Having invented the field of data literacy, Jordan has helped organizations around the world build and understand data literacy. Learning to ask the right question is key to data and analytics decision-making.
Jesse and Jordan dive into the data literacy skills that everyone needs to succeed, including mastering the 3Cs; the 4 levels of analytics; why you need an analytics-accountability friend; and, how to live with understanding that data and analytics is not perfect.
Welcome to the Soda Podcast. We're talking about the Data Dream Team with Jesse Anderson. There's a new approach needed to align how the organization, the team, the people are structured and organized around data. New roles, shifted accountability, breaking silos, and forging new channels of collaboration. The lineup of guests is fantastic. We're excited for everyone to listen, learn, and like. Without further ado, here's your host, Jesse Anderson.
Today, my guest is Jordan Morrow. He is the godfather of data literacy. We're going to dig into that and learn what data literacy is and how to be a godfather. We'll talk more about data science, some change management, how do we actually get to the point where we're thinking about data, change management of data, that mindset change that we have to do? And then from there, how do we get to data-driven results, thinking about data-driven, thinking about customer-centric data? Finally, we'll talk about some hiring and the issues that go on for that.
With that, Jordan, would you mind introducing yourself a little bit more?
Yeah, absolutely. And thank you for the introduction, and happy to be here. I'm always grateful for opportunities to share some thoughts on topics that I love. As you mentioned it, and I'm sure we'll talk about it more, my nickname, I've got two of them. The one that I like more is chief nerd officer. I still remember being on a call when someone said, "You, Jordan, you know your nickname is the godfather of data literacy?"
I did not know that was my nickname, but I think the reason they gave it to me is I helped pioneer and build the world's first data literacy program and invented that field. I'm excited to talk about it. I'm as nerdy as they come. I love data analytics, data science, statistics, you name it. I'm excited to talk about all these topics with you and share some thoughts there.
Hey, that's awesome. You mentioned a few things, godfather of data literacy. Does the godfather theme play in your head at times?
No, but a wonderful movie. For anyone who has never watched it, shame on you. Everybody should see that movie many times, but now, it's going through my head and I don't think it'll leave for a while.
Well, let me make an offer you can't refuse. Tell me about data literacy.
Yeah. I'll give you my background on where it all started. And that, I think gives more of a mindset of where it came from, et cetera. My background, I was working at American Express. This is about eight, nine years ago, running a large BI, business intelligence world, data analytics, democratizing data, et cetera. And what happened was I was also in charge of the training. I went to school for a while to be a mathematics teacher. I love to teach. I love doing that.
And I was teaching people how to use our dashboards. And while teaching them how to use the dashboard, the thought came to me, "Why don't we teach them to do more?" You could teach a person where to point and click with a dashboard, but that doesn't necessarily teach them how to use data. And so, I started. I created a whole business plan or a whole model around how to do that, but then I presented it to my leader there at American Express, my EVP, and was just flat told, "No, we're not going to do that." They're not ready for it, et cetera.
And I didn't know what I was stumbling upon, which was my first foray into data literacy, but the thought never left my mind. And then about a year later, American Express was changing my job. They wanted me to do more database administration, and that's not my cup of tea, not what I want to do. And I love data and analytics, teaching, et cetera. And Qlik actually hired me and basically said, "Be an entrepreneur, do what you want, build that vision that you have." A lot of credit needs to go to Qlik for giving me that opportunity. And the whole vision is to empower people of every background possible. Whether you want to be in data analytics or not, your job is affected by data today. Why don't we give you skills and abilities within those worlds that allow you to be confident and comfortable in using data?
And that's not to say we're making everybody a data scientist. Not everyone needs to be a data scientist, but everyone needs to be comfortable and confident in using data. It's a part of every job now. And so, as I started to build it, it took off. I mean, it took me all over the world, speaking at conferences, speaking to businesses on every continent, except for Antarctica, but I have a source that is going to get me to Antarctica. That's my goal, even helping the United Nations and things like that. It blew up. I never would've thought that this little idea of empowering everybody to use data, which intuitively, I think makes sense now, but back then, that's not how it was viewed. And the validating moment was a couple of years ago. Guess who came calling for help on data literacy? American Express.
It came full circle. I'm glad that they denied me the first time, because had they not, I would have just been doing it for one company, whereas Qlik gave me an opportunity to build what I envisioned, what I had thought about. And it blew up from there. And it did help that Gartner, about seven months after I started building the program, wrote an article. They didn't know I was already building it, but they wrote an article saying data literacy is coming. And they found out that I had already been building it. And so, I was on a call with them shortly thereafter, and we spent like an hour and a half just discussing my thoughts and stuff.
Data literacy is here to stay. It's now become pretty synonymous with the data and analytics world, and it makes me very happy. Then by definition, I just realized I never really did anything on definition for you. And it's the ability to read, work with, analyze, and communicate with data. And so, giving people skills in those areas.
Do you think that there's a top level or a bottom level that somebody should be at in order to be a data literate?
Yeah, it's a very good question. The way that I describe an answer to that, because people ask me that, when do I know I've arrived at data literacy? And what I like to tell people is, you're never going to arrive. What we find in the world of data and analytics is that things are transforming regularly, quickly all the time. The key is, there is a foundational knowledge, though, that I will say. I don't think there's an exact skillset you have to develop, but a foundational knowledge of what data is, what analytics are, how they work together, how it proceeds with data, and how to make a decision with data. Those would be those foundational skills. Then you progress to your point, to the more technical aspect with data visualizations, data science, statistics, mathematics, coding.
You've got that baseline foundation on what data is, how to use it, how to interpret visualizations, how to get insight. And then you advance to a more technical sphere, which would be how to do analytics, how to build visualization, statistics, et cetera. But again, the key is to never feel you've arrived, but that you're continually learning, because it was just three or four years ago when augmented analytics wasn't that big. And now, augmented analytics is becoming huge, where we don't have to do as much of the technical work ourselves, but the tool and technology will do it for us. Our job then becomes, can we interpret it and apply it? This is an ongoing thing that will continue to be, and we have to continue to adapt our data literacy learning to deal with where the technology and data take us.
With that thought in mind, do you think that there's a minimum level that somebody should have before they make a decision? In other words, do you have a worry that somebody as pretty novice at this takes a peek at the data and makes maybe the wrong decision or the opposite decision?
Absolutely. There is foundational knowledge, like, how do you understand when variables are correlated, it doesn't just mean causation? How to understand why trends go up and down. The way I describe this is through the four levels of analytics. And I write about this in my book. I actually speak about this often there, those four levels. There's descriptive analytics, which talks about what happened, diagnostic analytics, which talks about why it happened, predictive analytics, which shows, if we do X, this will then happen, and prescriptive analytics, with the tools and technology, can tell you what to do. There needs to be foundational knowledge in that.
I mean, the pandemic is one of the most illustrative examples of why data literacy matters. From every single political spectrum you could think of, things were misrepresented, misinterpreted. They still are. And so data literacy just doesn't go from a career perspective, it goes from an individual's personal life perspective. And we could see that the mishandling of data, where we bring our biases in, where we bring heuristics in, where we bring what we want to see happening, we drive poor decision-making.
And so, I do agree, yes, 100%, that we should have baseline abilities to analyze, to interpret, to dig into the information using key tests and things like that that allow us then to make smarter decisions, which of course, is the end goal of data. It's to make a smart data-driven decision. And if we don't have foundational skills in that, you're absolutely right, we could misrepresent a lot of things pretty quickly.
You started diving into biases, and that's a really interesting part of this, data will start to show your biases. How would you recommend somebody actually, not just identify, but start to work with them or deal with them at organizations?
Yeah, it's an absolutely great question. The first thing is to learn about biases and how many different ones there are. There's selection bias, confirmation bias. There's a massive list of them. And most people, I would bet, don't know a lot about biases. Now, this is not the bias and prejudice that exist in certain areas that we talk about and hear about. This is systemic biases that are built into the way we think about things, the way that analytics and data are set up, even the way algorithms are built.
One, the first thing people can do is study the different biases that exist out there. The more we learn about them, the more we know about them, the easier, I would say, it becomes for us to be able to deal with them, and to be better with them, and to understand how to... It's very hard to get rid of every single bias that exists, but it allows us to be able to acknowledge and recognize them.
The second thing, now, this is just a little tip that I tell people, is find analytics-accountability friends. What I mean by that is have people look at your work, just have them skim over it, and have them take a look to say, "Does this look right?" Far too often, we are so dead set on getting things out the door quickly versus getting things out the door correctly. And those are two very distinct things. And so if we get that accountability partner, as to how I describe them, as someone to be able to check your work. In fact, I volunteer all the time. My job isn't to break down what someone does, it's to look at it and say, "Did you think of this?"
The first thing that we could do is learn about them. That allows us to recognize them.
The second thing we could do is not assume that what we've built is perfect, but to get people to review it.
The third thing we have to do is organizational, and you brought this up in the introduction from a change management perspective. We have to live with an understanding that data and analytics is not perfect. And so it is an iterative process.
Far too often, when someone makes a prediction using data and it doesn't happen, people get frustrated and upset without this understanding that, let's say, it's 90% likelihood that something's going to occur. Well, that still means, one out of 10 times, 10 out of 100 times, 100 out of 1,000 times, it's not going to work.
And so, we have to include the fact that this is predictive, that these are predictions we're dealing with. When we do that correctly, the decisions that we're making, the biases are acknowledged, we're going to make smarter decisions. And understand that it's not always perfect, but it'll get there.
Excellent, then. So to help out people, you wrote a book?
It's called Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed. And I'm assuming that you talk a lot about this in your book. Is there some chapter that you'd like to call out for the audience and say, "If you're on this, you absolutely have to read this chapter"?
I would absolutely say that the key chapter, there's maybe two in there, but the number one would be the chapter I write on the four levels of analytics. I think far too often, people hear data and analytics, and they just think the analytics is going to solve everything, that it's all the same. But when you start to dig into the four different levels of analytics, you find out, one, where your skillset really dives into things. Where do things work together?
Two, how do we then drive decisions through it? I think that that's the key, is when we understand those four levels of analytics, we understand where our position is, where our part is, what skills we need to develop, et cetera. If we don't do that, we're going to struggle.
And so that would be the first chapter. And if I can say, the second one is on making decisions with data. How do you actually turn the data and information that we're receiving into an actual decision?
Oh, it's really interesting. I see that as a common initial problem that people have. Data, in and of itself, is useless.
It's only turned into value when there is a decision made based on that.
100%. I've got a friend and colleague, Donald Farmer, who basically said that data without analytics is just an expensive cost center. All you're doing is buying a lot of information that will sit there. Until something is applied against it, it doesn't do anything, and that could be a machine in the technology, or it could be a human. But until that happens, nothing's going to change.
Jordan's book will be linked on the soda-dream-team.io site. I believe the Soda team is going to be giving away a few free copies. Definitely check it out there. That's soda-dream-team.io. As we touched on, just briefly there, there are some, I guess, prevalent myths or common things that we see that are downfalls before we get started. Data-driven, what do you think that really means?
It is such a good question. During the pandemic, what was very interesting... And I'm just giving a little background for myself here. Before the pandemic, I was on the road two to four times a month, traveling the world, long trips, working with companies and organizations. When the shutdown occurred, I actually thought my calendar would open up. I was like, "Well, I'm not going to be traveling, so I'll be able to do some other things." The inverse happened. It actually got busier. And through my conversations, I discovered why.
It's pre-pandemic, a lot of organizations were tiptoeing into the swimming pool of data and analytics. Well, COVID came along and just shoved everybody in, and basically said, "Ready or not, you need to be data-driven." Many organizations found out, very quickly, they were not, that the fact that they were just sort of jumping into the deep end of data and analytics made it so. What they found was they couldn't make decisions with data. And to be data-driven, just basic definition is using data as a part of your business to drive decision-making. That's at a base level what being data-driven is.
And all these companies that have been, and we're talking some of the biggest companies you can think of. I won't say their names because that calls them out during this, but just think of the biggest companies. Some of them that you would think, you're probably more data-driven than they really are. And so they brought me on to improve their data literacy, because in the end, a lot of times what gets blamed is the tool and technology, but the reality is the tools and technology are fine. It's the data literacy level of the organization that struggles.
And so here, we had all these companies wanting to be data-driven, but the pandemic opened up their eyes to say, "Man, we are so far away from where we need to be." And that caused problems, especially in the early stages of the pandemic. And that's why you saw, in my opinion, why you saw that term, data-driven, is now one of the biggest hyped terms in the world of data and analytics, if you had all these organizations who thought they were there, only to find out they're not even close. And now, they're trying to catch up.
And then the reality of it is, over the next five to 10 years, those companies that become more data-driven, I believe we're going to see that they're the ones who succeed. And the ones who can't become data-driven are going to become like the blockbusters, and these other companies that get stepped over from this opportunity to use data effectively to make all these decisions.
Without giving a name, could you give us an example of a starting point of one of those companies and where they got to after working with you?
Oh, absolutely, multiple companies. It's really, I can use a name because it's a positive example, the United Nations. They got ahead of it. This was before the pandemic, before any of the shutdowns. The UN brought me out to see them and to speak. It's kind of funny, I went through more security to get into the United Nations building than I do at an airport. I had to go through four security checks, which was hilarious. But the reason I don't mind using their name is they've done an excellent job. They've done a great job. This is pre-pandemic.
What they did is they said, "We need to start becoming more data-driven. We need data to be essential," especially when you think about some of the initiatives that the UN is involved in, and some of the most remote parts of the world where they are collecting the data, where they are doing the work, and you're collecting it from different disparate sources. And so they started to formulate and said, "Okay, Jordan, how do we do this?" They got ahead of the game.
Another organization that did that, and this one is very pertinent to right now, because the Olympics just ended, was the US Olympics. The governing body in the United States is the US Olympic Committee. And they brought me in for advice, because a lot of people might not know this, but basically, every sport operates almost as a standalone business within the US Olympic Committee. And so they brought me in to speak to them, to speak at their event, and to help them understand, "How do we drive being data-driven, especially in the sports that don't get the funding that some of the bigger sports do?" When you think about the USA basketball team or USA track and field, versus some of the maybe lesser-known sports of the Olympics, they wanted to see, how do they become data-driven?
And then there's a company... I won't necessarily use this name, but it's a public company in one of the states here in the United States, that they started to invest with me before the pandemic, started to dive in, then they really started to invest as the pandemic was going. They brought in their C-suite. They brought in their executives to get them on board because you can't just have this haphazard sort of program going on. And what you saw was their executive team embrace and jump at this idea of, how do you make public works more effective using data? It's been interesting.
Then there are other companies that were far behind that, where they went from, was a company with major holes in their strategy. And you still see these. I worked with an insurance company about two months ago who was really trying to improve theirs, but what they did, and this would be a lesson to every organization out there, is they built a data literacy learning strategy, but the one question I kept asking over and over again in the meeting was, "Can you tie this back to your organization's business and data strategy?" And the people who were building it could not.
I said, "It's great, all this stuff that you've built, but it won't be effective. You have to learn what your business strategy is and what your data strategy is so that we can tie these things together." That was their next step. There's not a one-size-fits-all. Every organization is different, but what we find is different patterns that are occurring in these organizations.
Those two things that you call out match exactly what I've seen. Unless there is C-suite buy-in, these projects go off to die. You'll eventually need money. You'll need budget. And unless the C-suite has bought in, you won't get that, and you'll have spent a lot of team, a lot of political cachet on that.
The other one is that business. If your data team is at arm's length from your business, guess what? They're not creating business value.
You absolutely have to do this. Take both of our... I talk about it in my book, and it sounds like Jordan talks about it in his book as well. Oh, please do take this to heart. Please do. This is where good projects go to die.
Absolutely. What's so interesting is, what's the fastest-growing job in data right now? A lot of times, you hear the data storyteller. But what businesses don't understand is, the data storyteller is where you're removing the arm's length so that you're side by side. It blows me away that a data strategy doesn't tie to a business strategy. Whenever I go to an organization and I hear that, I just be like, "What are you tying it to? Are you just building cool databases, just building cool data repositories?" To not have those things tied together, to your point, it's a deathwish. Your project is going to die. Adoption is going to fail because no one sees the clear vision that you're trying to achieve with what you're doing.
Yeah. The other anti-pattern I'll see sometimes is, "Oh, yeah, my project has all kinds of funding from my executive. And yeah, eventually, that will change. Yep, please do reach out." Your business side is your best friend. They can help you.
Absolutely. Yeah, and I'm not sure where that disconnect came from. I don't know if people just got enamored with the data, with the tools, or whatever it was, but thankfully, we're starting to see those things bridged more and more because it's those companies that bridge it well are going to be the most successful.
Well, I've thought about that question a lot. And I think it comes from agile In agile, we're supposed to be doing this as well. We're supposed to have a product owner. We're supposed to be working with our stakeholders, and we don't. And I think this is just an extension of that, where we're still trying to be at arm's length from our stakeholders and somehow get what they want. Doesn't work.
Absolutely doesn't work.
Let's talk about what would work. When I talk about data teams, how do you think data literacy falls into those data teams? For my definition, just to give you the background, my definition of data team is data science, data engineering, and operations. I'm going to take a wild guess that data literacy is not in those, but they have to work with them. Tell me about how that works.
Yeah, you bring up a very good point. And I get that question asked a lot, is how does data literacy apply to a data scientist? Because they've got the technical skills. Or a data engineer, they've got the technical background. For me, a lot of it when it comes to data literacy is, there's a lot of soft skills that are involved with it. There's a lot of communication. There's a lot of leadership.
And so when I build data literacy plans with organizations, we take the most unskilled or the biggest beginner, if you will, from a data and analytics perspective. We have to make sure that the data engineer and data scientists can communicate effectively with them. That means, not only upskilling the beginner within data to be able to communicate and have that data fluency, that ability to talk around it, but at the same time, we have to teach data scientists and data engineers how to communicate back with the rest of the business.
A prime example is, I was meeting with the CEO of a data science company, and I asked him once, and I said, "How many data scientists would you have present to your board of directors or your C-suite?" And he stuck up his hand and he put a big zero. I don't think that's necessarily fair, because yes, some data scientists can communicate, but that's not what their background and skill is.
Part of it is growing together, driving together this background and ability for a holistic approach to data. Not only do we need to tie the business in from a data literacy learning, we need to tie the data team in with the business side, and create the skills within those areas where all of these things work holistically together.
When we do that right, then we start to see that success. But if we don't do that right, communication breaks down. We could build the most beautiful analysis in the world, and then it's not going to be adopted because, one, the communication was bad, two, the way it was presented was bad, whatever it may be. But on the flip side of that, again, is when we're creating business users who are confident with data, we're growing their skills to be able to do that effectively.
Could you go deeper into some of those behavioral aspects that the teams need to start doing?
Absolutely. From a data team perspective, the number one skill that I build for those who are technically sound is communication. Do you have the ability to public-speak? Do you have the ability to lead a meeting? Do you have the ability to sit in front of a C-suite and communicate effectively what's going to happen?
Secondly, it's the leadership and the strategic skills. Do you have the ability to lead a team, to lead a project that can then be turned into a decision, then once that decision is made, is employing that decision across business units, across different parts of the organization? This goes down to decision intelligence, all of that.
Three, we absolutely have to have the change management side of this. We spoke about that in the intro, where I think far too often, we think the biggest roadblocks to data and analytic success are the data and the technology. The reality is, the biggest roadblocks are the culture of an organization. You could have the most data-savvy team in the world, but if the rest of the organization's culture... And by that, I mean the rituals, the experiences, the traditions, what the organization is used to doing. If the rest of the organization is not used to using data, there's a big problem.
The third thing we have to get data teams better at is change management, understanding the processes, et cetera. This goes down to... There's an interesting question I get when I mentor people in their undergrad or graduate work who are going into data, data science, math, statistics. They always ask me, what courses should they be taking? And I think they're wanting me to say, "Take this data science course." I never tell them that.
I say, "Your program is going to teach you enough, so that when you leave, you have the technical wherewithal to succeed. The courses you should be taking are the non-technical courses, leadership, marketing," all these different things, improving the business side of their skills. That, to me, makes the most well-rounded data employee you can have. And when you put that into practice and upskill the rest of the organization with data literacy, you can start to see true data success, where adoption is happening, where the uptake of decision-making using data is happening, and all of these things.
I would echo yours 110%, cross-train, people start cross-training.
You'll find, since we're talking about math, there is a long tail of getting to that 99, to the 99.9, to the 100% of a skill. That last .9% takes a long time. And I think the ROI is just not there, frankly, for the vast majority of people. That time could be better spent on cross-training.
Absolutely. And that's the thing, is companies always are enamored with the tool and technology, but that's not what's breaking down. That's not the roadblock, it's this cross-training. We're not looking at these soft skills. For us, it makes sense now that data literacy seems intuitive, but you would be surprised. Eight years ago, when I started thinking about this, it was not even on the radar. It wasn't even talked about. It was all about the tools and technology. And that's wonderful, but we forget that tool and technology and the data has to be transformed into something tangible. Without it, we're not being successful.
Have you seen any organizational structure work well for this? Do you think you have those people be dotted-lined into the teams, or how do they interact?
To me, number one is, organizations have to have a chief data officer, end of story. And we're seeing it more and more, but if these programs... Nothing against CTOs or CIOs, but if they run out of a different C-suite position, the problem is they have different targets. They have different things that take their time. And so organizations need either a CDO, or CAO, or CDAOs. For those who don't know, a CAO is a chief analytics officer, same role. They need a place at the table. Then once you have the leadership in place, you need evangelists in the organization, not just for data and technology, but to drive data literacy in the cultural aspects. HR needs to be involved. Change management needs to be involved. What you're seeing from an org structure is it really is a top-down holistic approach.
There's an organization that I worked with. They brought me in to help with data literacy. And they said, "Each business unit in our company, we hired a data scientist, but they're not doing anything. We don't know what to do with them." Well, that's because your structure was just to hire a bunch of data scientists who don't know where they're going with it. They don't see the holistic strategy. There isn't a holistic strategy. It's not tied to the business. That company then flipped and hired, basically, a CDO who managed the whole data side and created this holistic approach.
I'm a big believer in that CDO center of excellence. Start with that nucleus, call it your brain, and then have things funnel out from there. But it does also start with, do you have the right strategy? Not only do you need the right org structure, you need the right strategy. When you do that correctly, yeah, you're going to be able to bounce things. This group might be working on one thing, this group on another, but as they do that, they holistically work together because everybody knows the direction they're moving in. But again, it starts with leadership. You need evangelists, you need change management. You do need the technical skills, but this goes back to the culture, you also need the business on board with it.
That's an interesting point about the technical skills. Let's say, imagine I was listening to this podcast, and I'm saying, "Oh, I want some of that data literacy, and I have this pool of not skilled labor, not skilled in this part, but skilled in their own part. What is this? Is this just a skills gap? Is this just understanding some new things? Where do I start there?"
It is absolutely a skills gap. We found, in studies that I've conducted, about one in four, one in five people are confident in data literacy skills. And so when you have that big of a gap... And you could see it. Gartner talks about the skills gap, the skills shortage, they call it. Gartner, Forrester, McKinsey, I believe all of them talk about this. And they talk that it's not going to be filled in the next couple of years. You're going to see this gap persist for a while, but it goes back to, it's not just a gap in technology. A lot of the gaps, they're non-technical. A lot of the gap is executives. We talked about biases, getting rid of their biases. It's the combination of the human element and the data element. All of this is why change management is so important within data and analytics, as data and analytics are tools in our toolbelt for our jobs to be successful. That's what they are. They're not to replace things per se.
But the problem is, far too often, it's the reverse. We think they're here to replace us. To me, I look at tools. We're doing this recording. That's a tool. My microphone is a tool. Zoom is a tool. Outlook is a tool. Slack is a tool. Excel is a tool. Power BI and Qlik are tools. Data is a tool. And until we start to ingrain that into a culture and weave that DNA into everybody, that's where those roadblocks come up. This skills gap, it's not just about taking a few courses and saying, "I now have taken a course on how to use Tableau." That's wonderful. Have you taken a course on how to make a decision with data? Have you taken a course on how to present that decision with data? Have you worked with leadership to the right hiring talent?
It's very interesting. Right now, I had a call earlier today. There are so many companies right now diving data literacy into their HR. As we see coming out of this pandemic, hiring practices are booming. All these things are trying to happen. They're trying to get the right talent. And so they're trying to use data to do it. We're seeing areas of companies do it correctly, I would say, but there's just so much to it. And it is absolutely not just a skills gap in the current workforce, there is a skills shortage.
How would I assess which skills I need?
For me, it always starts with the skills assessment. And there are multiple ones out there. In my current role here at Pluralsight, vice president of data, design, management skills, I have assessments. I never start a learning program without assessing a person's skill. The reason is, it's not a one-size-fits-all.
Jesse, your skill is different than my skill and data, which would be different than this person's skill, which is different than that person. What we do is we take an assessment and we say, "Okay, your skill level is at XYZ. Your data persona is this. This is how I would map your learning." We use those assessments to identify the gaps and the holes in an individual's skills, and then you fill them with the content.
Always start with an assessment because data is a massive, massive field. We know that. It's not getting smaller, it's just getting bigger. Can we use assessments to fill those gaps, to close the holes that allow us to put the proper learning in place? And when I say proper learning, we have to understand there's workshops, self-paced learning, assessments, but it needs to be applicability-based. I could teach you how to use and build a chart within Tableau, or within Qlik, or Power BI, but am I also teaching you where you would use that chart, and in what scenarios? That's also a key to closing this gap.
Now, one interesting part that I realized is, we're presupposing a decent amount. We're presupposing that these data literacy people have the infrastructure, have the data to do that. I just want to point that out for people listening, and I'm assuming that you agree there.
We need to specifically say, there has to be some foundational work that goes in before this. Could you talk a little bit more about that?
Yes, absolutely. There's something that I talk about at organizations. I love this idea of presupposing that everything's perfect. You ask a data scientist, "What do you spend the most time doing?" It's cleaning and making the data workable. I mean, there is so much. When I look at the whole data literacy umbrella, when I look at everything that is involved there, it does talk about data engineering. It talks about the back-end. It talks about teaching people on the front-end how to work with people on the back-end, who are building the data architecture, who are building these pieces. This drive to be truly data-driven is a massive undertaking. I think that organizations want that easy button. The easy button doesn't exist. And so, we have to understand that it is a big process.
I'm working with one federal organization right now that has an initiative to drive data literacy with 1.4 million people in their organization, but they understand this is going to take multi-year developments, and work, and things like that. It includes back-end. It includes front-end. It includes it all. That's why I always want to know, what is your data strategy, so that if we're building the right program for you, then we can make this work correctly?
That brings up an interesting thing. We're talking not about thousands. In that case, we're talking about millions. And if you look across other companies, we're talking about tens of millions, hundreds of millions. Let's fast forward this 10 years. What does this look like in 10 years, now that we have millions of people with high levels of data literacy?
To me, it means that individuals... From an individual perspective, individuals will be prepared to work in the economy in the future. I mean, that, to me, is the key. Data as a currency is not going away. Data is being built quite literally on an exponential basis. Data literacy isn't just for organizations, it's for individuals to be able to compete within the career market that is going to overtake us here in not too long. If we just study where technology is going, we need these different skills. We need to start to develop them.
From an organizational perspective, I think data literacy becomes synonymous, like Microsoft Excel, and using Microsoft Office becomes synonymous with just your job. I think data literacy needs to get to that point, where it's just something we do on a regular basis. We just use data to be there. This is not a slow evolution or a fast evolution. It's slow. In the '90s, internet and all that. And so that's how I see this transforming. In five, 10, 15 years, this is just going to be a part, and we're starting to see it.
I've worked with the US Department of Education. We're going to start to see this dovetail into elementary education. And we already are. And by building skills early on, this is just going to be synonymous with how life is done. Not that, again, everybody has to be technical, but we need skills to use data because it is just an everyday part of what we do, just like literacy, 400, 500 years ago. In some cases, it was illegal for people to read. That flipped over. It opened up opportunities for people. I think this ability to use this currency of data is going to do the same.
You bring up an interesting point that I was going to touch on. I know you have five kids. I have two kids. I think this stretches all the way. This theory or this issue of data literacy is, "I don't want my kids to be focusing on rote memorization. They need to be thinking about - how do I make a decision?" Have you done anything along those lines?
Oh, absolutely. And I've written a kid's book on data literacy. That was actually my first published book, it was a little kid's book. The reality, I agree with you. When I talk about data literacy, you'll find I'm not sitting here mentioning formulas. How many times have I said, go learn how to write formulas for derivatives, calculus, and statistics. It's this ability to actually use data to make decisions in your life. And eventually, technology to a degree, I would say, is going to get rid of our need to be able to memorize formulas. This ability to use data effectively is not rote memorization, just like you said. That's not what this is. It's an ability to make smart, cognitively sound decisions. We're talking decision intelligence and using data as a tool to make that happen. We're talking communication intelligence, presentation skills.
These are not skills that I can give you in a book. Even my book, you'll read it, and you'll find, hopefully, what it does is it sparks your curiosity to learn more and do more, to critically think more. Those are the skills. If I could teach one skill in elementary right now that... And with my five children, I ask, or I tell them to always ask questions, never stop. With five kids, I won't lie, it gets frustrating at times because questions are nonstop. But the reality is when we become adults, we stop asking questions for some reason. What I want children in education to learn is not how to memorize a formula. I want them to learn how to ask a question, because if they could do that correctly, and we create a culture where asking questions is fine, then as they get through college to enter their careers, data literacy is just a part of them, versus, "Oh my gosh, I have to relearn a new skill. I have to upskill. I have to do all these things."
No, let's just, to your point, build it at a young level, which we are seeing. We're starting to see this built into more of the education. But again, it is a transformation, to an extent, an evolution of how the education system works. Our education system was built off 60, 70 years ago for the agricultural sphere. We need to start transforming it. Sir Ken Robinson, if you've ever seen his Ted talks, does a great job of this, of transforming where education goes, preparing kids. I've got five. They better... Hopefully, they're data-literate by the time they hit college and stuff, and be able to process things differently as they enter the workforce.
Those of you who are listening, learn how to ask the right question. That is what's key about this. Somebody's going to give you some data, you need to learn how to ask the right question of that data. Related to that, we talk about that critical thinking in data literacy. Tell me more about that.
Absolutely. I think of the... I get asked the question, "What do I need to do to increase my data literacy?" And I've coined the term, the three Cs of data literacy, be curious, be creative, and critically think. It's this idea. There's a great book, The Demon-Haunted World by Carl Sagan, written in the early '90s. And those who don't know who Carl Sagan is, Neil deGrasse Tyson did Origins, but it was Carl Sagan who did that first. He basically says, "I lament a time when our news comes in 30-second sound bites," and all of these things. We live in what I call a very distracted world. Social media, all these things will post news on your site, and then in a matter of seconds, it's gone. How many of us sit back, take minutes a day, not even hours, just minutes a day, you sit there and think on a topic, and that's all you're doing? You shut down Slack, you shut down email, you shut down all these things, and you just think about the topic.
There's a great book out there by Cal Newport called Deep Work that shows the science says you have four hours of day, that's it, of deep work. And by deep work, I'm not talking about building a PowerPoint presentation, I'm not talking about answering emails, Slack, I'm talking about this ability to truly dig into information, think on it, and make a decision. We don't do that. Critical thinking is about questioning things, not in a bad way. I want to create a world of data skeptics, not data cynics. The world is doing a great job of creating data cynics. I want a world of data skeptics, where we question everything. It might be fine. It might be the right answer. It's not a bad thing to question it and say, "Oh, okay, that's fine. I just wanted to know."
We need to think deeper on the topics in front of us. When something is presented to you, ask the person, "Oh, why'd you build it this way? Why does the outcome look like this?" That's critical thinking. That's digging into an answer. And you said it really well. Learning to ask the right question might be the golden key to data and analytic decision-making, because asking the right questions gets you the right data, gets you to the point you need to be. It's this ability to just think differently, I think is wonderful. And then that's like Steve Jobs' whole mantra to a degree, is to think differently, be different. It's to dive into these things that way. And there are books on it. There's all these things. But I just want people to question things, just start asking questions. Yes, it gets annoying at times, I get it. But if everybody's doing it, it's an ability to really get to the root answer of things. And that's the key.
Oh, that really is the key. Another big plus one for Cal Newport's book, Deep Work. I've had various companies read that. Individual contributors, managers, read that book, please.
This is one of the problems in our society, or even in our corporate culture. "I'm going to give you 10 minutes to think deeply," doesn't work.
You need an hour. You need two hours to think deeply about that.
Yep. What I've told my team at work is they get 80% of their time. I want to do their job, doing your day-to-day job, and 20% of your time on a day, that's eight hours a week, developing yourself. I want dedicated learning. I want your Slack shut down. I want text messaging shut down. Shut your email down. And if people can't get a hold of you, trust me, so few things matter so much that an hour shut-down is not okay. And so, just shut everything down.
And if deep work for you is learning a different software technology, if it's studying a book, if it's doing a course, I don't care what it is, just do it. Google has this, just a day, a week, or whatever it is, where you just ideate, think about things. So far, we slam ourselves thinking that being busy means effective. And being busy does not mean effective, it just means you're busy. We need to start to really centralize our minds around, how do we be more effective?
I can say this personally. When I left corporate life to start my own company, I had to unlearn so many things. And one of those is busyness equals productivity. And that is not the case. That was a hard one.
It's very hard. I sit in meetings where for two hours, we're throwing virtual sticky notes up on a board ideating, and nothing comes of it. Nothing. Nothing effective comes of it, but the people walk away happy thinking they just had an effective meeting. I don't attend those meetings anymore. The moment I find out that's what it's going to be, I just won't attend, or I just leave it after 15 minutes, because two hours of time...
I think it's Elon Musk and Jeff Bezos, basically, both say, if you can't get done in 15 to 20 minutes, you're not ready. That's reality. There is so much fluff in business today that without doing it right... That's why I see so many people in leadership positions who probably shouldn't be there because they were promoted based on busyness and they were able to appear effective. And I actually find that a disservice to people, because when you put them then, and they have to make an effective decision, and it doesn't go well, or their anxiety rises, I think we do a disservice. You're right, 100% right. We have to unlearn so much that has been built into corporate culture, that it's tough. It is absolutely tough.
Well, let me ask you one last question. What do you never compromise on?
Oh, man, that's a great question. I'll never compromise on being a lifelong learner, ever. And I mean, I could go with traditional family answers, and this and that. You could say integrity. Those are all things people hear. For me, I will never compromise on thinking I've ever made it, because the reality is, we live in such an amazing world. There's no way in our lifetimes to learn everything. There's no way for me to even learn everything in the data sphere, and I'm as nerdy as you'll find. The reality is, I'll never compromise on saying that I may be wrong and I need to learn. Far too often, we get far too confident in where we are in our careers and things like that. It's okay to fail. It's okay to say, "I need to learn this."
One of my favorite quotes is by Nelson Mandela, where he said, "I never fail. I either win or I learn." And that, to me, is a lifelong learner. That, to me, is someone who is never going to stop growing, never going to stop progressing. And if you watch his life, that's how he lived it. And I think probably all of us know people like that. We know people who are lifelong learners from the truest sense of that term. And by lifelong learner, it means you can... I'm not saying I'm humble in any way, but you can be humble enough to say, "I failed, that I'm wrong, that there's a lot to learn." I could tell you right now, my book, yes, I'm proud of my book, but man, if I read it, I can tell you 50 things I'd probably want to add to it, hence why I'm writing another book. We should always be progressing, and that is something I will never compromise on.
Awesome. Thanks, Jordan. This has been great. A gentle reminder that Jordan's book will be linked on soda-dream-team.io. I encourage anyone looking to get some practical advice, different perspectives, and depth to go get it and read it. And keep an eye out for the next one available next year. Thanks again, Jordan.
Thank you so much.
Another great story, another perspective shared on data, and the tools, technologies, methodologies, and people that use it every day. I loved it. It was informative, refreshing, and just the right dose of inspiration. Remember to check dreamteam.soda.io for additional resources and more great episodes. We’ll meet you back here soon at the Soda Podcast.