Meeting Title: Brainforge Omni Training and Setup Date: 2025-12-10 Meeting participants: Demilade Agboola, Uttam Kumaran, Caitlyn Vaughn, Mustafa Raja
WEBVTT
1 00:00:17.370 ⇒ 00:00:19.160 Uttam Kumaran: Hey, sorry.
2 00:00:20.960 ⇒ 00:00:24.900 Uttam Kumaran: Just got a call, like, right before that I couldn’t miss.
3 00:00:27.270 ⇒ 00:00:28.980 Demilade Agboola: It happens, it happens.
4 00:00:28.980 ⇒ 00:00:33.759 Uttam Kumaran: I love that my Zoom is being used, like, everywhere in the company, so it’s like…
5 00:00:33.860 ⇒ 00:00:35.340 Uttam Kumaran: Just, like, a nightmare.
6 00:01:18.800 ⇒ 00:01:27.249 Uttam Kumaran: Hello, sorry, I just got a call, like, 2 minutes before, and I just, like, had to take it, and it’s, like, what a mess.
7 00:01:27.250 ⇒ 00:01:28.700 Caitlyn Vaughn: the choir. It’s totally fine.
8 00:01:28.700 ⇒ 00:01:36.180 Uttam Kumaran: My Zoom was… my Zoom’s been used in, like, half the company for other meetings. It’s like… and I tried to look, I’m like…
9 00:01:36.510 ⇒ 00:01:45.790 Uttam Kumaran: why can’t Zoom just let me host multiple meetings? And they’re like, oh yeah, that’s part of our, like, super enterprise plan, where you can host up to two meetings in parallel, currently.
10 00:01:45.790 ⇒ 00:01:47.240 Caitlyn Vaughn: Who? That’s it?
11 00:01:47.240 ⇒ 00:01:51.429 Uttam Kumaran: I was like, dude, how about 4 or 5 in a row?
12 00:01:54.380 ⇒ 00:01:57.199 Caitlyn Vaughn: Why don’t you just get everyone to get their own Zoom?
13 00:01:57.200 ⇒ 00:02:04.849 Uttam Kumaran: No, it’s not! Everybody has their own Zoom, but, like, I end up with the Zoom link, and then, like, it doesn’t end up getting swapped for me at any.
14 00:02:04.850 ⇒ 00:02:06.309 Caitlyn Vaughn: Yeah, yeah, yeah.
15 00:02:06.310 ⇒ 00:02:14.150 Uttam Kumaran: And then you get… you have to make a decision when it comes up, which is like, should I end the other meeting? Like, is that the meeting?
16 00:02:14.150 ⇒ 00:02:16.339 Demilade Agboola: So relatable, honestly.
17 00:02:16.340 ⇒ 00:02:17.930 Caitlyn Vaughn: Well, what’s up, girl?
18 00:02:18.640 ⇒ 00:02:21.509 Demilade Agboola: Thankfully, Dimladi zooms at the rescue, so…
19 00:02:21.510 ⇒ 00:02:23.370 Caitlyn Vaughn: Hey! Nice to meet you!
20 00:02:23.370 ⇒ 00:02:23.810 Uttam Kumaran: Thank you for.
21 00:02:23.810 ⇒ 00:02:25.259 Demilade Agboola: Nice to meet you, too.
22 00:02:26.270 ⇒ 00:02:28.610 Demilade Agboola: So I know…
23 00:02:28.610 ⇒ 00:02:31.820 Uttam Kumaran: Yeah, if you want to give a brief intro, I’m just gonna get…
24 00:02:32.230 ⇒ 00:02:33.880 Uttam Kumaran: Couple things up on my side.
25 00:02:34.320 ⇒ 00:02:45.360 Demilade Agboola: Alright, sounds good. So my name is Dimlady. I am an analytics engineer based in Malta, but I visit the US quite a bit, because my girlfriend is actually in the US right now.
26 00:02:45.360 ⇒ 00:02:45.889 Uttam Kumaran: We’re still here.
27 00:02:46.440 ⇒ 00:02:51.369 Demilade Agboola: Yeah, I live on… I live on Saturday. I’m back to Malta on Sunday.
28 00:02:51.600 ⇒ 00:02:55.909 Demilade Agboola: Unfortunately, though, because I’m in Minnesota, and it’s so cold, like, it’s like…
29 00:02:55.910 ⇒ 00:02:56.350 Caitlyn Vaughn: Ugh.
30 00:02:56.790 ⇒ 00:03:14.270 Demilade Agboola: It’s, like, negative 1 or negative 2 Fahrenheit. It’s that, it’s that sort of cold, and it snowed, like… the snow was crazy last night, so it’s gonna get colder, it’s gonna get, like, negative 4, negative 5, negative 6 this next couple of days. So, that’s fun. But, apart from… Where the fuck?
31 00:03:14.270 ⇒ 00:03:15.770 Caitlyn Vaughn: What the fuck is this?
32 00:03:15.940 ⇒ 00:03:17.609 Demilade Agboola: So it’s off the coast of Italy.
33 00:03:17.800 ⇒ 00:03:19.519 Caitlyn Vaughn: It’s an island off the coastal.
34 00:03:19.790 ⇒ 00:03:22.600 Demilade Agboola: Yeah, so it’s pretty… it’s pretty nice, for the most part. Small.
35 00:03:22.600 ⇒ 00:03:26.380 Uttam Kumaran: Well, yeah, Demi, where do you live, where do you live on… on… on the island?
36 00:03:27.080 ⇒ 00:03:31.099 Demilade Agboola: Can you… can you see that master region? Can you go… Bosets it.
37 00:03:31.100 ⇒ 00:03:31.900 Caitlyn Vaughn: This one?
38 00:03:32.480 ⇒ 00:03:38.510 Demilade Agboola: But, like, if you start to… okay, good. So now, I live in a place called Pieta. So Pieta is close…
39 00:03:39.070 ⇒ 00:03:41.470 Demilade Agboola: So, now, can you move to the right a bit?
40 00:03:42.930 ⇒ 00:03:49.859 Demilade Agboola: All right, so Msida, Pieta, so if you go… Msida is the university area, can you zoom in a bit more towards that side?
41 00:03:49.860 ⇒ 00:03:50.350 Caitlyn Vaughn: Wait, hu.
42 00:03:50.350 ⇒ 00:03:50.700 Demilade Agboola: I think it does.
43 00:03:50.700 ⇒ 00:03:51.570 Caitlyn Vaughn: Good.
44 00:03:52.100 ⇒ 00:03:55.360 Demilade Agboola: So, post the way… how do I put it? Post to the…
45 00:03:55.480 ⇒ 00:03:59.029 Demilade Agboola: the rights. Can you see San Juan, Jira.
46 00:03:59.300 ⇒ 00:03:59.849 Caitlyn Vaughn: Let’s see.
47 00:04:00.840 ⇒ 00:04:04.379 Demilade Agboola: If you zoom in on him, see that, Again, let’s see.
48 00:04:05.670 ⇒ 00:04:13.480 Demilade Agboola: But I kind of live between Msida and Nigira, so… yeah, I live… literally… can you see that trick, Marlina?
49 00:04:13.620 ⇒ 00:04:16.510 Demilade Agboola: that’s sort of where I live. I live close to the marina.
50 00:04:17.029 ⇒ 00:04:18.499 Caitlyn Vaughn: Oh, you live, like, over here?
51 00:04:18.720 ⇒ 00:04:23.890 Demilade Agboola: Yeah, so, like, where I live, there’s a bunch of, like, yachts parked and all that stuff, so you can kind of just see all of that.
52 00:04:23.890 ⇒ 00:04:25.350 Uttam Kumaran: You brag about it, come on.
53 00:04:25.350 ⇒ 00:04:26.939 Caitlyn Vaughn: Can we look at your house?
54 00:04:26.940 ⇒ 00:04:30.979 Demilade Agboola: Yeah, let’s do stream view. Give me the stream view. Let’s see.
55 00:04:30.980 ⇒ 00:04:35.250 Uttam Kumaran: This is awesome, dude. Is it always just, like, nice weather there, or, like, what’s the deal?
56 00:04:35.250 ⇒ 00:04:46.080 Demilade Agboola: Yeah, so that’s part of why I moved to Malta. So, Malta, like, doesn’t snow, so, like, you know how, like, continental Europe, like, Portugal, Spain, all of that, like, there’s snow? Malta doesn’t snow. I mean, it does get.
57 00:04:46.080 ⇒ 00:04:51.739 Uttam Kumaran: Yeah, yeah, I totally knew that. I’m so… I totally knew about snow patterns in Portugal.
58 00:04:51.740 ⇒ 00:04:53.800 Caitlyn Vaughn: I was like, what is Malta?
59 00:04:56.500 ⇒ 00:05:05.939 Demilade Agboola: I think… I think the beautiful part of it for me is, like, when it’s cold, it’s probably in, like, the 50s. That’s a cold… that’s a cold, day.
60 00:05:06.330 ⇒ 00:05:11.830 Demilade Agboola: But it’s… it’s… I think the thing about Malta and I, it’s… they’ve been, like.
61 00:05:11.970 ⇒ 00:05:14.889 Demilade Agboola: They were taken over by the Turks, by the British.
62 00:05:15.130 ⇒ 00:05:34.179 Demilade Agboola: So they have very, like, Arabian-looking architecture for some reason, because of the Turkish invasion. They also speak English because of, you know, the Brits were there. And so it’s kind of like this mixture of so many things going on, and they have a lot of churches, like, they have the best, like, the
63 00:05:34.320 ⇒ 00:05:41.010 Demilade Agboola: S density of churches in the world. So for the small… it’s a small island, but it has, like, 350 churches.
64 00:05:41.410 ⇒ 00:05:43.719 Caitlyn Vaughn: Wow. What? Like, if you…
65 00:05:43.720 ⇒ 00:05:47.859 Demilade Agboola: So if you close your eye and throw a stone, you’re probably gonna hit, like, a church or two.
66 00:05:48.180 ⇒ 00:05:54.560 Caitlyn Vaughn: Oh my god, that’s so crazy. Okay, wait, so wait, first, where are you originally from? You moved to Malta?
67 00:05:55.030 ⇒ 00:06:00.829 Demilade Agboola: Yeah, so I’m Nigerian. I lived in Nigeria all my life up until, like, 2023.
68 00:06:01.230 ⇒ 00:06:01.890 Demilade Agboola: Malta.
69 00:06:02.560 ⇒ 00:06:06.149 Caitlyn Vaughn: Wait, and how do you know about Malta? Who told you?
70 00:06:07.520 ⇒ 00:06:14.809 Demilade Agboola: I… I don’t know. I was just doing research, I guess, like… And also, like, kind of growing up, one of my favorite things to read was,
71 00:06:16.070 ⇒ 00:06:17.759 Demilade Agboola: What’s that thing called on Atlas?
72 00:06:17.860 ⇒ 00:06:19.809 Demilade Agboola: But I knew a bunch of countries.
73 00:06:20.290 ⇒ 00:06:28.950 Demilade Agboola: Yeah, it was literally just, like, a collection of books and geography of the world. That was literally how I kept myself entertained.
74 00:06:29.140 ⇒ 00:06:31.509 Demilade Agboola: So I know a bunch of random countries.
75 00:06:31.510 ⇒ 00:06:33.020 Caitlyn Vaughn: That’s really cool. I was just reading up.
76 00:06:33.020 ⇒ 00:06:37.939 Demilade Agboola: I was just reading up on it, and yeah, I saw some list of countries that you can move to.
77 00:06:38.540 ⇒ 00:06:42.649 Demilade Agboola: And I kind of just favored the English-speaking country, because I felt like
78 00:06:43.200 ⇒ 00:06:48.840 Demilade Agboola: I wouldn’t want to move to a place where, like, there’ll be a huge, huge language barrier.
79 00:06:49.070 ⇒ 00:06:49.510 Caitlyn Vaughn: Yeah.
80 00:06:49.510 ⇒ 00:06:55.110 Demilade Agboola: Plus, you know, the weather was a huge factor. Like, living in Nigeria, the weather is pretty warm.
81 00:06:55.740 ⇒ 00:07:00.359 Demilade Agboola: I mean, it does have its cold days, but even then, the cold days are probably, like, again, 40s, 50s.
82 00:07:00.360 ⇒ 00:07:00.750 Caitlyn Vaughn: Yeah.
83 00:07:00.750 ⇒ 00:07:04.779 Demilade Agboola: I didn’t want to go to a place where I was going to start dealing with the 10s, or…
84 00:07:04.780 ⇒ 00:07:06.190 Caitlyn Vaughn: Yeah. Cooler than that.
85 00:07:06.660 ⇒ 00:07:13.399 Caitlyn Vaughn: That is so cool, you’ve just become, like, the most interesting person on the Brainforge team in, like, 2 minutes.
86 00:07:14.190 ⇒ 00:07:14.819 Demilade Agboola: That’s what I do swap.
87 00:07:14.820 ⇒ 00:07:16.440 Uttam Kumaran: What are some other characters you got on here?
88 00:07:17.470 ⇒ 00:07:20.510 Caitlyn Vaughn: Also, are you a reader at all? Do you like books?
89 00:07:21.610 ⇒ 00:07:26.820 Demilade Agboola: I used to, I used to love reading. Nowadays, I think adulting has me in this, like.
90 00:07:26.950 ⇒ 00:07:37.939 Demilade Agboola: all about, like, making, like, self-development phase, versus, like, just the reading for pleasure phase. My girlfriend is a reader. In fact, my girlfriend has a book club in Minnesota.
91 00:07:38.160 ⇒ 00:07:41.300 Caitlyn Vaughn: Oh, I… it will… What else do you do in Minnesota, I mean?
92 00:07:42.700 ⇒ 00:07:52.250 Caitlyn Vaughn: That’s valid. Well, I’m also on the same page, but there’s this really good book called, Pillars of the Earth, and it talks about, like.
93 00:07:52.320 ⇒ 00:08:06.920 Caitlyn Vaughn: how, basically, churches were built in Europe, and, like, the thinking on the different, like, phases of architecture, and it’s, like, fiction, but it’s based in some reality. It’s pretty cool, it’s a good book. Would recommend, but there’s just so many…
94 00:08:07.900 ⇒ 00:08:09.310 Demilade Agboola: Can’t hold it?
95 00:08:09.560 ⇒ 00:08:10.380 Demilade Agboola: Yeah.
96 00:08:10.380 ⇒ 00:08:11.030 Caitlyn Vaughn: Yeah.
97 00:08:11.030 ⇒ 00:08:15.809 Demilade Agboola: That’s pretty cool. I will… I’ll just put it on my list of books to read.
98 00:08:16.080 ⇒ 00:08:16.650 Caitlyn Vaughn: Like, 5,000.
99 00:08:16.650 ⇒ 00:08:17.390 Demilade Agboola: Maybe.
100 00:08:17.390 ⇒ 00:08:18.780 Caitlyn Vaughn: So, yeah.
101 00:08:18.780 ⇒ 00:08:25.669 Demilade Agboola: That’s fine. One of the things I love about Europe, since I’ve been to Europe, is the architecture. Especially churches.
102 00:08:26.340 ⇒ 00:08:30.339 Uttam Kumaran: expect so much. You would like Chicago, dude. You would like Chicago a lot?
103 00:08:30.340 ⇒ 00:08:31.270 Caitlyn Vaughn: I feel like…
104 00:08:31.270 ⇒ 00:08:33.609 Uttam Kumaran: There’s another place in the U.S.
105 00:08:34.100 ⇒ 00:08:37.130 Uttam Kumaran: With architecture, like, that crazy.
106 00:08:37.570 ⇒ 00:08:38.000 Caitlyn Vaughn: New York.
107 00:08:38.000 ⇒ 00:08:44.569 Uttam Kumaran: You’re just… yeah, New York, yeah, Chicago, it just seems like you’re in, like, an old Chicago’s, like, gangster movie.
108 00:08:44.570 ⇒ 00:08:45.130 Caitlyn Vaughn: Hmm.
109 00:08:45.130 ⇒ 00:08:48.830 Uttam Kumaran: You’re walking around, it’s just, like, these, like, neon bulbs, and, like.
110 00:08:48.830 ⇒ 00:08:53.859 Caitlyn Vaughn: really cool architecture, yeah, like, Texas is, like, so beat, like, it’s so… So boring.
111 00:08:53.860 ⇒ 00:08:55.970 Uttam Kumaran: Dogs?
112 00:08:55.970 ⇒ 00:08:58.060 Demilade Agboola: Caitlin, are you in Texas as well?
113 00:08:58.410 ⇒ 00:09:02.920 Caitlyn Vaughn: Yeah, yeah, yeah, I’m in Austin. I know Utam, like, from years ago.
114 00:09:03.240 ⇒ 00:09:05.680 Demilade Agboola: Okay. I actually have a friend who lives in Austin.
115 00:09:05.790 ⇒ 00:09:07.910 Caitlyn Vaughn: Oh, really? I also have a friend who lives in Dallas.
116 00:09:08.020 ⇒ 00:09:08.730 Demilade Agboola: That is not.
117 00:09:08.730 ⇒ 00:09:09.120 Caitlyn Vaughn: Wait!
118 00:09:09.120 ⇒ 00:09:09.480 Demilade Agboola: So.
119 00:09:09.480 ⇒ 00:09:12.959 Uttam Kumaran: Do they do data work? Are they data people?
120 00:09:12.960 ⇒ 00:09:16.429 Demilade Agboola: No, no, my friend in Dallas, he’s the guy I went to his wedding in Dallas.
121 00:09:16.430 ⇒ 00:09:20.170 Uttam Kumaran: I was asking for a friend, I don’t know.
122 00:09:20.170 ⇒ 00:09:22.470 Demilade Agboola: He’s a DevOps person, so…
123 00:09:22.470 ⇒ 00:09:22.900 Uttam Kumaran: Okay.
124 00:09:22.900 ⇒ 00:09:24.820 Caitlyn Vaughn: Oh! Wait, that’s cool.
125 00:09:24.820 ⇒ 00:09:26.530 Uttam Kumaran: Caitlin’s like, oh, actually.
126 00:09:26.530 ⇒ 00:09:27.870 Caitlyn Vaughn: Wait a minute!
127 00:09:29.630 ⇒ 00:09:31.789 Demilade Agboola: My friend in Austin is doing a PhD.
128 00:09:32.290 ⇒ 00:09:32.690 Caitlyn Vaughn: Oh.
129 00:09:32.690 ⇒ 00:09:33.200 Demilade Agboola: So…
130 00:09:33.600 ⇒ 00:09:34.000 Uttam Kumaran: That’s cool.
131 00:09:34.000 ⇒ 00:09:35.489 Caitlyn Vaughn: Not that helpful, personally.
132 00:09:35.490 ⇒ 00:09:36.169 Demilade Agboola: Okay, bud.
133 00:09:36.170 ⇒ 00:09:37.329 Caitlyn Vaughn: There you go.
134 00:09:38.730 ⇒ 00:09:46.469 Caitlyn Vaughn: Okay, cool, guys, let’s go through Omni. I don’t… I need to, like, learn this tool so that I can do things. I can be self-sufficient.
135 00:09:46.610 ⇒ 00:09:50.070 Uttam Kumaran: So, I want to do, kind of, two things today. I want to just…
136 00:09:50.180 ⇒ 00:09:53.740 Uttam Kumaran: Sort of give you, like, a little bit of understanding of just, like.
137 00:09:54.010 ⇒ 00:09:57.079 Caitlyn Vaughn: how Omni works under the hood.
138 00:09:57.080 ⇒ 00:10:02.989 Uttam Kumaran: And then… kind of also let Mustafa and you kind of, like, guide through creating
139 00:10:03.150 ⇒ 00:10:07.709 Uttam Kumaran: like, creating a first dashboard and, like, creating a first report, and I honestly want
140 00:10:07.990 ⇒ 00:10:13.770 Uttam Kumaran: maybe, like, a mix… like, I honestly would prefer if you just want to try it, and we just sort of guide you, because I think there’s no better…
141 00:10:14.170 ⇒ 00:10:14.800 Caitlyn Vaughn: Oh, man.
142 00:10:14.800 ⇒ 00:10:30.399 Uttam Kumaran: That’s how I learned the best, but I do want to just start by giving you, like, an overview of, like, the platform. Like, these are… like, this is unfortunately a couple slides, but we can just go through them, and at least I’ll just show you everything so you have a sense of, like, what is…
143 00:10:30.550 ⇒ 00:10:33.060 Uttam Kumaran: what is here.
144 00:10:33.800 ⇒ 00:10:37.020 Uttam Kumaran: So, I think, like, this is really, like.
145 00:10:37.190 ⇒ 00:10:44.990 Uttam Kumaran: the core backend of Omni, you have workbooks, you have data models, and you have your database. So, as we mentioned, like.
146 00:10:44.990 ⇒ 00:11:02.140 Uttam Kumaran: we’re using mother. here. The shared data model, which I think, you know, we can… you’ll see sort of live when you’re in the product, is, like, how we’re actually taking, for example, like, Hyperline data, joining it with the Postgres data to show
147 00:11:02.460 ⇒ 00:11:04.330 Uttam Kumaran: Revenue per customer.
148 00:11:04.720 ⇒ 00:11:05.110 Caitlyn Vaughn: isn’t…
149 00:11:05.110 ⇒ 00:11:15.450 Uttam Kumaran: to join customer to Hyperline, you have to sum up all their subscriptions, and then you get revenue, right? So that sort of… those, like, all that SQL lives here in the data model layer.
150 00:11:15.810 ⇒ 00:11:31.750 Uttam Kumaran: books are all things where you can think about a workbook very similar to, like, a Tableau workbook, or honestly, it’s honestly closer to, like, a sheet in a Google Sheet, where you’re working on, like, a report, and then a workbook, you know, you end up, like, aggregating a couple different reports together.
151 00:11:31.750 ⇒ 00:11:36.710 Caitlyn Vaughn: Wait, wait, and then the shared data model, is that in Omni, or is that in Mother Duck?
152 00:11:36.710 ⇒ 00:11:54.419 Uttam Kumaran: So this is… this is in Omni. This is where there’s a couple of different ways of doing this architecture. It’s sort of the reason why, like, I’m looping in Demolade, kind of from the project, like, kind of from here on out, because I think he’ll kind of guide onto, like, the best architecture. Right now, you know.
153 00:11:54.440 ⇒ 00:12:13.100 Uttam Kumaran: to be frank, defaults data is small. And so we want to… we want to not introduce, like, a ton of tooling, and part of the beauty of Omni is you can actually do quite a bit of data modeling here. The one thing we want to avoid is having, like, logic on, like, how to join tables here, and have it somewhere else.
154 00:12:13.100 ⇒ 00:12:19.469 Uttam Kumaran: And so, we were just talking about this this morning. DBT is the most common tool for data modeling.
155 00:12:19.850 ⇒ 00:12:31.140 Uttam Kumaran: That is a tool that allows you to write SQL, join tables together, and I think, Mustafo, when we go into the product, you’ll actually see, like, where we’re actually doing those joins.
156 00:12:31.150 ⇒ 00:12:38.770 Uttam Kumaran: I think at this point, we would like to just keep all of that in Omni. You know, I’m sort of stealing a little bit of Demolade’s thunder, but, like.
157 00:12:38.780 ⇒ 00:12:55.259 Uttam Kumaran: typically, we at least want to have it… we just want to have it in one place, either dbt or in the BI tool. Typically, we recommend dbt, but right now, given that, like, I think still Thomas is, like, ramping up, we’re probably going to keep managing it. I think it’s best to just have everything here.
158 00:12:55.260 ⇒ 00:13:06.189 Uttam Kumaran: And as more engineers and people get involved, they can actually write data models and have that all in one place. You’re not paying for another tool, you’re not, like, having to manage another tool, and the…
159 00:13:06.320 ⇒ 00:13:09.260 Uttam Kumaran: Technical capabilities are the exact same.
160 00:13:10.580 ⇒ 00:13:20.309 Uttam Kumaran: So we’re just going to keep everything here. And again, again, data models, you just remind yourself, is just the joins, sums, that logic, you know, that we’re just keeping in here.
161 00:13:20.530 ⇒ 00:13:25.140 Caitlyn Vaughn: And then, just a quick question, how is the Thomas training going?
162 00:13:25.920 ⇒ 00:13:28.180 Uttam Kumaran: Slow?
163 00:13:28.180 ⇒ 00:13:29.000 Caitlyn Vaughn: Okay.
164 00:13:29.500 ⇒ 00:13:34.909 Uttam Kumaran: I think we’re still waiting on, kind of, the S3 thing to get figured out.
165 00:13:36.090 ⇒ 00:13:38.140 Uttam Kumaran: It shouldn’t be taking this long.
166 00:13:38.140 ⇒ 00:13:39.370 Caitlyn Vaughn: No, it should not.
167 00:13:39.370 ⇒ 00:13:44.390 Uttam Kumaran: I don’t wanna… yeah, like, it’s like a… probably, like, a one-hour thing.
168 00:13:44.390 ⇒ 00:13:45.010 Caitlyn Vaughn: Yeah.
169 00:13:45.010 ⇒ 00:13:45.640 Uttam Kumaran: So…
170 00:13:45.640 ⇒ 00:13:49.700 Caitlyn Vaughn: Wait, is it slow on the Victor approval side, or on the Thomas Building side?
171 00:13:50.470 ⇒ 00:13:55.409 Uttam Kumaran: I don’t have… like, I think VictorBase gave us the go-ahead, but…
172 00:13:55.660 ⇒ 00:14:00.329 Uttam Kumaran: at this point, it’s… you just have to configure S3 and, like, hook it up to Catalyst, like…
173 00:14:01.510 ⇒ 00:14:10.849 Uttam Kumaran: this is, like, a less than an hour task to do. Okay. I wonder, like, what… how you’d want us to proceed. Like, we can call Thomas and basically walk him through doing it.
174 00:14:11.020 ⇒ 00:14:15.080 Uttam Kumaran: But yeah, it’s sort of sitting there.
175 00:14:16.260 ⇒ 00:14:21.159 Caitlyn Vaughn: In 30 minutes… okay, I’m just gonna call him in 30.
176 00:14:21.210 ⇒ 00:14:21.910 Uttam Kumaran: Okay.
177 00:14:21.910 ⇒ 00:14:23.800 Caitlyn Vaughn: Yeah.
178 00:14:23.800 ⇒ 00:14:28.899 Uttam Kumaran: This is… it’s just a very… something we do all the time, it’s, like, not that… Relatively complicated, so…
179 00:14:28.900 ⇒ 00:14:31.580 Caitlyn Vaughn: He’s a, like, a baby engineer, he just graduated.
180 00:14:31.580 ⇒ 00:14:33.970 Uttam Kumaran: Yeah, so that’s why I’m worried he’s stuck somewhere.
181 00:14:33.970 ⇒ 00:14:34.570 Caitlyn Vaughn: It really is.
182 00:14:34.570 ⇒ 00:14:38.509 Uttam Kumaran: He should call us, or he should just call Mustafa, like, and they should just rip it together.
183 00:14:38.510 ⇒ 00:14:39.430 Caitlyn Vaughn: Yeah, totally.
184 00:14:39.430 ⇒ 00:14:40.690 Uttam Kumaran: learn, so…
185 00:14:41.310 ⇒ 00:14:42.230 Caitlyn Vaughn: Okay, cool.
186 00:14:42.500 ⇒ 00:14:46.370 Uttam Kumaran: So, like, this’ll kind of go through the UI, but…
187 00:14:46.740 ⇒ 00:15:02.119 Uttam Kumaran: this is, like, the home for Omni. I think the biggest things you just want to understand is, like, most of the place will happen just, like, in this home page. We’re gonna… as we start to do more, there’s gonna be more dashboards, and we have some guidance on, like, how to keep folders organized and stuff, but…
188 00:15:02.560 ⇒ 00:15:15.429 Uttam Kumaran: thing is just, like, here’s where you go to kind of create a new analysis. When you go, like, into, a report, you have, like, your filters, you have, like, things here.
189 00:15:15.590 ⇒ 00:15:35.149 Uttam Kumaran: there are actually really, really awesome things you can do in Omni, like, you can do images, you can do really great designs, you know, I know it’s not often, like, the biggest priority, but I do think that the UX of dashboards helps to gain adoption, and actually people trust the data more, so these are things that we can work on, but
190 00:15:35.490 ⇒ 00:15:42.310 Uttam Kumaran: And I’ll send you this deck, too, so you can just see, like… but, you know, you’ve used some reporting tools before, like, so, you know, you’re familiar with filters.
191 00:15:42.330 ⇒ 00:15:46.660 Uttam Kumaran: The other thing is, like, this is a data tool, so there’s a lot of, like.
192 00:15:46.660 ⇒ 00:16:06.179 Uttam Kumaran: things that you can do that most, like, typical reporting tools don’t allow you to do. For example, if you want to compare July 2025 with July 2024, in a typical reporting tool, it’s kind of, like, clunky. This is, like, a really great BI tool. They have tons of, like, great filters for you to be able to do that really quickly, versus, like.
193 00:16:06.420 ⇒ 00:16:10.150 Uttam Kumaran: Having to, like, drag a calendar You know, widget, and things.
194 00:16:10.900 ⇒ 00:16:19.140 Uttam Kumaran: And a lot of this is configurable, so if we’re often doing period-over-period analysis, or, like, year-over-year, we can do a lot of that.
195 00:16:20.090 ⇒ 00:16:23.500 Caitlyn Vaughn: This looks so much better than our Omni instance.
196 00:16:23.660 ⇒ 00:16:38.099 Uttam Kumaran: Yeah, yeah, so we’ll get there, we’ll get there. Downloads, so, like, you can download PDFs, PNGs to share. Ideally, we want to be sharing the live workbook, so people can just come in here and see the data.
197 00:16:39.190 ⇒ 00:16:42.909 Caitlyn Vaughn: And we can just have, we can have, like, unlimited viewers, right?
198 00:16:42.910 ⇒ 00:16:43.880 Uttam Kumaran: Yeah, yeah.
199 00:16:43.880 ⇒ 00:16:44.650 Caitlyn Vaughn: Okay, cool.
200 00:16:44.650 ⇒ 00:16:50.280 Uttam Kumaran: And then… and then for schedules, you can just schedule this to, like, your email, or schedule this to Slack.
201 00:16:50.540 ⇒ 00:16:55.130 Uttam Kumaran: I think the Slack schedule is really powerful, because you can basically send, like.
202 00:16:55.270 ⇒ 00:17:01.430 Uttam Kumaran: You can send, like, daily the latest Today’s product analytics, or subscription.
203 00:17:01.900 ⇒ 00:17:02.750 Uttam Kumaran: data.
204 00:17:02.750 ⇒ 00:17:03.840 Caitlyn Vaughn: Oh, cool!
205 00:17:04.010 ⇒ 00:17:12.129 Uttam Kumaran: Let’s talk about, like, workbook. So, workbook is really, like, the home for everything.
206 00:17:12.270 ⇒ 00:17:31.180 Uttam Kumaran: I think this is where, like, it starts to get a little overwhelming, but as you kind of see the components of the workbook and you just play around, I think it’ll get a lot more clear, like, what everything is. So this is where you can, like, come and do an ad hoc analysis, you can just run queries, like, just give me some sums so I can export, or you can create dashboards.
207 00:17:32.640 ⇒ 00:17:43.649 Uttam Kumaran: I think this is a good way of explaining it, is, like, workbooks are dashboards, and so basically every work… every dashboard has a workbook underneath.
208 00:17:43.930 ⇒ 00:17:49.250 Uttam Kumaran: And so, for example, each of these items itself is a workbook.
209 00:17:49.250 ⇒ 00:17:50.290 Caitlyn Vaughn: Got it.
210 00:17:50.290 ⇒ 00:17:57.440 Uttam Kumaran: And so, you can configure the display, but what is… what is this number? This is just, like, select sum of revenue.
211 00:17:57.860 ⇒ 00:18:00.989 Uttam Kumaran: filter by this. And so that itself is a workbook, and then you just
212 00:18:01.660 ⇒ 00:18:15.290 Uttam Kumaran: add it to your dashboard as a tile. So that way, it’s similar in Tableau, where you’re, like, dissecting a dashboard with the many tiles, it just creates this, like, workbook layer where, hey, I just want to drill into, like, this one piece, you could do that pretty easily.
213 00:18:17.000 ⇒ 00:18:26.199 Uttam Kumaran: this is where, again, there’s, like, there are these kind of workbook modes where you have topics, views, and SQL. And I think this is probably where…
214 00:18:27.190 ⇒ 00:18:44.769 Uttam Kumaran: Omni has, like, a unique view on how to do this, which is, like, a topic is similar to, like, a Looker Explorer, in that it’s just a series of columns that you can select. If you go into our Omni instance right now, there are tons and tons of
215 00:18:44.840 ⇒ 00:18:50.689 Uttam Kumaran: Like, there are tons and tons of fields, and, like, maybe 5 or 6 different,
216 00:18:50.930 ⇒ 00:19:03.160 Uttam Kumaran: ex… like, basically, views in a topic. So a topic is, like, revenue. So you may have customer, you may have rev… you may have, like, sales, you may have, like, CRM data.
217 00:19:03.200 ⇒ 00:19:12.390 Uttam Kumaran: And basically, the topic is, like, how do you join those two together? Like, okay, always join on customer ID. This is, like, what our team is basically gonna create.
218 00:19:12.600 ⇒ 00:19:23.109 Uttam Kumaran: And so we’re gonna create all the topics that your team needs to answer a variety of questions. This is… this is the data model, this is, like, the business logic.
219 00:19:23.570 ⇒ 00:19:27.239 Uttam Kumaran: A view is like, okay, if you need to just
220 00:19:27.670 ⇒ 00:19:45.660 Uttam Kumaran: you know, you just want to run a query on just the transactions table, that’s, like, what, you know, basically a view is, and then SQL is, like, completely ad hoc, like, if there are people that are going to run Direct SQL, that way they don’t have to go into Mother Duck to do this, they can do this directly here.
221 00:19:46.530 ⇒ 00:19:51.909 Uttam Kumaran: And so, I don’t think this is super relevant. This is probably…
222 00:19:52.370 ⇒ 00:19:57.859 Uttam Kumaran: where I want to start, which is, like, a workbook. So we’ll be going through this in this meeting, which is just, like, how to create
223 00:19:58.140 ⇒ 00:20:10.149 Uttam Kumaran: you know, your first dashboard, and so this is, like, what a workbook is. So you have your topic here, web event tracking. In a web event, you have sessions, users, their user orders, right? So these are the fields.
224 00:20:10.300 ⇒ 00:20:25.450 Uttam Kumaran: The nice thing is, you don’t have to think about how users are joined to sessions. That’s already in the data model. You as an analyst, just want to pick the things you need. Okay, I want to look at events and users by state. Okay, so I’m going to go and select state.
225 00:20:25.670 ⇒ 00:20:33.719 Uttam Kumaran: event and users. You can see here that you can press run, it’ll run, and it’ll create, like, a workbook here. And you can see…
226 00:20:33.720 ⇒ 00:20:38.449 Caitlyn Vaughn: Before you press run here, is it gonna give you, like, a little sample, basically?
227 00:20:38.850 ⇒ 00:20:47.389 Uttam Kumaran: Typically, like, right now, it’s… it will run just the 5 rows, like, this isn’t a ton… even though the numbers seem big.
228 00:20:47.500 ⇒ 00:20:53.570 Uttam Kumaran: it shouldn’t take very long to run. Like, most of the queries on your data warehouse will take less than, like, a couple seconds to run.
229 00:20:54.080 ⇒ 00:20:58.799 Caitlyn Vaughn: But I mean, right here, there’s 5 lines. So, if I was clicking, state.
230 00:20:59.110 ⇒ 00:21:07.110 Uttam Kumaran: Oh, yeah, it will auto-refresh. I think that’s a setting you can have, whether you want it to auto-query or, like, wait until you press play.
231 00:21:07.520 ⇒ 00:21:08.210 Caitlyn Vaughn: Okay.
232 00:21:08.210 ⇒ 00:21:13.660 Uttam Kumaran: that you can configure that. I don’t know what it is right now, if it, like, automatically does it, but…
233 00:21:14.840 ⇒ 00:21:15.360 Caitlyn Vaughn: Okay.
234 00:21:17.820 ⇒ 00:21:31.020 Uttam Kumaran: The other thing here is, like, this is your visualization. As you see here, this is just the results, so these are the rows, and then the next thing is, like, you would click… you basically would click chart to kind of see the chart. So, for example, this is the… if you were to take that and click chart.
235 00:21:31.120 ⇒ 00:21:42.359 Uttam Kumaran: you can see, like, okay, this is my order count, like, over time, for example, and you can go back to the results and just see, like, oh, what is the CSV that’s, like, powering this chart, basically? Like, what are the results that are powering this chart?
236 00:21:43.060 ⇒ 00:21:46.669 Uttam Kumaran: This is where it sort of gets really fancy, like, you can do every…
237 00:21:47.090 ⇒ 00:21:53.799 Uttam Kumaran: you could do every, like, Viz option you want. I feel like, for the most part, we’ll be talking about, like, whole numbers, bar charts.
238 00:21:53.930 ⇒ 00:22:00.370 Uttam Kumaran: and line charts. But as we do things like funnels, cohorting, like.
239 00:22:00.650 ⇒ 00:22:02.640 Uttam Kumaran: There’s, like, a lot more options.
240 00:22:06.050 ⇒ 00:22:10.370 Uttam Kumaran: Yeah, I think, like, I don’t know, do you want to talk about… Demi, do you have, like, a…
241 00:22:10.820 ⇒ 00:22:13.669 Uttam Kumaran: Any points to make about, like, dimensions and measures?
242 00:22:13.870 ⇒ 00:22:18.469 Uttam Kumaran: like, I don’t know if you have a good way to explain.
243 00:22:19.210 ⇒ 00:22:29.390 Demilade Agboola: So, I like to think of… Like, measures as, like, numerical values.
244 00:22:29.560 ⇒ 00:22:32.069 Demilade Agboola: That we use to, like.
245 00:22:32.840 ⇒ 00:22:45.750 Demilade Agboola: lack of, like, repeating the question, but we used to measure activities, we used to measure things going on in business. So when we think measures, we’re thinking things like the age, we’re thinking of things like
246 00:22:47.950 ⇒ 00:23:01.730 Demilade Agboola: the volume count of transactions, the revenue, those would always be measures. Those are always going to be numbers that point us to how well something is going, or, like, a characteristic of a certain
247 00:23:02.320 ⇒ 00:23:04.860 Demilade Agboola: group of users, so,
248 00:23:05.050 ⇒ 00:23:13.089 Demilade Agboola: But dimensions tend to be what we’re taking the measures of, so it will tend to be things like, oh, the ID,
249 00:23:13.140 ⇒ 00:23:32.450 Demilade Agboola: the names, the state. So, if I want to say, oh, like, how many orders do we get from a particular state, the orders will be the measure, the state would be the dimensions, and that’s kind of how I think of it. And that allows us, once we have an idea of that, that allows us to start doing things around
250 00:23:32.500 ⇒ 00:23:40.350 Demilade Agboola: modeling, because we need to know what dimensions we need for whatever chart. So if I say I want to see
251 00:23:41.220 ⇒ 00:23:45.039 Demilade Agboola: number of users. Also, dates is also a dimension.
252 00:23:45.170 ⇒ 00:23:53.319 Demilade Agboola: If I want to see our daily, chart of users by state, therefore I need to have the…
253 00:23:54.250 ⇒ 00:23:55.140 Demilade Agboola: 8.
254 00:23:55.620 ⇒ 00:23:56.510 Demilade Agboola: 8.
255 00:23:56.730 ⇒ 00:24:11.850 Demilade Agboola: Those are the two measure… the two dimensions we have, and the measure of… that we’re trying to keep track of would be maybe, like, the active user, so a count of the user IDs make up, you know, the coming a day. And that allows us to then create the model
256 00:24:12.200 ⇒ 00:24:18.780 Demilade Agboola: us to visualize it, and then we can start having charts where we can say, oh, over a number of days, this is
257 00:24:19.670 ⇒ 00:24:22.319 Demilade Agboola: We… this is how many users have commented.
258 00:24:23.610 ⇒ 00:24:31.569 Demilade Agboola: The other thing to note with that is, because we know what the dimensions and measures are, it also allows us to then know how we should
259 00:24:31.770 ⇒ 00:24:37.020 Demilade Agboola: visualize our data. So, like, dimensions do tend to be on the x-axis.
260 00:24:38.490 ⇒ 00:24:47.049 Demilade Agboola: And then, if it’s a chart, there tends to be an x-axis, and if it’s, the measures will be on the Y. So that’s why, when we see a chart of
261 00:24:47.400 ⇒ 00:24:49.319 Demilade Agboola: Users over time.
262 00:24:49.750 ⇒ 00:25:05.949 Demilade Agboola: we will have our data at the bottom, because the data is a dimension, and which is the count of the users, will be on the y-axis that shows us the change in the users all the time. So that’s kind of, like, once you understand what the dimensions and measures are, it allows you to know how you want to model.
263 00:25:05.950 ⇒ 00:25:11.229 Demilade Agboola: And it also allows you to know how, like, some of the best tips for visualizing as well.
264 00:25:12.520 ⇒ 00:25:13.740 Caitlyn Vaughn: Okay, amazing.
265 00:25:13.740 ⇒ 00:25:19.379 Uttam Kumaran: Put another way, it’s like, your dimensions describe, like, the events.
266 00:25:19.520 ⇒ 00:25:20.320 Uttam Kumaran: And so…
267 00:25:20.810 ⇒ 00:25:35.080 Uttam Kumaran: this is a user. You may have 100,000 users. They are described in this manner by the states that they’re from. And so, you can think of, like, you would… in this case, you wouldn’t do sum of state.
268 00:25:35.410 ⇒ 00:25:39.979 Uttam Kumaran: Because the state doesn’t describe, like, an aggregation.
269 00:25:40.100 ⇒ 00:25:57.769 Uttam Kumaran: The number of users, the number of events, the amount of money they bring in, those are things that you would aggregate. You do their count, if they are unique. For example, you may say, count all of the events where they clicked the login button, or sum all of the revenue.
270 00:25:57.850 ⇒ 00:26:03.709 Uttam Kumaran: But describe it to me and break it down by state. That’s the dimension.
271 00:26:03.710 ⇒ 00:26:22.559 Uttam Kumaran: You’ll see dimension, measures, metrics, like, I think it’s just helpful to learn the vernacular. This is, like, what data people have, like, invented to describe these concepts. You’ll just see this everywhere in the product, dimension, measures, dimension, measures. But, like, it’s helpful to realize, like.
272 00:26:22.560 ⇒ 00:26:24.890 Uttam Kumaran: Why you wouldn’t do sum of state.
273 00:26:25.000 ⇒ 00:26:29.360 Uttam Kumaran: Right? But you… why? Instead, you would say, aggregate
274 00:26:29.470 ⇒ 00:26:33.230 Uttam Kumaran: My users by the state dimension, you know?
275 00:26:33.230 ⇒ 00:26:36.440 Caitlyn Vaughn: Yeah. Okay, that makes sense.
276 00:26:36.810 ⇒ 00:26:44.280 Uttam Kumaran: And as you… actually, as you… like, right now, we’re talking about, like, fictional company. As you start to look at the default data, it’ll be, like.
277 00:26:44.320 ⇒ 00:26:59.280 Uttam Kumaran: you’ll really have a sense of, like, you know your product inside and out, so you’ll know, like, okay, users, you’ll know the properties that are available, you know, when they were created, when they first subscribed, things like that. So… so that’s, like, you know, basically…
278 00:26:59.480 ⇒ 00:27:07.779 Uttam Kumaran: Going in, you create, you know, your report, you create your results, and then you just go to the chart.
279 00:27:07.960 ⇒ 00:27:27.370 Uttam Kumaran: create the chart you want, and then you can go ahead and either stop there, or you can go ahead and add it to a dashboard. The other thing that… to go through is, like, filtering is gonna be big over time. The one major filter, for example, that we have on our dashboards is filtering out, like, internal default team members.
280 00:27:27.850 ⇒ 00:27:44.190 Uttam Kumaran: And so, there will be a lot to explore. For example, I want to filter out, users that… I only want to look at people that were created this month, like, users on this month. Okay, so you would do a filter on the created ad date, and so you go step 1,
281 00:27:44.190 ⇒ 00:27:48.579 Uttam Kumaran: Step 2, and then you basically say, create added date is in this month.
282 00:27:49.630 ⇒ 00:27:55.310 Uttam Kumaran: You’ll… so the reason why they highlight this is, like, filtering text versus numbers versus dates versus true-false.
283 00:27:55.480 ⇒ 00:27:59.490 Uttam Kumaran: there’ll just be a different dialogue. For example, shipping status.
284 00:27:59.610 ⇒ 00:28:05.240 Uttam Kumaran: They’re gonna show you the options, versus a date, they’re gonna show you a date picker.
285 00:28:05.410 ⇒ 00:28:21.149 Uttam Kumaran: But you can filter both at the dashboard level and at the individual workbook level. Like, let’s say you want to create one chart that is, like, I just want to look at people from California, and I want to create a chart next to them that’s people from Nevada.
286 00:28:21.150 ⇒ 00:28:29.119 Uttam Kumaran: You can just create those two workbooks, and then add them both to the dashboard, and those filters apply at the workbook level.
287 00:28:29.200 ⇒ 00:28:48.049 Uttam Kumaran: But an example of, like, filter out all default team members, that should be at the dashboard level. Or we can also just filter out of the data model entirely. Like, if you never want to see default team members, we can actually push that filter even further upstream, so that you never even have to think about
288 00:28:48.670 ⇒ 00:29:00.920 Uttam Kumaran: default team members never end up in reporting, because we’re filtering it at the data model level. So there’s just, like, options. That’ll be, I think, the things that, like, Demolade sort of thinks about, and sort of works on, like, what’s the…
289 00:29:01.240 ⇒ 00:29:02.240 Uttam Kumaran: structure.
290 00:29:02.820 ⇒ 00:29:14.059 Caitlyn Vaughn: Okay, and then were we able to figure out who the actual default users are, based on, like, joining people with a Hyperline ID with, like.
291 00:29:14.500 ⇒ 00:29:17.669 Caitlyn Vaughn: customer, like, team IDs were able to do that, or no?
292 00:29:18.260 ⇒ 00:29:20.850 Uttam Kumaran: Yeah, I think, Mustafa, we were able to do the join, right?
293 00:29:21.390 ⇒ 00:29:22.150 Mustafa Raja: Yep.
294 00:29:23.330 ⇒ 00:29:25.289 Uttam Kumaran: Yeah. Yeah, those are joined, yeah.
295 00:29:25.510 ⇒ 00:29:26.050 Uttam Kumaran: Yeah.
296 00:29:26.190 ⇒ 00:29:33.540 Uttam Kumaran: So we are able to basically find anyone with a default, and we are basically able to identify all users attached to the Hyperland subscription.
297 00:29:33.810 ⇒ 00:29:35.300 Caitlyn Vaughn: Okay, amazing. Yay!
298 00:29:36.930 ⇒ 00:29:51.430 Uttam Kumaran: I’ll kind of, like, skip past… maybe one thing I’ll show you is, like, one of the reasons we like Omni, and this may be something that, like, if anyone in finance, or anyone who’s, like, big Excel users, you can actually do, like.
299 00:29:51.740 ⇒ 00:30:01.909 Uttam Kumaran: spreadsheet work in Omni, and the reason you would do that is because typically, if people work on spreadsheets, they, like, export a CSV from Omni, open up a Google Sheet, and do work there.
300 00:30:01.920 ⇒ 00:30:11.480 Uttam Kumaran: you can actually… they basically replicated feature for feature everything in Google Sheets within Omni. So, like, if you wanted to, like.
301 00:30:11.480 ⇒ 00:30:21.359 Uttam Kumaran: basically represent calculations, or do, like, VLOOKUPs and things, and you wanted to do that sort of analysis in a spreadsheet sort of way.
302 00:30:21.360 ⇒ 00:30:34.479 Uttam Kumaran: you can do that in Omni so that it sits in the BI tool. It’s pulling from governed data sources, but you still get the power of, like, whatever folks are trying to do in Sheets that maybe they don’t know or they can’t do.
303 00:30:34.560 ⇒ 00:30:53.589 Uttam Kumaran: you know, the traditional way. Like, if you’re trying to do more advanced lookups, trying to do, like, financial analysis, like, build a P&L, for example, right now, if you were to build, like, a financial analysis, like, for example, the work that Amber did, I actually told her, I said, I would like to move all of that into Omni, so that it lives forever.
304 00:30:54.210 ⇒ 00:31:04.710 Uttam Kumaran: like a one-time thing that she did in a Google Sheet. This is something that the team can come back to and, like, you know, test new pricing and things like that, and it’s pulling in real data, you know?
305 00:31:04.980 ⇒ 00:31:07.439 Uttam Kumaran: So that’s… that’s something that I think will…
306 00:31:07.590 ⇒ 00:31:11.780 Uttam Kumaran: Explore further, and then maybe let me just see if there’s anything else…
307 00:31:12.300 ⇒ 00:31:15.360 Uttam Kumaran: this is sort of more about, like, the data modeling side, I think.
308 00:31:15.800 ⇒ 00:31:21.209 Uttam Kumaran: we… I want to start with workbooks, creating dashboards and reports. This is, like.
309 00:31:22.030 ⇒ 00:31:37.549 Uttam Kumaran: I don’t know, level 2, I would say, is, like, creating fields, creating joins. This is the stuff that, like, if any… if someone like Thomas or other people are gonna be in charge of, like, creating new models and topics.
310 00:31:37.550 ⇒ 00:31:43.400 Uttam Kumaran: this is the work that… that I think they’re gonna be familiar with. Definitely, like, a little bit more…
311 00:31:43.550 ⇒ 00:31:48.320 Uttam Kumaran: technical. And then there’s more stuff here about, like, other…
312 00:31:48.950 ⇒ 00:31:59.129 Uttam Kumaran: workbook things you could do. Maybe the other thing I just wanted to share, is just, like, a little bit about, like, modeling.
313 00:31:59.250 ⇒ 00:32:06.689 Uttam Kumaran: And this… this doc is great. It just has a lot about all the different keywords. We talked a little bit about this.
314 00:32:06.900 ⇒ 00:32:14.750 Uttam Kumaran: I wanna show the… Oh… Is it…
315 00:32:24.640 ⇒ 00:32:26.909 Uttam Kumaran: Okay, I feel like I didn’t have…
316 00:32:28.510 ⇒ 00:32:35.389 Uttam Kumaran: There’s something here about showing how to… how to do models, but maybe I… Covered it already, because…
317 00:32:37.250 ⇒ 00:32:37.810 Uttam Kumaran: I think.
318 00:32:37.810 ⇒ 00:32:41.680 Caitlyn Vaughn: Honestly, what might be helpful for me is to, like, actually go through the app and, like.
319 00:32:41.680 ⇒ 00:32:42.080 Uttam Kumaran: Yeah.
320 00:32:42.080 ⇒ 00:32:43.520 Caitlyn Vaughn: They’re building stuff with you?
321 00:32:43.520 ⇒ 00:32:48.309 Uttam Kumaran: So maybe if you want to just pull it up on your side, Caitlin, and I mentioned to Mustafa that…
322 00:32:48.470 ⇒ 00:32:52.990 Uttam Kumaran: We can just walk you through creating… a workbook?
323 00:32:52.990 ⇒ 00:32:54.910 Caitlyn Vaughn: And then creating a dashboard.
324 00:32:55.060 ⇒ 00:32:55.960 Uttam Kumaran: Okay.
325 00:32:56.830 ⇒ 00:33:00.370 Uttam Kumaran: So, the easiest thing is if you just want to go to home.
326 00:33:00.780 ⇒ 00:33:06.070 Uttam Kumaran: And then you just wanna, basically, create a new…
327 00:33:06.220 ⇒ 00:33:10.129 Uttam Kumaran: workbook, I could just also just pull this up alongside.
328 00:33:12.810 ⇒ 00:33:14.940 Uttam Kumaran: So you should see new on the top left.
329 00:33:16.720 ⇒ 00:33:17.920 Caitlyn Vaughn: Oh, over there.
330 00:33:20.150 ⇒ 00:33:25.950 Uttam Kumaran: And… Yeah, so you would… it’s just on one topic. Again, a topic is…
331 00:33:26.190 ⇒ 00:33:30.100 Uttam Kumaran: The joins between a bunch of views, so you would just click on the product topic.
332 00:33:34.060 ⇒ 00:33:40.349 Uttam Kumaran: And on the left side, you’re gonna see, like, all of the… this is all of the default data that we have available for the team.
333 00:33:41.090 ⇒ 00:33:47.710 Uttam Kumaran: So I think one thing, Demi, for you to kind of keep note of as we go is there’s definitely going to be some cleanup we want to do here. We’ve sort of just…
334 00:33:47.880 ⇒ 00:33:56.599 Uttam Kumaran: we just muscled through to get to the original dashboards, but I think there’s cleanup on… there are usual cleanup on views and… and duplicate… On everything.
335 00:33:56.850 ⇒ 00:33:58.089 Caitlyn Vaughn: Yeah, yeah.
336 00:33:58.140 ⇒ 00:34:02.080 Uttam Kumaran: But, like, let’s… let’s take an example, like, what’s a good…
337 00:34:02.230 ⇒ 00:34:05.639 Uttam Kumaran: Like, simple question to ask, like, we could do, like.
338 00:34:06.080 ⇒ 00:34:09.339 Uttam Kumaran: Forms over time, like, form submissions over time, maybe.
339 00:34:09.690 ⇒ 00:34:13.329 Caitlyn Vaughn: Yeah, can we do, workflow runs over time?
340 00:34:13.690 ⇒ 00:34:14.350 Uttam Kumaran: Yeah.
341 00:34:15.550 ⇒ 00:34:17.470 Caitlyn Vaughn: That’s actually forms, right?
342 00:34:17.650 ⇒ 00:34:18.420 Uttam Kumaran: Yes.
343 00:34:18.909 ⇒ 00:34:20.469 Caitlyn Vaughn: Or is that submissions?
344 00:34:20.469 ⇒ 00:34:23.699 Mustafa Raja: There would be submissions, yeah. So submissions are…
345 00:34:23.699 ⇒ 00:34:25.789 Uttam Kumaran: Forms is gonna be Form Creations.
346 00:34:26.130 ⇒ 00:34:26.709 Mustafa Raja: Yeah, yeah, yeah.
347 00:34:26.710 ⇒ 00:34:27.630 Uttam Kumaran: Okay, okay, okay.
348 00:34:28.810 ⇒ 00:34:31.580 Mustafa Raja: have form IDs, which are attached to forms.
349 00:34:31.860 ⇒ 00:34:33.589 Mustafa Raja: And then Hookie.
350 00:34:34.489 ⇒ 00:34:35.179 Mustafa Raja: Yeah.
351 00:34:35.699 ⇒ 00:34:37.919 Caitlyn Vaughn: should be the submission ID.
352 00:34:38.260 ⇒ 00:34:39.549 Mustafa Raja: Yeah, we can do that.
353 00:34:39.550 ⇒ 00:34:40.429 Caitlyn Vaughn: Click on it.
354 00:34:41.040 ⇒ 00:34:41.719 Mustafa Raja: Yeah.
355 00:34:41.989 ⇒ 00:34:49.199 Mustafa Raja: And then, we can say, create that, to, you know, see, if we want to,
356 00:34:49.510 ⇒ 00:34:53.880 Mustafa Raja: Make a view daily for daily, or weekly, or monthly, you know?
357 00:34:54.500 ⇒ 00:34:56.440 Caitlyn Vaughn: Hmm, so…
358 00:34:56.580 ⇒ 00:35:01.210 Mustafa Raja: If you would click week, we would say… we would see…
359 00:35:02.000 ⇒ 00:35:07.090 Mustafa Raja: Yeah, and maybe even, if you would scroll down a little, you’d see a, a measure.
360 00:35:07.570 ⇒ 00:35:17.599 Mustafa Raja: And it’s submissions count, right? So if you would, rather than ID, do submissions count, then what would happen is we will see weekly submissions, you know?
361 00:35:18.930 ⇒ 00:35:20.689 Caitlyn Vaughn: Submissions count out.
362 00:35:21.190 ⇒ 00:35:23.409 Mustafa Raja: Yeah, why is this one green?
363 00:35:23.410 ⇒ 00:35:24.369 Uttam Kumaran: So it’s green because.
364 00:35:24.370 ⇒ 00:35:25.930 Mustafa Raja: So it… yeah…
365 00:35:26.100 ⇒ 00:35:27.630 Caitlyn Vaughn: Because why?
366 00:35:28.120 ⇒ 00:35:29.390 Uttam Kumaran: Because it’s a measure.
367 00:35:30.580 ⇒ 00:35:31.360 Caitlyn Vaughn: Oh.
368 00:35:31.550 ⇒ 00:35:32.269 Caitlyn Vaughn: As opposed to…
369 00:35:33.280 ⇒ 00:35:39.199 Uttam Kumaran: Yes, exactly. So, for example, ID is a dimension, so you can actually, I think if you… yep.
370 00:35:39.200 ⇒ 00:35:39.910 Mustafa Raja: Yeah.
371 00:35:39.910 ⇒ 00:35:41.040 Uttam Kumaran: Exactly.
372 00:35:41.040 ⇒ 00:35:43.280 Caitlyn Vaughn: And then can I, like, drag this over here? Nice.
373 00:35:44.270 ⇒ 00:35:44.950 Mustafa Raja: Yeah.
374 00:35:45.170 ⇒ 00:35:50.389 Mustafa Raja: Now we, now we have a view of weekly, weekly submissions.
375 00:35:51.020 ⇒ 00:35:53.520 Demilade Agboola: Wait, just a quick question.
376 00:35:55.260 ⇒ 00:36:01.249 Demilade Agboola: Is this model data? Is there, like, the completed… Like, competitive equals to true.
377 00:36:02.700 ⇒ 00:36:03.490 Uttam Kumaran: Yes.
378 00:36:03.910 ⇒ 00:36:04.430 Demilade Agboola: Okay.
379 00:36:04.430 ⇒ 00:36:20.150 Uttam Kumaran: This is a completed Boolean, also, at the top. So right now, you’re seeing all submissions. I think also, Caitlin, what you can do is, like, let’s go through, like, setting a filter. So let’s go ahead and… if you… if you go to Status, and you… there should be a couple dots that come up to the right.
380 00:36:20.400 ⇒ 00:36:27.879 Uttam Kumaran: You can actually just filter, and I don’t know if Mustafa, like, status has the completed information.
381 00:36:28.700 ⇒ 00:36:34.030 Mustafa Raja: We can take a look at that, if you would go and, click on any value.
382 00:36:34.380 ⇒ 00:36:36.969 Mustafa Raja: You know? On, on, on, yeah.
383 00:36:38.560 ⇒ 00:36:41.969 Mustafa Raja: And then if you would, yeah, the values would… yeah.
384 00:36:42.360 ⇒ 00:36:44.560 Mustafa Raja: So this is the only value that’s in there.
385 00:36:44.560 ⇒ 00:36:48.149 Demilade Agboola: No, but there’s a column called completed. There should be a column completed.
386 00:36:48.150 ⇒ 00:36:54.270 Mustafa Raja: Yeah, so let’s look for that. Let’s remove this, remove this filter.
387 00:36:54.820 ⇒ 00:36:59.560 Mustafa Raja: There’s a cross when you hover it, hover over it, yeah.
388 00:37:00.230 ⇒ 00:37:05.280 Mustafa Raja: Yeah, so let’s look for the completed, dimension.
389 00:37:05.670 ⇒ 00:37:09.240 Mustafa Raja: It should be in here. No, no, no, the status one, the completed one?
390 00:37:10.200 ⇒ 00:37:12.210 Demilade Agboola: There’s a column called completer.
391 00:37:14.200 ⇒ 00:37:15.049 Mustafa Raja: Yeah, the completed.
392 00:37:16.450 ⇒ 00:37:19.719 Mustafa Raja: Now, if… now we should, filter over it.
393 00:37:20.900 ⇒ 00:37:21.830 Caitlyn Vaughn: We should what?
394 00:37:22.350 ⇒ 00:37:23.200 Demilade Agboola: I do have a filter.
395 00:37:23.200 ⇒ 00:37:23.870 Mustafa Raja: filter it.
396 00:37:23.870 ⇒ 00:37:24.390 Demilade Agboola: Yeah.
397 00:37:24.390 ⇒ 00:37:30.050 Mustafa Raja: Yeah, and now, then, yeah, yeah, it’s true. It’s true.
398 00:37:31.870 ⇒ 00:37:40.110 Mustafa Raja: And then let’s… let’s just cross it out on the right side. You’d see a cross? Yeah, let’s cross this one. Yeah, let’s update.
399 00:37:41.510 ⇒ 00:37:45.150 Mustafa Raja: Yeah, let’s update. Yeah. Yeah, this should be good now.
400 00:37:45.420 ⇒ 00:37:53.519 Uttam Kumaran: And so, if… but I guess, to show you, Caitlin, right now you’ve also brought in the submissions completed as a dimension.
401 00:37:53.700 ⇒ 00:37:57.790 Uttam Kumaran: So, you can actually remove it as a dimension, but keep it as a filter.
402 00:37:59.030 ⇒ 00:37:59.630 Mustafa Raja: Oh, yeah.
403 00:38:00.120 ⇒ 00:38:07.730 Uttam Kumaran: If you click on completed here, and you just remove it from your dimensions, you’re gonna end up with
404 00:38:08.490 ⇒ 00:38:11.469 Uttam Kumaran: Kind of a goal, which is completed submissions by week.
405 00:38:12.180 ⇒ 00:38:12.820 Uttam Kumaran: Hence.
406 00:38:14.650 ⇒ 00:38:15.190 Caitlyn Vaughn: Okay.
407 00:38:15.190 ⇒ 00:38:18.829 Demilade Agboola: If you… so if you remove column C, that’s what he’s trying to say, like…
408 00:38:20.420 ⇒ 00:38:22.070 Caitlyn Vaughn: If I remove column C, then what?
409 00:38:22.080 ⇒ 00:38:22.930 Mustafa Raja: Yes.
410 00:38:23.360 ⇒ 00:38:26.580 Uttam Kumaran: So then you’re just gonna get submissions by week.
411 00:38:28.450 ⇒ 00:38:30.349 Caitlyn Vaughn: Versus what is completed.
412 00:38:30.890 ⇒ 00:38:34.759 Uttam Kumaran: Versus right now, it’s sort of… you’re already filtering to true.
413 00:38:35.100 ⇒ 00:38:40.840 Uttam Kumaran: So, you don’t need to have the filter and have it in the result data set. You can actually…
414 00:38:41.010 ⇒ 00:38:47.580 Uttam Kumaran: Keep the filter, and just remove it from your results, because it’s always going to be true, because you’re always filtering to true.
415 00:38:47.980 ⇒ 00:38:50.730 Caitlyn Vaughn: Okay, but I don’t understand what completed is.
416 00:38:50.730 ⇒ 00:38:53.499 Uttam Kumaran: Completed is the submission is completed.
417 00:38:54.840 ⇒ 00:38:55.319 Caitlyn Vaughn: But what does that?
418 00:38:55.320 ⇒ 00:38:56.030 Demilade Agboola: Oh.
419 00:38:56.220 ⇒ 00:39:08.249 Demilade Agboola: Alright, so the way it works, and that’s kind of what I was looking at today, or yesterday, actually, when someone starts a form, it’s registered in the raw forms table, right?
420 00:39:08.540 ⇒ 00:39:11.280 Demilade Agboola: But as they work through that form.
421 00:39:11.590 ⇒ 00:39:17.160 Demilade Agboola: Every time they make an update or come back to it, it just stores the progress in there.
422 00:39:17.370 ⇒ 00:39:22.189 Demilade Agboola: But the completed flag, which is the completed column, will remain false.
423 00:39:22.630 ⇒ 00:39:32.030 Demilade Agboola: The moment, then, they then complete the entire flow of it, It will then say this… has been completed.
424 00:39:32.200 ⇒ 00:39:38.169 Demilade Agboola: But I actually sends an example to Mustafa, where there’s a form in particular.
425 00:39:38.890 ⇒ 00:39:53.230 Demilade Agboola: that has over 7,000 rows within this table, the submissions table, and every single column in there is false. So it was never completed, someone got in there multiple times to do whatever changes, but they never completed
426 00:39:54.840 ⇒ 00:39:57.219 Demilade Agboola: And they’ve never submitted it, so…
427 00:39:57.850 ⇒ 00:40:03.919 Demilade Agboola: that’s the idea of the completed e-cost proof lag. So, it allows you to sort of see who started what.
428 00:40:04.470 ⇒ 00:40:16.620 Demilade Agboola: and who eventually completed it, and you can kind of see if… which is the summary table I sent you, the CSV I sent you, you can see where people are getting stuck. Main things people are doing, starting.
429 00:40:16.620 ⇒ 00:40:24.849 Demilade Agboola: but never really completing. You can have a percentage column based off of that, and say, oh, okay, it appears this integration, people seem to struggle a lot with it.
430 00:40:24.850 ⇒ 00:40:44.340 Demilade Agboola: or they start with it, but just don’t seem to get over the line, and we can start… we can start a hypothesis on, like, why that is, right? Is it harder to do? Is it… did they change their minds in between? Do they think they need it, or are they just playing out? Like, we could try… trying to figure out, like, what exactly leads to a low competition rate for a particular
431 00:40:44.340 ⇒ 00:40:45.280 Demilade Agboola: It’s a good shot.
432 00:40:46.500 ⇒ 00:40:55.760 Caitlyn Vaughn: Okay, so I think where I was getting stuck, or what is confusing, is we have forms and submissions as two separate… what do we call them again?
433 00:40:55.760 ⇒ 00:40:56.590 Uttam Kumaran: Views.
434 00:40:57.040 ⇒ 00:40:58.080 Caitlyn Vaughn: Views.
435 00:40:59.590 ⇒ 00:41:05.320 Uttam Kumaran: Yeah, so I think Demolade feedback is like, yeah, we should basically nest them under one.
436 00:41:06.180 ⇒ 00:41:06.870 Demilade Agboola: Boom.
437 00:41:07.280 ⇒ 00:41:08.780 Caitlyn Vaughn: Yeah. So.
438 00:41:09.280 ⇒ 00:41:15.960 Demilade Agboola: Because I guess the way it is, because, like, I’m literally onboarding this week, so I guess catching up to speed.
439 00:41:16.100 ⇒ 00:41:19.730 Demilade Agboola: So the way it is is kind of how it is in the database right now.
440 00:41:20.100 ⇒ 00:41:32.200 Demilade Agboola: So the forms are in one place, submissions are in one place, and we tied them together using something called a join, which is why the submissions have something called a form ID, so we can always use that to go
441 00:41:32.380 ⇒ 00:41:38.759 Demilade Agboola: performs and say, this is what it is. What I could do for you, and do here, is have a larger
442 00:41:38.910 ⇒ 00:41:45.429 Demilade Agboola: Like, one view of everything, where we can start to see, okay, so for every farm.
443 00:41:46.180 ⇒ 00:41:52.910 Demilade Agboola: If we only care about when it was completed, we can start to see, like, It was completed, basically.
444 00:41:54.830 ⇒ 00:42:01.859 Caitlyn Vaughn: Okay, so also, if we have form submissions, then I’m just assuming that that equals workflow runs.
445 00:42:02.010 ⇒ 00:42:07.500 Caitlyn Vaughn: But it doesn’t necessarily, because not every single form is tied to a workflow.
446 00:42:09.390 ⇒ 00:42:10.420 Uttam Kumaran: So we should have…
447 00:42:10.420 ⇒ 00:42:11.139 Caitlyn Vaughn: This right here.
448 00:42:11.140 ⇒ 00:42:14.780 Uttam Kumaran: Like, is it… like, basically, is it a workflow or not?
449 00:42:16.740 ⇒ 00:42:17.620 Demilade Agboola: Yeah, so that…
450 00:42:18.110 ⇒ 00:42:20.279 Uttam Kumaran: Does the form contain a workflow run?
451 00:42:20.430 ⇒ 00:42:35.769 Uttam Kumaran: So that’s something that, like, let’s walk… let’s walk… even walk through, like, that feedback. So, Kayla, what our team would basically do, or whoever is data modeling, would say, great, I’m gonna go ahead and create a dimension that is… includes a workflow run, true, false.
452 00:42:36.130 ⇒ 00:42:38.720 Uttam Kumaran: So that you can toggle it. And so that’s something that…
453 00:42:38.910 ⇒ 00:42:42.330 Uttam Kumaran: we can go create pretty easily.
454 00:42:42.330 ⇒ 00:42:45.159 Demilade Agboola: Yeah, sure, but I think the question would be,
455 00:42:46.390 ⇒ 00:42:55.420 Demilade Agboola: Because from what I saw with Victor, or Victor’s message, so this is part of where I may need some clarification, is submission is when it is wrong.
456 00:42:55.750 ⇒ 00:42:57.459 Demilade Agboola: Would that be true or false?
457 00:43:01.470 ⇒ 00:43:02.379 Caitlyn Vaughn: I don’t know.
458 00:43:03.000 ⇒ 00:43:08.090 Uttam Kumaran: I believe what we found is that submission is on run, and so you can…
459 00:43:08.090 ⇒ 00:43:08.480 Mustafa Raja: Yeah.
460 00:43:08.480 ⇒ 00:43:17.959 Uttam Kumaran: You can start a… you can basically… you can create a form, and you can start a submission, but the submission may not be completed, and then if it’s not completed, there’s no workflow run.
461 00:43:19.620 ⇒ 00:43:26.660 Uttam Kumaran: like, for example, there are forms people create, but then don’t publish, and so they never get submission. There’s also forms published.
462 00:43:26.760 ⇒ 00:43:30.089 Uttam Kumaran: And then a submission is started, but it’s not, like, completed.
463 00:43:30.590 ⇒ 00:43:35.200 Caitlyn Vaughn: Yeah, but you can create a form and not attach a workflow to it. It could just be a form.
464 00:43:35.420 ⇒ 00:43:38.790 Uttam Kumaran: That’s also correct, yeah, but there’s still a submission attached
465 00:43:38.930 ⇒ 00:43:40.899 Uttam Kumaran: to it, there’s not a workflow run.
466 00:43:42.440 ⇒ 00:43:44.779 Uttam Kumaran: You know, not every… other…
467 00:43:45.150 ⇒ 00:43:48.359 Demilade Agboola: Are there… are there any integrations that…
468 00:43:49.500 ⇒ 00:43:52.569 Demilade Agboola: The goal is for the form to just be created.
469 00:43:53.100 ⇒ 00:43:57.870 Demilade Agboola: And there isn’t a run expected, like, isn’t a submission expected of it?
470 00:43:58.740 ⇒ 00:43:59.480 Demilade Agboola: Always the goal.
471 00:43:59.480 ⇒ 00:44:00.250 Caitlyn Vaughn: Say that again?
472 00:44:00.790 ⇒ 00:44:05.559 Demilade Agboola: Are there any, integrations where the goal itself is just…
473 00:44:05.940 ⇒ 00:44:12.210 Demilade Agboola: a form to be created versus an actual run on the form, a submission and then a run.
474 00:44:13.000 ⇒ 00:44:25.849 Caitlyn Vaughn: I’ll probably… what would be a good idea, Demi, is to, like, walk you through our platform so that you could see it, maybe later today. The only integrations that we have are inside of our workflow builder.
475 00:44:27.040 ⇒ 00:44:27.830 Demilade Agboola: Gotcha.
476 00:44:28.140 ⇒ 00:44:31.439 Demilade Agboola: So, the reason I ask that is because
477 00:44:31.890 ⇒ 00:44:35.750 Demilade Agboola: If we have the… okay, so the work, so…
478 00:44:37.250 ⇒ 00:44:38.510 Demilade Agboola: Do you want to do that now, or…
479 00:44:38.510 ⇒ 00:44:43.089 Uttam Kumaran: You can also go check, take a look. We have our… we have default as well, so you can go take a look at our…
480 00:44:43.950 ⇒ 00:44:48.460 Uttam Kumaran: our BrainForge AI.ai workflows and forms and shit.
481 00:44:49.350 ⇒ 00:44:55.070 Uttam Kumaran: Yeah, Ryan on our team set up a bunch with HubSpot and our inbound lead forms.
482 00:44:55.900 ⇒ 00:44:58.049 Demilade Agboola: Gotcha. No, I, I…
483 00:44:58.440 ⇒ 00:45:03.369 Demilade Agboola: I’ve been through the page, just not actually fully built a workflow, so that might be the…
484 00:45:03.930 ⇒ 00:45:04.480 Uttam Kumaran: Cool.
485 00:45:07.120 ⇒ 00:45:14.679 Uttam Kumaran: So I think, like, I just want to round out so we actually end up creating a dashboard. So at this point, Caitlin, let’s go ahead and click on just chart on the top.
486 00:45:15.080 ⇒ 00:45:18.019 Uttam Kumaran: And so, what you’re gonna see here is the chart.
487 00:45:19.270 ⇒ 00:45:19.960 Caitlyn Vaughn: nice.
488 00:45:19.960 ⇒ 00:45:23.540 Uttam Kumaran: And so, this is… You know, submissions.
489 00:45:23.940 ⇒ 00:45:24.260 Caitlyn Vaughn: Bye.
490 00:45:24.260 ⇒ 00:45:28.229 Uttam Kumaran: And so, on the right side, you have all your…
491 00:45:28.770 ⇒ 00:45:31.830 Uttam Kumaran: Like, you know, different chart types.
492 00:45:31.830 ⇒ 00:45:32.180 Caitlyn Vaughn: type.
493 00:45:32.180 ⇒ 00:45:37.139 Uttam Kumaran: Right now, you just have one measure and one dimension, so some of them are not gonna be, like.
494 00:45:38.340 ⇒ 00:45:41.340 Uttam Kumaran: Anything, but for example, we have, like, stacked bars, right?
495 00:45:41.450 ⇒ 00:45:49.900 Uttam Kumaran: In the stacked bar situation, we have multiple different dimensions, and you’re basically saying, okay, show me for the same time.
496 00:45:50.590 ⇒ 00:45:58.649 Uttam Kumaran: break down the measure by the various dimensions. So, if you were doing, like, submissions over time by state.
497 00:45:58.910 ⇒ 00:46:04.620 Uttam Kumaran: And you were to have those three dimensions, a stack bar is a perfect way for you to, like, visualize that.
498 00:46:05.560 ⇒ 00:46:10.459 Caitlyn Vaughn: Okay, so if I go back to results, could… do we have by state?
499 00:46:10.460 ⇒ 00:46:17.080 Uttam Kumaran: Yeah, so go to Submission, and let’s see what other dimensions we have access to. So we have…
500 00:46:17.730 ⇒ 00:46:21.450 Uttam Kumaran: Refer, response, submission, team ID,
501 00:46:21.730 ⇒ 00:46:26.970 Uttam Kumaran: We can actually probably just do, like, let’s go ahead and just do, like, team… ID.
502 00:46:27.260 ⇒ 00:46:30.250 Uttam Kumaran: Or, I don’t know, yeah…
503 00:46:32.870 ⇒ 00:46:34.169 Mustafa Raja: Yeah, there’s a name.
504 00:46:34.170 ⇒ 00:46:41.160 Uttam Kumaran: If you bring in the team name, you’ll be able to see submissions by forms owned by which team.
505 00:46:42.140 ⇒ 00:46:44.439 Uttam Kumaran: And so now, let’s go back to chart.
506 00:46:48.000 ⇒ 00:46:49.960 Caitlyn Vaughn: Okay, wait, I’m just looking at this…
507 00:46:49.960 ⇒ 00:46:50.550 Uttam Kumaran: Yeah.
508 00:46:50.890 ⇒ 00:46:57.670 Caitlyn Vaughn: Submission count, so the number of submissions per week, owned by…
509 00:46:58.200 ⇒ 00:46:59.649 Uttam Kumaran: That team on the right.
510 00:47:01.150 ⇒ 00:47:02.159 Demilade Agboola: But this is just a…
511 00:47:02.160 ⇒ 00:47:03.010 Caitlyn Vaughn: count.
512 00:47:03.550 ⇒ 00:47:04.300 Uttam Kumaran: Correct.
513 00:47:04.800 ⇒ 00:47:07.320 Caitlyn Vaughn: So why… what is the name for?
514 00:47:07.320 ⇒ 00:47:12.070 Uttam Kumaran: The name is the people that own the form on which the submissions occurred.
515 00:47:13.400 ⇒ 00:47:18.269 Uttam Kumaran: So this would be like, hey, I wanna… I wanna break down who’s getting the most submissions.
516 00:47:19.560 ⇒ 00:47:20.510 Uttam Kumaran: by week.
517 00:47:21.290 ⇒ 00:47:21.650 Demilade Agboola: Egypt.
518 00:47:21.650 ⇒ 00:47:23.160 Caitlyn Vaughn: Whoa, this is the most.
519 00:47:24.090 ⇒ 00:47:25.510 Uttam Kumaran: These are all of them.
520 00:47:26.070 ⇒ 00:47:28.709 Demilade Agboola: Yeah, but it’s not ordered, it’s just random.
521 00:47:28.710 ⇒ 00:47:31.450 Mustafa Raja: Yeah, it’s ordered. It’s actually ordered in the week.
522 00:47:31.570 ⇒ 00:47:33.060 Demilade Agboola: Yeah, so it’s not…
523 00:47:33.060 ⇒ 00:47:36.990 Mustafa Raja: So we can order it by submissions if we want, but that will mess up the time.
524 00:47:36.990 ⇒ 00:47:46.559 Uttam Kumaran: Let’s go back… let’s just go back to chart, Kayla, real quick. I’ll just show you what I mean. I think that visually, it’ll help. And then let’s go to… let’s do the second…
525 00:47:46.710 ⇒ 00:47:52.500 Uttam Kumaran: From the left on the chart selector, On the top right.
526 00:47:54.060 ⇒ 00:47:57.039 Uttam Kumaran: Do you see the stacked bar? Yeah.
527 00:47:57.770 ⇒ 00:47:59.390 Uttam Kumaran: So, stack column.
528 00:47:59.560 ⇒ 00:48:07.339 Uttam Kumaran: And then let’s actually drag in the name column. So, if you look at the bottom here, you’re gonna see name.
529 00:48:07.770 ⇒ 00:48:15.640 Uttam Kumaran: Let’s actually drag that in, to the, I think you have to drag it into the X…
530 00:48:16.680 ⇒ 00:48:20.889 Uttam Kumaran: axis, I believe, to, like, basically add… so you’re gonna drag it up here, yeah.
531 00:48:21.360 ⇒ 00:48:27.220 Uttam Kumaran: So, I think you either drag it there, or try dragging it onto the… Color and legend.
532 00:48:28.320 ⇒ 00:48:29.070 Uttam Kumaran: Yeah.
533 00:48:29.600 ⇒ 00:48:30.949 Uttam Kumaran: And so, what you’re gonna see here…
534 00:48:30.950 ⇒ 00:48:31.350 Caitlyn Vaughn: Hmm.
535 00:48:31.350 ⇒ 00:48:36.210 Uttam Kumaran: To the stacked bar of submissions based on who owns the form.
536 00:48:36.560 ⇒ 00:48:38.170 Caitlyn Vaughn: I see. Okay.
537 00:48:39.180 ⇒ 00:48:43.980 Caitlyn Vaughn: Okay, so after I have all of these fields in my results, then when.
538 00:48:43.980 ⇒ 00:49:01.390 Uttam Kumaran: And then try to visualize any way you want, yeah. So you’re not going to see any failed… you’re not going to see non-completed, because you have a filter, you’re filtering all those out. But let’s say you want to further segment and say, actually, just show me all submissions, no matter what, you can go ahead and nix this, or you can make it
539 00:49:01.920 ⇒ 00:49:11.189 Uttam Kumaran: any value, and then you can see everything. So right now, you’re seeing all form… completed form submissions by week, and by the team who owns the form.
540 00:49:12.430 ⇒ 00:49:14.520 Uttam Kumaran: Regardless if there’s a workflow or not.
541 00:49:14.670 ⇒ 00:49:17.930 Uttam Kumaran: You know, so that’s, like…
542 00:49:18.300 ⇒ 00:49:20.940 Uttam Kumaran: that is, like, what we arrived at here. And so.
543 00:49:22.400 ⇒ 00:49:26.610 Uttam Kumaran: I think maybe the last thing we could do is now just, like, save it to a dashboard.
544 00:49:27.100 ⇒ 00:49:27.970 Uttam Kumaran: Right.
545 00:49:28.150 ⇒ 00:49:30.759 Uttam Kumaran: So right now, exactly.
546 00:49:32.820 ⇒ 00:49:34.729 Uttam Kumaran: And you can go ahead and just…
547 00:49:34.970 ⇒ 00:49:37.520 Uttam Kumaran: Create a new one in your documents.
548 00:50:02.010 ⇒ 00:50:02.569 Uttam Kumaran: There it is.
549 00:50:02.570 ⇒ 00:50:03.100 Caitlyn Vaughn: Nice.
550 00:50:03.100 ⇒ 00:50:05.729 Uttam Kumaran: And so the other thing that’s going on here is now this is a draft.
551 00:50:06.100 ⇒ 00:50:09.579 Uttam Kumaran: And so at the top left, you can think about how this is just, like.
552 00:50:09.850 ⇒ 00:50:15.239 Uttam Kumaran: you can make edits, and then you can publish. And so the top left… the top right, you’ll see publish.
553 00:50:15.690 ⇒ 00:50:26.839 Uttam Kumaran: And so, one of the things that often happens is multiple people are editing, like, a dashboard over time, you can kind of step over each other, they have helpful features to just, like, prevent some of that. So you could publish, like, a draft.
554 00:50:27.300 ⇒ 00:50:31.250 Uttam Kumaran: And then… yeah, so if you go ahead and hit publish, then now…
555 00:50:31.550 ⇒ 00:50:38.339 Uttam Kumaran: anyone who has access to your documents folder can now see this. And so now you basically kind of get the sense of, like.
556 00:50:38.450 ⇒ 00:50:43.270 Uttam Kumaran: Okay, this is one chart, and now we’re gonna create the next one, we’re gonna create the next one, we’re gonna create the next one, you know?
557 00:50:43.610 ⇒ 00:50:47.189 Caitlyn Vaughn: Okay, so every time I want to create another
558 00:50:48.420 ⇒ 00:50:51.600 Caitlyn Vaughn: workbook inside of here. I could just add workbook right here.
559 00:50:51.600 ⇒ 00:50:53.160 Uttam Kumaran: Well, you guys should just go to Edit.
560 00:50:53.310 ⇒ 00:51:03.490 Uttam Kumaran: Oh yeah, or basically, yes, exactly. This, this is actually… you actually did it… yeah, there’s two ways to basically get there. You can hit edit, which will give you edit access to, like, the dashboard as a whole.
561 00:51:04.100 ⇒ 00:51:12.789 Uttam Kumaran: if you want to add dashboard-level filters, if you want to chat with the dashboard, but then, at this point, yes, exactly you’re right, you just, you would click on
562 00:51:13.150 ⇒ 00:51:16.449 Uttam Kumaran: Edit in Workbook, or you click on Edit in the top right, yeah.
563 00:51:16.980 ⇒ 00:51:20.799 Caitlyn Vaughn: Okay, and when I hit explore, that’s kind of like the drill down, right?
564 00:51:21.510 ⇒ 00:51:26.150 Uttam Kumaran: correct. Wait, where exactly did you hit explore?
565 00:51:30.410 ⇒ 00:51:37.620 Uttam Kumaran: Oh, yeah, so if you click Explore, it’s actually gonna take you out of the dashboard entirely. So it’ll be, like, just, like, starting fresh from the topic.
566 00:51:38.600 ⇒ 00:51:39.410 Caitlyn Vaughn: Okay.
567 00:51:40.390 ⇒ 00:51:45.560 Caitlyn Vaughn: And if I make changes in Explorer, it won’t get pushed back, right?
568 00:51:45.560 ⇒ 00:51:59.839 Uttam Kumaran: you would have to save that. It’s like starting fresh. For example, if you click on Explore from there, it’ll basically take the configuration of this to a new Explorer, and then you can save it into that workbook.
569 00:52:00.030 ⇒ 00:52:07.109 Uttam Kumaran: And so the workbook is here, so you can see this is one query. You’ll basically start to create multiple queries as part of that workbook.
570 00:52:07.450 ⇒ 00:52:08.470 Caitlyn Vaughn: Hmm…
571 00:52:09.320 ⇒ 00:52:15.920 Caitlyn Vaughn: So if I have a bunch of, like, dashboards, a bunch of charts in my dashboard, it’s a bunch of workbooks, and they’ll all show up here.
572 00:52:15.920 ⇒ 00:52:27.479 Uttam Kumaran: Yeah, so right now, this is, like, a brand new Explorer, so that you only have one. But if you were to go back to your dashboard and go to workbook, you’ll end up seeing, at the bottom.
573 00:52:28.000 ⇒ 00:52:33.889 Uttam Kumaran: I mean, you’ll end up seeing here. And so now you can click plus, and you can start to add more, and then they’ll all start to show up, basically.
574 00:52:34.060 ⇒ 00:52:38.730 Uttam Kumaran: And you can see here, this is the… this is the workbook that you’re working in, workflows over time by company.
575 00:52:39.530 ⇒ 00:52:40.700 Caitlyn Vaughn: Amazing.
576 00:52:40.870 ⇒ 00:52:41.929 Caitlyn Vaughn: Little thing.
577 00:52:42.550 ⇒ 00:52:46.139 Caitlyn Vaughn: Add new query, and then I just go back into product.
578 00:52:46.140 ⇒ 00:52:57.000 Uttam Kumaran: Yes. So there’s kind of, like, two things, so I think it’s helpful to kind of go through the manual process, but one of the reasons we selected Omni is they have a lot of great AI features that
579 00:52:57.000 ⇒ 00:53:06.429 Uttam Kumaran: Demolade will be configuring to make sure that you can create these faster. For example, I actually think if we had used the AI feature today, you probably could have said.
580 00:53:06.570 ⇒ 00:53:11.869 Uttam Kumaran: give me some missions completed over time, and it would have done the first version of the Explore.
581 00:53:12.680 ⇒ 00:53:16.949 Uttam Kumaran: The challenge is I still think it’s helpful for you to know how to fish without the help… without the…
582 00:53:16.950 ⇒ 00:53:19.570 Caitlyn Vaughn: Yeah. Yeah, I’d love to learn the manual version.
583 00:53:19.570 ⇒ 00:53:24.259 Uttam Kumaran: If it messes something up, and you publish it, and you’re like, I don’t even know how filters work, you’re kind of like…
584 00:53:24.460 ⇒ 00:53:25.000 Caitlyn Vaughn: Yeah.
585 00:53:25.000 ⇒ 00:53:26.020 Uttam Kumaran: Kinda tough, right?
586 00:53:26.020 ⇒ 00:53:26.880 Caitlyn Vaughn: Yeah, totally.
587 00:53:26.880 ⇒ 00:53:27.960 Uttam Kumaran: So…
588 00:53:28.100 ⇒ 00:53:35.390 Uttam Kumaran: this is sort of like the crawl, walk, run, but one thing I would love, Demi, if you want to own sort of, like.
589 00:53:36.000 ⇒ 00:53:48.399 Uttam Kumaran: building out the view names and, like, kind of configuring the topics in a way that’s super fresh, and then Mustafa is going to continue to own the, basically, the data ingestion and making sure that data is available.
590 00:53:48.530 ⇒ 00:53:50.500 Uttam Kumaran: Yeah.
591 00:53:51.250 ⇒ 00:54:03.869 Demilade Agboola: Couple questions, that would just help me with, like, figuring out how to build this is, are you going to be the major stakeholder in terms of, like, looking at the data across, like, figuring out
592 00:54:05.210 ⇒ 00:54:08.300 Demilade Agboola: In Army, are you going to be the one majorly looking at a lot of the data?
593 00:54:08.650 ⇒ 00:54:09.780 Caitlyn Vaughn: Yeah.
594 00:54:10.350 ⇒ 00:54:23.560 Demilade Agboola: Okay, alright. And also, I mean, we don’t answer that now, because I think time’s a little over, but I think we could just have a conversation on what you’d like to see, what decisions you need to make, and what data you kind of need to… those decisions.
595 00:54:23.970 ⇒ 00:54:39.989 Caitlyn Vaughn: Yeah, I think we’ve been, like, stacking data needs over time, but we’ve just been, like, waiting on getting this actual integration set up so that we can actually have live data, because Thomas has been, like, pushing data once a month, which is so silly.
596 00:54:40.490 ⇒ 00:54:44.240 Caitlyn Vaughn: But… yes, maybe we can debrief…
597 00:54:44.360 ⇒ 00:54:52.430 Caitlyn Vaughn: Oh, maybe I’ll send it in the chat async on, like, the different things that we’re trying to accomplish from, like, data, and then we can start building around it.
598 00:54:53.230 ⇒ 00:54:53.620 Demilade Agboola: Yeah, so that’s…
599 00:54:53.620 ⇒ 00:55:04.430 Uttam Kumaran: Yeah, can kind of own the strategy of, like, okay, what dashboards are needed, what topics, views, and models, but then I also do want to take time down a lot of to train the team to fish
600 00:55:04.510 ⇒ 00:55:21.049 Uttam Kumaran: you know, as much as possible as well. So as long as we get the topic set up, we train people on, like, how to query, and we also kind of, like, try to leverage the AI features to configure that. I think the default team will start creating, you know, content themselves, which is, like, would be the best.
601 00:55:21.460 ⇒ 00:55:24.390 Caitlyn Vaughn: And we have a full team training tomorrow.
602 00:55:24.390 ⇒ 00:55:25.060 Uttam Kumaran: Yes.
603 00:55:25.970 ⇒ 00:55:32.170 Uttam Kumaran: So what do you think, Kaylin? Is it… would it be helpful to have everybody Basically, side-by-side, create
604 00:55:32.280 ⇒ 00:55:35.360 Uttam Kumaran: a dashboard like this? Like, what was helpful?
605 00:55:35.790 ⇒ 00:55:41.440 Uttam Kumaran: in today that you, like, think would… would… would help others? Should we, like.
606 00:55:41.820 ⇒ 00:55:43.689 Uttam Kumaran: Yeah, like, what do you think?
607 00:55:44.170 ⇒ 00:55:45.569 Caitlyn Vaughn: Yeah, I think,
608 00:55:46.980 ⇒ 00:55:57.339 Caitlyn Vaughn: The concepts, like, the terminology concepts was helpful, the, like, view versus dimension versus… is it, like, metric?
609 00:55:57.520 ⇒ 00:55:58.020 Uttam Kumaran: Yeah.
610 00:55:58.020 ⇒ 00:55:59.710 Caitlyn Vaughn: It was helpful,
611 00:56:00.500 ⇒ 00:56:10.889 Caitlyn Vaughn: I don’t know that everybody on the team is gonna have build access. I’ll have to see in my Omni contract how many seats we have. Okay. I think we only have, like, a handful of seats.
612 00:56:12.900 ⇒ 00:56:20.350 Caitlyn Vaughn: But everybody that’s coming is interested in it, so they would potentially be using it, depending on, like, their role in the use case.
613 00:56:20.350 ⇒ 00:56:30.820 Uttam Kumaran: I mean, why don’t we walk through, like, why don’t we just share… I’ll kind of go through a condensed version of just, like, how to use filters, measures, dimensions.
614 00:56:31.080 ⇒ 00:56:38.270 Uttam Kumaran: And then, why don’t we walk through creating, like, a workbook live, and we just… I’ll just ask people
615 00:56:38.620 ⇒ 00:56:41.130 Uttam Kumaran: Like, to just do it alongside of us.
616 00:56:41.900 ⇒ 00:56:44.929 Uttam Kumaran: I want to keep some room for questions, for sure.
617 00:56:44.930 ⇒ 00:56:57.479 Caitlyn Vaughn: Yeah, yeah, that’s good. Go spend, like, 5 minutes going through the terminology high level, and then spend 20 minutes walking through how to create a dashboard. Let’s go with, like, a very specific,
618 00:56:57.560 ⇒ 00:57:07.289 Caitlyn Vaughn: Like, let’s start with, like, a really simple use case, like, teams created over time, or something like that. And then once everyone does that, then we can move to, like, a…
619 00:57:07.910 ⇒ 00:57:10.929 Caitlyn Vaughn: You know, even, like, an example that we did today would be good.
620 00:57:10.930 ⇒ 00:57:25.770 Uttam Kumaran: I think, basically, down the line, what we can do is we can layer on filters and more… more measures onto that. So we start with Teams Create over time, let’s start to… let’s then try to bring in revenue, let’s add a filter, let’s create a dashboard, let’s publish a workbook.
621 00:57:25.910 ⇒ 00:57:31.459 Uttam Kumaran: And then I think if we can try to leave Like, 15 minutes for questions.
622 00:57:31.990 ⇒ 00:57:35.059 Uttam Kumaran: Or basically, 15 minutes, we’re like, okay, does someone want to, like.
623 00:57:35.480 ⇒ 00:57:38.090 Uttam Kumaran: Give it a shot now, live, for everybody.
624 00:57:39.120 ⇒ 00:57:41.320 Uttam Kumaran: I think that would be… that would be perfect.
625 00:57:42.010 ⇒ 00:57:55.480 Demilade Agboola: I also think, like, the thing of, like, seeing which forms were started versus which forms were completed, and kind of seeing the trail of drop would also be a great example to kind of see, like, the power.
626 00:57:56.780 ⇒ 00:58:06.760 Demilade Agboola: Beta and being able to figure out, like, okay, so this is where these forms or these forms are things people struggle on the most, or to actually avoid trouble areas.
627 00:58:07.510 ⇒ 00:58:24.969 Caitlyn Vaughn: I also think that maybe having one person lead this, or, like, maybe having a second person on the call would be best. Three is kind of a lot for, like, learning when you guys are all jumping in, but if we could have, like, one main person just, like, leading the conversation, it’s easier to, like, focus on one… one voice.
628 00:58:25.230 ⇒ 00:58:35.419 Uttam Kumaran: Okay. Yeah, Demi, what do you think? I can own… I can own the explanation of, like, you know, Omni, the core terminology, and then maybe I can hand it to you to…
629 00:58:35.720 ⇒ 00:58:38.020 Uttam Kumaran: Walk through the creation process.
630 00:58:39.470 ⇒ 00:58:40.560 Demilade Agboola: Okay, sure.
631 00:58:41.500 ⇒ 00:58:41.820 Uttam Kumaran: Okay.
632 00:58:41.820 ⇒ 00:58:42.490 Caitlyn Vaughn: Cool.
633 00:58:43.580 ⇒ 00:58:55.140 Caitlyn Vaughn: Amazing. Okay, thank you guys so much for all your help setting up and basic teaching me. I’m gonna, like, spend some time in here, like, actually going throughout my own, so I’ll come with some more questions tomorrow.
634 00:58:55.930 ⇒ 00:58:58.850 Caitlyn Vaughn: And then I will bug Thomas right after this.
635 00:58:58.850 ⇒ 00:59:07.430 Uttam Kumaran: Yeah, and then we can start to use our, like, weekly, or, you know, I’ll… I told Demi to maybe grab time with you weekly, also, to just, like.
636 00:59:07.810 ⇒ 00:59:13.099 Uttam Kumaran: both, like, either walk through creating dashboards or answering questions on Omni.
637 00:59:13.100 ⇒ 00:59:13.590 Caitlyn Vaughn: Hmm.
638 00:59:13.590 ⇒ 00:59:21.699 Uttam Kumaran: I just want to make sure that, like, we’re… I know, like, this is sort of, like, I want to make sure that you also have time carved out to, like, do this, and even that’s just, like.
639 00:59:21.910 ⇒ 00:59:25.159 Uttam Kumaran: Hopping on a call with us and being like, let’s go create stuff together.
640 00:59:25.510 ⇒ 00:59:28.560 Uttam Kumaran: It’s a great way to sort of, like,
641 00:59:28.720 ⇒ 00:59:31.150 Uttam Kumaran: You know, work with us on that, so…
642 00:59:31.560 ⇒ 00:59:34.929 Caitlyn Vaughn: Yeah, I would love to still know how to do this after you guys leave.
643 00:59:34.930 ⇒ 00:59:40.259 Uttam Kumaran: Yeah, yeah, yeah, yeah, but I just don’t want you to, like, click into it again and be like, oh, I don’t know, like, I don’t know, get stuck, so…
644 00:59:40.260 ⇒ 00:59:40.770 Caitlyn Vaughn: Yeah.
645 00:59:40.770 ⇒ 00:59:44.290 Uttam Kumaran: We want to do one or two sessions, like, where we’re creating stuff together.
646 00:59:44.780 ⇒ 00:59:46.000 Caitlyn Vaughn: It’s perfectly fine.
647 00:59:46.170 ⇒ 00:59:48.109 Caitlyn Vaughn: Okay, amazing, that would be so helpful.
648 00:59:48.230 ⇒ 00:59:48.910 Uttam Kumaran: Okay.
649 00:59:49.310 ⇒ 00:59:52.829 Uttam Kumaran: Alright, cool. Yeah, let me… and then let me know how this stuff goes with Thomas.
650 00:59:52.830 ⇒ 00:59:55.300 Caitlyn Vaughn: Okay, well, I’ll ping you after. Alright.
651 00:59:55.300 ⇒ 00:59:55.779 Uttam Kumaran: Thank you.
652 00:59:55.780 ⇒ 00:59:56.800 Caitlyn Vaughn: Bye, guys!
653 00:59:57.290 ⇒ 00:59:57.930 Demilade Agboola: Bye.