Alter Everything Podcast

A podcast about data science and analytics culture.
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Andy Uttley, Alteryx ACE, makes music with Alteryx | Math + Music
Alteryx Community Team
Alteryx Community Team

We're joined by AJ Guisande and Michael Barone for a chat about data ethics and privacy, upcoming trends, and tips for sharing knowledge.

 


Panelists

 

Brian Oblinger@BrianOLinkedIn, Twitter
AJ Guisande@aguisandeLinkedin, Twitter
Mike Barone@mbaroneLinkedIn, Twitter


Topics

 


Community Picks

 

 

Artwork by @aguisandeArtwork by @aguisande

 


Transcript

 

Spoiler

AJ: 00:14 

[music] Bienvenidos a Alter Everything, un podcast acerca de ciencia de datos y cultura analítica.
Como habrán adivinado, no soy Brian Oblinger que será nuestro host hoy.
Yo soy Alberto Guisande y estoy con Mike Barone para conversar un poco sobre ética y privacidad en datos, tendencias y trucos para compartir conocimientos.
Así comenzamos esta edición de nuestro podcast.

BRIAN: 00:44 

All right. Mike and AJ, welcome to the show. 

AJ: 00:47 

Hey. How are you, Brian. Thanks for having us. 

MIKE: 00:50 

Hi, Brian. Glad to be here. 

BRIAN: 00:52 

Yeah, absolutely. Thanks for coming on, guys. It's going to be great. We have a lot of awesome topics to cover today. Before we do that, though, as always, I want to hear a little bit about you, a little bit about what you're up to, how you got into this. So maybe, AJ, we'll start with you, how did you come to analytics? What was that journey like for you? 

AJ: 01:11 

Oh, it was kind of an accident. Let me tell you, I was hired by the company to put some order within. They had been struggling with late projects and a lot of money losses. And I was hired to put them in order, and that company turns to be a [inaudible] company in that era, and that was my landing to the analytics world. It was by accident. I got totally hooked and here I am. 

BRIAN: 01:46 

Wow. Okay, great. And, Mike, what about you? 

MIKE: 01:49 

Mine was somewhat by accident as well. When I went to college, I was going to be a math teacher. And about halfway through my student teaching process, I decided that I didn't really want to be a math teacher, at least not at the secondary level. So then I turned my thoughts to maybe being a professor at the college level. So I started taking my master courses. Taught a semester of stats at a local community college, like it, but still wasn't really for me I didn't think. At the time, I was at a very large local company that was doing very well called Paychex. And I started out entry-level, just doing payrolls for local companies. And then, a few years later, I was promoted up to the corporate where I was a compliance analyst. And then about-- oh, boy, about eight years into that journey, a new department called predictive modeling opened up with one person. They contracted out to FICO, the credit people, to create a mathematical model to try to predict clients that will leave us. Took off really well. Great, great success. So the director decided to hire a few more people and bring that in-house and see if we can get that. And someone tapped me on the shoulder and said, "Hey, we know you're an analytic-type guy, you've got a math background. Want to come and join us? Don't know where this is going, what's going to happen, but it might be fun." And I said sure. And that was about 10-and-a-half, 11 years ago. 

BRIAN: 03:17 

Wow. Yeah, so we have a lot in common, the three of us, because I decided really early on in my life that I didn't want to be a math teacher [laughter]. And [laughter] everything else that I've done that's been successful has totally been an accident. So I appreciate your stories [laughter], and I think we're all on the same page there. Good. Good. All right. So let's get into our first topic. I wanted to kind of segue from your intros into-- so, Mike, for example, you just mentioned predictive, and AJ, you mentioned a couple of things you've been working on. What are you guys best at? What do you really like to do? What's the part of analytics? I'm sure that there's a spectrum of things that are not so fun and some things that are fun. Let's start with the fun stuff, what really gets you out of bed in the morning? Maybe, Mike, we'll start with you on this one. 

MIKE: 04:06 

Sure. I think what I do best and what I really enjoy is the beginning part of building something. So building something from the ground up for us here. It looks like-- we usually partner with other groups, other business units, and getting in on the ground floor and listening to them. Because I came to this department-- I currently have over 20 years of experience-- I've worked with all different departments, all different people, all different divisions. I know how the frontend, frontline users, what they do because I've done it. So when we're in requirements meetings and we're listening to the business unit describe what they're looking for, what issue they want to solve, what they might want to do, they usually bring me in early, my group, because I'm very good at listening and determining, "Okay, this is what's coming out of their mouth, but this how it might translate to our data at our company." So I seem to have a really good ability to do that, and that also allows me to execute very quickly, take out of that first meeting. And I can usually come out and write up a good step-by-step plan, getting at least a preliminary idea of what we need and putting together something to look at together as a group very quickly. So it's kind of existing that I can walk out of a meeting and a day later, bring my group together and say, "Hey, here's an initial data set that I pulled." So that's the fun part for me. 

BRIAN: 05:31 

Yeah. I think it's so important being able to synthesize all that information and kind of get something quickly, even if it's not the exact answer, at least have something to show for it. It's something that I think a lot of businesses struggle with, right? They embark on these big, long projects, and they spend a lot of time trying to get it perfect, where at least getting something that's 60 or 70 or 80% of the way there to start would get you so much further and kind of directional, where we need to go with this thing. So that's really cool. That's great to hear. What about you, AJ? What gets you out of bed in Panama every morning [laughter]? 

AJ: 06:07 

Well, since my background, even when I started-- I'm an [inaudible] engineer-- I work a little-- very little work in systems engineering-specific work like programming or architecting software. I was lucky enough to have a boss that saw some competences in me that were outside the technical lab and wanted to spend it on me. So I developed a career in management [paid?] by the bank I used to work for. And I learned a lot of things regarding the business, how the business works, what are the business needs, how to build a team, how to create everything from scratch. And I think what I'm very good at is to hear what the business needs and being able to translate it to a technical solution without, of course, losing the business [perspective?] or the objective of that need. And I think that's what made me get into this world very easily because I always know what to do with that data and do what I need to do. Sometimes I don't need the technical solutions, but I know what I have to get. So I end up figuring out how to get there and get the job done, and I believe that's what makes me good at it. 

BRIAN: 07:46 

Got it. Okay. And what about the least favorite parts? So I'm sure no one wants to really talk about the stuff that's not fun, but I think it is important to acknowledge, "What are the challenges? What are the hard things that we all grapple with in this industry, in our careers, and that kind of thing?" So maybe let's dive into that one, AJ, what's the thing that isn't so fun? And how do we make it better, I guess? Let's try to solution it. 

AJ: 08:15 

Yeah. I believe, "How can we make better?" is the right approach because, well, me as a consultant, I deal with customers all the time with different personalities and different goals and different expectations. And one thing that I "hate" in quotes because it's part of the deal, but I think we're not managing the information that we provide to our potential customers. In every sense, in the analytic world, our [inaudible] should be-- I find myself within C-level meetings where the CFO of a huge company believes that predictive model is going to take all the decisions for him, and it's going to avoid him to make mistakes, and it's going to even make the PowerPoint for him. And this is the message we are providing them on how they are reading those messages where we have these awesome predictive models that we are going to, "Put your business again into the competitive world." And I believe those expectations explode in our hands when we sit at the table and have to make these things work like they expect it work because we know predictive model has errors. We have a percentage of probability, and we don't have an accurate 100% effective model for everything. 

BRIAN: 09:52 

Yeah. What about you, Mike? 

MIKE: 09:55 

Well, I think in the data science analytics space itself, I wouldn't say it's my least favorite part but maybe the most challenging part is keeping up with the technology. It's a field that's exploding. There's new technology, new software, there's no tools in this space coming up all the time. And to be on the edge and keep yourself as a leader, you really need to, not just say, "Oh, I'm doing everything this way. It works great. I'm not going to look at these guys." Because it is coming out so fast, you kind of almost have to set time aside to evaluate new products, new tools, see if it's something worth looking into, worth considering. Other than that, I would say probably-- along the same lines; the technology. When you're in a large company like I am, and you have a large IT infrastructure, a lot of these solutions in this area tend to require-- not all of them but some of them do tend to require some IT intervention. And just getting that face time, again, because this area is so new-- yes, everybody reads about it and hears that it's the latest and greatest and could really provide great value to your company, but when you have a department that's so large-- an IT department, that is, that's so large-- it's kind of hard to get face time with them and really show them the value that this could provide. Or maybe not, maybe we just need to test it out because it might provide value. So it could be a challenge convincing them, "Hey, invest this time and these hours just so we can see it's something worth looking at. 

BRIAN: 11:23 

Yeah, I've heard others talk about that challenge of, "There's always something new. There's always a new technology. There's always a new vendor [laughter] out there trying to hock their wares. 

MIKE: 11:33 

Yep, yep, yep. 

BRIAN: 11:35 

And so I guess one question I'd ask is how often, in your experience, is that true? We always hear, "Oh, there's this new thing, and you should research it, and you've got to know all about it." Some of them pass. Some of them are "fads" or kind of passing fancies. What's the hit rate, and how do you try to circumnavigate that to make sure you're not spending too much time evaluating things that ultimately aren't going to be a real solution for you or your company? 

MIKE: 12:07 

Right, right. Well, we got-- boy, we got hit up, probably, at least two or three times a month with some kind of advertisement or email campaign or something like that. And we'll breeze through it. Usually, one of us will pick it out and say, "All right, I'm going to take a look at this." We'll read through the literature, read through their spiel. And then maybe go to the website, check it out. Before we talk to anybody on the other end of the phone or reach out, we'll fully investigate their websites, see if we can find any information on it, google them like you google everything [laughter] to see what it's all about. And after maybe spending maybe 10, 15 minutes, you can get a pretty good idea whether it falls into one of a few different categories. One category is, "Okay, totally not for us," two, "Maybe for us," or three, "Okay, maybe it's for us, but it's pretty much the same thing as the last guy. So we're not going to be interested," or, "We might be interested." 

BRIAN: 13:04 

And, AJ, you, I mean, as a consultant, you're all over the place [laughter]. So you must just see tons of different things and approaches. What's that like for you? How do you discover new stuff, and how do you block out the stuff that's not, ultimately, going to be that valuable? 

MIKE: 13:21 

Well, I have to be honest with you, I have it easy because you in the US and in more evolved markets do all the research. And when we have these needs in our market-- I work for Latin America-- so when we have these needs, I believe that kind of feature is already done at least 50%. So I have it more easy than you in this matter. It comes already featured for me. But, of course, I'm researching all the time and looking at new things every time. And what we're going through, [inaudible], this is part of our job. We need to keep our curiosity on top of our minds. We have to keep learning every day, and we cannot avoid those steps any minute of our professional career, we can't. 

BRIAN: 14:26 

Yeah. So what I'm hearing is that you definitely owe Mike a couple of cold beverages [laughter] for doing all the research for you. Is that what you're getting at? 

MIKE: 14:35 

Yes [laughter], something like that [laughter]. 

AJ: 14:38 

I'll take it [laughter]. 

MIKE: 14:39 

Yeah [laughter]. 

BRIAN: 14:40 

Inspire, 2019. Yeah, okay. Great. So in terms of-- let's talk about data. Let's kind of shift data. So we just spent a little bit there talking about vendors and platforms and technologies. There's a lot of stuff in the news about data and how it's being collected and how it's being managed and the security of that data and a lot of ethical dilemmas around the data. I don't want to name any names here or anything-- Facebook [laughter]-- but how do you guys see that right now? What's your outlook on-- are we collecting too much? Are we securing it properly? Are we managing it in a way that's going to be sustainable? I guess, Mike, maybe we'll start with you on that one. What's your view on the current state of things as far as "big data" goes? 

MIKE: 15:38 

Right. Well, that's a tough question. I don't think there's really any one truly objective answer to, "Are we collecting too much? Are we securing it?" First, for security, I think that no matter how secure something is built, someone will eventually will and can find a way to get into it. So I think we need to keep that in the back of our minds always. As far as what we collect and how much, it's not so much, for me, a question of, "Should we?" but, "Is it okay with the individual person?" Today, at work, at lunch, I might be googling, "Underwater waterproof cameras" for my upcoming vacation overseas or something, in Australia, who knows? Then when I get home at night and I'm surfing the web or on Facebook or whatever, I see 16 ads pop up for underwater cameras [laughter]. Now one person might say, "Oh, wow, great, look at all these-- just by looking at it earlier today, now I have all these options right in front of me, and I don't have to go and search them. I have different brands, different functionalities, different prices. That's awesome." Then you have another person that says, "Holy cow, that's really creepy. I don't like this [laughter]." So I think the way that things probably should go is the data collector, they really need to put it in front of the user, front and center in real language, in plain English, not legal-ese that only a lawyer could understand that "Hey, here's what we're collecting on you. Here's how we're going to use it. Are you okay?" And a lot of times, I see that it's either yes or no. "We'll collect your data, and we can do whatever we want with it." I'd like to see more, "We'll collect your data, and here's a few options of what we can or could do with it, and we'll give you the option to opt-in or not." I think we're a long way off from that, looking at the current landscape. But it probably comes down to the individual person. And, again, it goes back to the data collector. And ethics-wise, they have to put things in plain language that everybody can understand and right in front of them, not in some help account menu. They have to navigate through six different screens to get to their legal-ese documents of, "What we can do with their data." 

BRIAN: 17:53 

Yeah, I agree. I think one of the things you hit on that I think about myself personally quite a bit is, "What am I doing as an individual?" Right? I think that along the way, a lot of us became pretty comfortable with sharing a lot of data, a lot of things because the service was free. And so it was like, "Well, here's the exchange. I'm going to give you this data because the service is free." I do think that corporations and entities, obviously, do have a long way to go, and they should be focused on making sure these things are secure and private. And to your point, being very clear about, "What am I going to do with this data?" But I also don't know that I'm going to trust them [laughter] explicitly to do that. 

MIKE: 18:39 

That's a whole other conversation, yep [laughter]. 

BRIAN: 18:43 

Yeah. And so I think it's up to all of us as individuals to make sure that we're being thoughtful about what we're doing and how much we're giving and things like that. I mean, for example, I use a VPN. I have one of those services that you just put it on your phone and your whatever, and all of your traffic's funneled through there [laughter], and now that's a separate kind of issue because you're also trusting that they do what they say they're going to do in terms of treating-- they say, "Oh, we don't log anything and those kinds of things." So that's a whole separate thing, but at least trying to do my part to make sure that my data is either not shared or as secure as I'm comfortable with." But seguing, to you, AJ, I mean, is it different in Latin America? Is this a cultural thing in terms of how much people share or what's your outlook on that? 

AJ: 19:37 

Well, I actually think it's very similar. I don't think-- I always think that the real balance of things is not-- any excess is bad, and the truth is somewhere in the greys. It's not black or white. But in terms of personal data, I totally agree with you, but what I found sometimes or most often is that there are a lot of data that is being collected that is useless, and there is no quality in what we get, what we keep, what we share in terms of, "For what?" or, "What is the purpose of holding that data?" And I find myself with a lot of companies that say, "Well, we have all this information about the customers, which color of underwear they use? But, guys, you're selling cookies, why do you need the underwear color of the person [laughter]?" "No, just in case." And I believe this is what the problem in Latin America is most often; we try to keep all data just in case. And maybe if we can curate that information that we're keeping about people, of course, putting only yourself before-- as a matter of fact, as a need, we as individuals, might have control or we should have control of what we share. But if you decide to post your-- I don't know, your bank account and ATM password on Facebook, it's up to you, I guess. 

BRIAN: 21:31 

Yeah. Bosco, that's my ATM password [laughter]. 

AJ: 21:35 

Good. Good. 

BRIAN: 21:37 

For any of the Seinfeld fans out there, they'll get that reference. 

AJ: 21:40 

Yeah, absolutely. 

BRIAN: 21:42 

Yeah, I think it's interesting. It's something I'm keeping an eye on quite a bit. I think we have a long way to go worldwide [laughter] on the ethics around this. I do understand the desire to be able to leverage this data-- as you kind of pointed out before, Mike, the leverage recommendations and curated experience and those kinds of things. But I think we have to really think long and hard at this point about-- you kind of, I think, hit on it, which is-- the assumption was or is, I guess, that, "Oh, these recommendations are great. Everybody's going to want these, right? They're going to want tailored ads." And I don't really know that that's true, right? I don't know that consumers want ads at all, let alone ones that are tailored and targeted based on something they searched for six hours earlier, right? 

MIKE: 22:37 

I would say, more often than not, they don't. 

BRIAN: 22:39 

Yeah. I think people think it's creepy, and then they wonder what else is going on behind the scenes with their data, right? 

MIKE: 22:45 

Yeah, exactly. 

BRIAN: 22:46 

Yeah. 

MIKE: 22:46 

And same thing, just like with the-- even if a data collector did put it right in front of your face, plain English, "Here's what we're going to do. Opt-in, opt-out, whatever?" how do you enforce that or audit that? 

BRIAN: 22:59 

Yeah. So let me ask you this, if I spun up a Facebook competitor tomorrow and my business model was, "Hey, it's $2 a month or I'll give you a year for $20," or something like that. And the trade-off was, "We're neither going to collect a lot of data about you or what we have will be fully private and only really for your uses on the platform" because the model is-- your data isn't the model; you are the model now that you're paying for it. 

MIKE: 23:32 

Right, right. 

BRIAN: 23:33 

I think that a lot of people-- I hear people talk about that, and they say, "Oh, yeah, that'd be great, I'd love to do that," but I don't know that we've proven that, right? I think there's been several kind of pay-for social networks that are out there, and you also kind of gate access to that because some people maybe can't afford that or don't want to spend their money on that. And so then you have a problem of sort of bifurcating the network and making winners and losers based on the income part of it. But for you, personally, I guess I'll just ask both of you, is that a trade-off you'd be willing to make if Facebook popped up tomorrow and said, "Hey, for $5 a month or whatever, we will exclude you from these types of data collection and ad-targeting practices," what would you say to that? 

AJ: 24:20 

I would do that just to help you, Brian [laughter]. Yeah, just to help you with your startup. But I am not a huge social media. I don't use Facebook. I didn't use it for the last, I don't know, six, seven years, and I got some things over there, and it keeps bothering me and the email. But I believe that that kind of schema may be more controllable on my side. It could be a good deal. 

MIKE: 24:58 

Yeah. I would give it a serious consideration. I generally use Facebook for communicating with family, friends. Sharing pictures with family and friends and a couple of groups I belong to, that sort of thing. So I'm not really putting anything on there that I wouldn't be comfortable with the whole world seeing anyway. So I don't know if I would spend money for that sort of arrangement, but I would give it serious consideration, for sure. 

BRIAN: 25:24 

Yeah. I think just to round off this topic, for me, it's all about education, right? I think we talked about what the organizations should be doing in terms of educating people, but I think at this point when we're talking about school, actual physical schools [laughter] that children go to. I believe that we need to start having courses that are just as important as math and science and other things in terms of data literacy and understanding the internet and understanding privacy and understanding the risks of putting certain types of information out on, let's say, Snapchat and those kinds of things where the promise is that it's ephemeral, but it turns out that sometimes it's not, right? And I think if we're counting on organizations [laughter], and we're counting on advertisers to do the right thing, that might be part of the solution, but probably not. And I think that really educating people-- and you both just said it, right, as, "Hey, I either don't use it all that often," or, "I only put things that I'm okay if the world saw it." But I think a lot of people don't use it like that. I think a lot of people believe that it's private, especially in messaging apps, particularly. They believe everything they put in there is private, and they sort of are living their lives through that. And a lot of very personal, very painful things if they were to be exposed. So I'm on the education train. I think that it's up to all of us to push more for that and hopefully make it a formalized part of the curriculum of all-schooling from pretty much the beginning, all the way through college. I think it's paramount, this is where everything's headed, and if we don't understand it as consumers and participants and potential management of those things, then I think we're setting ourselves up for a pretty difficult road ahead. 

MIKE: 27:27 

Absolutely. 

AJ: 27:27 

[crosstalk]. 

MIKE: 27:28 

And not only that, but not too dissimilar to many other things of this nature. I believe that has to start at a young age at home as well. I have two daughters. One of them's seven, and she has an iPad, and both of them have serious conversations. These are the things that you do not ever expose when you're online. These are things that are okay. Always come talk to Mom and Dad in that discussion. This day and age, today, it has to start at home and then continue in the school as well. 

BRIAN: 27:59 

All right. Well, let's shift to a-- let's go to happy things [laughter]. 

AJ: 28:04 

Okay. 

BRIAN: 28:06 

What's the trend, right? So we kind of talked a little bit before about trends and technology and things like that, but maybe if you could put on your big blue sky thinking cap now. Where are we going with all of this; whether it's in business or just in general with data science and analytics? What do the next 10 years look like if you had to predict? And we're going to pull this thing out 10 years from now, and we're going to grade you to see if you were right. So kind of give us your thoughts. Maybe, AJ, we'll start with you on that one. 

AJ: 28:39 

Okay. And I'm totally amazed with this, I see a huge trend in micro-services, for example. I discover [inaudible] not long ago. I bought some smart speakers from several brands to manage my smart home, and I discovered these micro-services as a consumer not long ago. But I see a huge trend in using shared micro-services even in analytics for the next 10 years, five years. I think, for example, to take one example, I jumped into [inaudible] Amazon and found that it was so easy to build something. I created my own Alexa Skills or something like that, and I can share and I can pay just for what I use. And even being cautious and not being speaking about server lists or something like that that we tend to do to create a big theory about something, but I believe micro-services will be a big explosion in the years to come. 

MIKE: 30:02 

Probably not coincidentally, I have a lot of the same thoughts. The Internet of Things that you hear about, edge computing, with smart devices just starting out, sensor devices. Everyone has-- not everyone but many people have these FitBits, smart cars, smart TVs, alarm systems, fitness monitoring, Alexa, [inaudible], that sort of thing, if you think about it, one person-- and just one person with maybe three of those items in maybe less than half a day probably produces gigabytes if not terabytes of data. And that's one person. And when this explodes to the millions, even cloud services aren't going to be able to handle that. I think with this explosion of, again, "The Internet of Things," as I call it, handling all that data is going to have to be at the source, the edge computing where the data's being collected. And that would be my prediction that we're going to see-- in the next 10 years, I would say at the end of 10 years, we're going to see a lot more edge computing right within the device itself. 

BRIAN: 31:14 

Yeah. Yeah, I agree. I would say the same thing. I think sensors and the data they produce are probably the future of all of this data, especially when it comes to consumer electronics, for sure. 

MIKE: 31:26 

For sure. For sure, right. 

BRIAN: 31:27 

As AJ mentioned, I'm also on the smart home train. So I have [laughter] smart lights and some of these kind of things. And I do-- 

MIKE: 31:35 

I have Alexa, that's about it. 

BRIAN: 31:36 

--find the-- yeah, yeah, and I really do find the convenience in it, and it often kind of amazes me to think about me just shouting something out into the air and this cylinder hears me-- 

MIKE: 31:48 

Right [laughter]. 

BRIAN: 31:50 

--parses the data, goes through a workflow, essentially, that I've either built or was built by the engineers of the companies who produce these products, and then it does something. And there's sort of this magic-ness to it where it's like you're just speaking and the light turns on, but you think about what happens under the hood and all of the bits and bytes and data that's flowing through there to make that happen in almost real-time is really bizarre [laughter] when you think about it, actually [laughter]. 

AJ: 32:21 

So we're going back to the data collection subject, and all that that we do with this device is being recorded. So it's, again, us who have to decide what to do, what not to, and how. 

BRIAN: 32:41 

Yeah. All right. So on the education piece of that-- let's kind of segue. We're kind of coming through this, weaving our way through a couple of different ways. You're both thought leaders, as I think-- you're on this podcast. So that automatically makes you thought leaders [laughter]. Plus, everything else that you do. 

AJ: 32:57 

Yeah [laughter]. 

BRIAN: 32:57 

Right? I'm sort of being facetious here. 

MIKE: 32:59 

Of course. 

BRIAN: 32:59 

But one of the things we talk about a lot, and it seems like more so as time goes on here is there are so many wonderful people out there that have real subject matter knowledge, but if you don't share it, then it's just sort of in your head. And we all need to be sharing as much as possible of our experiences and what we're learning and our takes on data and analytics and these kinds of things. And so I just want to ask you both, how do you recommend that other people-- in big or small ways, whatever it is-- how do they get involved in the sharing of that knowledge? Maybe, Mike, we'll start with you. What do you do today to share your knowledge? And how do you recommend other folks get started with that? 

MIKE: 33:50 

Well, for subject areas like this, there's countless numbers of groups and communities you can become involved with, local or national or online, and just meet up and talk about things, share ideas, post ideas, internally, with the people you work. Obviously, that's a just a conversation, a shout over the wall. But otherwise, get involved in the community-- different communities out there and talk. Blogging now is big. Anybody can do that. LinkedIn. Everybody has a LinkedIn page pretty much. You could post things there and just kind of spread the word. I think more and more today things are going to electronic spreading of information, but it certainly is done the old fashion way as well. 

BRIAN: 34:34 

You just want people to generate more data [laughter]. 

MIKE: 34:37 

Right, right. So we can collect it [laughter]. 

AJ: 34:39 

Well, in my case, yeah, I believe there is something that we bond with, which is the need to teach, to share knowledge. I don't think everybody has it, but if you at least some time in your life question yourself to be able to share that, please do, and keep learning and keep being curious. It's part of teaching people, too. And when you share chocolate, and the other person has half. But when you share knowledge, you double the assets because you got your knowledge, and you are not going to forget what you know, but you are doubling that knowledge into another person. So I love to teach. I have more than 700 hours of teaching Alteryx in this case but in Tableau and BI and analytics. And I love to teach. 

BRIAN: 35:50 

That was beautiful. And I'm hungry for chocolate all of the sudden, as well. Yeah. 

MIKE: 35:55 

Yeah, that was a great analogy. 

BRIAN: 35:56 

Great. 

MIKE: 35:56 

I know, right [laughter]. 

BRIAN: 35:59 

No, I was just saying that that was awesome, and I think, actually, a perfect segue to our community picks. So let's share a little bit. So what have you two seen lately out there in the world that we want to point people to that they should go check out after they get done listening to the episode here? 

MIKE: 36:16 

For me, I think everybody who begins their path in the data analytics space or data, in general, needs to watch-- there's a gentleman that actually presented at one of Alteryx's Inspire. I believe it was 2014 in San Diego, Jer Thorp. He has a TED Talk out there of pretty much the same thing presentation that he gave at Inspire, and it's a great one about humanizing data. It's out there, TED Talks, Jer Thorp, "Make Data More Human." And you could look it up. It's very easy to find, and it's just a great, great presentation. Great speech with examples, and what I would say, he is a visualization master [laughter]. Master at visualizing data in different and unique ways. But he also explains and shows how it's not just zeroes and ones. It's people's data. Bringing the humanity back to it. And just always keep that in mind when you're messing around with data that you always got to keep your human side close to you. 

AJ: 37:19 

I'm going to use this first part of the community picks to make a popular [inaudible]. And to everybody that's listening to this, we are very eager to help you in the community. I'm talking about the Alteryx community in this case. When you have a problem or a question or a post, please try to be as thoughtful as you can with the use case so we can help you because we want to help you in the community. There are a lot of people-- very clever people to help you that we need those questions and those posts are as complete as they can. We cannot grab, "I'm getting this error. Can you help me?" from scratch and help you. We need to know what you're doing. This is my advertising about how the community can help us to build a better one. But as a peak in the community, I came up-- a week ago, I came with some posts from [inaudible] that are absolutely amazing in the data science blog, please go check them because even if you don't know anything about statistics, models, and that stuff, you can understand them. And what she's doing is great. I don't know her, and I'm going to find her at Inspire. But I realize that those posts are amazing. And another one for you, Brian, I miss the hang-outs, man [laughter]. 

BRIAN: 39:09 

You want more hang-outs? 

AJ: 39:11 

Yeah, I miss them. Yeah, absolutely. 

BRIAN: 39:13 

Okay. All right. I'll see what I can do. I have accepted your challenge. 

AJ: 39:18 

Good. 

MIKE: 39:19 

Another one I don't think that can be underestimated is the tool mastery index on the knowledge base. Though, yes, I'm an ace. I've been using Alteryx for six years. I still go there. There's still tools in the software that I haven't covered yet, and whenever I encounter a new tool-- if the configuration isn't painfully obvious like maybe a union tool is, if it seems a little complex, I would start with a mastery index. I can't emphasize that enough. 

BRIAN: 39:47 

Great. All right. Well, we'll put all the links to that in the show notes. I'll round us out. And I have one that's a little, I guess, off the wall. But I think our audience might appreciate it. I was watching 60 minutes just the other day, and-- it's a television program here in the US and investigative journalism. And they did an episode on these geospatial satellites. There's a company out there. I believe the company's just called Planet. And basically, all of the satellite imagery that you get today comes from just a handful of satellites because they're really expensive to launch and maintain, and there's governmental regulations and all these kinds of things. And so just to get to the point that we're at today to map the Earth has taken a couple of decades, right? And so what the mission of this company is is they're launching these micro-satellites. And so they're basically the size of a cat [laughter]. They're pretty small. And I think they've launched-- I don't want to misquote this, but I think somewhere between 50 and 100 of these things are up there floating around. And they're able to map the entire Earth in a very short period of time and then continue mapping it. And so one of the benefits that they were talking about in the episode was that you get sort of this timeline of changes. And so you can imagine whether you're looking at climate or you're looking at city development or whatever it is you might be interested in from a geospatial imagery perspective, you have these snapshots that are pretty close in-between each other and ongoing, right? So you can look and see, "Oh, okay, here's what it looked like six months ago. Here's what it looked like yesterday. Here's what it looked like three weeks ago," whatever the case might be. And they were talking about the immense nature of the data because they're mapping so much that they basically can map the whole world every-- whatever it was, every week or something crazy like that. 

MIKE: 41:51 

Wow. 

AJ: 41:52 

Wow. 

BRIAN: 41:53 

And so they were talking about the data, and like, "Okay, great, so now we have all these maps. But how do we actually analyze it?" And they were talking to this guy who-- he works at the governmental agency here in the United States that looks at geospatial data probably more for the defense perspective. But he was saying that they crunched some numbers, and they would have to hire eight million people [laughter] or something like that to properly analyze the data, and obviously, that wasn't going to happen. So then they were talking about, "Okay, we need to look at machine learning and AI and other things like that to be able to parse through this data intelligently in a reasonable period of time." So I'll put the link to that with everything you guys mentioned as well in the show notes. It's really interesting I think, and also kind of flows back to our roots here in geospatial, which I'm always excited about. 

AJ: 42:45 

Great. 

BRIAN: 42:46 

All right. Well, hey, thanks guys for being on. This was wonderful. I really appreciate your intuition and your thought leadership, and I will see you on Facebook. 

MIKE: 42:58 

Well, yeah [laughter]. 

AJ: 42:59 

Not me, my friend [laughter]. 

MIKE: 43:04 

Great. Well, thank you. Thanks for having us. 

AJ: 43:06 

Thank you. 

MIKE: 43:06 

It was very fun. 

AJ: 43:08 

Yeah, absolutely. 

BRIAN: 43:08 

Absolutely. 

AJ: 43:09 

And we have to do a special podcast about data collection-- 

BRIAN: 43:13 

Indeed. 

AJ: 43:14 

--just on that. 

MIKE: 43:16 

Oh, sure. 

BRIAN: 43:16 

Indeed. We will do. Thanks, guys. 

MIKE: 43:18 

Yeah. 

AJ: 43:19 

Thank you, Brian. 

MIKE: 43:20 

Thanks. 

 

[music] 

BRIAN: 43:30 

Thanks for listening to Alter Everything. Go to community.alteryx.com/podcast for show notes, information about our guests, episodes and more. If you've got feedback, tweet us using the hashtag AlterEverything or drop us an email at podcast@alteryx.com. Catch you next time. 

 

[music] 

BRIAN: 44:01 

So, AJ, I have to tell you that I was sitting at home this weekend watching some TV and looking at my email as all of us do now. We can't stop. We're addicted to our emails. 

AJ: 44:17 

Yeah, absolutely. 

BRIAN: 44:18 

And I see this email from one AJ Guisande come through. And I thought, "That's interesting. What's he got on a Sunday for me?" And I was greeted when I opened up my email with this amazing illustration of my face, and somehow you managed to make me look better than I do in real life, which probably isn't hard. It's a low bar. But I was blown away by this. I have to tell you. I just sat there, and I looked at it, and I smiled, and I laughed. And I was so excited, but I didn't want to email you back. I wanted to talk with you live here on the show about it. Where did you learn how to-- and we'll put this in the show notes so people can see this because it's incredible stuff-- where did you learn how to do this? And how do you do it? 

AJ: 45:12 

Well, I'm self-taught. When I was young, one of the most valuable toys I had was a pencil and a piece of paper. And I never took courses of illustration. I just figured it out, and I started moving my hands. And once I got serious, I wasn't a kid that doodles anymore, I was trying to draw seriously. I start working on my skills with two kinds of concept of deliberate practice where you grab-- hands are pretty difficult to draw. So how can I make-- this time I'm going to draw-- or how can I improve my hand-drawing? And I was spending, I don't know, one hour, two hours a day drawing hands until I mastered them or until I got good at it. And that concept of deliberate practice helped me all my life. And that's how I got from doodling on a page to cash several checks drawing comics. Yeah. 

BRIAN: 46:31 

Wow. Wow. Okay, so you got some income? I'm not surprised by that because this, to me, just looked amazing. 

AJ: 46:38 

Yeah. 

BRIAN: 46:40 

And, Mike, did you see this thing? I think I sent it over to you. 

MIKE: 46:43 

I saw the prototype. 

AJ: 46:45 

Yeah. You saw kind of the sketch [crosstalk]. The thing is, as I always say, I rather like to finish it. Even if it's not perfect, it has all the-- it lacks from the production phase, which I should grab another tool and do vector lines on the lines you have, but I wanted to send you the raw drawing where all the imperfections are from my hands since my hand is not as steady as it used to be. For production, I will grab that drawing and put it into-- I don't know-- illustrator or some tools like that and do the vector lines to figure them or to make them look like perfect lines. 

MIKE: 47:40 

That, in and of itself, is pretty incredible [laughter]. 

BRIAN: 47:42 

Looks pretty perfect to me. I got to tell you. And I love all the little details. I see that I'm wearing an Alteryx for Good shirt. I see there's some funny things written on the boxes, which I won't spoil for people. We'll let them discover that on their own. Yeah, so I [laughter] appreciate it, and I've got to tell you, it really kind of blew me away, and thank you so much for that. 

AJ: 48:05 

Thank you. I don't like to come empty-handed to an invitation. So that's why [laughter] I sent you that. I couldn't send you a bottle of wine so I made you a drawing. 

MIKE: 48:18 

Nice. 

BRIAN: 48:18 

Well, thank you, sir. I appreciate that. That's wonderful. 

AJ: 48:20 

Oh, absolutely. Anytime. 

 

 


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

Comments
Alteryx Community Team
Alteryx Community Team

@aguisande can you check our transcription of your introduction? 🙂

Alteryx Certified Partner
Alteryx Certified Partner

Hi @NeilR 

Here is the right transcript:

 

[music] Bienvenidos a Alter Everything, un podcast acerca de ciencia de datos y cultura analítica.
Como habrán adivinado, no soy Brian Oblinger que será nuestro host hoy.
Yo soy Alberto Guisande y estoy con Mike Barone para conversar un poco sobre ética y privacidad en datos, tendencias y trucos para compartir conocimientos.
Así comenzamos esta edición de nuestro podcast.

Alteryx Community Team
Alteryx Community Team

@aguisande muchas gracias

8 - Asteroid

Is there a way to listen to this at a faster rate?

Alteryx Community Team
Alteryx Community Team

Hey @treepruner, I would recommend checking out a podcast listening app like Spotify or Overcast, as the apps let you adjust the play speed. You can also download a listening app on your phone so you can listen on the go.

 

Hope this helps!

6 - Meteoroid

i wish to ask if there is any dataset newbies can play with on alteryx while learning. I hope my comment is understood.

Alteryx Community Team
Alteryx Community Team

@MikaelO there are sample datasets built into sample workflows in Alteryx Designer. Go to Help -> Sample Workflows to find those. Also find loads of free data sets here.