#103 How Data Literacy Skills Help You Succeed

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Hello everyone, this is Adele, Data science educator and evangelist at datacamp. Today marks the first episode as part of our Data Literacy Month special, and there's no better person to kick us off than Jordan Murrow. Jordan Murrow is known as the Godfather of data literacy, having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership around the topic. He also wrote the book on data literacy called Be Data Literate the Data Literacy Skills Everyone Needs to Succeed. Jordan is currently the Vice President and Head of Data analytics at Brainstorm, Inc. And a global trailblazer in the world of data literacy. Throughout his career, he has helped companies and organizations around the world, including the United nations, build and understand data literacy. Throughout the episode, we speak about his definition of data literacy, why organizations need data literacy to drive impact with data, how data literacy creates a more informed citizenry, how to build organization data literacy, and much more. In case you missed it, Jordan's episode is part of a wider agenda of events and podcasts this month all dedicated around data literacy, so make sure to check that out and check out all the great events we have in store and sign up for our upcoming webinars. Jordan will be hosting a webinar this Thursday and will be happy to take any questions, so make sure to sign up by following the link in the description below. And now onto today's episode. Jordan, it's great to have you on the show.

Oh, it's such a pleasure to be here. It's my honor, that is, for sure.

Thank you for having me that's awesome. So I'm extremely happy you're joining us for Data Literacy Month, and I'm very thrilled to deep dive with you on why data literacy is so important. The different dimensions of data literacy, how both individuals and organizations should think about adapting to the age of data literacy and more. But before, can you give us a bit of a background about yourself and maybe let us know why you're called the godfather of data literacy?

Oh, absolutely. So I'm. I'm as nerdy as they come, right? Like, background wise, you know, I love mathematics. I think statistics are awesome. But I was working in American Express, and when I was there, I ran a. Basically call it a business intelligence group. We were democratizing data. And this is maybe anywhere from seven to 10 years ago, if I have my timing right. And while I was there, I was training people on how to use the dashboards. I was training people in that manner. And I would say that's how training on data and analytics was back then. You train on the tool. And I still think in a lot of cases, that's what's happening today. But I'm like, why don't I build a curriculum? Or I don't remember my exact thoughts, but maybe along these lines. So I built a curriculum around teaching my consumers of what I was building how to do, like basic statistics. And basically it was these beginning stages, if you will, of data literacy. I showed the plan to my executive vice president, and she just flat said, no, they're not ready for it. Maybe in the future. So it's maybe not a flat no, but a yeah, they're not ready for this. And I didn't know what I was stumbling upon. So that's kind of my first foray to these thoughts around data literacy. And then I got hired to basically be an entrepreneur in 2016 by click. It was an analytics curriculum manager. It was product agnostic. And I started building while it was in February, March, I believe, of 2017 that Gartner launched a report that talked about data literacy. They didn't know this little nerd named Jordan in Utah was building it. And they talked to me in June or July. And it's kind of funny. And I guess I could say the rest is history, right? You know, traveled the world, spoke at the United nations, the US Olympic Committee, big companies, and I still do to this day. I'm lucky enough to be on this podcast now where the nickname came from. It's kind of funny as I was on a call with the person, I think she. She might have Been the sole author or a co author or whatever on that Gartner article, Valerie Logan. And she was the one who told me or asked me. She's like, you know what they call you? You know what your nickname is, right? And I'm like, no. And she was like, it's the godfather of data literacy and she's the godmother of data literacy. And so that's kind of where it came from. But I mean, if you know me, you know my passion, you know what I do, data literacy is it. And I believe there's so much power in it. I've been in it now since 2016, but my ideas came before that. And I just love this world now from a personal perspective. You know, married, five children. I'm very lucky with the kids. I have two dogs and a bunny. It's a chaotic house, but it's wonderful. I'm an ultra marathon runner and just ran one in July. And so I'm passionate. I love what I do, whether it's inside my career or out. So there's a little background, hopefully. That was good for you.

That's awesome. And I really appreciate this holistic background and I don't know how do it. Keeping up with the family, as well as ultra marathoning, but also writing books on data literacy, it's very impressive. So before deep diving into the ins and outs of data literacy, I want to set the stage for today's conversation by trying to contextualize data literacy into the broader moment that we live in. Right. Both from a society's perspective, from how organizations are evolving, the technology landscape, and the first chapter of your book, Be Data Literate, I think you do a fantastic job at setting up that context. So maybe in your own words, can you walk us through why data literacy is so important?

Well, I think it's no secret that we truly live in a data inundated world. It's everywhere. Just think of the pandemic itself, right? How often were we inundated and shown numbers, whether they were accurate or not? Let's move that aside, right? We were shown data all the time in organizations. People are being probably hit depending on the organization and leadership and whatever being told, you need to use data. Are we using data? There are all these tools and technologies out there to utilize and empower us with data. But let me point one clear thing out. This might sound weird from the godfather of data literacy, but who cares about all that data, right? The reality is data is a tool that should be supporting us in decision making. That could be from Our individual lives. Like, think back to the pandemic being inundated with these numbers. How do we know if they're accurate? Is it politically driven? Is it personally driven? Are corporations doing this? And do we have that ability to question it appropriately, to dissect it, to find answers that impact or can help us positively impact our lives? Right. I've got a family. There are seven of us. Last summer, I think it was last. Yeah, right. Last summer we had another person from South America living with us. Right. There's all these things happening around us, whether it's Covid in our careers, all these things. Data literacy matters because we might feel overwhelmed. We're being told, maybe on a regular basis, you need to use data here, you need to use data here. And that can be intimidating. It can be fearful. A lot of people don't go to school for a background in utilizing these fields. That's increasing, but it's not their background, it's not their forte. It's not what they want to be doing. So data literacy matters because we don't need everybody to be a data scientist. But can we empower people to utilize data both in their personal lives, their families, et cetera, and in their career? Can we empower them to utilize data to make smarter decisions? That should be, if we think about it, in why data literacy matters, what does it mean? What does it do for us? That's it. Right? End of podcast recording done. Right. It's. Are we utilizing it and empowering ourselves and others to be able to utilize data to effectively bring to life the strategies we have, whether it's on our personal life, like ultramarathons. I could use data to help illuminate how I train for that. It's not the end all game. It's a tool I can use in our careers. If you're in marketing, am I using data to support this? We don't need you to be so technically sound and have all these hard technical skills, but can we just give you a little bit more skills to do your things better? That, to me, is data literacy and the right context, the right messaging around data literacy matters greatly. We don't want people to be intimidated, to have fear, et cetera, but it is such an empowering skill and it's wonderful the data camp is doing an entire month on this topic because it is vital. Again, we don't want to make people be fearful, but can we enhance their skills in data to just be more successful in what they're doing?

I couldn't agree more. I love that setting the stage that you put here, both from how it empowers us as citizens to become more responsible citizens, challenge and understand different components or different phenomenons that we see in society. For example, on the data literacy side here you can extend that definition also to a certain extent and see how AI literacy can help us understand fake news deepfakes better and understand and rebut that and have a better inoculation against it while demystifying that data. Fear is so important to be able to equip people to be confident in their day to day roles in today's age. A key component of why your book and the message it carries is so important is because there is a massive data literacy skill gap with an organization. There is quite a lot of fear that people have when it comes to how do I deal with data on a regular basis. So can you walk us through why this skill gap exists and what are the main drivers behind it?

One of the catalysts, if I can word it that way, that we maybe didn't even have control over and that is the advent of the production of data. And at the rate that came to be right now, if we were on video and chatting with each other, I could hold up my smartphone and just think about how much data that smartphone is producing. It's unbelievable, right? The apps that we use, the phone, it's maybe itself, it's mainly probably driven by the apps, not so much the phone, but even then, maybe the phone, right? Think about that. The first smartphone, I believe. The first iPhone, 2007, I believe, right? So that's roughly 15 years if we're going off calendar years right now. So when we think about the advent of just how much data started to be produced, there is so much of it. And so think organizations said or thought to themselves, man, we could capitalize on this. So you start investing in tools, you start to source the data, you could do all these things much easier than maybe 20, 30, 40, 50 years ago. That is a catalyst that I would say is probably out of our control to a degree, right? It's yes, we're creating the smartphone, we're inventing these things, but the byproduct of all this data, there's just so much out there. So let's face it, not everyone's as nerdy as me. They don't love math, they don't love statistics. And so I think historically in businesses it was commonly seen that it's the technical people, it's it, it's these different areas that need to utilize data well all of a sudden we get into this area of self service analytics, business intelligence tools like Qlik, Tableau, Power, bi these different technologies that make the democratization of data easy so we could put it into the hands of the masses. But here's the problem. If you put a tool or technology into the hand of the masses and their background is not designed to use the data, we can train you on a tool that does not necessarily train you on the data. So we have all this tools and technology, we're producing all this data, etc. Plus there was this quote by Harvard, amazing, wonderful university, but I believe it's October 2012, said Sexiest Job of the 21st Century. If I'm saying this quote right, is the data scientist. So here we're thinking a data scientist in its pure form. They're technical, they've got good, hard technical skills. Organizations start to seek out data scientists, well, guess what was forgotten. And I don't mean this in a negative light, but the common person, right? And again, not saying people are common, but hopefully this is coming across right in the sense that we're focused on tools and technology, sourcing of data, getting all this set up. Here's an example. If you have a company of 10,000 employees, how many of them are truly going to be data scientists? I don't know, 50 to 100, maybe a couple hundred. That gives you 9,800 plus. If going off my numbers of people who are not data scientists, but we are asking them to use data, that's not their training, it's not what they've done. So these things pushed forward, we want to utilize them. Unfortunately, I would say for a while data literacy is here now, but for a while, the true training, not the training on the tools and technology, but on the true training of data and analytics, that was forgotten. And we are seeing increases in STEM and STEAM education. And I will also say, because I think some people have an assumption that the younger generations who grow up in the digital era are automatically data literate. No, no, they're digitally literate. That doesn't mean they know how to use data. So there is still this, I want to say pressing need, but it's not meant maybe in that way to help people be comfortable with data. And so it's almost this perfect storm for when I start building data literacy for it to come to be, because the ROI and things like that on data is not as strong as I think organizations would want it to be.

Yeah, and I couldn't agree more, especially on that notion of organizations Invested a lot in tools and technology and tool based training rather than the fundamentals for the rest of the organization. I think organizations are really attracted to the technology aspect of data science and AI and its applications, which leads to massive investments in tools and in talent that's working on really hardcore problems. But the real value of data within the organization comes from everyone participating in data and having democratized access to solve simple problems with data. Would you say that's correct?

Spot on. There are four levels of analytics, right? Descriptive, diagnostic, predictive, prescriptive. We see companies are stuck at level one, which is descriptive, that we build a lot of dashboards, KPIs, metrics, all these things. But a lot of investment goes into predictive and prescriptive. You're exactly right. There is a shiny object out there. AI. Oh my gosh, machine learning. Look at that. Data science. Look at this. Do all these fantastic things. Not only that, you have salespeople who can make these tools and technologies just shine. Right. They're using, I would say in a lot of cases, manicured data sets or data sets that will enable the tool to look amazing. You know what I mean? So it's this counter intuitive or counter to what truly should be done. You're exactly right. We're missing out on the second level, if you will, of analytics and that's diagnostic. Can we get to the why behind things? The insight. I don't give a crap how cool and shiny and amazing your AI and machine learning tool is. Show me to the point that I can utilize it to find insight. And maybe more importantly, with the data literacy discussion we're having, show me how a person who does not have a background in this can utilize this for success. We need to take a step back as organizations. The shiny object can be a part of the roadmap, but it should be down the line. Do we have good data engineering and architecture is the front end. The democratization of data, putting it into the hands of the masses. Are they able to find insight in the data, the simplistic form of it. Right. In a lot of cases, I would argue that the dashboards companies have, the descriptive analytics that they have aren't very good. People think data and analytics is a panacea. This amazing pot of gold at the end of the rainbow versus data and analytics being a tool to empower decisions. And I think if we can get that mindset and in this mindset, that the empowerment of the entire workforce to use data effectively. Again, not all the hard skills, but can we ask good questions, do we Know how to interpret data correctly. That's what companies should focus on. Even in my own company, there was one goal that we had. Like, they wanted AI by the end of the year. I come out and say, no, no, guys, not, not even close. We have to get other things before that. That's two or three years down the road. Let's get these things done now. And I think organizations need to take a step back if they want true roi, true data and analytics success. There absolutely needs to be a better holistic data strategy at the company. They need a chief data officer, and things need to be reevaluated to get to the point where they're seeing true success.

I couldn't agree more. And so we teased this out throughout our conversation so far, but I'd love for you to give us an official definition of data literacy. So can you walk us through your definition of data literacy and what do you think are the different components of that definition?

So while I was at Qlik, which is where I was helping to pioneer this field, we used a definition that came from MIT and Emerson. The definition was the ability to read, work with, analyze and argue with data. While at Qlik, we changed that. Argue with. I think that that term confused people too much. Plus I think the word communicate is more powerful. So the definition I use is it's the ability to read, work with, analyze and communicate with data. And that was the definition, give Click the full credit there that we changed it to while I was there. Those are the four components. To me, the most important of those components is the ability to read the data. Because if you can't read it, which would mean to understand it, how are you going to work with it properly? How are you going to analyze it properly and can you even communicate with it? But there's an understanding that if we think about it, take that academic or formal definition out. Essentially, to me, data literacy is creating a comfort in people to be able to utilize data. That's it. And so for some people, that means, yeah, let's go full bore, let's become a machine learning engineer, let's be a data scientist. For a lot of people, it's probably just, can you interpret the data, can you ask questions of it? And for them, they might not have to do much heavy lifting and analysis because they can communicate effectively. The fourth characteristic with those who are technically sound. Hey, Jordan, can someone on your team run this type of analysis for me? And then the communication works between us to figure out what we're truly looking for. So that definition and Understanding that we just want to create a comfort and a confidence in utilizing data, hopefully that puts them at ease, to help them realize they don't have to become some super technical person. But can we just empower you to use data effectively in in your job and hopefully then it translates also into your personal life?

That's really awesome. I love that definition, especially on creating comfort in using and removing that data fear. Would you say that from an organization's perspective, taking that definition in and kind of extending it to the organization, that an organization that is data literate has a spectrum of data skills from people who are comfortable with data, but also people who are necessarily technically gifted are like data scientists, data analysts, etc.

You're so spot on here. When I speak about organizations and utilization of data, I talk about what I call a holistic data strategy. And a holistic data strategy is a data strategy that ties to the business's strategy or ties to the business objectives. So that gives the data and analytical work a direction to follow. We are using data to hit these business objectives now for it to work well. The number one roadblock to data and analytics success is the culture of an organization. That's the people. And to your point, you said really well, there is a spectrum of skills from a person who only knows how to read the data but asks questions and then that permeates out to the most advanced technical person in the organization and people in between. It's not a one size fits all. You can't just send someone an email that says, take this two hour online course, your data literate. No, it's taking an assessment. The program I built at Qlik, we would. One of the starting points is taking an assessment to see where you are, then give you a prescriptive learning path. So it's yes, you need various skills throughout the organization. And one thing I want to hit home on here is that it's not just the various data skills. Everybody has a seat at the data table. No matter what your background is, you might be an English major or an art history major and you'll be like, man, I can't be at the data table. Bull. Yes you can, because you have personal experience that can be applied to the data. I think that right there maybe adele be one of the big fears, right? And that is data and analytics is going to take over my job when reality. We don't want to eliminate the human element. We want to combine the human element and the data element together. And in some cases the data element will supersede the Human element. And in some cases the human element will supersede the data element. But we don't want to rid the human element. If you have a gut feel, awesome. Share your gut feel. Now we have to have that objective mindset. We can't look for data to support it, but we can study the data. And in some cases the data is going to show us our gut feel is off. Some cases it will support it. And so never forget that not only are you going to have data literacy skills, but I want your human skills, your experience, your background, your creativity, the human side of this attached or applied with the data. And that varies both the human skill and the data skill across this spectrum that you're talking about.

Yeah, there's a lot of subject matter experts within organizations with just a bit of data skills can become really super superheroes at their job and really augment what they do. So that subject matter expertise is often not talked about when thinking about data literacy, because it's so important when combined with data literacy, because otherwise just pure data literacy skills are not going to be useful with that business expertise.

They're not. And that's a key. I think historically you could silo off the business side. And I shouldn't say you could, you shouldn't have. But that's how it was done. Right? You'd silo the business side, silo the data side. In reality, we need those things working together. And I think if we can change the mindset around data and analytics to, to help people understand they are tools to help you do better in your job, then every business employee can enhance their data literacy skills. To enhance their business skills. Right. To enhance what they're doing. And I think there shouldn't be a gap anymore. Right. There shouldn't be this divide between these worlds. They need to go together.

I couldn't agree more. So the definition that you lay out for data literacy involves reading, working with, analyzing and communicating with data. Right. I'm going to try this thought exercise. You know, if I'm someone that has basic skills, I don't have any data skills. I know that I need to grow my data literacy skills, but I don't know what that end state of data literate looks like. Like, can you walk us a bit in more detail what each element looks like in that definition?

Yeah. So I have a friend, his name is Brent Dykes, I'm going to lunch with him tomorrow and he came up with a term that I have grown to use and I really love minimum viable proficiency.

Right.

And that's that's like an mvp. You get minimum viable product. Here we're going to call minimum viable proficiency. And I'm not going to necessarily use what he did. He has an article or a blog post on his website about it. But we have to think if you don't have much skills there, number one, can you interpret data? And in a lot of cases that data might come to you in a visualization. Do you have a real good ability to interpret and understand what that visualization is saying to you? That's like a descriptive analytic, right? Do you have that ability to understand the line charts, the bar charts, the dimensions of the visualization that is in front of you? Minimum viable proficiency? And do you have the ability to work with it? Can you filter it? Can you adjust it? Can you get new viewpoints? Can you look at it differently? Can you analyze it to help find insight? This might be though, and I don't know if this is how my mind truly thinks about it, but I say this because analyzing it can be complicated to think about. When you think of the four levels of analytics, it might just be that you are good at asking questions of it and you know the right people to communicate with and you can effectively communicate with those people and say, look, I manipulated, I worked with, I changed the data visualization to look like this. I personally think this is what is happening behind the scenes that is driving the visualization to look this way. Can you help me understand? If that's right, then the technical person then can take that away. There should be constant communication between these people to understand if the interpretation was right, can they get to the insight? Then maybe the final MVP thing here is can people make a decision with what they're finding? Right? Because the end goal of data and analytics should be to empower decision making. That's what it should be. And so it's great if you could read a data visualization, work with it, maybe even build one, make it so beautiful and analyze it, communicate with it. And then if you don't know how to make a decision, I'm sorry, I'm not sure what you did all that for. Right. And it might be the decision could be just a stand pat. The decision might be we just want to learn about these things, but hopefully that learning or in probability that learning will take you somewhere in a decision later on. But that's how to look at this. And then, then you can. There's varying levels of MVP based on roles, right? A data analyst, a data scientist, a data engineer. But maybe the one skill universally across these in a data literacy perspective that all either one need to improve on or we will call it the secret sauce of data and analytics. And that's the ability to communicate with data. If you are a newcomer to this, can you communicate effectively to the technical people? If you are a technical person, can you simplify the language that you are using in data analytics so you don't lose your audience? Those are things that like I call data fluency or the ability to communicate with data. I probably make it interchangeably between those two. The secret sauce of data and analytics, because you could do everything right, but then not be able to communicate it well. And then what was the point? And so those are like the premise of this question was around like the minimum skills and stuff. Hopefully I've done a good job of communicating some of those out.

Yeah, definitely, 100%. I couldn't agree more on the importance of data storytelling and communication skills. I think the biggest pitfall technical folks, for example, fall into is that they have an executive in front of them and they're like, this is this machine learning model. It's a support vector machine. We have been able to improve the accuracy by 0.01% by improving this feature and that hyperparameter. And then you lose the room even if your solution is super useful.

Absolutely. And I was with the CEO of I would call it a tool or technology that simplifies data science. That might be how I describe it. And I asked that CEO, how many data science. I don't remember exactly how I worded it, but how many data scientists would you have present to like your C suite or your executive team, your board? And he just put up a zero with his hand. And I don't think that's necessarily fair in the sense there are data scientists who can communicate. But I would say the argument or the premise of why he put up a 0 rings true. A data scientist, a data engineer, machine learning engineer, they're not trained on communication. And so we have to empower everybody with better data storytelling and communication ability so that we aren't losing things. Right? That we aren't missing out on some of these cool analyses and things that we could do.

So what's wonderful as well about the book is that you also go beyond definitions. You really talk about, you know, how the literacy impacts different aspects of the organization, especially how it relates to its entire data analytical strategy. And you call this the data literacy umbrella. So can you walk us through at a high level what that data literacy umbrella looks like?

Well, if you think about Everything. Like someone might say to me, how does data literacy impact or work with data governance? Well, if you've improved data literacy in people, hopefully you're improving their understanding of why data governance matters. Right. If under your data literacy umbrella, part of it is machine learning. You said AI literacy earlier. Right. I don't need everybody to learn how to code machine learning or truly understand what the AI is doing behind the background, but they better have literacy around it so that you can communicate, you could talk about it. Hopefully we're removing fear of the unknown. Again, data scientists, right under the data literacy umbrella, you might have data science. You don't have to be the technical one doing the statistics, doing those models, but do you have an understanding of what it's doing and why it's working? So there's this fundamental. If you think about data and analytics holistically, that umbrella probably covers every single topic. Doesn't mean that you have to be technical or proficient in them, but you're understanding your foundational level. Do you have a comfort with it? Can you confidently talk about it? Could you read it? Like, you don't need to know what the AI is doing on the back end, but it might spit out some results to you? Can you read it and interpret it? So think about that as the umbrella. Data literacy encompasses all those spaces so that we can just be comfortable with it and it takes time to get there. I understand that people need to realize that. Again, you can't take a two hour course and say, boom, I've got it all. But you can put strategies in place to empower this and you can find different areas. Oh, you're the marketing team, while the data science team has deployed a machine learning algorithm. Let's get their data literacy on that started. It could be, you have data analysts, all these different things, but it's not that one size fits all. So find those areas, find where you fit under the umbrella, build a holistic data literacy strategy and empower a workforce to succeed with data.

I couldn't agree more, and especially on the AI literacy and understanding what an AI system is and whether its outputs necessarily make sense. I think that also empowers the workforce to not only challenge an AI system if it has maybe biased results or unethical results that you may not see as a data scientist if you're working on, but it also helps you get out of that data fear and go out of that mindset of algorithmic determinants. Anything a machine learning system does is correct because it's an AI. Right. And it Helps you become much more critical about it within the organization.

Well, you're touching on something that I think should permeate throughout organizations, especially from a data literacy perspective, and that is healthy data skepticism, not cynicism. Right. The world will create data cynicism. Right. How many times do we see numbers and political parties manipulate data and businesses do it? All these things. We need healthy skepticism in a business. It's not wrong to question things, even if whatever is being presented to you could be 100% right, but couldn't. Can we ask questions of it? Could we look at it this way? Could we look at it that way? What about doing this? What about doing that? In the end, that we need to be questioning everything. I don't know what it is, right? Kids. I've got five kids. How many questions do you think I get regularly? And trust me, I get frustrated by it because they don't stop. My wife gets frustrated by it. But I hope they understand. I never want them to stop. Adults stop questioning things. I don't know why. We just get in routines. Whether we're distracted, someone presents data that must be true. I want all of us to just question things. Bring back a natural childlike curiosity to what you're doing that is part and should be a part of the culture 100%. It should not be wrong that if a senior vice president presents something to a marketing analyst and the analyst says, oh, that's interesting, could we look at it a little differently? That analyst should not get in trouble. They are doing data literacy work right there. And it might be that their question sparks something powerful. But organizations don't operate that way. It was the U.S. army, a military organization. And the military is not designed to question. You're given orders, you do things. But I had a brigadier general in there talking about. And I'm not. We're not talking about questioning things in a cynical or insulting way, Right? We're talking about questioning things in a data literacy and data driven way. And she backed it and she was there for it. And that to me is power. Can a marketing analyst question the CEO of a company if that doesn't exist in your culture, help it grow? Because it doesn't matter who's presenting. Being able to ask smart questions, we need to enhance our ability to do that. But being able to enhance smart or enhance our ability to ask smart questions, that's power. That is part of data literacy. That could be maybe the most powerful part of it. And I want everybody, let's get everybody to do that. Everyone who listens to this. Anyone who participates in Data Camp's Data Literacy Month, please start asking more questions.

That is awesome and so inspiring as always, Jordan. So we've covered what data literacy is. We've covered how data literacy impacts different aspects of the organization. I think it marks a great segue now to discuss what needs to happen with an organization for it to fulfill its data literacy potential. Right. So start off first. I think it's important to discuss where learning fits into a broader data analytics strategy. So can you walk us through where does a data literacy plan fall within the organization's data strategy or should it fall.

Absolutely. Should be a part of that data strategy. Because it's one thing to have a data and analytics strategy where you've got the architecture on the back end, the analytics on the front end, the tools and technology. That's wonderful. But you could build it up, make it amazing, and then find that you have a very poor data literacy. Now, I want to make one thing clear when I say poor data literacy. Everyone has data literacy skills. Everybody is data literate to an extent. I think that sometimes we hear that term and we're like, oh, my gosh, am I data illiterate? Are they insulting me? Everyone has data literacy skills. If you use a weather app to interpret the weather, data literacy skills right there. So I want everyone to understand that. But when you build out a data strategy, data in a work environment might be different for people. They might not be comfortable with it. So you can have this data strategy tied to your business strategy. Forget data literacy and then think about it. You roll it out. How successful do you think you would be? I would bet that's to a degree how a lot of organizations have done data and analytics. Data literacy should be operated in parallel to the execution of your data strategy. It should be a key component because you're investing all this money. You hire amazing people, talented people. You want them to be successful. You want them to use the data strategy, data and analytics. But if you forget them from a data literacy strategy perspective, that that could frustrate an organization's ability to be data driven. Here's an anecdotal story about that. This is. And I don't. I' studied it exactly. But prior to the pandemic, I worked at QLIK at the time and I worked remotely. But I would travel one probably one to four times a month. Could be all over the world. When the pandemic hit, I thought my calendar is gonna get is gonna open up because I'm not traveling right. The conferences are Done. I'm not traveling. I'm not in airports. I'm not doing these things. The opposite occurred. My calendar got busier. My anecdotal. What I've been saying for a while is anecdotally, I think the reason that happened was companies wanted to be data driven and found out they weren't. So let's call the data literacy guy up and talk to him. For organizations to be able to be successful there, we cannot forget data literacy. And I think that's one reason why I think Covid sped up data literacy's adoption at full scale. If that's how we want to describe it. There was a prediction and maybe it came out in 2019 from Gartner. I believe that said it was like five to 10 years away for, like, organizations. I think I might mess up the wording around fully embracing it. I think Covid sped that up because companies think about the power that data could have given companies to be more data driven in a time of great uncertainty. And so now it's like. But it wasn't necessarily successful. So now you've got, oh, man, we need to upskill everybody. And my speaking engagements are like, off the charts in a way. Like, prior to my current company and prior to this year, you know, I'd get invited by the companies I was from maybe a little bit outside the organizations I was in. Now it's like getting requests all the time. And it's wonderful like it is. I never. And one of them, I worked at pluralsight before I came to this company, I think almost. Almost right when I left them and started at my new company to a degree. They're like, will you come? Do you want to be a speaker for us? Right, Because. And I guess I'm going to guess I'm not. I don't know for sure, but I'm probably their number one requested speaker or at least one of them in the data space. I think that I've been told. So it's. We're seeing it. I never would have guessed Adele. Never would have guessed in 2016 when I started this journey and I started it more at American Express. Never would have guessed it would have come to where it is. But back to that question. Data literacy. Anyone listening to this? Who's in a leadership position? If you're not in a leadership position, make it so your organization does this. Data literacy is a key component to data and analytics success and make it a part of your data strategy.

That's awesome. And you know, a lot of organizations had buffer before COVID 19 to a certain extent because they could rely on non digital channels, non digital products and kind of still keep that status quo. But COVID 19, once it sped up that digitization really exposed that skill gap. And without data literacy skills, you're not able to iterate on digital products and improve them.

Oh, it's one of those where if we think about data and analytics from the perspective of it being a tool, a tool to enhance decision making, think about that during COVID Think of its power on how do we drive better digital adoption? How do we understand digital literacy more? How do we make supply chain and logistics operate better? Right. When you have the skills gap as big as it is. Let me share a couple numbers. When I was at results of a survey, this might have been 2017 or 2018 that showed one in five people were fully confident in their data literacy skills. Fast forward to I think it was this year in like February or March, they launched another study. That number went down, it went from 20. And I think it's because there's a more solid understanding of what data literacy is. It was 11%. So you go from 20% and then it's like four or five years later we've gone backwards. I don't think we've necessarily gone backwards. I think it's because maybe people now fully understand what it means when, when they first, when Click first conducts this study. I don't know if people truly understood it. We could define it for you or not. Not me. I mean I, I was the data literacy guy or whatever, but it was marketing who built this study. I want to. I don't even know if I helped on the original one. So let me give credit to Qlik and not say we but Click. I think we could tell people what data literacy was but that doesn't mean they understand it. I think people probably were more confident than they should have been. Now here we are four or five years later. Maybe Covid illuminated it all this data and people are like, yeah, it's now roughly 1 out of 10 people, 11%. So that's. That should show people. If you want data and analytics success, data literacy needs to be a part of what you're doing.

Yeah. And connecting that survey you seeing like decreasing numbers because the goal post not has changed but has become clearer. The new Vantage Partner survey, for example, they run a yearly survey where they discuss, you know, CXO's responses on their analytics and AI investment. And you see every year the percentage of people who say that we have realized investments or we are Actually data driven, that number goes down despite investments going up because I think they are realizing truly what that data driven data literature organization looks like.

Well and it makes me happy that the realization of where companies truly are is being illuminated and that people are realizing, man, we're not where we thought we were. We need to do some things like do we even have a chief data officer? I was working, I won't mention the name of the company out of consideration form but massive world renowned company don't believe they have a cdo. And I spoke with them a couple times last week and the enthusiasm from the data literacy talks is fantastic. And so it's, it's this idea we've got to be doing more now. Maybe they have a cdo. My understanding though, from someone I've chatted with there, they don't. And so it's like, okay, so data could enhance your organization, could empower it. Let's get that first step is hire a true CDO and get them going with and I think organizations should take a step back. I understand you want all the cool things, you want the shiny object, but just take a step back. If you don't have a cdo, start there. If you have a cdo, make sure they have data literacy. Just kind of take those steps back to evaluate better and then march forward with your strategy and what you want to do.

So having a data literacy strategy and how central it is for the overall data analytics strategy, who should own the data literacy plan within the organization?

Oh, the cdo without a doubt. You get other people involved, right? You get executive buy in. You can get the learning and development group in there so that they make sure it's all there. But I want to say that the thought that just popped in my head, I haven't really fettered this through my mind yet. To me, data literacy is a part of that data strategy. And who owns the data strategy? The chief data officer. And I personally like chief data analytics officer. I don't want that analytics should be a. Data literacy should be a part of it with the proper sponsorship, the partner, proper partnerships, getting full buy in so that the culture understand it. So you're getting your HR group, you're learning and development change management in there. You're getting other executives to fully support it and get behind it. You're getting that mid level leadership who's going to probably be leading the charge on the initiative, get them to understand it and getting the participants that that fundamental understanding of why an organization is doing this is paramount. Because if people are People could look at it and be like, oh, I got another email that I have mandatory training. You don't want them to look at it that way. You want them to look at that, understand why the company is doing this, what they're doing, and maybe, maybe most importantly, why they're doing it and how it's going to improve their job. Right. I think people want to get better at their jobs. I hope they do. I think people, I probably every single person you would ask would tell you they have a busy job. Okay, so what if we tell you that we're going to give you some skills that will make it so you don't feel so stressed down, bogged down. I hopefully a lot of people are saying, yeah, well, you've got to make the messaging right, because, oh, we're going to teach you data and analytics. Oh my gosh, I don't want to learn that. Okay, let's change it. We're going to teach you data literacy. Here's what that means. But yeah, that cdo, cdao. They need to own this and they need to buy in.

So beyond ownership, what should go into a data literacy program within an organization? Maybe clipping this question slightly, if you had an infinite budget and resources, what are the different aspects that you'd incorporate in a data literacy program?

Yeah, so I'm gonna, I'm gonna pull from the Click program to give them credit, that's the one I built, and then maybe add a few pieces in. But for me it's not a one size fits all. So you, you need good assessments, at least one good assessment to analyze where people are so that you can set the path for what learning they need to take. You need good communication plans where those communication plans aren't just you have mandatory training, but they're communications around what we're doing, why we're doing it, why does this matter? What's the program going to look like? How will we put it in place? I believe in having internal like webinars and TED talk style talks. Bring in this, I do a lot of these for companies. I don't know if they're doing it from a full data literacy perspective or just to teach people what it is, but get people excited about it. Bring in outside experts. Part of, part of the issue is when you do it internally because you're an internal employee, sometimes that doesn't get the weight and the steam that it should. Right. And so you bring in this external speaker who, who's a subject matter expert. Expert excites people about it. I Believe a key to a data literacy program is teaching the executives. Really why data, what data literacy is, why does it matter? What will the program entail? And hopefully from that you're getting full buy in from the executive team. These are pieces that need to be in there. And I think that one of the final things is benchmarking. Right. You start the program, you run it for six months, you benchmark at the beginning, benchmark at the end, figure out are we making progress and if not, what could we do differently? Can we iterate on it? Iteration is a huge part of data and analytics in general. Make data literacy iterative, learn from it, build it differently. Those are key pieces. You can look at the program that I built at Qlik. I believe they use the exact same one. I think they just launched something new which was like a data driven seven step process. But I think the premise those, those parts that I just spoke about can be empowering to or to an organization. But you do want a good strategic learning plan that's not one size fits all you do. I like doing it in cohort learning and you build it out and you just empower these people to do things differently or to use data more effectively.

Yeah. Of all of these principles, the way we think about it at data camp, you know, you need to personalize the learning path because everyone has a different relationship with data. You need to use assessments and reporting analytics to be able to measure the impact of your program. And you need to think creatively as well. Like one example that we saw that was from Bloomberg where they had a program and upskilling with Python. Right. One way they were able to measure actual reactions of people was by looking at the data platform within Bloomberg and seeing how many API calls it has post training, for example. And these are all very creative ways to like understand the impact of a training or understand the impact of a data. Data upskilling program.

Yeah, you're touching on something that's I think hard at times. Data and analytical work can be intangible. Meaning I use data and analytics here to make this decision and then the decision gets made. But there's, there may be multiple parts that goes into it. So you might get questions around, how do I measure data literacy? Well, there's assessments and things, but like Bloomberg, what, what a good way, right. To, to do this, it's to analyze after effects. Are we seeing more API work? Do we see better dashboards? Do we see. So it's almost bringing in these vicarious or proxy things. I love hearing that Bloomberg did that I spoke to them on site and maybe virtual. So I've spoken and been there at least once. It's like, do things, use creativity, Find ways to measure this. You can find very tangible ways. How many people took courses? Did they score better on assessments? Then find intangible ways? Do we have proxy ways of looking at things that show, man, look at this. People are diving into the data more. We have traction and there are tools that, that I believe can help do that. Like castor. Right. Castor can document data lineage and things like that or the most popular tables that are being searched. Right. So it's, it's use different ways to measure it and then communicate out to the organization your success stories. Right. Let people see the effectiveness of what's going on. And hopefully that might drive those who were naysayers that didn't want to be a part be like, oh, look, there's success here. That's something maybe I'm now interested in.

Now, of course, Jordan, as we close up, while I have you here, I'd be remiss not to talk about your current and upcoming projects. Your next book will be around how organizations can become data driven. Now, of course, we talked at length today about data literacy, but what can listeners expect in your upcoming book? And how does data literacy relate to building a data driven organization?

Yeah. So I'll touch upon that latter part first. And that is to be truly data driven, you have to have a data. You. You have to have a data literate or at least an organization that's improving in their data literacy. Right. And to be data driven, thinking about it. Right. Is me defining it right now. So my book probably has some nice long definition, but let's, let's define it right now. And that is utilizing data to help in your business decisions. That's data driven. It's not necessarily something complex. Where I believe I came up with this book was if you watched in the I that term data driven was. Was gaining steam. And I said, all right, I'm going to write a book on this. Then last year, 2021, as the pandemic just rolls on and call it the doldrums of the pandemic, that term I think kind of faded away as everyone's just in maybe this rut, et cetera, and it has come back full steam ahead. So I'm, I feel very lucky that my book launched August 3rd internationally. I think it's August 30th in the US on B. Data Driven, if I get my title right there. Right in that it's coming at the right time and it's to be data driven. There's aspects, right? Is there a culture? How can an organization build a data driven organization? Can they harness the power of data? And hopefully that's everything that I've written in this book. I haven't been through it in a while. I'm waiting for my copies to get here. I'm excited. But that term and there are people that have an issue with it because I think for some it's like, oh, data drives every. No, no. Like I get a little maybe frustrated that people get so hung up on terminology versus the context of what we're trying to achieve. And that is, can an organization harness the power of data to be a more impactful and effective organization? There you go. There's. There's the book in a nutshell. And that empowers us to be successful with data and data literacy, 100% has to be a part of it. And the third book, which I'm in the process of writing, is called Be Data Analytical. And if you remember from my first book, the Four Levels of Analytics, this book is an expansion on those levels of analytics, teaching people how to do it. And I tie in my first two books into it. So maybe think of it as a series or that maybe that wasn't the intention originally with book two, but now with book three, it's like, yeah, tie these things together, you have data literacy, data driven, four levels of analytics. We can call that the Trident, like the Zelda Trident or whatever it is. And where it's. We want data literacy. We want to be data driven. We need skills and it varies across who is doing what with data literacy and it dry and we can button those things up together. That book launches next year in May. So again, I never thought I'd be where I am. I think it's amazing. I think it's fun. And as you can probably tell through the podcast, I love this stuff. I think there's power. I think that we could do a lot of good in giving people these skills. I think organizations, when doing it right, we're doing it ethically. And using data effectively can be powerful organizations. I think that there's a lot of good that comes from effective use of data and analytics in our lives.

That is so exciting. And I cannot wait to read the books myself now. Jordan, it was great to have you on the show. Do you have any final call to action before we wrap up today's episode for anybody?

What I want you to we're talking data literacy. What I want you to do is realize not everyone needs to be a data scientist. Right? That's not what we're trying to do. But everyone needs to develop confidence and skills and data literacy. So find your path. Find an area that makes you excited. If you don't like statistics, don't dive into statistics. If you don't care about AI, don't dive in there. Eventually you might get there, but find an area in data and analytics that can empower you. And I would say, connect with me. I'm on LinkedIn. I'm a big, powerful voice in data literacy on LinkedIn, award winning. And I, I'm an open book, right? Adele probably would, would attest to that. People who work with me would attest to that. I just come chat with me if you need a mentor, if you're trying to figure out where to start any of that, shoot me a message. And now at times, of course, I'm busy and I'll say, okay, I can't meet until next week. Does that work for you? But I would say you don't have to be a data scientist and connect with me. Those would be the, the two key, maybe two things to walk away with as we close up here.

All right, that is awesome. Thank you so much, Jordan, for coming on Data Framed.

Oh, thank you so much for having me. You've been listening to Data Framed, a podcast by Data Camp.

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