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How Data Literacy Skills Help You Succeed

Jordan Morrow shares his insights on data literacy, why organizations need data literacy in order to use data properly and drive business impact, how to increase organizational data literacy, and more.

Sep 2022

Photo of Jordan Morrow
Jordan Morrow

Jordan Morrow is the VP and Head of Data Analytics at Brainstorm, Inc., is known as the Godfather of Data Literacy, and is the author of Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed. Jordan has been a fierce advocate for data literacy throughout his career, including helping the United Nations understand and utilize data literacy effectively.

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Quotes

Data literacy is creating a comfort in people so they are able to utilize data. For some people that may look like becoming a full-fledged machine learning engineer or data scientist. But, for many others, creating comfort just means learning to interpret data, ask questions of data, and communicate effectively with those who specialize in data science and who are technically sound. It’s just about creating comfort and confidence in utilizing data, and it doesn’t mean everyone has to become super technical or be something they are not.

Historically, businesses have always seen the technical people as the ones that need to utilize and understand data, and while self-service analytics and business intelligence tools streamlined data democratization, most people don’t have a technical background in data, so they are trained on how to use the tool, but they still lack training on how to properly understand, interpret, and utilize the data itself these tools give them access to. We need training on the tools and technology, and we need training on the data and analytics.

Key Takeaways


Being data literate does not mean you have to become a full-fledged data practitioner. You just need to be comfortable interacting with data in a meaningful way so you can understand, interpret, and utilize it.


When it comes to data, everybody has a seat at the table, no matter what their background or role is, because everyone has personal experience that can be applied to the data.


Successful data literacy programs educate executives, secure their buy-in, benchmark throughout all stages of the program, and continuously iterate.


Adel Nehme: Hello, everyone. This is Adele, a data science educator and evangelist at data camp. 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 Morrow. Jordan Morrow 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 know’. Jordan is currently the vice president and head of data analytics at Brainstorm Inc and is 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 organizational 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, check all the great events we have in store, and sign up for our upcoming webinars. Jordan will be hosting a webinar this Thursday, and we'll be happy to take any questions. So make sure to sign up by f... See more

ollowing the link in the description below and now onto today's episode. Jordan. It's great to have you on the show.

Jordan Morrow: Oh, it's such a pleasure to be here. It's my honor. That is for sure. Thank you for having me

Adel Nehme: 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 it'll help us know why you're called the godfather of data.

Jordan Morrow: 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 BA, basically call it a business intelligence group and we were democratizing data. And this is maybe anywhere from 7- 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 build 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 be an entrepreneur in 2016 by Qlik. It was an analytics curriculum manager. It was product agnostic, and I started building. While it was in February and March, I believe, of 2017 that Gartner launched a report 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 it's kind of funny. And I guess I could say the rest is history, right, you know, travelled the world, spoke at the United Nations, the US Olympic committee. Big companies. And I still do to this day, and 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 might have been the sole author or a co-author on that Gartner article, Valerie Logan. And she was the one who told me, or, or asked me, and she's like, you know what they call you, you know what your nickname is. 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, my passion, and 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. 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, I love what I do, whether it's inside my career out. So there's a little background. Hopefully, that was good for you.

What is Data Literacy?

Adel Nehme: That's awesome. And I really appreciate the holistic background and don't know how you do it—keeping up with the family, ultra marathoning, and 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 this stage for today's conversation by trying to contextualize data literacy into the broader moment 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?

Jordan Morrow: So I. 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 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 0.1 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 support us in decision making. That could be from our individual lives, like think back to the pandemic, being inundated with these. How do we know if they're accurate? Is it politically driven? Is it personally driven? Are corporations doing this? And do we have the ability to question it appropriately, dissect it, and 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. Last summer we had another person from South America living with us right there. All these things happening around us, whether it's COVID in our careers or data literacy matters because we might feel overwhelmed. We're being told maybe regularly, you need to use data here. You need to use data here and that can be intimidating. It could be fearful. A lot of people don't go to school for a background in utilizing these fields. It's that's increasing, but. It's not their background. It's not their forte. It's not what they wanna 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. And 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 utilize data to effectively bring to life the strategies we have, whether it's on our personal life.

Like ultra-marathons, 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 in 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 fearful, but can we enhance their data skills to be more successful in what they're doing?

Data Literacy Gap

Adel Nehme: I couldn't agree more. I love setting the stage that you put here, both from how it empowers us as citizens to become more responsible citizens, a challenge, and understand different components or different phenomenon 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, deep fakes, better and understand and rebut. And have a better inoculation against it. Well, 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 regularly? So can you walk us through why the skill gap exists and its main drivers?

Jordan Morrow: One of the catalysts, if I can word it that way. We maybe didn't even have control over it. 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 I 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 is 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 while all of a sudden we get into this area of self-service analytics, business intelligence tools, like click 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, et cetera. 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 I, again, I'm 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 employee. How many of them are truly gonna be data scientists. I don't know, 50 to a hundred, 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 the, 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 wanna 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.

The Real Value of Data

Adel Nehme: Yeah, and I couldn't agree more, especially on that notion of organizations invested a lot in tools in technology and tool-based training rather than the fundamentals for the rest of the organization.

I think organizations are attracted to the technology aspect of data science and AI and its applications, which leads to massive investments. 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?

Jordan Morrow: 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 sales people who can make these tools and technologies just shine, right? They're using, I would say in a lot cases, manicured data sets or data sets that will enable the tool to look amazing.

You know what I mean? So it's this. Counterintuitive 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. 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 many cases, I would argue that the dashboards companies have the descriptive analytics 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, we had one goal, like they wanted AI by the end of the year I come on. It's like, 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 analytic 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.

Define Data Literacy

Adel Nehme: I couldn't agree more. And so we can sit 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?

Jordan Morrow: So, while I was at click, which is where I was PI helping to pioneer this field, we used a definition that came from MIT and Emerson, that 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. Plus, I think the word communicate is more powerful. So the definition I use is 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 gonna work with it properly? How are you gonna analyze it properly? And can you even communicate with it? But there, there's an understanding that if we think about it, take that academic or formal definition.

Now, essentially to me, data literacy is creating a comfort in people to be able to utilize. That's it. And so in for some people that means, yeah, let's go full bore. Let's become a machine learning engineer and 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 the fourth characteristic effectively 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 comfort and 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 your job and hopefully then it also translates into your personal.

Adel Nehme: That's really awesome. I love that definition, especially on creating comfort and using and removing that data fear. Would you say that from an organization's perspective, taking that definition in and kinda extending it to the organization, that 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 or like data scientists, data analyst, et cetera

Jordan Morrow: 100 percent. You're so spot on here, like. When I speak about organizations and the utilization of data, I talk about what I call a holistic data strategy. A holistic data strategy is a data strategy that ties to the business strategy or the business objective. 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 an organization's culture. 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 it, 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 you're data literate. No it's taking an assessment. The, the program I build at click, 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, the organization. And one thing I wanna 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. Yes you can becausese you have personal experience that can be applied to the data. I think that right there may be, Adele might be one of the big fears, right. And that is data and analytics is gonna take over my job. When in reality, we don't want to eliminate the human. 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 gonna show us. Our gut feel is off some cases, it will support it. And so never forget that. Not only are you gonna 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. That varies, both the human skill and the data skill across this spectrum that you're talking about. 

Adele Nehme: 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 like 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, that business expertise.

Jordan Morrow: They're not that. And, and that's, that's a key, I think historically you could silo off the business. And, 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 their 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.

Data Literate- The Goal

Adel Nehme: 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 gonna 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. Can you walk us bit in more detail? What each element looks like in that definition?

Jordan Morrow: 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 termvthat I have grown to use, and I love minimum viable proficiency. Right? And that's, that's like an MVP. You get minimum viable product here. We're gonna call minimum viable proficiency, and I'm not gonna necessarily use what he did. He has a, an article or a blog post on his website about it, but we, 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-analytics, right? Do you have that ability to understand? The line charts, the bar charts, the dimensions of the visualization, are 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, I don't know. If this is how my mind truly thinks about it, but I, 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 co 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, the interpretation was right. Can they get to the insight then? Maybe the final, 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 can read a data visualization, work with it, maybe even build one, make it so beautiful, 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 to 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. Like I, I call data fluency or the ability to communicate with data. I, 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. 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. Yeah, definitely a hundred

Adel Nehme: 100 percent. I couldn't agree more on the importance of data storytelling and like 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 factor machine. We have been able to improve the accuracy by 0.0-pound percent by improving this feature and that hyperparameter. And then you lose the room, even if your solution is super useful.

Jordan Morrow: Absolutely. And I was, I was with the CEO of I would call it a, a tool or technology that simplifies data science. That might be how I describe it. And I asked that CEO, how many data sci- and 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 a sense there are data scientists who can communicate, but I would say the argument or the premise of why he put up a zero 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 we aren't losing things. We aren't missing out on some of these cool analyses and things we could do.

Data Literacy Umbrella

Adel Nehme: So what's wonderful as well about the book is that you also go beyond definitions.

You really talk about, you know, how data 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?

Jordan Morrow: 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 understand what it's doing and why it's working?

So there's this fundamental. Suppose you think about data and analytics holistically. In that case, that umbrella probably covers every single topic doesn't mean that you have to be technical or proficient in them, but you understand 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. Well, the data science team has deployed a machine learning algorithm.

Let's get their data literacy on that started and 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.

Adel Nehme: I couldn't agree more.And I, especially on the AI literacy and understanding what an AI system is and whether it's outputs necessarily make sense. I think that also empowers the workforce to not only challenge an AI system, if it has maybe bias 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.

Jordan Morrow: Well, you're, you're touching on something that I think should permeate throughout organizations, especially from a data literacy perspective and that.

Healthy data skepticism, not cynicism, right? The world will create data cynicism. Right? How often do we see numbers and political parties manipulate data and, 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 a hundred percent 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, 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 into routines, whether we're distracted or someone presents data. That must be true. I want all of us just to question things, and bring back a natural, childlike curiosity to what we'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 us 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, 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 present. 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.

Please start asking more questions for everyone who listens to this and participates in DataCamp’s data literacy month. That is awesome and so inspiring as always Jordan.

Data Literacy in an Oranization’s Strategy

Adel Nehme: 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 within 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 a data literacy plan falls within the organization's data strategy? Or should it fall? 

Jordan Morrow: 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 a wonder. But you could build it up, make it amazing, and then find that you have very poor data literacy. Now I wanna make, 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 weather to interpret the weather data literacy skills right there. So I want everyone to understand that, but when you build 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, then think about it. You roll it out. How successful do you think you would be? I would be. 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 could frustrate an organization's ability to be bdata-drivenen.

Here's an anecdotal story about that. This is, and, and I don't, I've never 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 was going to 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. 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 that 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 wanna describe it, there was a prediction and maybe it came out in 2019 from Gartner.

I believe it was 5 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 C. To be more data driven in a time of great uncertainty. And so now it's like, but the, it, 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, like before my current company. And before 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, it is, I, I never, and, and one of 'em I worked at Pluralsight before I came into this company. I think almost right when I left them and started at my new company to a degree. They're like, will you come, do you wanna be a speaker for us? Right. Because, and I, 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. I've been told. So it's, we're seeing it. I never would've guessed Adele never would've guessed in 2016 when I started this journey and I started it more at American express, never would've guessed it. I would've 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. 

Adel Nehme: That's awesome. And you know, many organizations had a buffer before COVID 19 to a certain extent because they could rely on non-digital channels, non-digital products and still keep that status quo. But COVID 19, once it's sped up that digitization really exposes that skill gap and without data literacy skills, you cannot iterate on digital products and improve them.

Jordan Morrow: Oh, it, it, 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. It, when, when you have the skills gap, as big as it is, let me share a couple numbers. When I was at the results of a survey, this might have been 2017 or 2018 which 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 wanna, I don't even know if I helped on the original one. So let me give credit to click and not say we, but click. I, I think we could tell people what data literacy was, but that doesn't mean they understand.

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 one 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.

Adel Nehme: Yeah. And connecting that survey, 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 invest. And you see every year, the percentage of people who say that we have realized investments, or we are data driven, that number goes down despite investments going up, because I think they're realizing truly what the data driven data organization looks like.

Jordan Morrow: Well and it, it makes me happy that the realization of where companies truly are, is being illuminated and that, and that people are realizing, man, we're not where we thought we were. We need to do things like, do we even have a chief data officer? I was working. I won't mention the company's name to out of consideration form, but massive world-renowned company doesn'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'm chatted with there, they don't. And so it's like, okay, so data could enhance your organization, could empower. 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 wanna do. 

Adel Nehme: Having a data literacy strategy and how central it is for the overall data analytics strategy, who should own the data literacy plan. 

Jordan Morrow: Oh, the CDO, without a doubt, you, 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 wanna 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 and I, I personally like chief data analytics officer. I don't want that. Analytics should be, but 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, your 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 gonna 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.

Cause 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 gonna improve their.

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 gonna 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 gotta make the messaging, right.

Because, oh, we're gonna teach you data and analytics. Oh my gosh. I don't wanna learn that. Okay. Let's change it. We're gonna 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.

The Perfect Data Literacy Program

Adel Nehme: 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?

Jordan Morrow: Yeah. So I'm gonna, I'm gonna pull from the Qlik 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 their communications around. What we're doing, why we're doing it.

Why does this matter? What's the program gonna look like? How will we put it in place? I, I believe in having internal, like webinars and Ted talk style talks, bring in this. I, 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, be part, part of the issue is. When you do it internally because you're an internal employee, sometimes that doesn't get the weight and steam it should. Right. And so you bring in this external speaker who's a subject matter 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, 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 the, one of the final things is benchmarking, right? You, 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? If not, what could we do differently? Can we iterate on 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 click.

I believe they used 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, and you build it out and you just empower these people to do things differently or use data more effectively.

Adel Nehme: Love all of these principles, the way we think about it at data camp, you know, you need to personalize the learning path, cuz 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 we saw from Bloomberg, where they had a program and upstanding with Python, right? One way they could measure people's actual reactions 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 training or understand the impact of a data, data upscaling program.

Jordan Morrow: 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, I love hearing that Bloomberg did that. I've I spoke to them at onsite and, and maybe virtual. So I've spoken and, and been there at least once it's like, do things, use creativity, find ways.

To measure this, you could 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 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 used different ways. To measure it and then communicate your success stories to the organization, 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.

Building Data Literacy in a Data-driven Organization

Adel Nehme: Now, of course, Jordan, as we close up while I have you here, I'd be remiss to talk about your current and upcoming projects. Uh, 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?

Jordan Morrow: 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-drivenen, thinking about it right. Is, 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. Necessarily something complex where I believe I came up with this book was if you watched in the I, that term data driven was gaining steam.

And I said, I'm gonna write a book on this. Then last year, 2021, as the pandemic just rolls. On and call it the doldrums of the pandemic. I think that term faded away as everyone's just in maybe this rut 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 being data driven. If I get my title right there, right in that it's, it's coming at the right time. And it's to be data driven there's aspects. Right. Is there culture, how can an organization. Build a data driven organization count, 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 your 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, I get a little maybe frustrated that people get so hung up on terminology versus the context of what we're trying to achieve.

Can an organization harness the power of data to be a more impactful and effective organization? There you go. There's the book in a nutshell, which 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 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 can be powerful organizations when doing it right with doing it ethically and using data effectively. I think that a lot of good comes from effective use of data and analytics in our lives. 

Call To Action

Adel Nehme: 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?

Jordan Morrow: What I want you to we're talking data literacyt I want you trealize thatze not everyone needs to be a data scientist. Right. That's not what we're trying to do, but everyone needs to develop confidence, 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'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. All right.

Adel Nehme: That is awesome. Thank you so much, Jordan, for coming on DataFramed.

Jordan Morrow:  Aw, thank you so much for having me.



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