Unpacking Data Literacy

Loris Marini - Podcast Host Discovering Data

There is an expectation that if you are under 40 and you have an internet connection you naturally "get" data. That's not that simple and we are leaving people behind. The solution? Follow me as I speak with Ben Jones, founder of dataliteracy.com.

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Episode transcripts

Loris: [00:00:00] The promise of data is not to replace humans with bots, but to assist our thinking so that we can address bigger problems while being, on average, more responsive and more effective. But for most organizations, this is not quite within reach.

On the one hand, most of our data is treated as a byproduct of the business operations, an inconvenience almost - we wish it took care of itself. And this leads to poorly managed data and effectively breaks the value chain and makes it nearly impossible to do anything meaningful with it.

On the other hand, there's a confidence gap between the experts (the analysts, engineers, and scientists), and the self-proclaimed non-data people. Not everybody can be a data expert, but most should at least be data literate.

I believe that our ability to make informed decisions at scale really depends on closing these two gaps. So to learn more about data literacy, today I have the absolute pleasure of speaking with Ben Jones, founder of dataliteracy.com.

Ben graduated with a mechanical engineering degree 20 years ago. He worked in that industry for five years then spent another seven in the medical sector. In 2013, he joined Tableau, starting in product, then moving to marketing and landing what I think is the coolest job of all: technical evangelism director (which I’m burning to know more about). In 2018, he started Data Literacy to address data-phobia and help people from all industries and disciplines obtain the knowledge and skills they need to confidently participate in the dialogue around data.

Ben has authored a number of books, including the bestselling business communication book, Avoiding Data Pitfalls. He’s also published another two short books which are part of a series on data literacy. So, here I am with Ben Jones. Ben, thank you for being with me today, and welcome to The Data Project.

Ben: [00:02:02] Well, thank you very much for having me.

Data Literacy as a Language

Loris: [00:02:04] What is data literacy, Ben? How do you think about it?

Ben: [00:02:06] People often ask: what does this term mean? I see data literacy as the ability to read, understand, create, and communicate data as information. And so it's a critical skill that everyone really needs to have in today's world, and it involves many different activities.

It is relevant in every industry and discipline. It's almost too big in scope so we are looking into finding ways to make an impact in many different avenues. So that's what I see regarding data literacy. It’s really about understanding our environment and having a conversation with other people and using data to make better decisions.

I think that it really comes down to those factors. Data literacy involves hard skills and technical skills - being able to work with numbers and charts and graphs. It also involves domain expertise. If you're in marketing, it's slightly different in a dialogue or a dialect than if you're in HR or finance. So those are some things to take into account. But ultimately, it’s a language. Data is a language and it's something we need to get better at speaking with one another in order to navigate the world we live in.

Why data literacy is so relevant now?

Loris: [00:03:30] And it's something that we can learn! We’ll dive a lot more into this in a short while. But before we do that, I would like to understand why data literacy is so important today. We’ve been collecting data for more than 50 years, and we know that the volume of data is increasing, but is that the only reason or is there something else?

Ben: [00:03:54] I think over the course of our careers in the last 20 to 30 years or so, ever since the PC revolution began in the late seventies, obviously data was already relevant much earlier than that, but it became an every person concern right around that time. Companies have been spending a lot of time and money and effort to gather data. We've also seen a huge evolution of tools in the last 10 to 15 years in the self-service analytics space. But the problem now is that many people don't know how to make use of it. And so I think that's part of the reason that data literacy is relevant.

Another reason, by looking at what happened in 2020, it isn't just about our jobs or the companies we work for. I mean, we needed to understand data in order to keep our families healthy, understand what is happening in the world, participate in the democratic process, understand social issues, understand global issues and try to work to solve them.

Data is an angle and a lens. It's a tool we can use to try to improve the world we live in. And that's probably the bigger reason why it's more important now more than ever is that data can be applied to solve some issues that are becoming fairly urgent for each one of us.

Data Literacy Fundamentals

Loris: [00:05:22] Yeah the numbers are absolutely mind-boggling. The IDC, a global provider of market intelligence, has published a forecast expecting a tenfold increase in the total volume of data by 2025. I think you're right. If we can have all the data in the world, but we cannot make sense of it and use it to make better decisions, then there's really no point in storing all that data.

So I’m really excited to dive into this. I’ll be referring to your book - and I may be a bad host because I’m jumping my own guns but I’m so excited to talk about it - “Data Literacy Fundamentals: Understanding the Power and Value of Data”. It is an absolutely fantastic read. It’s a compelling read and it taught me a lot more than I thought.

I've been in the data space for some time and you've done an excellent job. One of the things that really impressed me is the clarity with which you explained the progression from data to wisdom. Would you like to dive into it a little bit?

Ben: [00:06:45] Yeah, sure.

Loris: [00:06:46] I think it's really interesting.

Ben: [00:06:48] So we were able to learn a lot from this DIKW (Data, Information, Knowledge, Wisdom) pyramid, which has been circulating for the last few decades in some different fields like Information science. And actually, we pulled all the way back from a pageant play by T.S. Eliot in the 1930s, where he says, “Where is the wisdom we have lost in knowledge? Where's the knowledge we have lost in information?” And of course in our day, in our era, we've added this layer below those three factors, which is the data itself.

And so I think the problem is like you're saying, organizations have been hoarding data even to their detriment, even in ways that have resulted in privacy and security issues for them and for their stakeholders. That's another story but an important one as it relates to data ethics.

But let's say we take all the data and we need people to be able to make sense of it. Well, how do they do that? What's the process that they have to follow in order to do that? And so part of it is a very human process because it brings into the equation our assumptions, our priorities, our values - what is it that we're trying to accomplish? What are we trying to use data to do?

But we need to do things that are not foolproof, like interpret the data. What are the metrics? What are the units? You know this is not a trivial concern because it resulted in a Mars Rover crashing on the surface of Mars in the 1990s because a vendor of NASA used the wrong units. So it's very easy for us in our world to get the units wrong: time zones, currencies - these are things that are important. That's a fundamental part of the data that we're sitting on top of. What are the units of measure? How is the data collected? What are the potential issues?

The contextual nature of data

I was actually just doing a training session earlier today, where we were talking about urbanization and the percentage of people in each country that live in a city. If you look at the World Bank data site, they have some interesting details about that.

They admit and call attention to the fact that every country defines city dwelling in a slightly different way. Maybe it's proximity to a city center or maybe it's the density in that place. The UN is trying to collect all that information from different reporting bodies, collate it and smooth it. And we're trying to use it to understand the world we live in. And so that's part of this process of converting data into information and interpret it.

We then need to turn it into knowledge where a single piece of information floating around doesn't do us a lot of good. We need to make associations and connect the dots with other pieces of information. Now we understand more about the world we live in. We turn information into knowledge by making those connections and associations.

And of course, the wisdom step happens by actually applying it. Let’s say you're an executive leader in an organization. Are you allowing the data talent on your team to give you advice, to give input into some of these important decisions that you're making? Or, are you retaining all of that decision-making authority and ability and closing them out of the conversation? In which case, they're going to get frustrated and leave. You've wasted a lot of your organization's time and money by collecting all this data.

So we spend a little bit of time right there at the beginning helping people understand some of the reasons why this chain from data to information, to knowledge, to wisdom breaks down. And then how can we make sure that doesn't happen in our own organizations? Not just in our companies, like I said, but in our communities, societies, even in our own families and our personal lives, we need to find ways to make sure that chain is intact.

And we try to go beyond the ideologies and into some practical examples of how we can make that conversion happen well because I think that that's what a data-savvy person does.

Loris: [00:10:41] Yes. And that's what a resilient organization should aim to do. And I'm so passionate about this topic. Every time I hear someone speak about the ladder of knowledge or wisdom - however you wanna call it - I get excited. I like the fact that data has a human element everywhere across every single step from data to decision-making, to wisdom.

It’s the ability to know when to apply the knowledge of creating relationships between pieces of information and understanding data. Data without a structure will never turn into information. It doesn't tell you anything.

Making space for the data conversation

You make a beautiful example in the book of the different ways in which different countries write down a date: year first, then the month, and the day, or the other way around. So in all those examples, you have data because you get a string of numbers, like a sequence from the integers. In one case it conveys information, while in the other carries misinformation, just because of the way we interpret it - and that means structure and agreeing on standards. All of that involves people and within an organization, different types of people. So that conversation has to happen.

Gardner published this article by Mike Duncan and Valeria Logan recently. They found that most CTOs favor the tactical type of work versus strategic work. There is an obsession with short-term achievements and a never-ending thirst for delivery. There is this sense that data is almost seen as an inconvenience.

Like I said at the beginning of the episode, they need to get rid of the problem and tick the box. And so there's this thinking that all you need to do is just hire a contractor or someone that can work really fast and they can take the data from A to B and solve the problem.

Unfortunately, that's not the case because it skips over the human element. And when you don't take that into account, since data is eventually used by humans for humans, there's really little use in having that data in the fanciest, fastest, and most scalable data storage solution that “The Cloud” can provide.

How big is the data literacy gap?

Let's look at the numbers: we should be data literate, but we're not. How far are we in 2021?

Ben: [00:13:38] I think that there's a massive gap and I think it's really hard to quantify. I know that there are some studies that have been out there, notably, a recent one by Forrester about the great data literacy gap. And in that study are the results from surveys from both academic decision-makers, people deciding on curricula at US universities, as well as recruiters at corporations trying to find talent. There's an agreement on both sides that there aren’t data skills that are necessary, and universities aren't always doing everything they can to close the gap.

So for example, out of 156 university academic decision-makers, only 48% said that they have programs in place to address and give data skills to their students. That number is even less for students who are in the arts and humanities.

Whereas if you talk to recruiters, they'll say that the demand for data literacy is actually the fastest-growing one. And as the study shows, it applies in any and all departments: HR, finance, operations, marketing, you name it.

And so I think companies are feeling the pinch. They're trying to find data talent. They’re recognizing entry-level candidates who have data skills as being highly valuable. Universities are a little slow right now to try to respond to that need. They have been developing analytics programs left and right over the past few decades, I would say since the early 2000s, there were always statistics programs. And so in many ways, they're flavors of versions of a statistics program.

Loris: [00:15:24] And I have a horror story about that in a second.

Ben: [00:15:26] Oh, yeah. Many people break out into a cold sweat when I say the word STATS101 from personal memory. But yeah, I think that universities are going to play a role in closing the data literacy gap. That's hard to measure, frankly, but I don't think that that's the only avenue.

There are many people who don't have access to higher education. And so it becomes an even more egregious problem for individuals who are at a disadvantage, either because of structural racism or also because of socioeconomic factors. So how are we helping them? Is this going to be another asset of the haves and the have nots are yet again left out? I mean, I think that that's not going to help us address some of the social issues we're facing today. So that's another part of the conversation as well: how are we helping people of all different backgrounds become capable of getting the training, the resources, the knowledge, and the skills they need?

But I don't know if I can say, well, here's the number that I would call out as the single metric that tells us about data literacy. There have been some studies. Jane Crofts down in Australia, her organization tried to come up with a sort of current state, if you will, a state of data literacy around the world.

And a lot of it comes down to people's confidence. How well-equipped do they feel they are to tackle the data challenges that they're faced with? And I think the fact of the matter is, there's far too many that don't feel that. Many of these different surveys, you mentioned Gartner, Forrester, or Jane Crofts (Data To The People), time and time again, they're surfacing.

And also we do a data literacy score assessment where we get feedback from individuals within companies and from the thousands of people we've talked to, many of them are lost. They don't know where to get access to data. They don't feel like they have the breathing room and frankly, they just like they’re being left behind. And I think that that to me confirms what I felt when I left Tableau a few years ago, that this is important enough for me to try to focus on full-time.

Data Literacy as a Strategic Imperative

Loris: [00:17:35] I mean, the future is definitely bright if you ask me. I'm always very optimistic.

Ben: [00:17:44] An optimist. Right. Me too.

Loris: [00:17:46] When there's a problem and everybody complains, there is at least one business opportunity. Oftentimes when you close your eyes and you really think hard about it, with a bit of creativity, you can find the 2, 3, 4, or 5 business opportunities, especially when the problem is this big.

There’s a number that freaks me out. And one that excites me. The one that freaks me out is that in the paper that I mentioned before, if you ask a business’s decision-maker, nine times out of 10 they would tell you, “Oh yeah, sure. Data literacy is super important”, but then, only one in five reports that the business actually provides resources for their employees to become more confident with their data.

And based on what I see, I don't know if the numbers match exactly, but this is a problem across all of data, including data management, or even more so for data information and knowledge management.

If you as a CEO “how much data matters to you?” They will answer “100% we couldn't live without data.” But if you ask “okay, what’s your priority, what are you trying to do now?” they’ll say “Oh, we are just trying to hire a unicorn, someone that can fixes all our data problems."

How does that make any sense?

Ben: [00:19:06] There’s a lot of lip service that's being paid to the importance of data. And so there are some, I think, attitudinal problems. There are also some myths about its value and what it can accomplish for us. And so I think that's part of the problem, frankly, that people are ready to admit that data is important but not necessarily prepared to roll up their sleeves and try to find a way to make progress.

But that's changing. Here at my company, we get to work with many organizations, not just companies, but nonprofits and governments who are trying to do something about it. I think that's starting to rise up now, a pretty big wave of focus with many organizations placing it as their strategic focus - finding a way to become more data literate, more data fluent.

They're calling it different things, but they're really all trying to accomplish the same thing, which is that they want their team members to be able to be well-equipped to tackle and to make use of the data that they have.

It's starting to change, but I agree with you when that article was written back in 2020, I know Valerie Logan quite well, actually she's a close friend. She runs The Data Lodge, which specializes in helping organizations, running these boot camps for organizations that are trying to figure out what their playbook is for addressing this gap.

When they wrote that report a couple of years ago, that was what they were noticing is that no one had a plan. Everyone was saying data was important, no one had a plan. And I think what we've seen more and more, especially in the Forrester report that just came out that Tableau sponsored is they're noticing that organizations more than ever are starting to invest their time and effort and their budget behind this problem to close the gap.

And so I think it's moving in the right direction, but it's still a long way to go.

Loris: [00:21:00] I think in the same article that we're mentioning, by 2022, we expect 90% of corporate strategies to explicitly mention information as an actual asset. And, 30% of the CDO is sort of expected to start liaising with the CFOs to actually put a dollar value on information. And I really believe that that's going to be a key driver to actually allocate enough funding to start treating this stuff seriously. Not just like, “Everybody's doing it, we need to do it too to be the cool kids in town”, but as, “Hey, if we don't do it, we're going to leave money on the table.”

Ben: [00:21:40] It's the lifeblood of organizations now. Absolutely. Companies are monetizing it. Now, it's about being competitive. It's about being relevant. It's about understanding what's happening and you really cannot do that unless you have data-savvy people, data and tools, and a culture that allows them to make use of it. It’s a multifaceted sort of a thing.

And hopefully, they have a bedrock of ethics so that they're doing that in ways that don’t harm anyone. Or they seek to minimize the likelihood of that because I think we've seen that pop up quite often here in the last decade or so, organizations rushing ahead to try to make use of data in an almost a frenzied attempt while maybe not putting in place some of the safeguards that are going to protect their stakeholders, their customers.

Overcoming Data Phobia

Loris: [00:22:28] And I think that the literacy element is important, having that intelligent conversation because otherwise fear kicks in when you don't understand something and you don't even have a reference framework to be able to start a conversation or to be part of the dialogue.

It's going to be hard because our natural human reaction when we don't understand something, it feels threatening you just sort of close down, you stop thinking and you react instead of actually taking more of a deliberate and balanced approach and say, “Okay, this is one way of using data and it's definitely bad, but it doesn't mean all data is bad. It doesn't mean every company is trying to leverage data as an asset. They don’t have a dark secret plan to conquer our soul or to impair our freedom.”

Ben: [00:23:18] Absolutely. And you mentioned this idea of fear. That was our focus for the first year of our business, what we call the data phobic individual, that is really feeling left behind. They don't feel like it's their thing. They notice data is becoming more and more relevant. They're being asked to do things that they don’t feel they have the skills for, and so how can we equip them? How can we make them confident?

That's the first question we were trying to solve. We're working on programs now that address the data worker: people who have embraced data for some time. But we didn't want to get very far along the road before we started to come up with real resources for individuals that were data phobic.

And so you're right. What they'll do if their data phobic is they’ll become defensive, they'll start to criticize and find holes to poke in the entire edifice of data within their companies. And so they'll undermine the organization and their ability to leverage data.

Data doesn’t know better

Why wouldn't they, I mean, this is not comfortable for them? It is, in some ways, maybe making them feel like their contributions aren't as important or valid. Their experiences or their intuitions are not even valid, but they're dangerous because data is somehow better.

Loris: [00:24:32] Data “knows more”?

Ben: [00:24:33] Data is better. Data is this perfect decision-making magical engine, but that's also not true at all. And that's a myth we need to dispel and so that people can feel more confident and comfortable. Actually, we want their intuition and experience. We want them to bring that to the table because that might help us understand when there's an issue with the data.

We've all been in that experience, in that situation where we said, “this doesn't feel right to me”. Let's have a conversation about that. We realized there are some issues that need to be resolved with the underlying data. And then the flip side is true as well. Sometimes the data tells us our intuitions are wrong and lo and behold we've been wrong all along.

And so I think we need to be creating cultures in which it's okay to have the data prove us wrong. And it's also okay to question the data because both of those things, to me, are both engines now firing if you're coupling them together, as opposed to the overly simplistic route where you've cut one off and you only use one. I think that’s not the answer. That's not gonna help organizations get where they need to get to.

The many roles of great mentors

Loris: [00:25:39] You raised so many really interesting points. So I want to start by sharing a personal story. When I was in high school, I believe in year three, we were doing these classes on trigonometry over and over. I remember absolutely hating the subject. The professor was not the best person to teach it. He was clearly not passionate at all. He would just show up and draw sin(x) and cos(x) on the whiteboard. Those classes felt to me as if it's been an hour and we were only 5% through. I hated it.

This story is probably going to resonate with a lot of people. One day, this professor had a health issue, unfortunately, and he had to leave the school for three months. So they got us a replacement. And the one that replaced him was the exact opposite. He was passionate, he knew the topic well. He would jump to an actual real-life example.

I remember one day he said, “imagine you have to build the profile of the wings of a plane. How would you go about it?” And he used that to motivate us about why you need to project things on a set of axes and all that stuff. And it was so, so interesting and it literally converted me from, “I hate mathematics” to “I want to know more”. And that's what led me to feel confident enough to join an engineering school. And I graduated and then that confidence was important for the next decision to getting a Ph.D. in physics.

So I believe that no one is born confident. It’s something you build over time and the role of a mentor is extremely important, they can make it or break it. So the fact that I see individuals like you super passionate about the topic, really trying to raise that bar… that's inspiring.

I guess the message I want to say is if you're listening to this and you're like, “oh, I don't touch this stuff, I'm not a data person”. I totally get it. You know, first of all, I get it. And second is, it's not just mathematics.

Spread the passion, spread the language

Ben: [00:28:17] No, it's not, it's very practical. Even when you were describing what caused you to have this turnaround. It was about something practical, right? It was about building something, making something that intrigued you, and this brought you to a place where you were far more interested. It wasn't some abstract, meaningless puzzle on the whiteboard. It was something that was more, almost an engineering kind of example.

People, along the way, have told themselves they're not good at math. They’ve heard that story from probably a few sources. I don't know. And I think mostly all they're missing like you're saying is exposure to someone with passion. So find someone with a passion for the topic.

It's like a language. If you want to learn a foreign language, you need to surround yourself with people who are good at speaking it. Maybe it's on the radio. Maybe it's on the television. Maybe you travel to a country and live with the family. This is what they do. This is the way they communicate and they love doing it. And so the more you are around people like that, the more you surround yourself and your career with individuals who are passionate about data, the more you seek out mentors who are on fire about the way this can also be helpful and useful for you as you grow your career. I think you're going to be better off.

And to those of us, like you and me who are in this field who really get it and understand how valuable this is, that's on us to spread the language. Can we find ways to instill others with confidence? A lot of it for me is helping them see that they actually already know a lot more than they think they do. Data visualization is a great entryway into that - which is that second book, Learning to see Data - because we already know how to see it. It's just a question of becoming more aware of it.

We've been given a visual cortex that allows us to take in these symbols and shapes and pay attention to things like outliers or colors. And our attention is a very powerful thing as it relates to visual data. And so that entire course and book is all about helping people see that our brains are wired a certain way and many times our confusion or frustration when we come across a chart that doesn’t make sense to us, is as much about the way it was conceived as it relates to the questions we have or the tasks we're trying to accomplish.

And so if we can see that, then there's just a disconnect and it isn't our fault necessarily. And we need to be able to learn to have a conversation that allows us to move to a place where these do become effective visual aids for us. They help us do something better. They help us accomplish tasks in a more efficient way.

Because I can make a visual that works great for you, but for someone else, it might be useless, you know? And so it isn't that it's good or bad. It's a question of, is it well suited to that person's needs? So we're trying to help people understand that that is a way they can advocate for themselves when they're interacting with data, whether it's in their workforce or talking about what's happening in their community.

But to your point, a big part of it is helping them see, “oh, wait a minute. I actually do speak this language already. I didn't think I did, but I do.” And so there are still things to learn. It's not like they’re already fluent in everything, but there are some things that they're much more fluent in than they know.

And when they become aware of that, that's exciting. That's when they have that light bulb moment. And when you were a part of that with someone that's addictive for me to be a part of.

I can really relate to that teacher of yours. I aspire to be like that individual and to have that effect on people like that trigonometry substitute teacher had on you. And if I can do that, I think I'll have accomplished what I want to do here.

Loris: [00:31:58] Yeah, I definitely feel the same way about this. And it's a fantastic experience to see that  spark firing, the eyes getting slightly bigger when someone really understands something. It's also very disappointing when you try your best and people don't seem to care very much.

Ben: [00:32:18] We all have those days.

Psychology at every step of the value chain

Loris: [00:32:19] Yes, we have those days too. And a message here that I really like to stress is that data is made of many different parts. In the book, you mentioned there are seven data activities. You can call it a value chain. You can call it a pipeline. Whatever the language, somewhere we start by creating a data source that is stored on a physical medium, somewhere being a database in the cloud, on-premises, whatever.

Then we need to build a data source, which means structuring information and giving the right names to manage the metadata. Then we need to prepare it for analysis because the structure in the database is not necessarily ready for what we want to do. And that's sort of where data starts almost, the behind-the-scenes work, but then the process continues and you can analyze it.

And then at some point, you'll have to present it and someone will have to consume it. So you don't have to be passionate about mathematics or database and coding, but you have to have a passion for graphic design or the study of how we perceive visual information as opposed to sequential serial text.

And you may be interested in combining the two to supercharge your reports and make sure that people understand the message no matter how they see it, or no matter how they look at it. That's also an avenue. People who specialize in the first one that comes to mind is Georgia Loopy. I think that you mentioned, yeah, I absolutely love her work.

Ben: [00:33:53] Me too. She’s great.

Loris: [00:33:54] Yeah. And then the consumption and making sure that people feel comfortable using it, they trust it. Someone else will have to interpret that and feel that they trust what they're seeing to the point that they're going like, “okay, I'm going to use it now to make this decision. And we'll either hire or we expand or we don't expand.” Getting to that point, that decision is incredibly challenging and fascinating.

Psychology enters the equation pretty much at every level. When you store data, psychology is important because we interact with data. We do stuff with it. We create copies. So all of our behavior around it, it's going to affect ultimately the quality, the level of trust we associate with it, but psychology percolates in the last mile of the value chain, which is where we build plots and stuff. So there's plenty of work to be done.

Data as a co-pilot

There is one point that you made which is the difference between the data dreamed and data-informed. And the fact that data is never complete. Elaborate on that for me a little bit. Because I think this stuff is really, really intense.

Ben: [00:35:01] Yeah. So this kind of a pet peeve of mine, the phrase data-driven, I mean, I get it. People are just trying to increase the relevance and prominence of data and I'm all behind that. I just think that for those who think that data is going to make the decision for them, maybe that's a little over-exuberant and it's actually overvaluing data and I don't think that does any of us service.

Because the reality is, to your point, it's always a little incomplete. I mean, even thinking about last year with governments trying to make decisions about what to do in the face of a data set that billions of people around the planet were looking at at the same time. I don't think that's ever happened before in the history of the human species, but was it clear, was it obvious what to do? Were there questions? Were there uncertainties? Were there gaps in the data where absolutely confirmed cases aren't actual cases? Those rates are difficult to compute.

Should we look at the absolute count? Should we look at the rate? There are many different angles, but which angle is the most important one? And so, there's uncertainty in the equation and we need to also apply our best judgment. We need to also apply our intuition. We need to piece these together into a tapestry that allows us to make a good decision.

And, data is not a panacea. It doesn't solve all of our problems and make all of our decisions for us to the degree that people think it will. I think that they're going to be disappointed when they realize that it's messier than that in reality. And that's I think the truth about data.

So I think it's important to speak that truth and to embrace it, and find ways to work with it. And so that to me is what we try to teach, because I think if people are going into it with their eyes open with the right expectations, then they're going to be better suited to thrive when those challenges arise, they're not going to be surprised.

They're going to understand that that's part of the game and the rules of the game. Our data is dirty. Data is not perfect. Data is incomplete. And it's giving us an idea, an angle, but not the only angle. There's a human angle and there are many, many unknowns still in the most important decisions we have to make. And even sometimes in tactical decisions that we have to make. That's what we try to teach in our programs.

And I think that that's the reality. I gotta be honest. I actually kind of love that about data, when you might say, “oh, how depressing”, no, I think that that actually makes it far more interesting. And I think that makes sense. You mentioned all those activities along the workflow and the fact that each step involves humans and even the data that we’re collecting in the first place. That's a statement of our values. That's a statement of what matters to us. I mean, there are a million things that we could measure about our environment at any given time.

What are you going to measure now? I'm running a business. We're about three years in, what are we looking at? What are we measuring? Are we looking at burn rate? Are we looking at page views? Are we looking at customer satisfaction? Are we looking at all of those?

I mean, we could drown in data and so we need to cut through all of the noise, focus on the things that our intuition tells us is important, and then pay attention to what it's telling us, understanding full well that it's not going to give us the full picture.

Loris: [00:38:18] Absolutely. I come from a background of using data and worrying about data transfer between a machine and another machine. I mean the mobile phones we use - smartphones. I don’t think anybody calls them mobile phones.

Ben: [00:38:31] It was a cell tower. Yeah.

Loris: [00:38:33] Yeah. We take it for granted, but the stuff behind it is incredibly complicated, but that's an example where you have everything controlled. There's no human element there. It's a machine that talks to another machine. So yeah, sure. In a world full of bots and computers, we can imagine the system that's engineered almost to perfection, where data flows and decisions are made, but that's not the world we live in. It might be 50 or 100 years from now.

But today we have a lot of dirty data. We have a lot of important decisions to make, and we need to piece this story together by merging intuition and data one hundred percent.

How DataLiteracy was born

So what was the spark that got you to start Data Literacy LLC?

Ben: [00:39:17] So there were a couple of things. Teaching at the University of Washington in a data visualization program and helping them design that program got me hooked on this experience of helping people along their data journey. When I was leading the Tableau public platform, we were traveling around the country, teaching journalists, and there many of them, at least those who were in our training classes, who didn't really see themselves as data people, you know?

And so we were helping them get over that hurdle. And I started to realize that the tool was very powerful, but in many cases, it was the confidence of the individuals using it that was the barrier, the limiting factor if you will. And then one day it just hit me like a ton of bricks that I think it was like about lunchtime.

One day, I was sitting in my office in Fremont, Seattle, and I wondered how many people had used the software I was helping to market to come up with the completely wrong understanding about their environment? And that was part of also the impetus behind Avoiding Data Pitfalls, the book that I started while I was at Tableau and then finished just after I left. But just this idea that I was seeing my students at university make many of the same mistakes that I made early on, and even then I would still continue to make.

And so a silly example is I was at a lunch. Not far from that office I was describing and right outside that office, there's this bridge called the Fremont bridge. I think actually I looked it up on Wikipedia. It's the bridge that goes up and down the most often in the entire country, it's very low, every little sailboat that comes through makes this thing go up and down.

Anyway, it has bike counters on it, long story short, those bike counters, which Seattle's department of transportation installed in order to promote and understand the extent of bike ridership in the city, count bikes, and you can go download that data right now, on the open data portal of the City of Seattle. And I was presenting that to a group at a luncheon, not far from the bridge itself.

And we looked at the data and there was a massive spike. And I said to them, and I didn't know what it was. I actually hadn't had time to look at the data I have to admit prior to the luncheon. It was the first time I was a little under-prepared, but I asked the people in the room, what do you think that is? What might've caused that? And I, and they, and everyone, came up with great ideas about why bike ridership may have spiked that day. And nobody knew.

So we moved on with the presentation, but about 15 to 20 minutes later, a gentleman in the back of the room is waving his phone. He says, “I looked it up. I Googled it. I figured out the reason behind the spike.” Well, it turns out a bike blogger started emailing back and forth with an employee at the department of transportation, lo and behold, the bike counter was glitched. It was probably the batteries. They replaced the bag. And then all of a sudden the crazy anomalies went away.

And the thing that hit me was the fact that no one when we originally brainstormed what might've happened. We talked about bike races. We talked about bike to work day. We talked about normally sunny, beautiful days, all of these reasons why, and no one sat there and said, “wait a minute, maybe there's something wrong with the bike counter.”

And so those are the kinds of things I started seeing people do and mistakes that I was making as well. And then I realized, I think that that's actually what I need to do. Not so much promote or help any company sell their tool. I think they're doing a great job at that. They need to keep doing that. Absolutely. They're teaching people how to use their tools very well. But someone needs to also go beyond that and teach some of these basic concepts and some of the problems behind using these powerful tools, which aren't necessarily the most convenient message to convey for the tool vendors themselves.

So I thought that that was going to be part of what I could do, what I could contribute. The spotlight wasn't being shined on those topics and issues as much as I think it needed to be. And part of that is a fallout of mistakes that I made myself. Part of that was a fallout of a realization that this was happening all over the place.

The Role of a Structured Course

Loris: [00:43:10] Here is a paradox, right? We have access to the internet through one of the most powerful search engines ever designed. But it doesn't feel like we are on top, there's access to so much information but that doesn’t make it any easier to commit to upskilling, even if you are self-motivated, even if you are consistent in your work and you really want to improve.

I'm an avid user of YouTube. Even if you know how to use it, you can't replace a structured course with a bunch of videos on YouTube. I definitely had to stitch together many different channels, many different books, and, and ultimately that's how I do my learning, but it's a lot of work to find all this information and then put it together, and then you'll never know how far into the learning path you are. Because there's no reference point. You're sort of building an intuition for it as you go. And if you have all the time in the world, maybe that's fine. But if time matters to you, then there's no replacement for a course. How do you see this?

Ben: [00:44:34] Yeah. So I think that those free sources you're referring to are super helpful and they play a massively important role in our ability to close the gap of data literacy in the world. And you're right. Not everyone is self-motivated and self-guided enough to piece together a learning path that is going to get them where they want to go.

But some are very good at that. Whether it's blogs or books or YouTube videos, they're going to figure out a way. Khan academy is a great example of learning at scale in a free way. So I think that that is a big part of the equation.

Especially as I mentioned at the beginning of our conversation, those who can't afford a fancy course or a university degree can still become data-savvy with just free content. And that is definitely true. And you're right. It takes effort. Sometimes it's a problem of not knowing what to ask. Sometimes that's part of the limiting factor. Then if you're self-guided and self-motivated, you still need to know what questions to ask.

You still need to know how to apply it. That’s also part of what you get out of interacting with an instructor who has put together a course, who has applied it in the real world, sort of like an apprentice-type scenario.

It wasn't long ago when, if you wanted to learn a craft, you went and worked with the blacksmith for a few years until you could do it yourself. And so that's still also an effective way. And I think we're seeing user groups pop up. We're seeing different communities online, forums, where that is essentially what's happening - apprentices are learning from master tradesmen and women.

What I'm trying to do is build courses that we can deploy cost-effectively across organizations where they can then begin to speak the same language, because if everyone has their own self-guided pathway, how can they speak the same language? How can they be on the same page?

And so part of that is having resources that are common across different disciplines and departments. So I think that that's also part of the equation, but neither is the full solution set. I think that they can be complementary. I think they can be used together. And it takes a lot of that.

I was lucky early in my time, working and teaching at the University of Washington, I’d be paired with an instructional designer and I had developed some courses and I sat down with him. I said I'll talk to you about my courses. Absolutely, no problem. But by the time I was done with that first hour, I realized, I didn't know anything about building courses. And I had a lot to learn and this person helped me see that there were many things about the course that I'd created that were very confusing to the students that I was trying to teach.

And that was a big barrier. And so, yeah, I started to appreciate that this ability to create a curriculum and to create the content within that curriculum that is easy to understand which is a very valuable thing. And it's actually a discipline in and of itself. And I've been lucky enough to have some instructional designers on my board of advisors that have helped me continue along that learning path as an instructor, as a course creator.

I do think that there are multiple tools in the toolkit of any team. And actually, in our organization, part of what we're doing is to put out free resources. So we just partnered with a really talented GRA data designer named Allie Torbin and she created our first comic strip, which you can find on our blog. And it's a story.

She's so talented and she made this comic strip of this person, her name is Becky, and this friend of hers, which happens to be a plant on her desk at work named Fern, they're trying to figure out how to win the sales meeting.

They're trying to figure out how to convey a message that's going to be easy to understand for their audience. And so, and they're trying to pick a chart and you know, which chart do I choose and what are the pros and cons, right? So these are the things we all go through. And so we're putting that out there on our blog for free because we believe that we need to invest, as a company, in these free resources so that other people can have fun in their learning journey.

And also they don't have to pay for everything. If they have to pay for everything, then here's what's going to happen. Only the people who can afford it will be able to get access to learning. And that's just not going to work. We have training programs we offer that people pay for, and that's certainly part of what we do.

What I learned at Tableau and running the Tableau public platform, which is a free tool for individuals, students, researchers, journalists, is to tell the data stories of our time on the web. What I learned from the Tableau and those founders is that you don't have to worry about counting the value of that investment. It's going to help others. It's going to help you as a business.

Every business should be doing this. What are the ways in which we can put free tools and resources and content in the hands of everyone? I think that to the degree that they learn how to do that, they're going to create a world in which their products and services are more in demand. So it's an investment and that's what we're trying to do. We're following suit from what I learned from those Tableau founders, with whom I was very fortunate to be able to work with for many years.

Are you the product?

Loris: [00:50:04] I definitely agree. When you pay for content, you're getting a service, right? And that service has been designed to be the best service that the content provider can give you at that time. When you are on free platforms, and I'm particularly referring to YouTube. When you don't know where to look, you Google, and the first thing you find is a video on YouTube.

The content you see there is not necessarily what you should see at that time, rather is what keeps you more engaged. Because that's the whole algorithm and the way that it's designed. And so it’s a completely different experience.

Ben: [00:50:51] Hmm. Yeah. The content provider is more interested in you staying engaged than necessarily solving your problem and leaving. I didn't think about it that way, but yeah, it's absolutely true. I mean, the power of it is that you have access to this massive repository of content, and that's something that you really can't replace with a fancy course.

Loris: [00:51:11] No, but the one that you see is likely going to be the one that keeps you engaged and makes it to the very tip of the iceberg, because all of the other videos, no matter how insightful they are, they just don't make it. And they're not going to be recommended if they don't make it on that first page, which is 20 thumbnails? Then as far as you're concerned, they don’t even exist.

Ben: [00:51:33] They don’t even exist yet. How long are you willing to scroll? Right.

Loris: [00:51:36] Yeah.

Ben: [00:51:37] If you have all day, maybe you'll get to something. The needle in the haystack happens to be hidden beneath pages and pages of other content. That's not going to help you. And that's the other drawback is that maybe it isn't as polished.

I'm actually not a big believer in polished, per se, or polished for polished sake. I would take a passionate, interesting person over a well-rehearsed person almost every time, but the quality of the content relative to your task or your question can be a little bit of a mixed bag.

I'm thinking of times I've tried to fix a clogged drain in my bathroom, you know, oh, this guy is setting now. There's also a bunch of videos that give a totally different solution that wouldn't help me in any way, they would probably make the situation worse for me. And the same is true of data.

Loris: [00:52:30] So data literacy fundamentals is, I would say, understanding the power and value of data. That’s where I would start if you're looking to get into this topic for sure. And then your courses are available on data literacy.com.

Ben: [00:52:42] Yeah, we launched our own online learning platform about a year and a half ago. And so you can take those courses on demand. We're just starting to work with some trainers. We're going to get back to offering cohorts of instructor-led courses. Virtual for now.

I would say start at fundamentals if you're data phobic, no doubt. I think if you're already into data, you might be able to skip that one. It's really designed for the data phobic individuals that don't feel like data is their thing. But also included in it are some things that I have unlearned. Maybe as someone who has worked in data for some time. That might be helpful for some misconceptions that I'm helping people unlearn who are data professionals. But the next version of learning to see data is really for anyone who reads charts and graphs on a regular basis, certainly for anyone who creates. You're not going to learn how to create them, but you're going to learn about the language of the visual language of data.

We're going to take graduate-level coursework there and make it simpler for everyone to understand. And then we're working right now on the next book and a course called Read, Write, Think Data. We just got that title and ISB and numbers for it. The course is Data Literacy Level II: How to Work Effectively with Data. It's for people who roll up their sleeves and work with raw data in the form of spreadsheets and databases. And we're trying to teach a framework there that you can use no matter what tools you're using. It's about the process of converting data. And we have a flow chart that you can see on our site that we go into detail to help people learn this process step right.

Then hopefully they can get rid of the idea of a process and it becomes more natural for them. That's the idea, that it becomes more second nature. We need to go through it slowly. We need to understand every step and what are the pitfalls along the way, you know, and how to do those steps well.

Loris: [00:54:25] And practice makes perfect right?

Ben: [00:54:26] Yeah, that's so important, you know, you've gotta be doing it. You've got to be using data on a regular basis for your own projects at work. You've got to be in the data and working with it.

Loris: [00:54:38] Neuroplasticity.

Ben: [00:54:39] Yeah, exactly. Your mind is going to adjust as you continue to get your hands in the mix there.

Loris: [00:54:47] Awesome. Where’s the best place to follow you?

Ben: [00:54:50] Oh, I spend way too much time on Twitter. So I'm @dataremixed on Twitter and on LinkedIn, I think my profile is Ben R. Jones. You can just find me on LinkedIn if you search for Ben Jones, data literacy, I should pop up there. I spend more and more time on LinkedIn these days, interacting with people about this topic. We have our own little LinkedIn group called Data Literacy Advocates. And if you put through a request to join that, we'll go ahead and approve that. That's got a couple of thousand people now who are interested in this topic of how do we become more effective at helping people speak the language of data.

So lots of great resources and interesting questions and conversations pop up. We work really hard to make sure it doesn't turn into one big advertising stream, which actually takes work. So it's intended to be a place of knowledge, not self-promotion. So that's another place if they want to interact with me a little bit more specifically around this topic, data literacy advocates on LinkedIn, if you look for that group and you put through a request, we'll check it out and then let you in.

Loris: [00:55:59] So if you don't follow Ben Jones do it now, hop on LinkedIn, and hit that follow button because as you’ve heard, there's going to be a lot of interesting content coming up in the next month. So Ben, what pleasure. I want to thank you for taking the time to be with me, to talk about all things, data literacy, and data in general. And I'm so excited for the bright future that awaits us.

Ben: [00:56:23] Yeah, me too. We'll stay optimistic and we'll help each other stay there. And thank you so much again for having me and looking forward to seeing the conversation that comes out of our chat. I think it was a great one. I appreciate the time and looking forward to hearing what your audience and listeners think about our conversation points today.

Loris: [00:56:40] Absolutely. Thank you, Ben, enjoy the rest of your day.

Ben: [00:57:29] You too!

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