015 - The fundamentals of data literacy with Jordan Morrow

Data has the power to change lives . . .  when it’s understood correctly. That’s where “data literacy” comes in. After years of refining his approach, Jordan Morrow walks us through the essential steps in becoming more data literate. (And no, being a data scientist isn’t required.)

• Jordan's Pluralsight author profile
• Jordan's Twitter profile
• Qlik.com
• TED Talk: Why everyone should be data literate

If you enjoy this episode, please consider leaving a review on Apple Podcasts or wherever you listen.

Please send any questions or comments to podcast@pluralsight.com.

Jeremy:
Hello and welcome to All Hands on Tech, where today's leaders talk tomorrow's technology. I'm Jeremy Morgan. Data is everywhere and people are using data to change our quality of life every day. Data literacy is the ability to read, work with, analyze and argue with data. Both organizations and individuals can benefit by being data literate. Jordan Morrow is the chief nerd officer at Qlik, and he's been a pioneer of the field of data literacy. He's developed methods and models for becoming more data literate. In this podcast we talk about how you can learn to understand and interpret data better. We talk about what data literacy is and the four levels of analytics. Let's welcome Jordan Morrow.

Jeremy:
Can you tell us a little bit about who you are and what you do for Qlik?

Jordan:
Yeah, yeah. My name's Jordan Morrow. I'm the global head of data literacy for Qlik. All that really means ... My nickname is officially Qlik's chief nerd officer. My entire world revolves around empowering people with being in enterprises with the ability to use data more effectively.

Jeremy:
Awesome. How did you land in this position, doing what you're doing?

Jordan:
Yeah, that's a good question. Well, I've been at Qlik since 2016, and before that I was actually at a really large financial service firm managing a large BI world. I had my first ideas of data literacy while in that role, so about five or six years ago. When I presented those ideas I was told no, they wanted me just to kind of do the standard training, this and that. And then at Qlik, a role opened up and it was all around analytical learning, which is what I was trying to do with data literacy and everything. I was pretty much hired on the spot. Started the journey and was one of, if not, the pioneer who really got this field going. Yeah, four years going this year and loving it. There's so much to do on it.

Jeremy:
Yeah, that is awesome. I would definitely say you're a pioneer. I had never even heard the term before we talked last summer, and now it seems like it's kind of popped up in other places and I'm seeing it kind of pop up. For the people out there who have never heard of data literacy, what is data literacy?

Jordan:
Yeah. Data literacy, if you take it from kind of a nonconventional definition, it's basically your comfort or confidence to be able to use data. And then if you want to go to a pure definition, it's this ability to read, work with, analyze, and then communicate with data. And I will emphasize, notice we didn't say data science in there. Not everyone needs to be a data scientist. In fact, the vast majority of people do not. But in this day and age you have to have confidence with data and being able to ask questions of it, communicate with it, and have dialogue. That's the essence of data literacy itself.

Jeremy:
Yeah, that's interesting because a lot of times when I speak with people about data, that's one of the first things I say, is I don't know anything about data science, I don't know anything about math, I don't know anything about any of this so I don't want to know any of it period. And so the idea of data literacy seems to be more like if you're not a scientist, you're not an engineer, you're not any of these things, you don't have to be, but it can help you with your existing job or your existing role that you're in right now. Do I have a clear idea of that, that data literacy kind of can apply to almost anybody that's out there?

Jordan:
Anybody. Yeah, you're spot on. Because again, if you look at the size of the global workforce, a very small fraction are those technical jobs that use data or are comfortable with databases, with coding, et cetera, like statistician, data scientist, engineer, et cetera. The vast majority of people, whether you go into the arts and humanities or you go into marketing, or all these different things, regardless of whether a person wants to believe it or not, everything has become a role where data can touch it, data can be used in it. And so data literacy is there to help alleviate any stress people have in using data, and really to empower them with confidence that says, "Hey, you're fine. Use your background. Use the skills you developed in the arts. Use your skills you developed in whatever degree you got in school."

Jeremy:
Yeah, I think people would be a little bit apprehensive not knowing a lot about it, especially if you spent an entire career ... You and I are probably roughly the same age, so you remember what it was like 20 years ago. I don't know how we survived. I don't know how businesses moved with the amount of data that was being used back then compared to what it is now. I think there probably is a little bit of apprehension from people who are like, "I don't really want to get involved in that. I've been spending my entire career getting really good at what I do." So what would you say to somebody like that? Well, I could use myself as an example of being a software engineer up until recently. I could say, "I've been focused on being a software engineer, what do I need data literacy for?"

Jordan:
Yeah, you bring up a really good point. I'm going to address the apprehension part and then dive right into what you would need it for. We just recently launched, just a week ago basically, a brand new research report that we did with over around 9,000 or 9,000 plus respondents. We were trying to assess, essentially, that apprehension that you're talking about, that overwhelming feeling that people have had for a long time now.

Jordan:
Because if you take a step back, when you look at the topic of data, math, statistics, all these things, historically they've been intimidating topics, right? You think, "I have to go advanced, I have to have these technical skills," and all of this. Plus, employees, who to your point have been doing their job for a long time, how often are they hearing time and time again, "Oh, we invested in this software for the data and that, and we're doing this now and we're doing that now." It becomes overwhelming and that's what this research report found.

Jordan:
We found quite literally that each employee, so per employee per company, loses roughly over 40 hours a year, of working hours, due to this overwhelming and stressful feeling with data and technology. And when you equated that to numbers in the United States alone, that was the equivalent of $100 billion or so lost. That's billion with a B, $100 billion lost because of this apprehension, this stress. And I think it's because people do feel, one, the topics are intimidated, two, let's be honest, they're sick of hearing about it, and three, they're tired of having something pushed down on them again.

Jordan:
What I say to them is, "Look, that's not what we're trying to do. Data literacy looks to remove that stigma, and all it's doing is saying to you, 'Look, you're right, you've been doing your job and you're really good at it. You've been doing it for 10, 15, 20 years.' All we're trying to do with data literacy is say, 'Hey, let's give you some more skills that will allow you to maybe do that job even better. That will allow you to do that job more effectively. Freeing up some of your time. Giving you more ability to maybe do some of the things you've never been able to do.' And that's what that confidence comes with when you become more data literate because you're no longer looking at all those things being pushed down on you as a bombarding thing, more so you can use it as a weapon for yourself to do your job more effectively."

Jeremy:
Yeah, and that makes perfect sense. If an organization does adopt data literacy and they get a significant amount of their people up and running, and really data literate as you would say, what kind of effect do you think that has on the company or the organization as a whole?

Jordan:
Great question. In my world, what we've done is we've conducted a few big research projects because of questions just like that. It's one thing for me to go around and say, "Hey, look what data literacy can do," but it was another for us to kind of quantify it. So we did a different study that really studied what benefit comes from being a more data literate organization. We partnered with the Wharton School of business and another organization to build this big, nerdy study, if you want to call it that. What we found was those organizations that were top tier data literate had 3 to 5% more enterprise value. So if you're isolating, or looking at I should say, the quality of data literacy, those that were top tier, that 3 to 5% value equated to literally hundreds of millions of dollars. We also found the organizations that are more data literate, they're quicker to market, they have better return on sales, they had better return on equity. That's the enterprise wide, and I think that makes sense. Intuitively, it makes sense that if you're better with data, you're going to get more value.

Jordan:
For the individuals themselves, what they can do for you is one, help you eliminate that apprehension and the overwhelming stress that you get, but two, people that were found to be more confident in their data literacy skills felt they had more credibility at work. They felt that they were performing really well at their job compared to their counterparts who did not feel that confidence with data. So there's all these plus benefits that come from it. And again, I think that makes a lot of intuitive sense because instead of feeling stressed and overwhelmed, you see this power of data and using it for good. That can benefit your career and in essence it can also benefit the enterprise itself.

Jeremy:
Yeah, and that does make sense. I'm just kind of assuming, just based on what you've just told me, that probably where the waste is being cut is in decision making. So basically, not chasing down the wrong thing and then coming back to the right thing that you should have done. With data literacy and being able to analyze and use data properly, maybe you'll make better decisions quicker. Would that be kind of an accurate assumption?

Jordan:
It is. If you think about data literacy, probably the number one essence that comes from it is the ability to make a smarter data-informed decision. I stress the word informed because what we're saying with data literacy is let's combine the human element with the data element, not replace one or the other. And by empowering people to make smarter decisions because they have data to back it up, data to support, et cetera, to your point, you're able to sift maybe through the weeds faster, you're able to diagnose and find insight faster, and you're able to get things out there faster. And when you're more data literate, you understand that it's an iterative approach. With an iterative approach you understand it might not be perfect but that you can come back to it, so on and so forth. There's just a ton of benefits that it really can bring to you.

Jeremy:
Yeah, absolutely. I worked for, years ago in the beginning of my career, for a very large video store, video retailer. One of the positions they had there, they had a couple of people who would sit and watch movies all day long. Which sounds like a dream job if you like movies but both of them were like, "No, this is real work." So they would watch movies-

Jordan:
I think it would get old pretty quickly.

Jeremy:
Yeah, I would imagine. What they would do is take the movie and say it's this type of movie and people who like this movie might like this movie, and map it all together. It was a very, very manual process because this was 20 years ago. And now Netflix does that in a split of a second, at least. That's one of the things I think where people get apprehensive about it's going to take all our jobs. I think they think of cases like that. There's no more two people sitting in a cubicle watching movies by hand, we have this algorithm that's doing it. But somebody has to build the algorithm, I think, is the answer to that. And somebody has to maintain the data and maintain whether or not that's accurate. Is that something that you'd agree with?

Jordan:
Yep. 100%. There's a report out there and if you look at every industrial revolution or every big industrial revolution shift, more jobs are created than are eliminated, and that is the same prediction that is going on now. The McKinsey report is a really good one that talks about this, that more jobs will be created during this revolution shift that's going on to this digital world. But what it requires is, one, it's happening faster, and that just makes sense with technology, but two, it does require maybe the re-skilling and the upskilling and new education, and things like that. And that's what data literacy looks to do, is someone who might not have had that skill before now does. And so yes, I completely agree. Yes, jobs will be eliminated. I don't think anybody would disagree with that. But if you re-skill them and give them new tools to succeed in this new era, then why does that matter? It's all about that re-skilling and upskilling of people to be able to do it.

Jeremy:
Yeah, absolutely. And you've said before that this is a skill that anyone can learn, pretty much anyone from any walk of life, so what is something that someone could do to get started in being better at data literacy?

Jordan:
Yeah, for me, when I think of the beginning, I always ... I've coined what I call the three Cs of data literacy. The first one is something that actually comes naturally to humans, but when we become adults we forget it. I have children, and anybody who has children, in a test, they ask a lot of questions. But for whatever reason, when we become adults, guess what we stop doing? We stop asking questions. Our curiosity goes away. The first thing I tell people to get started on a data literacy journey is you don't need to worry about all this learning and all that, just ask more questions. If someone puts a chart in front of you or a dashboard or whatever it is, ask 50 questions about it. Just be curious and start digging in. That to me is the key to data literacy.

Jordan:
The second C is be creative. If someone brings you all this data, get creative with it, tell a story with it. And then the third C is critical thinking. It's actually thinking on it. Now, when we go to the learning side of things for Pluralsight, for example, I've built eight executive briefing videos. They're short, concise, all about data literacy. And the thing you don't find in there, math formulas. It's all the theories. It's all the what this is, what that is. Through my role at Qlik, everything I built is product-agnostic pretty much, so ... And, by the way, free, so anybody can go in there, assess themselves, find where their starting point is and just start taking courses. That to me is still a part of the first C, which was curiosity.

Jordan:
You need to get out there and study what's out there. Watch the executive briefings that are on Pluralsight. You'll hear me talk about different topics within data literacy that will spark curiosity, that will spark you to read different things and do those things. I'm an open book. Heck, connect with me on LinkedIn. I'm an open book and I'll give you a thousand things to read. I love to read. So it's all these different things to be able to do it, but it just has to start with curiosity. That right there is that fundamental foundation to just get going.

Jeremy:
What is something that you wish you knew about data 20 years ago?

Jordan:
20 years ago, man. I wish I would've been able to see back then just how quickly it was going to come about. What I mean by that is, data literacy, when I started this thing a few years ago, that term really wasn't anywhere. Now it's everywhere. 10 years ago, 15 years ago, iPhones didn't exist. Now smartphones are synonymous everywhere. You probably wouldn't have really heard the term internet of things 10, 15 years ago, now the internet of things collects data everywhere. So 20 years ago, I wish I would've known. I would have changed my study habits, you know what I mean?

Jeremy:
Yeah.

Jordan:
Because I wish I would've known just how prevalent this was going to become. But the other thing, I wish I would have known just how easy it is to be good at asking questions of data and being curious. When you're in your college days or early in your career and doing things, you just kind of get in the mode of this is what you do and you do it every day. I wish I would have been more curious 20 years ago and asking different things of the data. I think that could of projected things even faster for me.

Jeremy:
What are some common misconceptions you think people have about data literacy?

Jordan:
I think the number one is the one I kind of mentioned earlier and that is I think a lot of people ... And it's because that's the term we hear. I think a lot of people think that data literacy means data science, and technical learning, and statistics, and databases. It means none of that per se. There was a quote, it's almost a decade old now, but back in I think it was 2012 that said, "The sexiest job of the 21st century is the data scientist." All sexy nerds, right? We were never called sexy, but now we're going to own it, right?

Jordan:
But the reality is I think that quote did a disservice, because then everybody went out pushing for data scientist, data scientist, data scientist, and that overstepped the broader audience, the massive workforce that is not going to be a data scientist. And so I think it's kind of in the infrastructure of our brains that data science is the end all be all, when in reality so few people have those skills, and so few people need those skills, it created this massive assumption and misconception on what is really needed with data literacy.

Jordan:
That right there is the first. And I think the second big misconception is that technology, that software and technology will solve all the problems. For far too long, companies are spending so much money on technology and software thinking it's going to be the magic potion, thinking it's going to be this magic solution only to find out they don't get a return on investment with it. That is because the people that they're trying to get that technology and software to use it, they can't adopt it. They don't know how. They don't have skills in data literacy. And so, that right there, when I talk to organizations where I go and speak somewhere, is what we try and flip in the mind is that, "Yeah, this technology and software will not do it for you. Your workforce has to be confident with data before they will adopt anything you've invested in." By adoption, I mean truly use it to the effect that you want it to be used.

Jeremy:
Yeah, I think that makes a lot of sense. I think a lot of organizations, at least in my experience, have kind of worked backwards where they're like, "We need to get the technology, we need to start collecting the data. Now we're going to have a data scientist and this person's going to produce all of these neat nifty things." And then, I think they run into exactly what you spoke about. They've produced all this data and have all these nifty things and then they pass it down to others. And then they're like, "Okay, what do I do with this?" Make some guesses and things like that. What you're saying is kind of a backwards approach, so building it out to the people who aren't directly involved with the technical part of it and kind of moving inward, is that a correct assumption?

Jordan:
Yeah, yeah. No, and the way that I tell companies is ... You're exactly right. Historically, what they've done is they invest in the technology and then they try and force fit a strategy into that technology. Versus the flip, you need to start with a strategy first, and then the technology will fall into place based on the strategy. And a huge portion of that strategy is how are you going to get your people up to speed to be able to do this right?

Jordan:
That's the key to getting this started, is start with a good data and analytical strategy that includes the empowerment of your workforce to use data effectively. Then, as you start to expand the strategy and start to roll it out, you'll realize, "Oh, I need a BI tool. Oh, I need this sort of database or this cloud, or whatever it is," and that will fall into place. All the while in parallel, your workforce is being stilled up to be able to use whatever technologies you find to help facilitate the strategy. Tools should not be the strategy, they should be there to facilitate the strategy. For far too long, tools were the strategy, and so that is that ... You're exactly right, it is flipping it on its head, and when that is done properly, you can see data and analytical success. But I'm sorry, far too often what I see is that technology first and then they're not getting the fruit of what they've invested in.

Jeremy:
Yeah, I ran into that a lot as a consultant. One of the first things that I would say as I was coming in there, like, "We have this and we have this," and start talking about the technologies and everything. And then I would shake everybody up and say, "Okay, wait a minute. What problem are we trying to solve? Let's start there." And then all of a sudden everybody's like, "Whoa. Well, we're trying to do this," and it's like you have to kind of work from there, so I can see a lot of similarities there.

Jordan:
All I was going to say is you're spot on. With data and analytics, just like you talked about, it's the same. What problem are you trying to solve? You can't just invest in a lot of data and think, "Oh, this is fantastic." The analogy I use all the time is, if you like to go fishing, you don't go to a Lake, hold your arms up and hope a fish jumps out of the water into your arms. It's the same thing with data. You can't just buy all this software and technology and source all this data, and hope that insight's going to come out of it. You need problems to solve. You need outcomes that you're trying to get to, just like you just described.

Jeremy:
Absolutely. One of the things, and you have a really good analogy for this, but one of the things I think that's a good place for people to start are talking about the four levels of analytic. Could you talk about that a little bit?

Jordan:
Yeah. The four levels of analytics far too often ... and this goes right back to this problem that we're describing, that we've invested in technology and that's going to solve the problems. The problem with that is there are actually four levels of analytics, not just one. It's not just analytics. The four levels are descriptive, diagnostic, predictive, and prescriptive. I love to describe this like going to a doctor.

Jordan:
Imagine you're sick. Hey, you've been sick for a week. People can look at you and tell. You go into your doctor's office. You're waiting in the doctor's office. The doctor comes in and looks at you, says, "You are sick," leaves the room never to come back. How many people would go back to that doctor? What that doctor just did was descriptive analytics. And this is what's happening, companies around the world spend the majority of their time in descriptive analytics. All descriptive analytics is, is describing what has happened in the past. That's descriptive analytics. Other words for this are data visualizations, dashboards, reporting, KPIs. These are things that are necessary, don't get me wrong, but we should not be spending the majority of our time there.

Jordan:
So we go back to the doctor. Now imagine the doctor says, "You are sick and here is why." You've gotten to the why side of it. That's the second level of analytics, which is diagnostic analytics. Data literacy seeks to truly help individuals get good at diagnostic analytics. That's where the majority of organizations should spend their time. The third level is predictive analytics. Same thing with the doctor. The doctor's going to say, "I predict you take this medicine, you get bed rest, you do this, you do that, you'll overcome it." Sometimes you're not right and that is one of those things that people need to understand about data and analytics. Not every prediction is going to come true. That's not the point of predicting something. If there's an 80% chance of something happening, that means one out of five times it's not going to happen and you have to be okay with that.

Jordan:
And then the fourth level is prescriptive analytics, and this is where we're getting really advanced. It's similar to predictive, but in prescriptive, your data and analytics will be telling you what you should be doing. It will prescribe it for you. Most companies are stuck at level one, and they spend the majority of their money at levels three and four. All the while, the human element, which is ... the human element, is diagnostic analytics, level two. Don't get me wrong, there's human element in all of it. But if you create a data literate workforce who knows how to work through these four levels and make it known that it's not like these four levels are divided equally, you don't spend a quarter of your time and age. No, no. The majority of your people will spend the majority of their time on level two when they get better at data literacy. When you absorb those four levels of analytics, that's when your strategy can really succeed.

Jeremy:
Yeah. That's really helpful to have that framework, I think, to kind of step back and look at everything and look at the approach. And I think the doctor analogy for that is perfect because we don't give a lot of thought to the steps and the process, sometimes with these things it's just kind of go at it, look at it, and figure it out and move. So yeah, that kind of framework is pretty nice. So, what do you think is coming down the road for data literacy or data in general? What things are kind of exciting that are coming?

Jordan:
Yeah. I think we're going to see more ... At Qlik we like to call AI, augmented intelligence. I think that's what we're going to see with data and analytics. The human element needs to be there. The creativity, the curiosity, all of that. You can't just leave it to the data. If we do that, we've seen what happens with black box algorithms and things like that. They can be biased, racist, all these different things that are so negative that come from the data. But what I think we're going to see advancing within data and analytics and data literacy is the ability of the tool to augment us even more.

Jordan:
Imagine if, historically, something you were working on took you two to three hours to build a dashboard or whatever. Let's say the tool augments it now and it only takes 15 minutes. You've now freed up two to three hours of your time to be more curious, ask more questions, to find the diagnoses within the data more and more. So to me, I think that augmented analytics is going to become bigger. Where, yeah, the tools and technology can do more than they've done before. Freeing up the human capability to combine it with that augmented intelligence and allowing us to do more. I think that's one of those keys that's coming down even more, and we're seeing it. We're starting to see it already. But I think some of these advances that are coming in this world are really going to free up people's time to bring that creativity, to bring the ability to tell stories to the data, which makes it more human, which makes it more impactful. I think those things are here and I'm excited for them, I think they're awesome.

Jeremy:
Yeah, that is pretty exciting. And I think that that's a good way to sell it to people, for lack of a better word, as far as getting people excited about data literacy is, "This will give you time to do more of what you do and allow you to pour more of you and what you're good at into what you're doing." What are you working on right now? Is there anything you could ... any cool projects you can tell us about?

Jordan:
Yeah, the big one we just did is just last week at our company sales kickoff, we actually announced to the world our brand new data literacy consulting. And so a lot of my time, as you can imagine, is spent on that now because it just launched to the world. What that is is, if you think about it, all my stuff, pretty much everything is free and it's product-agnostic. I think there's one course out there that uses our Qlik product to show you how to do R and Python, which is more for the technical side. But other than that it's free, product-agnostic, it allows people to just go play in there, but the feedback that we got was, we need more help. We need people to be with us to drive these new strategies. We've tried for years, as we all know, and nothing's been working, so we launched this large business line to provide that.

Jordan:
That's something I'm spending a ton of time on. I'm writing a lot. I'm doing a lot for Pluralsight, which makes me excited. Getting more and more involved there. Driving more things for the nontechnical area, which is ... Pluralsight's been so focused on the technical, and rightfully so, computer science and everything, and they've asked me to drive more into that nontechnical side with courses, with writing, with podcasts. Yeah, so there's a ton going on. It's like my nickname of being chief nerd officer. I geek out on this stuff so that is a very formidable nickname that I have.

Jeremy:
Yeah, I love that title. I'm quite jealous of that title actually. I'm sometimes called the resident nerd here at the workplace and I wear it as a badge of honor.

Jordan:
Oh, for sure. My son wants me to get him a shirt that says junior chief nerd officer.

Jeremy:
That's awesome. That's pretty cool. I have one final question for you, and this is my surprise question of the podcast. Imagine you've been given an elephant and you can't give it away or sell it, what would you do with that elephant?

Jordan:
Oh dude, I'd keep it as a pet, in a heartbeat. I would keep it as a pet. Knowing my personality, I would dig in and figure out, how in the world do you take care of an elephant? And I would become best friends with that elephant. I'm all up for challenges. I'm all up for learning, and in this case, if I have to learn how to take care of that big elephant, finding land for it or whatever, dude, I'm going to rock it and I'm going to have an elephant.

Jeremy:
Awesome. That's a great answer. Is there anything that you want to talk about that I didn't ask?

Jordan:
The only thing I would say is for anyone listening, please connect with me on LinkedIn. As you know, I'm an open book. I will share the world and what I can on this topic. I want people to feel that, again, they don't have to be a data scientist, just get going with this. Research it, reach out to me, let's help you on your journey. That would be that final point I share is, don't hesitate to reach out to people who have an expertise in the things that interest you. One of the number one things you can do is build your network and to be genuine about it. Don't be fake of course, but be genuine about it and reach out to me. I will help you along your journey to make this happen.

Jeremy:
Awesome. Well, thank you very much for talking with us today.

Jordan:
Well, thank you. It's been fun. Always.

Jeremy:
Jordan Morrows, the chief nerd officer at Qlik, a leading business intelligence company. You can check them out on the web at qlik.com. Be sure to look for Jordan's TED Talk, Why everyone should be data literate, on YouTube. Thank you for listening to All Hands on Tech. If you like it, please rate us. You can see episode transcripts and more at pluralsight.com/podcast.