Session Transcripts

A live transcription team captured the SRCCON sessions that were most conducive to a written record—about half the sessions, in all.

How do we convey technical ideas without dumbing things down? AKA Making Effective Data Visualizations

Session facilitator(s): Bo Peng

Day & Time: Thursday, 10:30-11:30am

Room: Boardroom

BO: Does everyone have, like, markers? There’s not enough paper. Welcome. Welcome to SRCCON. How many of you, is it your first time here at SRCCON? How many of you, is it not your first time here? Okay. Cool. Then you guys are actually—this is my first time at SRCCON. One of my former coworkers, raved about the very first time she came here. And so I just, like, had to get in here. And she’s hero, too, somewhere. Lori, if you see here. Well, with, to your very first session, and also my very first session. So I’m, like, a little bit nervous. Today, we’re going to be talking about data visualizations in the context of journalism, but also I found. So I’m not a journalist; I’m a data science consultant. But I think a lot of the key ideas overlap, and so, I want to share, with you some of the things that I’ve learned, making data visualizations for my clients, but also, in respecting the one over in time talking, you know, in a room, I want to make sure that everybody has a chance to speak up, and draw their own data visualizations, or pretend data visualizations and make sure that everyone learns from each other, because I just as much want to learn from you, as you might want to learn from each other, around this stuff.

So being the very first session, I think it is appropriate to do some introductions. So just, like, your name, and if you want to say, like, where you work, or what you do, and whatnot, but I’m interested in your name, and how you use data visualizations in your life. So I’ll go ahead and get started. My name is Bo. I make data visualizations for a living but not necessarily for a wide audience like you might. I make visualizations specifically for clients, so that is an interesting, kind of, twist in that, often, I’m making these graphs, and dashboards, and web apps, and things like that. And throughout, I’m able to ask them, “Does this make sense?” Can you understand this?” versus, it’s a luxury of making a very specific thing, for very specific people versus a wider audience. So that’s me. I want to go around and let’s start with you.

PARTICIPANT: I’m Marie Connelly, and how do I do an introduction? I don’t so much right now, but I would like for that to change. I do community work with a book team at Vox Media and a lot of my job is communicating to people who use our platform. So folks who are writing for us, and may contain technical information—putting in technical information is sometimes a hard challenge in my day to day job.

PARTICIPANT: I’m B. Cordelia. I use data visualizations to teach people who don’t really understand technical things—technical things. So my last one that I was really proud of was, like, a completely physical poster that was interactive that I could move around to teach people the publishing process, and the digital publishing process.

PARTICIPANT: Hi, everyone, my name is Jeff. And I’m with the Coral Project. I currently don’t use data visualizations a lot but I do have a background with data on the store side. So building databases and things like that. And I’m actually interested in getting more into the visualizing data. In particular, wanting to know, how can you take arbitrary datasets and recommend intelligent visualizations for users up front and allow them to customize those as needed.

PARTICIPANT: Hi, my name is Pietro, and I’m one of the first ones this year. I’m with Vox Media, and I’m working with video tools so really won’t with visualizations and I’m trying to see if there’s any overlap with the things that I know.

PARTICIPANT: Hi, my name is Anu and I’m a data reporter with the Washington Post, and usually I use data visualizations to kind of understand a bunch of numbers, and to make sense of those quickly.

PARTICIPANT: I’m Shaelyn, and I’m another Simple Commons, and I use data visualizations mostly for analytics to see how people are using our website.

PARTICIPANT: I’m Lydia, and I’m also Simple. And I use data visualization in similar ways—just understanding massive amounts of numbers, and, kind of, seeing overall patterns in those.

PARTICIPANT: Hi, I’m Brittany. I’m with the Blue Bird Graphics Team. I use data visualizations for storytelling and for exploration, too, with data science.

PARTICIPANT: Hi, I’m Kanya, and I’m with Studio Networks and I make data visualizations, and tools for data visualizations.

PARTICIPANT: Hi, I’m Josh. I’m a freelance journalist and web developer, and I do data visualization, oftentimes, maybe less-well-defined data. So I’m interested in, like, better ways to visualize more qualitative data that isn’t necessarily well standardized in commerce networks.

PARTICIPANT: Hi, I’m Anna I work in Product at Condé Nast. I don’t currently use data visualizations and I’m really interested in how you can communicate really technical ideas to a really non-technical audience and I’m interested in how you can use data visualization to help do that.

PARTICIPANT: I’m Dawn Garcia, and I’m with the Joules Fellowship at Stanford. And I don’t do data visualizations but I’m a fan of visualizations. And I’m trying to understand how to better story tell with data.

PARTICIPANT: I’m Andrew Tran, I’m a data—we use visualizations to distill and communicate effectively, and delightfully if we can.

PARTICIPANT: I’m Hunter Owens. I use data visualizations to help teachers and students to understand how they’re learning.

PARTICIPANT: I’m Michael Andre, I’m a developer at the Milwaukee Journal Sentinel newsroom and I use virginses to help reporters understand the data they have.

PARTICIPANT: I’m Josh, and I’m just getting into data visualizations excited about the wealth of information that’s available to them, both in exploring open data in San Francisco, and actually analyzing data, and meeting up.

PARTICIPANT: I’m Melly, I’m a software engineer at Mapbox, so I use data visualizations to basically map. So I use it a lot. And yeah, I’m interested in how maps can help us tell their stories.

PARTICIPANT: Hi, I’m Madeline, and I’m an editor at The Guardian mobile lab. I don’t use visualizations, but we’re compiling a map, figuring out how we’re going to implement it.

PARTICIPANT: I’m Lydia. I don’t personally use data visualizations. But we use a lot of data. So I’m interested in learning how we could use visualizations for our team.

PARTICIPANT: My name is Emily Withrow, and I’m faculty at Northwestern University. I teach primarily in the Night Lab but I also teach information design for our grad students.

PARTICIPANT: My name is Katie Park. I make graphics at NPR Visuals. And, I use data viz both for my own reporting and analysis purposes but also, primarily for storytelling and presentation.

PARTICIPANT: I’m Brian. I work for a culls called Boucoup. We have a great data visualization team that I’m not on. So I mostly admire from the sidelines. So I use it to derive joy and information when it’s well done, and anger and frustration when it’s not.

PARTICIPANT: I’m Patty Reeves. I’m a UX developer at Alley Interactive, which is a web development company for publishers, I, in a previous life was an audience editor at a metro newspaper, Main. So I did a lot of data visualizations on explaining our audience data to the rest of our company, and when needed, explaining stories our audience.

PARTICIPANT: Hi, I’m Juan, I work as a web app developer at NPR, Visuals, and I use data SREURPBGS mainly for explore tore analysis and for presentation storytelling.

PARTICIPANT: Hi, I’m Martha, I’m a software developer, when I use data visualization, it’s just to understand the mounds of data that I have, and I don’t know exactly what I have, so I look for patterns and I look for information out of the data.

PARTICIPANT: I’m Ariana Giorgi, I’m a computational journalist at the Dallas Marine News, and most of my job is to make data viz for storytelling purposes.

BO: Cool. Well, thank you, everybody for sharing. So it seems like about half the room may be already doing graphics or data visualizations, or even teach visualization. So you probably know more than me. And I would love it for you all to learn from you folks. So I will try to incorporate some things equally in the activity portion but in the after-discussions to kind of had illuminate some of that. So here’s how it’s going to go. For the next 45 minutes or so, we are going to have two parts. Okay. So the first part is this activity session in which you are all going to be data science consultants. Where your project is to create a series of “data”—pretend you have data—visualizations with these markers, and papers, and things like that. And then—so after a couple of minutes of breaking off into groups and doing that, I will alot, like, 15 minutes for the drawing portion. Then each team of, like, three or four people, however you want to divide amongst yourselves, will present. And each individual will present if you want, or if you feel comfortable doing that, at least as a team.

And then, finally, I’ll do some discussion on how our team actually did the visualization, and what we learned, and what we didn’t learn. Does that sound like a good plan?

Okay. So this exercise… now you’re all data scientists. You’re all data science consultants. And part of your job is to create data visualizations for your client. And this project is, you’re visualizing office space usage.

Okay. So you’re all data scientists at Datascope. Welcome. Your client, their name is Sensors, Inc. They are creating a prototype app. It’s going to be, like, a mobile thing but they’re also going to be install sensors. So this is the problem that they’re going to try to solve. So if any of you ever working in an office space, where it’s, like, a hundred people, and there’s multiple floors, and hundred people, something like that, and there’s multiple meeting rooms that you can book, especially for large, or growing companies, it becomes increasingly hard for people to just book rooms because either you’ll want to find a time when all of you are free, and that’s hard enough, but enyou can’t find a room that’s available, or you book a room, and you walk there, and the previous room is going over, or someone’s just like squatting there, and maybe they’re higher up, so you can’t exactly kick them out. `

Or oftentimes, it seems like things are being booked, but people are not using the space. They just book a full day because, you know, space is rare. So does everyone, like, understand that kind of general prompt? So this Sensors Inc. is developing this app that is designed to hopefully solve this meeting room/booking problem. So they’re building—they’ve built a prototype like a mobile app in which you, like, go to a room, and you can say, live-book it right now. Or it tells you realtime, Meetings are going on, and what’s not going on. And how many—what capacity your meeting room size is versus how many people you need in your meeting room, and things like that. And they do that realtime thing by having sensors in the room. So it’s a mobile app but it’s also, like, physical sensors in the room that can tell you if the room is occupied or empty. So they built this prototype stuff. And they’ve built the prototype sensors, and they’re testing it on their client, let’s call it Busybee, LLC. So let’s pretend—I just, like, made this up, obviously—this is Busybee’s office space. And there’s different bookable rooms in different sizes and capacities, and things like that. And so Sensors, Inc. has installed sensors in each room, and also with the mobile app that they’ve developed, they can track who’s meeting. Again, it’s all anonymized, and there’s no privacy issues. So Sensors Inc. lets people realtime see, and book the rooms.

So this was—so now, Sensors, Inc. is coming to you, data scientist, data viz experts, to use data visualizations to explore one of these questions. In real life we had to explore, like, a whole list, and it was a multi, several-week project. But for 15 minutes, I want you to break off into groups of three or four, and then within your groups, within your group of data science/data viz experts, choose one of these questions, and then try to use the next 15 minutes to draw out some solutions. And then we’ll share at the end of 15 minutes. So let’s go over each of your questions to try to see what you’re trying to answer. One, how might we help facilities managers at Buzzbee, LLC match demand with available space by providing actionable recommendations. So the actual clients, the people that you’re showing these data visualizations to are facilities managers at Busybee. And so it’s very accommodating for groups of companies of at least a hundred or more, they have staff, or a group of staff that are managing. There’s meeting rooms. And it’s put really easy to tear down walls and rearrange the rooms and things like that. So that’s your audience. They don’t have data background. Often, the last math class they took, probably in high school, or something like that. So keep that in mind when you’re designing for facilities managers at Busybee. Two, how might we infer social dynamics from a product usage. If this product is successful and build out to many people, they may be able to infer more population-wide social dynamics from certain types of companies, you know? And so they’re trying to collect data from this, but also, the audience for this might be a more general audience. You know, anybody who might be interested in, like, social dynamics within a office space as meeting rooms are booked and unbooked in a way. So that’s the wider audience and you don’t know exactly who that audience is going to be yet. And number three, how might we demonstrate to Sensors, Inc. how this prototype app/product helps to improve workplace performance? And this was real. Sensors Inc. came to us—or Sensors Inc. is coming to you wanting to develop this prototype app. But they have the or own analysts. So they have their own technical analysts that think these are more quantitative, and with a more quantitative background. And they want to use data viz to show whether this app is even helping this supply and demand technical audience. So that’s your audience. The additional constraint is that you’re not coding this up in a web app or anything. This is a low-budget thing. And so they want the deliverables to be on paper, of course. So for printable reports. And this was true, too. They wanted something not where you can click around and zoom out and zoom in, but, like, they want printable reports. Okay. So does this make sense? Do you have any questions so far?

PARTICIPANT: What factors do these sensors collect, or can we make that up?

BO: You can make it up. Yeah. In real life, the sensors only collected whether the room was occupied or unoccupied. So somebody’s—at least one person is in the room, or it’s totally empty. And that was a binary thing. But for the purposes of this exercise, just pretend that they can collect, like, whatever you wanted to account for. Any other questions before we get started? Is this exercise clear?


BO: Okay. Great. Well, break off amongst yourselves and I’ll set a timer for 15 minutes and then we’ll present at the end of 15 minutes.

[ Group Work ]

If you have haven’t already, you should start drawing.

Five minutes.

All right!

PARTICIPANT: Hey, YO! You’re welcome.

BO: All right. That was actually, like, 18 minutes because what I forgot to say was, in addition to talking amongst yourselves about which question and what your angle’s going to be, also, this is the time to draw. So I think every group has a couple of graphs and visualization sets. So let’s go this way. Where each group, and hopefully, each individual, but at least each group will present their thought process within their brainstorming session. Let’s start with those groups who did number one. Did anybody talk a little more? Does your team want to start, and then we’ll go onto the next one?

PARTICIPANT: Do you want me to start?

PARTICIPANT: So okay. So we were trying to decide on a way that we could, like, present this to someone, so that it would be useful to them, in a way that they could visualize the room. We thought about the way that it’s conveyed, but in addition to how often, how many people are using the room, and, in addition to that, when are people using this room. And so we tried to put it in the simplest format that we could think of. And so, on this side, we have all the rooms and they’re actually… ideally, they would be organized by popularity. So we have a few just floating around. And they also, they’re drawn by size. So you would be able to, like, get a feel for how big that space is. And then, over here, this would be, like, a histogram of how many people were using the room at a certain time. And so you could see if the people were using the room in the morning, or in the afternoon. And also, we decided to put in this, down the line at the top for, what the capacity of the room actually is. And, that way, if there’s, like —

PARTICIPANT: In that case, right? In that case, you would see that this is, like, occupied throughout the day. But we, the people that are in that room are under usage, under capacity of that room. So you could create actionable things, like, create a threshold that would be allow you to book that big room for two people that want to use that room, or kind of like that. Another thought would be, I don’t know if you want to —

PARTICIPANT: With the national park example, we see that, that means that we should be able to see what’s happening in that room, maybe it’s too hot in the morning, or too cold in the morning. Or something like that. So change based on what time you start during the day.

PARTICIPANT: Yeah, so we thought this would be a good first hit of which ones would be popular, and it would be good to help them with that, and even with the office spaces.

PARTICIPANT: Even for room number one, that’s occupied, and is, like, near full capacity, you could even poll your employees and say why is this room so popular. Like, if you were going to move to another building, try to make the rooms as that one because people like it, right? So that was our thought.

BO: Great. Anyone have any questions for this group? Cool. It’s interesting that you say that there are certain rooms which are only booked in the afternoon, or something. That actually happened and it turns out exactly like what you said, this room was actually, at certain times too hot to do anything. And they found that out. And there were other things that if the room was consistently under-utilized, the actionable insight was to cut the room in half, make two tiny meeting rooms, because from the calendar data we actually see that the rooms are actually being used for one-on-one meetings. So if you have a ton of, like, two-person meetings, then why do you have so many ten person meetings, in a big room like this? Who was the other group that did number one?


BO: Both did?

PARTICIPANT: We were four.

BO: Four. Okay. Got it. Why don’t you guys go.

PARTICIPANT: So we made two different graphs that kind of had the same approach where we wanted to chart the usage of the rooms over the work day. And so I have a graph of people walking in and out of this room. And so we were, like, the biggest meeting is usually in the morning, and there’s a bunch of people meeting in this it enperson room. And we know we imagined, like, having a 40 person room, and then finding out that they only use it on conference days. So it’s usually only two people hanging out in there, and then usually everyone goes in there for lunch.

PARTICIPANT: Yeah, and so we figured that we could use that information, and make some proposed shifts for what the room’s capacities would be. So you would go from something like this, where you have one room just being used way more than the others, and then if you kind of shift things around, you have a lot more consistent usage in all of the rooms.

PARTICIPANT: So the patterns denote, like, the room capacity. So if you saw that the really highly used rooms are the same capacity, clearly we need more of those, versus the ones that no one’s using.

BO: Very nice. Anybody have any questions or comments?

PARTICIPANT: No, that was not a comment. We were saying, we did the same question, too.

BO: Why don’t you?

PARTICIPANT: Well, if nobody else has questions.

[ Laughter ]

I don’t want to hijack the whole table. All right. Yeah, so the main assumption we made is that the actionable items, whoever the facilities manager is reconfiguring the room makes sure that they’re dynamically sized and the occupancy, or the size of the room is configurable. So we kind of ended up with this, like, ten where it’s the days of the week, and three time zones—time slots, morning, afternoon, and evening. And it’s just a preferred configuration of the rooms. So on Monday mornings, you probably need more small rooms, and then on Friday, you have sort of all hands sort of thing, with bigger things. So it’s based on usage data and usage history.

BO: That’s interesting because there’s, like, a thing that some office facilities managers, companies are doing where there are, like, movable walls. So it’s actually—that’s something that, you know, might be useful for you. Others?

PARTICIPANT: Yeah, I think, like, checking the different times, like, sometimes if—when you are, like, exploring information, the time span that you grasp, or that you take is important. It shows you patterns that they don’t show you. Like, if you go to a week, then you see some patterns and usage. But if you go to a day, you don’t see things like that. So sometimes… I mean, when you do that, like, for real, you need, like, to say, let’s do it for months, let’s do it for weeks, let’s do it for days, let’s do it for hours. Something like that.

PARTICIPANT: I think also, keeping in mind the intended user for this, I think we ended up with something similar because we felt a lot of the dynamic planning and optimization shouldn’t be on the facilities person, right? Like, that’s done on the background. That’s the analytics part. Like, they don’t want to have to, like, kind of constantly, like, use this. It’s just like these configurations that are preset are going to get you 90% through your day.

BO: Um, okay. Which, actually, so in response to that, all of these questions kind of bleed into each other. They’re not completely independent, right? So the next one, social dynamics talks exactly about that. So maybe for whatever meme people, nobody wants Monday-morning meetings. I don’t know why anybody would want that. Maybe you shouldn’t have meeting rooms, or something like an open space or something. So did anybody talk about number two?


BO: How about your team goes first, and then your team.

PARTICIPANT: So we did see some overlap with the kinds of questions that you were looking at. One of them was looking at which rooms are used most often with kind of a heatmap display. But we were kind of—we were trying to ask questions about the social dynamics of, like, who’s involved in booking meetings and participating in meetings, and how do certain patterns of equality play out, or efficiency. So we looked at small multiples for who’s booking meetings. So asking, like, what level of seniority are they at, what is their gender, what team are they on, to try to see if there are any sort of patterns. One thing that Emily noted was that a lot of times women are asked to book meetings, or do, like, administrative tasks more often.

We were wondering about efficiency. As you have, like—so do your meetings get more or less efficient with the number of senior managers that are involved?

[ Laughter ]

Possibly, this curve could have been a little steeper. Who knows.

[ Laughter ]

Seeing when people prefer to meet, which is kind of related to some of the first questions. And I really liked this idea of, I don’t know if this is an effective visualization, but the kind of distribution of floorspace, or floor time. So, depending on your meeting size, or the makeup of your meeting, are people speaking with, like, an equal duration, or are they speaking in a certain order?

PARTICIPANT: We imagined that the sensors could differentiate between the voices of the room. So, it’s sort of the… yeah.

PARTICIPANT: Did we leave anything out?

PARTICIPANT: Yeah, and this one is team performance plotted against frequency of meetings.

[ Laughter ]

… which is not what—this is not the positive… yeah.

[ Laughter ]

That’s a leading question.

PARTICIPANT: We also talked about, like, if you had, like, you know, more data about the employees, a personality test, if you could ask any of those questions against that kind of data.

BO: Yup. Cool, in the interest of time I want to move on to the next group who did social dynamics.

PARTICIPANT: Okay! Here we go. All right. So we did some very similar things in terms of thinking about, you know, looking at this space that’s being used over time, and how many, you know, people were supposed to be in that meeting, how many people actually showed up, who was using the space when a meeting is not scheduled, that might start to show you about how people are kind of working together, both officially, and unofficially, I don’t know, informally? There you go. And then thinking about that, over the course of, again, not just a day, a week, but also look quarterly, you know, depending on the type of organization that you work for. Maybe there are things like, oh, everybody’s slammed into meeting at the end of quarter, because it’s crunch time, what could we do about that, or the end of the year, if you’re in parts of media organizations, are there certain times of the year, or certain events that, I don’t know, political conventions that either bring people into this space and encourage them to collaborate, or disperse everybody and encourage them to be staring at and working on their computers, working. We were also looking at if you were tied into the calendar system, and you could see what’s the meeting invite subject, you can start to get… this would be more challenging on paper than dynamically, but you could start to get a sense of what are the—what’s the meeting about?

Or when are people kind of having all their one-on-ones, versus having some planning sessions, versus other kinds of things.

There was more to this. We did also talk about, sort of, like, team, you know, looking at it team-by-team, who is in meetings, maybe a lot? And, you know, both as an indication of who’s collaborating across teams, but also, as an indication of, oh, is this team constantly just in meetings, and maybe not hitting some of the things, you know, their deliverables are, or whatever, you know, they’re supposed to be accomplishing, or are they in meetings all the time, still getting a lot of shit done, which would be exciting. I think that was…

BO: That actually speaks to a point that I wanted to address, which was that, when we are making visualizations, it’s really important, not only to understand who the audience—of course, that’s already kind of implied in these three points because I very specifically told you who the audience may or may not be, but also, what the audience is interested in. So a lot of you were looking in, oh, are people more efficient if you have different types of meetings, or frequencies or meetings, or things like that. That is clearly something that somebody from a place like Busybee would want to know. So using a visualization answering a more complicated question, they would want to read it even if it’s multidimensions or not. So anybody else who did the social one? Let’s do the last but not least final one for Sensors, Inc.

PARTICIPANT: All right. So we kind of thought about, after using all these particular insights and getting information about each of these rooms, this is, like, a big presentation, you give to the execs at the end saying, oh, well, this justifies the cost for this particular service. So who we want to do is include this in—distill it down to two kind of concepts, to save you money. So the way that we thought the way that we would illustrate that was measuring out and averaging out the duration of the meeting time to see when a service comes into effect and people were learning from the analytics from all the conference rooms. And then, on top of that, whether the occupancy of the room, of the spaces actually increases, as well. So you could kind of say, okay, well, the duration of the meetings, they shrink maybe, like, 30 minutes, and then divide that by the median amount, or the salary per hour, and so that saves you this much money over time. And then you can say, because you realized that you know putting not enough people in to fill these rooms, and then divide that in half, and so you spend—you saved this much money by putting people more efficiently into the space, and you got rid of half, of the cost constraints. And you save this much money. So it’s kind of an over-time comparison.

BO: Yeah, the Sensors, Inc. loved seeing, like, well, how many dollars are we saving here? So that, also, keeping that in mind is also a good point. Did anyone else go?

PARTICIPANT: We did the third one, as well.

PARTICIPANT: Yeah, we started by asking similar questions that I think many of us asked. Like, what is the average time length of the meeting, how many people are in a room at a given time. Like, if it’s a big room and there’s a bunch of people in it, you could be using a smaller room, the percentage of reservations that use all of the scheduled time. How many people need the room but there is no availability. And then we kind of came up with this idea that we could do this in room capacity measures. So instead of looking inside a room, it’s tied in with, I’ve got three people who have to go to this meeting, I have three meeting rooms available. So it eliminates that problem, you’ve got a meeting for four people but there’s only the teeny tiny room available. So it kind of solves for that. And we basically, well, we arbitrarily decided that the best capacity for the meeting rooms at all times is 85% full, so we made a distribution graph to show what an ideal spread would look like. So it would show the executive of the board, or the committee, or whatever company it is you want to have your prototype app, what they could be getting with your app.

BO: Especially for the analysts at the Sensors, Inc. group, they loved seeing the distributions. But the people, the facilities managers at the Busybee Corporation, they didn’t care, they wanted the simple bar graphs and everything. But the simple bell curves was something that they liked. We have two minutes left. It’s 11, 28. If you leave now, I won’t be offended at all. I know that’s the time we have. But the last slides we have are the real mock-ups of what we ended up doing. This was a separate client. One thing that I wanted to…

Yes, so the heatmap idea, we absolutely did. This was one of our first examples. So the floorplan, everything is made up. All the data is made up. This was before we had the data. This was part of our brainstorming deliverable action. So there’s a concept of how many people can fit in a room, and how many people are actually in the room, and then the percentage full, right?

So we had this gradient scale for every single room. Perhaps, this would be over, like, a week, or something like that. And this was, like, a nice and pretty graph, or so we thought, but to derive any instant actionable insights from this, it’s kind of difficult because all of the graphs, they just look kind of like a haze, you know? Even if we simplified it just to be fewer buckets, they’re still hard to understand. The really technical folks loved this idea. This was too much. But they liked the really simplified idea because then they could spend a couple minutes looking at the floorplan, and seeing for themselves, what’s a well used room, and what’s an under-utilized room. But a lot of people just liked this. You can understand, during this last week, about 40% of the times, our rooms were completely empty, accounting for business hours, and things like that. And so, what we learned from this exercise was that some clients liked to really nerd out, and explore and things, but some people wanted just the answer right then and there.

Another thing. A lot of their facilities managers really loved this concept. So the concept of, like, you know, was the room reserved, or not reserved, and was the room actually used, or not used? So that’s kind of four possibilities, right? So that’s there and they wanted a pie chart. And, you know, it does seem kind of like a pie-chart idea, that it’s like—oh, this is a full scope after day, four possibilities, of how it was used. A lot of…

So your brain is not that really good at determining whether a pie slice like this is bigger or smaller than a pie slice like this, or this. Certainly, much less so when the pie slices are stacked like this. Can you tell if the red one, or the light purple one is bigger, or smaller. Maybe, but by how much. But it’s very difficult. So unless you have a really good reason for wanting a pie chart, generally not a good idea, in our experience. This is much clearer, and if you guys disagree, I would love to hear your thoughts, too. But this is my… I think this is better. Another final idea, this was tracking for certain people on the app. Like, I wanted an extra large room in the small room at different times of the day, and then there’s a heatmap regarding the number of searches and things like that. And again, for the analyst, this was really exciting to see, because you can explore different types of room demand versus time of day, or day of week, and things like that, but, actually, a lot of people just thought that this was too much for—you know, your brain only has a certain capacity to, like, process things before it’s able to make conclusions before they forget, okay, what were the axes again? So accounting for that, especially if you’re wanting to deliver this in a report that they skim, unless you circle something like this, which was okay. Like we… this was a more complicated diagram which we offered to walk them through, step by step. Okay, this is the x-axis, this is the y-axis, this is the actionable thing, where small rooms across the board are searched for higher. More people want smaller rooms whatever time of day it is. That’s fine. So, like, stepping through a visualization they liked. But they didn’t want to just see it and explore it for themselves. Some people liked to explore it for themselves, and some people really don’t. And so that—those were the types of the I think so that we wanted to say. And, just really quickly, the real final thing that I wanted to show you was this was, like, a completely different client, completely different use case, but this was, like, a—and it was the actual words are kind of anonymized out. But this was, like, a heatmap that was multidimensional. And you had to really read it. It took, like, five minutes to really read this. But this particular client really liked that because, they were then able to present this, and once everybody in the room was able to understand it. They were, like, oh, that’s what that deep red rectangle means, and it was really insightful. And it really showed that our client was the expert in the room. And so that was a completely different use case which we learned, and I was completely surprised about that because we wanted, something that was really insightful but if anybody else saw it without her, they wouldn’t understand.

[ Laughter ]

Which is true. A lot of people want that, and find it useful and a lot of their presentees, might find that useful, as well. So yeah, are there any other points that you wanted to discuss before the conclusion of this?


BO: Great. Well, thank you so much for attending my session and I hope to see you future sessions today and tomorrow.

PARTICIPANT: Okay. Thank you.

[ Applause ]