Alter Everything Podcast

MaddieJ · ‎09-25-2018

We're joined by Ben Sullins for a chat about data science, the world of Teslanomics, and why soft skills are the future of your career.

Guests

Ben Sullins - Twitter
Brian Oblinger - @BrianO, Twitter

Topics

Transcript

Episode Transcription

BRIAN 00:06	[music] Welcome to Alter Everything, a podcast about data and analytics culture. I'm Brian Oblinger, and I'll be your host. We're joined by Ben Sullins for a chat about data science, the world of Teslanomics, and why soft skills are the future of your career. Let's get right into it. All right, Ben. Welcome to the show.
BEN 00:28	Hey. Thanks for having me, man. Glad to be here.
BRIAN 00:30	Yeah. No, I'm super excited. So let's start with you. Tell us about you and your journey kind of to analytics, through analytics.
BEN 00:38	Yeah. It's been a wild one. I've been a lifelong data geek is how I like to describe it. I was very fortunate early in my life to find this bug for technology where I just wanted to figure it out. To me, it was the ultimate hacking ability was to be able to manipulate and get technology to do what you want. And at a early age, I was fortunate enough to land a job as a intern at MCI, a phone company that no one knew exists but, at the time, was the pinnacle of tech. Right? Google didn't exist. Facebook didn't exist. The internet was a very nascent kind of thing. So the tech companies of the world, at the time, were telecom companies. And I was able, on the help desk there, to get a taste of everything, and it was nice because now I had this broad sense of what technology was. And I really found my niche working with data, and at the time, that would be Excel 97 with Microsoft Access. This is prior to even Office 2000 and any of that stuff. This was super early days. Dot net didn't exist yet. Right? This was like ancient history in the tech world. But I found my love for being able to look at these numbers and manipulate them and do Excel formulas and stuff and then give that information to different teams to help them do their jobs better. And through those efforts, we had some wild stuff going on at the time that, even by today's standards, I would say is-- in terms of what we were able to do, real-time analytics off of a Unix-based system that was giving predictions for call volumes for our sales staff, I mean, by the minute--
BRIAN 02:22	Mind-blowing analytics happening.
BEN 02:24	I mean, in the late '90s by hand using Visual Basic for Applications, wild stuff. We were able to actually grow the call center to be the number one sales call center for the whole company for several quarters in a row until Verizon bought us and shut everyone down. But yeah, since then, I just had this insane passion. And throughout my career, there've always been opportunities to do other things that were maybe more sexy or more fun or maybe made more money. Back in the early '00s, everyone was going into real estate because they could make all this money selling mortgages.
BRIAN 02:58	Yeah. You should have done that, Ben. You missed out. You should have got in there. Maybe right around '08, you should have--
BEN 03:02	Yeah. Yeah. Exactly. Right? It's one of those things where it was like I just stuck with what I liked, and I liked turning data, just raw basic information, event logs and things, into something that people could then use to improve their business. So it's always been my passion, and I just was lucky enough or fortunate enough to see that and not stray from it for almost 20 years until I decided to quit my job and start making YouTube videos.
BRIAN 03:31	So before we get to the quitting your job and making YouTube videos, tell us a little bit about-- I know that, at one point, you had the title of data scientist, I believe.
BEN 03:40	It's interesting. So this audience, I would consider myself more of a data analyst, but I've also held the role of data engineer, front-end web developer because, unlike today, if you wanted to build a website where somebody could go view a chart or even just a table of data, you had to hand code that. Right? So I've a myriad of roles. I don't think any of my roles have ever been what I would call-- what people that are listening to this would consider a data scientist, but if I'm talking to Joe Rogan about it, to the general population, we're all data scientists. Right? So I think that term to somebody that knows what it means, I wouldn't say that's ever been a job I've had, but I consider the whole field to be data science because that just seems to be the term that has generally been adopted now. If you say business intelligence or something, people, no clue. Right? But you say data science, it's like, "Oh, Nate Silver, DJ Patil," these kind of famous data scientists. So yeah, so that's kind of where I land on that whole thing.
BRIAN 04:39	Cool. Okay. So you conquered the world in the data science world, and then you quit it. So tell me about that.
BEN 04:46	Well, there's a funny thing that happens when you bring another human into this world. They are very demanding. And when your job goes from super fun start-up, 50 people, you were just doing everything, bringing all of your knowledge and experience to make a difference every day and grow this company to sitting in meetings, going to executive off-sites, just explaining why your team exists, what you do, when you're in a remote location, the dynamic changes dramatically. And so my job went from being super fun - I was crazy engaged, passionate about it - into more typical corporate kind of political environment, which is fine, but when I had my little guy, it was like, "I don't want to be done with work and then still be stressed all night thinking about what someone said or the meeting I have coming up." I just looked at my life at the time and thought, "Man." I had already made, I think, seven or eight online courses that were generating some residual income, and so I kind of said, "All right. When that catches up to my salary, I'm done." And it's one of those things where if you have this side hustle and you're trying to grow this business, whatever it may be, you get to the point where like, "Okay. If I had more time to do it, I could turn this thing into a billion-dollar enterprise." Right? But you can't get there unless you leave your current job. So I got to the point where I wasn't enjoying my work anymore. It wasn't the fun start-up. I had a very demanding little human, and I wanted to do something more. And so at that time, I was like, "All right. Cool. I've done almost 20 years in corporate America at every level in the data field you can imagine." And it was one of those things where I was like, "Cool. Now it's my time to do my own thing, to take that next step." And so yeah. So I left and started my own business.
BRIAN 06:37	Cool. So tell me about that business. What does that look like?
BEN 06:40	Well, I called it Green Bar Data, and it's not a name you hear me advertise. That's the LLC. But it's in reference to green bar reports, which maybe some people listening to this remember. But in dot matrix printer days-- or actually, it's not dot matrix. It's the one where you had the perforated edges. And this was what in MCI, originally. You'd get what we call green bar reports, and it would be the stats for the day. And it was basically just numbers, like if you could just type it in the smallest, most concise format what the sales numbers were or whatever. And so you'd peel them off the printer and deliver them. So that was the name of it. The idea is I have now 26 online courses, all in the data field, from things that are very general purpose like what I call data analysis fundamentals, something that I think anybody in this field at any level should have, this base knowledge of how it all fits together, how to communicate, how to figure out what your users need versus what they're asking for, right, things like that, to building force-directed layout graphs in D3, which was something not everyone in the world is going do or want to do. So that was what my business was as soon as I left my full-time role was just make online courses. And I cranked out a lot of them, 26 of them now, before I stopped doing that and started doing YouTube, so.
BRIAN 08:08	Yeah. So I think we've buried the lead here a little bit. We've been on for a little while, and we're now going to talk about YouTube. So tell me about YouTube.
BEN 08:15	Yeah. It's a fickle thing. I've mixed emotions about YouTube. I love it and I hate it. I get to do and have some of the most fun ever in my career by geeking out about Tesla which is a company that I've been fascinated with ever since I first got to ride in one - jeez, I don't know - six years ago or something because they were the only company that I ever felt made a car that was worthy of my dollar. And what I mean is the car I had prior to that - it was a Honda Civic or a Honda Accord or something - I remember them trying to sell me this DVD navigation package that could be built into it for $2,000.
BRIAN 08:54	Only $2,000.
BEN 08:55	Yeah. And then every year, you have to load a DVD in and update it. And I thought, "This is the dumbest thing ever. I've this little tiny supercomputer in my pocket that blows this whole car out of the water." I'm like, "How do you not find room for this thing?" And so Tesla did that. Right? They really started as a tech company with bringing that sense of software and all the things you'd expect from a smartphone into the car. So I was fascinated with them from early on. And before I left my full-time job, I bought one, a used one, and a lot of it was predicated on the fact that we'd be saving money on gas. And so a year after that, my wife asked me, "Okay, data guy, prove it to me. Show me the numbers." Right? And so I did.
BRIAN 09:36	Very smart, that one you got.
BEN 09:38	Yeah. Yeah. Yeah. So I don't even think we had it for a year. It was almost a year. And so what I had been doing on YouTube prior to that was just making these teaser videos, like, "Here's the number one function you need to know in Excel. Everybody in the world needs to know this, the VLOOKUP. Right? You have to know that. You just have to," and so doing little videos like that as like, "Hey, this is a teaser. Go watch my full course where I go into this." Right? Not realizing it at the time, but that was basically a fruitless effort. It just didn't make any difference. 1,000 people could go watch my course, and it didn't make any real difference in the revenue I got, so. One of those videos, though, was answering my wife's question about, "Okay. Well, how much money are we saving?" And it was a terrible video. It still is a terrible video. It was shot with 720p with a webcam, horrible lighting. The audio was good because I'd been making all these online courses, so I had a good microphone, and that was good. But it was me talking over a spreadsheet I'd put together that was just showing gas prices, how many miles we drove, what the average miles per gallon would be for a similar car, how much money that would cost versus how much we spent on electricity, which was kind of hard to get too, but anyways. Yeah. I mean, that was the video. It was hastily done. And within the first week, it had like 200,000 views on YouTube which, by any standard, even the biggest YouTubers, I think that's okay. For me, I think I had 800 subscribers at the time, so I was blown away by that. So that's kind of the spark that started Teslanomics which is now my full-time job.
BRIAN 11:12	Right. So I watch a lot of your videos. That's how I found you. And so I think it's Tableau that you're using, but you're still doing that same thing. Right? You're putting this data in. You're trying to solve problems. You're trying to answer questions people are lobbing at you. Tell me a little bit about that process and what do you do there and sort of what are you really trying to convey to people beyond just the obvious stories about Tesla, I guess.
BEN 11:40	Yeah. Yeah. I definitely wanted to bring me, my personality, my background, into this space. At the time and still today, a lot of what you see-- anything Tesla related on YouTube or online is about either autopilot safety or just how sexy these cars are or racing, stuff like that. But me with my background, I thought I had something unique to offer which was a more analytical view on the company and what it means for consumers. So I try to stay focused on a lot of consumer stuff. Elon doesn't make that easy on us always with kind of the drama. But mostly, my focus is around what it means to you. So I built a lot of tools, and I also have an app, and so I've a lot of data around Teslas, like how efficient they are, phantom drain, how much it'll actually cost you, looking at charging rates and how much it'll cost to charge. And so I've built online tools, and then I analyze the results of people using those tools, and I have all these things. So I'm trying to bring a more objective kind of, yeah, data-driven approach to understanding a company that's really changing our world, unlike many others.
BRIAN 12:52	Yeah. It's not surprising to me you've been successful in this because I think that as people shift from traditional gas cars which are a known quantity, right - people understand miles per gallon; they understand how it's going to drive; they get all of that - launching in now to this EV world, there's so many questions people have about, to your point, what is it going cost me? What are the actual economics or the Teslanomics of these things? So that's definitely sort of where I got on to your videos too is because I'm thinking, "Well, hey, here's two things I like." I'm looking at these cars but also exploring the data and trying to understand how is this going to be different, how is this going to change the world, those kind of things. So yeah, I love it and will definitely point people towards the channel.
BEN 13:36	Yeah. Yeah. That's the idea. I'm trying to help people understand what's going on because I think we're at a really interesting time in history where we're seeing a shift, and Tesla kind of is the one driving it. They're setting the bar in that all the other automakers are trying to kind of catch up to, and I think that that's what's going to drive. People compare them to Apple a lot with the iPhone, and I think that's a good comparison, but the good news about that is that if you look at the smartphone market, Apple has 15% of it, whereas we maybe, in Southern California, think of everyone having an iPhone. Really, worldwide, they have 15%, which is a huge percent for a single company, but that means 85% of the smartphone market is not an iPhone-- are not iPhones. And so if we think that same thing is going to play out with Tesla, that's a great story because Tesla will still capture the majority of the profits because they make premium products. They don't make ugly or slow or short-range EVs of the first generation.
BEN 14:40	But because of that, because of what they do and how they've changed people's perception-- I mean, besides the old iPhones, there are very few companies, if any, where people will stand in line in the rain to put down a deposit on something that they may or may not see two to three years down the road. There are people literally standing in line in Europe for a car that still, to this day, does not exist in Europe. That was over two years ago. That just doesn't happen. And so because of their ability to capture people's product lust, I think it's foreseeing or even opening the door for Jaguar or BMW or Mercedes, who's launching an EV soon, to make really legit good electric vehicles. So I think they're the catalyst that's going to push us there, and I want to be here to help understand how that's happening because I think it's a fascinating story, and we're going to look back on this when my son's older thinking, "What the hell were you doing driving this gas car? What is going on?" That doesn't make any sense to him - you know what I mean? - because I think the gas car will be displaced much quicker than people maybe think right now.
BRIAN 15:47	Yeah. Yeah. Okay. So kind of shifting back to the data science aspect of your life and what you've done, what's the coolest thing you ever did from a data analytics, data science, however you want to put it-- I'm just kind of looking for-- you've been in this game a long time. Just kind of trying to explore what's the thing you're most proud of, I guess.
BEN 16:09	Man, a lot of things come to mind. Still, probably, one of the funnest things that I ever helped build was back at MCI, believe it or not. So I mentioned it already or touched on it. So our sales systems were AS400 VAX terminals. I mean, this is as old school as old school gets. Right? There was no mouse for these systems. And working with the manager of the IT desk, the help desk, in the call center, me and a couple of other guys in my team built this real-time analytics engine off of this Unix-based system. And if you were thinking about doing that today, you might think, "Hmm, okay. Maybe there's a database. I can just connect to the database, query the database, or maybe there's an API or something, whatever, I can get the data from." No, none of that stuff existed. This was total black hat. The corporate IT did not want us doing this.
BRIAN 17:03	I bet they were sort of like, "Uh, no."
BEN 17:06	And actually, a funny story, a lesson learned then actually was applied when I was at Pluralsight not too long ago where one of the guys in my team, Mike Roberts, built a PowerShell script to do a completely unattended installation of Tableau server. So I don't know if they have that now, but at the time, this little technique we used way back in like '98, '99 was used just a couple of years ago to accomplish a similar thing. So what it did is it was a VBA macro that would literally take a screenshot of this Unix terminal, paste it into Excel, and then we had other macros it would call in Access to pull that data into Access, and then we built reports that I could just refresh. And I would literally work until 10:00 PM with the managers of the call center that were doing outbound telemarketing at the time, which we all hate but it worked. And so at the time, I'm real-time communicating with these managers going, "Hey, you have too many people on this call campaign. They're burning through these leads. We're going to run out hours before our call time is over," because there's laws about how long. And I mean, I did that for months, and we crushed it.
BEN 18:19	And that right there, still-- imagine trying to do that today, real-time analytics on your outbound call operations, adjusting on-the-fly stuff. Even with today's technology, that's not easy. That's not something you can just get out of the box somewhere. Right? And so I look back at that as like, "That was one of the coolest, funnest things I've ever done," and it had a serious impact for our business and for all the people working there. We had like 1,200 employees. So yeah, it was a blast. I mean, I've had so many fun things, growing teams, growing organizations, help companies go from basically not having a data program to having one that I think is well respected in the industry, things like that, so. It's been a long journey, almost 20 years, but I look back to those little things as like, "Wow." I guess that goes to the hacker mentality I have of just like, "Look, I want to get it done. I want the answer, and I want to make a decision. I don't care about-- we can figure out the proper data model and architectures once we validate that what we're doing is of use."
BRIAN 19:20	Yeah. Okay. So let's look forward now. So from your perspective, where are we going with this whole data science, analytics? What's your view on that?
BEN 19:31	Man, I have a utopian view and a dystopian view.
BRIAN 19:37	Let's get both. This'll be great.
BEN 19:39	Okay. So at Pluralsight, we tried to do some fun stuff along this route, and it looks like there's been some development, but the idea that a person has to sift through your daily numbers and figure out what's important, I think, is going to go away. I think we're going to be able to make bots and machine-learning algorithms and build systems around them that will predict what is of interest and needs to be brought to someone's attention now versus maybe someone else can look at later. So I think things like that, kind of automated insights, will become more prevalent and, I think, will become, for business owners, just fantastic. And actually, even in YouTube, they've updated their analytics recently, and one of the fascinating things I saw the other day is I logged in after I posted a video to see how it was doing, and it gave me a little thing that said, "Oh, this video had X number of views within the first hour which is higher than your previous videos. That means it'll probably do good." I'm like, "Wow. Brilliant." Typically, that would take a person to tell me that, or me being me, I would just figure that out because I have that context built in, but it was able to deliver an insight to me on its own.
BEN 21:00	I think we're going to see more and more of that, like, "Hey, plug in all your data here. Set up your KPIs for your different parts of your business or whatever, and then we're going to highlight ones we think are of interest to you. You tell us whether or not those are interesting." Now you're training a data model to tell it what's important and not. You're still going to have people there figuring it out and really adding the context of the team or whatever to add the kind of deep value, but a lot of that service-level stuff of like, "Hey, why were sales down 20% yesterday?" "Oh, well, because we had an issue with the website," right, I think a lot of that stuff will just be figured out automatically. And so that will save time for people to go deeper into stuff to find greater valuable bits of gold buried in the data. But I think that's something I'm looking forward to and, I think, a lot of folks, business folks especially, are wanting.
BRIAN 21:50	Or if they don't know they want it, they're going to want it. Right? I think--
BEN 21:52	No doubt. No doubt.
BRIAN 21:53	--that's the other thing. I think there's still some of the, "Hey, we've been doing it this way for 20 years. We'll keep doing it this way," contingent. And I guess that sort of brings us full circle back to the Tesla thing, right, which is I think people don't know they want it maybe until they've ridden in one or they've understood. And when I think about-- so first of all, I think you're totally right. I think the automation is going to be the thing. And when I think about myself - what do I believe is the biggest analytical challenge we're going to solve in the next couple of decades? - I do look at things like autonomous-driving cars, right, because it occurs to me that this is essentially streaming data from radar, lidar, sensors, whatever it is, and basically, the computer in there has to do all this in real time to keep you safe and navigate the road and--
BEN 22:39	Yeah. And on the latest earnings call, that was fascinating. I don't know if you listen to that. Not many people are probably listening to Tesla earnings calls, but I do. And they actually brought the autopilot team on the call which was unusual. Right? Usually, it's just Elon, Deepak, and JB, the kind of C-level folks. And they had them talk about that because they've had to make new chips in order to process this data in real time. I did, this past January, spend an afternoon with the Mercedes self-driving team, and I got to drive in their-- or ride in their Level 5 S-Class. Super cool. And that was a big question I posed to them. I'm like, "Well, look, it's fine if the roads are a known quantity." And so I have a mixed view on this, but I think true autonomous vehicles are maybe 10, 15 years away. I think what we'll see in between here and there are what people will call self-driving vehicles but aren't truly fully autonomous. A good example would be at maybe an airport or maybe at a university where the roads are well maintained. They're known. The thing only has to go five miles an hour to be useful. So those things, I think, we'll see today and tomorrow. That's easy if you can hard code, essentially, the route it's going, what to expect, etc. The impossible thing is to figure out traffic, construction on the road, snow. Things like that are incredibly difficult to try to figure out in real time when the car has never encountered it before. Right? That's what machine learning is aiming to do, and it does work when you have it connected with a fiber channel and mountains of RAM and GPUs to process stuff on. Right? Google can figure out what you want before you want it because they have the entire giant data center powering that one query. Your car isn't there yet. So I think it's fascinating, but I think, for self-driving cars to be a reality, there are some hardware challenges that we have to solve first.
BRIAN 24:40	Sure. Yeah. And spoiler alert, I am a huge nerd. I do listen to the earnings calls, and I was super excited. I'm like, "Wait. They have data people on the earnings call. This is amazing." So I think you're starting to see that come into more focus for these businesses, not just Tesla but a lot of businesses of a lot of different stripes, right, of how important data science and analytics is, how they're embracing it, and you start to see these signals kind of slowly leaking out of their earnings calls or the news that they put out about how they see this as being a pillar of their future, so.
BEN 25:14	Yeah. Yeah. Completely. I think of companies-- I mean, wow. It's funny too after spending a few years consulting in Silicon Valley. I mean, data is the name of the game. Right? If you go to work at Facebook and you're in the HR department, right, and you're a recruiter, you spend six weeks at data camp figuring out how to use data. Why? Because the entire company is full of data geeks that just have the greatest amount of data and tools available to them known to the world. So it's one of those things. Those companies get it. And those that do, I think, will thrive, and other companies will wonder how they were able to figure that out, right, how they were able to come up with that program that works so well. Well, because you had some really smart people experimenting with an idea, seeing if it would work. You didn't just go off your gut instinct. And I mean, that's, at this point, hopefully proven. You need that no matter what type of organization you have.
BRIAN 26:05	Yeah. Maybe we'll get to the point where, on everybody's résumés, instead of putting Microsoft Office as their proficient skill, they'll just say data analytics or data science, whatever, which brings me actually to another great topic. So you talked a little bit before about the courses that you've created over time and how that's gone. I know that you've recently kind of announced a new one, so tell us a little bit about that.
BEN 26:27	Yeah. So I've been wanting to do this for a while, and I feel I'm uniquely kind of positioned to help people get their first job in the field of data science and do various different roles as well as to advance their careers. So I'm launching what I call the Free the Data Academy, and the idea is essentially that it's soft skills for people in the data industry. So one of the challenges that I've found over the years is you can have people that are just tremendous what I would call doers. They can write code. They can do analysis. They can come up with findings that are truly valuable. But none of that matters if you can't stand in front of a room of executives and convince them that your experiment is valid and they should act on it. It does not matter how good your experiment was or how elegant your code was. That is worth zero unless you can actually communicate that in a way that moves people. Now, when I was building teams at organizations, this would be the difference that I saw between a director and just a regular individual contributor. Right? So you could be a fantastic data scientist running really, really great experiments, finding beautiful things, but I am not going to put you in front of the CEO and have you try to present that idea. Right? If you're the director of data science, I'm going to expect that you can do both of those things well. And so to me, that is a huge piece that's missing.
BEN 27:57	In fact, just last night-- because I'm adding several different aspects. There's the soft skills. I am going to add a technical foundations for people that are kind of new to this, and also I think the technical foundations would be good for people that are super niche focused in one area. A good example is when I worked at Mozilla. We had, and they still, I'm sure, have a tremendous team of real what we would call data scientists, statisticians, essentially, that can code. And so we had people there, I remember, that were working on their PhDs in stats from Harvard that could not install software on their laptop. I think that's a shame. You should have a base-level understanding of all technology if you are going to try to be functioning in a role at an extremely high level. Right? I'm not going to pay you 150 grand a year and then you tell me you can't install Alteryx or Tableau or whatever the hell tools we use at our business. It's just unacceptable. You need to have that base level. So I've got my soft skills which I think are critical for people to succeed because when it comes down to it, we're all human, and we all respond to the same psychological triggers that we've been evolved to respond to for millions of years. So no matter how much time we spent at university or in business or whatever, we still behave very similarly given similar triggers.
BEN 29:22	And so that's what I want to help people understand is that when you have a finding, when you have something that needs to be communicated and needs to be acted upon, how do you do that? And the answer isn't a chart, or I mean, it could be a chart, but that's not the answer. Right? And it's not a better font or something like that. It is using the same presentation and storytelling methods that we as humans, still to this day, respond to in very similar ways. So that is, I think, a key piece, and none of the courses I've ever taught, none of the platforms I've ever taught on, cover this. I haven't found anything besides a 10-week, $20,000, off-site boot camp that will actually solve that. So I wanted to do some more stuff, and this time, I'm doing it on my own. So that's the Free the Data Academy, and that's the first thing, soft skills, technical skills, and I'll probably add an interviewing section as well for people where it's first job. Maybe you have a degree. It's amazing you can get a degree in some data field now. When I went to school, didn't exist.
BRIAN 30:27	That wasn't around. Yeah.
BEN 30:28	Yeah. None of that stuff was there. So I think that is what my focus is now because we need people that are skilled at this that can also make a difference. It's not enough just to be a good coder anymore or to be a good mathematician. That's not enough to be successful in business. And so that's why I'm trying to bridge that gap. I'm trying to help people go from, "Hey, I know how to use R or Alteryx or whatever. I know how to actually do the data part, but I don't know how to actually make a difference." So that's the main focus.
BRIAN 31:02	Very cool, man. All right. Well, we'll make sure we put links to the show notes in that for you. So I think that's about it from my end. Where do people go to get more Ben Sullins?
BEN 31:11	Yeah. So you can find me at teslanomics.co for all the Tesla-related stuff or just to bensullins.com where I'm starting up my blog again. I've had that blog going for like 12 years now. It's crazy. It used to just be when I would figure something out because I was a Microsoft business intelligence consultant many moons ago, and Microsoft is very great at not documenting everything fully, so back then, it was just, "Hey, I figured this out. I'm going to write it down. Maybe someone else will like that." So at bensullins.com, that's where you get all the data stuff, and you can learn more about the academy there and all that kind of thing, so.
BRIAN 31:44	Awesome. And I follow you on Twitter, so we'll make sure we point people in that direction. Yeah. Thanks for being on, man. We appreciate it.
BEN 31:52	Absolutely. Glad to be here. [music]

BRIAN 00:06 [music] Welcome to Alter Everything, a podcast about data and analytics culture. I'm Brian Oblinger, and I'll be your host. We're joined by Ben Sullins for a chat about data science, the world of Teslanomics, and why soft skills are the future of your career. Let's get right into it. All right, Ben. Welcome to the show. BEN 00:28 Hey. Thanks for having me, man. Glad to be here. BRIAN 00:30 Yeah. No, I'm super excited. So let's start with you. Tell us about you and your journey kind of to analytics, through analytics. BEN 00:38 Yeah. It's been a wild one. I've been a lifelong data geek is how I like to describe it. I was very fortunate early in my life to find this bug for technology where I just wanted to figure it out. To me, it was the ultimate hacking ability was to be able to manipulate and get technology to do what you want. And at a early age, I was fortunate enough to land a job as a intern at MCI, a phone company that no one knew exists but, at the time, was the pinnacle of tech. Right? Google didn't exist. Facebook didn't exist. The internet was a very nascent kind of thing. So the tech companies of the world, at the time, were telecom companies. And I was able, on the help desk there, to get a taste of everything, and it was nice because now I had this broad sense of what technology was. And I really found my niche working with data, and at the time, that would be Excel 97 with Microsoft Access. This is prior to even Office 2000 and any of that stuff. This was super early days. Dot net didn't exist yet. Right? This was like ancient history in the tech world. But I found my love for being able to look at these numbers and manipulate them and do Excel formulas and stuff and then give that information to different teams to help them do their jobs better. And through those efforts, we had some wild stuff going on at the time that, even by today's standards, I would say is-- in terms of what we were able to do, real-time analytics off of a Unix-based system that was giving predictions for call volumes for our sales staff, I mean, by the minute-- BRIAN 02:22 Mind-blowing analytics happening. BEN 02:24 I mean, in the late '90s by hand using Visual Basic for Applications, wild stuff. We were able to actually grow the call center to be the number one sales call center for the whole company for several quarters in a row until Verizon bought us and shut everyone down. But yeah, since then, I just had this insane passion. And throughout my career, there've always been opportunities to do other things that were maybe more sexy or more fun or maybe made more money. Back in the early '00s, everyone was going into real estate because they could make all this money selling mortgages. BRIAN 02:58 Yeah. You should have done that, Ben. You missed out. You should have got in there. Maybe right around '08, you should have-- BEN 03:02 Yeah. Yeah. Exactly. Right? It's one of those things where it was like I just stuck with what I liked, and I liked turning data, just raw basic information, event logs and things, into something that people could then use to improve their business. So it's always been my passion, and I just was lucky enough or fortunate enough to see that and not stray from it for almost 20 years until I decided to quit my job and start making YouTube videos. BRIAN 03:31 So before we get to the quitting your job and making YouTube videos, tell us a little bit about-- I know that, at one point, you had the title of data scientist, I believe. BEN 03:40 It's interesting. So this audience, I would consider myself more of a data analyst, but I've also held the role of data engineer, front-end web developer because, unlike today, if you wanted to build a website where somebody could go view a chart or even just a table of data, you had to hand code that. Right? So I've a myriad of roles. I don't think any of my roles have ever been what I would call-- what people that are listening to this would consider a data scientist, but if I'm talking to Joe Rogan about it, to the general population, we're all data scientists. Right? So I think that term to somebody that knows what it means, I wouldn't say that's ever been a job I've had, but I consider the whole field to be data science because that just seems to be the term that has generally been adopted now. If you say business intelligence or something, people, no clue. Right? But you say data science, it's like, "Oh, Nate Silver, DJ Patil," these kind of famous data scientists. So yeah, so that's kind of where I land on that whole thing. BRIAN 04:39 Cool. Okay. So you conquered the world in the data science world, and then you quit it. So tell me about that. BEN 04:46 Well, there's a funny thing that happens when you bring another human into this world. They are very demanding. And when your job goes from super fun start-up, 50 people, you were just doing everything, bringing all of your knowledge and experience to make a difference every day and grow this company to sitting in meetings, going to executive off-sites, just explaining why your team exists, what you do, when you're in a remote location, the dynamic changes dramatically. And so my job went from being super fun - I was crazy engaged, passionate about it - into more typical corporate kind of political environment, which is fine, but when I had my little guy, it was like, "I don't want to be done with work and then still be stressed all night thinking about what someone said or the meeting I have coming up." I just looked at my life at the time and thought, "Man." I had already made, I think, seven or eight online courses that were generating some residual income, and so I kind of said, "All right. When that catches up to my salary, I'm done." And it's one of those things where if you have this side hustle and you're trying to grow this business, whatever it may be, you get to the point where like, "Okay. If I had more time to do it, I could turn this thing into a billion-dollar enterprise." Right? But you can't get there unless you leave your current job. So I got to the point where I wasn't enjoying my work anymore. It wasn't the fun start-up. I had a very demanding little human, and I wanted to do something more. And so at that time, I was like, "All right. Cool. I've done almost 20 years in corporate America at every level in the data field you can imagine." And it was one of those things where I was like, "Cool. Now it's my time to do my own thing, to take that next step." And so yeah. So I left and started my own business. BRIAN 06:37 Cool. So tell me about that business. What does that look like? BEN 06:40 Well, I called it Green Bar Data, and it's not a name you hear me advertise. That's the LLC. But it's in reference to green bar reports, which maybe some people listening to this remember. But in dot matrix printer days-- or actually, it's not dot matrix. It's the one where you had the perforated edges. And this was what in MCI, originally. You'd get what we call green bar reports, and it would be the stats for the day. And it was basically just numbers, like if you could just type it in the smallest, most concise format what the sales numbers were or whatever. And so you'd peel them off the printer and deliver them. So that was the name of it. The idea is I have now 26 online courses, all in the data field, from things that are very general purpose like what I call data analysis fundamentals, something that I think anybody in this field at any level should have, this base knowledge of how it all fits together, how to communicate, how to figure out what your users need versus what they're asking for, right, things like that, to building force-directed layout graphs in D3, which was something not everyone in the world is going do or want to do. So that was what my business was as soon as I left my full-time role was just make online courses. And I cranked out a lot of them, 26 of them now, before I stopped doing that and started doing YouTube, so. BRIAN 08:08 Yeah. So I think we've buried the lead here a little bit. We've been on for a little while, and we're now going to talk about YouTube. So tell me about YouTube. BEN 08:15 Yeah. It's a fickle thing. I've mixed emotions about YouTube. I love it and I hate it. I get to do and have some of the most fun ever in my career by geeking out about Tesla which is a company that I've been fascinated with ever since I first got to ride in one - jeez, I don't know - six years ago or something because they were the only company that I ever felt made a car that was worthy of my dollar. And what I mean is the car I had prior to that - it was a Honda Civic or a Honda Accord or something - I remember them trying to sell me this DVD navigation package that could be built into it for $2,000. BRIAN 08:54 Only $2,000. BEN 08:55 Yeah. And then every year, you have to load a DVD in and update it. And I thought, "This is the dumbest thing ever. I've this little tiny supercomputer in my pocket that blows this whole car out of the water." I'm like, "How do you not find room for this thing?" And so Tesla did that. Right? They really started as a tech company with bringing that sense of software and all the things you'd expect from a smartphone into the car. So I was fascinated with them from early on. And before I left my full-time job, I bought one, a used one, and a lot of it was predicated on the fact that we'd be saving money on gas. And so a year after that, my wife asked me, "Okay, data guy, prove it to me. Show me the numbers." Right? And so I did. BRIAN 09:36 Very smart, that one you got. BEN 09:38 Yeah. Yeah. Yeah. So I don't even think we had it for a year. It was almost a year. And so what I had been doing on YouTube prior to that was just making these teaser videos, like, "Here's the number one function you need to know in Excel. Everybody in the world needs to know this, the VLOOKUP. Right? You have to know that. You just have to," and so doing little videos like that as like, "Hey, this is a teaser. Go watch my full course where I go into this." Right? Not realizing it at the time, but that was basically a fruitless effort. It just didn't make any difference. 1,000 people could go watch my course, and it didn't make any real difference in the revenue I got, so. One of those videos, though, was answering my wife's question about, "Okay. Well, how much money are we saving?" And it was a terrible video. It still is a terrible video. It was shot with 720p with a webcam, horrible lighting. The audio was good because I'd been making all these online courses, so I had a good microphone, and that was good. But it was me talking over a spreadsheet I'd put together that was just showing gas prices, how many miles we drove, what the average miles per gallon would be for a similar car, how much money that would cost versus how much we spent on electricity, which was kind of hard to get too, but anyways. Yeah. I mean, that was the video. It was hastily done. And within the first week, it had like 200,000 views on YouTube which, by any standard, even the biggest YouTubers, I think that's okay. For me, I think I had 800 subscribers at the time, so I was blown away by that. So that's kind of the spark that started Teslanomics which is now my full-time job. BRIAN 11:12 Right. So I watch a lot of your videos. That's how I found you. And so I think it's Tableau that you're using, but you're still doing that same thing. Right? You're putting this data in. You're trying to solve problems. You're trying to answer questions people are lobbing at you. Tell me a little bit about that process and what do you do there and sort of what are you really trying to convey to people beyond just the obvious stories about Tesla, I guess. BEN 11:40 Yeah. Yeah. I definitely wanted to bring me, my personality, my background, into this space. At the time and still today, a lot of what you see-- anything Tesla related on YouTube or online is about either autopilot safety or just how sexy these cars are or racing, stuff like that. But me with my background, I thought I had something unique to offer which was a more analytical view on the company and what it means for consumers. So I try to stay focused on a lot of consumer stuff. Elon doesn't make that easy on us always with kind of the drama. But mostly, my focus is around what it means to you. So I built a lot of tools, and I also have an app, and so I've a lot of data around Teslas, like how efficient they are, phantom drain, how much it'll actually cost you, looking at charging rates and how much it'll cost to charge. And so I've built online tools, and then I analyze the results of people using those tools, and I have all these things. So I'm trying to bring a more objective kind of, yeah, data-driven approach to understanding a company that's really changing our world, unlike many others. BRIAN 12:52 Yeah. It's not surprising to me you've been successful in this because I think that as people shift from traditional gas cars which are a known quantity, right - people understand miles per gallon; they understand how it's going to drive; they get all of that - launching in now to this EV world, there's so many questions people have about, to your point, what is it going cost me? What are the actual economics or the Teslanomics of these things? So that's definitely sort of where I got on to your videos too is because I'm thinking, "Well, hey, here's two things I like." I'm looking at these cars but also exploring the data and trying to understand how is this going to be different, how is this going to change the world, those kind of things. So yeah, I love it and will definitely point people towards the channel. BEN 13:36 Yeah. Yeah. That's the idea. I'm trying to help people understand what's going on because I think we're at a really interesting time in history where we're seeing a shift, and Tesla kind of is the one driving it. They're setting the bar in that all the other automakers are trying to kind of catch up to, and I think that that's what's going to drive. People compare them to Apple a lot with the iPhone, and I think that's a good comparison, but the good news about that is that if you look at the smartphone market, Apple has 15% of it, whereas we maybe, in Southern California, think of everyone having an iPhone. Really, worldwide, they have 15%, which is a huge percent for a single company, but that means 85% of the smartphone market is not an iPhone-- are not iPhones. And so if we think that same thing is going to play out with Tesla, that's a great story because Tesla will still capture the majority of the profits because they make premium products. They don't make ugly or slow or short-range EVs of the first generation. BEN 14:40 But because of that, because of what they do and how they've changed people's perception-- I mean, besides the old iPhones, there are very few companies, if any, where people will stand in line in the rain to put down a deposit on something that they may or may not see two to three years down the road. There are people literally standing in line in Europe for a car that still, to this day, does not exist in Europe. That was over two years ago. That just doesn't happen. And so because of their ability to capture people's product lust, I think it's foreseeing or even opening the door for Jaguar or BMW or Mercedes, who's launching an EV soon, to make really legit good electric vehicles. So I think they're the catalyst that's going to push us there, and I want to be here to help understand how that's happening because I think it's a fascinating story, and we're going to look back on this when my son's older thinking, "What the hell were you doing driving this gas car? What is going on?" That doesn't make any sense to him - you know what I mean? - because I think the gas car will be displaced much quicker than people maybe think right now. BRIAN 15:47 Yeah. Yeah. Okay. So kind of shifting back to the data science aspect of your life and what you've done, what's the coolest thing you ever did from a data analytics, data science, however you want to put it-- I'm just kind of looking for-- you've been in this game a long time. Just kind of trying to explore what's the thing you're most proud of, I guess. BEN 16:09 Man, a lot of things come to mind. Still, probably, one of the funnest things that I ever helped build was back at MCI, believe it or not. So I mentioned it already or touched on it. So our sales systems were AS400 VAX terminals. I mean, this is as old school as old school gets. Right? There was no mouse for these systems. And working with the manager of the IT desk, the help desk, in the call center, me and a couple of other guys in my team built this real-time analytics engine off of this Unix-based system. And if you were thinking about doing that today, you might think, "Hmm, okay. Maybe there's a database. I can just connect to the database, query the database, or maybe there's an API or something, whatever, I can get the data from." No, none of that stuff existed. This was total black hat. The corporate IT did not want us doing this. BRIAN 17:03 I bet they were sort of like, "Uh, no." BEN 17:06 And actually, a funny story, a lesson learned then actually was applied when I was at Pluralsight not too long ago where one of the guys in my team, Mike Roberts, built a PowerShell script to do a completely unattended installation of Tableau server. So I don't know if they have that now, but at the time, this little technique we used way back in like '98, '99 was used just a couple of years ago to accomplish a similar thing. So what it did is it was a VBA macro that would literally take a screenshot of this Unix terminal, paste it into Excel, and then we had other macros it would call in Access to pull that data into Access, and then we built reports that I could just refresh. And I would literally work until 10:00 PM with the managers of the call center that were doing outbound telemarketing at the time, which we all hate but it worked. And so at the time, I'm real-time communicating with these managers going, "Hey, you have too many people on this call campaign. They're burning through these leads. We're going to run out hours before our call time is over," because there's laws about how long. And I mean, I did that for months, and we crushed it. BEN 18:19 And that right there, still-- imagine trying to do that today, real-time analytics on your outbound call operations, adjusting on-the-fly stuff. Even with today's technology, that's not easy. That's not something you can just get out of the box somewhere. Right? And so I look back at that as like, "That was one of the coolest, funnest things I've ever done," and it had a serious impact for our business and for all the people working there. We had like 1,200 employees. So yeah, it was a blast. I mean, I've had so many fun things, growing teams, growing organizations, help companies go from basically not having a data program to having one that I think is well respected in the industry, things like that, so. It's been a long journey, almost 20 years, but I look back to those little things as like, "Wow." I guess that goes to the hacker mentality I have of just like, "Look, I want to get it done. I want the answer, and I want to make a decision. I don't care about-- we can figure out the proper data model and architectures once we validate that what we're doing is of use." BRIAN 19:20 Yeah. Okay. So let's look forward now. So from your perspective, where are we going with this whole data science, analytics? What's your view on that? BEN 19:31 Man, I have a utopian view and a dystopian view. BRIAN 19:37 Let's get both. This'll be great. BEN 19:39 Okay. So at Pluralsight, we tried to do some fun stuff along this route, and it looks like there's been some development, but the idea that a person has to sift through your daily numbers and figure out what's important, I think, is going to go away. I think we're going to be able to make bots and machine-learning algorithms and build systems around them that will predict what is of interest and needs to be brought to someone's attention now versus maybe someone else can look at later. So I think things like that, kind of automated insights, will become more prevalent and, I think, will become, for business owners, just fantastic. And actually, even in YouTube, they've updated their analytics recently, and one of the fascinating things I saw the other day is I logged in after I posted a video to see how it was doing, and it gave me a little thing that said, "Oh, this video had X number of views within the first hour which is higher than your previous videos. That means it'll probably do good." I'm like, "Wow. Brilliant." Typically, that would take a person to tell me that, or me being me, I would just figure that out because I have that context built in, but it was able to deliver an insight to me on its own. BEN 21:00 I think we're going to see more and more of that, like, "Hey, plug in all your data here. Set up your KPIs for your different parts of your business or whatever, and then we're going to highlight ones we think are of interest to you. You tell us whether or not those are interesting." Now you're training a data model to tell it what's important and not. You're still going to have people there figuring it out and really adding the context of the team or whatever to add the kind of deep value, but a lot of that service-level stuff of like, "Hey, why were sales down 20% yesterday?" "Oh, well, because we had an issue with the website," right, I think a lot of that stuff will just be figured out automatically. And so that will save time for people to go deeper into stuff to find greater valuable bits of gold buried in the data. But I think that's something I'm looking forward to and, I think, a lot of folks, business folks especially, are wanting. BRIAN 21:50 Or if they don't know they want it, they're going to want it. Right? I think-- BEN 21:52 No doubt. No doubt. BRIAN 21:53 --that's the other thing. I think there's still some of the, "Hey, we've been doing it this way for 20 years. We'll keep doing it this way," contingent. And I guess that sort of brings us full circle back to the Tesla thing, right, which is I think people don't know they want it maybe until they've ridden in one or they've understood. And when I think about-- so first of all, I think you're totally right. I think the automation is going to be the thing. And when I think about myself - what do I believe is the biggest analytical challenge we're going to solve in the next couple of decades? - I do look at things like autonomous-driving cars, right, because it occurs to me that this is essentially streaming data from radar, lidar, sensors, whatever it is, and basically, the computer in there has to do all this in real time to keep you safe and navigate the road and-- BEN 22:39 Yeah. And on the latest earnings call, that was fascinating. I don't know if you listen to that. Not many people are probably listening to Tesla earnings calls, but I do. And they actually brought the autopilot team on the call which was unusual. Right? Usually, it's just Elon, Deepak, and JB, the kind of C-level folks. And they had them talk about that because they've had to make new chips in order to process this data in real time. I did, this past January, spend an afternoon with the Mercedes self-driving team, and I got to drive in their-- or ride in their Level 5 S-Class. Super cool. And that was a big question I posed to them. I'm like, "Well, look, it's fine if the roads are a known quantity." And so I have a mixed view on this, but I think true autonomous vehicles are maybe 10, 15 years away. I think what we'll see in between here and there are what people will call self-driving vehicles but aren't truly fully autonomous. A good example would be at maybe an airport or maybe at a university where the roads are well maintained. They're known. The thing only has to go five miles an hour to be useful. So those things, I think, we'll see today and tomorrow. That's easy if you can hard code, essentially, the route it's going, what to expect, etc. The impossible thing is to figure out traffic, construction on the road, snow. Things like that are incredibly difficult to try to figure out in real time when the car has never encountered it before. Right? That's what machine learning is aiming to do, and it does work when you have it connected with a fiber channel and mountains of RAM and GPUs to process stuff on. Right? Google can figure out what you want before you want it because they have the entire giant data center powering that one query. Your car isn't there yet. So I think it's fascinating, but I think, for self-driving cars to be a reality, there are some hardware challenges that we have to solve first. BRIAN 24:40 Sure. Yeah. And spoiler alert, I am a huge nerd. I do listen to the earnings calls, and I was super excited. I'm like, "Wait. They have data people on the earnings call. This is amazing." So I think you're starting to see that come into more focus for these businesses, not just Tesla but a lot of businesses of a lot of different stripes, right, of how important data science and analytics is, how they're embracing it, and you start to see these signals kind of slowly leaking out of their earnings calls or the news that they put out about how they see this as being a pillar of their future, so. BEN 25:14 Yeah. Yeah. Completely. I think of companies-- I mean, wow. It's funny too after spending a few years consulting in Silicon Valley. I mean, data is the name of the game. Right? If you go to work at Facebook and you're in the HR department, right, and you're a recruiter, you spend six weeks at data camp figuring out how to use data. Why? Because the entire company is full of data geeks that just have the greatest amount of data and tools available to them known to the world. So it's one of those things. Those companies get it. And those that do, I think, will thrive, and other companies will wonder how they were able to figure that out, right, how they were able to come up with that program that works so well. Well, because you had some really smart people experimenting with an idea, seeing if it would work. You didn't just go off your gut instinct. And I mean, that's, at this point, hopefully proven. You need that no matter what type of organization you have. BRIAN 26:05 Yeah. Maybe we'll get to the point where, on everybody's résumés, instead of putting Microsoft Office as their proficient skill, they'll just say data analytics or data science, whatever, which brings me actually to another great topic. So you talked a little bit before about the courses that you've created over time and how that's gone. I know that you've recently kind of announced a new one, so tell us a little bit about that. BEN 26:27 Yeah. So I've been wanting to do this for a while, and I feel I'm uniquely kind of positioned to help people get their first job in the field of data science and do various different roles as well as to advance their careers. So I'm launching what I call the Free the Data Academy, and the idea is essentially that it's soft skills for people in the data industry. So one of the challenges that I've found over the years is you can have people that are just tremendous what I would call doers. They can write code. They can do analysis. They can come up with findings that are truly valuable. But none of that matters if you can't stand in front of a room of executives and convince them that your experiment is valid and they should act on it. It does not matter how good your experiment was or how elegant your code was. That is worth zero unless you can actually communicate that in a way that moves people. Now, when I was building teams at organizations, this would be the difference that I saw between a director and just a regular individual contributor. Right? So you could be a fantastic data scientist running really, really great experiments, finding beautiful things, but I am not going to put you in front of the CEO and have you try to present that idea. Right? If you're the director of data science, I'm going to expect that you can do both of those things well. And so to me, that is a huge piece that's missing. BEN 27:57 In fact, just last night-- because I'm adding several different aspects. There's the soft skills. I am going to add a technical foundations for people that are kind of new to this, and also I think the technical foundations would be good for people that are super niche focused in one area. A good example is when I worked at Mozilla. We had, and they still, I'm sure, have a tremendous team of real what we would call data scientists, statisticians, essentially, that can code. And so we had people there, I remember, that were working on their PhDs in stats from Harvard that could not install software on their laptop. I think that's a shame. You should have a base-level understanding of all technology if you are going to try to be functioning in a role at an extremely high level. Right? I'm not going to pay you 150 grand a year and then you tell me you can't install Alteryx or Tableau or whatever the hell tools we use at our business. It's just unacceptable. You need to have that base level. So I've got my soft skills which I think are critical for people to succeed because when it comes down to it, we're all human, and we all respond to the same psychological triggers that we've been evolved to respond to for millions of years. So no matter how much time we spent at university or in business or whatever, we still behave very similarly given similar triggers. BEN 29:22 And so that's what I want to help people understand is that when you have a finding, when you have something that needs to be communicated and needs to be acted upon, how do you do that? And the answer isn't a chart, or I mean, it could be a chart, but that's not the answer. Right? And it's not a better font or something like that. It is using the same presentation and storytelling methods that we as humans, still to this day, respond to in very similar ways. So that is, I think, a key piece, and none of the courses I've ever taught, none of the platforms I've ever taught on, cover this. I haven't found anything besides a 10-week, $20,000, off-site boot camp that will actually solve that. So I wanted to do some more stuff, and this time, I'm doing it on my own. So that's the Free the Data Academy, and that's the first thing, soft skills, technical skills, and I'll probably add an interviewing section as well for people where it's first job. Maybe you have a degree. It's amazing you can get a degree in some data field now. When I went to school, didn't exist. BRIAN 30:27 That wasn't around. Yeah. BEN 30:28 Yeah. None of that stuff was there. So I think that is what my focus is now because we need people that are skilled at this that can also make a difference. It's not enough just to be a good coder anymore or to be a good mathematician. That's not enough to be successful in business. And so that's why I'm trying to bridge that gap. I'm trying to help people go from, "Hey, I know how to use R or Alteryx or whatever. I know how to actually do the data part, but I don't know how to actually make a difference." So that's the main focus. BRIAN 31:02 Very cool, man. All right. Well, we'll make sure we put links to the show notes in that for you. So I think that's about it from my end. Where do people go to get more Ben Sullins? BEN 31:11 Yeah. So you can find me at teslanomics.co for all the Tesla-related stuff or just to bensullins.com where I'm starting up my blog again. I've had that blog going for like 12 years now. It's crazy. It used to just be when I would figure something out because I was a Microsoft business intelligence consultant many moons ago, and Microsoft is very great at not documenting everything fully, so back then, it was just, "Hey, I figured this out. I'm going to write it down. Maybe someone else will like that." So at bensullins.com, that's where you get all the data stuff, and you can learn more about the academy there and all that kind of thing, so. BRIAN 31:44 Awesome. And I follow you on Twitter, so we'll make sure we point people in that direction. Yeah. Thanks for being on, man. We appreciate it. BEN 31:52 Absolutely. Glad to be here. [music]

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).

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17: Free the data and your mind will follow

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