Meeting Title: MatterMore x Brainforge | Standup Date: 2025-05-14 Meeting participants: Annie Yu, Luke Daque, Trevor Cohen, Uttam Kumaran, Amber Lin, Mathew
WEBVTT
1 00:01:50.040 ⇒ 00:01:51.050 Amber Lin: Hi! There!
2 00:01:52.960 ⇒ 00:01:54.480 Luke Daque: Hello! Everyone.
3 00:01:54.990 ⇒ 00:01:55.889 Mathew: I am here.
4 00:01:59.090 ⇒ 00:02:00.180 Luke Daque: Hi, Matthew!
5 00:02:00.410 ⇒ 00:02:01.407 Luke Daque: How’s it going.
6 00:02:01.930 ⇒ 00:02:04.600 Mathew: Good, good I just texted Trevor.
7 00:02:05.413 ⇒ 00:02:10.909 Mathew: Amber. I don’t need to participate for the 1st part. So do you want to just like.
8 00:02:11.390 ⇒ 00:02:14.529 Mathew: DM, me, when you guys want to talk analytics, though.
9 00:02:14.530 ⇒ 00:02:15.520 Amber Lin: Yeah, sure.
10 00:02:15.920 ⇒ 00:02:27.529 Mathew: Alright cool, so I’ll just hide. And then, whenever you need me, just just DM me on slack, or tell Trevor to text me, but I’ll I’ll pay attention. Okay, cool.
11 00:03:40.380 ⇒ 00:03:41.170 Trevor Cohen: Hey!
12 00:03:44.600 ⇒ 00:03:45.590 Luke Daque: Hey! Trevor.
13 00:03:47.420 ⇒ 00:03:47.970 Trevor Cohen: How’s it going.
14 00:03:47.970 ⇒ 00:03:48.550 Luke Daque: Good.
15 00:03:50.694 ⇒ 00:03:57.499 Trevor Cohen: Good. Actually, amber, do I think we? I think we’re okay doing Async on Wednesdays. What do you think about that?
16 00:03:57.800 ⇒ 00:04:07.099 Amber Lin: Yeah, sounds good. I think today was mostly we wanted to discuss the analytics with Matthew. I can send you all the async updates.
17 00:04:07.270 ⇒ 00:04:10.559 Trevor Cohen: Okay, cool, cool. Alright, Matt, do you want to stay on and talk analytics?
18 00:04:11.061 ⇒ 00:04:14.820 Mathew: Yes. Do we need Tom for that, or should we just do you and me.
19 00:04:15.452 ⇒ 00:04:20.757 Amber Lin: I can relate to that. I just asked him if he’s joining
20 00:04:21.320 ⇒ 00:04:25.440 Amber Lin: He said he was before, but maybe he’s still on a sales call, I believe.
21 00:04:26.390 ⇒ 00:04:27.550 Trevor Cohen: I’m gonna hop, guys.
22 00:04:28.680 ⇒ 00:04:29.670 Mathew: Trav, thanks!
23 00:04:29.670 ⇒ 00:04:30.100 Trevor Cohen: I.
24 00:04:30.100 ⇒ 00:04:30.505 Amber Lin: Yeah.
25 00:04:31.542 ⇒ 00:04:39.719 Amber Lin: Luke, I think also, if you don’t, if you mind, can you send the updates Async, in the Channel? And I think you’ll be, you’ll be good.
26 00:04:39.930 ⇒ 00:04:40.729 Amber Lin: I can still.
27 00:04:40.730 ⇒ 00:04:42.570 Luke Daque: Sounds, good thanks.
28 00:04:42.990 ⇒ 00:04:43.540 Amber Lin: Okay.
29 00:04:43.540 ⇒ 00:04:44.160 Luke Daque: Like.
30 00:04:51.990 ⇒ 00:05:03.640 Amber Lin: Okay. Let’s get started. I can pull up our document that you sent when we 1st got started, and then I’ll take any notes that
31 00:05:04.310 ⇒ 00:05:06.969 Amber Lin: I can relay to Utam as well.
32 00:05:07.590 ⇒ 00:05:08.430 Mathew: Very cool.
33 00:05:11.440 ⇒ 00:05:15.129 Mathew: Yeah. So what did I? What did I originally send you.
34 00:05:19.090 ⇒ 00:05:28.299 Amber Lin: Yeah, the Enterprise Productivity Intelligence project. So I’m going off of that document.
35 00:05:28.300 ⇒ 00:05:37.090 Mathew: Got it, got it? Got it? Got it? Okay? Cool. Yeah. So for that document, the goal is to be able to do what’s in phase, one to start.
36 00:05:38.640 ⇒ 00:05:44.989 Mathew: So we’re measuring activity by segment.
37 00:05:47.490 ⇒ 00:05:50.510 Mathew: Meetings, emails and messages on teams.
38 00:05:51.910 ⇒ 00:05:55.539 Mathew: And and also onedrive activity.
39 00:05:57.130 ⇒ 00:06:07.045 Mathew: So we want to be able to show that raw. Well like, and I imagine we’ll want to even be able to show that for each
40 00:06:08.160 ⇒ 00:06:09.100 Mathew: like
41 00:06:09.970 ⇒ 00:06:18.490 Mathew: like, imagine even a view that shows like these are this is this, segments, meetings, emails, and onedrive activity, or teams, chats, etc.
42 00:06:19.055 ⇒ 00:06:24.310 Mathew: As we build those tableau dashboards. And then the second half of this document
43 00:06:24.740 ⇒ 00:06:51.089 Mathew: is showing that how that stuff looks over like, if you like, based on badge swipe data. So how does that? How does that change when you’re in office versus remote? How does that change? What’s a filter for for teams that are remote versus in office. And then finally, the the thing that you’re seeing there is the historical look back. So we then should be able to look back and show. What are the what are these workplace
44 00:06:51.190 ⇒ 00:06:53.540 Mathew: patterns? Like?
45 00:06:54.430 ⇒ 00:06:59.310 Mathew: How does it look 12 months ago, or like across over over the last 12 months. Rather.
46 00:07:01.450 ⇒ 00:07:16.299 Amber Lin: so to some of what I heard so far, so right now, we have all this data of like, we want the data about meeting emails, messages, onedrive, etc. But most importantly, we want to show the difference between in office versus remote.
47 00:07:16.300 ⇒ 00:07:20.530 Mathew: Well, I wouldn’t. I wouldn’t call that most important, I would say. That’s an important distinguisher as well.
48 00:07:20.530 ⇒ 00:07:27.509 Amber Lin: I see, so we want to be at least be able to filter and see the comparison between those. I bet there.
49 00:07:27.510 ⇒ 00:07:30.859 Mathew: Between badge activity, yeah, badge, badge activity data. Exactly.
50 00:07:30.860 ⇒ 00:07:41.730 Amber Lin: Hmm, yeah. And we also want like a historical look back. So we can spot trends based on a time, maybe compare different times to year, how things might look different.
51 00:07:41.730 ⇒ 00:07:49.589 Mathew: Correct, correct, and then and then exactly. And then you could see like this is showing how this looks over weeks like week over week.
52 00:07:50.073 ⇒ 00:07:57.640 Mathew: You’re seeing time of day like how things look time of day, because we are scanning for different thresholds like weekend activity
53 00:07:57.830 ⇒ 00:08:08.199 Mathew: after hours and pre hours activity, and also how is that affected by? If you’re remote like, you don’t have to commute? If you are, if you are in office, how does that affect? You know.
54 00:08:10.910 ⇒ 00:08:12.110 Amber Lin: Sounds good.
55 00:08:13.590 ⇒ 00:08:16.670 Amber Lin: Okay, great sounds. Good.
56 00:08:17.450 ⇒ 00:08:24.050 Amber Lin: Any particular thing you want to point out, or are, we good on just taking that and starting.
57 00:08:24.050 ⇒ 00:08:27.310 Mathew: I would, I would. Yeah, I would start with these.
58 00:08:28.040 ⇒ 00:08:34.405 Mathew: because this is what we’ve committed to. This is what our decision makers excited about and and
59 00:08:35.010 ⇒ 00:08:39.402 Mathew: what I’ll do with, I’ll also basically start to
60 00:08:41.020 ⇒ 00:08:47.279 Mathew: So so like, what you have in here is is like very much what the stakeholder, like wants already.
61 00:08:47.640 ⇒ 00:08:48.310 Amber Lin: And.
62 00:08:48.560 ⇒ 00:08:55.810 Mathew: There are also going to be other new correlations that we’d love for you guys to help this suss out right? Like, just what? What do you see that?
63 00:08:55.940 ⇒ 00:09:04.410 Mathew: What are you seeing from this data? What are interesting call outs and trends? And how do we? How do we almost like, build, make that into build that into our process. You know.
64 00:09:04.890 ⇒ 00:09:05.900 Amber Lin: Oh, okay.
65 00:09:06.450 ⇒ 00:09:17.280 Amber Lin: so example. Would you provide an example of that correlation? I know Annie is here as well. So this just helps us. The more you talk about it, the more we’ll be able to understand.
66 00:09:17.280 ⇒ 00:09:25.000 Mathew: Yeah, I would. I would. I would be like, I’m not a data analyst or scientist, but I would be like trying to find correlations.
67 00:09:27.520 ⇒ 00:09:40.579 Mathew: So almost sorry. There’s an alarm going off behind me so almost like you tell me. And like, is there a way where you can actually like, just like unleash, like something that’s scanning for correlations. And then
68 00:09:40.850 ⇒ 00:09:52.760 Mathew: and then like almost seeing like what’s actually statistically significant, or what’s and then saying like, Oh, we’re seeing, because this is this is like, I think, the heart of the like, the IP. And what’s really interesting to us is to be.
69 00:09:52.760 ⇒ 00:09:53.160 Amber Lin: Able to see.
70 00:09:53.160 ⇒ 00:09:57.739 Mathew: Like, we’re going to scan for all this data to find what those correlations are too.
71 00:09:57.740 ⇒ 00:09:59.569 Amber Lin: Oh, I see, I see. So you want.
72 00:09:59.570 ⇒ 00:10:00.279 Mathew: You know what I mean?
73 00:10:00.280 ⇒ 00:10:14.000 Amber Lin: Yeah, when I was when I was still doing analyst work, I think that’s a like a correlation matrix would be very interesting. And then from that you can see. Okay, maybe these 2 can be related. There might be some cause.
74 00:10:14.000 ⇒ 00:10:33.019 Mathew: Exactly exactly so correlation scanning. So and it could be things like that we don’t even know. Like, for example, you know, these types of teams when after they became remote. These are these correlations or teams that are, you know.
75 00:10:33.430 ⇒ 00:10:35.440 Mathew: teams that are, you know.
76 00:10:36.610 ⇒ 00:10:44.840 Mathew: 90 80% remote tend to have, you know, their their activity tends to skew this way. So something like that.
77 00:10:45.110 ⇒ 00:10:48.189 Amber Lin: Okay, that sounds good. Those 2 examples are awesome.
78 00:10:48.560 ⇒ 00:10:52.449 Mathew: Yeah. So so the 1st part is just like, what are the actual work patterns.
79 00:10:52.450 ⇒ 00:10:52.960 Amber Lin: Yes.
80 00:10:52.960 ⇒ 00:11:07.742 Mathew: 2 would be correlations like, and then analyzing them against those specific thresholds, like after hours. Burnout like after hours. Weekend activity, you know, commute, etc. And then finally,
81 00:11:08.600 ⇒ 00:11:10.889 Mathew: correlation scanning would be. Then the other.
82 00:11:10.890 ⇒ 00:11:11.250 Amber Lin: There you go!
83 00:11:11.250 ⇒ 00:11:17.059 Mathew: That would be like the most, I think, like the most like uncertain but but exciting interesting thing behind that.
84 00:11:17.570 ⇒ 00:11:34.219 Amber Lin: I see that’s great. I have a question to follow up on that. So I’m looking at the document. And I remember you said, you guys have a few Phds as also doing these analyses. I I kinda wanna know what we should avoid overlapping because I don’t wanna.
85 00:11:34.688 ⇒ 00:11:40.311 Mathew: This, this, this stuff, this stuff that’s on this Doc is not
86 00:11:41.040 ⇒ 00:11:44.179 Mathew: is not a Ph. It doesn’t require a Ph.
87 00:11:44.512 ⇒ 00:11:45.509 Amber Lin: Okay. Sounds good.
88 00:11:45.510 ⇒ 00:11:55.000 Mathew: Yeah, yeah, this is basic, just like work. This is literally just like, what are the volumes. And then and then starting to cross, analyze those in the net in phase. 2.
89 00:11:56.150 ⇒ 00:12:10.669 Mathew: If you look at phase 2 of the doc like when you get into those clusters and whatnot, that’s where some Phd level analysis comes in, but quite honestly, that that also can be done using just like some of the off the shelf network analysis stuff.
90 00:12:10.880 ⇒ 00:12:19.239 Amber Lin: Okay, awesome. I think. I was looking a little bit ahead. I was scrolling down here. I was like, Oh, I’m not sure if we’re supposed to do that. So we
91 00:12:19.750 ⇒ 00:12:21.679 Amber Lin: stay within mostly phase one.
92 00:12:23.240 ⇒ 00:12:28.930 Mathew: Correct, we should right now we should be focusing on phase one and getting that right. Because that’s the 1st 3 months of this engagement.
93 00:12:29.545 ⇒ 00:12:30.775 Amber Lin: Sounds good.
94 00:12:31.607 ⇒ 00:12:37.050 Mathew: And do you have the do? You have the larger deck that I shared that like that breaks out a little more of these slides.
95 00:12:37.210 ⇒ 00:12:40.771 Amber Lin: Yes, it’s a very long deck.
96 00:12:41.280 ⇒ 00:12:43.340 Mathew: Yeah. Yeah. Cause the first, st like.
97 00:12:43.650 ⇒ 00:12:49.799 Mathew: 13 slides of that will show, or like slides 8 through 13 show you like a few more cuts of these.
98 00:12:51.680 ⇒ 00:12:59.530 Amber Lin: I see. It’s the one I mean. You only shared one. So it’s the organizational productivity, intelligence.
99 00:12:59.530 ⇒ 00:13:09.610 Mathew: That’s right. That’s right. Yeah, that shows you the thresholds in the in orange letters like baseline Fridays, weekend activity, weekend burden commute after hours. That’s yeah.
100 00:13:09.940 ⇒ 00:13:13.269 Amber Lin: Hmm! Sounds good. I think we have enough information from.
101 00:13:13.270 ⇒ 00:13:15.589 Mathew: Oh, sorry, and also 1617.
102 00:13:16.470 ⇒ 00:13:22.599 Mathew: 6 slides 1516, 17. Are also really interesting stuff.
103 00:13:22.600 ⇒ 00:13:26.150 Amber Lin: Okay, sounds good. We’ll look at the 1st 20 ish slides. Then.
104 00:13:26.150 ⇒ 00:13:28.290 Mathew: Yeah, exactly. Exactly.
105 00:13:28.560 ⇒ 00:13:39.420 Amber Lin: Awesome. I think I have a most of the requirements from you. The rest. I can work on the team work Witham to fill that in. And then if I have questions, I can email you.
106 00:13:39.570 ⇒ 00:13:46.550 Mathew: Cool, and as we’re getting closer starting, I’ll circle up with our decision with our stakeholder, and I’ll ask, and I’ll get more of what he wants to.
107 00:13:47.430 ⇒ 00:13:47.795 Amber Lin: Hmm.
108 00:13:48.500 ⇒ 00:14:00.880 Amber Lin: yeah, I think definitely, at least for the 1st part, before we strong correlations like just the basic data and a comparison and feeling identifying those stuff. I think that’s pretty straightforward.
109 00:14:00.880 ⇒ 00:14:01.510 Mathew: Great.
110 00:14:01.813 ⇒ 00:14:14.549 Amber Lin: I’ll work with Annie, and we’ll give you guys how we vision how we plan on visualizing it. Honestly, in your document, you said there’s already examples. So I think this 1st part would be pretty straightforward. Once we get
111 00:14:14.550 ⇒ 00:14:15.230 Amber Lin: awesome.
112 00:14:16.320 ⇒ 00:14:16.970 Mathew: Perfect.
113 00:14:16.970 ⇒ 00:14:17.520 Amber Lin: Nope.
114 00:14:18.100 ⇒ 00:14:19.130 Mathew: Okay. Sounds good.
115 00:14:19.130 ⇒ 00:14:21.780 Amber Lin: Okay, yeah, thank you for coming to this meeting.
116 00:14:22.270 ⇒ 00:14:28.209 Mathew: Thank you. I’m glad that I was able to at least get you started with that. I’m curious. Is Annie here right now?
117 00:14:28.210 ⇒ 00:14:29.619 Amber Lin: Yeah. Annie, is here.
118 00:14:29.889 ⇒ 00:14:35.009 Mathew: I’m curious. If any let me know when you’re when I got when I have your attention.
119 00:14:35.986 ⇒ 00:14:41.109 Annie Yu: Yeah, I think I just do have one question, cause I know I think
120 00:14:41.320 ⇒ 00:15:02.470 Annie Yu: a joint in the middle. I think you were saying for face one. We want to show the patterns, and when you say correlation, I I will be careful in using correlation. Just because I want to ask, are we trying to see like actual, like, proven inferential statistics, or just things like you said, like more like descriptive
121 00:15:02.910 ⇒ 00:15:04.390 Annie Yu: analysis, like 80%?
122 00:15:04.390 ⇒ 00:15:06.179 Annie Yu: Well, so yes, that’s what I was trying to.
123 00:15:06.400 ⇒ 00:15:32.950 Mathew: Yeah, that’s what I was trying to convey to. Amber is like, it’s like a there’s different tiers. So one is just like what’s going on right? What’s happening? 2 is where we we like. You know we can. We can like give some color to the thresholds that we’re seeing. But then 3 is what I’m calling, and you would know better than me, like actual correlation scanning. And you could then say, like within correlation, scanning, there’s like the actual, proven, statistical, significant.
124 00:15:33.346 ⇒ 00:15:44.509 Mathew: Things that you can stand on and say we’re really confident about this. But then we could also say, there are things that might be happening that we’re not as confident about, but are alluded to in the data.
125 00:15:44.800 ⇒ 00:15:45.379 Annie Yu: So we.
126 00:15:45.380 ⇒ 00:15:50.459 Mathew: Want to. We want to educate the customer. And so, by virtue, you’re going to educate us.
127 00:15:51.680 ⇒ 00:15:56.970 Annie Yu: Okay. Okay, alright, that’s great to know.
128 00:15:57.210 ⇒ 00:16:06.239 Annie Yu: So that means we probably will not be able to do everything within power bi. But other tools. Is that okay?
129 00:16:06.240 ⇒ 00:16:07.050 Mathew: Likewise.
130 00:16:08.900 ⇒ 00:16:15.800 Annie Yu: I I was thinking, if we do want to see correlation, I will use python. But I don’t know. Okay.
131 00:16:16.160 ⇒ 00:16:19.690 Mathew: We love Python Trevor Trevor works in Python, and so
132 00:16:20.107 ⇒ 00:16:30.110 Mathew: to the anything that like to the degree that we can build out whatever analysis that we’re doing that could be done in Python, and then we could then reuse that in python and other places.
133 00:16:30.990 ⇒ 00:16:32.879 Uttam Kumaran: I think you could do it in a notebook and.
134 00:16:32.880 ⇒ 00:16:34.229 Mathew: Oh! Here he is!
135 00:16:34.430 ⇒ 00:16:41.800 Uttam Kumaran: Hey, guys, sorry I was delayed. I think you guys could do it in a notebook or a Google Collab and like.
136 00:16:42.310 ⇒ 00:16:44.500 Uttam Kumaran: get to get to a graph first.st
137 00:16:46.020 ⇒ 00:16:51.880 Mathew: Agreed, like whatever whatever we could, because we’re obviously working out of Big 3 or whatever so whatever
138 00:16:52.060 ⇒ 00:16:59.910 Mathew: like, we, whatever’s most reusable and then easy for Trevor if he has to make any tweaks understandably. So I’m a new Tom. What I was basically saying is.
139 00:17:00.030 ⇒ 00:17:03.509 Mathew: if you look at this, I’ll just share my screen.
140 00:17:05.790 ⇒ 00:17:07.050 Mathew: Why, it’s not scanning
141 00:17:12.069 ⇒ 00:17:12.980 Mathew: here.
142 00:17:15.270 ⇒ 00:17:22.200 Mathew: And maybe you have a better way of translating what I’m trying to say here, basically for stuff in phase one here.
143 00:17:22.480 ⇒ 00:17:25.059 Mathew: This is all just like what’s going on, and what are the actual.
144 00:17:25.060 ⇒ 00:17:25.579 Uttam Kumaran: Yeah.
145 00:17:25.589 ⇒ 00:17:27.129 Mathew: Work patterns.
146 00:17:27.130 ⇒ 00:17:27.660 Uttam Kumaran: Yeah.
147 00:17:27.660 ⇒ 00:17:36.189 Mathew: But eventually I call this correlation scanning like, let’s let’s actually like, however, is possible. Find out what those patterns are of.
148 00:17:36.190 ⇒ 00:17:46.150 Uttam Kumaran: Exactly so on our side, like the way we do that is, we, I mean, I mean, and any I’ll this is like sort of a lot of the work on statistical analysis is like, yeah, finding correlations between 2 metrics.
149 00:17:46.150 ⇒ 00:17:46.820 Mathew: Yes.
150 00:17:46.820 ⇒ 00:17:51.420 Uttam Kumaran: Given. Given a bunch of features what are correlated weekly versus strongly.
151 00:17:51.420 ⇒ 00:17:53.510 Mathew: S, yeah, yeah.
152 00:17:53.510 ⇒ 00:17:58.299 Uttam Kumaran: What’s what’s helpful from you is like, if there are a couple of charts here that you’re like.
153 00:17:58.440 ⇒ 00:18:02.910 Uttam Kumaran: these are incredible, these are.
154 00:18:02.910 ⇒ 00:18:05.900 Uttam Kumaran: If these are all them. Then like, this is what we’re going to.
155 00:18:06.170 ⇒ 00:18:13.989 Uttam Kumaran: We’ll start with trying to just fit toward. And we’ll we’ll we’ll like. We’ll give you that in something that sits on the live data, basically.
156 00:18:13.990 ⇒ 00:18:14.540 Mathew: Yep.
157 00:18:14.680 ⇒ 00:18:26.130 Mathew: these 1st 24 slides are like the things that got the client really excited that they want. But I want us to then take it. A level deeper is assuming. It’s not like I don’t want to be chasing, you know.
158 00:18:26.130 ⇒ 00:18:26.590 Uttam Kumaran: Yes.
159 00:18:26.590 ⇒ 00:18:43.730 Mathew: Shiny objects like it should be within the realm where we could start to say like, Oh! And, by the way, teams like this correlate with like these trends, or when teams seem to have more chats and meetings, then their focus time goes down like, I want to be able to show that.
160 00:18:44.010 ⇒ 00:18:55.030 Uttam Kumaran: Yeah. So so one, maybe one follow up question for me is can you tell me about like what the when you put this data in front of clients. Sorry, this is like a basic question. But I just want to drive this point when you put this in front.
161 00:18:55.790 ⇒ 00:19:01.190 Uttam Kumaran: What are some questions you’re expecting clients. What are some decisions you’re expecting clients?
162 00:19:03.000 ⇒ 00:19:05.500 Uttam Kumaran: Are they doing like a return to office thing?
163 00:19:05.500 ⇒ 00:19:06.840 Mathew: Yeah, yeah, yeah.
164 00:19:06.840 ⇒ 00:19:08.550 Uttam Kumaran: Yeah, so we talk about that. Yeah.
165 00:19:08.550 ⇒ 00:19:11.420 Mathew: Yeah, yeah, they want to figure out like, well.
166 00:19:11.740 ⇒ 00:19:14.660 Mathew: I’ll give you this like, even this slide. Can you see my screen right now?
167 00:19:14.820 ⇒ 00:19:15.500 Uttam Kumaran: Yes.
168 00:19:15.970 ⇒ 00:19:19.460 Mathew: With this one they want to determine. Do people actually work from home on Fridays?
169 00:19:19.460 ⇒ 00:19:20.529 Uttam Kumaran: Great, cool.
170 00:19:20.530 ⇒ 00:19:37.539 Mathew: Right like, are people were, are people actually working? Are they doing anything on Friday? So you could see that for meetings, emails and team chats like that’s an obvious one. Are people working on weekends which implies either they’re like super committed, or it means that they’re overwhelmed. They have too much stuff going on during the week, like, What’s that threshold?
171 00:19:39.460 ⇒ 00:19:59.459 Mathew: this one is like? Are people working after hours or and like, how does the and and how does that like when did? When are they starting based on their schedules? And how does that differ? Based on teams? And then, most not most importantly, most like relevant to return to offices. And how does that? How does that change? If people were at home, or if they were in the office.
172 00:19:59.460 ⇒ 00:20:11.430 Uttam Kumaran: Yes, okay, okay, so great. So we, there’s 2 things on home versus office. There’s like the timeframe of working. The other thing. I think that’s probably really helpful is time in an application. Or, like you have email sent.
173 00:20:11.430 ⇒ 00:20:13.329 Mathew: So that’s all. This.
174 00:20:14.090 ⇒ 00:20:18.459 Uttam Kumaran: I think about that for our company, which is like emails, meetings per person emails per person.
175 00:20:18.460 ⇒ 00:20:19.030 Mathew: Yep.
176 00:20:19.580 ⇒ 00:20:19.980 Uttam Kumaran: Wow!
177 00:20:19.980 ⇒ 00:20:42.139 Mathew: So that’s yeah. So that’s email, that’s emails, meetings and chats. And that’s all activity. Right? That’s called, we’re calling that workplace activity. When you get here. So this is, I think, is really important. That we all are aware of. This is your. This is how much time you’re actually spending in your work tools for them. It’s gonna be mostly office 3, 65, right? And this should look like the opposite. You notice how that one is like
178 00:20:42.210 ⇒ 00:20:50.499 Mathew: opposite to this one right? Because the more meaning the meetings that I have that ideally reverse correlate with, like the amount of work I’m actually getting done. Less meetings.
179 00:20:50.960 ⇒ 00:20:51.350 Uttam Kumaran: Increase.
180 00:20:51.350 ⇒ 00:21:11.059 Mathew: So we want to be able to show this too. And productivity in your tools also relates to focus time. Because we want to show. We actually want to show like, how almost like, how how like, how scattered is your workforce? Are they in calls and meetings all the time? Or do they actually have space for prep time, focus time or flow time?
181 00:21:11.850 ⇒ 00:21:13.190 Mathew: You, you follow.
182 00:21:14.100 ⇒ 00:21:19.170 Uttam Kumaran: I follow? Yeah. So I I think part of like, what I wanna share is like you want thought, provoking
183 00:21:19.700 ⇒ 00:21:42.479 Uttam Kumaran: graphs like you want to propose like, Hey, emails are getting sent here versus when our home. What is this like? Can you guys tell us a story behind this? Right? So I think Amy and amber like that’s what we want to find is we want like, this is what we found when we go to do analysis like this like I don’t want. I don’t necessarily want to come in and be like, here’s a graph and based on what I know about like just working in general. Here’s what I think. Instead.
184 00:21:42.480 ⇒ 00:21:43.339 Mathew: No, no.
185 00:21:43.340 ⇒ 00:21:53.209 Uttam Kumaran: Here’s iheart hypothesis. But you guys know your company best. You may maybe never seen it laid out this way. What’s going on? What do you think is going on here? How can we go one layer deeper? So
186 00:21:53.360 ⇒ 00:22:18.280 Uttam Kumaran: I think probably what’s best is we’ll we’ll take these slides. We’ll focus on a couple of these, and then, if if you don’t care about like the ugliness factor at this point, then I think doing it this in python, and having this set up is fine. If you care about the visuals, I mean, I don’t know amber like. What do you think you’re sort of in your consulting background, like
187 00:22:18.590 ⇒ 00:22:24.200 Uttam Kumaran: I think maybe it’s helpful for amber, for even to even for you to show
188 00:22:24.619 ⇒ 00:22:33.489 Uttam Kumaran: the slides we did for pool parts where it’s like, it’s that’s sort of the stuff like, yeah, like, do you want it to look like this? Or do you care like if it looks like so.
189 00:22:33.490 ⇒ 00:22:42.880 Mathew: Internally whatever’s fastest to get it working. But when we do show the deliverables to the customer, it it ultimately needs to be in power bi because they’re gonna need to work with it. We’re delivering something for them.
190 00:22:42.880 ⇒ 00:22:43.370 Uttam Kumaran: Yes.
191 00:22:43.370 ⇒ 00:22:48.490 Mathew: To work in. So this is, this is like what he sent me today from better up like.
192 00:22:48.490 ⇒ 00:22:48.840 Uttam Kumaran: Yes.
193 00:22:48.840 ⇒ 00:22:54.109 Mathew: That’s telling a story like this is how performance like we don’t have performance per se. But we can show, for example.
194 00:22:54.110 ⇒ 00:22:58.259 Uttam Kumaran: See that you know. The thing about these graphs is that’s a designer. Drew these.
195 00:22:58.480 ⇒ 00:22:59.370 Mathew: Right, right.
196 00:22:59.370 ⇒ 00:23:01.320 Uttam Kumaran: This is not online, like, like.
197 00:23:01.320 ⇒ 00:23:01.870 Mathew: Yeah, that’s a.
198 00:23:01.870 ⇒ 00:23:05.099 Uttam Kumaran: Thing is like, well, well, there’s not a tool in the.
199 00:23:05.100 ⇒ 00:23:11.710 Mathew: Sorry. Sorry I’m not pulling. Sorry I’m not. I’m not pulling this up to to show the the graph. I’m showing it like this.
200 00:23:11.710 ⇒ 00:23:12.560 Uttam Kumaran: Story. Okay, okay.
201 00:23:12.560 ⇒ 00:23:13.569 Mathew: So like sure.
202 00:23:13.570 ⇒ 00:23:13.970 Uttam Kumaran: Yes.
203 00:23:13.970 ⇒ 00:23:14.440 Mathew: So this week.
204 00:23:14.440 ⇒ 00:23:33.020 Uttam Kumaran: The story slide needs to look like this. There needs to be a commentary. There needs to be like a graph or some visual of the data. And then there needs to be like an open question, which again, amber, this is like your bread and butter on like what is like, what builds what’s like a great management consulting slide on like analyzing a specific thing. You know.
205 00:23:33.020 ⇒ 00:23:47.799 Mathew: Yeah, and I can. And I can play a role in telling this story like, I know exactly. I know a lot of what they’re talking about here like focus, we we actually their definitions a little wonky. But like we could talk about how much focus time you have, which then implies, because if people don’t have space to focus, aren’t getting work done.
206 00:23:47.800 ⇒ 00:23:48.370 Uttam Kumaran: Yes.
207 00:23:48.370 ⇒ 00:23:51.880 Mathew: Connectivity. This will come in phase 2. But yeah, you know what I’m saying.
208 00:23:52.440 ⇒ 00:23:57.490 Uttam Kumaran: I see. So if you look at if you look at Amber’s screen, this is something where we did. Yeah, yeah, go ahead. Go ahead.
209 00:23:58.530 ⇒ 00:24:03.040 Amber Lin: Yeah, let me real quick to share. This is a very short analysis. It’s only.
210 00:24:03.040 ⇒ 00:24:05.499 Mathew: Yeah, I can’t see your screen. Are you sharing.
211 00:24:05.500 ⇒ 00:24:07.870 Amber Lin: Yeah.
212 00:24:07.870 ⇒ 00:24:08.600 Uttam Kumaran: Seeing it. Yeah.
213 00:24:08.600 ⇒ 00:24:09.339 Amber Lin: You know.
214 00:24:09.340 ⇒ 00:24:14.700 Mathew: Hold on. Maybe I’ll have to. I have to switch tabs into this little tab thing. How do I? Okay, cool. Alright. I’m good.
215 00:24:15.020 ⇒ 00:24:17.690 Amber Lin: Yeah. And so we did analysis for
216 00:24:17.840 ⇒ 00:24:25.592 Amber Lin: a pricing change. Right? So this company, they want to see what’s actually, if a change of price would affect
217 00:24:26.020 ⇒ 00:24:37.220 Amber Lin: and they’re perfect. And so so essentially, we did analysis, we have insights. We have recommendations. And essentially, we’re telling a story of, okay, why are things
218 00:24:37.220 ⇒ 00:24:57.069 Amber Lin: the way they are? And we’re backing them up with data insights. So I would say, the analysis here is not that complicated? But it’s drawing insights from these and showing them, okay, what’s happening behind the scenes? What are some factors you might not have considered before, because you’ve never seen the data layout in this way.
219 00:24:57.677 ⇒ 00:25:03.659 Amber Lin: An overall arriving at a recommendation. What what you can do to based on.
220 00:25:03.660 ⇒ 00:25:05.459 Mathew: Yeah, I love that. I love that.
221 00:25:07.760 ⇒ 00:25:09.910 Amber Lin: So so can I share? Can I just share one.
222 00:25:10.720 ⇒ 00:25:13.598 Uttam Kumaran: Yeah, I’ll share one, as after your
223 00:25:14.230 ⇒ 00:25:17.619 Uttam Kumaran: done as well. I mean again, this is just like classic
224 00:25:17.940 ⇒ 00:25:22.300 Uttam Kumaran: consulting decks that we would just try to like, get you the graph.
225 00:25:22.450 ⇒ 00:25:43.069 Uttam Kumaran: And then, even if you, if you want us to help, make the deck, or whatever like we can help with that. But I think for our side amber. This is the exercise we want to move towards is once we have the data in a good place. We then want to supply Matthew with something that you can build it. Build a story around.
226 00:25:43.420 ⇒ 00:25:52.430 Mathew: Yes, yeah, yeah. Cause you have to imagine it like this, we wanna as quickly as possible get him a deck. My, and by him. I mean my our decision maker. He’s gonna take that to the CEO
227 00:25:52.560 ⇒ 00:26:08.810 Mathew: right? He’s taking that to the chief people officer. So we want that to. We want him to have to do as little work as possible. But then, right after trailing that, he needs access to the chat, to the power Bi, because he’s gonna then hand that to his team and they’re gonna then start running more stuff on top of that.
228 00:26:10.260 ⇒ 00:26:30.689 Mathew: So it’s not like we’re just delivering the deck. It’s like, first, st like tip of the spear, the insights coming out like with our story deck, he’s gonna ask questions. We’re going to iterate it, etc. But really it should be all powered on top of a like. The underlying things should be like, you know, if it’s python, so be it. But if it’s like really power, Bi is what they’re expecting their team to eventually be able to like work with.
229 00:26:32.000 ⇒ 00:26:46.419 Uttam Kumaran: So let’s see, I mean, that’s, I think, on our side, let’s see what we can do in power. Bi. I I would say, if if they have like data people on their side, they’re not gonna be opposed to like it, just being like as a notebook with the with simple graphs like that’ll that’ll.
230 00:26:46.420 ⇒ 00:26:47.000 Mathew: Yeah, yeah.
231 00:26:47.000 ⇒ 00:26:48.380 Uttam Kumaran: I don’t think they’ll be opposed to it.
232 00:26:48.380 ⇒ 00:26:57.020 Mathew: Our main person on the people analytic their main person who’s gonna be on the dashboarding team. She’s a tableau person, but they have to use power Bi, because they’re a Microsoft shop. So.
233 00:26:57.020 ⇒ 00:26:57.420 Uttam Kumaran: Okay.
234 00:26:57.420 ⇒ 00:26:57.910 Mathew: I was like.
235 00:26:57.910 ⇒ 00:27:00.630 Uttam Kumaran: She knows she’ll she knows like what, how shitty it is. Okay, cool.
236 00:27:00.630 ⇒ 00:27:10.680 Mathew: Yeah, exactly. And she’s she. I told her. We’re gonna hold her hand so she doesn’t have to like struggle herself through it. But she’s not like, gonna be there being like, Hey, guys, why isn’t this right now?
237 00:27:10.680 ⇒ 00:27:11.110 Mathew: Yeah.
238 00:27:11.110 ⇒ 00:27:15.250 Mathew: Rbi, I need to. Yeah. She’s he’s she’s just happy that we’re here, you know.
239 00:27:15.650 ⇒ 00:27:21.010 Uttam Kumaran: So should we? Do you want us to spin up a power bi or like? Do you guys want to do that? And like.
240 00:27:21.010 ⇒ 00:27:29.499 Mathew: Yeah, I, yeah, I thought that was like, well, I thought that was like the original engagement, because, like, we don’t have that capability. So I figured you guys would be.
241 00:27:29.500 ⇒ 00:27:39.549 Uttam Kumaran: Yeah, no, no, not not about like building it more like, do you want us to just procure it like, or I guess we would. One of the next steps here is just like Trevor. Do you want to just sign up for.
242 00:27:39.550 ⇒ 00:27:40.480 Mathew: Covers on your side.
243 00:27:40.480 ⇒ 00:27:44.649 Uttam Kumaran: Oh, sorry sorry. It’s just note.
244 00:27:44.650 ⇒ 00:27:45.610 Mathew: There’s no taker.
245 00:27:45.930 ⇒ 00:27:48.012 Uttam Kumaran: Yeah, or or if you wanna
246 00:27:48.630 ⇒ 00:27:49.319 Mathew: I think it should be.
247 00:27:49.320 ⇒ 00:27:53.540 Uttam Kumaran: From power bi through your account, or we could sign up for it, and then invite you all, and then transfer.
248 00:27:53.540 ⇒ 00:27:58.880 Mathew: No, I think I think I should do it because I don’t I? So how do I do it? Just go to Microsoft Power bi got
249 00:27:59.360 ⇒ 00:28:02.970 Mathew: Microsoft, so should I do buy, buy now, or start free.
250 00:28:03.830 ⇒ 00:28:06.020 Uttam Kumaran: I would just do whatever the free one is.
251 00:28:06.020 ⇒ 00:28:06.680 Mathew: Okay.
252 00:28:07.260 ⇒ 00:28:15.060 Uttam Kumaran: Yeah, do. Whatever is the just do, whatever the the cheapest one is, it’s pretty cheap, so like it, it may be, and we can just all share accounts and stuff like that.
253 00:28:15.060 ⇒ 00:28:16.819 Mathew: And I’ll just invite Brain Forge.
254 00:28:17.160 ⇒ 00:28:21.279 Uttam Kumaran: Just invite the brain forge@mattermore.ai.
255 00:28:24.560 ⇒ 00:28:25.110 Mathew: Cool. It’s.
256 00:28:25.110 ⇒ 00:28:25.480 Uttam Kumaran: And.
257 00:28:25.480 ⇒ 00:28:27.459 Mathew: I think, yeah, yeah.
258 00:28:27.780 ⇒ 00:28:30.090 Uttam Kumaran: It’s gonna be. It’s gonna it may take a sec, because.
259 00:28:30.090 ⇒ 00:28:33.449 Mathew: It’s having me do a Captcha. That is like it seems impossible.
260 00:28:35.250 ⇒ 00:28:36.699 Mathew: Got me dude. I’m.
261 00:28:37.080 ⇒ 00:28:38.600 Uttam Kumaran: Shape, which shape is.
262 00:28:38.980 ⇒ 00:28:41.399 Mathew: This is bad. I can’t. I might need to see your guys.
263 00:28:41.400 ⇒ 00:28:46.460 Uttam Kumaran: Some of the captures I’ve been getting wrong. I honestly have been getting wrong, and I have to think of like
264 00:28:46.950 ⇒ 00:28:53.250 Uttam Kumaran: which shape is which it’s like name. 3 of these shapes that are the same.
265 00:28:53.590 ⇒ 00:28:56.610 Mathew: I was. Yeah, that’s the one that just got me like, what’s what’s showing. I.
266 00:28:58.890 ⇒ 00:28:59.650 Uttam Kumaran: That’s funny.
267 00:28:59.650 ⇒ 00:29:02.180 Mathew: Alright. So I’m gonna so I’m gonna I’m gonna.
268 00:29:02.180 ⇒ 00:29:04.210 Uttam Kumaran: Sign up and invite us. That’s it. Yeah.
269 00:29:04.210 ⇒ 00:29:04.820 Mathew: Yeah.
270 00:29:04.820 ⇒ 00:29:06.840 Uttam Kumaran: And we’ll we’ll set it up from there. Yeah, that’s what I meant.
271 00:29:06.840 ⇒ 00:29:20.365 Mathew: Yeah. And then I, we’re we’re like at the last stage of the contract right now. We’re just signing up on like we. We made it through risk. INFOSEC all of that bullshit. So we’re up to. Yeah, we’re we’re literally just finalizing pricing terms. And
272 00:29:20.660 ⇒ 00:29:29.770 Uttam Kumaran: So give us like I mean, apart from like yesterday, give us like a reasonable deadline, for, like getting you graphs so you can form a deck around.
273 00:29:30.430 ⇒ 00:29:49.679 Mathew: I mean, I feel like we should have the decks from our the deck that I shared with you off the gate, and, like you know, I’m I’m aggressive. So I would love it to be like ready, because I assume we’re gonna start June 1st with them. I almost want that ready. I almost want that ready by June first, st so that we can go show them to that in the workshop and say, like.
274 00:29:49.800 ⇒ 00:29:50.880 Mathew: Yeah.
275 00:29:51.130 ⇒ 00:30:06.309 Uttam Kumaran: I think that’s fair. So let’s so amber, let’s say so. June. So June second is the Monday, so let’s aim for us to at least have something by June 26, so that we have that week to iterate with you on.
276 00:30:06.310 ⇒ 00:30:07.139 Amber Lin: 26.
277 00:30:07.380 ⇒ 00:30:08.170 Uttam Kumaran: Oh, sorry. May.
278 00:30:08.670 ⇒ 00:30:09.559 Uttam Kumaran: Yeah. May 26.
279 00:30:10.831 ⇒ 00:30:17.057 Uttam Kumaran: That way. We have something that we can iterate on, and that’ll that. That gives us enough time. That’s that’s
280 00:30:17.900 ⇒ 00:30:20.219 Uttam Kumaran: That’s 2. That’s 2 and a half weeks from now.
281 00:30:20.744 ⇒ 00:30:24.110 Uttam Kumaran: Amber, you have that deck that that Matthew shared.
282 00:30:24.110 ⇒ 00:30:26.459 Amber Lin: Yeah, I have all the information.
283 00:30:26.460 ⇒ 00:30:27.060 Uttam Kumaran: But let’s.
284 00:30:27.060 ⇒ 00:30:30.090 Amber Lin: Clarify on the exact deliverables.
285 00:30:30.360 ⇒ 00:30:36.379 Amber Lin: So we want. Do we want either power Bi or Jupiter mail book? Or do we want like?
286 00:30:36.590 ⇒ 00:30:38.510 Amber Lin: Do let’s start. Let’s start with.
287 00:30:38.510 ⇒ 00:30:38.900 Uttam Kumaran: Let’s start.
288 00:30:38.900 ⇒ 00:30:40.350 Uttam Kumaran: Start with Jupiter notebook first.st
289 00:30:40.820 ⇒ 00:30:45.200 Uttam Kumaran: I mean, Annie, correct me if I’m wrong, but I think that’ll be quicker.
290 00:30:47.510 ⇒ 00:31:02.300 Amber Lin: Yeah, I’ll let any answer that. But I think in tandem once I have once we have somewhat of a notebook. Matthew, you can review it, we can iterate and talk about what’s working, what’s not. And also it allows me to start on a deck
291 00:31:03.095 ⇒ 00:31:05.764 Amber Lin: to do insights as Annie does.
292 00:31:09.190 ⇒ 00:31:14.630 Uttam Kumaran: Yeah, Annie, Annie, what do you think about having this in Google? Colab first.st
293 00:31:15.658 ⇒ 00:31:19.569 Annie Yu: Yeah, I think for the ones, I guess
294 00:31:19.680 ⇒ 00:31:25.929 Annie Yu: initially, if we want, if we want to just explore what’s going on, we can do that directly in
295 00:31:26.563 ⇒ 00:31:36.980 Annie Yu: in power. Bi. And if we want to draw that like statistical correlation, that we probably be easier to start with. Python.
296 00:31:37.450 ⇒ 00:31:44.389 Uttam Kumaran: Okay, yeah. So let’s let’s start there. We’ll use Google Collab hopefully. I think the collab integration with bigquery
297 00:31:44.570 ⇒ 00:31:47.299 Uttam Kumaran: will be nice as well. We’ll play nice.
298 00:31:47.400 ⇒ 00:31:55.030 Uttam Kumaran: Let’s on our side. Let’s create a couple. Let’s create a 1 ticket around each of the core graphs that we want to support.
299 00:31:55.591 ⇒ 00:32:01.009 Uttam Kumaran: And then amber, or they can be like arranged towards the slides. And then the graph is just
300 00:32:01.470 ⇒ 00:32:04.460 Uttam Kumaran: part of the the graph ticket is blocking that
301 00:32:05.800 ⇒ 00:32:11.149 Uttam Kumaran: So then maybe on internally on our side, let’s pick the 5 or 6 we want to go after.
302 00:32:11.260 ⇒ 00:32:15.869 Uttam Kumaran: and then we can drive from there. So yeah, let’s start in. Let’s start in colab,
303 00:32:17.420 ⇒ 00:32:35.730 Uttam Kumaran: we can pick the 5. We’re going after. We can discuss internally how to get the data to fit that story, and then amber. As soon as the the viz. Is ready, you can start working the slides as soon as the viz. Is ready. We’ll then move towards making the viz. Available in power bi in one way or another.
304 00:32:36.248 ⇒ 00:32:38.399 Uttam Kumaran: But I want to prioritize that
305 00:32:38.560 ⇒ 00:32:51.899 Uttam Kumaran: if power bi, like the risk here is that power Bi really stinks, and it may take longer. So I want to have the deck getting the deck ready, for Matthew is the number one goal. So let’s like.
306 00:32:52.200 ⇒ 00:32:55.799 Uttam Kumaran: let’s do that with the ugly graph from python first.st
307 00:32:55.800 ⇒ 00:33:02.770 Uttam Kumaran: Yep, yep, substitute power Bi. It links to the live one once that’s once that’s done.
308 00:33:03.570 ⇒ 00:33:18.680 Amber Lin: Awesome. I have just one more question. I it might. It might be a little silly. So my question is right. Now we’re basing office synthetic data. And so, therefore, the insights I draw from it might not be.
309 00:33:18.680 ⇒ 00:33:21.959 Mathew: No, no, yeah, you’re not gonna draw actual insights yet. Right now.
310 00:33:21.960 ⇒ 00:33:23.809 Uttam Kumaran: It’s just a demonstrative. Yeah.
311 00:33:23.810 ⇒ 00:33:34.369 Mathew: Yeah, the synthetic data is to allow us to like, know that when we spin up these graphs, that when we have the actual data. You don’t have to figure out how to create any of the
312 00:33:34.960 ⇒ 00:33:39.969 Mathew: like that. We just know how to go from Microsoft graph data to populating these graphs right.
313 00:33:40.470 ⇒ 00:33:44.280 Uttam Kumaran: Yes, that’s exactly right. So I don’t. I don’t think we necessarily need
314 00:33:44.380 ⇒ 00:33:48.269 Uttam Kumaran: like the insights. But we’re, I also think, like part of this is like.
315 00:33:48.450 ⇒ 00:33:54.850 Uttam Kumaran: this is something that amber. Matthew will then take and sell to a lot of different firms. So we want to have this be something.
316 00:33:54.850 ⇒ 00:33:55.179 Mathew: Got it.
317 00:33:55.180 ⇒ 00:33:57.689 Uttam Kumaran: Can reuse. So I don’t know. I think we can.
318 00:33:57.690 ⇒ 00:33:58.330 Uttam Kumaran: We can fit.
319 00:33:58.330 ⇒ 00:34:09.390 Uttam Kumaran: You can like change this. The source data, like, I think part of this is like you. You’re like creative director of these slides. Think about the stories you want to tell. And Luke can actually just change the data to fit that.
320 00:34:09.750 ⇒ 00:34:18.620 Uttam Kumaran: So like, because Amy will will focus on getting the graphs. The graphs will show whatever the data is, and then you’ll you can draw whatever insight. So we actually have control, a lot of control here.
321 00:34:18.739 ⇒ 00:34:28.120 Uttam Kumaran: It’s kind of an interesting. It’s like this is not typically like the data goes right. We we’re waiting. But of course we can tell the story. That’s the sexiest to tell.
322 00:34:29.628 ⇒ 00:34:36.820 Mathew: And, by the way, I do, I do love the look and feel of the one I just showed you, or should I? Should I send that to you.
323 00:34:37.510 ⇒ 00:34:40.460 Uttam Kumaran: And send any of the ones that you that you like in terms of install.
324 00:34:41.280 ⇒ 00:34:47.039 Mathew: But okay, I’m like multi tabbing. Hold on. I’m just gonna drop it in so black.
325 00:34:47.300 ⇒ 00:34:48.989 Amber Lin: Yeah, that would be awesome.
326 00:34:49.528 ⇒ 00:34:57.420 Amber Lin: So what I hear is that what I’m doing for you to text is not really actual insights. I draw from them but possible
327 00:34:57.550 ⇒ 00:35:06.350 Amber Lin: ways of looking at the data and what potential relationships and potential insights they can draw. Not really.
328 00:35:06.350 ⇒ 00:35:11.479 Uttam Kumaran: I would I would. Well, there’s this is a fake company. This is a fake data, so there’s no like
329 00:35:11.760 ⇒ 00:35:18.079 Uttam Kumaran: there’s no there’s no like people on the other end that are gonna act on it. But I think we want to go through like a real exercise where.
330 00:35:18.080 ⇒ 00:35:18.400 Amber Lin: Yep.
331 00:35:18.400 ⇒ 00:35:29.626 Uttam Kumaran: You should, you should have insights that you draw from that. Consider it like real data, draw real insights. The lovely thing is, if there are no insights. We can change the data so that there are.
332 00:35:30.100 ⇒ 00:35:32.850 Uttam Kumaran: Yeah, it’s a little bit, Meta. But like, that’s the.
333 00:35:33.210 ⇒ 00:35:38.129 Uttam Kumaran: So you actually, so ideally, once the graphs are produced. If you’re like, Hey, these are.
334 00:35:38.400 ⇒ 00:35:46.409 Uttam Kumaran: These are like, very. These are like nonlinear relationships. And you want them to be linear. Luke, you know, tweak the data to like fit the story.
335 00:35:46.410 ⇒ 00:35:46.930 Amber Lin: Sounds.
336 00:35:46.930 ⇒ 00:36:03.009 Uttam Kumaran: So you need to be a little bit create. You need to be a little bit creative, and I can work with you on that if you’re like, hey, what are the couple of stories you want to tell? To give you an example like I I maybe the last thing I’ll mention is like I worked on like we work. I worked a lot on the key card swipe data. So we told a lot of stories about like.
337 00:36:03.060 ⇒ 00:36:22.290 Uttam Kumaran: when are people working from home versus in office? How much square footage you need? One time people coming in, going to lunch, leaving like, are you overloaded? So that’s I. I. There’s a lot of really easy stories. The other thing is, I think once you get the graphs and shove them in the chat. Gpt. It’ll give you a couple of like I mean, I’m sure you’ll do that. But
338 00:36:22.740 ⇒ 00:36:23.090 Uttam Kumaran: inside.
339 00:36:23.090 ⇒ 00:36:27.689 Amber Lin: Sounds good. So once I’ll draft a few initial stories, and then.
340 00:36:27.690 ⇒ 00:36:28.240 Uttam Kumaran: Sure.
341 00:36:28.240 ⇒ 00:36:31.200 Amber Lin: With you guys. So we can tweak the data as soon as possible.
342 00:36:32.150 ⇒ 00:36:32.890 Uttam Kumaran: Perfect.
343 00:36:32.890 ⇒ 00:36:38.219 Amber Lin: Yeah, sounds good. I don’t have any more questions. Does anyone else have any last comments?
344 00:36:39.430 ⇒ 00:36:47.330 Uttam Kumaran: I think. Maybe just one quick thing in terms of next steps. And if you want to give a shot at like getting a collab notebook spun up or slack me if
345 00:36:47.690 ⇒ 00:36:49.759 Uttam Kumaran: if that’s not working, Matthew, as soon as you get.
346 00:36:49.760 ⇒ 00:36:50.260 Mathew: Did that be done?
347 00:36:50.260 ⇒ 00:36:50.779 Uttam Kumaran: And we should.
348 00:36:50.780 ⇒ 00:36:53.280 Mathew: Be done under our name or the brain, for it’s gonna be.
349 00:36:53.280 ⇒ 00:36:54.190 Mathew: It’s so. It’s.
350 00:36:54.190 ⇒ 00:36:56.309 Uttam Kumaran: Colab is going to be within your bigquery.
351 00:36:56.310 ⇒ 00:36:57.370 Mathew: Okay. Cool. Cool.
352 00:36:57.370 ⇒ 00:37:09.410 Uttam Kumaran: Your Google Workspace and stuff. So we’ll share all that and then as soon as you get power, Bi, if you can share it, and then, yeah, let’s let’s set what’s again, our rough goal is June. First, st
353 00:37:09.600 ⇒ 00:37:14.249 Uttam Kumaran: again, let’s just work backwards from there. I I kind of want to have something
354 00:37:14.370 ⇒ 00:37:17.030 Uttam Kumaran: I want to aim for, something finalized
355 00:37:17.320 ⇒ 00:37:28.800 Uttam Kumaran: like that is ready to go by the week before the 26.th That way. If anything tweaks. We can tweak it if we have the rest of the week to just make it better. We have the time to get better, so.
356 00:37:31.370 ⇒ 00:37:35.099 Mathew: Oh, my God! This is horrible! Branch forge!
357 00:37:36.250 ⇒ 00:37:37.719 Mathew: This is really rough.
358 00:37:39.690 ⇒ 00:37:45.120 Amber Lin: I think the username is actually bring forward user.
359 00:37:45.120 ⇒ 00:37:46.080 Mathew: Really.
360 00:37:46.420 ⇒ 00:37:48.010 Amber Lin: You should confirm, Yeah.
361 00:37:48.410 ⇒ 00:37:55.840 Mathew: Hold on. Okay. So this is, it’s actually pretty complicated to get a user provision after, like, so it’s.
362 00:37:56.161 ⇒ 00:38:00.260 Uttam Kumaran: To ask Trevor. He’ll do. I think he’ll or yeah, I feel like.
363 00:38:00.260 ⇒ 00:38:02.660 Mathew: I’m in. I’m in like azure.
364 00:38:02.660 ⇒ 00:38:04.769 Uttam Kumaran: Do. You’re like azure console right?
365 00:38:04.770 ⇒ 00:38:05.690 Mathew: Yeah.
366 00:38:05.690 ⇒ 00:38:06.900 Uttam Kumaran: Yeah, which?
367 00:38:06.900 ⇒ 00:38:09.040 Mathew: Wait. Trevor’s already done this or no, I don’t.
368 00:38:09.040 ⇒ 00:38:12.410 Uttam Kumaran: No, no, he it’ll take him like 90 seconds, though.
369 00:38:13.600 ⇒ 00:38:19.500 Mathew: Oh, I’m in, I’m in. Well, no, I think it will take him longer, because you have to do like annoying stuff like authenticator, which I doubt he’s done.
370 00:38:20.060 ⇒ 00:38:25.440 Uttam Kumaran: Well, no, it’s very similar to the bigquery. It’s like exactly like what it take to like. Do all the bigquery stuff. So.
371 00:38:25.440 ⇒ 00:38:29.969 Mathew: This sucks? No, yeah. Okay. So yeah, you’re right. Brainforge, user fuck.
372 00:38:32.780 ⇒ 00:38:36.019 Uttam Kumaran: You could just give us as many power bi roles as possible.
373 00:38:36.990 ⇒ 00:38:39.359 Mathew: Oh, my God, this is so annoying!
374 00:38:40.680 ⇒ 00:38:43.490 Mathew: Actually, I’m in like Microsoft. Intro. Have you worked in that.
375 00:38:43.490 ⇒ 00:38:50.210 Uttam Kumaran: Yes, yeah, it’s it’s like their Iam product. I mean, it’s it’s a nightmare. It’s a complete nightmare.
376 00:38:52.570 ⇒ 00:38:53.870 Mathew: Guess than my.
377 00:38:53.870 ⇒ 00:38:55.450 Uttam Kumaran: Microsoft experience sucks.
378 00:38:56.010 ⇒ 00:39:02.159 Mathew: Alright. I just hit, invite to user cannot be invited because the domain of their email address
379 00:39:02.450 ⇒ 00:39:05.969 Mathew: is a verified domain of this Whatsapprob.
380 00:39:06.960 ⇒ 00:39:09.193 Mathew: Sorry for all my cursing.
381 00:39:12.560 ⇒ 00:39:16.969 Mathew: create a new internal user in your organization. Wow! This is but interrupting
382 00:39:18.470 ⇒ 00:39:21.199 Mathew: alright. Whatever I’ll I’ll do stuff.
383 00:39:23.040 ⇒ 00:39:25.069 Mathew: It’s not like you need this like right now, right.
384 00:39:25.550 ⇒ 00:39:26.289 Uttam Kumaran: No, no, no.
385 00:39:26.640 ⇒ 00:39:27.590 Mathew: Okay. Paul.
386 00:39:28.880 ⇒ 00:39:33.419 Uttam Kumaran: This week. Ideally, I’ll work on this for the rep for the next few
387 00:39:33.420 ⇒ 00:39:35.049 Uttam Kumaran: for the rest of my life.
388 00:39:35.050 ⇒ 00:39:36.050 Mathew: Few days. Yeah.
389 00:39:37.790 ⇒ 00:39:40.469 Uttam Kumaran: Or call or call me if you wanna do it together at some point.
390 00:39:40.470 ⇒ 00:39:45.220 Mathew: No, no, no, I mean I’m I’m trying right now, like I could just share my screen if you and I help me.
391 00:39:45.410 ⇒ 00:39:47.510 Uttam Kumaran: Yeah, yeah. Everyone else.
392 00:39:47.940 ⇒ 00:39:48.450 Uttam Kumaran: I think I’ll.
393 00:39:48.450 ⇒ 00:39:50.869 Mathew: Everyone else can go. Yeah, unless you guys have anything else.
394 00:39:52.950 ⇒ 00:39:56.170 Amber Lin: Now, do you guys want the meeting room? Or are you gonna call.
395 00:39:58.300 ⇒ 00:39:59.750 Uttam Kumaran: Can you make me? Host? Yeah, I’ll.
396 00:39:59.750 ⇒ 00:40:01.130 Amber Lin: Okay, all right.
397 00:40:02.350 ⇒ 00:40:03.260 Amber Lin: Thank you.
398 00:40:03.260 ⇒ 00:40:07.339 Annie Yu: Question about Collab. So what’s
399 00:40:07.490 ⇒ 00:40:15.039 Annie Yu: I don’t really know. Once I like, I guess, open one. How how do I ensure
400 00:40:16.280 ⇒ 00:40:19.399 Annie Yu: like what’s cause? I thought, colab is just
401 00:40:20.660 ⇒ 00:40:23.789 Annie Yu: like, kind of like Google Sheet. You can just share it.
402 00:40:24.550 ⇒ 00:40:25.040 Uttam Kumaran: Yes.
403 00:40:25.040 ⇒ 00:40:27.760 Annie Yu: Layer of security we have to go through.
404 00:40:28.723 ⇒ 00:40:33.549 Uttam Kumaran: It’s so you’ve used like Jupyter notebook. Right? It’s just like Jupyter notebook. It’s just cloud based. Jupyter notebook.
405 00:40:34.040 ⇒ 00:40:38.510 Annie Yu: So do I. I guess open one under Brainfort user.
406 00:40:38.510 ⇒ 00:40:39.260 Uttam Kumaran: Yes.
407 00:40:39.460 ⇒ 00:40:40.170 Uttam Kumaran: Correct.
408 00:40:41.010 ⇒ 00:40:42.270 Annie Yu: Okay. Cool.
409 00:40:43.720 ⇒ 00:40:44.970 Uttam Kumaran: You may have to.
410 00:40:45.398 ⇒ 00:40:50.110 Uttam Kumaran: You can slack me. I think there’s gonna be some things on how to cook up bigquery to colab.
411 00:40:50.320 ⇒ 00:40:51.880 Uttam Kumaran: But if you Google it, I think.
412 00:40:51.880 ⇒ 00:40:55.270 Annie Yu: I probably will have to look into.
413 00:40:55.550 ⇒ 00:40:56.200 Uttam Kumaran: Yeah.
414 00:40:59.220 ⇒ 00:41:01.249 Amber Lin: Okay, bye, guys, you have to.
415 00:41:01.250 ⇒ 00:41:02.060 Mathew: I.
416 00:41:02.360 ⇒ 00:41:03.080 Uttam Kumaran: But.
417 00:41:04.450 ⇒ 00:41:05.130 Mathew: All right.
418 00:41:06.360 ⇒ 00:41:07.426 Mathew: No, no.
419 00:41:08.120 ⇒ 00:41:11.189 Mathew: You say apple pie, you’re right.
420 00:41:13.790 ⇒ 00:41:16.480 Mathew: Good luck. If it runs just, I’ll give you a card.
421 00:41:17.070 ⇒ 00:41:19.480 Mathew: Alright. So you see my screen.
422 00:41:19.770 ⇒ 00:41:20.560 Uttam Kumaran: Yes.
423 00:41:21.730 ⇒ 00:41:22.699 Uttam Kumaran: And by the way.
424 00:41:22.700 ⇒ 00:41:46.089 Mathew: By the way, by the way, Trevor, for for these calls Trevor is good for like clearing whatever you need from a data perspective when it comes to like product ownership, creative direction, like ultimate requirements and deliverables. Just ping me if you’ll if you, if you’ll have stuff to show about that cool. I feel I feel like he’s done enough of a clearing over the last few weeks to like, get you guys going. But, like, now, we’re getting back into like account management, or whatever.
425 00:41:46.270 ⇒ 00:41:47.079 Uttam Kumaran: Yeah, this isn’t working.
426 00:41:47.080 ⇒ 00:41:47.430 Uttam Kumaran: It’s on.
427 00:41:47.430 ⇒ 00:41:49.609 Mathew: Create new. I did this already. It didn’t work.
428 00:41:51.032 ⇒ 00:41:52.389 Uttam Kumaran: Can you go?
429 00:41:56.220 ⇒ 00:41:57.186 Uttam Kumaran: Let’s see.
430 00:42:00.201 ⇒ 00:42:07.040 Uttam Kumaran: Where is? Can you go to identity? Oh, can you go to your end? Users here? Okay all users.
431 00:42:13.910 ⇒ 00:42:16.270 Uttam Kumaran: And then when you hit new user, the 1st one doesn’t work.
432 00:42:17.150 ⇒ 00:42:21.659 Mathew: No, maybe cause I’m like a freebie or something.
433 00:42:23.496 ⇒ 00:42:27.250 Uttam Kumaran: Can you go here to identity governance.
434 00:42:43.890 ⇒ 00:42:45.900 Mathew: I hope you’re talking, and Judge Tp.
435 00:42:47.300 ⇒ 00:42:51.670 Uttam Kumaran: Can you click on roles? Just type in roles? Here, top.
436 00:42:52.880 ⇒ 00:42:56.300 Uttam Kumaran: what typically happens is you have to create where.
437 00:42:56.300 ⇒ 00:42:57.310 Mathew: Where’s your role?
438 00:42:57.480 ⇒ 00:42:59.010 Uttam Kumaran: Here. I put this on the search.
439 00:43:03.330 ⇒ 00:43:03.960 Mathew: Here.
440 00:43:05.430 ⇒ 00:43:07.220 Uttam Kumaran: And
441 00:43:10.760 ⇒ 00:43:14.829 Uttam Kumaran: yeah, so I just did this yesterday for us, too. So
442 00:43:19.240 ⇒ 00:43:23.930 Uttam Kumaran: I just don’t know why it’s not letting you add users, because that’s not online.
443 00:43:28.100 ⇒ 00:43:29.620 Uttam Kumaran: Let me open up.
444 00:43:34.350 ⇒ 00:43:38.620 Uttam Kumaran: You should be able to. Just I mean, did
445 00:43:39.420 ⇒ 00:43:41.899 Uttam Kumaran: th- this one didn’t work when you, when you try this.
446 00:43:43.560 ⇒ 00:43:45.140 Mathew: Now look what happens.
447 00:43:47.400 ⇒ 00:43:51.929 Mathew: Right? Brainforge, user, AI invite. And then it’s top up here
448 00:43:52.180 ⇒ 00:44:02.460 Mathew: cannot be invited because of the domain of your email is a verified domain of this directory that implies that you’re not external. But then the button to invite you internally doesn’t work.
449 00:44:04.340 ⇒ 00:44:06.080 Uttam Kumaran: Okay, so let me see.
450 00:44:21.050 ⇒ 00:44:25.279 Mathew: Actually can’t do anything up here. These these 3 are over.
451 00:44:26.160 ⇒ 00:44:32.979 Mathew: Oh, wait! Oh, no, no, maybe I don’t have like the permission to add new users.
452 00:44:33.150 ⇒ 00:44:34.970 Uttam Kumaran: Yeah, can you go to.
453 00:44:34.970 ⇒ 00:44:35.950 Mathew: To myself.
454 00:44:37.550 ⇒ 00:44:50.106 Uttam Kumaran: Can you go to? So there is a azure portal can you go to? Can you type in just like azure active directory? Actually like, leave leave. Oh, yeah, here. Actually, you should be fine here. Just click on
455 00:44:51.746 ⇒ 00:44:58.380 Uttam Kumaran: Security here, see? You don’t have any of these which implies that
456 00:45:01.740 ⇒ 00:45:05.310 Uttam Kumaran: someone else’s admin, like I have a feeling that you’re not the
457 00:45:05.480 ⇒ 00:45:08.100 Uttam Kumaran: like. Did you set up this Microsoft thing, or or.
458 00:45:08.100 ⇒ 00:45:11.239 Mathew: Think I’m I just set it up. Yeah, I don’t think anyone else has set it up.
459 00:45:12.620 ⇒ 00:45:13.670 Uttam Kumaran: Okay.
460 00:45:15.576 ⇒ 00:45:18.879 Mathew: Can you go to? Can you? Can you go to go to your user?
461 00:45:19.770 ⇒ 00:45:24.920 Uttam Kumaran: Go to either permissions here or go to yours. Like, go to permissions here and see if you can see.
462 00:45:29.490 ⇒ 00:45:31.270 Uttam Kumaran: Yeah, just click on launch.
463 00:45:32.150 ⇒ 00:45:34.679 Uttam Kumaran: Basically, you want to make sure you have admin
464 00:45:35.050 ⇒ 00:45:37.280 Uttam Kumaran: that’s the that’s the problem right now.
465 00:45:37.280 ⇒ 00:45:39.910 Mathew: That’d be nice. Your onboarding failed.
466 00:45:40.090 ⇒ 00:45:48.800 Uttam Kumaran: Get out of this. Sorry. Get out of this thing. Go go to go back, go to users, user settings, or like wherever it says all users, wherever you saw your name
467 00:45:50.145 ⇒ 00:45:53.189 Uttam Kumaran: and wait wait for this to load.
468 00:45:53.190 ⇒ 00:45:54.590 Mathew: Oh, Admin Center.
469 00:45:58.390 ⇒ 00:46:04.129 Uttam Kumaran: So go to all users and see if you can edit your user.
470 00:46:05.721 ⇒ 00:46:09.228 Uttam Kumaran: Can you somehow like edit? Yeah. Edit
471 00:46:10.270 ⇒ 00:46:11.789 Mathew: No, we can’t do any of these.
472 00:46:12.070 ⇒ 00:46:16.869 Uttam Kumaran: Oh, shit what is this link right here? This one?
473 00:46:21.050 ⇒ 00:46:23.169 Uttam Kumaran: Oh, okay, never mind.
474 00:46:23.370 ⇒ 00:46:27.660 Uttam Kumaran: Are you sure, like Trevor didn’t create one? And you’re just like on the account.
475 00:46:30.090 ⇒ 00:46:30.729 Mathew: Bro. I don’t know.
476 00:46:30.730 ⇒ 00:46:31.700 Uttam Kumaran: I feel like.
477 00:46:34.360 ⇒ 00:46:35.870 Mathew: What am I asking him?
478 00:46:36.020 ⇒ 00:46:41.700 Mathew: I don’t think he’s touched this yet, but why would he? He would have no reason to well enjoy.
479 00:46:41.700 ⇒ 00:46:48.160 Uttam Kumaran: I mean, in case if, in case he created a Microsoft account before then, he just added, you as sort of a user
480 00:46:52.300 ⇒ 00:46:54.889 Mathew: But you’re sort of back in. So click on.
481 00:46:57.350 ⇒ 00:46:58.939 Uttam Kumaran: See if you can do yours.
482 00:47:00.440 ⇒ 00:47:01.530 Mathew: Again.
483 00:47:01.910 ⇒ 00:47:02.560 Uttam Kumaran: Yeah.
484 00:47:02.930 ⇒ 00:47:05.660 Mathew: Oh, this is a different one, Matt, do you matter more.
485 00:47:05.660 ⇒ 00:47:15.239 Uttam Kumaran: Yes, yes, so so yeah. So it’ll it’ll ask you to create. So for me, I had to do this, too. I have a I have a Utah this and that one. So go to go to assignments.
486 00:47:15.470 ⇒ 00:47:20.620 Uttam Kumaran: So yeah, go to go to proper sorry go to properties, and there’s a member change it to admin
487 00:47:21.170 ⇒ 00:47:24.040 Uttam Kumaran: at the top here. Yeah, remember.
488 00:47:24.230 ⇒ 00:47:27.780 Uttam Kumaran: Oh, never mind. Okay, and then go to assignments and then give yourself global admin.
489 00:47:27.780 ⇒ 00:47:29.710 Mathew: I can’t! I can’t!
490 00:47:29.710 ⇒ 00:47:30.480 Uttam Kumaran: So.
491 00:47:31.660 ⇒ 00:47:33.470 Mathew: I guess I’m just a basic bitch.
492 00:47:35.200 ⇒ 00:47:43.799 Uttam Kumaran: I think I think you’re in. I think I don’t know. My my guess is that you’re in. You’re in an account already, and like. Maybe Trevor created it.
493 00:47:44.500 ⇒ 00:47:49.030 Uttam Kumaran: I would just ask him ask him if he set up azure before for a matter more.
494 00:48:07.190 ⇒ 00:48:08.370 Mathew: He said, Nope.
495 00:48:09.840 ⇒ 00:48:10.910 Uttam Kumaran: Oh!
496 00:48:45.450 ⇒ 00:48:49.329 Mathew: I just delete this this whole thing. We could just tear this whole thing down.
497 00:48:50.940 ⇒ 00:48:53.194 Uttam Kumaran: No, wait! Hold on! Go to
498 00:48:53.820 ⇒ 00:48:57.180 Uttam Kumaran: Is there roles and administrators somewhere on the left.
499 00:49:00.330 ⇒ 00:49:02.939 Mathew: Or you can type try typing it in up here.
500 00:49:04.010 ⇒ 00:49:05.519 Uttam Kumaran: Just do roles.
501 00:49:05.670 ⇒ 00:49:07.569 Uttam Kumaran: Yeah, Microsoft, this one
502 00:49:11.110 ⇒ 00:49:17.730 Uttam Kumaran: and then click on just type in admin. Here.
503 00:49:21.030 ⇒ 00:49:22.930 Mathew: Maybe they shouldn’t English, which didn’t.
504 00:49:23.130 ⇒ 00:49:25.480 Uttam Kumaran: There is a global admin, I think.
505 00:49:26.400 ⇒ 00:49:30.779 Uttam Kumaran: like, if you scroll down, yeah, there should be yeah, global administrators. So click on that.
506 00:49:31.160 ⇒ 00:49:33.209 Mathew: Why is this number one?
507 00:49:33.780 ⇒ 00:49:36.680 Mathew: Who the hell is this? Who the hell is this.
508 00:49:37.049 ⇒ 00:49:39.639 Uttam Kumaran: Alright go back, go back, go back
509 00:49:41.360 ⇒ 00:49:45.140 Uttam Kumaran: like, go back type in global type, in global admin.
510 00:49:45.920 ⇒ 00:49:47.399 Mathew: Is in my account.
511 00:49:47.400 ⇒ 00:49:48.789 Uttam Kumaran: Click on, click on this.
512 00:49:51.490 ⇒ 00:49:55.050 Uttam Kumaran: Oh, God! Why can’t you grant this to anyone?
513 00:49:55.490 ⇒ 00:49:56.910 Mathew: I gotta see who this is
514 00:49:57.390 ⇒ 00:50:00.640 Mathew: who the fuck thinks they can be the admin in my in. My.
515 00:50:02.110 ⇒ 00:50:05.560 Mathew: please be aware of both operations. Okay, whatever.
516 00:50:06.130 ⇒ 00:50:07.470 Uttam Kumaran: I don’t.
517 00:50:14.350 ⇒ 00:50:17.580 Uttam Kumaran: This is really confusing cause.
518 00:50:17.580 ⇒ 00:50:18.040 Mathew: I’m always.
519 00:50:18.040 ⇒ 00:50:21.399 Uttam Kumaran: This is a role, and you can’t. How do you?
520 00:50:21.620 ⇒ 00:50:24.620 Uttam Kumaran: How can? Why can’t you do add new assignment.
521 00:50:24.620 ⇒ 00:50:25.880 Mathew: Because I’m nothing.
522 00:50:26.430 ⇒ 00:50:27.719 Mathew: I’m a nothing.
523 00:50:28.150 ⇒ 00:50:29.790 Uttam Kumaran: Well, that’s what I’m saying.
524 00:50:29.790 ⇒ 00:50:33.370 Mathew: This ran this random, this random fucking Agent Smith and.
525 00:50:33.370 ⇒ 00:50:38.670 Uttam Kumaran: No, no, this is like this is your. This is your Google docs and stuff. This is your
526 00:50:38.670 ⇒ 00:50:39.819 Uttam Kumaran: King hazard.
527 00:50:41.190 ⇒ 00:50:44.200 Mathew: Hold on. Let’s see if I got an email from you. These fuckers.
528 00:50:52.180 ⇒ 00:50:53.120 Mathew: Thank you.
529 00:50:53.520 ⇒ 00:50:58.080 Mathew: Yeah. I mean, it sucks.
530 00:50:58.430 ⇒ 00:51:02.255 Uttam Kumaran: Yeah, I may just support. Do a support ticket
531 00:51:02.680 ⇒ 00:51:04.180 Mathew: A support ticket.
532 00:51:05.540 ⇒ 00:51:07.314 Uttam Kumaran: Did you when you when you
533 00:51:08.390 ⇒ 00:51:09.989 Uttam Kumaran: can you click log in here?
534 00:51:10.180 ⇒ 00:51:12.350 Uttam Kumaran: Can you? Can you do this? Can you log out
535 00:51:19.800 ⇒ 00:51:23.800 Uttam Kumaran: sign out, Yeah, and then just sign sign back in.
536 00:51:25.740 ⇒ 00:51:27.289 Mathew: Oh, it’s really come to this.
537 00:51:30.770 ⇒ 00:51:36.669 Uttam Kumaran: Did you get you? Did you think they would have sent you like a different email, like a@microsoft.microsoft? Something.
538 00:51:36.990 ⇒ 00:51:38.050 Mathew: Well, they dance.
539 00:51:41.550 ⇒ 00:51:44.990 Uttam Kumaran: Okay, then my only suggestion is, okay.
540 00:51:45.560 ⇒ 00:51:47.820 Mathew: That’s a horrible suggestion. If I ever say.
541 00:51:47.820 ⇒ 00:51:49.439 Uttam Kumaran: I don’t know. Man.
542 00:51:49.440 ⇒ 00:51:51.109 Mathew: I would say so myself.
543 00:51:51.345 ⇒ 00:51:53.695 Uttam Kumaran: Can solve a lot of problems with this. One is,
544 00:51:53.930 ⇒ 00:52:00.860 Mathew: You guys want to just try to create it with Brainforge and then make me an admin and sure.
545 00:52:00.860 ⇒ 00:52:01.400 Uttam Kumaran: The.
546 00:52:01.400 ⇒ 00:52:02.759 Mathew: $50 for the hour.
547 00:52:02.760 ⇒ 00:52:08.209 Uttam Kumaran: No, no, no, I would just sign in and submit a ticket. They’ll help you out.
548 00:52:09.100 ⇒ 00:52:11.439 Uttam Kumaran: Oh, my God! I wanna fucking
549 00:52:11.440 ⇒ 00:52:14.490 Uttam Kumaran: chat, chat with this chat with the guy. And and yeah.
550 00:52:15.010 ⇒ 00:52:16.149 Mathew: Out with the guy.
551 00:52:16.860 ⇒ 00:52:19.499 Uttam Kumaran: That would be the chat bot thing.
552 00:52:19.500 ⇒ 00:52:21.599 Mathew: Oh, my God! I can’t find the password!
553 00:52:26.420 ⇒ 00:52:31.610 Mathew: You have to be kidding me! You didn’t not not nobody wanted nobody saved it.
554 00:52:32.250 ⇒ 00:52:34.109 Mathew: Are you fucking, kidding me, guys?
555 00:52:36.240 ⇒ 00:52:36.830 Mathew: What?
556 00:52:39.050 ⇒ 00:52:41.919 Mathew: No like? Not on my last. Nothing.
557 00:52:43.290 ⇒ 00:52:46.680 Mathew: Alright. Well, I’m still signed in here. So that that accounts for something.
558 00:52:48.140 ⇒ 00:52:49.599 Mathew: Okay. What am I doing?
559 00:52:50.886 ⇒ 00:52:53.020 Uttam Kumaran: Click on the yeah.
560 00:52:55.590 ⇒ 00:53:05.009 Uttam Kumaran: and then just let them know that like, or now this isn’t, I’ll actually go click on the this one. This is a ticket.
561 00:53:05.450 ⇒ 00:53:08.210 Uttam Kumaran: they said, that’s a feedback thing. So like, yeah.
562 00:53:08.770 ⇒ 00:53:13.609 Uttam Kumaran: type it in here. You could just say, like, I’m the only user. But I’m but I need to be global admin.
563 00:53:21.230 ⇒ 00:53:23.710 Mathew: And you’re telling me Microsoft stock has been going up.
564 00:53:25.290 ⇒ 00:53:30.125 Uttam Kumaran: Yeah dude, because they they just lock. This is the thing the products suck. But they
565 00:53:30.970 ⇒ 00:53:34.319 Uttam Kumaran: they’re the most secure. And so they just win a lot of agreements.
566 00:53:34.920 ⇒ 00:53:37.580 Uttam Kumaran: cause the it. People make the decision, not the users.
567 00:53:38.280 ⇒ 00:53:39.450 Mathew: This is not true.
568 00:53:39.870 ⇒ 00:53:41.380 Uttam Kumaran: Yeah, that’s horrible.
569 00:53:41.660 ⇒ 00:53:45.259 Mathew: It’s just incredible. It’s just it’s just really incredible that they could even like.
570 00:53:46.920 ⇒ 00:53:48.469 Uttam Kumaran: Tell something like this.
571 00:53:52.880 ⇒ 00:53:54.830 Mathew: Alright, I’m gonna I’m gonna kill someone
572 00:53:58.450 ⇒ 00:54:00.400 Mathew: view support plans.
573 00:54:21.400 ⇒ 00:54:22.480 Uttam Kumaran: What I’m saying.
574 00:54:29.670 ⇒ 00:54:32.730 Mathew: This isn’t right, it’s not right.
575 00:54:32.730 ⇒ 00:54:34.050 Mathew: Sit next.
576 00:54:35.560 ⇒ 00:54:36.730 Uttam Kumaran: Unreal.
577 00:54:40.810 ⇒ 00:54:42.000 Mathew: About to throw up.
578 00:54:55.920 ⇒ 00:54:57.609 Uttam Kumaran: Hey? Click! Here you may find it.
579 00:54:58.340 ⇒ 00:54:59.690 Uttam Kumaran: Subscription. Id.
580 00:55:00.020 ⇒ 00:55:00.640 Mathew: Right.
581 00:55:01.510 ⇒ 00:55:02.360 Uttam Kumaran: Click on this
582 00:55:02.360 ⇒ 00:55:07.150 Uttam Kumaran: you may see you may click on your name. Here you may see the subscription id somewhere. Okay, never mind.
583 00:55:07.560 ⇒ 00:55:08.820 Uttam Kumaran: Yeah, or or check.
584 00:55:09.870 ⇒ 00:55:11.559 Mathew: Well, I can’t. Can’t log back in.
585 00:55:12.300 ⇒ 00:55:13.109 Mathew: It’s done in my past.
586 00:55:13.545 ⇒ 00:55:16.160 Uttam Kumaran: Just XY. Just do. Xyz.
587 00:55:25.170 ⇒ 00:55:29.549 Mathew: Yeah, that’s how we decided to switch her out of working with.
588 00:55:30.940 ⇒ 00:55:34.880 Mathew: okay, what just happened. You’re not gonna give me a success date.
589 00:55:37.220 ⇒ 00:55:39.500 Mathew: I had no new support. Request.
590 00:55:48.090 ⇒ 00:55:53.009 Uttam Kumaran: Okay, okay. As soon as they
591 00:55:53.710 ⇒ 00:55:54.930 Uttam Kumaran: As soon as I get back.
592 00:55:54.930 ⇒ 00:55:55.610 Mathew: Me, up.
593 00:55:55.830 ⇒ 00:55:58.910 Uttam Kumaran: Can you just CC, just CC, me on this, too.
594 00:55:59.710 ⇒ 00:56:04.800 Uttam Kumaran: You’re talking about brainforge.ai, yeah.
595 00:56:07.000 ⇒ 00:56:09.159 Mathew: It’s a phone call. Maybe I should change it to email.
596 00:56:10.150 ⇒ 00:56:13.200 Mathew: I’m I’m not gonna pick up the phone from these bastards.
597 00:56:17.670 ⇒ 00:56:19.700 Mathew: Alright, I’m gonna go. I’m gonna go pass out.
598 00:56:20.360 ⇒ 00:56:24.087 Uttam Kumaran: Okay, try this, I would say the only if they don’t get back to you today,
599 00:56:24.690 ⇒ 00:56:28.819 Uttam Kumaran: I will. I can help create one with the with the brain forge, that matter more.
600 00:56:28.820 ⇒ 00:56:30.360 Mathew: You guys just do it honestly.
601 00:56:30.360 ⇒ 00:56:31.470 Uttam Kumaran: Okay. Okay. Okay.
602 00:56:31.470 ⇒ 00:56:32.619 Mathew: Yeah, this is, how.
603 00:56:32.920 ⇒ 00:56:34.420 Uttam Kumaran: Okay, okay, let me give it a go.
604 00:56:34.780 ⇒ 00:56:35.353 Mathew: Thank you.
605 00:56:35.790 ⇒ 00:56:38.439 Uttam Kumaran: Okay. Alright, thanks, dude. How’s everything else?
606 00:56:38.730 ⇒ 00:56:41.209 Mathew: Everything’s good. Man. Yeah, everything’s good. How about you?
607 00:56:41.700 ⇒ 00:56:46.679 Uttam Kumaran: Good dude we just we’re doing more content going to a conference. Things are growing. It’s good.
608 00:56:46.680 ⇒ 00:56:47.580 Mathew: X.
609 00:56:47.980 ⇒ 00:56:55.029 Uttam Kumaran: Yeah, I wanna interview you for something sometime soon about like workspace analytics and stuff. And I think it’ll give you guys some good content and clips.
610 00:56:55.030 ⇒ 00:56:56.420 Mathew: I’ll take it, man, we’ll take it.
611 00:56:56.420 ⇒ 00:57:01.109 Uttam Kumaran: Yeah, we’ll hand over all that to you, so I’ll I’ll I’ll tell you about it soon. Once we get out of this
612 00:57:01.430 ⇒ 00:57:03.629 Uttam Kumaran: this next year. I’m glad this thing is moving along. Dude.
613 00:57:03.980 ⇒ 00:57:07.120 Mathew: Damp, same.
614 00:57:07.120 ⇒ 00:57:12.390 Uttam Kumaran: What are the odds? Are the odds of it closing like? I mean, it’s close. It’s like technically closed or like, what’s the.
615 00:57:12.580 ⇒ 00:57:18.806 Mathew: We have the verbal. It’s just now we’re we’re finalizing. We just went through all of their INFOSEC requirements. So that was
616 00:57:19.510 ⇒ 00:57:25.380 Mathew: that was a big thing, big. That was. That was a big hurdle to get over, which we did. And now we’re just
617 00:57:25.640 ⇒ 00:57:33.499 Mathew: they sent us a really thick data privacy addendum, so we just redlined and we’re just waiting for their comments. Otherwise we’re good.
618 00:57:34.360 ⇒ 00:57:35.889 Uttam Kumaran: Okay, wow, let’s go.
619 00:57:35.890 ⇒ 00:57:41.010 Mathew: Yeah. But they wanna move forward. It’s just a matter of like making sure we we do it right.
620 00:57:41.580 ⇒ 00:57:42.240 Uttam Kumaran: Okay.
621 00:57:42.670 ⇒ 00:57:47.410 Uttam Kumaran: yeah, okay. Well, anything you need me for I’m here. We’ll move this along and try to get you something
622 00:57:47.660 ⇒ 00:57:52.569 Uttam Kumaran: like that deck so that you could work with that. And yeah, I don’t know. I want you guys to take something that you can pitch
623 00:57:52.920 ⇒ 00:57:54.370 Uttam Kumaran: to everybody right? Like cause it’s.
624 00:57:54.370 ⇒ 00:58:00.970 Mathew: I mean. Look, I’ve been. I’ve been. I’ve been pitching. I’ve been pitching with this deck, but I prefer one that’s like our own homegrown, you know.
625 00:58:01.540 ⇒ 00:58:09.050 Uttam Kumaran: Yeah. And like, if you build it off the synthetic data, then you could just pull that up to be like, here’s a version of what we have like.
626 00:58:09.050 ⇒ 00:58:09.630 Mathew: Yep.
627 00:58:12.270 ⇒ 00:58:13.679 Uttam Kumaran: Okay, cool. Alright.
628 00:58:15.040 ⇒ 00:58:16.729 Uttam Kumaran: Thank you. I’ll talk to you soon.
629 00:58:16.910 ⇒ 00:58:18.529 Mathew: Awesome. Thank you so much. Bro.
630 00:58:19.260 ⇒ 00:58:20.010 Uttam Kumaran: Bye.