DataFramed
DataFramed

Episode · 2 months ago

#103 How Data Literacy Skills Help You Succeed

ABOUT THIS EPISODE

Data Literacy is increasingly becoming a skill that every role needs to have, regardless of whether their role a data-oriented or not. No one knows this better than Jordan Morrow, who is known as the Godfather of Data Literacy.

Jordan is the VP and Head of Data Analytics at Brainstorm, Inc., and is the author of Be Data Literate: The 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.

Throughout the episode, we define 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.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

You're listening to data framed, a podcast by data camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is adult 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 ass off than Jordan Murrow. 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 the data literally the day 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 their literacy to drive impact with data, how their 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 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 following the link in the description below. And now on to 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. It's awesome. So I'm extremely happy you're joining us for the literacy month and I'm very thrilled to deep by 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 also know why you're called the Godfather at 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, and we were democratizing data, and this is maybe anywhere from seven to ten years ago, if I had 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. That's why 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 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 seen that Gartner launched a report that talked about data literacy. They didn't know this little NERD named Jordan's 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 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 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 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 you do it keeping up with a family as well as ultra marathon ng 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 accept the stage for today's conversation by trying to contextual lies 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 the 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? Our 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 use data here, you need to use data here, and that could be intimidating, it can be fearful. A lot of people don't go to school for a background and utilizing these fields. It's 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 mean everybody to be a data scientist. But can we empower people to utilize data both in their personal lives, their families, etcetera, and in their career? Can we empower them to utilize data to makes murder 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 up 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 ultra marathons, I could use data to help illuminate how I trained 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 round data literacy matters greatly. We don't want people to be intimidated to and fear, etcetera, 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 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 treacy side. Here you can extend that definition also to a certain extent and see how AI literacy can help us understand fake news, the fakes better and understand and rebut that and have a better inoculation against it. Well, the mystifying that data fear is so important to be able to equip people to be confident in their data day roles. In today's age, a key component of wire a 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 is 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, two thousand seven, I believe, right. So that's roughly fifteen 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's 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 fifty 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 smart when 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 I t, 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 B I, these different technology is 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 we have all the 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 said sexiest job of the 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 heart, 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 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 the set up. Here's an example. If you have a company of ten thousand employees, how many of them are truly going to be data scientists? I don't know, fifty to a hundred, maybe a couple hundred. That gives you D 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 from when I start building data literacy for it to come to be, because the r o y 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 de marketized 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 long cases, manicure 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 have, 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 in gineering 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 a I by the end of the year. I come out and said, 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 R O Y, true data and analytic success, they're 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 can use it that 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 Dataacy and what do you think are the different components of that definition? So, while I was at Click, which is where I was helping to pioneer this field, we used a definition that came from M I t and Emerson that the definition was the ability to read, work with, analyze and argue with data. While it click we change 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's, 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 and 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 and 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 are necessarily technically gifted or like data scientists, data and listen. You're so spot on here. Like 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 businesses 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 analytic 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 on 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. That the program I built, a click we withold 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 and 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 Ada might be one of the big fears right and that is data and analytics is going to take over my job, when in 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 gonna have data literacy, the 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 the literacy because it's so important when combined with data literacy, because otherwise just pure the literacy skills are not going to be useful with that that business expertise. They're not that and and that's that's a key. I think historically you could silo off the business side and and and I shouldn't say you could, you shouldn't have, but that's how it was done. Right, you 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 too, to help people understand they are tools to help you do earn 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 their 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 day literacy looks like. Can you work a more detail which 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 M v P. 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 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 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 find herbal 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 a stand pack. 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 M v P based on roles, right, a data analyst, data scientists, 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 I call data fluency or the ability to communicate with data. I probably make it interchangeably between those two the secret source 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 at 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. 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 vector machine. We have been able to improve the accuracy by zero point zero pound percent by improving this feature and that hyper parameter, and then you lose the room, even if your solution is super useful. Absolutely, and I was. 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 signed 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 the sense there are data scientists who can communicate, but I would say the argument for 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 that we aren't losing things right, that we aren't missing out on some of these cool analyzes 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 day literacy impacts different aspects of the organization, especially how it relates to its entire data and political strategy, and you call this the day 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 and 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 can 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. Well, 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 if it's also 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. Understanding what an AI system is and whether it's outputs necessarily makes sense. I think that also empowers the workforce to not only challenge an AI system...

...if it has maybe biased results. Are An ethical results that you may not see as a data scientists, 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 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 what? 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 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. 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 getting 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. 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 analysts 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 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 camps, data literacy month, please start asking more questions. That is awesome and so inspiring. As always, joined in. So we've covered what day literacy is. We've covered how dat literacy impacts that 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 it's data literacy potential. Right. So I 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 you have a data and analytic strategy where you've got the architecture on the back end, the analytics on the front and 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'm one the way. 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, a my 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 from 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 bet that's too would agree 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've never studied it exactly, but prior to the pandemic, I worked at Click at the time and I worked remotely, but I would travel want probably one to four times a month, could be all over the world. When the pandemic hit, I thought my calendar is gonna get it is 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 things. The opposite occurred. My calendar got busier. My anecdotal what 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 right 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 that maybe it came out in twenty nineteen from Gardner. I believe that. said, it was like five to ten 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 that 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 company as 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 and one of them. I worked at plural site before I came to this company. I think, almost almost right when I left them and started at my new company, to agree. They're like, when you come, do you want to be a speaker for us? Right, because, and I guess I'm gonna 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 when I started this journey, and I started it more at American Express. Ever 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 analytic success and make it a part of your data strategy. That's awesome. And you know, a lot of organizations had buffer before covid nineteen to certain extent, because they could rely on non digital channels, non digital products, and kind of still keep that status quo. But covid nineteen wants. 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, when you have the skills gap as big as it is? Let me share a couple of numbers. When I was in results of a survey, this might have been seventeen or twenty eighteen. 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 twenty and I think it's because there's a more solid understanding of what data literacy is. It was eleven. So you go from 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 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 click and not say we, but click. I think we could tell people what data teracy 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 one out of ten people eleven. 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 seeing like decreasing numbers, because the goalpost not has changed but has become clever. The new advantage partners survey, for example, they run a yearly survey where they discuss, you know, c xos 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 they had driven. The elected organization looks like well and and it makes me happy that the realization of where companies truly are is being illuminated and 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 and too out of consideration form, but massive world renowned company. Don't believe they have a CDO and I spoke with them a couple of 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 it. Let's get that. First step is hired a true C do 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. It's so having a the literacy strategy and how central it is for the overall datalytic strategy. Who should own the DETA literacy plan within the organization? 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 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 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 buying 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 them participants. That that fundamental understanding of why an organization is doing this is paramount because if people are people could look at 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, 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 got to make the messaging right, because, oh, we're gonna teach you data and analytics. Oh my gosh, I don't want to learn that. Okay, let's change it. We're gonna teach you data literacy. Here's what that means. But yeah, that's C do, C D Ao. They need to own this and they need to buy in. So, beyond ownership, what should go into it? The 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 incorporate in a day 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 a few pieces in. But for me it's not a one size fits all. So 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 going to look like? How will we put it in place? I believe in having internal like webinars and Ted talk style talks bringing 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 the part of the issue is when you do it internally because you're an internal employee, sometimes that doesn't get the weight in the steam that it should right and so you bring in as external speaker who who's the 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 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. Your benchmark at the beginning, benchmark at the end. figure out are we making progress and if not, what can 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 it click. 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 to an organization. But you do want a good strategic learning plan that's out one...

...size fits all. You do I I like doing in cohort learning and you build it out and you just empowered these people to do things differently or to use data more effectively. Of all of these principles the way we think about a data camp. You know, you need to personalize the learning path because everyone has a different relationship with data. 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 upscinning 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 scanting program. Yeah, you're 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 at on site and 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 their tools that I believe can help do that, like castor right castor can document data lineage and things like that are 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 and be like, Oh look, there's success here. That 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 and of course we talked at length of day about data literacy. But what can listeners expect into your upcoming book and how does the data literacy relate to building a data driven organization? Yeah, so I'll touch upon that ladder part first, and that is to be truly data driven you have to have a data 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 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 that term data driven was gaining steam and I said, all right, I'm gonna write a book on this. Then last year is the pandemic just rolls on and call it the dull drums of the pandemic. That term, I think, kind of faded away as everyone's just in maybe this rot, etcetera, and it has come back full steam ahead. So I'm I feel very lucky that my book launched August third internationally. I think it's August thirty in the US. On be 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 their 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 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 may be 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. One 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 tri dent like the Ze to try it end 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 and giving people these skills. I think organizations, when doing it right, when 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's. 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 here. We're talking data literacy. What I want you to do is realized 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 a I 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 and 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 what 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 two key maybe two things to walk away with as we close up here. Alright, 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. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time,.

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