Meeting Title: Samsung CES Report Sync Date: 2025-12-23 Meeting participants: Ashwini Sharma, Katherine Bayless
WEBVTT
1 00:00:54.420 ⇒ 00:00:55.760 Ashwini Sharma: Hello, Catherine.
2 00:01:00.030 ⇒ 00:01:01.329 Katherine Bayless: Hello! Hello, 11.
3 00:01:01.690 ⇒ 00:01:02.630 Ashwini Sharma: Hello.
4 00:01:04.629 ⇒ 00:01:06.419 Ashwini Sharma: I’m not able to hear you.
5 00:01:07.030 ⇒ 00:01:08.250 Katherine Bayless: Oh, can you hear me now?
6 00:01:08.250 ⇒ 00:01:13.029 Ashwini Sharma: I can, I can, no, I can, yeah. Sometimes it takes a second, sorry.
7 00:01:13.030 ⇒ 00:01:14.660 Katherine Bayless: Yeah. How’s it going?
8 00:01:14.800 ⇒ 00:01:16.550 Ashwini Sharma: Yeah, pretty good. How are you?
9 00:01:17.090 ⇒ 00:01:23.339 Katherine Bayless: I’m alright. Hanging in there, yeah, holding down the fort, because both Kyle and Kai are out today, so… but it’s quiet, so…
10 00:01:23.340 ⇒ 00:01:24.810 Ashwini Sharma: Oh, okay.
11 00:01:25.300 ⇒ 00:01:25.980 Katherine Bayless: Yeah.
12 00:01:25.980 ⇒ 00:01:28.139 Ashwini Sharma: You didn’t take a vacation this week.
13 00:01:28.630 ⇒ 00:01:35.639 Katherine Bayless: No, I’ll probably take some time after we get back from the conference, just try to, like, get through all of it first, I think, yeah.
14 00:01:35.640 ⇒ 00:01:44.459 Ashwini Sharma: Awesome. All right. Yeah, I just wanted to sync up with Kyle and understand, that report that he was talking about, Power BI report, right? And,
15 00:01:45.250 ⇒ 00:01:48.519 Ashwini Sharma: maybe, like, I could start with something, you know.
16 00:01:49.250 ⇒ 00:02:05.050 Katherine Bayless: Yeah, let me take a… because I have it up here, so I can, like, kind of show it to you, and then we can kind of talk through what the pieces are, because, I’ve got thoughts and questions, and I’ll pick your brain for, expertise, so let’s take a look at the report first.
17 00:02:06.400 ⇒ 00:02:11.480 Katherine Bayless: Okay, let’s see… Okay, so…
18 00:02:11.940 ⇒ 00:02:27.230 Katherine Bayless: The report, I guess I should say, you know, eventually this will all look very different, but this is the, you know, current state of our, sort of, member engagement reporting. The reason that we’re hoping… well.
19 00:02:27.780 ⇒ 00:02:31.230 Katherine Bayless: Somehow or other, we will get this updated before CES.
20 00:02:31.860 ⇒ 00:02:32.300 Ashwini Sharma: Okay.
21 00:02:32.300 ⇒ 00:02:42.130 Katherine Bayless: sort of, you know, up to us to figure out how nicely or not nicely we want to do that, I guess. But the reason that we need it for CES is because we operate a couple member lounges on site.
22 00:02:42.130 ⇒ 00:02:54.459 Katherine Bayless: And so, this report will be used by the team that’s staffing those lounges, so that if somebody comes in from Samsung, right, they can kind of pull this up and say, like, okay, gotcha, they’re a current member, they were at CES last year, they have a booth, that kind of thing.
23 00:02:54.460 ⇒ 00:02:59.089 Katherine Bayless: And so it is a tool that they’ll be using on-site, which is why they’ve asked to have it updated.
24 00:03:00.280 ⇒ 00:03:20.009 Katherine Bayless: So I’ll just kind of walk through the component pieces. So, the ability to filter on membership status, the customer ID from Impexium slash remembers, or the company name. These fields are probably all of the things that you’ve already got in that active members model.
25 00:03:20.230 ⇒ 00:03:29.110 Katherine Bayless: The only one that might not be in there is the dues level, but I would say that’s probably either easy to add or fine to leave off.
26 00:03:29.270 ⇒ 00:03:30.770 Katherine Bayless: Okay. For the moment.
27 00:03:30.910 ⇒ 00:03:40.259 Katherine Bayless: Then these are sort of calculated fields based on the other data, but all of this would be able to be pulled from that Impexium data.
28 00:03:40.260 ⇒ 00:03:41.450 Ashwini Sharma: Okay, cool.
29 00:03:41.890 ⇒ 00:03:57.300 Katherine Bayless: The rest of it, however, is essentially flat files right now. So, in the engagement section, we’re giving them data on, like, the total number of people that were registered for CES in the last 3 years.
30 00:03:57.300 ⇒ 00:04:04.959 Katherine Bayless: And as well as attended, and so we have that, you know, difference between how many people signed up versus showed up.
31 00:04:04.970 ⇒ 00:04:10.360 Katherine Bayless: This data we do have, it would just be, like I said, yeah, like, flat files.
32 00:04:10.760 ⇒ 00:04:14.830 Ashwini Sharma: Is this still related to Samsung, or is it different now?
33 00:04:14.830 ⇒ 00:04:17.600 Katherine Bayless: Yeah, no, this is all just for Samsung.
34 00:04:17.820 ⇒ 00:04:23.319 Ashwini Sharma: No, no, the engagement section. This is still Samsung, okay. Alright, alright.
35 00:04:23.320 ⇒ 00:04:27.490 Katherine Bayless: Yeah, yeah, so the company at the top filters the total report.
36 00:04:27.490 ⇒ 00:04:28.240 Ashwini Sharma: Okay.
37 00:04:28.740 ⇒ 00:04:36.319 Katherine Bayless: Yeah, I know, right? It’s a lot of people. They get 3,000 badges, I think, and so they only end up using about half of them, yeah.
38 00:04:36.320 ⇒ 00:04:37.980 Ashwini Sharma: Okay, cool, alright.
39 00:04:37.980 ⇒ 00:04:49.280 Katherine Bayless: The badges that they’re allowed to have are calculated based on booth space, and so, like, Samsung’s one of our biggest exhibitors, so they get a large number of badges to give away. Or, not give away, but used, yeah.
40 00:04:49.280 ⇒ 00:04:49.630 Ashwini Sharma: Yeah.
41 00:04:50.530 ⇒ 00:05:01.099 Katherine Bayless: And so same thing, the exhibit status, whether or not they had an exhibit. 2025, definitely, they were an exhibitor. I’m not sure what that data is.
42 00:05:01.310 ⇒ 00:05:07.689 Katherine Bayless: why it’s wrong, but but this would also be flat file historical exhibitor data, which we have.
43 00:05:08.380 ⇒ 00:05:11.090 Katherine Bayless: The CES savings piece…
44 00:05:11.320 ⇒ 00:05:16.300 Katherine Bayless: I actually, to be totally honest, off the top of my head, I’m not sure if it’s…
45 00:05:16.590 ⇒ 00:05:34.889 Katherine Bayless: it’s definitely not captured explicitly anywhere I know of, but I’m assuming there is a formula for this that we could recreate, that’s basically, like, how much would this have cost, how much did you pay for it, kind of a thing. But I haven’t gone in on the back end to see how that’s actually happening.
46 00:05:35.820 ⇒ 00:05:40.400 Katherine Bayless: The Innovation Awards, same thing, flat files that we have.
47 00:05:41.060 ⇒ 00:05:49.130 Katherine Bayless: These are whether or not there are any board members, from the company. Plat file for this as well, tiny one.
48 00:05:49.130 ⇒ 00:05:49.750 Ashwini Sharma: Yeah.
49 00:05:49.750 ⇒ 00:05:53.000 Katherine Bayless: Committee participation
50 00:05:53.170 ⇒ 00:06:06.200 Katherine Bayless: This is in the Impexium data. It’s gonna be a little bit interesting to kind of get our hands around, potentially, but it is something we could build a dbt model with that, remembers data share.
51 00:06:06.200 ⇒ 00:06:10.800 Ashwini Sharma: To recreate this. If not, we do have the flat files for it as well.
52 00:06:11.660 ⇒ 00:06:25.380 Katherine Bayless: Research downloads, this is flat files. This is coming from Shopify, generally speaking, but we aren’t integrated with Shopify anywhere anyway, so it’s just flat files behind this.
53 00:06:25.430 ⇒ 00:06:26.710 Ashwini Sharma: Okay.
54 00:06:27.500 ⇒ 00:06:30.059 Katherine Bayless: And then, event attendance…
55 00:06:30.390 ⇒ 00:06:38.669 Katherine Bayless: So, there’s a couple buckets. There’s the 2024, 2025, then there’s a call-out for standards events.
56 00:06:39.040 ⇒ 00:06:40.649 Katherine Bayless: And for GLA events.
57 00:06:41.320 ⇒ 00:06:59.319 Katherine Bayless: This is all coming from, I think, Cvent, based on the things that I’m seeing here, but I think there is a little bit of Zoom data as well. In terms of the data that’s being displayed currently, it is a flat file, in terms of getting the, you know, data for the last
58 00:06:59.430 ⇒ 00:07:17.800 Katherine Bayless: window of time since this was last updated, I could probably get more flat files just to add to it, rather than dealing with, like, consuming it live from Cvent or Zoom, especially knowing there will be no additional events between now and CES, right? So, like, the flat files would cover all of the data that exists anyway.
59 00:07:20.550 ⇒ 00:07:27.619 Katherine Bayless: And then, Matchmaker, I’m gonna be honest, I have no idea what this is. So, we can put a pin in that until I know how to answer that question. Okay, cool.
60 00:07:28.200 ⇒ 00:07:44.890 Katherine Bayless: The speaker data does come from EventPoint, but again, it’s a flat file in this dashboard, and I could just move that flat file into a new place, and we could use it there, because I think we’ll look at integrating with EventPoint via Polytomic next year.
61 00:07:45.170 ⇒ 00:07:45.810 Ashwini Sharma: Yeah.
62 00:07:46.650 ⇒ 00:08:01.840 Katherine Bayless: Media opportunities, I am not sure, but I’m also noticing that, like, the last one in here is 2023, so it might be that this is just old data that we are surfacing, but I can… similar to the matchmaker, I can figure out where this comes from.
63 00:08:02.980 ⇒ 00:08:20.820 Katherine Bayless: And then this is the same. So really, I guess I should say, matchmaker, media opportunities, and other engagements. These three, I’m not sure where the data comes from. My suspicion is that it’s a spreadsheet somewhere that someone maintains, and so…
64 00:08:20.820 ⇒ 00:08:38.430 Katherine Bayless: I could at least give you the flat files that are being used currently. In terms of getting the latest data, I’ll see what I can track down, but I think in terms of the things that are most critical for CES and the usage of this report, what the membership team really needs are the pieces
65 00:08:38.600 ⇒ 00:08:40.380 Katherine Bayless: That we’ve already built, more or less.
66 00:08:40.380 ⇒ 00:08:40.990 Ashwini Sharma: Yeah.
67 00:08:40.990 ⇒ 00:08:56.279 Katherine Bayless: as well as the, like, did they go to CES, that kind of stuff. Like, I think some of these smaller things are less critical, but as long as we could at least give them the equivalent stale data via the flat files, we’d be in okay shape.
68 00:08:57.300 ⇒ 00:09:01.330 Ashwini Sharma: And these flat files, where do they come from? Flat files?
69 00:09:01.780 ⇒ 00:09:11.270 Katherine Bayless: Yeah, so they… right now, this dashboard is connected to the old data warehouse, which has not been updated.
70 00:09:11.750 ⇒ 00:09:25.970 Katherine Bayless: the files that were in the data… or the data that was in the data warehouse, I have exported to files that are in that S3 bucket that could be brought into Snowflake. So we could hit them that way from the S3 bucket.
71 00:09:26.120 ⇒ 00:09:37.280 Katherine Bayless: Or, I could bring these into our Postgres data warehouse. Same idea, right? Just take the flat file and create the tables in Postgres. But I do have all of the data in S3.
72 00:09:37.510 ⇒ 00:09:39.459 Ashwini Sharma: Okay, cool.
73 00:09:39.580 ⇒ 00:09:47.629 Ashwini Sharma: Can you put them in a folder that I can access? Maybe, like, when I get time, I’ll create tables out of them, the way that you had done for…
74 00:09:48.330 ⇒ 00:09:52.159 Ashwini Sharma: One of the… Tables and webhooks, something?
75 00:09:52.170 ⇒ 00:09:53.450 Katherine Bayless: Yeah.
76 00:09:56.510 ⇒ 00:09:59.999 Ashwini Sharma: Yeah, what was that name? Yeah, webhooks.something, right?
77 00:10:00.570 ⇒ 00:10:01.270 Katherine Bayless: Yeah.
78 00:10:01.270 ⇒ 00:10:04.749 Ashwini Sharma: Yeah, so I could do that. I think there is…
79 00:10:04.860 ⇒ 00:10:10.760 Katherine Bayless: Probably in the new year, we can sort of explore it further, but, like, a question around, like.
80 00:10:11.390 ⇒ 00:10:23.359 Katherine Bayless: There’s a lot of data in the old data warehouse, and a lot of it is probably valuable for, like, historic or archival stuff like this, and so eventually it might make sense to consider it
81 00:10:23.680 ⇒ 00:10:34.690 Katherine Bayless: a legacy database or something like that, and kind of build out some of the things. But yes, for the moment, I could put just this set of the files into an S3 bucket and give you that.
82 00:10:35.680 ⇒ 00:10:36.670 Ashwini Sharma: Awesome, yeah.
83 00:10:36.670 ⇒ 00:10:37.320 Katherine Bayless: Yeah.
84 00:10:39.090 ⇒ 00:10:47.520 Ashwini Sharma: So, yeah, maybe we can work on… on the first part of the report, which is, like, more or less already done, right? Account summary.
85 00:10:49.430 ⇒ 00:10:57.280 Ashwini Sharma: And the engagement, and whatever flat files you put into it, right? We can start looking into that.
86 00:10:58.090 ⇒ 00:10:58.700 Katherine Bayless: Okay.
87 00:10:59.630 ⇒ 00:11:01.240 Ashwini Sharma: Alright.
88 00:11:03.030 ⇒ 00:11:07.990 Katherine Bayless: Do you need… We don’t have access to this report, right?
89 00:11:08.280 ⇒ 00:11:21.480 Katherine Bayless: That’s what, actually, I was gonna ask. Yeah, so if you want, I don’t think we have you guys all, like, set up and assigned Power BI licenses, but, like, I can export it if you want, and put it into the S3 bucket.
90 00:11:21.750 ⇒ 00:11:22.239 Ashwini Sharma: Yeah, you can take a…
91 00:11:22.240 ⇒ 00:11:24.049 Katherine Bayless: screen grab, if you want, it depends.
92 00:11:24.050 ⇒ 00:11:27.230 Ashwini Sharma: Yeah, anything, anything is fine, yeah, anything is fine.
93 00:11:27.600 ⇒ 00:11:44.880 Katherine Bayless: Okay. I mean, I would also say it doesn’t necessarily have to look exactly like this, like, if it was something that’s just kind of, you know, maybe a little less, branded, that’d be okay, too. It’s the data is more important than the look and feel, if that makes sense. But yeah, I can definitely…
94 00:11:46.450 ⇒ 00:11:52.290 Ashwini Sharma: And this visualization will be done in Power BI itself, or is there another tool in mind?
95 00:11:52.820 ⇒ 00:11:53.510 Katherine Bayless: I think…
96 00:11:53.650 ⇒ 00:12:11.339 Katherine Bayless: probably the Power BI is the way to go, because that’s where the team is already looking for it. I kind of wanted to say, like, no, let’s put it in Snowflake and, like, make a little Snowflake dashboard, but then I’ve got to train them to go somewhere else and all that stuff, and given the short timeline, I think…
97 00:12:11.350 ⇒ 00:12:15.549 Katherine Bayless: Yeah, just creating a new version in Power BI makes the most sense.
98 00:12:17.210 ⇒ 00:12:18.500 Ashwini Sharma: Cool. Okay.
99 00:12:19.070 ⇒ 00:12:33.929 Katherine Bayless: Oh, and actually, to that end, I should say, we do have the Power BI Gateway set up for things to be connected from, like, the new data warehouse to Power BI, and I assume we could also connect Snowflake to Power BI. We just haven’t done it yet.
100 00:12:34.530 ⇒ 00:12:35.210 Ashwini Sharma: Cool.
101 00:12:38.560 ⇒ 00:12:42.140 Katherine Bayless: Hmm, let’s see… put this under…
102 00:13:04.340 ⇒ 00:13:05.969 Katherine Bayless: Let’s see what this looks like.
103 00:13:07.240 ⇒ 00:13:09.690 Katherine Bayless: Okay, yeah, so this is a PDF of it.
104 00:13:09.690 ⇒ 00:13:10.270 Ashwini Sharma: Yeah.
105 00:13:10.780 ⇒ 00:13:12.910 Katherine Bayless: So I can put this into the S3 bucket.
106 00:13:15.170 ⇒ 00:13:17.269 Katherine Bayless: Do you want, like, the raw, like.
107 00:13:17.420 ⇒ 00:13:20.970 Katherine Bayless: Power BI file, or is that not helpful?
108 00:13:20.970 ⇒ 00:13:24.610 Ashwini Sharma: No, that, probably won’t be any helpful to me.
109 00:13:24.610 ⇒ 00:13:27.150 Katherine Bayless: Yeah, okay, that’s kind of what I figured, but yeah.
110 00:13:28.500 ⇒ 00:13:29.200 Katherine Bayless: Okay.
111 00:13:30.470 ⇒ 00:13:34.100 Katherine Bayless: Okay, so what I can do is I’ll…
112 00:13:34.380 ⇒ 00:13:42.489 Katherine Bayless: I’ll put things into an S3 bucket. I’ll add that S3 bucket to the S3 integration stage thing in Snowflake.
113 00:13:42.980 ⇒ 00:13:55.260 Katherine Bayless: I’ll let you know what the name of it is, and then I’ll work on tracking down, like, where these three things come from, as well as what the sort of formula is for this.
114 00:13:55.620 ⇒ 00:13:56.750 Ashwini Sharma: Sure, yeah.
115 00:13:57.330 ⇒ 00:13:58.870 Katherine Bayless: Okay, cool.
116 00:13:58.870 ⇒ 00:14:00.180 Ashwini Sharma: Alright, yeah.
117 00:14:00.550 ⇒ 00:14:01.360 Ashwini Sharma: Great.
118 00:14:01.610 ⇒ 00:14:02.580 Ashwini Sharma: Thank you.
119 00:14:04.790 ⇒ 00:14:12.709 Ashwini Sharma: All right, yeah. Thank you, Catherine, that was helpful. And drop me a message once you put it into S3.
120 00:14:13.710 ⇒ 00:14:15.100 Ashwini Sharma: Awesome, awesome, yeah.
121 00:14:15.200 ⇒ 00:14:20.440 Ashwini Sharma: Thank you, and have a nice week, yeah.
122 00:14:20.660 ⇒ 00:14:22.370 Katherine Bayless: Okay, cool, cool. Talk to you later.