we created, so kind of like views, so more, like, you can search up Omni’s definitions, so this is…
95 00:08:56.869 ⇒ 00:08:59.399 Amber Lin: The talk is created.
96 00:08:59.979 ⇒ 00:09:15.579 Amber Lin: And if you want to explore the data, you can always click here, click New, and then you can select the different topics, or if you want to look at… explore the tables, you can go here as well.
97 00:09:15.719 ⇒ 00:09:24.299 Amber Lin: And you should also have access to Snowflake via OnePass. If you have been added to the element
98 00:09:24.689 ⇒ 00:09:27.959 Amber Lin: OnePath’s fault, should be able to…
99 00:09:27.960 ⇒ 00:09:28.310 Advait Nandakumar Menon: Okay.
100 00:09:28.310 ⇒ 00:09:32.080 Amber Lin: link, and then log into Snowflake to…
101 00:09:32.490 ⇒ 00:09:35.039 Amber Lin: Take a slope, so let’s see…
102 00:09:35.890 ⇒ 00:09:45.390 Amber Lin: We can click sign in, and then… It will be… It will be this one.
103 00:09:46.680 ⇒ 00:09:56.719 Amber Lin: And then… When you come in here, you’ll… you should be able to see the different tables here.
104 00:09:56.880 ⇒ 00:10:06.920 Amber Lin: We usually look at Pradmars, but if you want to go further and explore, this is where our main retail is at, so this immersion table.
105 00:10:07.000 ⇒ 00:10:18.230 Amber Lin: And then you can see, of course, like, you have Shopify, which is some of where the wholesale data comes from, and…
106 00:10:19.790 ⇒ 00:10:22.670 Amber Lin: just wholesale customers.
107 00:10:23.890 ⇒ 00:10:26.619 Amber Lin: It’s also where the other things are at.
108 00:10:27.480 ⇒ 00:10:28.210 Advait Nandakumar Menon: Okay.
109 00:10:28.720 ⇒ 00:10:29.340 Amber Lin: Yes.
110 00:10:29.960 ⇒ 00:10:39.890 Amber Lin: Yeah, that’s an overview of what we have right now. I believe what they want you on this project, is to look at
111 00:10:40.490 ⇒ 00:10:56.720 Amber Lin: other, say, prepare for other departments and how we would help them visualize, or you’ll be working directly work with their stakeholders, but I think first, just to understand what data we have currently,
112 00:10:57.640 ⇒ 00:11:00.470 Amber Lin: Probably you can look at e-commerce.
113 00:11:00.610 ⇒ 00:11:01.450 Amber Lin: Maybe?
114 00:11:01.450 ⇒ 00:11:02.030 Advait Nandakumar Menon: Okay.
115 00:11:02.030 ⇒ 00:11:03.249 Amber Lin: you would take.
116 00:11:03.630 ⇒ 00:11:08.829 Amber Lin: And… Explore Omni, get familiar with the data.
117 00:11:09.000 ⇒ 00:11:11.630 Amber Lin: I would say that would be her next step.
118 00:11:12.730 ⇒ 00:11:13.410 Advait Nandakumar Menon: Okay.
119 00:11:13.630 ⇒ 00:11:13.960 Amber Lin: Yeah.
120 00:11:13.960 ⇒ 00:11:21.970 Advait Nandakumar Menon: Just to summarize, all the spreadsheet data is there, and that, data platform documentation is what you mentioned, right?
121 00:11:22.630 ⇒ 00:11:30.070 Amber Lin: Yes, so spreadsheets, we’re going to move away and move into Omni, so…
122 00:11:30.070 ⇒ 00:11:30.640 Advait Nandakumar Menon: Okay.
123 00:11:30.640 ⇒ 00:11:33.589 Amber Lin: And all the data is from our data.
124 00:11:33.590 ⇒ 00:11:35.809 Advait Nandakumar Menon: In Snowflake. Yes. Yeah, okay.
125 00:11:36.130 ⇒ 00:11:36.940 Advait Nandakumar Menon: Okay.
126 00:11:37.300 ⇒ 00:11:43.499 Advait Nandakumar Menon: And, you would want me looking at just the whole data and…
127 00:11:43.700 ⇒ 00:11:48.089 Advait Nandakumar Menon: How it flows, and, like, just play around with it.
128 00:11:48.830 ⇒ 00:11:49.960 Amber Lin: Yeah, yeah.
129 00:11:49.960 ⇒ 00:11:53.510 Advait Nandakumar Menon: Can we look into a department like e-commerce as well.
130 00:11:53.790 ⇒ 00:12:00.910 Amber Lin: Yeah, explore the dashboards, explore our data sources, and…
131 00:12:01.150 ⇒ 00:12:07.079 Amber Lin: I think Robert or Jasmine should give you next steps pretty soon.
132 00:12:07.080 ⇒ 00:12:07.710 Advait Nandakumar Menon: Okay.
133 00:12:08.540 ⇒ 00:12:16.580 Amber Lin: So, they will let you know what department it would be, but right now, just get familiar with tools and the data.
134 00:12:17.990 ⇒ 00:12:18.660 Advait Nandakumar Menon: Okay.
135 00:12:19.440 ⇒ 00:12:20.420 Amber Lin: Okay. Awesome.
136 00:12:20.420 ⇒ 00:12:24.569 Advait Nandakumar Menon: And with respect to the access to 1Password, like.
137 00:12:24.760 ⇒ 00:12:33.819 Advait Nandakumar Menon: I don’t think I’ve been added with respect to anything to Element, or Notion, or Clockify, or even 1Password, so…
138 00:12:33.820 ⇒ 00:12:37.990 Amber Lin: Gotcha. Should I tag Rico on access items?
139 00:12:38.450 ⇒ 00:12:39.399 Advait Nandakumar Menon: Okay, okay.
140 00:12:39.400 ⇒ 00:12:42.279 Amber Lin: He should be able to add you to all of these.
141 00:12:42.810 ⇒ 00:12:47.799 Advait Nandakumar Menon: Okay. I have to just tell them to add, anything related to Element.
142 00:12:47.800 ⇒ 00:12:48.380 Amber Lin: Yeah.
143 00:12:49.280 ⇒ 00:12:49.950 Advait Nandakumar Menon: Okay.
144 00:12:51.970 ⇒ 00:12:57.439 Amber Lin: Awesome. I have a stand-up, so I need to hop, but shoot me questions in Slack.
145 00:12:58.070 ⇒ 00:13:00.570 Advait Nandakumar Menon: Sure. Alright. Thanks, thanks, Sam.
146 00:13:01.190 ⇒ 00:13:02.130 Amber Lin: Right.
147 00:13:02.500 ⇒ 00:13:03.180 Advait Nandakumar Menon: Bye.