2026-03-11_brainforge_lmnt_omni_kick-off_d0bb75b6

Meeting Title: Brainforge <> LMNT Omni kick-off Date: 2026-03-11 Meeting participants: Greg Stoutenburg, Shivani Amar, Uttam Kumaran, Bess Ross

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WEBVTT

1 00:00:38.990 ⇒ 00:00:41.750 Greg Stoutenburg: Right now, right?

2 00:00:48.680 ⇒ 00:00:49.670 Shivani Amar: Hello!

3 00:00:49.890 ⇒ 00:00:51.769 Greg Stoutenburg: Hi, Shivani, I’m Greg, nice to meet you.

4 00:00:52.680 ⇒ 00:00:54.790 Shivani Amar: Nice to meet you. Where are you based?

5 00:00:54.970 ⇒ 00:00:59.669 Greg Stoutenburg: I’m in York, Pennsylvania, which is near Harrisburg, Lancaster, Baltimore.

6 00:00:59.840 ⇒ 00:01:03.169 Shivani Amar: Cool. Okay, nice. And you’re going down to Florida?

7 00:01:03.210 ⇒ 00:01:16.669 Greg Stoutenburg: I am, I am. I wanted to make sure everyone knows, because it’s so nice out there. Yeah, yep, going to, going to see my, my girlfriend’s father, who lives near Tampa.

8 00:01:16.670 ⇒ 00:01:17.500 Shivani Amar: Okay, cool.

9 00:01:17.720 ⇒ 00:01:18.360 Greg Stoutenburg: Yep.

10 00:01:18.360 ⇒ 00:01:24.959 Shivani Amar: Yeah, it’s been… it’s like the weather has gotten so much nicer, but I think it’ll get cold again in the next few days, so it’s nice.

11 00:01:24.960 ⇒ 00:01:29.500 Greg Stoutenburg: Yeah. Yeah, that’s a very Northeastern worry. Where are you?

12 00:01:29.500 ⇒ 00:01:33.679 Shivani Amar: Yeah, I’m in, I’m in Williamsburg in Brooklyn.

13 00:01:33.680 ⇒ 00:01:40.879 Greg Stoutenburg: Okay, alright, okay, yeah. So, yeah, you’ve been probably living in some kind of slush palace out on the roads for a couple months now.

14 00:01:40.880 ⇒ 00:01:48.950 Shivani Amar: Right? Yeah, yeah, okay. And so, it’s like the snow has melted, which just, like, makes the sidewalks more clear. Like, it’s been nice to just walk around lately.

15 00:01:48.950 ⇒ 00:01:51.349 Greg Stoutenburg: Yeah. Yeah, you can almost see it, the sky is.

16 00:01:52.040 ⇒ 00:01:56.879 Shivani Amar: Blue-ish today, which is nice. Yeah, that’s what I would call it, too.

17 00:01:56.880 ⇒ 00:01:57.354 Greg Stoutenburg: Oh.

18 00:01:57.830 ⇒ 00:02:02.759 Shivani Amar: But I’m super excited to understand more about Omni, and…

19 00:02:02.760 ⇒ 00:02:03.620 Greg Stoutenburg: Yeah.

20 00:02:03.880 ⇒ 00:02:08.839 Shivani Amar: like… Like, I… my previous world was…

21 00:02:08.949 ⇒ 00:02:12.999 Shivani Amar: looking at data in… Hey, Utham! Okay, he’s joining.

22 00:02:13.000 ⇒ 00:02:14.740 Greg Stoutenburg: Hello.

23 00:02:14.980 ⇒ 00:02:15.740 Shivani Amar: What?

24 00:02:15.890 ⇒ 00:02:18.750 Shivani Amar: like, when I worked at DaVita, we used Anaplan.

25 00:02:18.750 ⇒ 00:02:19.270 Greg Stoutenburg: Okay.

26 00:02:19.270 ⇒ 00:02:24.249 Shivani Amar: Which is, like a… if you heard of Anaplan, it’s, like, big…

27 00:02:24.250 ⇒ 00:02:25.609 Greg Stoutenburg: Yeah, I mean, I’ve heard the name, I’ve not used.

28 00:02:25.610 ⇒ 00:02:40.630 Shivani Amar: We used Anaplan, which was, like, pretty… pretty awesome. Like, the level of, like, things that I could look at as a facility admission or a regional operational director of, like, my clinics and stuff was, like, pretty dialed, and there was always room to… I was, like, imp…

29 00:02:40.630 ⇒ 00:02:59.690 Shivani Amar: impressed with the dashboards that were, like, already built out for people. And then when I worked at Brave Health most recently, we worked in Tableau, but it was, like, me often saying, like, what I wanted the dashboards to look like for different stakeholders across the business as the chief of staff. But now, we’re entering this world which is hopefully more self-serve, so…

30 00:03:00.060 ⇒ 00:03:02.200 Greg Stoutenburg: That’s the vision. Yep. That’s the vision.

31 00:03:02.200 ⇒ 00:03:05.219 Uttam Kumaran: It’s always been the vision, but it’s so hard, like…

32 00:03:05.570 ⇒ 00:03:11.170 Uttam Kumaran: It requires people that are really knowledgeable, and the foundation to be, like, really solid.

33 00:03:11.170 ⇒ 00:03:11.720 Shivani Amar: Yeah.

34 00:03:11.720 ⇒ 00:03:23.170 Uttam Kumaran: And usually, in order to train people, you have to stop working on a foundation, and so it’s, like, such a trade-off. The AI thing, I think, is really… I mean, Greg, I think you’re…

35 00:03:23.320 ⇒ 00:03:33.889 Uttam Kumaran: the… seeing your stuff on some of our other clients, like, it’s… it’s really working for them, like, to use the AI pieces, like, incredibly well, to ask questions.

36 00:03:33.890 ⇒ 00:03:46.369 Greg Stoutenburg: Yep, we have a, we have a big client, signing a contract with Omni this week after the pilot that we did for them, because their leadership team is just so impressed. I mean, I was… I was attempting to show, you know.

37 00:03:46.370 ⇒ 00:03:51.890 Uttam Kumaran: Sort of, you get a little nervous when you’re talking to the whole leadership team, so attempting to show what we just built in the previous, like, week.

38 00:03:51.890 ⇒ 00:04:00.199 Greg Stoutenburg: And and he just interrupted me and shared the screen, and was like, alright, I’m gonna test out this AI thing, and just got this result right away for this new chart, and he was just like, okay, this is…

39 00:04:00.200 ⇒ 00:04:03.019 Uttam Kumaran: I get intense just hearing the story, like…

40 00:04:03.020 ⇒ 00:04:08.670 Greg Stoutenburg: Okay. I’m like, I was actually mid, you know, I was mid-sentence, and he just, he took over the screen share.

41 00:04:08.670 ⇒ 00:04:09.150 Shivani Amar: Yeah.

42 00:04:09.150 ⇒ 00:04:14.630 Greg Stoutenburg: And it was, you know, it was one of those moments where it’s like, okay, well, once that happened, I was able to just sit back and…

43 00:04:14.630 ⇒ 00:04:18.099 Shivani Amar: Yeah. The deal was done. Watch them self-serve, that’s right.

44 00:04:18.100 ⇒ 00:04:35.459 Greg Stoutenburg: Yeah, and that, yeah, I mean, that really is, you know, that’s the pitch and the promise of it, is, you know, like Utam said, right, the goal has always been self-service, but, like, something isn’t all that self-service if someone else has to build it, and then you can just, by yourself, go retrieve it, right?

45 00:04:35.460 ⇒ 00:04:44.369 Shivani Amar: Yeah, like, self-service also, like, the thing that I maybe get nervous about, so we can, like, talk about the…

46 00:04:44.370 ⇒ 00:04:44.940 Greg Stoutenburg: Yes.

47 00:04:44.940 ⇒ 00:04:51.420 Shivani Amar: The flip side is, like, self-service without clear definitions, so, like, documentation, like, that’s what we’ll kind of…

48 00:04:51.420 ⇒ 00:05:08.620 Shivani Amar: touch on, right? Like, really making clear what’s in this tool, what’s not in this tool, like, so you don’t just think of revenue as a complete picture, but you know that, like, currently it’s only being fed by wholesale and two of our retailers or something. So, like, really, like, making the…

49 00:05:09.250 ⇒ 00:05:15.559 Shivani Amar: constraints or whatever, like, very clear to the user, I think is important. Yes.

50 00:05:15.940 ⇒ 00:05:25.620 Shivani Amar: I’d say the other piece, and you… you can probably speak more from experience, and I’m just briffing based off, like, what I’m, like, feeling into. The, other piece is, like.

51 00:05:25.890 ⇒ 00:05:34.270 Shivani Amar: A proliferation of, like, Reports and data and things versus, like, standard views of the, like.

52 00:05:34.270 ⇒ 00:05:52.280 Shivani Amar: common language of, like, the things that we think are most important. Like, people always want to… like, the way I describe it is, like, there are so many different cuts of data one could want, right? Like, it’s like, actually, I care about the fourth order, not the third order anymore. Right. Actually, I’m, like, doing this one initiative to try and make, like, this one subset, like.

53 00:05:52.330 ⇒ 00:06:02.150 Shivani Amar: act this way, and so therefore I, like, just need information on that. And so, like, I think the self-serve allows for that, but then, like, having some macro, like.

54 00:06:02.220 ⇒ 00:06:18.150 Shivani Amar: blocks and organization of, like, the Omni? Like, okay, what’s our folder system? Like, when do you decide to publish a dashboard for everybody to see versus, like, keeping it for yourself and your own analysis? Like, those are all things that I’m kind of curious about, and hopefully we can touch on.

55 00:06:18.730 ⇒ 00:06:26.090 Shivani Amar: But in terms of just, like, the flow for today, what do you guys have in mind? Like, what… what do you… what should we be discussing today?

56 00:06:26.880 ⇒ 00:06:37.579 Greg Stoutenburg: Well, the flow that I had in mind was just to, go over some of the questions that you and Jason raised in the, in the Establishing Omni Accelerator doc.

57 00:06:37.960 ⇒ 00:07:02.029 Greg Stoutenburg: And… which is… which is similar to what you just said now, so I thought I would speak to the questions that you have, and, you know, when we feel like we’re aligned on what exactly you want to get from Omni, which you’ve already actually just painted a pretty good picture, what we’ll do is we’ll kind of come away from that, we’ll come up with a plan for how… what the needs are, and how we’ll phase this out in the form of, you know, like, deliverables week over week, and

58 00:07:02.050 ⇒ 00:07:05.000 Greg Stoutenburg: Yeah, and then we’ll move forward to deliver it.

59 00:07:05.000 ⇒ 00:07:12.429 Shivani Amar: And so, with them, like, honest, honest, like, question for you is, like, okay, we said we want to pilot it in March.

60 00:07:12.430 ⇒ 00:07:13.560 Shivani Amar: It’s March 11th.

61 00:07:13.560 ⇒ 00:07:36.209 Shivani Amar: we’ve ended our rest and assess week. I wasn’t sure if we could put, like, hands… like, you guys in the background were, like, feeding it the wholesale and retail data, and, like, we can start playing with it by tomorrow, right? Like, I’m like, when I hear Greg’s version, I’m like, oh, this feels very, like, programmatic, and, like, like, I’m just kind of like, what is the exact timeline? So I’m hoping today we can walk away with, like, okay.

62 00:07:36.210 ⇒ 00:07:48.070 Shivani Amar: by X date, we’re gonna have the data fed, by Y date, we can do a quick training. By, like, whatever. And so I know, Greg, you kind of touched on that, but I’m very eager to get going. So…

63 00:07:48.560 ⇒ 00:07:50.610 Shivani Amar: Yes, Utham, any thoughts there?

64 00:07:51.240 ⇒ 00:08:09.119 Uttam Kumaran: Yeah, I mean, I think that’s exactly… so I don’t know, Greg, if we want to just walk through that document, I think, basically, yeah, all Greg needs is… he’s coming on this project new, so I just want to make sure that he has a really clear sense, and then he’s gonna drive this piece. What we kind of do in the background is we ticket everything out, and then, I’m…

65 00:08:09.190 ⇒ 00:08:19.809 Uttam Kumaran: we first will drive towards getting all the topics in place, and then, yes, as fast as we can, drive to onboard, you and Jason, and then.

66 00:08:19.810 ⇒ 00:08:20.610 Shivani Amar: And Dan.

67 00:08:20.610 ⇒ 00:08:24.170 Uttam Kumaran: basically… and you, Jason, and Dan, and then,

68 00:08:24.360 ⇒ 00:08:33.760 Uttam Kumaran: figure out, like, who’s next to start the trial. And then, in parallel, I want to see as fast as we can get you in the platform to start testing, it’ll make the

69 00:08:33.780 ⇒ 00:08:46.759 Uttam Kumaran: conversation about, like, seats and things a lot more clear, which is, like, what Omni will… will start to come in on. So, yeah, I don’t know, Greg, can you just set the stage for maybe even, like, how it’s worked in the past? And then, yeah, like.

70 00:08:46.840 ⇒ 00:08:49.830 Uttam Kumaran: answer Shivani’s question about, like, how we’re gonna phase it out, basically.

71 00:08:50.050 ⇒ 00:09:00.229 Greg Stoutenburg: Yeah, sure. So, the fastest way to do this is get data into Omni, build something, you look at it, and then we go, what do you think?

72 00:09:00.360 ⇒ 00:09:05.249 Greg Stoutenburg: And then we make changes from there. So that’s… that’s the fastest way to go.

73 00:09:05.550 ⇒ 00:09:29.859 Greg Stoutenburg: But to install some of the things that you’ve just mentioned, well, let me just speak to some of the things that you just mentioned. So, right, the goal is self-service, but also a level of control and order. So the way that I’ve done this for another client, for example, is we made folders in what Omni calls their hub, sort of like a central area. And I’d love to show it to you, but it’s all their data, so I can’t do that. It’s got, like, a sort of central page where you go.

74 00:09:29.860 ⇒ 00:09:36.269 Greg Stoutenburg: there are these dedicated folders that Brainforge has created, where these are the, we’ll just call them the official dashboards, the ones that we know

75 00:09:36.270 ⇒ 00:09:51.029 Greg Stoutenburg: that the stakeholders that need access to the BI tool will access regularly. They’re the canonical versions, they’re the ones that, we do things like send a snapshot via Slack and email every day to leadership so they know, you know, here’s your update from the official version.

76 00:09:51.460 ⇒ 00:09:57.130 Greg Stoutenburg: Now, you can control who gets that, you can control who sees those things,

77 00:09:57.150 ⇒ 00:10:08.479 Greg Stoutenburg: Next layer of order is the self-service piece. So, maybe I only care about the fourth order and not the third, like you say. But I probably don’t need… that probably doesn’t need to be something that everybody sees.

78 00:10:08.490 ⇒ 00:10:19.579 Greg Stoutenburg: Necessarily. So, every individual person can actually make their own private workspace, where if there’s something that’s going to be specific only to you, or maybe even to your small team, because you can share it selectively, you can

79 00:10:19.580 ⇒ 00:10:30.190 Greg Stoutenburg: create charts and dashboards that live there. So that self-service element, then, doesn’t have to conflict with the canonical versions that everybody in the company needs to see and align on.

80 00:10:30.270 ⇒ 00:10:49.460 Greg Stoutenburg: So, so that’s another way that we sort of establish order. And then another one is, you know, you gave an example where, like, we need to have the same understanding of revenue, and if we’re, if we’re asking revenue questions, they should be questions that are about, you know, particular sources, right? Like, you want to look at, retail and wholesale.

81 00:10:49.460 ⇒ 00:10:51.160 Greg Stoutenburg: For example, you mentioned in the doc.

82 00:10:51.160 ⇒ 00:11:11.499 Greg Stoutenburg: The way that Omni works, and this is, I don’t want to sound too salesy, like, I don’t get a cut, but the way that they solve this problem to make sure that you’re looking at the right data is they have this concept of a topic. So you’ve got all your… you’ve got all your, your tables that live in a database in, you know, in the background somewhere that are connected to Omni.

83 00:11:11.500 ⇒ 00:11:30.790 Greg Stoutenburg: What Omni allows you to do is build what they call topics, which are subject matter joins of tables that live in your source. So, if you want to have someone asking questions about revenue, where it’s, it’s decided that those questions should be about

84 00:11:30.900 ⇒ 00:11:36.729 Greg Stoutenburg: You know, some particular channel, we can make a topic that’s, like, revenue for this channel.

85 00:11:37.170 ⇒ 00:11:57.130 Greg Stoutenburg: or revenue for these channels, and it’ll take those sources, and it’ll combine them, so that when you ask a question, even if you just use the AI to ask questions, the… the AI will look at the topic structure that you have, select the most relevant topic, and then surface answers by… this is kind of invisible to you…

86 00:11:57.220 ⇒ 00:12:09.419 Greg Stoutenburg: writing SQL queries for those particular tables, or those combinations of tables. So then your… the information that’s surfaced to you is, you know, is curated appropriately, even while it’s self-serviced through, you know, AI queries.

87 00:12:09.650 ⇒ 00:12:24.369 Uttam Kumaran: So they created these, like, new types of, like, these new objects that basically allow you to, like, segment types of data at different levels, depending on what you need. Yeah. Not just, like, you can’t see finance data, but a little bit more creative than that, yeah.

88 00:12:24.670 ⇒ 00:12:25.150 Greg Stoutenburg: Yeah.

89 00:12:25.150 ⇒ 00:12:34.119 Shivani Amar: Makes sense. And it probably then saves time versus having to, like, have the SQL query, like, do the join, like… Yep.

90 00:12:34.950 ⇒ 00:12:35.480 Shivani Amar: like.

91 00:12:35.480 ⇒ 00:12:42.690 Uttam Kumaran: Or, in a Looker environment, typically people will throw everything into one, so you’ll be, like, there’ll be, like, 100 tables joined.

92 00:12:42.970 ⇒ 00:12:50.840 Uttam Kumaran: Right? And, like, that’s also, like, nonsense. And so, thinking about, like, from a persona perspective, someone on this team typically

93 00:12:50.840 ⇒ 00:13:06.569 Uttam Kumaran: ask questions about these topics is sort of, like, what they’re getting at. And the topics we then decide, like, yes, on the data side, it’s typically joined between these marts, or, like, these tables within the mart. And instead, we’re left with, like, topics, and it still takes some curation. So this is, like.

94 00:13:06.680 ⇒ 00:13:26.629 Uttam Kumaran: because of the flexibility, the time has to go into, like, making sure that this person in this role at Element uses these topics to answer questions, but then this is where we actually feed in, and Greg, I’ll steal your thunders, where we feed in all the semantic stuff. So, for each table and each topic, all the way down to the metric, we’re adding descriptions.

95 00:13:26.630 ⇒ 00:13:36.910 Uttam Kumaran: So that when you ask the AI, it uses your persona to pick from the proper topic to then answer the question, with all of those… using all those descriptions, right?

96 00:13:37.240 ⇒ 00:13:55.139 Uttam Kumaran: And so again, it’s… you’re alleviating that… the user to have to… basically, one, have to know which things to join, but also, two, like, even, like, what topic to use, and a lot of that, right? And it’s getting them to an answer that’s referenceable, you know, that they can see where it came from really, really quickly.

97 00:13:57.380 ⇒ 00:14:08.030 Greg Stoutenburg: Yep. It’s like a nice, safe playground for whoever has to access data, right? Yeah, they won’t get into trouble by running into the wrong things, because our team will curate the topics.

98 00:14:08.030 ⇒ 00:14:17.670 Greg Stoutenburg: So, what Omni has done that’s clever is the place where engineers and the people who are managing data intervene, it’s in building the semantic layer.

99 00:14:17.670 ⇒ 00:14:37.330 Greg Stoutenburg: It’s in building the topics. And then, what else is cool about it is that if your backend data is in good shape, then you don’t have to do data engineering work then in order to see what you want to see in Omni. You shape it in Omni, rather than there. So that is an improvement as well.

100 00:14:37.700 ⇒ 00:14:46.579 Shivani Amar: And, like, to double-click into, like, an example for a second, right? Like, when I see fit with Omnichannel Revenue Plan. Like, with them, when you think about

101 00:14:46.740 ⇒ 00:14:56.559 Shivani Amar: what Amber’s been learning about how we think about, like, sales, discounts, refunds, like, in the wholesale world, versus what you’re finding in the retail world. If I were to…

102 00:14:56.660 ⇒ 00:14:59.979 Shivani Amar: like, if we were to upload those tables to Omni today, and I were to say.

103 00:14:59.980 ⇒ 00:15:00.330 Uttam Kumaran: Yeah.

104 00:15:00.330 ⇒ 00:15:11.119 Shivani Amar: what is revenue across channels in California, and, you know, what was revenue across channels in California in February? Do you feel like it could, like.

105 00:15:11.240 ⇒ 00:15:15.990 Shivani Amar: Give me a clear sense of what we’re defining revenue to be for each channel.

106 00:15:16.800 ⇒ 00:15:23.590 Uttam Kumaran: Yeah, so, like, I would say we’re gonna approach it in a couple different ways. One is, we will likely have an omni-channel

107 00:15:23.930 ⇒ 00:15:27.279 Uttam Kumaran: like, revenue view. Yeah. Of course, like.

108 00:15:27.610 ⇒ 00:15:43.570 Uttam Kumaran: when you go deeper into a channel, the amount of dimensions you have increase, right? The dimensions we have in wholesale are not the same as retail, is not the same as this. So, our alternatives… our options are, one, just take the dimensions that are in everything and bring it up.

109 00:15:43.830 ⇒ 00:15:55.429 Uttam Kumaran: But if you want to go deeper into a channel, you’ll have to go deeper into that channel’s related topic, and then ask that question. So, if you’re asking a question like, what is this channel’s California revenue, we will…

110 00:15:55.580 ⇒ 00:15:57.309 Uttam Kumaran: Definitely have state.

111 00:15:57.420 ⇒ 00:16:06.670 Uttam Kumaran: And revenue for all of those. But then if you’re like, well, tell me, like, the wholesale partners that contributed to it, Omni will actually, like, move to the topic that’s related to wholesale.

112 00:16:06.700 ⇒ 00:16:21.589 Uttam Kumaran: the way we have it… the way we’ll structure it is that those will match, meaning the sums will match, but then when you cut it, it’s gonna split up, right? So when you ask a question about, like, which… which wholesale partners contributed, that may not live in that, like.

113 00:16:21.930 ⇒ 00:16:23.770 Uttam Kumaran: exact mark, like.

114 00:16:24.060 ⇒ 00:16:24.570 Shivani Amar: That makes sense.

115 00:16:24.570 ⇒ 00:16:25.340 Uttam Kumaran: you.

116 00:16:25.340 ⇒ 00:16:28.790 Shivani Amar: It’ll go one… it’ll go one layer deeper, and Omni will be able to do that.

117 00:16:29.120 ⇒ 00:16:45.029 Shivani Amar: Yeah, okay, that makes sense. But, sorry, I think, like, the question behind the question that I’m asking is, like, do you now feel clear on what a definition of revenue is? Like, if I were to ask it revenue, do you feel clear on what the definitions of revenue are across those two channels right now?

118 00:16:45.030 ⇒ 00:16:56.570 Uttam Kumaran: Yeah, so in two ways. One, and I think, Greg, you’re gonna talk today about, like, the metric stuff. Maybe, do you want to just chat about, yeah, do you want to just go into that, the actual, like, metric confirmation stuff?

119 00:16:56.840 ⇒ 00:17:20.319 Greg Stoutenburg: Yeah, so I have, I have your data platform documentation, with all of your definitions that are in use already. So I see the data sources, and we’ll rely on these when building your semantic layer in Omni. So, and whatever it is you’re used to using for those definitions, we’ll rely on those as we build in Omni.

120 00:17:20.920 ⇒ 00:17:23.150 Uttam Kumaran: I think it’s in core… in Core Metrics, right?

121 00:17:25.990 ⇒ 00:17:31.089 Uttam Kumaran: So these definitions, I think what we talked about basically was, like, I want to confirm

122 00:17:31.780 ⇒ 00:17:37.289 Uttam Kumaran: The ones that are most relevant to the pilot first, which is… you mentioned in a comment, like.

123 00:17:38.020 ⇒ 00:17:45.729 Uttam Kumaran: should we confirm all these? Like, what can I do async? What our ask is to confirm the ones related to wholesale and retail first.

124 00:17:45.840 ⇒ 00:17:51.130 Uttam Kumaran: And so we’ve gone in and we’ve updated these based on, like, our conversations.

125 00:17:51.350 ⇒ 00:17:55.439 Shivani Amar: Can you filter by wholesale and retail and business domain right now, so we can actually look at them?

126 00:17:55.840 ⇒ 00:17:56.450 Uttam Kumaran: Yeah.

127 00:17:56.800 ⇒ 00:17:57.580 Shivani Amar: Can you filter?

128 00:17:57.580 ⇒ 00:17:59.290 Uttam Kumaran: Your business domain, right? Yeah.

129 00:17:59.290 ⇒ 00:18:00.859 Greg Stoutenburg: Sorry. Yep.

130 00:18:01.970 ⇒ 00:18:02.720 Greg Stoutenburg: We’ll do…

131 00:18:02.720 ⇒ 00:18:03.450 Uttam Kumaran: And then…

132 00:18:06.010 ⇒ 00:18:07.480 Greg Stoutenburg: She said wholesale retail.

133 00:18:07.870 ⇒ 00:18:08.490 Uttam Kumaran: Yeah.

134 00:18:18.830 ⇒ 00:18:23.840 Shivani Amar: So we don’t have… we have point-of-sale revenue, but we don’t have…

135 00:18:29.140 ⇒ 00:18:31.600 Uttam Kumaran: So, wholesale total revenue is, is,

136 00:18:32.320 ⇒ 00:18:41.309 Uttam Kumaran: So, one thing we… what we’ve done is basically tried to match this to the actual, like, column. And so, a couple things we need on, like, when we think about QAs, one.

137 00:18:41.470 ⇒ 00:18:51.009 Uttam Kumaran: the… what is the business semantic definition, looking at the columns like G to K, and then we can go rename this to either, like.

138 00:18:51.650 ⇒ 00:18:58.710 Uttam Kumaran: Our naming convention right now is trying to do, like, the domain underscore… metric.

139 00:18:59.560 ⇒ 00:19:12.549 Uttam Kumaran: So we can remove, like, total if we want to do some, but again, a lot of that, it shouldn’t be as much of a concern. I want to keep that as, like, a… that’s more concerning, like, how we do the naming in the data model. It’s not going to appear in Omni that way.

140 00:19:12.930 ⇒ 00:19:13.620 Greg Stoutenburg: Yup.

141 00:19:13.620 ⇒ 00:19:16.500 Shivani Amar: So, we’re gonna just… let’s just stay here for a minute instead.

142 00:19:16.500 ⇒ 00:19:16.840 Uttam Kumaran: Yeah.

143 00:19:17.160 ⇒ 00:19:22.540 Shivani Amar: Stay here. So, wholesale total revenue. Total revenue from wholesale channel.

144 00:19:22.590 ⇒ 00:19:37.209 Shivani Amar: Like, that doesn’t actually tell me anything. It doesn’t tell me that you’ve done some version of, like, I’ve looked at sales minus refunds. Or maybe you haven’t done minus refunds, or minus discounts, like, it doesn’t actually tell me anything.

145 00:19:37.710 ⇒ 00:19:45.059 Shivani Amar: Like, the methodology that you’ve done to reach revenue is very poorly documented in my… from my perspective in this row.

146 00:19:45.260 ⇒ 00:19:58.270 Shivani Amar: Yeah. And then there’s no retail underscore revenue, which would incorporate, I think, trade spend or chargebacks, I forget which one, right? Like, there’s… one, there’s no even metric for that right now for me to, like, react to.

147 00:19:58.270 ⇒ 00:20:10.799 Shivani Amar: So if I were to go into Omni, thinking that you’ve made marts for retail and for wholesale, I have no understanding of what I’m even pulling. Are they apples to apples? Do they tie to what finance says? Like, these definitions are pretty bland.

148 00:20:11.610 ⇒ 00:20:22.709 Uttam Kumaran: Yeah, so, I mean, so, I… one… yeah, this is what we have to just confirm now, so we can go one step further and go all the way to the source. I guess my question is, like, what’s relevant to…

149 00:20:23.180 ⇒ 00:20:30.409 Uttam Kumaran: the Omni, like… what’s relevant to the Omni user? Seeing, like, all the way down to that detail, or is it…

150 00:20:31.000 ⇒ 00:20:31.699 Greg Stoutenburg: Okay, terribly.

151 00:20:31.700 ⇒ 00:20:38.440 Shivani Amar: I mean, I want to be able to trust your methodology for how you’re getting to revenue, and I can’t even trust it with this definition, because it just says revenue is revenue.

152 00:20:39.120 ⇒ 00:20:39.690 Shivani Amar: Yeah.

153 00:20:40.020 ⇒ 00:20:42.299 Shivani Amar: So, like, like, that tells me nothing.

154 00:20:42.780 ⇒ 00:20:51.690 Shivani Amar: So, like, trade spend is just trade spend. Like, discounts are just discounts, like, the definitions are very self… very…

155 00:20:51.690 ⇒ 00:20:58.089 Uttam Kumaran: We take, like, 61, like, is there anywhere deeper we need to go on, like, new applicants? Like, do we need, for example…

156 00:20:58.210 ⇒ 00:20:59.960 Shivani Amar: Do we need to talk about, like, the…

157 00:21:00.000 ⇒ 00:21:00.800 Uttam Kumaran: Okay.

158 00:21:01.290 ⇒ 00:21:05.749 Shivani Amar: I think that’s fine, but you could, like, in a separate column, you could say, like.

159 00:21:05.860 ⇒ 00:21:19.320 Shivani Amar: the source for this is the, like, CRM, right? Like, so it really, like, walks people through… this is where we’re talking about that layer of documentation being really sound, and it’s the part that I probably feel the shakiest on.

160 00:21:19.380 ⇒ 00:21:33.749 Shivani Amar: Right? Because, like, I haven’t seen it in a way that has been tangible to me, be like, okay, I’m, like, really getting how the documentation is going. So, when I look at this, it’s like, count of new host applicants in a period. Okay, fine, but, like, where are you deriving that from?

161 00:21:33.910 ⇒ 00:21:39.070 Shivani Amar: And it’s like, this is stemming from the Google Sheets CRM, or this is stemming from

162 00:21:39.180 ⇒ 00:21:47.549 Shivani Amar: You know, like, the emails we receive that go… like, just that level of detail and, like, what is this actually derived from would be very helpful.

163 00:21:48.200 ⇒ 00:21:50.419 Greg Stoutenburg: You want to see the source and the calculation here as well.

164 00:21:50.420 ⇒ 00:22:01.450 Shivani Amar: Yes, yeah. Like, that’s, like, the level of rigor that would make me feel more confident in, like, like, if we’re going to embark on using Omni, right now, if I were to say, what was my…

165 00:22:01.710 ⇒ 00:22:06.179 Shivani Amar: retail versus wholesale revenue in California last month.

166 00:22:06.480 ⇒ 00:22:09.749 Shivani Amar: Okay? One, I don’t even think there is a retail revenue.

167 00:22:09.860 ⇒ 00:22:29.369 Shivani Amar: Two, I don’t really know, like, it should caveat it in Omni from my perspective, or be really clearly defined that, like, currently out of scope right now is trade spend and chargebacks. Like, we actually don’t have that. Or maybe we do in Emerson, I don’t remember, right? But, like, to make it clear that, like, the way that we’re naming revenue is based off this.

168 00:22:31.270 ⇒ 00:22:33.850 Shivani Amar: And, like, if you were to flip,

169 00:22:34.120 ⇒ 00:22:42.540 Shivani Amar: I don’t know who’s sharing their screen right now, but, Greg, have you seen the, like, retail dashboard that Amber has put together?

170 00:22:43.260 ⇒ 00:22:47.379 Greg Stoutenburg: I mean, I’ve clicked through some of the sources, I haven’t studied the dashboards, no.

171 00:22:47.380 ⇒ 00:22:51.570 Shivani Amar: Gotcha. Do you have… do you happen to have it? Like, if you… you can stop sharing your screen, I was just curious.

172 00:22:51.570 ⇒ 00:22:53.349 Greg Stoutenburg: Hey, Tom, is it here? Is it on this…

173 00:22:53.350 ⇒ 00:22:56.419 Uttam Kumaran: Yeah, it’s in… it’s in your… I sent… I slacked it to you yesterday.

174 00:22:56.420 ⇒ 00:22:56.840 Greg Stoutenburg: Okay.

175 00:22:58.590 ⇒ 00:23:01.730 Greg Stoutenburg: Feel free to take a beat. Let me grab that one. Yep.

176 00:23:02.150 ⇒ 00:23:14.060 Shivani Amar: I know I’m kind of, like, taking us on a pivot, but, like, I think it’s just, like, for me, like, before we, like, kick something off, I’m like, where am I reviewing this documentation to be like, yup, I clear it, it makes sense to me, otherwise I’m just like…

177 00:23:14.060 ⇒ 00:23:14.589 Uttam Kumaran: Yeah, yeah.

178 00:23:14.590 ⇒ 00:23:16.130 Shivani Amar: I’m piloting.

179 00:23:24.870 ⇒ 00:23:26.360 Uttam Kumaran: Do you have it, Greg, or do you want me to…

180 00:23:26.560 ⇒ 00:23:30.760 Greg Stoutenburg: Yeah, I mean, if you have it and can pull it up faster than I can, yeah, I’m searching for it.

181 00:23:37.550 ⇒ 00:23:47.859 Uttam Kumaran: So, like, basically, my thought is that these… these all will get inferred from that same list. So these are, like, now these are a little bit dis… these are a little bit disjointed, but…

182 00:23:48.290 ⇒ 00:23:52.190 Uttam Kumaran: basically, yes. This will pull from that list, and these should all…

183 00:23:53.180 ⇒ 00:23:54.769 Uttam Kumaran: Like, kind of join and match.

184 00:23:54.770 ⇒ 00:23:58.500 Shivani Amar: Yeah, okay, now let’s go to the daily report as an example.

185 00:23:59.130 ⇒ 00:24:03.650 Shivani Amar: Okay, so this is POS, which you had in your thing, like, point of sales…

186 00:24:04.650 ⇒ 00:24:09.700 Shivani Amar: data, right? And so this is, like, probably very clearly named in Emerson.

187 00:24:10.490 ⇒ 00:24:22.720 Shivani Amar: Right? So, like, there’s… that’s helpful, like, this is, like, point-of-sales data, but if I were to say, like, how much revenue have I gotten from Target, I imagine that’s different from my point-of-sales data. That’s, like…

188 00:24:23.010 ⇒ 00:24:23.670 Uttam Kumaran: Yeah.

189 00:24:23.670 ⇒ 00:24:27.600 Shivani Amar: Right? And so, like, then maybe I shouldn’t even be querying revenue right now.

190 00:24:28.020 ⇒ 00:24:28.660 Uttam Kumaran: Sure.

191 00:24:28.660 ⇒ 00:24:41.050 Shivani Amar: Right? Unless we’ve, like, come up with the full structure, and that’s where I’m saying what is in scope and out of scope needs to be so clear to me, because we keep saying the words omnichannel revenue, which is where, like, is one of my true norths of where I want to get to.

192 00:24:41.080 ⇒ 00:24:53.849 Shivani Amar: But, like, I don’t even know all the steps to get there, and that’s kind of where we’re… we’re almost tying into the other conversation with them, with the inputs and the outputs. It’s like, at what point can we, like, clearly say a metric is, like, modeled correctly?

193 00:24:54.050 ⇒ 00:25:07.570 Shivani Amar: Sure. Right? And it’s like, once we have Confido in… I’m making things up, like, once we have Confido and we have this, then I can say revenue. Once I have this and this, then I can name point of sales, which I… that’s already done.

194 00:25:07.830 ⇒ 00:25:08.230 Uttam Kumaran: Sure.

195 00:25:08.230 ⇒ 00:25:11.350 Shivani Amar: And then it’s like, then it kind of tells you, like, okay, these are the things you can, like.

196 00:25:11.600 ⇒ 00:25:13.089 Shivani Amar: query from.

197 00:25:13.300 ⇒ 00:25:31.010 Shivani Amar: But these are things that are out of scope. So, if we’re combining wholesale and retail into Omni, what are the things that I can glean? If I have point of sales from one, and then I have revenue from the other, like, what can I query in Omni to, like, get insights about both channels together? I don’t even know.

198 00:25:31.970 ⇒ 00:25:32.490 Uttam Kumaran: Yeah.

199 00:25:32.990 ⇒ 00:25:34.389 Shivani Amar: Do you have any examples?

200 00:25:35.630 ⇒ 00:25:38.960 Uttam Kumaran: Oh, like, in terms of, like, if you combine those, I mean, one…

201 00:25:39.090 ⇒ 00:25:48.759 Uttam Kumaran: I mean, basically, you’re… you can look at, like, revenue acceleration, you can look at, like, what channels, like, what… you can look… there’s a ton of geo-analysis you can do across both.

202 00:25:48.940 ⇒ 00:25:50.740 Uttam Kumaran: Right? .

203 00:25:50.740 ⇒ 00:25:58.390 Shivani Amar: But if we don’t have revenue, then what terms should I use? Or is it gonna tell me that it’s not quite revenue? Or is it gonna use point of sales as a proxy? Like…

204 00:25:58.390 ⇒ 00:25:59.500 Uttam Kumaran: No, I…

205 00:25:59.500 ⇒ 00:26:05.930 Greg Stoutenburg: Yeah, I, sorry, I, I, yeah, so point heard about having the documentation improved to

206 00:26:05.930 ⇒ 00:26:19.679 Greg Stoutenburg: population and source. To the question about what’s going to happen in Omni, if you ask a question that basically the topic can’t actually answer, is that it’ll return that. It’s not going to just relabel something, and this is the value of the semantic layer.

207 00:26:19.680 ⇒ 00:26:20.090 Shivani Amar: Perfect.

208 00:26:20.090 ⇒ 00:26:43.389 Greg Stoutenburg: that, yeah, so whatever dashboards are, sorry, whatever sources are set in, whatever joins we set up using topics, it’s going to surface information for, you know, at that join, or at those joins. So if there isn’t a place where there is some column where all these other, you know, all the channels are going to connect, it’s not going to say, it’s not going to just sort of pick from,

209 00:26:43.390 ⇒ 00:26:46.099 Greg Stoutenburg: From one of the tables, it’ll just return that, you know, it doesn’t have that.

210 00:26:46.100 ⇒ 00:26:57.120 Shivani Amar: So, so then for the pilot to feel like it’s useful and wowing people, which is what we want, right? We want people to be like, oh yeah, I love Omni, like, at the end of some set amount of time, a few weeks or something.

211 00:26:57.810 ⇒ 00:26:58.230 Greg Stoutenburg: Yep.

212 00:26:58.230 ⇒ 00:27:16.090 Shivani Amar: Then I’m like, what can I actually query from Omni, knowing that, like, maybe I don’t have revenue for retail right now, and I have something for wholesale? Like, if I’m Dan, who’s in finance, and finally eager to get, like, some, like, omni-channel view situation across a couple channels, or slices of a couple channels.

213 00:27:16.090 ⇒ 00:27:20.920 Shivani Amar: Like, I’m just looking for an example of, like, a win that could come from…

214 00:27:21.070 ⇒ 00:27:22.250 Greg Stoutenburg: Doing this.

215 00:27:22.940 ⇒ 00:27:24.120 Greg Stoutenburg: Yeah, yeah.

216 00:27:24.120 ⇒ 00:27:29.020 Shivani Amar: I don’t know if I… I think we can think about it, we don’t have to declare it on this call, but I…

217 00:27:29.680 ⇒ 00:27:31.409 Shivani Amar: full of, like… Yeah.

218 00:27:31.410 ⇒ 00:27:38.319 Uttam Kumaran: We’re basically gonna script the demo. We’re gonna script the best demo, so Greg, you can talk about how we did it for M, yeah.

219 00:27:38.670 ⇒ 00:28:02.860 Greg Stoutenburg: Yeah, yeah, I mean, yeah, I’ll tell you how I did this before. So, knowing that I had basically 3 personas that were going to look at dashboards in Omni, what I did was, sort of perfectly build the dashboards that I know that they’re going to want to look at, make sure that they’re, you know, the data is exactly as it should be, right down to things like you called out in the document, like what…

220 00:28:02.860 ⇒ 00:28:27.860 Greg Stoutenburg: what’s going to be considered the start of the day. So, if I say, you know, I want to look at last week’s such and such, right, I’m going to get from… if what I want is 12am Sunday to 11.59 p.m. Saturday, you know, I get that, rather than 5 AM on Monday, and so on. So, we executed at that level of precision, we understand the personas and the type of questions they’re going to ask. How do we do that? Actually, by scraping transcripts from the times that we’ve talked to them.

221 00:28:27.860 ⇒ 00:28:42.849 Greg Stoutenburg: to go, what kinds of questions do they ask? And then we looked at those questions that they ask, and actually used those to build the topics. So that’s a way to… that’s a way to surface that, and then just show someone who you’ve never talked to, look, check it out. Omni already knows.

222 00:28:42.850 ⇒ 00:28:49.449 Greg Stoutenburg: what you’re interested in. Or, you know, we just ask. We just have a conversation like this, where, you know,

223 00:28:49.490 ⇒ 00:28:56.769 Greg Stoutenburg: Whether you’re the revenue person who’s interested in it, or you’ve been giving questions as though you are.

224 00:28:56.890 ⇒ 00:29:05.399 Greg Stoutenburg: we will build a topic so that when you ask revenue questions, it gives information that is relevant and correct. Gotcha. Yeah, yeah, so…

225 00:29:05.740 ⇒ 00:29:11.279 Shivani Amar: Helpful. So I think this will give you a flavor of, like, yeah, there might be one type of user that’s

226 00:29:11.470 ⇒ 00:29:24.470 Shivani Amar: Like, I haven’t even talked to Laura about the Omnipilot, right? But, like, Laura is the head of wholes, like, leads wholesale. So, if Laura was just wanting to query wholesale data, then I feel confident that that could be useful. Right.

227 00:29:24.520 ⇒ 00:29:36.739 Shivani Amar: the people I want to vow… I mean, sorry, the people I want to wow with this BI tool is not just, like, one slice of the business, it’s, like, people who can see things stitched across different parts of the business.

228 00:29:36.740 ⇒ 00:29:37.070 Greg Stoutenburg: Yes.

229 00:29:37.070 ⇒ 00:29:41.560 Shivani Amar: That’s, like, I’d say, like, Greg, context for you is, like, we started D2C.

230 00:29:41.910 ⇒ 00:29:44.910 Shivani Amar: It’s like, I know you’re coming in, and sorry, I, like, barely, like.

231 00:29:44.910 ⇒ 00:29:52.140 Greg Stoutenburg: onboarded you to Element. I’m sure, I’m sure people have, like, shared some stuff with you, but, like, you started as a D2C company just, like, on Shopify.

232 00:29:52.140 ⇒ 00:29:56.280 Shivani Amar: Now, we sell on Amazon, like, if you just think about e-commerce, we sell on Amazon also.

233 00:29:56.340 ⇒ 00:30:14.210 Shivani Amar: So, if we’re like, okay, we’re selling on Amazon and Shopify, like, the data modeling rigor is gonna come in in, like, matching the week to the week across those two, and matching, like, you know, like, all of the different, like, they have different ways they name the products and the SKUs, and, like, being able to make sense of all of that.

234 00:30:14.210 ⇒ 00:30:26.740 Shivani Amar: And then, we, like, started selling at Target, and Walmart, and Costco Canada, and things like that. And so, like, what used to happen in this business, which is common for, I’m sure, a lot of other businesses that you’ve worked in.

235 00:30:26.840 ⇒ 00:30:37.220 Shivani Amar: the people in retail would go in and pull their reports from their source systems, and then the person from e-commerce would pull some reports from Shopify and pull some reports from Amazon, try to make sense of it.

236 00:30:37.290 ⇒ 00:30:48.320 Shivani Amar: And now we’re, like, we’re so hungry for this data foundation to show us, like, the, like, the view across all the things. Right. Like, stitching everything together is where the juice really comes.

237 00:30:48.320 ⇒ 00:30:48.820 Greg Stoutenburg: Yes.

238 00:30:48.820 ⇒ 00:30:53.619 Shivani Amar: Like, were we, like, are we able to really squeeze that juice out of this? And so…

239 00:30:54.230 ⇒ 00:31:06.700 Shivani Amar: great if Madison and Laura can, like, query their stuff a little bit better, that’s awesome. Great. But, like, that’s not gonna be the thing that convinces our CEO that, like, Omni is the tool to use.

240 00:31:07.410 ⇒ 00:31:13.430 Greg Stoutenburg: Yeah. Yep, understood. Heard all of it. Yeah, I mean, the promise of…

241 00:31:13.450 ⇒ 00:31:38.419 Greg Stoutenburg: Well, really, part of the promise of any BI tool is that you ought to be able to take your data connections, send them into the tool, and get some information, you know, across those, provided that your tables are structured in such a way that you’re going to be able to do that, right? There’s no such thing as an inner join if there isn’t some column that’s got, you know, the same key. So, it has to exist, but then the additional promise of.

242 00:31:38.420 ⇒ 00:31:42.990 Greg Stoutenburg: Omni is being able to self-serve it so effectively. Now, to…

243 00:31:42.990 ⇒ 00:32:01.140 Greg Stoutenburg: beyond just any BI tool, why Omni specifically? It’s because… it’s because of the topic thing, again. And it’s like, it can’t really be… I think it probably can’t be understated how important the topic concept is for understanding the value of Omni, because we will build for all those personas. Like, everyone you just mentioned who’s got their, you know.

244 00:32:01.170 ⇒ 00:32:08.760 Greg Stoutenburg: unique interests for why they want reporting, we’ll make them a topic, or we’ll make that group a topic. So, just like

245 00:32:08.850 ⇒ 00:32:09.700 Greg Stoutenburg: you know.

246 00:32:09.850 ⇒ 00:32:29.279 Greg Stoutenburg: hypothetically, if you’ve got 5 tables, and everyone’s been looking at these 5 different tables, no one wants to or knows how to write SQL to do their own, you know, querying on this, what Omni’s going to allow us to do is define a topic so that person 1, who needs to see information from tables, A and C,

247 00:32:29.450 ⇒ 00:32:37.850 Greg Stoutenburg: there’s a topic where there’s a join for A and C, right? Person 2, who needs to see topics from A and C and D, is going to have a topic that does that.

248 00:32:37.850 ⇒ 00:32:51.329 Greg Stoutenburg: And… and so on. So, everyone’s getting something that’s, like, sort of curated, but it’s… what enables it is the work that we do to create the topic, and also the structure of the tables on the backend. Yes, perfect.

249 00:32:53.720 ⇒ 00:32:59.109 Shivani Amar: So this is, like, the prehistoric age style, where everything gets joined, and then you’re, like.

250 00:32:59.620 ⇒ 00:33:04.139 Uttam Kumaran: why… who cares about, like, what, you know? And then this is more of, like.

251 00:33:04.200 ⇒ 00:33:20.020 Uttam Kumaran: okay, these are topics, but again, I actually think that this world is moving towards even, like, less understanding of this, and more about, like, I have a colloquial understanding of a metric at Element, and I’m gonna ask the question that I’m used to asking.

252 00:33:20.020 ⇒ 00:33:31.530 Uttam Kumaran: And we are gonna glean that from our notes, our transcripts, all the things we’ve learned, and, like, try to do that one-to-one. And, like, that’s really… that’s the gap, is, like.

253 00:33:32.260 ⇒ 00:33:40.260 Uttam Kumaran: less about, like, people who need to ask, like, oh, I’m looking for revenue sum underscore, or, like, that is, like, kind of so stupid, but…

254 00:33:40.400 ⇒ 00:33:52.889 Uttam Kumaran: I haven’t… there’s been no solution to that until, like, some tools like this, which is why we’re, like, pushing for it, because it’s going further and further that people… people should have never really had to know

255 00:33:53.040 ⇒ 00:34:04.649 Uttam Kumaran: that they needed, like, the double underscore, oh no, pull it from this table, not that table, like, it’s, like, the worst thing ever. So we are gonna try to implement as much context into the metric.

256 00:34:04.650 ⇒ 00:34:15.320 Uttam Kumaran: The table, and then the other layer, so that the agent will traverse all of that, and then basically propose, and then we can tune, like, how liberal or conservative, like, we want it to answer.

257 00:34:16.780 ⇒ 00:34:26.490 Uttam Kumaran: You know, and so that’s… I know it’s tough to not see, like, a working example, but, like, I think we’re probably, like, a week from, like, having something to play with.

258 00:34:26.670 ⇒ 00:34:30.500 Shivani Amar: Totally. And a question for you, Greg, like, do you… are you, like…

259 00:34:33.060 ⇒ 00:34:36.710 Shivani Amar: Are you hoping from me to get those archetypes?

260 00:34:37.370 ⇒ 00:34:45.290 Shivani Amar: are you hoping to say, okay, Shivani, who are the users gonna be? What are the types of questions they want to ask? Because I can start putting that together.

261 00:34:45.290 ⇒ 00:34:45.999 Greg Stoutenburg: That would be great, yeah.

262 00:34:46.000 ⇒ 00:34:54.120 Shivani Amar: So, I think if you define the deliverable that you need for me really clearly, I can move on it. It’s what I’m hearing, but not, like, explicitly, like, here’s your.

263 00:34:54.120 ⇒ 00:34:54.949 Greg Stoutenburg: Yeah, yeah, yeah.

264 00:34:54.949 ⇒ 00:35:19.950 Greg Stoutenburg: Well, yeah, it… sure, it’s an ask. As far as which stage we’re at, right? So, as we continue to build out Omni for you, getting the topics right is going to be critical, so that when folks log in, they’re like, wow, look what, you know, look what Shivani set us up with. Like, this is exactly the tool I’ve needed so that I can see all the information I’m trying to get and get that holistic view. Which, only during this side note, I’m just gonna take a sidebar, because I think of things like

265 00:35:19.950 ⇒ 00:35:22.679 Greg Stoutenburg: this sometimes. That has to be why they called it Omni, huh?

266 00:35:23.050 ⇒ 00:35:23.530 Uttam Kumaran: Boy.

267 00:35:23.530 ⇒ 00:35:25.740 Greg Stoutenburg: Like, every… like, everything? Just all?

268 00:35:25.790 ⇒ 00:35:27.059 Uttam Kumaran: Oh, I don’t know why.

269 00:35:27.290 ⇒ 00:35:27.940 Uttam Kumaran: I don’t have.

270 00:35:27.940 ⇒ 00:35:29.030 Greg Stoutenburg: I think too much about it.

271 00:35:29.030 ⇒ 00:35:31.939 Uttam Kumaran: Like, what does Tableau mean? I don’t know. Yeah.

272 00:35:31.940 ⇒ 00:35:33.320 Greg Stoutenburg: Yeah, I’ve… I think.

273 00:35:33.320 ⇒ 00:35:37.679 Shivani Amar: It’s like my North Star’s omni-channel views, right? Like, it’s like, that’s the…

274 00:35:37.680 ⇒ 00:35:44.110 Uttam Kumaran: Well, that’s the problem, is we kept saying Omni twice in this doc, and Greg’s like, why are… what does that mean? I’m like, no, no, no, like, wait.

275 00:35:44.110 ⇒ 00:35:44.700 Greg Stoutenburg: Omni on me.

276 00:35:44.700 ⇒ 00:35:45.769 Uttam Kumaran: There’s no…

277 00:35:45.770 ⇒ 00:35:48.599 Greg Stoutenburg: That’s like infinity plus 1, like, that’s not more.

278 00:35:48.600 ⇒ 00:35:56.630 Uttam Kumaran: Greg… well, Greg… Greg is, like, a professor on the side, PhD, and so when I talk about Greek stuff, maybe you’re the… you gotta tell us what all these words mean, because I don’t…

279 00:35:56.630 ⇒ 00:36:00.110 Greg Stoutenburg: Yeah, I’m a philosophy PhD. I was a professor before this.

280 00:36:00.950 ⇒ 00:36:19.030 Shivani Amar: That’s awesome. I, the, like, the person we’re kind of exploring as, like, internal hire, like, the person I used to work with at Brave Health is, I think, also… I think you’ve also been a philosophy major, but literally, as we’ve been talking, I’m just very much reminded of him, and I’m like, if he comes on board, you guys will… you guys will buy.

281 00:36:19.030 ⇒ 00:36:36.549 Greg Stoutenburg: We’ll get it. We’ll each get it. Trading looks and stuff. Yeah, I mean, well, I can tell you, Tableau means, like, slate. Like, when John Locke had this idea that all ideas are gotten through experience, he said that the mind is a tabula rasa, meaning, like, it’s a blank slate, so it’s not until you get sensory experience.

282 00:36:36.550 ⇒ 00:36:40.939 Uttam Kumaran: Dude, you’re so… you’re so smart, like, I… I… I couldn’t even like…

283 00:36:40.940 ⇒ 00:36:45.009 Shivani Amar: Yeah, he majored in… okay, I’m looking now, he went to Colgate, majored in philosophy and applied math.

284 00:36:45.010 ⇒ 00:36:46.320 Uttam Kumaran: I have so many data…

285 00:36:46.320 ⇒ 00:36:48.530 Shivani Amar: And I’m like, you guys are gonna… you guys are gonna click.

286 00:36:48.730 ⇒ 00:36:53.989 Uttam Kumaran: I have a lot of data friends that are psychology majors, English majors, or philosophy majors. Majority of them are not

287 00:36:54.110 ⇒ 00:36:55.610 Uttam Kumaran: engineers, which is…

288 00:36:56.030 ⇒ 00:36:58.569 Uttam Kumaran: Very surprising, but a lot of them end up in data.

289 00:36:58.730 ⇒ 00:36:59.990 Uttam Kumaran: One way or another.

290 00:36:59.990 ⇒ 00:37:00.880 Greg Stoutenburg: Yeah.

291 00:37:02.210 ⇒ 00:37:19.879 Greg Stoutenburg: Anyway, like I was saying, yeah, so, yeah, so Omni, but before that, oh yeah, understanding the personas. So, it depends, you know, like, where we are in the project. If what we really need is, like, a small, tidy, beautiful pilot that just shows retail and wholesale together, we can just

292 00:37:19.880 ⇒ 00:37:27.179 Greg Stoutenburg: We can just, you know, build some particular view, and look at some particular view, have that, that small

293 00:37:27.180 ⇒ 00:37:45.339 Greg Stoutenburg: scripted demo. The further we want to go, the more we need to understand everyone who’s going to be using Omni, what they’re going to be using it for, so that we can, you know, continue to really expand, but also refine what those topics are going to be, so that everyone is able to self-serve in the way that they would expect to. Yeah.

294 00:37:45.340 ⇒ 00:37:45.930 Shivani Amar: Perfect.

295 00:37:47.260 ⇒ 00:37:48.310 Shivani Amar: Cool.

296 00:37:48.440 ⇒ 00:37:54.649 Shivani Amar: Okay, should we go through any next steps in timeline and just crystallize these things?

297 00:37:56.270 ⇒ 00:37:57.310 Greg Stoutenburg: Yeah.

298 00:37:57.310 ⇒ 00:37:58.230 Shivani Amar: Perfect.

299 00:37:58.780 ⇒ 00:38:08.610 Shivani Amar: No, I’m very excited about this. I think I’m just, like, I’m, like, with them, I’m like, we gotta get that documentation rigor, so I feel confident, and I still think that that’s, like, a…

300 00:38:09.030 ⇒ 00:38:09.640 Shivani Amar: I think we got.

301 00:38:09.640 ⇒ 00:38:13.710 Uttam Kumaran: Yes. We oughta spend some time there, which we have time today, okay?

302 00:38:13.710 ⇒ 00:38:18.540 Greg Stoutenburg: Yep. Yes. Yep. So, let me… I will go back to the screen share then.

303 00:38:18.540 ⇒ 00:38:19.240 Shivani Amar: Perfect.

304 00:38:23.030 ⇒ 00:38:23.680 Greg Stoutenburg: Yeah.

305 00:38:23.800 ⇒ 00:38:25.879 Greg Stoutenburg: Okay, so…

306 00:38:26.220 ⇒ 00:38:32.329 Greg Stoutenburg: In the accelerator doc, what we put is, you know, high-level, 1-2 month plan, assuming a four-week pilot.

307 00:38:32.330 ⇒ 00:38:54.910 Greg Stoutenburg: starting here. And now, of course, you know, something that… we laid this out, so this is, like, very rigorous, very, you know, step-by-step, but also, we’ll of course find ways to accelerate this at every stage that we can, and have it be both, you know, rigorous and precise. You’ll get… you’ll get the opportunity to sign off at every stage as we’re moving forward, even… even while we go at that… at that Brainforge fast speed.

308 00:38:55.110 ⇒ 00:38:59.409 Greg Stoutenburg: So, so what we’re looking at here, 4-week pilot for now.

309 00:39:00.360 ⇒ 00:39:03.740 Greg Stoutenburg: The very beginnings, just to connect Omni to Snowflake, already done.

310 00:39:04.140 ⇒ 00:39:04.830 Shivani Amar: Great.

311 00:39:05.020 ⇒ 00:39:12.990 Greg Stoutenburg: Second stage of that is exactly what we’ve been talking about, is to, lock the pilot domain and definitions. And I’ve been hearing you say,

312 00:39:13.440 ⇒ 00:39:17.609 Greg Stoutenburg: these things, right? Wholesale 360, unified revenue across channels.

313 00:39:18.730 ⇒ 00:39:19.700 Greg Stoutenburg: Okay, so…

314 00:39:19.700 ⇒ 00:39:27.090 Shivani Amar: So, a big caveat that I don’t know if it’s revenue or sales, and that we should just articulate what that is, which is fine. Yes, yeah, yeah, yeah, yeah.

315 00:39:27.090 ⇒ 00:39:40.780 Greg Stoutenburg: Yeah. Yeah, so this is going to mean, for that data taxonomy, this is going to mean doing those calculations, providing the source information that you asked for in, in the data platform documentation, so that… so that

316 00:39:40.780 ⇒ 00:39:50.010 Greg Stoutenburg: as we’re… as we’re setting things up in Omni, we go, you know, someone types in revenue, and it pulls from a table, the definition is the one that you see reflected in the spreadsheet.

317 00:39:50.010 ⇒ 00:39:50.730 Shivani Amar: Perfect.

318 00:39:50.730 ⇒ 00:39:55.960 Greg Stoutenburg: Okay. So then what we’ll do is build the first topics.

319 00:39:56.210 ⇒ 00:40:02.689 Greg Stoutenburg: Which, to start with, you know, just one or two, based on the personas that will need to see a demo.

320 00:40:03.150 ⇒ 00:40:15.790 Greg Stoutenburg: You know, to be impressed and say we want to move forward with this. This explains what that is. We’ll build the semantic layer for the first couple of dashboards,

321 00:40:16.010 ⇒ 00:40:22.169 Greg Stoutenburg: Sorry, I got ahead, that’s what this is saying. Enable blobby, that’s just the word for the AI assistant.

322 00:40:22.170 ⇒ 00:40:43.229 Greg Stoutenburg: Okay. Very cute. And then build a couple of dashboards. And so then, once we have those dashboards in there, starting with you, we’ll, you know, we’ll ask you to go in and type some stuff in and see what the AI assistant gives back to you, and hopefully you’ll have the same reaction. We expect that you’ll have the same reaction that, you know, that our other clients’ leadership did, which is just like, wow, this actually did

323 00:40:43.260 ⇒ 00:40:46.579 Greg Stoutenburg: exactly what I always wanted a BI tool to do.

324 00:40:46.580 ⇒ 00:40:52.510 Uttam Kumaran: Giovanni, do you have a sense of what Dan and Jason, like, care about? Like, initially?

325 00:40:52.780 ⇒ 00:40:55.129 Shivani Amar: is probably less of a user, he’s just like.

326 00:40:55.130 ⇒ 00:40:55.590 Uttam Kumaran: Okay, okay.

327 00:40:55.610 ⇒ 00:40:56.710 Shivani Amar: tech member.

328 00:40:56.710 ⇒ 00:40:59.770 Uttam Kumaran: Make sure he’s in there, and he, like, sees that it’s, like, working.

329 00:40:59.770 ⇒ 00:41:00.100 Shivani Amar: Yeah.

330 00:41:00.100 ⇒ 00:41:01.300 Uttam Kumaran: I’ve passed that to whatever.

331 00:41:01.300 ⇒ 00:41:12.639 Shivani Amar: I don’t think Jason’s like, okay, now I want to know, like, sales, right? Whereas Dan, I think, is, like, he’s been hungry for, like, sales across channels for years. He was like, when I started at…

332 00:41:12.640 ⇒ 00:41:23.619 Shivani Amar: element that was, like, my project that I thought I was gonna be able to, like, make more streamlined, right? So he… and then what does Dan care about long-term is improving how we do supply-demand forecasting.

333 00:41:23.850 ⇒ 00:41:28.369 Shivani Amar: Right? Yeah. But, like, that might be, like, what we’re exploring with that other company with them, but I think that.

334 00:41:28.370 ⇒ 00:41:28.889 Uttam Kumaran: Yeah, yeah, yeah.

335 00:41:28.890 ⇒ 00:41:33.210 Shivani Amar: But it’s like, what are all my demand signals across my channels? What are all the, like.

336 00:41:33.370 ⇒ 00:41:48.530 Shivani Amar: what are the SKUs that are selling the fastest? Like, what are the flavors that are selling the fastest across my channels? Like, these are all types of questions that people might be curious about. Like, across wholesale, if all we have right now is wholesale and two big retailers, which flavors are the most popular?

337 00:41:49.670 ⇒ 00:42:00.539 Uttam Kumaran: Yeah, no, we’re, amber, in our retail call, is gonna talk about… we have some inventory stuff, so I think, like, I guess my… more of my question is, like, how raw can we be working with Dan on this stuff? Like…

338 00:42:00.910 ⇒ 00:42:02.680 Uttam Kumaran: Like, should we wait to, like…

339 00:42:03.700 ⇒ 00:42:08.939 Uttam Kumaran: QA everything, or is it, like… yeah, like, how do you think, like, the.

340 00:42:08.940 ⇒ 00:42:17.400 Shivani Amar: I think part of the QA comes from somebody saying, I just queried… like, I think Dan can be at a place, like, I just queried for this, and I was surprised that it didn’t have it.

341 00:42:17.560 ⇒ 00:42:31.879 Shivani Amar: Okay, okay, okay. Right? Like, I queried for this, and I thought the numbers were a little bit off, and I think that’s why Dan is actually the right person. He has a very good, holistic view of the business, and, like, he’s so,

342 00:42:32.900 ⇒ 00:42:37.720 Shivani Amar: Engaged with, like, all the critical metrics in our business, because he’s managing supply-demand.

343 00:42:38.360 ⇒ 00:42:41.379 Shivani Amar: Right? Okay. Okay. So, I think, I think, like.

344 00:42:41.780 ⇒ 00:42:44.660 Shivani Amar: me and him putting hands to keyboard and saying, like.

345 00:42:44.660 ⇒ 00:42:45.729 Uttam Kumaran: I did what I… yeah.

346 00:42:45.730 ⇒ 00:43:01.319 Shivani Amar: It’s like, we’re, like, the working group versus him being like, and then maybe then after me and Dan, like, Dan and I play around with this, it’s like, then we’re showing Phil some, like, dashboards that we’ve, like, all lined on that we feel good about, but, like, Dan and I can be the ones to, like, riff on things with you, Greg.

347 00:43:01.690 ⇒ 00:43:07.520 Greg Stoutenburg: Great. Great. Perfect. Yeah. That’s perfect. Having… having partners in that is really helpful.

348 00:43:07.520 ⇒ 00:43:29.089 Greg Stoutenburg: And that is where you do identify things that seem like discrepancies. Like, someone’s like, these two charts don’t align. That’s because this tool said, again, like, the week started this day, the other one says it starts this day, and so you end up with this 15% discrepancy. But really, it’s just because you were looking at Saturday and not Monday. Right. You know, things like that. Okay, yeah, so that’s good. So.

349 00:43:29.090 ⇒ 00:43:36.160 Uttam Kumaran: So that’s what I think I just want to make clear, is, like, I think Greg, Shivani, and Dan are the, I think, our initial folks, just to, like.

350 00:43:36.610 ⇒ 00:43:46.870 Uttam Kumaran: try to get something into their hands that they can test. Both testing, like, the ergonomics of Omni is, like, one big piece that, like, I think, like, I’m very… we’ll…

351 00:43:46.870 ⇒ 00:43:56.959 Uttam Kumaran: you’ll solve that, and then the data… the accuracy of the recall from the agent, and then, like, I guess, Shiovanni, the dashboarding capabilities are, like.

352 00:43:57.170 ⇒ 00:44:01.679 Uttam Kumaran: everything you expect. So we’ll… we’ll spin up dashboards that look great, but again, like.

353 00:44:01.930 ⇒ 00:44:06.099 Uttam Kumaran: more subjective, I think, once we start talking to teams about what they want to see.

354 00:44:06.380 ⇒ 00:44:14.680 Uttam Kumaran: Like, we can re… we’ll end up recreating, at minimum, everything that’s in the spreadsheet, so, like, the table view of everything, but…

355 00:44:14.940 ⇒ 00:44:19.430 Uttam Kumaran: we’ll create visuals and things like that, but the AI thing nailing that.

356 00:44:19.520 ⇒ 00:44:27.540 Uttam Kumaran: is gonna be the star of the show, and so I think, Greg, you kind of heard, like, both of those stakeholders, the people to, like, that are gonna be our…

357 00:44:27.590 ⇒ 00:44:42.270 Uttam Kumaran: folks that we work with, and then driving, you know, Shivani and Dan to be like, hell yeah, this is, like, working super well, then focusing on, like, what is the star set of, like, demos that we can really just, like, hone in and make sure.

358 00:44:42.470 ⇒ 00:45:00.370 Greg Stoutenburg: Yep. Yep. Yep, that all sounds good. And then… and that is what I did for, you know, for the other presentation as well, is I… I had some questions that I already knew that the AI was gonna do a good job of, based on the topic, but, you know, but also said, like, alright, now that you’ve seen how this is supposed to work, go ahead and give it a try.

359 00:45:00.370 ⇒ 00:45:00.750 Shivani Amar: Right.

360 00:45:00.750 ⇒ 00:45:09.460 Greg Stoutenburg: in, you know, in the topic that’s gonna be of interest to you. So, you know, it’ll be, you know, it’ll be curated, it’ll be polished, but that’s not the same thing as saying it’s, you know, fake.

361 00:45:09.720 ⇒ 00:45:14.240 Shivani Amar: Yeah, and the other person that I think would be interesting is, like, if you are feeding this

362 00:45:14.430 ⇒ 00:45:17.840 Shivani Amar: wholesale data, Madison would probably be a person that I just want to, like.

363 00:45:17.840 ⇒ 00:45:22.280 Greg Stoutenburg: with her scope being more just wholesale-specific, right? Okay.

364 00:45:22.280 ⇒ 00:45:40.459 Shivani Amar: So those would be the users, and I… I think, for her, it’s just about, like, it’s like, we can onboard her to the thing, but that’s not the thing that’s gonna prove out that Omni is awesome, necessarily. And so I think it’s more just to get a feel for, like, oh, like, can a… can a person who is

365 00:45:40.710 ⇒ 00:45:47.050 Shivani Amar: It’s more focused on one area, gleaning insights from having this, like, this tool to play around with now.

366 00:45:47.180 ⇒ 00:45:53.740 Shivani Amar: Which is obviously so useful to Element at large, but, like, I’m really hungry to show, like, the OmniView.

367 00:45:54.320 ⇒ 00:45:55.440 Shivani Amar: Yep, yep, yep.

368 00:45:55.800 ⇒ 00:46:09.099 Greg Stoutenburg: Yep. Yeah, and the OmniView is very cool, and… but also, like you’re indicating, some folks will only need to see, like, one dashboard, and that’ll… that’ll be all they need, and that’s okay, and we can do… we can still do neat things, like allow them to query it from Slack, or…

369 00:46:09.100 ⇒ 00:46:23.030 Uttam Kumaran: Yeah, you read my mind. I was about to say, like, yeah, there’s a lot of, like, really neat features on, like, sending… sending alerts, sending reports, and then querying via Slack. So all those things, Greg, I think, nail the baseline, and then, like.

370 00:46:23.030 ⇒ 00:46:23.560 Greg Stoutenburg: Yeah.

371 00:46:23.980 ⇒ 00:46:25.969 Uttam Kumaran: Extra credit, she can get it.

372 00:46:26.420 ⇒ 00:46:32.419 Greg Stoutenburg: Yeah, I mean, once all the… once all the data is connected and has the right structure, like, Omni kind of takes care of it from there.

373 00:46:33.670 ⇒ 00:46:34.300 Greg Stoutenburg: Yep.

374 00:46:34.970 ⇒ 00:46:35.620 Shivani Amar: Cool.

375 00:46:35.620 ⇒ 00:46:36.880 Greg Stoutenburg: OmniView, yeah.

376 00:46:37.300 ⇒ 00:46:38.140 Greg Stoutenburg: Okay.

377 00:46:38.350 ⇒ 00:46:38.690 Shivani Amar: Exciting?

378 00:46:38.690 ⇒ 00:46:39.120 Uttam Kumaran: So…

379 00:46:39.120 ⇒ 00:46:46.390 Shivani Amar: Okay, so Snowflake already connected, me telling you a little bit more about the users, I can, like, write a couple things to you. If you have.

380 00:46:46.390 ⇒ 00:46:46.750 Greg Stoutenburg: Yep.

381 00:46:46.750 ⇒ 00:46:56.089 Shivani Amar: if you have actual questions for me to answer, like, something structured, I can fill it out. Okay. And then, what would the next step be in terms of, like, the post…

382 00:46:56.480 ⇒ 00:46:58.130 Shivani Amar: like, post that, like, for the… Yep.

383 00:46:58.130 ⇒ 00:47:06.969 Greg Stoutenburg: This phase right now that’s still in what we’re calling, you know, phase one foundation and pilot scope, is to get all those metric definitions nailed down. So, we need to,

384 00:47:06.970 ⇒ 00:47:18.149 Greg Stoutenburg: Add those calculations, say a little bit more for the definition, as you requested before, and make sure that we’re solid on that, and then we can move forward to starting to build topics.

385 00:47:18.150 ⇒ 00:47:18.790 Shivani Amar: Perfect.

386 00:47:19.140 ⇒ 00:47:19.820 Greg Stoutenburg: Yep. Cool.

387 00:47:19.820 ⇒ 00:47:23.739 Uttam Kumaran: So I think, Greg, what would also be helpful is, like, yeah, I think we can…

388 00:47:23.910 ⇒ 00:47:26.200 Uttam Kumaran: Basically, go put all the tickets together.

389 00:47:26.200 ⇒ 00:47:26.520 Greg Stoutenburg: Yeah.

390 00:47:26.590 ⇒ 00:47:35.489 Uttam Kumaran: And then… I think if you can send Shivani, like, the clear tasks in the spreadsheet, where to go update things. I also think, like, maybe we should…

391 00:47:35.650 ⇒ 00:47:38.260 Uttam Kumaran: put the persona-related stuff

392 00:47:38.640 ⇒ 00:47:44.779 Uttam Kumaran: In the stakeholders part of the spreadsheet, like, I don’t know if there’s pieces we can add there about, like.

393 00:47:45.700 ⇒ 00:47:53.730 Uttam Kumaran: like, what, like, some of the common questions per person, or things they care about, maybe this is a good place to centralize it. Otherwise, yeah.

394 00:47:54.430 ⇒ 00:47:55.740 Greg Stoutenburg: Yeah, that’s a good idea.

395 00:47:56.610 ⇒ 00:47:57.170 Uttam Kumaran: Okay.

396 00:47:58.710 ⇒ 00:48:00.260 Greg Stoutenburg: Say, key…

397 00:48:00.690 ⇒ 00:48:08.499 Uttam Kumaran: Yeah, key questions, or that way, like, this… this being maintained is gonna make it, so otherwise it’s only gonna live in Omni. I’m kind of, like, trying not to…

398 00:48:08.640 ⇒ 00:48:10.290 Shivani Amar: Yep, I think… you’re trying to have…

399 00:48:10.290 ⇒ 00:48:10.750 Greg Stoutenburg: Whoa.

400 00:48:10.750 ⇒ 00:48:11.410 Shivani Amar: Yeah.

401 00:48:11.710 ⇒ 00:48:13.840 Uttam Kumaran: And I don’t think this is, like, anything, like,

402 00:48:13.990 ⇒ 00:48:28.569 Uttam Kumaran: sensitive. It’s just, like, what does the person care about? Like, eventually, I think this can link to their place in the OKR sheet, which I can share to you. Things like that, like, that’s gonna where… that’s gonna be where, like, what we’re gonna find is people are gonna ask a question that

403 00:48:28.890 ⇒ 00:48:30.179 Uttam Kumaran: they, they, like…

404 00:48:30.330 ⇒ 00:48:40.009 Uttam Kumaran: we didn’t expect them to ask. Like, they may start to ask, like, how does this compare to my OKRs? Or, like, I want to try to, like… that’s all extra credits, I want to see how far we can go.

405 00:48:40.010 ⇒ 00:48:40.980 Greg Stoutenburg: Yeah. Up front.

406 00:48:41.040 ⇒ 00:48:45.360 Uttam Kumaran: Because if that person has that surprise moment, like, they’ll be… everyone will be super bought in.

407 00:48:45.360 ⇒ 00:48:46.750 Greg Stoutenburg: Yep. Yep.

408 00:48:47.750 ⇒ 00:48:49.200 Greg Stoutenburg: Yeah, agreed.

409 00:48:49.370 ⇒ 00:49:03.269 Greg Stoutenburg: Yes, so I can… I can send over some specific questions. Mostly it’ll be about just what kind of questions do they ask, what do they want data for? It’ll be, it’ll be that sort of thing. So, but I’ll write it up, and…

410 00:49:05.100 ⇒ 00:49:17.219 Greg Stoutenburg: Sorry, I’m fighting my browser for a moment. Yeah, and then we’ll… we’ll work out those definitions, and then once we have that finished, we move on to the next phase, which is we start actually building an Omni.

411 00:49:17.460 ⇒ 00:49:19.970 Shivani Amar: Okay, and like, when do we think that will be?

412 00:49:20.160 ⇒ 00:49:29.139 Greg Stoutenburg: I think… I mean, Utam, help me with this one. I’ll… I’ll be out for a couple days, but I can do this… this handoff work before I… I go.

413 00:49:29.140 ⇒ 00:49:34.920 Uttam Kumaran: Yeah, I think we’re gonna work on all the model setups, so if you’re back on Monday.

414 00:49:35.500 ⇒ 00:49:41.790 Uttam Kumaran: I’m fairly sure that all of the models we’re dealing with with both of these will be ready for you to topic.

415 00:49:41.790 ⇒ 00:49:46.539 Greg Stoutenburg: Yeah, yeah, so we can probably start working on topics, middle to end of next week.

416 00:49:46.540 ⇒ 00:49:47.280 Shivani Amar: Perfect.

417 00:49:47.920 ⇒ 00:50:02.029 Uttam Kumaran: Yeah, and then I think, Shivani, like, probably the only other thing is, like, we… we added dis… the two things to figure out about both the two areas we’re focusing on is the discounts and refunds, which I sent a note to Beth, but maybe I can re-up, or I can…

418 00:50:02.270 ⇒ 00:50:05.770 Uttam Kumaran: Just message her. And the second piece is to talk about inventory today.

419 00:50:06.120 ⇒ 00:50:10.649 Uttam Kumaran: But roughly, if we feel like those spreadsheets are in a good place, the data’s QA’d.

420 00:50:11.150 ⇒ 00:50:18.139 Uttam Kumaran: then, like, I don’t have to think about that going into this phase. Yeah. So, I just want to… let’s… maybe I can help make sure

421 00:50:18.910 ⇒ 00:50:33.170 Uttam Kumaran: coming from, like, last week, that the rest. So today, we’re gonna go through the retail sheet. I think I’ll have enough action items, if anything comes out of that, to hand the Amber to execute. Yeah. I mean, making sure discounts and refunds, but the wholesale piece, I feel like we… we beat that down, and…

422 00:50:33.170 ⇒ 00:50:33.590 Shivani Amar: I…

423 00:50:33.590 ⇒ 00:50:35.710 Uttam Kumaran: We’re good. I think we’re good there.

424 00:50:35.710 ⇒ 00:50:36.029 Shivani Amar: I think.

425 00:50:36.030 ⇒ 00:50:42.260 Uttam Kumaran: Or at least we’re, like, way better than what it was, like, anyone knew about it before, so… yeah.

426 00:50:42.580 ⇒ 00:50:44.310 Shivani Amar: Cool? Okay, great.

427 00:50:45.770 ⇒ 00:50:47.890 Shivani Amar: Thank you, Greg, so nice to meet you, I hope you have a.

428 00:50:47.890 ⇒ 00:50:48.400 Greg Stoutenburg: Definitely.

429 00:50:48.400 ⇒ 00:50:49.270 Shivani Amar: I’m in…

430 00:50:49.270 ⇒ 00:50:49.720 Greg Stoutenburg: Yeah, thank you.

431 00:50:49.790 ⇒ 00:50:52.120 Shivani Amar: Consistent sunshine, hopefully.

432 00:50:52.390 ⇒ 00:50:52.820 Greg Stoutenburg: Okay.

433 00:50:52.820 ⇒ 00:50:55.359 Shivani Amar: But then, I know we have time, like, later to kind of.

434 00:50:55.360 ⇒ 00:50:56.030 Uttam Kumaran: Yeah, Lucas…

435 00:50:56.030 ⇒ 00:50:58.840 Shivani Amar: Does that sound good versus topic switching right now?

436 00:50:58.840 ⇒ 00:51:06.140 Uttam Kumaran: That’s fine, that’s fine. Yeah, we have the retail call, so for this, discounts, refunds, can I just re-up best to just…

437 00:51:07.080 ⇒ 00:51:08.899 Uttam Kumaran: This number? Or, like, what do you think?

438 00:51:08.900 ⇒ 00:51:12.629 Shivani Amar: Yeah, so, Greg, do you have to hop off? I feel like you have to hit the road soon, so…

439 00:51:12.630 ⇒ 00:51:16.449 Greg Stoutenburg: I… I will actually have to go in just a couple minutes here. Okay.

440 00:51:16.450 ⇒ 00:51:17.319 Shivani Amar: I’m like, you can hop…

441 00:51:17.320 ⇒ 00:51:17.949 Uttam Kumaran: Yeah, you’re free to go.

442 00:51:17.950 ⇒ 00:51:19.670 Shivani Amar: This is probably not super…

443 00:51:19.670 ⇒ 00:51:22.809 Greg Stoutenburg: Yeah, this has been great, great to meet you, looking forward.

444 00:51:22.810 ⇒ 00:51:28.519 Shivani Amar: And impressing the heck out of everybody with Omni. Okay, it’s gonna be fun. Bye.

445 00:51:28.520 ⇒ 00:51:29.150 Uttam Kumaran: Thanks, Greg.

446 00:51:31.360 ⇒ 00:51:34.299 Uttam Kumaran: How is jet lag? How are you even, like, awake right now?

447 00:51:34.300 ⇒ 00:51:41.059 Shivani Amar: I’ve been waking up so early, so I don’t know. Yesterday, I took, like, a nap in the afternoon. So…

448 00:51:41.350 ⇒ 00:51:45.960 Shivani Amar: The… The discounts and.

449 00:51:45.960 ⇒ 00:51:46.370 Uttam Kumaran: Yes.

450 00:51:46.370 ⇒ 00:51:47.130 Shivani Amar: funds.

451 00:51:47.400 ⇒ 00:51:55.769 Shivani Amar: say the question again, like, you’re like, I want to finalize our methodology is correct for calculating these, or, like, what is the, what is the goal?

452 00:51:56.230 ⇒ 00:52:01.230 Uttam Kumaran: That is correct. So, we… we were asked by, Beth.

453 00:52:01.420 ⇒ 00:52:09.700 Uttam Kumaran: to get returns and discounts. She basically said, can we get returns and discounts in our reporting? Because we use those in our journal entry.

454 00:52:11.050 ⇒ 00:52:14.589 Uttam Kumaran: And, sorry, I’ll just… I’ll just show you, like, what the thing is.

455 00:52:14.890 ⇒ 00:52:18.010 Uttam Kumaran: and… let’s see…

456 00:52:21.450 ⇒ 00:52:23.070 Uttam Kumaran: Yeah, so she said.

457 00:52:24.420 ⇒ 00:52:32.839 Uttam Kumaran: Hey, Amber, this is great. I’d like to get returns and discounts in there as we use those in our journal entry. The small discrepancies coming from cell G1.

458 00:52:33.010 ⇒ 00:52:45.340 Uttam Kumaran: And then here, we sent a message saying, hey, hi team, happy Monday, we’ve updated the monthly sales report by SKU, it includes discounts from return, we verified using the summary data, and then a note on the, like.

459 00:52:45.830 ⇒ 00:52:51.839 Uttam Kumaran: just, like, the definition, and then that’s actually… so this is what we generated for… for Bess.

460 00:52:52.610 ⇒ 00:53:01.129 Uttam Kumaran: And so, this gives Beth, like, the finance view of, like, literally every single product. It’s almost replicating, like, her view.

461 00:53:01.480 ⇒ 00:53:07.620 Uttam Kumaran: In the spreadsheet, because ultimately, we needed to do that to, like, speed up the QA.

462 00:53:07.860 ⇒ 00:53:12.649 Uttam Kumaran: So, we’ve just added discounts and returns here.

463 00:53:13.190 ⇒ 00:53:17.160 Uttam Kumaran: So… Just need, like, a…

464 00:53:18.090 ⇒ 00:53:20.740 Uttam Kumaran: This is matching, this is good.

465 00:53:21.160 ⇒ 00:53:23.380 Uttam Kumaran: And, like… Yeah.

466 00:53:24.110 ⇒ 00:53:41.620 Uttam Kumaran: Ultimately, again, I’m still, like, I don’t… Best doesn’t necessarily need to use this, because they have a process, but we basically needed to recreate that. It’s also all on the same Shopify data, so that’s okay. We needed to recreate that to, like, start doing the QA, and isolate, like, where the issues were.

467 00:53:43.160 ⇒ 00:53:52.780 Uttam Kumaran: And now also our process of, like… like, for example, if they were to close books and give us that, like, sheet again, it’s very easiest for us to go look at the discrepancies, if there are any.

468 00:53:52.780 ⇒ 00:53:53.250 Shivani Amar: Yeah.

469 00:53:53.250 ⇒ 00:54:01.209 Uttam Kumaran: And so anytime we’re pushing a model change out, we’re looking at if there are any discrepancies between what finance has published, but… Yeah.

470 00:54:02.010 ⇒ 00:54:03.479 Uttam Kumaran: I just think she just…

471 00:54:03.750 ⇒ 00:54:08.540 Uttam Kumaran: That’s already approved the rest of this. I think I just need to get her approval on these two columns and close the sale.

472 00:54:08.540 ⇒ 00:54:15.120 Shivani Amar: Perfect. Okay, that sounds good. I… I just pinged her to be like, hey, are you free to pop into Zoom real quick? And she hasn’t.

473 00:54:15.120 ⇒ 00:54:15.490 Uttam Kumaran: Okay.

474 00:54:15.490 ⇒ 00:54:26.169 Shivani Amar: But, like, I think if she… when she replies later, I can say, we were just looking over this tab, and, like, I think they’re hungry for you to, like, okay their methodology and let us know.

475 00:54:26.170 ⇒ 00:54:26.530 Uttam Kumaran: Okay.

476 00:54:26.530 ⇒ 00:54:34.040 Shivani Amar: see anything. Like, no, it’s not urgent for today, but if you can do it by end of week, I think that would help them kind of, like.

477 00:54:34.040 ⇒ 00:54:34.540 Uttam Kumaran: Okay.

478 00:54:34.540 ⇒ 00:54:36.789 Shivani Amar: feel good about their whatever. So, I’ll.

479 00:54:36.790 ⇒ 00:54:37.370 Uttam Kumaran: And then…

480 00:54:37.370 ⇒ 00:54:39.900 Shivani Amar: I’ll ping her instead of you having to ping her.

481 00:54:39.900 ⇒ 00:54:40.790 Uttam Kumaran: Okay.

482 00:54:41.040 ⇒ 00:54:44.330 Uttam Kumaran: Okay, and then on the retail report,

483 00:54:45.050 ⇒ 00:54:48.040 Uttam Kumaran: Really, this is where we’re gonna start… we can show, like.

484 00:54:48.300 ⇒ 00:54:48.950 Shivani Amar: So, so…

485 00:54:48.950 ⇒ 00:54:49.679 Uttam Kumaran: Some of this stuff.

486 00:54:49.680 ⇒ 00:55:01.800 Shivani Amar: Let me clarify one thing, because, like, the retail report, you are very hungry to clear your inventory piece. Russell has just, like, looked at this, and he’s like, I see it as 1% off what I’m looking at, I just want to understand their methodology.

487 00:55:01.800 ⇒ 00:55:02.730 Uttam Kumaran: Okay, okay, okay, okay.

488 00:55:02.730 ⇒ 00:55:04.209 Shivani Amar: Russell is just, like…

489 00:55:04.610 ⇒ 00:55:05.110 Uttam Kumaran: Thank you.

490 00:55:05.110 ⇒ 00:55:07.680 Shivani Amar: To, like, go through it macro. So, like, it’s not.

491 00:55:07.680 ⇒ 00:55:08.220 Uttam Kumaran: Okay.

492 00:55:08.220 ⇒ 00:55:12.999 Shivani Amar: If Brainforge’s agenda item is talking about inventory, he might have no insight on.

493 00:55:13.000 ⇒ 00:55:14.410 Uttam Kumaran: Sure, sure, sure, sure, sure.

494 00:55:14.410 ⇒ 00:55:27.230 Shivani Amar: So it’s like, I think we… you and I can go through that, and say, like, okay, like, how did you define this? Does it make sense. I’m totally game. Or Phil can be a person to be, like, gut-checking things, that’s fine.

495 00:55:27.230 ⇒ 00:55:34.570 Shivani Amar: But you might get Russell’s buy-in on that section, but I think just so you know, like, not even starting with the summary report.

496 00:55:34.570 ⇒ 00:55:35.350 Uttam Kumaran: Yeah, yeah.

497 00:55:35.350 ⇒ 00:55:41.530 Shivani Amar: lived in, beth says she’s free, by the way. Okay. I’m gonna just invite her.

498 00:55:41.720 ⇒ 00:55:44.030 Uttam Kumaran: I can just tell him that, like, we have this.

499 00:55:44.470 ⇒ 00:55:44.800 Shivani Amar: Yeah.

500 00:55:44.800 ⇒ 00:55:46.140 Uttam Kumaran: And we’re QA-ing it.

501 00:55:46.140 ⇒ 00:55:47.560 Shivani Amar: Exactly, or just saying, like…

502 00:55:47.560 ⇒ 00:55:48.709 Uttam Kumaran: If he wants to go deeper.

503 00:55:48.710 ⇒ 00:56:03.569 Shivani Amar: We’re actively queuing this. It seems like you have, like, some 1% delta in some of the numbers you’re looking at. What’s your methodology? And then he can talk to his methodology, where he’s… where he has a question, and then… and then we can say, like, okay, here are our next steps to tie the… that loop.

504 00:56:03.570 ⇒ 00:56:10.369 Shivani Amar: And you can say, hey, like, by the way, would love somebody to gut check this inventory. Is that you, or is that somebody else?

505 00:56:11.100 ⇒ 00:56:11.930 Uttam Kumaran: Okay, okay.

506 00:56:11.930 ⇒ 00:56:12.760 Shivani Amar: Hi, Bess!

507 00:56:13.350 ⇒ 00:56:13.760 Uttam Kumaran: Yes.

508 00:56:14.140 ⇒ 00:56:15.619 Shivani Amar: Good to see you, how are you doing?

509 00:56:15.620 ⇒ 00:56:17.160 Bess Ross: I’m doing good.

510 00:56:17.160 ⇒ 00:56:19.620 Shivani Amar: Are those birds chirping in the background? That’s so nice.

511 00:56:19.620 ⇒ 00:56:20.500 Bess Ross: They are.

512 00:56:20.500 ⇒ 00:56:23.549 Shivani Amar: Wow, okay, good life. I don’t know, sounds so pleasant.

513 00:56:24.040 ⇒ 00:56:27.169 Shivani Amar: It does sound very quaint.

514 00:56:27.170 ⇒ 00:56:44.550 Shivani Amar: We were just looking at the wholesale, just checking in on the status of some stuff over here, and this wholesale summary report that they made… no, sorry, gross sales by SKU, you can see, I don’t know if you’ve looked at this, Bess, but they’ve added columns now for discounts and refunds.

515 00:56:44.550 ⇒ 00:56:47.140 Bess Ross: Okay, great. Yeah, I haven’t had a chance to look at it, but…

516 00:56:47.140 ⇒ 00:56:58.520 Shivani Amar: And it’s not, like, urgent today, but I think the thing that I’ll share, like, the macro context, is we’re trying to, like, feed the wholesale data into a BI tool.

517 00:56:59.450 ⇒ 00:57:04.719 Shivani Amar: To start testing out the efficacy of this, like, do we like this BI tool to…

518 00:57:04.720 ⇒ 00:57:05.130 Bess Ross: Got it.

519 00:57:05.130 ⇒ 00:57:20.020 Shivani Amar: BI tool. You know, I don’t know if you’ve interacted with, like, Tableau in previous iterations or Power BI, but this is something different that would, like, help us, like, visualize the data and stuff like that. So I think they’re looking for, like, a stamp of approval. Brainforge is, like, looking at a stamp of approval on how they did the discounts and…

520 00:57:20.020 ⇒ 00:57:33.190 Shivani Amar: refunds, and if you’re like, no, there’s a really big delta, then I think then we deep dive into it. But if you have time by end of week to just look at this and see if it’s far off from what you have, or if it’s in line, then we can, like, kind of move forward and feed the data to the BI tool.

521 00:57:33.190 ⇒ 00:57:37.390 Bess Ross: I should have… I should. Let me block off some time.

522 00:57:37.390 ⇒ 00:57:38.440 Uttam Kumaran: Thank you.

523 00:57:38.440 ⇒ 00:57:45.989 Bess Ross: That way, we just wanna put a pin in, like… My one hang-up with it…

524 00:57:46.090 ⇒ 00:57:55.909 Bess Ross: is… Amber, and I understand why, like, you guys have this kind of as a static report, yes. My one…

525 00:57:55.910 ⇒ 00:57:57.629 Uttam Kumaran: hang up, because…

526 00:57:57.630 ⇒ 00:58:15.920 Bess Ross: not being able to pull in new SKUs, because the way I have my file set up is… is basically… it does a check for new SKUs, basically, it does a completeness check, and that tells… if my completeness check is off, then that tells me that I’m missing new SKUs.

527 00:58:15.920 ⇒ 00:58:16.430 Uttam Kumaran: same.

528 00:58:16.430 ⇒ 00:58:17.020 Bess Ross: And so…

529 00:58:17.020 ⇒ 00:58:21.329 Uttam Kumaran: Okay, so we can address that, too. I feel like… okay, let me… I can take that back.

530 00:58:21.330 ⇒ 00:58:21.960 Bess Ross: Okay.

531 00:58:21.960 ⇒ 00:58:37.480 Uttam Kumaran: sure that new SKUs enter this. It would be… basically, the behavior should be very similar to, like, if you were just going to run that Shopify report again. And we are… this spreadsheet, it’s doing that, actually, but let me just double-check again that new SKUs will enter into the right category here.

532 00:58:37.480 ⇒ 00:58:37.900 Bess Ross: Okay.

533 00:58:37.990 ⇒ 00:58:39.330 Uttam Kumaran: So I can…

534 00:58:39.330 ⇒ 00:58:42.949 Bess Ross: That would be the only… because otherwise, I’ll have to go still do my process to make sure.

535 00:58:42.950 ⇒ 00:58:43.490 Uttam Kumaran: Yes.

536 00:58:43.490 ⇒ 00:58:46.929 Bess Ross: briefing. So in order… I did…

537 00:58:47.050 ⇒ 00:58:54.289 Bess Ross: like, I did briefly look at this prior to running my process last time, and it was pretty close with… it just didn’t have the discounts and the returns, so I…

538 00:58:54.290 ⇒ 00:58:54.690 Uttam Kumaran: Okay.

539 00:58:54.690 ⇒ 00:59:19.359 Bess Ross: I need those for my entry. I ended up doing the normal process, but there were new SKUs for 12-ounce that popped in this month, so it did make me think about that, so I’m glad that it kind of came up. But if those couple things get incorporated, and obviously I’ll jump in here and tie out a couple months, other than December and make sure it’s working correctly, but if so, I mean, I think this would be great. I’ll still have other things to pull, but this will take a big

540 00:59:19.360 ⇒ 00:59:23.089 Bess Ross: piece of that reconciliation, just make it a lot quicker, so…

541 00:59:23.090 ⇒ 00:59:38.469 Uttam Kumaran: Yeah, so that’s also what I want to confirm, is, like, I think two things. One, of course, I mentioned this to Shivani, our initial goal here is, like, to power whatever we need for omni-channel reporting, but I said along the way, we’re gonna find wins like this, where, like, we’re already pulling this data.

542 00:59:38.470 ⇒ 00:59:55.339 Uttam Kumaran: we’re gonna need it in a very similar way, and we can make it apparent. So I think, Shivani, there’s… I mean, in two ways. One, if this can start being used adjacent, or, like, at least to start your process, that would be great. And then I think on our side, just knowing that you’re gonna move to this, I want to go make sure this is, like, hardened.

543 00:59:55.340 ⇒ 00:59:58.670 Uttam Kumaran: And, like, we’re able to have some observability on the fact that this is running.

544 00:59:59.720 ⇒ 01:00:02.569 Uttam Kumaran: So that’s sort of just what my call-out,

545 01:00:02.570 ⇒ 01:00:15.760 Shivani Amar: Yeah, I think that’s definitely the goal, that this is part of, right? And so, like, it sounds like your next item is checking to make sure the SKUs get incorporated, and if they don’t have a home that’s clear that we, like, talk about, like.

546 01:00:15.760 ⇒ 01:00:32.750 Shivani Amar: what is that methodology to do the SKU check? Or, like, I don’t know, whatever the background thing needs to happen for the SKUs to flow in. And then my hope is that when Bess is… when doing, like, the March reporting, that she’s completely using this, and doesn’t have to,

547 01:00:33.020 ⇒ 01:00:36.860 Shivani Amar: doesn’t have to do the methodology she’s been doing otherwise, so…

548 01:00:37.050 ⇒ 01:00:37.950 Uttam Kumaran: Yes, okay.

549 01:00:37.950 ⇒ 01:00:43.289 Shivani Amar: We’re all aligned on the goal, I think. SKU check, and then a quick gut check on the numbers.

550 01:00:43.290 ⇒ 01:01:03.050 Bess Ross: Yeah, and I think the easiest way to do the SKU check, what I do every month is I just, kind of like I mentioned before, I look at the total, see if my totals here agree to that total, and if they don’t, I know I’m missing SKUs. So, a quick, like, standard completeness check, usually does the trick. And,

551 01:01:04.520 ⇒ 01:01:23.400 Bess Ross: And then I have some… and if you have the file, but I have a… on the Shopify Raw Data tile file, I have a formula that basically pulls in the combination of the SKUs and checks them to the summary tab, and then it tells me if I need to add it. Okay, okay. So, you can probably follow that same logic, but…

552 01:01:23.400 ⇒ 01:01:32.300 Bess Ross: Or you may have a better way. But yeah, I’ll try and set a… I don’t think it’ll take very long, I’ll try and set aside some time. It probably won’t be until Friday.

553 01:01:32.470 ⇒ 01:01:33.150 Uttam Kumaran: Okay.

554 01:01:33.150 ⇒ 01:01:37.459 Bess Ross: Probably won’t be till Friday afternoon, but I’ll try to get it done this week.

555 01:01:37.700 ⇒ 01:01:51.159 Uttam Kumaran: Yeah, and as soon as you send that, I’ll just try to make sure we’re on, so if you’re on, we can, like, rapidly try to check things, but yeah, I’m super excited. I’m hopeful that this replaced, and we’re gonna try to make it so you can go back to one area, and then…

556 01:01:51.280 ⇒ 01:01:57.210 Uttam Kumaran: a lot of this should move out of Spreadsheet world, hopefully, into some BI tool, so I’m glad. Like, that’s great.

557 01:01:57.430 ⇒ 01:02:05.579 Bess Ross: Yeah, yeah, that would be awesome. Well, Udom, would you rather us put, like, 30 minutes, 30, 45 minutes on the calendar on Friday, and…

558 01:02:05.580 ⇒ 01:02:07.310 Uttam Kumaran: Yeah, I would, yeah, I would prefer…

559 01:02:07.310 ⇒ 01:02:09.429 Bess Ross: Address things that come up, if they do.

560 01:02:09.430 ⇒ 01:02:12.579 Uttam Kumaran: Yeah, I would… that would be amazing. So, you tell me…

561 01:02:13.280 ⇒ 01:02:16.640 Bess Ross: Do you have anything around 2.30 Eastern?

562 01:02:19.740 ⇒ 01:02:22.550 Uttam Kumaran: I can do 2.30 Eastern. Yeah.

563 01:02:22.880 ⇒ 01:02:23.360 Uttam Kumaran: Yes.

564 01:02:24.750 ⇒ 01:02:32.590 Uttam Kumaran: I will be in an interview, but I don’t think you guys need me for this, so… Yeah, so I’ll… if you can, I can send it, and I’ll… I can add Amber as well.

565 01:02:32.590 ⇒ 01:02:37.350 Bess Ross: Okay, yeah, that’d be great. If you just want to send out an invite, we can hop on.

566 01:02:37.920 ⇒ 01:02:40.500 Bess Ross: Live in real time, and just knock it out.

567 01:02:40.620 ⇒ 01:02:43.530 Bess Ross: Okay. We can check a couple different months, just to…

568 01:02:43.740 ⇒ 01:02:44.290 Uttam Kumaran: Yes.

569 01:02:44.400 ⇒ 01:02:56.820 Bess Ross: And I’m not pulling in, I think you guys know this, but this month, like, I checked their refund report, but I’m not pulling it in. I don’t think that’s a necessary step, given where those are landing currently.

570 01:02:57.070 ⇒ 01:03:05.579 Bess Ross: In months past, there were much larger numbers that we needed to look at, but now they’re not. So, I think that takes a wrinkle out of it as well.

571 01:03:06.280 ⇒ 01:03:06.880 Shivani Amar: Perfect.

572 01:03:07.410 ⇒ 01:03:07.770 Bess Ross: Cool.

573 01:03:07.770 ⇒ 01:03:16.419 Shivani Amar: Okay, great. Thank you so much for popping in, Bess. I was just, like, at the most, like, what’s the status of this? I was like, let’s just, let’s just have her join us, because I don’t really know.

574 01:03:16.420 ⇒ 01:03:20.740 Bess Ross: Now, in fairness, I have not had, really, any time to dig into it deeply.

575 01:03:20.740 ⇒ 01:03:22.449 Shivani Amar: No, totally, there’s no…

576 01:03:22.810 ⇒ 01:03:26.439 Shivani Amar: like, there was a deadline for this at all. I was more just like, I don’t really know where this is.

577 01:03:26.440 ⇒ 01:03:29.400 Bess Ross: No, but when I did pop in, I was like, this will be nice.

578 01:03:29.400 ⇒ 01:03:30.150 Shivani Amar: Yeah.

579 01:03:30.150 ⇒ 01:03:30.500 Bess Ross: Great call.

580 01:03:30.800 ⇒ 01:03:39.109 Bess Ross: making sure my formulas are working and all that, so I definitely want to hammer it out, so hopefully in March I can use it.

581 01:03:39.110 ⇒ 01:03:40.520 Shivani Amar: Okay, thank you so much.

582 01:03:40.520 ⇒ 01:03:41.490 Bess Ross: Cool. Thanks, guys.

583 01:03:41.620 ⇒ 01:03:42.330 Shivani Amar: Bye.

584 01:03:42.330 ⇒ 01:03:43.440 Uttam Kumaran: Okay, bye.

585 01:03:43.440 ⇒ 01:03:48.740 Shivani Amar: Utam will talk… so, retail, he might be game to talk inventory.

586 01:03:48.740 ⇒ 01:03:49.370 Uttam Kumaran: Okay, so…

587 01:03:49.370 ⇒ 01:03:57.470 Shivani Amar: You and I can double-check into it, but I’m more just hearing that he’s, like… Phil commented at him, like, have you looked at this? He was like, oh, let me look at it now.

588 01:03:57.580 ⇒ 01:04:03.059 Shivani Amar: He reports to Will. He was the person I thought we were gonna do the discovery call with. We ended up doing the discovery call with Will.

589 01:04:03.580 ⇒ 01:04:07.960 Shivani Amar: And he was like, oh, I’m seeing numbers, like, 1% off, I just want to understand their methodology.

590 01:04:07.960 ⇒ 01:04:11.689 Uttam Kumaran: Okay, okay, so then I’ll just try to come with answers, like, to…

591 01:04:11.690 ⇒ 01:04:16.560 Shivani Amar: Yeah, yeah, versus questions, because I don’t know if you’ll get your questions answered.

592 01:04:16.560 ⇒ 01:04:18.570 Uttam Kumaran: Okay, well, someone needs to answer them.

593 01:04:18.570 ⇒ 01:04:23.940 Shivani Amar: Yeah, yeah, so if you have clear questions, if you’re like, hey, like, I just want somebody to gut check these numbers, or…

594 01:04:23.940 ⇒ 01:04:24.510 Uttam Kumaran: Okay.

595 01:04:24.510 ⇒ 01:04:42.350 Shivani Amar: if you’re like, hey, like, this seemed off to me, and I want to understand, like, if we did the right methodology, or here’s how we defined the metric, is that kind of how you would think about it? That will help, and then I can, like, give that to Phil or Will, or we can even ask Russell and see, but just, like, making those crisp will be helpful for me to know who the right person is.

596 01:04:42.350 ⇒ 01:04:43.540 Uttam Kumaran: Okay, okay.

597 01:04:44.650 ⇒ 01:04:53.349 Uttam Kumaran: And we kicked off… we kicked off Amazon today. I called Golub yesterday and walked him through, so our meeting on Friday should be productive.

598 01:04:53.510 ⇒ 01:04:57.079 Uttam Kumaran: Yeah, I walked them through all of our sources.

599 01:04:57.340 ⇒ 01:05:02.120 Uttam Kumaran: And like, I think we’re gonna… we’re just gonna kick off… again, we’re trying to kick off as many as we can…

600 01:05:02.340 ⇒ 01:05:04.680 Uttam Kumaran: do at a time. And then Amazon today.

601 01:05:04.820 ⇒ 01:05:08.079 Uttam Kumaran: I’m just gonna make sure that the backfill is happening from the front.

602 01:05:08.900 ⇒ 01:05:09.320 Shivani Amar: Yeah.

603 01:05:09.540 ⇒ 01:05:10.360 Uttam Kumaran: Yeah.

604 01:05:10.600 ⇒ 01:05:15.580 Shivani Amar: For context for you, it’s like… Like, let’s say in, like.

605 01:05:16.080 ⇒ 01:05:21.560 Shivani Amar: there was a world in which he was, like, feeling blocked on Gorgeous, and that was delaying Amazon.

606 01:05:21.560 ⇒ 01:05:22.600 Uttam Kumaran: That was the case.

607 01:05:22.600 ⇒ 01:05:24.449 Shivani Amar: If he had said that to me.

608 01:05:24.450 ⇒ 01:05:25.060 Uttam Kumaran: Yes.

609 01:05:25.060 ⇒ 01:05:28.640 Shivani Amar: What would I have said? Forget gorgeous right now, and…

610 01:05:28.640 ⇒ 01:05:34.219 Uttam Kumaran: I told him that… he was like, that’s… he’s like, oh, that’s on me, I have to answer. I said, okay, yeah, so…

611 01:05:34.220 ⇒ 01:05:35.909 Shivani Amar: Okay, so I will ask him about that.

612 01:05:35.910 ⇒ 01:05:36.340 Uttam Kumaran: Yes.

613 01:05:36.340 ⇒ 01:05:39.680 Shivani Amar: But I just want to say, like, are we…

614 01:05:39.770 ⇒ 01:05:59.159 Shivani Amar: yes, you’re communicating with Polytomic, but I want to have my own communication channel with them, which is great, like, to say, like, we totally align on what the priority set looks like. And so, Jason, when OAS yesterday was like, I want to get… start getting all these, like, marketing sources in, Jason was like, are these the next priority? They say P2. Have we completed P1?

615 01:05:59.350 ⇒ 01:06:01.440 Shivani Amar: Right? And so I think it’s just, like.

616 01:06:01.780 ⇒ 01:06:02.260 Uttam Kumaran: Okay.

617 01:06:02.260 ⇒ 01:06:02.960 Shivani Amar: before…

618 01:06:02.960 ⇒ 01:06:05.809 Uttam Kumaran: Could you also tell me what’s helpful to indicate, because…

619 01:06:06.130 ⇒ 01:06:20.040 Uttam Kumaran: we’re… we’re basically… some are being built, so then we move to try to get as many live. I think, yes, we’re a little bit brute-forced with, like, can we get these, but if we can be more verbose and, like, we… this is our process…

620 01:06:20.040 ⇒ 01:06:22.499 Shivani Amar: Now you have a bunch of P2s, right? So then it’s…

621 01:06:22.500 ⇒ 01:06:22.950 Uttam Kumaran: Yeah, we’re.

622 01:06:23.210 ⇒ 01:06:38.529 Shivani Amar: what happened with P1? And at the same time, I told, I told… and P0, right? I told Jason yesterday, I was like, we’re blocked on some things in P0, and we can’t just pause and say we’re not gonna even tackle P2 until we finish P0.

623 01:06:38.530 ⇒ 01:06:40.890 Uttam Kumaran: Because, because effectively, somebody’s gonna click a button.

624 01:06:41.040 ⇒ 01:06:44.260 Uttam Kumaran: As we talked about, so I want to, like… yeah.

625 01:06:44.550 ⇒ 01:06:47.589 Shivani Amar: But I can be… we can be more… more verbose with, like.

626 01:06:47.590 ⇒ 01:06:50.509 Uttam Kumaran: Here’s where we are now. Because of this, we wanna…

627 01:06:50.780 ⇒ 01:06:52.520 Shivani Amar: What about being verbose?

628 01:06:52.850 ⇒ 01:06:55.850 Shivani Amar: the wrong takeaway. Verbose is not the goal, it’s.

629 01:06:55.850 ⇒ 01:06:56.230 Uttam Kumaran: Okay.

630 01:06:56.230 ⇒ 01:06:59.180 Shivani Amar: Right? Okay.

631 01:06:59.180 ⇒ 01:07:00.979 Uttam Kumaran: But then I… but then my expectation is that…

632 01:07:00.980 ⇒ 01:07:02.310 Shivani Amar: Happy 3.

633 01:07:02.310 ⇒ 01:07:04.400 Uttam Kumaran: No, but then I’m like, but then I’m like.

634 01:07:04.610 ⇒ 01:07:11.020 Uttam Kumaran: Jason, well, you just gotta trust us that we’re, like, not… we’re gonna… we’re gonna hook these in as we discussed, right?

635 01:07:11.020 ⇒ 01:07:26.290 Shivani Amar: No, no, no, and there’s no stress. There’s no stress at all. I’m more just, like, before we give other people work to do, it’s just a little bit of comms around, like, we’re waiting on these… while we wait on these connectors to be built, in parallel, we want to start, like.

636 01:07:26.700 ⇒ 01:07:34.389 Shivani Amar: we want to start getting API access for these, right? And I think your Gantt chart will kind of make that clear. It’s like.

637 01:07:34.710 ⇒ 01:07:37.180 Shivani Amar: Ingestion has pre-work before it.

638 01:07:37.700 ⇒ 01:07:38.110 Uttam Kumaran: Yeah.

639 01:07:38.110 ⇒ 01:07:54.139 Shivani Amar: Right? Like, ingestion equals, like, establish the connection, like, get the key, like, whatever to get in there before starting ingestion. Build the connection. And so it’s like, when you say, I want to start ingesting, AdRoll next week, then.

640 01:07:54.140 ⇒ 01:07:54.480 Uttam Kumaran: Yeah.

641 01:07:54.480 ⇒ 01:08:09.129 Shivani Amar: pre-work for that, right? And so I think the conversation around the Gantt will be super helpful when you go to that level of, like, these are all the inputs, and then the outputs, I think, was very informative today. It’s just like, once you get Confido, or once you get this, I unlock this metric.

642 01:08:09.490 ⇒ 01:08:10.050 Uttam Kumaran: Yeah.

643 01:08:10.050 ⇒ 01:08:16.999 Shivani Amar: Right? And it’s like, the dashboard will come, the dashboard, the VP-level dashboard, fine, that’s, like, one output.

644 01:08:17.270 ⇒ 01:08:27.889 Shivani Amar: But I think the interesting thing is saying, like, what metrics you can actually start to name. So it’s like, right now, I could say, I can… I can actually just tell you what Shopify sales were for D2C. I could do that, but if.

645 01:08:27.899 ⇒ 01:08:28.299 Uttam Kumaran: Yeah.

646 01:08:28.300 ⇒ 01:08:35.950 Shivani Amar: e-commerce revenue, I still need to get Amazon… like, the output, once I get Amazon and Walmart to come, is now e-commerce sales.

647 01:08:35.950 ⇒ 01:08:37.140 Uttam Kumaran: Yeah, yeah.

648 01:08:37.140 ⇒ 01:08:43.490 Shivani Amar: Right? And so, like, I think that’s, like, the inputs, outputs. Okay. And it doesn’t have to be… don’t make it into a GAMP. Just make.

649 01:08:43.490 ⇒ 01:08:49.149 Uttam Kumaran: No, no, no, no, it’s a table and a list. My question is gonna be, like, more or less.

650 01:08:49.260 ⇒ 01:08:58.819 Uttam Kumaran: Yeah, so we can go through that. My other… my one other question here is, like, should we split e-com into marketing or digital advertising? Because some of these are not, like.

651 01:08:59.870 ⇒ 01:09:02.340 Uttam Kumaran: it’s all kind of under Carlos.

652 01:09:02.479 ⇒ 01:09:04.349 Uttam Kumaran: But I guess, like, I don’t know how Element…

653 01:09:04.359 ⇒ 01:09:12.339 Shivani Amar: I don’t have a person that’s different from the thing, so I get that it’s him, but I think it makes sense to say that this section right here is marketing.

654 01:09:13.029 ⇒ 01:09:15.549 Uttam Kumaran: Yeah, because I think, and this is maybe also helpful for, like.

655 01:09:16.189 ⇒ 01:09:27.799 Uttam Kumaran: I guess also thinking about, is the business domain gonna be mapped to one of the new VPs? Because typically, we see these split, even if there are people that are, like, within both, so, like, I would prefer to

656 01:09:27.909 ⇒ 01:09:31.409 Uttam Kumaran: split this into marketing, or I can just say digital advertising.

657 01:09:31.410 ⇒ 01:09:32.100 Shivani Amar: Yeah, let’s say.

658 01:09:32.100 ⇒ 01:09:33.050 Uttam Kumaran: Understood, that is true.

659 01:09:33.050 ⇒ 01:09:38.039 Shivani Amar: Yeah. And then one day, if there’s a leader of digital advertising, they kind of know these are my metrics.

660 01:09:38.040 ⇒ 01:09:38.370 Uttam Kumaran: Okay.

661 01:09:38.450 ⇒ 01:09:39.250 Shivani Amar: Right?

662 01:09:39.359 ⇒ 01:09:40.319 Shivani Amar: Okay.

663 01:09:40.689 ⇒ 01:09:44.949 Shivani Amar: Because anyways, it’s like, now, like, there’s all this stuff around, like, if we’re in…

664 01:09:45.479 ⇒ 01:09:48.120 Shivani Amar: Retail, like, what’s the efficiency.

665 01:09:48.120 ⇒ 01:09:58.940 Uttam Kumaran: Well, you may not… you just may not care about the ad spend when you talk about e-com. Like, there’s… it’s just so… such a… too… it’s too broad, so, like, this is where we’re gonna go from domain to topic.

666 01:09:58.940 ⇒ 01:09:59.620 Shivani Amar: Yeah.

667 01:09:59.620 ⇒ 01:10:01.829 Uttam Kumaran: And then I want just that to be clear.

668 01:10:02.040 ⇒ 01:10:04.819 Uttam Kumaran: for Greg, because this is, like, the highest level.

669 01:10:04.820 ⇒ 01:10:05.150 Shivani Amar: Yeah.

670 01:10:05.150 ⇒ 01:10:07.190 Uttam Kumaran: It’ll… it’ll basically go from, like.

671 01:10:07.500 ⇒ 01:10:15.350 Uttam Kumaran: Domain, to topic, to the… to basically the tables, relationships, down to, like, the metric.

672 01:10:15.600 ⇒ 01:10:16.230 Shivani Amar: Yeah.

673 01:10:16.810 ⇒ 01:10:17.740 Uttam Kumaran: Perfect.

674 01:10:18.910 ⇒ 01:10:21.889 Shivani Amar: Okay, cool, we’ll talk later, but I feel like we’re…

675 01:10:22.890 ⇒ 01:10:23.490 Uttam Kumaran: Okay.

676 01:10:23.490 ⇒ 01:10:25.789 Shivani Amar: starting to crystallize some things, and… Okay.

677 01:10:25.790 ⇒ 01:10:26.320 Uttam Kumaran: Okay.

678 01:10:26.320 ⇒ 01:10:30.580 Shivani Amar: And then on this, like, piece around… the tab…

679 01:10:30.580 ⇒ 01:10:31.600 Uttam Kumaran: the stakeholders?

680 01:10:31.890 ⇒ 01:10:35.190 Shivani Amar: No, the tab. I’ll add the stuff in the stakeholders, but on the.

681 01:10:35.190 ⇒ 01:10:35.660 Uttam Kumaran: Oh, yeah.

682 01:10:36.050 ⇒ 01:10:38.160 Shivani Amar: just… We don’t have to talk.

683 01:10:38.160 ⇒ 01:10:38.889 Uttam Kumaran: I’ll do, we’re doing.

684 01:10:38.890 ⇒ 01:10:39.280 Shivani Amar: private.

685 01:10:39.280 ⇒ 01:10:40.360 Uttam Kumaran: Yeah, yeah.

686 01:10:40.550 ⇒ 01:10:47.790 Shivani Amar: I don’t want to keep, like, repeating myself, but I think I’m just like, how do we get to a place where we’re like, yep, this feels really good.

687 01:10:48.750 ⇒ 01:10:50.300 Uttam Kumaran: Rest of sheet, I feel really good about.

688 01:10:50.300 ⇒ 01:10:53.129 Shivani Amar: Okay, great. I’m just, like, honing in on… I know I’m like, I’m.

689 01:10:53.130 ⇒ 01:10:55.550 Uttam Kumaran: No, no, no, that’s fair. That’s fair. It has to be right.

690 01:10:55.550 ⇒ 01:11:01.489 Shivani Amar: venue is, like, the one I’m gonna just, like, jump to as a way to be like, okay, is this starting to make sense to me?

691 01:11:01.720 ⇒ 01:11:02.530 Uttam Kumaran: Yeah, okay.

692 01:11:02.530 ⇒ 01:11:09.989 Shivani Amar: Okay, cool. Thank you, I’ll talk to you at the retail conversation, and we’ll… we have time one-on-one in the afternoon. Okay, perfect. Okay, thank you. Bye.

693 01:11:09.990 ⇒ 01:11:10.400 Uttam Kumaran: Bye.