#102: Data and Disasters (of the Natural Kind)

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00:04 (Announcer): Welcome to The Digital Analytics Power Hour. Tim, Michael, Moe, and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at facebook.com/analyticshour, and their website, analyticshour.io. And now, The Digital Analytics Power Hour.

00:27 Michael Helbling: Hi, everyone, welcome to The Digital Analytics Power Hour. This is episode 102. In our digital lives, the definition of disaster might be leaving the AdWords campaign turned on over the weekend, or having our website go dark on Cyber Monday. But in the real world, there’s nothing bigger in the disaster category than this big old hunk of rock we call home. The earth just does what it does, and few things serve better as a reminder of our small place in the grander scheme of things than a natural disaster, and we do our best in the face of hurricanes, and earthquakes, volcanoes, tornadoes. But there is always the quest for being better informed, being better at predicting, managing those risks. And guess what we use to do this? Data. Hence the tie-in for this show. [chuckle] So let’s get to know our co-hosts.

01:26 Moe Kiss: Hi, there, I’m Moe. I like to do stuff with data and analytics.

01:30 Tim Wilson: I’m Tim, I live on Earth, and have periodically experienced weather.

01:36 MH: And I’m Michael Helbling, also living here on Earth. [laughter] And, like Moe, do things with data and analytics. Okay, we don’t know anything about natural disasters or the data that we need to manage them, but we went out and found an expert in this area, somebody who marshals the data, chases the storms, so that we could talk to her. Sherilyn Burris is a Resilience and Disaster Risk Reduction Consultant. She’s held numerous emergency management roles, most recently as Emergency Management Chief of Manatee County, Florida, and today, she runs Cascia Consulting, helping organizations develop personalized risk assessments from available data. And she is one of only two emergency managers in Florida who has been on Comedy Central, and now she’s on our show. Welcome, Sherilyn.

02:32 Sherilyn Burris: Hi, guys.

02:33 MH: Awesome.

02:33 TW: Big step up from Comedy Central.

02:34 MH: Yeah, exactly.

02:35 SB: Woo-hoo!

02:36 TW: You actually planned to be on this show.

02:37 SB: Oh, that’s even better.

02:38 TW: Is it Manity or Manatee? Is it Manatee County? Manatee, or Manity?

02:42 SB: Manatee, like the animal.

02:43 MH: Manatee.

02:44 TW: Manatee, like the animal.

02:45 MH: How did I say it?

02:46 TW: You said Manity.

02:46 MH: Manity?

02:47 TW: Manity.

02:48 MH: Oh, the humanity?

02:49 SB: It’s a Southern thing.

02:50 TW: Oh, the humanity.

02:51 MH: Manatee.

02:51 TW: It’s Manatee County.

02:52 MH: It’s okay, I also pronounced Yehoshua wrong, apparently.

02:56 SB: What is that?

02:56 MH: It’s a guy we know.

[laughter]

03:02 SB: I thought that was like a breed of dog or something, I’m like I need to…

03:04 TW: You have no idea how many people have asked that question when they have met him in person.

03:08 SB: That’s okay, that’s okay. No one can pronounce my name if I go to Starbucks and have to give your name to write it on their cup or something, I just give some kind of really fake obnoxious name, because I have the same…

03:20 TW: Wait, did Michael pronounce your name correctly…

03:21 SB: Yes, yes he did.

03:21 TW: Or was he close?

03:22 MH: Oh, yeah. It’s a Southern name.

03:24 TW: That just makes Yehoshua feel that much worse.

03:26 MH: Yeah, there you go.

03:27 TW: So that’s perfect.

03:28 MH: Well… Okay, let’s get back on track.

[laughter]

03:30 MH: Yehoshua being a natural disaster of a different kind.

[laughter]

03:33 MH: No, I’m just kidding. A natural… I don’t know, phenomenon, I think, is the right word. Sherilyn, it’s great to have you on the show. I think to get us all on the same page, I’d love to hear just a little bit about your background and what you do with your company and those kinds of things, just as a way to set the table and get the conversation started.

03:54 SB: Cool. My background is a little long. I would say I never intended for this to be my career track, and it has been one of those fortuitous accidents. In 2004, I was working for the state of Florida in Tallahassee, and I was doing marketing research on agriculture. And because it’s such a big agricultural state, when the 2004 hurricane season hit, we had just billions of dollars in losses, and part of my job was to tally that and see really how much money we were looking at for our losses. And in the end of 2004, I transferred over to the Florida Division of Emergency Management, and I had no idea what I was getting into. I was a Public Information Officer, so mostly I talked to the media and to the general public.

04:38 SB: And then the 2005 hurricane season happened, and it sucked even worse than the year before, and I went to Hurricane Dennis outside of Pensacola, and then Hurricane Katrina in Mississippi, Hurricane Wilma in Broward County, and then I really haven’t gotten off that boat, it hasn’t slowed down, the train hasn’t stopped, just one disaster after another. And I really just fell in love with the way weather and technology interact and exchange with either really good or really bad human choices and the things that we can do to provide information. But I think largely because my first emergency management job was in information, I was kind of privileged.

05:18 SB: This is gonna come out kind of odd, but during Hurricane Katrina, we didn’t have a lot of people on hand, and when I got sent out there, there just weren’t enough resources to go around. And this sounds awful saying it out loud, but I was a young woman, I was in my early mid 20s and I knew how to type, and because I knew how to type and I was the only one that knew how to type with these bunch of old guys sitting around a fire station, I got invited to a lot of meetings that I probably wouldn’t have been invited to otherwise because you don’t really wanna tell that public information officer anything ’cause they’re gonna go tell the media. But I got to sit in the back and listen to all of this information being exchanged without having a stake in it: Where were people in need? Where were resources available? Where’s the link between those two? And I just got to soak all of it up like a sponge. And ever since then, I’ve worked to be the bridge between what people think they know and what I think they need to know.

06:17 SB: And I can tell when people aren’t understanding the information that they’re getting. So for disasters, I can just watch their little faces glaze over when I tell them, “This is how high the water is gonna be in your house.” And a lot of my background now has just fully evolved to be just like I said, kind of a bridge of information, so that I know where people want to go with their organization or with their plans, and then I just help them find random strengths that they didn’t know they had for it. So a lot of that is very stakeholder-based and community-based, just how to get the people involved in the conversation.

06:54 TW: I thought we’re gonna not have this tied directly to digital analytics, and then you talk about how to effectively communicate to stakeholders and make sure that they’re actually [chuckle] understanding what you’re talking about.

07:04 SB: But they don’t know anything about analytics though, and it’s kind of… You’re just giving them a nice little fluffy product at the end, otherwise they’re not on that journey because it’s like trying to explain statistics to a bunch of third graders maybe. They just want the outcome, and they just want the product. So maybe it is and maybe it’s not, but maybe it’s a mix of both.

07:28 TW: So this may be a slight little tangent, but do you deal with the expectation that what you’re saying is certain or the truth? I almost feel like weather is one of those areas where people are a little more aware of the uncertainty in the data, but maybe not. They’re expecting if you say the water’s gonna be two feet high in your house give or take 18 inches, they’re gonna be like, “No, how high is it gonna be?” Do you have to deal with that?

[chuckle]

08:02 SB: Oh, yeah, I laugh, because in hurricanes, we have what’s called the cone of certainty. And we call it the cone of death, actually, because it’s not certain at all. Those cones are designed to only be right two out of three times, so it’s got a 66% chance of being certain.

08:22 TW: Oh, was that literally like a plus or minus… That would be one standard deviation.

08:28 SB: Yeah, yeah. Every third time the storm goes out of that range of that cone. And it’s designed to be that way. People don’t know anything about weather probability, we suck at weather probability, because if I say that we’ve got a 30% chance of rain today, what does everybody go out and say? They say, “Oh my God, it’s gonna rain today.” And I have to say, “Uh, uh, uh, that’s not what I said, I said there was a 30% chance.” And we’re even worse when it comes to the risk part of it. So if we say it’s a 100-year flood, people will go, “Oh my God, that means it’s only gonna flood once every 100 years,” and like, “No, that’s not even remotely close to what that means.” But we would like to think people understand the risk behind all of that or what it means, but no, no, they don’t.

09:14 TW: One of the show favorites people is a guy named Matt Gershoff, who talks about decision-making under uncertainty and probabilistic thinking and I don’t know, this is fascinating. ‘Cause I looked at it and I don’t know if you’re familiar with Nate Silver and “The Signal and the Noise”. And so he talks about weather a lot and talks about how forecasts have gotten way better within 100 miles instead of 350 miles of where it’s gonna hit, still sucks at predicting the intensity. But he talks about sort of the cone of uncertainty or the cone of uncertainty? Cone of certainty. Cone of… What’s it called?

09:53 SB: Oh, now I’m gonna have to look it up ’cause I always call it the cone of death and now I feel like I should know that. [chuckle]

09:57 TW: The cone of… Well, and so I said, “Ah, this is one area that we’ve found a way to communicate to the stakeholders writ large that there’s variability,” but then you’re like, “Well, no, because the actual variability is so wide that people would look at it and probably totally discount it because the true cone of uncertainty or whatever it’s called would be so broad that people would say, ‘I can’t work with that.’” So you have to narrow it, and then people get irked if it’s not…

10:27 SB: When it’s wrong, yep.

10:29 TW: Fascinating.

10:30 SB: It’s almost like the cone… It’s a very discrete area. If it were shaded and tapered off instead of just being a fine line between, “I’m in danger,” or “I’m not in danger,” if it was a nice little red to pink to whatever instead, maybe that would communicate it a little differently.

10:50 MK: So what are some of the consequences? Obviously, you have to make some big decisions. What are the consequences on when you’re actually doing analysis about… If you make the call that a storm’s gonna hit somewhere and so you evacuate all these people, and then it doesn’t happen, do you guys quadruple check everything? How does that actually impact the process that you go through when you’re doing your analysis?

11:17 SB: We only evacuate people if the threat of bodily harm is worse if they stay than it is to evacuate, because evacuations are dangerous, too. But in Florida especially, and unfortunately all the other states have it way better than we do, we have two roads that go north out of our state and we have 20 million people. When you do the math, that is pretty unfortunate for us. So we have to make evacuation decisions based on how big our roads are and how many people we put on them all at the same time. And in South Florida’s case, we had six million people evacuate for Hurricane Irma. It took about five days, and five days away we had no idea where that storm was really gonna go. We had a pretty solid idea, but even 24 hours before the storm we didn’t have a great idea. And so we have to make those evacuation decisions well in advance before we have a solid idea of where it’s gonna go. It just depends on how certain the forecasters are, and a lot of the National Hurricane Center, they’re very transparent with their language, but you have to be very familiar with how they present stuff.

12:21 SB: A couple of weeks ago I had a quasi-conversation argument with someone because they were talking about hail being reported, and I said, “Did the weather service say it was reported or did they just say it was probable?” And the person goes, “Well, I don’t know.” I’m like, “Well, there’s a huge difference. Did they just say there might be hail, or did they say there is hail?” And you could just see the person, they didn’t understand the difference. A lot of that is they’re just so caught up on the word “hail” and they just turn their blinders on to everything else. If you say “disaster”, people just kind of shut down and they get very narrow-focused instead of saying, “Well, your risk of being eaten by a shark is pretty low, but aren’t we all still afraid of sharks?” Well, yeah. And a lot of that is just the way we process fear or the way we learn from past experiences to be able to even understand what a disaster actually is.

13:18 MK: So the stakes are really high when it comes to disaster management. How do you keep your cool, or what strategies do you use when you’re trying to communicate the difference between these tiny pieces of language to the listener, they might not really even think about or notice, but to you the difference between it being likely and it’s literally happening outside right now are very different? What kind of strategies do you use yourself to manage that? ‘Cause during a crisis, everyone just goes a bit bananas.

13:53 SB: Yeah, yeah they do. I use very small words and very complete sentences. So you’ll notice if you watch some of my press conferences, I speak very succinctly on what I want people to know and what I want people to do, and I don’t give them a lot of information. The biggest difference that I saw when I studied some of the other folks was they would just drone on for 20 minutes on storm surge. And I’m like, “I don’t need to do that, I just need people to leave.” People will, I think, subconsciously start to listen to that. So if you drone on for 20 minutes then you distract them, and you scare them, and you confuse them, and now they don’t know what to do. If you just say, “Here’s what’s going on, here’s what you’re gonna do,” and leave it at that… When a disaster is already happening it’s way too late to provide any of that background information. So the more you can provide well in advance, any public education that you can get out in the months leading up well before a storm, you’re gonna be better off because you don’t have to go back and explain to somebody what storm surge is while it’s already coming your way.

14:54 SB: But personally, I just try to get people to detach a little bit from it so they don’t take it as personally. I will tell you, when we had a Category 4 coming our way, I had a couple of very scary moments where I really thought, “I’m gonna go into work and when I come back out, Florida is just gonna be wiped off the map.” And that’s a very hard, hard realization to know that nothing is gonna be the same when you get to see it again. When the daylight comes back out, everything you know and love is gonna be gone. But if you can get very focused and get lots of sleep and try to block other things out, you generally do a little better. The details are great, but at that point I don’t think we need that many.

15:39 MK: So I wanna shift gears a little bit and ask about the type of data that you use. Primarily, my experience with lots of government data sources is it can be pretty messy, it can be really manual, like being dumped in CSVs. What kind of state is the data sources that you use and the rest of the team, and is there reaching a level of maturity in your industry, or is it all still… Yeah?

16:08 SB: The data is so raw, and I don’t know if it’s because people don’t have time to use it. Emergency management offices are generally very small, they’re not analysts. These are people that worked as EMS or law enforcement, just kind of inherited a job. But a lot of it was also because it’s collected at the federal level and then sent back down, it’s maybe not the data that we would want to look at. So this morning I pulled up FEMA’s open data source, and just out of wild curiosity downloaded their housing assistance. And it’s such a huge… You’re right, it’s a CSV file. And I know what I’m looking at when I go through it, it’s the housing type and then how bad the damage was, and how many units, and when it was inspected. And as a professional, I can understand it, but as a person who didn’t do this for a living, I just know it would just be a bunch of numbers.

16:58 SB: And what I think we’re missing in our industry is, nobody has time to go back through all of these spreadsheets and say, “Wow, this zip code just got hammered. We need to focus on why they had so much damage in this one particular area so that next time we can figure out was it a very old neighborhood? Was it a very low-lying neighborhood? Was it people that lived in poverty and didn’t have insurance? Why? Why was that damage so bad?” But we don’t, we don’t have time to…

17:28 TW: So what are the… There’s weather data, which presumably in the States that’s National Weather Service. You just mentioned FEMA data, which is presumably post-event data. Is there also whatever local municipality or whatever… What are the… I don’t know, I’m trying to think of it as, what’s the data, but then also what are the stages? Presumably, there’s an emergency preparedness need that may be looking at the data for X, Y, and Z, but then there’s, “Hey, this event’s gonna happen.” Is that the same data or different data? Do you have a mental framework of the different aspects of emergency management and the different data sources and how they dovetail?

18:17 SB: There’s endless data sources. I think if you can use it for anything, you can use it for emergency management. If it’s on how many people you have go get gas on Tuesday afternoon versus how many people go on Saturday afternoon, even though that sounds really silly, that’s important to us to be able to make a decision on saying, “Okay, well, if we’re gonna have to shut down fuel ports, how do we know when people have already gotten gas?” And it’s kind of a trivial example, but the weather data is super cool lately with crowd-sourced data and citizen science data being able to report what they see on the ground, because maybe that’s not always what forecasters see on their radar. And so we’ve got that publicly available data. And then you’re right, every city or jurisdiction or county or state has their own sociodemographic data. And the trick is lining those two up on top of each other to see where your gaps are. And one of the silly examples is if it rains six inches at my house, my car is under water. If it rains six inches at your house, you’re probably still out playing golf. There’s things that we just can’t look at a spreadsheet and be able to tell.

19:32 SB: So, somebody’s got to really sit down and go through and say, “Well, this is, historically the areas that we’ve seen flooding.” And we can usually get that from 911 data because when people drive their car through a flooded road, the first thing they always do is call 911 or even the same with trees down being in the road. What we did in Florida is we can take 911 call location data and what kind of call it was, and kind of even put it over our weather map of either where a tornado went through or a hurricane or a flood happened or a wild fire. And you can see the majority of the calls are exactly overlayed with where that storm damage was. And it’s neat to then take socioeconomic data and lay that on top and say, “Well, these are the people that might need extra help.” So, if all of that damage was in a trailer park or a lower level of economic status or a retirement home or perhaps it’s a tourist industry and nobody actually lives over there. So, it’s a bit of a… I would really hope my job gets computerized one day because there’s gotta be a better way than going one-by-one on that little mental checklist.

[chuckle]

20:43 TW: What… So, that’s a… What are the… So there’s CSVs and Excel, are there people using visualization, mapping visualization tools, R, Python? What’s kind of the, in the world that you’re in, what is actually being used to, I guess, crunch the data? And then presumably in the last 14 years or so, it sounds like you’re saying, “Yes, the whole open data movement and digitization has made way more data accessible. So, has that then shifted the way or the, where you’re spending your time? The data is easier to get at, but now there’s more work, there’s more of it, more accessible but it’s more work to crunch it or kinda how does that all work?

21:27 SB: A lot of it is. So with the maps, we rely, as an industry, almost exclusively on Esri to be able to crunch that data for us.

21:36 TW: That was the phrase…

21:37 MH: Yeah.

21:37 TW: I was looking… Esri, got it.

21:38 SB: Right. And…

21:39 TW: Yeah. I was like, “I know there’s something out there that’s a stand… ” Yeah, Esri, okay.

21:42 SB: There’s more than that…

21:43 TW: Yeah.

21:43 SB: Not that I’m gonna be an endorsement, but it’s so much easier to just show somebody a picture, than say, “Here’s a spreadsheet.” I don’t have time to read a spreadsheet. I don’t have time to basically even read an email. Just show me a picture of where the damage is. Maybe it’s a heat map or maybe it’s a regular green, Safe, kind of Safe/Unsafe. It’s just gotta be pretty fast. So, we use different software systems, but mostly it is Esri to do the lovely, beautiful story maps and things like that, to show where the damage is or what kind it is.

22:17 TW: And does that work such that if you bring in data that’s got geocoding in it, can you add in different datasets to Esri? I haven’t worked with it.

22:27 SB: Yeah.

22:27 TW: Okay.

22:28 SB: Yeah. So they’ve done some really cool stuff on Esri in a lot of counties all over the States, it’s not just us in Florida, use it to do damage assessment. So for instance, say I work for Orlando. I’ve got this nice little phone with me. I’ve got the Esri, whatever, downloaded on it, and my job is to go out and inspect buildings to see what’s damaged and what’s not. So I’ve got this system and I’ve got my little app and I can walk over to a house and it’s gone, and I can just punch in on my phone, “That’s a five out of five, as far as damage goes.” And that goes automatically into whatever spreadsheet and database gets processed, and we can spit out a number on the other side if we’ve connected to our, kind of the property assessment guys who know the value of that property, so we can tell you, “Here’s where the damage is and here’s the economic cost of what that damage is.”

23:19 TW: Do insurance… Do claims adjusters… Do private insurance companies’ data go into a publicly available dataset?

23:26 SB: No, they don’t give up their data at all.

23:28 MH: ‘Cause how will they screw you later if they let you into that data?

[laughter]

23:34 SB: Remember that they’re in the business of making money and the government usually is not. So, most private industries don’t turn over their data. Power companies and cell phone companies have gotten a lot better about turning over theirs just because they wanna be transparent and the whole corporate responsibility movement. But as far as the insurance company damage, that is probably some priceless data because you know exactly what’s wrong with that home, or how many people needed help.

24:03 MH: Yeah. Well, since we’re talking about data sets, one of the ones that gets mentioned in the news a lot is the Waffle House Index. So, what… Is that real? Do people actually use that?

[chuckle]

24:16 SB: I love this one. So, when I first started working in emergency management, I worked for Craig Fugate who came up with that. And…

24:24 TW: You wanna explain what the Waffle House Index is just for the…

24:27 MH: Oh yeah. So that people…

24:28 SB: It is… [laughter]

24:29 TW: I’m assuming that is not a globally…

24:30 MH: Not a universally…

24:30 TW: I am vaguely aware of it, but…

24:33 SB: So, we have a lot of Waffle Houses in Florida and Georgia. I don’t know where there is a lot of Waffle Houses otherwise.

24:40 MK: Currently Googling Waffle House.

24:41 MH: Oh, Moe, you’ve got to come.

24:41 SB: How many of them and where are they?

24:43 MK: Oh you… Like a restaurant?

24:44 TW: It’s a restaurant.

24:45 MH: It’s a restaurant.

24:46 TW: Yeah. It’s a 24-hour diner.

24:48 SB: I need to way back peddle then.

24:50 MK: I thought it was some kind of a house.

24:52 SB: They got waffles. So, it’s a 24-hour diner.

24:54 MH: It’s so good, Moe.

24:56 SB: You can get your hash browns whatever way you want. So, if Waffle House is open, your town is generally doing okay. You might have some damage, you might have some debris, or your electricity is out. So, if Waffle House is open, you need people to work. You need food. You need electricity. You need some kind of way to run a credit card if you need to. Your building has to be there and you should probably have a parking lot. And then of course you need customers. So, the Waffle House Index is saying, “Physically, we’re okay. Economically, okay, logistically with supplies, we’re okay.” But if all your Waffle Houses are closed, your town is having a pretty bad day. It’s not a real index, but it was one of those ways I think that we try to make light of the situation a little bit. That healthy, happy coping mechanism. So, working in death and destruction every day.

25:44 MH: Oh, I was hoping it was real. That was… Okay.

25:49 TW: Does Waffle House, do they embrace, or do they… You would think that would be positive for them from a marketing perspective ’cause they’re like…

25:57 MH: It’s a lot of good PR for them I would imagine.

26:00 SB: It is. And they embraced it. They appreciated it. It’s not that it’s not real. It’s as real as you want it to be, but they have a corporate response team. I think they kind of probably made it because Craig Fugate really pushed that Waffle House Index. So, they embrace it and they’ll come out and get their Waffle Houses up and running because they know a hot meal makes a big difference after a disaster.

26:24 MH: Yeah, personally having dealt with a Waffle House in a natural disaster, that was when we got two inches of snow here in Atlanta.

[chuckle]

26:34 SB: Total disaster. Epic. Epic proportions.

[laughter]

26:37 MH: It actually was. That was the sad thing about it.

26:41 TW: There was the remote worker from the Bay Area who had flown in for the week and wound up sleeping on a…

26:47 MH: We all have our stories, but we went to a Waffle House. And we were just sitting there. And it was a very interesting experience. Those poor people at the Waffle House I think had been there for 36 hours. It was really bad. Anyways. Sorry. The Waffle House index is just amazingly humorous to me because of that experience, but I’m glad to hear that it’s not necessarily a real way that we manage things.

27:11 SB: No, it’s a lot more technical than that, but like the 500-year flood thing is just… If we gotta communicate it in 30 seconds or less is the way we gotta do it.

27:20 TW: So, Florida is obviously kind of tornado alley. I assume there are different areas of the US that are more or less prone in the similar sort of things they’re dealing with. Do you have a sense of how other countries kind of tend to address just kind of structurally what data is available, how they manage, even their governmental services, infrastructure? Is there sharing of best practices or different structural differences?

27:53 SB: We really do try to study people that have done it right. So the Netherlands does a lot with flood mitigation, because they’re under sea level, kind of like New Orleans. And so they built a lot of dykes and levees and all sorts of things to help prevent them from being totally under water. And we study what the Japanese do to prepare for tsunamis. I actually study a lot of what other countries do to communicate risk to people. So maybe it’s not necessarily how they build their buildings but, what are they telling their residents to be able to make better decisions for themselves? In Australia they have hurricanes as well. So it’s kind of neat to study somewhere on the other side of the world, having the same exact problems that we do. But it’s a little harder to compare if we can’t build something like that in America, if it’s too expensive to do it. Or Japan’s risk is higher or their topography is different, or their resources are different. So as much as we can, we really do try to pilfer and steal other people’s best practices and some of them we just can’t.

28:54 TW: What part of Australia… Moe, where do hurricanes hit?

28:57 MK: We have cyclones, and I’ve…

29:00 TW: Cyclones, sorry, which hemisphere? Sorry.

29:01 MK: I used to live in an area that was cyclone prone and had to catch an inflatable boat over to our office, because there was so much flooding. So yeah, definitely, definitely have been there. It’s pretty scary. But I’m curious about how your messaging can get politicized, because that happens to us in the analytics space as well, where someone in the company has a particular agenda that they wanna push, or a message, or something they want people to action. And in this case, the stakes are so much higher. How have you managed that?

29:35 SB: Oh, we smile and nod a lot. Disasters are so politicized here in the States and it’s kind of used as a divisive tool. One of the better examples, and I won’t tell you who it was, but it was a long time ago, during Hurricane Wilma. I was working in south Florida and I think I had been sleeping on the floor for about two weeks by this point, and I was working in a county that had about 30 different cities. Some of the cities had 200-300 people, and then some of the cities had 200,000-300,000 people. And there was a lot of wind damage in this community, so people wanted blue tarps to put on their roof; it’s because it rains every day.

30:11 SB: And so some of these mayors got 500 tarps. And then you’d have another mayor come over and say, “Well you gave him 500 tarps. You need to give me 500 tarps. And I would say, “You don’t have 500 homes in your community and, you know, I can’t… ” And it was like a, excuse my language, but it was a pissing contest on, everybody wants the same things. So, I think politicians have a big problem with absolute versus reference numbers to say, “Hey, you know, I don’t… ” I try to help politicians where I can, but you’re right, they want you to help their people without regards to the information behind it to say, “Do you need help?”

30:48 SB: The cost of disasters, I think is one of the reasons why it just becomes a political agenda, because we’re looking at millions if not billions of dollars for disasters and it’s almost the way we have it set up in the States. It’s like winning the ugliest dog contest. So all these politicians get together and they want to see who can get the most money from FEMA. Well, if you get the most money from FEMA it means that you have done the worst job trying to prepare your residents. So, that’s not an achievement and that’s not a good thing, but yet that’s the mentality. And so I try to work to say, “Here’s… You’re not gonna get $10 million ’cause you didn’t get $10 million of damage and that’s a good thing.” But it just doesn’t really work like that all of the time.

31:30 TW: But it’s a structural… So on the one hand they want, the politicians are gonna wanna say, “We put in the right preparation. We managed the actual event itself.”

31:41 SB: Right.

31:41 TW: “We did the best we possibly could.” At the same time they also want to go and say, “We maximized the amount of federal relief we got.” And those, you’re saying those are kind of… There’s a tension there and they kinda have to sort of speak out of both sides of their mouths to be effective politicians, I guess.

[chuckle]

32:01 MK: Yeah, I guess it’s that typical old adage, right? Those that prepare the best are probably gonna have hopefully less troubles, but then at the end, won’t get the reward and the accolades I suppose, when it comes to funding or perceived funding. Man, your job sounds tough.

[laughter]

32:21 TW: Well where does the… And where does the… ‘Cause to me the preparedness is kind of this fascinating… Like you said, I can’t remember it was before we were recording or now but the 100-year… You’re on a 100-year floodplain. So, what does that mean? How much should I be investing in flood preparedness or flood insurance or whatever the ways I can prepare for flood given that it’s a 100-year flood. Like, where does that… And I assume the same is for municipalities saying how much of our taxpayers dollars do we want to invest in a low probability, but certainly not a zero probability event. Do you wind up… And is that what you’re doing? Is that now what you’re doing now in the consultancy is largely the preparedness piece?

33:11 SB: Yeah, and it’s… It’s how do you walk that fine line between what’s most likely to happen and then what’s most awful if it happened. So, we kinda have to do a trade off between… The 500 and 100-year flood are a little easier to communicate, because there are solid ways that you can decrease your risk or promote getting insurance or, “Hey, quit building in a flood zone.” But there are different things that don’t really have those nice little box step solutions, like an infectious disease outbreak or a terrorist attack. So even though those things are very unlikely to happen, when they do happen, it’s so much worse, with infectious disease, because it really impacts how we live our lives as humans and how we go to the mall or get on an airplane and how we interact every day of our lives. And then of course with an active shooter you have that huge emotional mental health piece. And those things, those kind of outcomes and impacts for those events don’t have a dollar amount behind them, and so it’s very hard to get communities to pay money to prepare for something that doesn’t have a monetary return on it.

34:20 MK: So how much of your job then would be in helping areas prepare versus respond after an event?

34:28 SB: I would prefer it be 100% on the preparedness and none on the response, because at the response phase it’s almost too late at that point to do anything about it. So, responders get all of the credit, they rush in, it’s very sexy, it’s on TV, they got the uniforms on and they give out water and it’s all very, very glamorous. But when it comes down to it…

34:48 TW: They throw toilet paper… Paper towels.

34:50 SB: Right, yeah, ’cause…

34:52 TW: That’s… That just.

34:53 MK: In Australia they save koalas.

34:55 MH: Yeah.

34:55 MK: You always see the fireman with a koala.

34:57 MH: Oh, that’s where all the photo ops with the politicians, right.

35:00 SB: Right, given out that bottle of water.

35:01 MH: They’re not the control center looking at data curves to determine whether an evacuation is necessary. They are pulling stuff off a truck afterwards handing it to a little old lady.

35:15 SB: Right, and that’s really what sells votes and it’s kind of crappy with their response, but… I used the example of Biloxi, Mississippi, if you guys are familiar with it, if you went before Hurricane Katrina, and then you went now, God it’s been 13 years. It took them over 10 years to get to their economic point where they were before the storm. But America spent billions of dollars on this response. All on their response, but it didn’t actually make measurable difference on whether that community recovered. So I have slept on the ground, I have slept in empty abandoned ammunitions factory, I’ve slept under desks. That’s what you do in response. So if there’s anything I can ever do to have to avoid that, I will. I will gladly prepare, prepare, prepare all day long.

36:03 TW: So if… Maybe you can’t even make this. If Katrina exactly hit again today, is that a thought experiment? Or does that go into the planning? Like, how does Biloxi, Mississippi come out of Katrina hitting today than it did hitting 13 years ago? Is part of that rebuilding, is that we’ve had this, so therefore I’m now receptive to a preparedness message for some finite window, which again I’m thinking of like the website crashes on Cyber Monday, and all of the sudden the IT department gets very well funded for their capacity for the website load? Is that a phenomenon as well that there’s a window after the disaster, where all of the sudden people are listening to you saying, “What do I need to do?” “What do I need to do to make this work better?”

36:54 SB: Yeah, you’ll notice all of our funding, after anything bad happens, all of our funding goes up after. So, after 9/11, all of our funding for terrorism skyrocketed. After SARS all of our funding for infectious disease just skyrocketed. And after hurricanes, it does the same thing, but after a couple of years go by, that there’s nothing bad happening, all that funding goes back down. And unfortunately, when the funding goes down, our capability to respond also goes down. So, it’s just this vicious circle that we can’t get out of, because we can’t keep the funding at a sustainable level to keep it from happening again. So if Katrina hit again, I honestly think that the exact same thing would happen, because we haven’t had a steady stream of funding into it.

37:35 TW: It’s a good thing that weather is tapering off and kinda getting milder over time. [chuckle]

37:40 MK: Ohh, that is depressing.

[chuckle]

37:46 TW: Interesting. This was, as you were talking about, the things like the SARS or the active shooter that are very low. I’m reading a book, it’s taking me a while to get through about Bayesian thinking and it talks about during the Cold War, there was a discussion of, “What’s the likelihood of a nuclear disaster?” Because there had not been any, outside of test lab development. And there was kind of the frequentist school of thinking is saying, “It’s never happened, we therefore can’t predict its probability”. Whereas there was kind of the Bayesians were saying, “Yeah, we can. We can think logically, apply some subject matter expertise, assign probabilities to the different failure points”. And supposedly, coming out of that, there was a recognition that there was actually a decent level of risk that a plane would crash that was carrying a nuclear warhead and would cause a major disaster, which led to a bunch of preparedness investments, because they were trying to predict the likelihood of something happening that had never really happened, and it was kind of a fascinating… But even telling that story, they still had to get somebody controlling the purse strings to buy into the approach. It does seem like, if you talk about pandemics, a pandemic hasn’t hit the US, so…

39:09 SB: Don’t say stuff like that. We’re super superstitious.

[laughter]

39:12 TW: Oh, I’m sorry. Sorry.

39:14 SB: It’s the 100-year anniversary of the Spanish Influenza outbreak, so let’s not do that.

39:19 TW: But I think that’s the point. If it hasn’t happened, doesn’t mean it won’t. But from a communication, that is the sort of challenge… I feel like we’ve been talking extreme weather events, and all of a sudden, you’re like, “Hello, there are lots of other types of disasters that have nothing to do with the weather”.

39:35 MH: Well, and then the other thing is, do you find that there are people who are also over-hyping… ‘Cause I think local news and meteorologists want you to think that, “This thunderstorm is gonna be the worst one to ever hit your neighborhood.” And so, how do you deal with sort of the deadening effect, I guess, of just hearing it all the time? Also, the new Weather Channel things where they show the flood going up around the person on the video screen? I don’t know if you’ve seen those YouTube videos? Pretty frigging awesome.

[laughter]

40:05 MH: No, but what is the response to that? Because that’s sort of the other side of people are just like, “Nah, I’m good. I’ll ride this one out” kind of…

40:16 SB: We try to get really good at predicting weather, and we’re gonna shoot ourselves in the foot one of these days with how close it’s gonna be to somebody’s house, but… When I see those people on TV and when I… I worked with the media during the 2005 hurricane season. I just had to laugh a little bit, because they are trying to sell news, so they’ve got a bottom line and I didn’t. A lot of what their message was, was unfortunately driven at a corporate level and not a public safety level. And in my job now, I just make a point to be very good friends with pretty much every reporter I find, so that I can educate them on what’s going on so that they can look like a really good subject matter expert on TV and say all the right things and get the right message out there. And a lot of it is, if the media doesn’t trust the local official and what they’re saying, then yeah, they’re probably gonna go off the rails. They’re probably also gonna go out and put themselves in danger, which is just… I really wish they would stop doing that. I think they even gave Jim Cantore an award for it the other day, and I’m like, please don’t do that.

[chuckle]

41:17 SB: Right. Don’t get on the news and tell people all their kids are gonna die, or something like that, it’s horrible.

41:21 TW: Stop reinforcing bad decision making.

[laughter]

41:23 SB: Though somebody did that, so in fact, that was actually Hurricane Harvey. Thank goodness it wasn’t in Florida. But, we have the opposite too, where all of these people put out horrible messages like, “Evacuate now”, “Leave now”, “Your house is gonna be gone”. And then, like Moe was saying in the beginning, and then nothing happens. So then, how do you get people back on board to say, “Oh, it was just the chance that your house could’ve been wiped away. It wasn’t that it was gonna get wiped away”.

41:49 MH: Well, here in Georgia, we still go buy all the bread, eggs, and milk anyways.

41:53 SB: We call that the French toast scale, or the French… Yeah.

41:55 MH: Yeah. That’s right.

[laughter]

42:00 MH: It’s ridiculous.

42:00 SB: French toast effect.

42:00 MH: It’s the only three things people… They empty out the store of those three items and that’s it. Everything else is still there.

42:06 MK: Really?

42:07 MH: Yes, it’s the most ridiculous thing. Anyways, it’s…

42:10 MK: I think we’re the opposite. We go straight for the canned goods and the beer.

42:13 MH: No, you gotta get them staples.

[laughter]

42:16 TW: I don’t get it either. It’s so crazy. If we can’t get to Waffle House, we better make French toast. That’s…

[laughter]

42:28 SB: Now did you guys… You guys must have… You don’t lose power during one of those snow storms, apparently. It’s like… Moe was saying. We get everything in a can because we don’t have electricity to make French toast, so you better drink that beer warm.

42:40 MH: Yeah, it all depends. There’s people that lose power and those kinds of things. Anyways, do not mean to minimize storms and people’s responses to that.

42:49 SB: No. Again, we just…

42:50 MH: It’s just…

42:50 SB: We have to laugh to keep from crying.

42:52 MH: There you go. Well… And…

42:53 MK: Sorry, but what Sherilyn just made a really good point that I wanna hone in on. So apparently, to be the quintessential analyst, I just need to make friends with everyone and make them look really smart and know what to say when, and then like, job’s done, kind of… I had a mini epiphany.

43:12 SB: Yeah but the catch is, you can’t ever be wrong.

43:17 MK: Oh, wow.

[laughter]

43:18 MK: We really are the quintessential analysts now. [chuckle]

43:20 MH: Yeah. There you go.

43:21 TW: But other than that, you’re right.

43:24 MH: Alright. Well, we have gotta wrap up and I wish we didn’t, ’cause this is actually a really fun conversation. And thank you, for coming on. One of the things we love to do is go around the horn and just share a our last call. Something we found in the last few weeks, that we think is interesting; it might be of interest to our listeners. Sherilyn, you’re our guest. Do you have a last call you wanna share?

43:48 SB: My dog has an Instagram and he is really funny. [chuckle]

43:53 MH: Nice.

43:55 SB: We call him a our Hurrik9. It’s a little plan.

43:58 TW: Wait, how old is this dog? Right. ‘Cause you were saying…

44:01 SB: He… Yeah, so my old chihuahua died in January 19. And in March, I got a phone call from an animal rescuer and she said, “I have a puppy. It was a confiscation. I need you to just take care of the puppy because they don’t do well in the shelters.” I was like, “Oh, sure.” I walk over, and I go to pick up this little dog, and he was so stinking cute. And I took the dog to work with me all the time, so he is extremely friendly. He’s very, very well versed in hurricanes. His name is Cooper. And he is about, I would think… I think he was probably born in February, so maybe he’s about eight months old. I don’t know.

44:40 MH: Alright.

44:40 MK: So sorry. Hurricane Cooper, is that his…

44:43 SB: Hurrik9. It’s kind of a little play on words. It’s Cooper Hurrik9.

44:47 MK: Oh, got it.

44:47 SB: But I’ll send it to you.

44:48 MH: Alright. Well, cool. We’ll hopefully get the big Analyst…

44:52 SB: Yeah.

44:52 MH: Power Hour bump to his…

44:53 SB: This little storm dog.

44:54 MH: Instagram followers. I don’t think that’s actually a thing. I think we’ve tried to test it. I don’t believe we provide a bump. So, sorry about that.

45:00 SB: No worries.

45:02 TW: Alright, I just… I kinda wonder if by mentioning the dog, the last dog that has died, we have not yet kinda worked in “The Adventures of Butter Bean and Yoga Dog”. I’m not gonna make that…

45:13 MH: Oh, well.

45:13 TW: My last call, but we…

45:15 MH: Oh, I mean, you could do twofer, Tim.

45:18 TW: I never do twofers.

[chuckle]

45:19 MH: That’s right.

45:19 TW: Mine are always shorts and…

45:21 MH: In many years.

45:22 MK: Currently receiving a stern look from one Moe Kiss.

45:25 MH: That’s right.

[laughter]

45:29 TW: That would be Sherilyn’s book. We will also link to that.

45:32 MH: There you go.

45:32 TW: So you can kinda look to retire soon based on the Amazon book sales of the…

45:37 SB: You gotta have a pet preparedness plan.

45:38 MH: There you go. That’s true.

45:38 TW: There you go.

[laughter]

45:39 MH: Alright, Tim, what is your last call?

45:42 TW: So I am going to call an Audible, because it’s actually… I saw it recently and it is actually related to what we are talking about here. There was a guy on Twitter named @EvanTachovsky, who basically has posted a R code for making New York Times style building maps for whatever your zip code is. So for the R users out there, there is a little bit of code where you basically have to download some files then you get kinda the like building map level detail of whatever zip code you’re interested in, which seems like it has a lot of potential to have some fun with some visualization of overlaying weather or other related data on it.

46:27 MK: That’s so show appropriate.

46:29 TW: Well, I just changed it. I was frantically looking for it ’cause it came up.

46:33 MH: You’re like, “Oh, I’ll use that instead.” Alright, Moe, what do you got?

46:37 MK: Alright. I want to share an article by Elena Grewal? I think I’m saying her name right. Hence, the inflection. She leads the data science team at Airbnb, which is like a 120 people plus now. But the article is on how one data science job doesn’t fit them all. But there’s something in the article in particular that I really liked the idea of, and I think it’s something we face in analytics. She was talking about how lots of the team members who were doing analytics work felt like their work wasn’t as valued by the business as those doing machine learning work, even though what they were doing was completely critical. They basically re-aligned their teams into three teams, so data science analytics, data science algorithms, and data science inference.

47:24 TW: Did you say inference?

47:26 MK: Yeah. And they’ve kind of just gone down the approach of calling everyone a data scientist, even if they’re doing analytics work. But I really do like how they heard and addressed and dealt with the fact that those who were not doing like the sexy machine learning stuff still added a lot of value to the business, and how did they make sure that their staff members felt that their work was valued. And so, it’s a really interesting article on LinkedIn, and we’ll share that in the show notes.

47:52 TW: That’s awesome. Michael, as a data science manager. What’s your… [chuckle] What’s your last call?

[laughter]

48:01 MH: Tim, actually, mine is a twofer. So I’ve discovered something very interesting in that, I switched to rum for this recording because we’re talking a lot about hurricanes.

[laughter]

48:14 TW: Oh. ‘Cause we’re talking about hurricanes. Nicely done.

48:16 MH: But I actually think it makes my face red. There you go. That’s a really key learning I found out tonight. So, that’s good. Not actually…

48:24 MK: I’m glad you had two today.

48:26 MH: Yeah. No. That’s really important… No. My real one is actually a book that I’m reading. Tim listens to podcasts, I read books. It’s called “Measure What Matters” by John Doerr, and it’s about…

48:37 TW: OKR Man.

48:38 MH: It’s about objectives and key results or OKRs, which is a way of mapping what you intend to accomplish to the outcomes or the metrics that you’re gonna use to measure it. We use it in our business. But I actually just really have enjoyed reading the book and all the cool stories in it. Highly recommend it. And okay. There we go. Last c…

49:00 TW: But now, I’m realizing we literally did not ask Sherilyn the question of how is performance measured in the disaster management?

49:09 SB: Very badly.

49:09 MH: Yeah. I mean, I was like, “What does success… ” It sounds like success…

49:13 TW: Maybe that was the theme of the show.

49:14 MH: Success is measured in a lot of different ways, because if you’re a local official, it’s by more FEMA money, and if you’re a preparedness person, it’s by, maybe, people not dying, and I don’t know. But, yeah.

49:28 SB: Yeah. I… We have a lot of people call and complain, and I would… I know this sounds really awful, but when they would call and complain about things like, they didn’t like their chicken nuggets at the hurricane shelter. I would say something really awful that goes, “You’re alive to complain. I’m doing the heart of my job.” I mean, a lot of it is just a little bit of perspective with… My job, it’s I need to make sure we save lives, but sometimes it’s making sure those chickens nuggets are tasty.

49:57 TW: That’s a misalignment on Maslov’s hierarchy perspective, it sounds like, between…

50:01 MH: Yeah. Let’s take some pride in our nuggets here people, though. Let’s step it up next time. [chuckle] So that Sherilyn doesn’t have to deal with that. Okay, if you’ve been listening, there’s two distinct segments of our audience I wanna address at this moment. The first is everyone with the typical end-of-show, we’d love to hear from you. The second is analysts who are looking to advance and accelerate their careers. I think this episode actually represents an amazing opportunity. There’s a ton of publicly available data, and publicly available tools around disaster analysis, and recovery, and preparation. And if you are an analyst, who is looking for ways to showcase your skills, this might actually be an extremely viable avenue for you to both burnish your reputation and resume, as well as do the public some good. I’m just throwing that out there. There’s a lot of data sources, and maybe if you’re really nice, you can even follow Sherilyn on Twitter and ask for questions, which, if she has time, she may even answer. And there’s lots of other things out there.

51:09 TW: If not, her dog might answer.

51:09 MH: Her dog is also available to field the questions on his Instagram. But, I just wanna throw that out there, ’cause it has been ringing in my head like, “Man, there’s a lot of people and who are like, ‘How do I get experience? How do I show that I can do this?’” Sounds like there’s publicly available data, and a real need, and that’s an opportunity. That’s something I just wanna throw that out there. For the rest of you, who are like, don’t care about your careers, and doing good, just keep listening to the show. We’d love to hear from you, or if you’ve been in a hurricane or a natural disaster, I’m sure Sherilyn wants to hear your story. No. I’m just kidding. I’m sorry.

[laughter]

51:44 MH: I actually… No, it’s only because I almost started talking about mine…

51:47 SB: I do. I do wanna hear those stories. It’s…

51:49 MH: From the Atlanta apocalypse and I was like, “Stop, Michael. Everyone has a story. You don’t need to share yours right now.”

[chuckle]

51:56 SB: No. Maybe if you shared your story though, people will learn from what to do or not to do during their emergency.

52:02 MH: Well, my story was best showcased by the SNL sketch with the Seth Meyers news segment. If you would recall, that one. Sethery.

52:17 TW: I think he did a few news segments. I think we need a little more detail.

52:19 MH: No. He had a guy on from the South, who was in the storm and he was like, “It was terrible, Sethery.” It’s like, “This white stuff floated down from the sky. [chuckle] So, I did the only logical thing. I jumped into my white Escalade and got on the interstate.”

52:37 SB: Oh God.

[laughter]

52:39 MH: Yeah. Anyways…

52:40 MK: This is the slowest wrap-up in history. [chuckle] Our poor listeners were like, “Oh they’re on last calls, this is nearly done.” No. No.

52:47 MH: Moe, see, I’m trying a lot of new things, Moe. [chuckle] We’re Episode 102. We’ve gotta drum up the personal interest side of this a little more. Just go with it. Anyways, we would love to hear from you. Tim can edit this all in post. It’s not a big deal.

[laughter]

53:05 MH: For my two co-hosts, who are sort of amazed by my amazing closure skills, and our guest Sherilyn. For all of you out there, no matter what you do, and in any kind of weather, keep analyzing.

[music]

53:23 (Announcer): Thanks for listening and don’t forget to join the conversation on Facebook, Twitter, or Measure Slack group. We welcome your comments and questions. Visit us on the web at AnalyticsHour.io, facebook.com/analyticshour or @AnalyticsHour on Twitter.

[music]

53:43 S6: So, smart guys wanted to fit in so they made up a term called Analytic. Analytics don’t work.

[music]

53:52 TW: Micheal, did you wanna overtly call this out as you one of the like, “Hey, this is not a digital analytics topic, not like we’ve done in the past.” Or no, you just figure it’ll be obvious?

54:00 MH: Yeah, I think people will get it.

[laughter]

54:04 TW: I’m just [54:05] ____ this show’s not about digital analytics.

[music]

54:10 SB: Our governor wouldn’t let him stay the word “climate change” during a Senate hearing, or a Congressional hearing of some sort. And so you can watch this guy’s face use the word “atmospheric redeployment”.

[laughter]

54:22 SB: And you could just see him, just trying to bite his tongue.

[music]

54:26 MH: I’m good at the internet. I’ll figure it out.

54:28 S?: Okay. [54:29] ____.

[laughter]
[music]

54:32 MH: She’s guarding the front door because she’s certain that someone’s coming to kill us. So…

54:38 TW: Okay.

[laughter]
[music]

54:41 TW: Oh good. You were recording that. So if you wanna make it… [54:45] ____.

[laughter]
[music]

54:47 MK: He was out to buy a suit and I told him he could wear a jacket and chinos. So it’s really not…

54:52 TW: I did not buy a suit because you said… I was showing pictures of you and Jamie to the guy at the Joseph A. Bank saying, “We were told cocktail attire with bow ties and then they sent us this picture. This is the bride and the groom and she said this would work. So I want a freaking bow tie.”

[laughter]

55:12 MH: You’re gonna wear…

55:12 TW: Carry on.

55:13 MK: Oh shit.

55:13 TW: A bow tie? That’s gonna be amazing.

[music]

55:15 TW: No, I talk over Moe and then everybody says I’m mean to Moe and then so…

55:21 MK: No, we talk over each other.

55:23 MH: Yeah.

55:23 MK: It’s just not a you thing. Anyway.

55:24 TW: Moe, you’re up.

[music]

55:27 MK: So, Sherilyn, I’m trying to find your dog on Instagram via my own dog’s Instagram, and I’m still struggling, because apparently, Hurrik9 is a very common dog Instagram name.

55:39 S?: Still recording! We’re still recording!

[laughter]

55:40 MK: That makes it all the better. No.

[music]

55:44 TW: Rock flag and natural disasters.

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