Episode 39: Does Data Exist?

Data has been heralded as the next oil! Given this, it is clear that people are very excited about what data will be able to do for humanity.

However, data is very different than oil (obviously). Today’s episode, and the slate of episodes this week, work out what we actually mean by calling data a raw material.

Of course, data exists, but it exists in a way that all other natural raw materials do not. Namely, data is something purely constructed by humans to understand the world around us. Tune in to the shows this week to consider what this raw material is and how we will use it to change our world!

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Music for the show: Foolish Game / God Don’t Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: Snowflake

Transcripts:

Welcome to data couture the podcast about data culture at work at home. And on the go. I’m your host, Jordan Bohall. To stay up to date with everything data controller, be sure to like and subscribe down below. Furthermore, be sure to follow us around the internet at data to her pod on Twitter, at data couture podcast on Instagram, and at data couture pod on Facebook. Of course, if you’d like to help keep the show going, then consider becoming a patron at patreon. com forward slash data couture. Now, now onto the show,

Welcome to data couture to I’m your host Jordan and on today’s episode, we’re going to be asking the question, does data exist? Well, because my pragmatists, the simple answer is yes. But that’s not the reason why today’s episodes interesting. We’ll get into that more and the first act of the show. However, before we get there, I want to say again, thank you to Lance Gibbs and all of BP three global for hosting an amazing conference, the Griffin conference in Austin, Texas. It was so much fun, and I really enjoyed my presentation and there will be a video probably coming shortly of me talking about this very podcast. So stay tuned from the various social media channels of the show to see that. And the other piece of officiousness I suppose we’ve got is the hat I have launched a Kickstarter campaign.

And so to get to that, and to help me fund this amazing podcast at least it’s amazing. Go to Kickstarter. com forward slash data tour forward slash dedicated to our podcast and you will find my Kickstarter campaign I’m looking for a fairly sizable pledge, or I guess fairly sizable amount of money for backing. But that’s because the show is rather expensive to produce as well as to get a few better bits of sound recording software as well as hardware to make this an even better experience for you. So if you’re interested head over to Kickstarter and help fund the show. There’s all sorts of interesting prizes and backer level.

I don’t know that I gifs, necessarily, but they’re they’re the things that you get for being a backer, so I really appreciate it ahead of time. Now, for the show, we’re going to be talking about somewhat philosophical matters. But more importantly, we’re going to be talking about the notion that data is the new oil, I’m sure you’ve heard that the news or whatever blogs or whatever, trade pubs or whatever social media site, and I’m, I’m not really sure what that means. I do get their point that data is this amazing resource. And we’re doing all these sorts of cool things with it. But this show is all about well, what really does that mean? Does data really even exist? And if so, how are we going to use it to change the future? So stay with me after the break, and we will get into it?

So I’ve already mentioned in the intro to the show that Yeah, data exists and why does it matter, then I’m a pragmatist. And that implies that data exists. Well, because we use data and pragmatism is all about, well, if we use something, then it probably exists, right? What I’m more concerned about is this notion that data is this raw material, it’s if we consider it literally like a raw material like oil, or like any precious metal or anything like that. Well, then maybe we should treat it like that. And if we treat it like that, that implies that data itself is this super fundamental piece of I don’t know, I’m silly garbage. But it is. It’s basically useless if it’s just left untapped. And, frankly, this is how most consultants or most companies that try to sell analytics products talk about it, they say, Oh, you have all this data, and it’s trapped under this layer of on usage. And if it’s trapped under this layer of non usage, then it’s good as meaningless, which Sure, Gold’s meaningless if it’s just living in the earth, their oils meaningless if it’s underneath the sea floor bed, or, I don’t know, I had really don’t know much about precious minerals, or, or mining or anything like that, you get my point, right? data is this thing that is very similar to these raw materials.

And once we transform the raw materials into something useful, and by useful, I mean, other sort of base materials that we can then produce even higher level materials are more abstracted materials. So for the case of gold, I guess we mined gold, we refine it, and then we turn it into jewelry or bits for our electronics, or I don’t know, whatever else people do with gold, I guess gold leaf for your art? Not? Yeah, I don’t know, what do people do with gold? trade it I guess, I guess it has a currency line. Anyways, the point is, this data is all around us. And we are using it constantly. So does data exist? Well, there’s, there’s this interesting thing about data that seems to be different from oil, or gold, or any other Precious Mineral or precious commodity that we get out of the dirt. And that’s that data comes in all sorts of forms.

And so there is the kind of data that is more or less just measurements of activities. So example, for example, bank account information, well, that’s a measure of how much money that you’ve trusted the bank to hold. Or, I don’t know how about how tall I am. That’s a measure of my height in relationship to the ground and our units of measurement that we use to understand the world and some sort of mathematical way. Similarly, there’s all sorts of descriptive types of data so that I have red hair, if you didn’t know that. Yep, I’m a ginger. I’m sorry, if that turns off some of you.

Though, I am bald, which means that my beards The only thing that’s ginger on me, but I still consider myself a ginger. The point is, that fact is descriptive me and it identifies me among I don’t know a group of people who are lined up and who don’t have red hair. Well, if they know that I’m a ginger, they can point me out. Similarly, facts about my standing in society. And so my social security number, for example, or my home address, or my phone number, or my email address, or these sorts of things, they describe a certain type of location data about me about who I am in the world, so that I can, I guess, be tracked or you can send stuff to me or whatever may be. The cool part is that data is unlike these other natural resources.

They’re purely a product of man, they’re purely a product of our ability and our nature to measure and to describe, and to try to figure out the world around us. Now, for any philosophers that might be listening, this might fly in the face of your favorite it metaphysics or metaphysical theory. But I firmly believe that things like numbers don’t exist. Mathematics doesn’t really exist, the theories, and the theorems and the various pieces of mathematics, the methods, they don’t exist, all they do is help us understand the world. Now this flies in the face of say, some metaphysician, say platonic realists who think that number is really do exist, they exist just as much as, say, the perfect form of a chair, or the perfect form of a blade of grass or the perfect form of a tree. I don’t subscribe to that, you can see my dissertation if you care to understand why, because I don’t want to get into it in this podcast. But nevertheless, I think that data and a particular data is just a way for us to understand the world. And so this natural resource is purely artificial, it’s purely this kind of thing that only we have, I don’t think that you could go into the rain forest and ask some beautiful animal or plant there.

Well, you know, what, how old are you are? How fast do you travel or any other sort of thing that we care to really measure quantify about a particular thing? Know, we use it to measure the world around us describe the world around us. And that’s fascinating. And so it’s more or less the only natural resource that I can think of, for calling it a natural resource that is purely manmade, which implies that this industry is going to be built upon the resources of this raw material that only we have created that we have built in our imagination almost. And I know that you’re going to say, Well, no, no, we have all these data warehouses, and we have all these ways to store data.

Well, that data didn’t get to that warehouse by magic, right? Somebody input that data or a sensor, read that data or something else that we’ve created, has measured that data and then pass it along. And so our frontline staff say, and the banking world has input that data or the government has assigned that data or a researcher has gone out into the field and literally taken a tape measure to determine the length of a particular type of plant species. And so in this sense, data is a different kind of natural resource, a different type of raw resource. And I find that massively interesting, massively interesting. And, you know, the impetus for this, this episode of the podcast was from a conversation that I had with somebody that I am gaining deep respect for in this industry last week.

And it’s because like gold like oil, like any other Precious Mineral, or precious resources we have on this earth, we are transforming the way that we do business, the way that we live the way that everything happens based on this built up raw material. And so that’s what I want to talk about in the second section of this podcast, namely, what what are we doing with the raw material. And after that, I want to talk about the future because frankly, I am very excited to see what this raw material becomes. So stay with me.

So welcome back. Now, as I mentioned in the first section, data is like a raw material and that it can be used for all sorts of things. However, unlike a raw material, it’s not something that’s natural. It’s something that we have you sir, we’ve built, I suppose we’ve developed as this way of measuring or describing the world around us. And so what I want to talk about now is well, what does that really mean when people say that data is the new oil? Let’s look at oil, oil is very useful, and so many different aspects. One, we can power our vehicles with it, we can turn it into, I don’t know, like more products and more goods, consumer goods, and I can consider or think of, we can use it for all sorts of pieces for our electronics and our technology, we can use it literally everywhere. And so what I think people mean, when they say that data is the new oil is that data is going to have this massive impact on literally every aspect of our life.

Now you can’t make makeup, for example, out of data, at least I don’t think you can. So those YouTubers, and those other people who have the makeup tutorials on platforms, like YouTube, won’t be able to just apply data based things to their face. Nevertheless, with data we’re going to have, at least the hope is we’re going to have all these amazing and incredible never before seen applications for it. And so like oil, even though it as I discussed is not necessarily a raw material. And I don’t know Earth Science, since I’m not sure if I’m even using that correctly. It is the sort of thing is sort of item that we can transform and create the the fourth industrial revolution or industrial or industry 4.0, I think is the buzz term for that.

Nevertheless, what does that mean? Well, right now we’re in the early days, let’s think of all these cool technologies like artificial intelligence, machine learning AI, I guess already said, A, predictive analytics, deep learning neural networks, all that, well, to be quite honest with you all those algorithms as they have existed since the 30s, and 40s. And so the algorithms were using, okay, they might be like, slightly more sophisticated, but the math is more or less the same. The only thing that’s really changed is our computing power and our storage capability. And that’s allowed us to unleash the these algorithms, from pencil and paper into machines that can compute millions of lines of code fairly quickly and robustly.

That’s great. But I see this as a stepping stone. This is literally like somebody’s getting fire and then realize they’re realizing that they can put this other piece of stick her wood into the fire and then have a torch, right? It’s it’s kind of the first step. Same with data visualization. Same with any sort of analysis or stats physical analyses that we do. It’s it’s first level, it’s, it’s, it’s the first thing that we thought have to do with all this amazing data that we have about the world around us. So what I really want to know is where is it going to be. So let’s consider the first time that human beings discovered fire got fire, they saw perhaps lightning strike, burn a tree, they got the fire from the tree, they nurtured the fire kept it alive, they then put a stick into the fire and had a torch. Great. I, I suspect and I claim that’s where we’re really at with understanding this resource.

But if we look at things like oil, the I really don’t know the history of oil or her the manufacturing that goes along with it. But oil by itself is very useful. And it can make fires and can do all these sorts of things. And so just like data, oil was probably first used is just a way to keep the fire going. However, these people that discovered fire, they had no idea that we’d be on the road rolling around with these things attached to us. I guess if you wear a seatbelt.

I guess when I ride a motorcycle, I don’t wear a seat belt, I think that’s less safe. I don’t know, any case, they could not possibly imagine that we would be rolling around with these machines that literally have explosions going on constantly, that propel us from one place to the next and fairly rapid succession. Similarly, we as data professionals, and anybody who interacts with data, ie literally all of us can’t imagine what’s going to be coming down the pike. We’re so early in this investigation, we’re so new it to think that the the data profession is that all mature is naive, it’s deeply naive. So what is the combustion engine for the data profession? What is it that will really be a game changer, that’ll be I don’t know, just absolutely monumental for the continuance of the human race. I’m excited about that. I like I you know, I see these robotic Process Automation pieces that are helping us get away from the drudgery of certain sort of point and click work.

I see AI and machine learning and predictive analytics, helping us make decisions based on past data. I see all the beautiful visualizations that we can create from trends in our data. But what I can’t see is where is this leading? I don’t know. I, I don’t know if you know, I don’t know if anybody knows for that matter. But I’m very fast and and what’s to come? Well, well stay with me to the third part where I’ll be talking about some of the grave concerns I I have about this transition, because I truly fear for what’s going to happen to our workforce and the way that we exist. So stay tuned.

 

Welcome to the third part of the show. Now, I mentioned in the end of the second that I have grave concerns, and that’s not false. You know, in previous episodes, I’ve spoken at length about the need for data literacy, for digital literacy, for all these kinds of things that will make us citizens of the 21st century. But what I found is that so many companies are out there, attempting to ease that burden, make it make it like your smartphone, you might not know how your smartphone works. And that’s fine, how you probably don’t even know how your car works. And that’s pretty typical. And for that matter, you don’t know how most of the things around your work. And it’s because at some point in time, somebody invented a way to automate that aspect of your life. It’s not as though I’m in my yard, generating electricity, so that I can turn the light switch on and something will happen, right. And frankly, I’m amazed by the nuclear energy, which is how I get my power, or solar energy or wind power, or unfortunately, coal power, makes electricity. Of course, that’ll be changing, let’s be honest.

But it’s the same with data, all these companies are out there making it so that you don’t really know have to know how the thing works with robotic process automation, for example, you don’t necessarily know or have to know how the robot works in order to get your work done. Because the robots are very good at doing point and click kind of automation. Same with artificial intelligence and machine learning. I would argue that maybe the hardest problem in machine learning and AI and all these are similar areas, is how to explain exactly what the black box is doing. Because frankly, I understand the math, I understand the coding, I understand the implementation.

But at the end of the day, I don’t know the exact moves are the exact choices that my algorithms have made I it would be very difficult for me to explain down like to a very precise, very detailed very, in the weeds way exactly why the results that I generated, were generated. By my point is simply this. It’s unclear whether or not the level, digital understanding that digital literacy or data literacy that we’re expecting from our world, from our fellow citizens or from our fellow colleagues at work. It’s unclear how deep they’ll have to go in order to successfully perform their job. Because frankly, everybody interacts with something that they don’t understand myself included. And yet, we get along just fine.

And so when I’m worried about is given that we’re in this, push, stick into fire, get a torch, part of our data history. What’s going to happen when this is abstracted into something far far more abstract, far more detailed, far more technical and interesting. Where will we be then? Will we be purely reliance on these on these types of data? Using copper? Like I don’t even know how to explain it. But the point is, will we be numb when something goes wrong? Well, we know how to fix it. Well, we know what to do in our lives when these pieces break down because let’s face it all technology breaks down. I don’t care what it is, that will break. Well, I would love to hear your thoughts, your ideas vastly and deeply interested in this notion.

So let’s talk about it. Be sure to like and subscribe and leave your comments down below and I will definitely interact well. Till next time. See you soon. That’s it for the show. Thank you for listening and if you liked what you’ve heard, then consider leaving a comment or like down below. To stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod to consider becoming a patron@patreon.com forward slash data couture music for the podcast. It’s called foolish game. God don’t work on commission by the artist spin Meister used under the Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is
by your host, Jordan Bohall.

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