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  • Writer's pictureKen Jee

Are Your Goals Holding You Back in Data Science? (Ben Sullins) - KNN Ep. 94

Updated: May 2, 2022

Ben has spent his entire adult life trying to understand how the world works by looking at the stories buried in data. Ben's career in data started in 1998 in the telecom industry with the company MCI. Since then Ben has consulted at silicon valley’s biggest companies and led the charge at Pluralsight to become a data-driven organization and help millions elevate their skillsets. In 2016, Ben shifted gears towards helping fight climate change and leave a better world for future generations. Ben's efforts in that space involve helping people decide to drive electric as well as live more sustainably with renewable energy solutions. Today, Ben continues to help people develop their skillsets at Free the Data Academy and inspire people to live more sustainably on YouTube. In our conversation today, we learn about Ben’s journey into data and why he thinks goal setting is complete BS. I think you will enjoy our healthy debate on the topic!



[00:00:00] Ben: So we didn't have individual goals effectively. I mean, as far as HR was concerned, we did, but effectively what we had were team goals, because it was like, if we didn't achieve these things, if we didn't deliver on the, if we didn't make progress to a satisfactory level, like building a data Mart or delivering some dashboards or whatever, the thing was, my team was working on, then we failed. I don't care if you put all the data in there, but it never got to the. ,It doesn't matter, right? You could score a hundred points in a game and still lose the game.

[00:00:37] Ken: Today, I had the pleasure of interviewing Ben Sullins. Ben spent his entire adult life trying to understand how the world works by looking at the stories buried in data. Ben's career in data started in 1998 at the telecom industry with a company MCI. Since then Ben has consulted at Silicon Valley's biggest companies and led the charge at Pluralsight to become a data-driven organization and help millions elevate their skillsets. In 2016, Ben shifted gears towards helping fight climate change and leave a better world for future generations. Ben's efforts in that space involve helping people decide to drive electric vehicles as well as living more sustainably with renewable energy. Today, Ben continues to help people develop their skillsets at Free the Data Academy and inspires people to live more sustainably on YouTube. In our conversation today, we learned about Ben's incredible journey into data, why he thinks school setting might be completed.

I think you'll really enjoy our healthy debate on the topic. Ben, thank you so much for coming on the Ken's Nearest Neighbors Podcast. I was just recently on your, your podcast Free the Data, and I'm so happy to be able to have you on to tell your story, talk about your journey on YouTube, and also talk about some of the other fun projects that you're working on. So again, thank you for coming on the show.

[00:01:53] Ben: Absolutely, man. Happy to be here.

[00:01:55] Ken: Excellent. Well, so the first thing that I always like to start off with to get the listeners just a little bit more familiar with you and your story is understanding how you first got introduced to data. So did you become interested in data because of one pivotal moment or something that happened to you in the past, or has it been a slow progression over time.

[00:02:14] Ben: I mean, so I'm sort of a dinosaur, I guess, in this industry at this point. I mean, I have my first job where I was doing data things was in 1998. I was 17 years old and I was just a techie kid. I built my own computer when I was 15. You know, this is before cable internet was a thing. When you had like AOL CDs, you had to dial up and do the thing.

I mean, so I was just always interested in technology and I got a job working at the help desk at a company called MCI, which at the time was the world's largest communications company. And then later massive scandal and bought by Verizon, whatever. So, but at the time this was like the pinnacle of tech.

And remember the internet was like not really a thing. It was barely. Like, Oh, the Internet's cool. It was still like, most grown-ups didn't know what it was or whatever. And at that job at the help desk, I got to do everything. I got to, you know, tune phone systems. I got to replace vacs, terminals. I got to do tape backups on in the little data center we had and all these things.

And one of the things we got to do was to work with what was then called the reporting team and the reporting team. Basically, we're just doing things in Excel. It was at a call center in Phoenix, Arizona, and we had outbound and inbound calls coming in outbound sales, inbound sales, customer service, that kind of stuff.

And the reporting team always had to figure out how many sales did we make? And it would print out reports given to people as a sales supervisors, inbound people had to figure out how many, you know, staffing levels and things like that. As I was just there on the help desk, I got to touch all of these different things.

And I sort of just found myself working in helping the reporting team more and more do their work and Excel. And this was office 97. So we're like Excel 97 and Microsoft Access where the tools we use primarily and, you know, SQL Server, like I don't think was even a thing yet. Maybe SQL Server. It was, I think it was still Sybase.

So, I mean, it was very early days compared to like what we, the technology we have now. And yeah, I just gravitated towards doing it. It was like something I was better and better at or, you know, finding myself to growing in those skills. And then later they invited me to join their team. And then I got a job on the reporting team and ever since then, I mean, like I said, 98, I just, I found it was interesting to me.

I found that people value debt and it was always growing and changing. So it wasn't boring. Right. It was like, Oh, there's something new that people are changing this. Let me try that. And back in those days, it was very much the wild west, like putting a chart on a webpage would, would have been considered revolutionary at the time.

Like just having a table of data on a webpage meant that you had to write code at the time. I think like the first one I did was in classic Wasn't even a thing yet. Having to like hack your way together. I just loved. and so I just really kind of grew an affinity for it at that early age, at that earliest stage, even in what we call data science maybe today.

And I just stuck with it for my whole career, because every turn that my career took in my life, I had the ability to help people make better decisions using data. And that was always a valued thing, no matter where I lived or what the situation called for. So that was really like how it started. And then I just stuck with it for, I mean, almost 20 years, I think.

[00:05:38] Ken: Well, I love that so much. I mean, something you described is that you're not ever going to get bored within that domain because things were constantly changing. And I think it's really important that you had that mindset, right? If you're on the other side of that, where you're like, Oh my goodness, things are changing so fast.

I'm completely overwhelmed. Probably wouldn't have had that much success in this career so far. And I think that that's something that everyone can can look at is that, Hey, you know, the data domain, data science, and in particular, it is changing really fast, but that means that the potential for the things you can do is constantly growing and expanding.

It means that if there are new avenues that you're looking to explore. That you haven't yet. They're almost definitely open there. Your people are going to advance in a lot of the different more like specific or specialized areas within the domain. So, you know, in my mind, and in your mind, that's an incredibly good thing.

That's an awesome thing. It means out there's so much. That we still haven't done, but I see so many people that are sort of on the other side of that, where they're trapped, where they have this overload of information, you know, how, how have you dealt with that? like, Oh my goodness, there's so much stuff to learn.

How do I know what to go into? You know, as you progress through your career. I mean, obviously you've done a lot but how did you make those decisions to say, Hey, I'm going to follow this. And like ignore a lot of the other cool stuff that's going on. All those shiny objects. What is that process?

[00:07:03] Ben: Well, I think for me, I was always very close to the business in my career. I was always working hand in hand with the people that were on the business side, making the decisions, doing the thing that made the company go or organization go. And so being close to that, I always shared that vision or that purpose of like, Oh yeah, I do this because.

Customer service is going to be handled better, which means you have better customers, which means the company does better. I always was able to tie what I did directly to some sort of business goal. Because of that, the tool sets and the different skills and how we approach things always we're really just about delivering results.

It was never academic in any way. Right? Like I I've just built so many, like let's just hack together system. That worked beautifully delivered, amazing results. And as a result, I saw my career grow and progress and, you know, get promoted and all of these things, not because I was the best coder in the world, but because I had the same belief and same focus as the people that I was serving.

So I shared that vision with them. And so. You know, and you can't let, like you're, you're something you learned in school or something you read online, get in the way of delivering because when you're not delivering stuff now, now in the business world, I'll say in academia, it's totally different, but in most people's jobs, That focus on getting things done and helping people make better decisions, always like bared fruit.

And so people didn't care what I did as long as I delivered results. So I had the opportunity in a lot of companies where, you know, they just w they wouldn't even question how I was getting something done. So, as I went through my career, I just always made sure that what I was doing, I could tie it back to something.

And that might be hard for some folks that are maybe way more backend, like data engineering, data architecture, where you're setting up these servers and these systems and these pipelines. But you have no idea how that ties to a sales person's job or a customer service person's job or whatever. So for me, anyways, that was always, my focus is like, how can I help these people get their job done quicker better?

Because I know that that's going to make a real difference. And being in that position, I was always looked at, I think favorably from the people that made those decisions of like, you know, where do we spend our money? Who, you know, who do we hire? How do we, how do we grow this thing? Let's bring Ben in.

Let's bring his team in. Let's make sure that they're a part of it because we know that they can help and data science specifically. And, you know, we'll use that term as sort of like the statisticians that can code. And I think it can get a bit more muddy. and that's where, when I've led data science teams, I've always tried to try to instill that of like, why are we doing any of this?

Sure. We can find some, you know, p-value or R-squared or K or whatever. But unless that translates to something that matters to the people that we're to, to our organizations or our business, no, one's going to care. And so trying to like focus on the core elements of like being you know, helping move the needle forward, it really just. Made everything else simpler because if something didn't help me achieve that goal, then I wouldn't focus on it. I wouldn't do it.

[00:10:18] Ken: I liked what you said. There was so much, I think that, you know, if ,,,,,we take away the beautiful stories that you had in there at the most basic level, what you were able to do is you had a fairly clear unifying goal throughout your career and all the things that you learned, all the things you hacked together were pointed at.

Achieving that goal in a certain way. And when we talked about, you know, in my episode, on the, on the free data pot, free the data podcast how, when I was pursuing playing golf, all I did all my projects were to make me a better golfer. Right. I was collecting this information. I was collecting this data and I learned some really cool skills.

All in the name of making me improve my golf scores. Right. And to me, it doesn't matter if it's your work. It doesn't matter if it's your personal projects. It doesn't matter if it's your health and fitness. If there's something that's unifying it all together, you learn all those other things. Out of the initiative to improve or to take action on the goal.

And then it doesn't becomes overwhelming. Cause it's all pointed to the same thing. I think that, you know, that concept was revolutionary to me. And it seems like you were able to identify pretty, pretty earlier in your career, especially as it relates to business. I will say, I don't think in academia, it necessarily has to be that different.

I mean, the goal could be pursuit of knowledge. And if your mindset is that, Hey, like I want to learn all this information. I want to understand it and digest it. It's probably not that intimidating to you because like that's like what you're destined to be doing. That's what the goal is. It shouldn't be overwhelming if you're like refining it in that sense.

But again, I really liked that. And I'm interested in how you leverage that to further your career. You know, you started working at this company and data at a fairly young age. How did it progress from you? Essentially like working for other people, trying to accomplish business objectives who eventually leading teams and addressing the business objectives at even greater.

[00:12:16] Ben: Yeah. So throughout most of my career, I was what you call an individual contributor, right? I was the person writing the code, making the charts you know, doing the things that made the sausage, so to speak. And so as my career, as I got better and better at that, and I had more and more experience, eventually I got asked to like lead a team that did that thing.

It was actually a tough transition for me because it's one thing to write the code in and make the machine do what you want to. It's really different to take a step back and try to embrace and build a team of humans that will also kind of operate in the same way. I think this is maybe a big problem with a lot of management, people in management now is that they went from being the best skilled person, add a thing to know you should be a manager now, but they never took the time to learn how different management is to being an individual contributor. And so throughout, so, you know, I struggled with it at first, but my mindset is very all in on anything I do. And so, yeah, I liked full on, took a lot of courses at community colleges. I took a lot of like specialty training programs where I'd go to like an offsite for a weekend about being a manager and focusing on more of the like philosophical ways that working with humans is different than working with machines. And so that transition was initially really tough, but then once I got past that, it was mind-blowingly simple, you know, it was as a manager of a group of data people, or a group of any people.

My job was to make people love their jobs so much that they would do their best work. And so I had to fine tune every single person, what they were focused on, how that was helping us as a business and make sure. You know, an introvert didn't have to be giving presentations every week and an extrovert didn't have to go sit and think for three weeks on a project that they could be out there talking to people.

And so management was such a different skill set. It was more like playing, you know, like conducting an orchestra than playing an individual instrument. It was a career, you know, so it took me a while. I think I started in management maybe like 2006 or 2007. That's what it kind of when I made that transition from individual contributor.

And then from there, you know, it's just because my teams were always, because all I cared about was their wellbeing, essentially. Like I would often. Like, you know, not lie, but kind of like BS all the other people above me about what we were doing or whatever, because trying to explain to them how I was setting goals, which I don't believe in at all, how I was like, kind of like hacking their little systems that they want to play from the business book they read last week to make my team more productive.

Because of that, my teams are always super engaged and I ended up just growing and growing and growing, getting more people that wanted to work on my team, you know, and to the point where I ended up becoming the chief data officer for Pluralsight, which was a, there is a online education company where I had like, I dunno, three teams spread across the country, 30, 40 people, eventually things like that.

And it was always that same mindset of just like these people are smarter than me. There's so much better than me. and I want that because all my job then can be to help them have their best job. I want every person that ever worked for me to go back and be like, Oh man, whenever, you know, our relationship, when we, our paths diverge, I want those people to be like, damn, I miss working for Ben because he got it.

You know? so it was a different, it was a very different skillset. You know, my focus, whenever I dive into something is like, really go hard on it. And so I feel like I was able to do that. And it was pretty rewarding, you know, for, until I left corporate America in 2016.

[00:16:06] Ken: So something I really like in there is that you're able to maximize the collective outcome. By focusing on the individual outcomes by paying really close attention to your employees, by making sure they're getting what they want. And I feel like that's a pretty delicate song and dance, but that's also what the manager is supposed to be doing. You know, I've talked to people where, you know, their manager, hands them a book and is like, Ah, everyone's reading this.

I think this is relevant to, this is going to make our company great. And a one size fits all approach. In my mind is not how you can manage people effectively, unless you've hired very specifically to have one type of person on your team, which usually doesn't create great outcomes. Right?

[00:16:49] Ben: Yeah. It's hard. It's hard to balance that.

[00:16:51] Ken: Something you mentioned is that, you know, you don't believe in like the goal setting process or whatever that might be. Can you expand on what that means from an individual level and like a team management level?

[00:17:02] Ben: Yeah, sure. There there's this big focus in I don't know our society, certainly in business of, of setting a goal and potentially setting goals that are way too hard to reach and that, you know, you'll shoot for them.

Shoot for the stars land on the moon, kind of a thing. I think this is all just bogus. I think it's dumb. I think it's counterproductive. Because ultimately, goal after you achieve a goal. What's next? Oh, another goal. Okay, great. And let me just add another goal. I believe in milestones. Hey, you hit a million subscribers on YouTube.

Thumbs up. Let's have a party. I get that. I get the idea of celebrating certain milestones. But I believe in more of a direction and focusing on progress. and so here's the thing is, is goals. When we, when we have this, like laser-like focus in this big obsession with them, we often will sacrifice other things in pursuit of the goal.

So if I have a goal of making a million cars this year, I may say screw quality, screw customer service, because I don't care. That's not my goal. And so. When you set goals at an individual level, what you're automatically doing is saying to a person, Hey, this is the number one thing that's important for you to be focused on which means instead of helping your teammate, who's struggling.

I want you to focus on you. It's almost like, imagine if Kobe Bryant, you know, when he was playing the late great Kobe Bryant was playing and yes, he was a tremendous score, but he also helped his teammates were like, Jordan, you know, Jordan was so great. Not because he was the best score on the team, but because everyone else around him became better.

And so if it was, Hey Kobe, Michael, I want you to score as many points as possible. Chances are, they would lose a lot of games. Right. So you have to, in my mind kind of like focus on the the, team or as big as the team can be. If you're the leader of a company, the entire company around the directions of things you want to do, because when you have goals, you're, you're setting up people for failure settings.

You're setting up things. Either they're going to achieve the goal. And then what's next, you have this sense of despair until the next goal is higher and bigger. And then eventually you get really fatigued with this notion of just like this hedonic treadmill more and more and more, better, better, better.

And if you do it at an individual level, you really are not helping the team get better. You're helping that individual get better. So as an example, what I would do. The first management role I had, we had, we, I had to do this, like our company, big company. We had these like HR systems. I had to go put in goals or whatever.

And so instead of like saying, Okay, can you do this? And then Jeff, over here, you do this. I said, everybody's goals. The exact same percentage for all of the. So we didn't have individual goals effectively. I mean, as far as HR was concerned, we did, but effectively what we had were team goals, because it was like, if we didn't achieve these things, if we didn't deliver on the, if we didn't make progress to a satisfactory level, like building a data Mart or delivering some dashboards or whatever, the thing was, my team was working on, then we failed.

I don't care if you put all the data in there, but it never got to the. It doesn't matter, right? You could score a hundred points in a game and still lose the game. So what matters here, the team or the individual. And so to me, I always try to rearrange things to where everybody was interested in helping each other out and that way we can, we can kind of grow together and get better together and not just, Oh, this person is a superstar and this person sucks. I hate that. I think it's toxic and it can. Interpersonal kind of problems and it doesn't help the company overall.

[00:20:54] Ken: So I really liked that. And I agree with you. And I also like disagree with you a little bit, and I think that that's like a fun thing to dive into. So the first thing, in terms of like major agreement, I think so many, especially large companies get into massive problems.

And you actually talked about this in a, in a YouTube video recently, but it's something I see constantly is chasing quarterly profits over long-term growth and benefit. Right. I effectively, when companies reach around 150 employees, the incentive structure starts to change where you see people when their their major motivation is to see company.

And at around that a hundred in the early stages, like, Hey, we're part of this really cool thing. We want to see it grow. What, what could be possible once you hit this certain magic number around one 50 or 200, it starts to shift to individual incentives. How do I get promoted? How do I do this? How do I do that? And that is effectively toxic for a company, unless you can figure out how to create really good systems and really good accountability structures. To mitigate a lot of those...

[00:21:57] Ben: Me versus you. It's not us as a team versus the rest of the competition. It's me versus you. And that's just not the recipe for a good team or a good company.

[00:22:08] Ken: Yeah, and, you know, I think a lot of companies that have had really good long-term success, I think like historically apple has done an incredible job at this. Is that. Measure success in larger chunks, or they measure success in, I mean, obviously they have quarterly reports. They have these things that that affect their shareholders and whatever it is.

But there isn't this feeling of urgency that they need to perform every quarter because they've created this atmosphere about, Hey, we're waiting for the next big thing for me. And, you know, we're going to see what's going to come out. Right. I think all, a couple of other companies have done that really well, but getting away from that, like really short-term goal structure, a hundred percent, I think is fundamentally wrong.

Well, I fundamentally. Inefficient or ineffective. I will say I like, I agree with you on sort of like the team goal setting. But I think you can have success with individual goals, but you have to be very, very meticulous with how you set them up. Like just what you described. I'd say 80, 90% of goals are counterproductive because they're framed in the wrong way.

It's like, Oh, I want this thing, namely, let's say, take the million YouTube subscribers, something like, I want this. But I'm not going to hit it right. And XYZ amount of time because I have very little control directly over my subscriber account. Like, yes, there are things I can do. But the things that I do are loosely correlated and I don't have a whole lot of control over achieving that thing.

On the other hand, I think if you're setting goals where you have a lot of control over them, it's like, Okay, well, you know, on my team, I'm going to. Do XYZ every day, I'm going to try and do code review with my peers. I'm going to do X, Y, Z to contribute to the learning of, of my group. I think those can be really constructive.

And you also, every individual has complete control over them. To that point, I think people should create their own goals, like a hundred percent. Right. You creating goals for an individual, I think is so fundamentally flawed. And it goes against the philosophy you just described, right. Is that you're trying to match the specific experience of each individual to their, to their work, right.

Or, to make an enjoyable as possible. Right. And so, you know, I think, what I'm trying to say is I think we agree 100%. We just describe it in slightly different ways. And I might view goals a little bit differently than you do.

[00:24:36] Ben: Yeah. Yeah, and I think for me, it's a bit of a thing where, you know, we have this just massive obsession with them.

And so my reaction to even cut through that, because if you just have a reasonable opinion on a thing, no one cares. So you have to like go hard the other way to even get people's attention. Yeah, but no, I agree with what you're saying. One, one fascinating thing that I learned about there's a guy named W. Edwards Deming who he's passed at this point.

He's sort of a business philosophy guy. He helped rebuild or build the Japanese automotive industry after world war II, after you know, it was destroyed. and he's, you know, largely credited for how Japanese automakers have. The world sort of, so they, in fact, in Japan, there's a thing called the Deming prize, which is about efficiency and innovation and all these things.

So it's like, he's just like really, really well-known guy, whatever. And one of his things from one of his teachings was. Yeah, you have to have a systems approach to your business. So you have to think about you can't have one part of the business do really well. And another part suffered. That's not okay.

You have to address it. Like it's all a singular unit and everything. So if you increase sales, right, like let's say we double the size of our sales team. Great. Chances are, you're going to get more sales. If you double, if you put more money and time and effort into it, what are you going to do with customer service?

Maybe it doesn't have to grow at the same pace. But if you double the amount of customers you have, and you don't invest a dollar in customer service, what's going to happen to those customers. It's not going to be good. So his whole thing is like you have to have a systems thinking approach and as such designing a system for maximum efficiency and output is the key goals.

The key approach. And this studying that they did. And this was all based on, you know, fifties, sixties, seventies, work that he had worked with all these companies they found, and you can fact check this. I might be off by a little bit, but it was that an individual, an individual, an individual employee is accountable for 6% of the output of their function.

Meaning 94% of a person's output. If you turn the crank X amount of. 94% of that was not your individual doing so they have this philosophy of like individual goals are kind of now, well, you're talking about are not like goals in the traditional sense of like, Hey, read books, go give speeches. Like self-improvement goals.

It's more like make widgets goals. They said, you know, you can't hold people accountable to them because they fundamentally are not responsible for in any significant way for the outcome of itself. Imagine if you are on a manufacturing line and the line stops, but your job is to make, make parts while the line is stopped.

Somebody else up there stopped it. Well, I have no control over my situation. So, it's one of those things where I think different roles and different functions and, you know, the world's obviously evolved a lot since he was, you know, studying this stuff. But I think that there is a healthy discussion or thought experiment about how much is an individual person's contribution actually attributable to the outcome because I think it's, you know, if you make sandwiches for a living and no one comes in to make, to buy sandwiches and your goal was based on the number of sandwiches you made, it's hard to hit that goal. Right? What are you going to do?

There, there is something there about how the system is designed for people to achieve these things. And if you set up goals there, people are going to, I mean, look at what happened to Wells Fargo, with opening accounts, that is a classic example. You set goals for people to hit, to get bonuses for how many accounts you open and guess what they start fraudulently opening accounts for everybody and everybody's cheering and happening and giving each other bonuses.

Meanwhile, it's all .... It's all nonsense. It doesn't exist. So. Here's a real, I think it's just a real problem. And so I rail against it hard just to hope that we find some middle ground there between where we're at in most places. Whereas I think what would be most effective.

[00:28:43] Ken: This episode of Ken's Nearest Neighbors is brought to you by Z by HP. HP's high compute, workstation-grade line of products and solution. Z is specifically made for high performance data science solutions.

And I personally use the Z Book Studio and the Z4 workstation. I really love that the Z line can come standard with Linux and they also can be configured with the data science stack. With the software stack, you can get right into the work of doing data science on day 1 without the overhead of having to completely reconfigure your new machine.

Now, back to our show. I love what you said about essentially things derailing. If people don't have control over the outcomes, right? I think for me that if you are going to set goals, the most important thing is having things that you have complete control over. One of my favorite books is called The 4 Disciplines of Execution and they described like any way to hit a larger company goal or is by having people meet the goals that they've set for themselves, that they have complete control over. So your sandwich example, I love that one because it's like they have no control over how many people come in. Right? If, I mean, they could influence it, but they could not control it.

Right. A good goal in that circumstance would be for every customer that did come. If they, you know, made sure they greeted them with a smile, if they did XYZ in the process, if they were able to like you know, like put their best foot forward in some quantified way, I think.

[00:30:12] Ben: Yeah. Ask for an upgrade. Do you want to have a ... on that? Whatever, right, exactly.

[00:30:17] Ken: And I think that those can be effective, but they can have those runaway consequences like you described, like, you know, maybe it's not a company issue that you're, supersizing everything, but you're like, wow, everyone's fat now, so.

[00:30:27] Ben: Yeah. I mean, and so that's my point with this whole, this whole, my whole position on it is like, it's so much what you hear online and read and books and all this, just like this, what I would say.

Like better sizing goals to the point of like guests, that's how you succeed. And I just disagree with that entirely. You know what I mean? I think they can be useful and effective, but you have to do it right. And I've literally never been anywhere. Actually. No, I take it back when I was at Pluralsight, we, we did it in a better way, but I also was the one that designed that better way. So I'm biased towards how I feel about those ones.

[00:31:02] Ken: So, what is the opposite of a golden? Like what is the con the so direction versus goal and what, what differentiates direction versus goal.

[00:31:13] Ben: Well, a goal has an end point and a direction is just what, you know, the thing you're doing progress towards in a certain path.

So, for example, if you are selling, you know, like we were software subscriptions, essentially. You know, we can look at like how many sales you made, but we also, on the other side, could look at how many people were repeat buyers and how long those people were with us. And if they referred people, right?

So it's a more comprehensive thing of like, how are you treating customers? How are you actually building the business? Because if you're only, you're only focus is, you know, get number of X, number of people signed up, you are gonna like, you know Lie, cheat and steal to achieve that goal. So that way you, as an individual can get some kind of bonus or some kind of thing.

So, for example, a Pluralsight and I have no idea how it is now, but when I was there in the very early days, I guess we were like 50 people when I joined we w like salespeople did not have commissions. There was no such thing as commission on sales. and the reason was basically this whole idea of like, No, let's pay salespeople.

What they should get paid. And as a result, they will treat our customers better because they're not just trying to squeeze every nickel and dime out of them. What they're trying to do is make sure that that person is taken care of. And if you've worked in sales and, you know, sales, you know, that sales is a business of relationships.

It's not a transactional-type job. It is all about helping people and being there for them because situations arise potentially years later, where you land a massive deal because this person has trusted you. And you've been there for that whole time. But like, how do you measure trust among customers surveys?

I mean, you're a data scientist. I mean, let's talk about the validity of customer opinion. And, you know what I mean? It's just not a thing. so like when I was our salespeople, for example, did not have commission structures and there's a whole lot of research on this and whether or not those are good or bad.

And like I said, I don't know I've been gone from there for many years now, but that was just an example of like how we would think differently about these things. So that way we would do what we felt was better for the whole company which was, you know, have these longstanding relationships instead of these more transactional.

[00:33:27] Ken: I really like that. I mean, it's sort of, it's weird that I remember this concept, but the idea, I think it's something it's called a B Corp. Are you familiar with the, and so rather than having just a profit motivation, B corporations are supposed to have like a social group. And a couple it's called a Triple bottom line.

So they're evaluated, not just on profit, but it's significantly more complex. And they're trying to do good across a range of things. And I think that that's, you know, correct me if I'm wrong. That's something at a high level you're describing is that goals can oversimplify things and create negative negative externalities.

Like maybe mission might be better maybe. Yeah. For some reason, I don't like love the just like direction description should I think, I think the idea that it's just so much more complex than what a lot of business books are. A lot of people make it out to be is something that's really rich and has a lot of things.

Like, you know, if your goal, I tell people this all the time, like a lot of people are like, Oh, my goal is to get a data science. Right. And I don't think that that's a good goal because as you described, after you get the job, you might hate it. You might be like, wow, all this hard. and, you know, yes, I got this income, but I hate going into work every day.

I mean, in my mind it's still a goal, but it would be to say, Hey, like I want to create a career that I, that I love. That is my goal. And like, part of that could be landing a job as a data scientist, but there's so much for further further ahead. I mean, I think if you're setting goals with. I don't know exactly how to describe it.

I love the idea that that you have like a path, right? A destination, not a destination, but like an open-ended I guess direction. Yeah, exactly. Yeah. And my philosophy is that you can set good goals to help you get to that long thing. But if the goal is the destination. You're absolutely screwed because once you get there, you're done and the incentives are going to be completely misaligned.

[00:35:33] Ben: One of my favorite authors in the space is Simon Sinek and he has a lot of great books if you guys never heard of him. But w the latest one, I believe that I've read is called the infinite game. And it talks about companies. That that play in, you know, in their, their, their approach to business and competition and stuff.

You have different types of players. You have finite players and infinite players and a finite game. The goal, the goal is to. Right. I think of like a football game yet you win or you lose at the end of the day. Right. But an infinite player wants to remain in the game. And one of the great examples he uses was the iPod versus the Microsoft zoom.

And if you guys remember this, but the iPod came out and then Microsoft was like, Oh crap, what do we do? And so they made the Zune, which was an MP3 player at the time. And objectively the Zune was better by far. It was better in almost every. Apple did not care at all because see Microsoft was out to sell more Zunes than iPods.

Apple was out to change the world with their technology. And so just as Microsoft has the Zune and it's coming out and it's starting to build steam, there's people with tattoos on it. You can go Google, put tattoos of the Zune logo. Yeah. Bad choices. Microsoft is playing to beat the iPod just as they're getting ready to do.

Apple unveils the. And the iPhone. So apple was playing an infinite game. Microsoft was not Microsoft lost, the Zunes canceled, everything. People are fired, whatever it was a disaster. So you can think of a lot of these goals in that. 'cause some of them, like you said, I don't call them goals. I call them milestones.

You know, those milestones, those check marks. I, you know, quarterly reports, all these things are fine. Those are just like reporting what's going on. But if you're, if your obsession is with achieving a thing by. You're going to find yourself as a finite player and in an infinite game. And I think that's how you lose.

I mean, look at, you know, quick aside about sports, it's kind of nuts, but if you listen to Michael Phillips on Tony Robbins podcast, he talked about like having deep depression and suicidal thoughts after winning eight gold medals, because what's left, you are now the most metaled Olympian in all. There is none.

There is arguably never been a better swimmer on this planet ever. What's next for you? What's next. By the time the next Olympics roll around, you'll be a lot older. The guys will be faster. Like, so he fell into this deep depression. He had suicidal thoughts. Sean White was on the same podcast. They're talking with them about it.

I think it has to do with an obsession of goals because once you achieve a goal like that, a finite goal winning the super bowl, you know, getting a gold medal, whatever it is, what's that. It's a great sense of despair. Just like you're saying with the data science career, if your job is to get that job great. Now, what now? What do I do? Where do I go from here? It's so yeah, like we, we should be thinking like infinite players here, because the goal is to this, to stay in the game, not to achieve.

[00:38:46] Ken: I have to read that book. I read another book called Finite and infinite games. I imagine it touches on some of the similar things.

[00:38:52] Ben: Is that a very old book? A very, very old book, yeah. Yes. That is the foundation for his book. And he credits it and talked to John something. Right. Yeah, but he talks about that forever with author names.

[00:39:07] Ken: No, I'm going to, I definitely want to refresh because the older book is very academic sounding and I don't necessarily do too great with the academic literature.

[00:39:16] Ben: Simon Sinek is probably my favorite kind of business philosopher out there. And it's just fantastic. All of these thoughts, I mean, they all ring true, not just for me being a fan of it. Somebody that lived it for how long? How long was I at America? 98 to 2016. Yeah. So 18 years. Yeah, four. I just said enough of that.

[00:39:38] Ken: Well, let's talk about what happens after enough of that. So I know that you have been involved in data education. You also have a incredible YouTube presence talking about electric vehicles. How did all those, what's the origin story behind, behind those and, you know, what's, what has been the next step of your career after 2016?

[00:39:57] Ben: Yeah. So 2016 was when I officially left Pluralsight. I April 1st, ironically, which is kind of funny. But. Before I joined Pluralsight, I had been doing a lot of individual contributor stuff. I worked at Facebook. I worked at Mozilla, a lot of big tech companies doing really cool stuff. I remember like at Mozilla, we were launching Firefox OS, which was like a mobile OSTP that was all open source and everything, you know, based on their principles and all that.

And we were launching in all these, like like like Latin American countries and in other places where like the iPhone is like stupid, expensive, nobody owns it because it's just like three months salary to own one kind of a thing. And so we were launching this there and I was doing all the data and analytics for it.

It was a really cool time because I mean, I was working with like, our data warehouse was relatively small compared to Facebook's. Six to 10 petabytes worth of data that I was with, that I was working with. So, and we're talking hundreds of millions of users around the world. Like this is fascinating stuff like as a researcher, as someone who's just interested in stuff.

And in Mozilla is a fantastic organization. I love being a part of it. So I was there and then a friend of mine who became a customer. Pluralsight as for software development courses. So, Hey, they don't have any data courses at all. You should just start teaching data courses at Pluralsight and see what's up.

And I was like, all right, well, I don't know. So, cause I'd been doing like in-person trainings and stuff like that for companies I'd been at. And I was a consulting for Tableau prior to that, where I was doing Tableau trainings on their behalf and stuff. You know, I'd kind of been a trainer educator for a long time.

And then, so I started talking to Pluralsight and I became an author for them making online courses on data stuff, on all kinds of different things. You know, what is big data to data analytics and data science and all this. I started making courses for Pluralsight met Aaron scanner, the founder, we became fast friends really, you know, had a bromance there at one of these off sites.

It was a really good time just a great guy and we really hit it off and he was really hot on me coming to work for them. And I just, I told them, no, it was. I do it. I'm working in Silicon Valley on protecting the internet from big corporations as we saw at Mozilla you know, the Facebooks and Googles of the world, like trying to fight against what they do.

And I'm like, I have the most interesting data in the world. I have all this autonomy. I do all this great research. It's so fascinating. I get paid really well. The benefits are stupid. Like, I'm sorry, I'm not going to come work for a startup out of Utah. Just knock it out. And and so he came back to me, he said, Okay, well, what would be in.

It was like, please, like I want our company to become data-driven. I don't know how to get there. Let's talk about it. And I said, Okay, so let's, let's do this. And I came back a couple of weeks later with a plan, a whole business plan, essentially to make, to build the data wing of the company to make the company data-driven and he loved it.

We went back and forth on it. I presented it to the board and then they hired me as the VP of Data to build a data team three data teams, actually at different levels of the organization out here in San Diego, where. I quickly got to work on that fast forward a few years, the company grew a hundred X, you know, I forget.

I dunno, maybe we were like, we're making a lot of money when I started, but then we were making 50 X that two years later, three years later. So we were like a unicorn, you know, we got all this funding and all these things and the company just became, like you said, once you get to a certain scale, it's just a different, different animal altogether.

And my job was just not something I'm just, I'm not good at big companies. I just realized that, you know, and so I hit a point where I'm like, look, I, it's not for me to go. But I still wanted to keep making courses for them. So I left officially as a full-time employee, but I kept making online courses for Pluralsight that lasted for another year or so.

And then LinkedIn learning approached me and Pluralsight, Ted kind of slowed down on the data content that they wanted. So, and LinkedIn learning came in. No, like we want every date. Of course you can. Every, any idea you have, let's make a course on it and just go. And so LinkedIn learning came in and were like, yes, do you know, do that?

And so, you know, after, from 2016, when I left to maybe the end of 2017, so a year and a half later, you're in three quarters later, I probably put out 20 new courses on LinkedIn learning in Pluralsight, just like tons of content. I mean, it was my full-time job. At that point throughout that process, we had bought a 10.

And I had sold my wife on the idea that I would, that we would save money by owning it. You know, it's cheaper to own operate, float, whatever fuel, all this, you know, I had basically been fed that line, but I hadn't actually researched it myself. She said, yes, we had a young kid at the time. So I was like, Okay, you can't drive that old beat up truck anymore.

You need a real car. And so we got, we got a Tesla a year later. I did a video of. Owning that Tesla and saving money. And it just went absolutely nuts on YouTube. I think I had 800 subscribers and it, the video within the first week at 200,000 views and I was just like, Oh my God, this is not sin being a good data guy.

I'm like, well, you know, what do you do when you see a signal? You do try to do that more. Right. That worked. Let's try that again. And so I did another test, a video and another test video, and it was. Barry bonds on steroids, just home run after home run after home. Right. I legit thought after like two months of doing YouTube that, well in three months I should be getting five to 7 million views a month because every video gets four or 500,000 views.

Obviously it's just how they do you know, I had a lot to learn, but. That was like the Dawn of YouTube. And it all came from me already being sort of a content creator. Not really like in the YouTube sense, but making online courses for like my first online course I published with Pluralsight was in 2013, you know?

So by the time I got to YouTube by order, I had already been making online, making content for a living for five, six years.

[00:45:52] Ken: Oh, that's awesome. I, you know, I think it's so cool to see. Sort of pet projects, things that you're interesting, get legs and eventually lead to income. And. No like financial benefit.

[00:46:05] Ben: It's a whole business now., I'm like officially a car journalist somehow. I mean, and I say that just as a way to explain it to people, not as an insult to actual journalists, because whatever I do is not that, but people understand that when you say that word, kind of like if I call myself a data scientist, it's a matter of of using a term that someone might be familiar with to give them a clue about what I actually do.

I'm not a data scientist in the traditional sense at all, but to like the lay person that has no idea the difference. Yeah. I'm a data scientist. Fine. You know, so I call myself a photojournalist.

[00:46:41] Ken: The quality of your work is significantly higher than a lot of the journalists that I've seen out there. So I think what you're talking about on that front, but, you know, so you really like working on a lot of projects, it seems like you have a lot of freedom to do that.

Now. What are some of the projects you're working on? What are you most passionate about it at this period of time and welcome people would kind of expect from you in the future on that front?

[00:47:03] Ben: Well, You know, the YouTube channel, focusing on sustainability and EVs, I've had, it's been an interesting ride with a lot of, you know, boat rocking back and forth as to what I do and what I don't do.

But I really settled in on I review electric cars on YouTube and I have a lot of great relationships with a lot of the automakers. And so I would say expect a lot of videos about electric cars, a lot, a lot of, you know, analysis of them as well as just opinions of them. You know, I started my YouTube channel looking at Tesla as a data guy.

Every video had charts and graphs, and I was analyzing the data about Tesla. And that was a unique perspective. And I think that's why it went well. But as I got more and more into cars, I realized that cars are very difficult things. To quantify you can tell someone the wheelbase of a car or the zero to 60, or the horsepower, this and that, you know, but there's something to be said about when you get in the car, the feeling that it gives you, or when you look at it, you know, cars are more.

There's more soul to them, then there are specs. And so I kind of, you know, I still include a lot of that, but I just, I try to review electric cars now from a total consumer standpoint of just I'm a dad with two kids in San Diego or Southern California what's life, like in this vehicle, what's it really like?

So I've got a bunch of that. I do have my podcast, as you mentioned for the data podcast where I interview folks like yourself and others in the data space, I don't pretend to teach data stuff directly anymore. I like to just get people on that are much smarter than me and learn from them. And hopefully through that process, other people can learn from them as well.

So that's out there. I do have a data academy and I have a team that is building our own online courses that are all very like short. It's kind of a funny thing about short versus long form content. You know, there's a. In the, in the public world of like tick talk versus say a 20-minute YouTube video, you know, TikTok is clearly winning right now.

Th this battle of this like short form video content and in the online education space, it's always been longer, was better, but. A lot of times when that's your incentive is just to have more watch time. It's kind of like it's having a goal. it's it's now you have people dragging things out and adding filler stories for no reason, other than getting you to watch longer, this course on Python programming or something.

So I'm trying to go against that a little bit and build these like shorter tutorial type courses as well. And it's not even me. It's like, I might be the guy on camera, but I have a team of actors. Practitioners that know this stuff. And then, you know, but aren't maybe good at presenters and then I'll make a course on it.

So I've got online courses coming out. I've got the podcast, I've got I've got the YouTube channel and then one day I'll have a golf app. I swear. My goal is still I, cause I have a golf, YouTube channel where I, where I do these. Driving tours or sort of, I put a GoPro on my golf cart, in my golf bag as I go play a really beautiful course. And if that becomes monetized, everything else will immediately be canceled because my job then will just be to go play golf. And that is really all I want.

[00:50:13] Ken: That doesn't sound bad, get paid to do the things you love. I mean, you're able to do that with electric vehicles. Why not? Why not golf in the future? That is so incredible.

[00:50:20] Ben: I would probably still review electric cars cause yeah, it's just so fun. I mean, I get to go drive brand new, amazing vehicles on race tracks, going off-roading trips. I mean, it's just, it's a very, I've never had a job that that's that's that cool. Other than like, I was the coach at a skateboard camp when I was 17 years old as well over the summer, that was probably more fun. Cause I just got to skate all day. But other than that, you know? Yeah. It's hard to be.

[00:50:46] Ken: Well, so you had mentioned with the Free the Data podcast that had a pretty interesting origin story for the name. I'd like to think the Ken's Nearest Neighbors Podcast also had an interesting origin story. I just outsourced the name. I did a, you named the podcast type of thing, but I'd love to hear that on the, on the way.

[00:51:04] Ben: Okay, so, Oh, what is the band free your mind? And the it's like a, it's like a nineties pop band. Oh man. I can't, I can't remember the name of it right now, but my wife and I were in Nicaragua on a, what they called surf and yoga retreat, but let's just be clear if you do two hours of yoga a day. And you surf once across five days, you shouldn't put surf as the first name in the surf and yoga retreat, just saying, all right, anyways, we're on a surfing yoga retreat and they had these it was a great time though. You don't do don't get me wrong, but I expect more surfing.

They had these shirts that said free your hips and your mind will follow. And I was like, Oh, that's genius. and I just translated that to free the data. And your mind will follow because. Th the thing I learned in my career throughout, you know, all, everything we've talked about, everything I had done was that we tend to get stuck in our ruts and our ways of thinking and data can come in and kind of mess all that up.

Right. It can really throw a wrench in like what works, what doesn't. And it's hard to get out of that. It is really hard to change your way of thinking. The older you get, the more experience you have and whatever the thing is, you just know this is what works. I don't care what your data sets. And so the slogan was really to try to do the opposite.

It was to like, have the data be the thing that you follow, right? So you free the data and then your mind will follow. It was more like once you see the data. And you and you, and you hear it again and again, and again, maybe your mind will start to listen to what the data says. And I just think it's hard to do.

I think it's really, really hard. But that was the origin of the name. It was from this surfing or yoga and yoga retreat that my wife and I went on in Nicaragua.

Caribbean enthusiasm where like David Schwimmer's on there and he like had a cashew and raisins little thing that he was selling and it was like all raisins and like one cashew. He was like, Come on, you can't call this cashews and raisins.

[00:53:13] Ken: So how can people learn more about you? How can people get in touch with you? If that's something you're interested in. I'll link all of your YouTube content and your LinkedIn in the description, but are there any other places where you're accessible.

[00:53:31] Ben: That's pretty much it. Yeah. If you search my name on YouTube, you can learn all about electric cars. If you search for Free the Data, you can hear the podcast interviews. And we have a, like I said, a website with a bunch of courses on there and stuff like that. Also LinkedIn Learning, I am publishing a lot of my stuff still.

So if you guys are on LinkedIn Learning, you can find me there. Otherwise social media, I'm not really on, so I'm not on Instagram. I'm not on Twitter. LinkedIn, I guess I'm on. But like I go on there, but I'm always, just don't know why I'm on there. It's kind of a weird thing. I liked the LinkedIn Learning platform, that seems to have good content, but yeah, that's it search me on YouTube is probably the main place.

[00:54:06] Ken: Excellent. Thank you so much again for coming on. I really enjoyed this.

[00:54:09] Ben: Yeah. Happy to be here, man.

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