#323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy

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When you look at the definition of data literacy, reading is the most important characteristic to me because if you don't read it right, you can't do the others right. But the secret sauce to making data and analytics and data literacy initiatives succeed is communication. It 100% is. So the first pillar of a data driven culture is data fluency. And the fluency to be able to have conversations with out getting people to look at you like they have deer in the headlights. Some of the first steps of a change management within data AI, any of that is number one, understanding what the true objective is. And the objective is not to teach data literacy course. The objective is to change people's behavior. Right? That is very hard. You can't just send people an email that says you have mandatory training. The moment you do that, you've lost. There needs to be messaging around it. They need to understand what's in it for them. They need to have ensure that leaders truly buy in. They need to understand how much time this is going to take. All these things, things add up.

Welcome to Data Framed. This is Richie. Back in 2016, today's guest started championing the idea that everyone needs data skills regardless of their role. The message is simple. Whatever your job title, you need some data skills. And this will help you at work and in life. Our guest is the godfather of data literacy himself, Jordan Morrow. Jordan is Senior VP of Data and AI Transformation at Agile1. He's also the founder of Bodhi Data and he's currently working on his fifth book about data literacy. I want to know what progress you've made in the last decade towards worldwide data literacy, how it relates to AI literacy, and how you go about making your colleagues data literate. So let's find out how to be data literate. Hey Jordan, welcome to the show.

Good to see you, my friend.

In fact, welcome back. I should say yes. You've done this before.

Absolutely. Always happy to chat nerdy things. Let's party. Wonderful. Yeah.

So I mean, I was thinking you've been talking about data literacy for must be coming up a decade now almost. I'm curious, do you think we're making progress? Is the world getting more data illiterate or less data literate?

I think the best answer for that is yes. What I mean in some instances, absolutely. We see organizations doing better and attacking it better. And then all of a sudden Covid hit and I think critical thinking skills went out the door at times. And I do, but I do think there's a balance. And I, what I, what's interesting is when you study the trends and everything, I do think there's like a, almost like full circle, people are coming back to we gotta be using the data. And I, I'm like, yes, we do. So I think it's, it's a yes and no. I think in some areas 100% we see more effective use in data and understanding of how data feeds AI. And then in some cases it's just shrug your shoulders and be like, okay, I can't worry about that.

Yeah, that's true. So we still got some work to do, but we're, we're making progress in some areas, which is a good sign. Okay, so maybe we need some, some positive motivation here. So have you got some success stories then from maybe organizations or people using data to get better?

One of the first things I did in my day to day job, I'm essentially chief data AI officer. I'm senior vice president of data and AI transformation. And one of the very first things I put into place coming into the organization when I'm leading vision around AI and data was literacy training because it wasn't going to be any good if I build all these products and build these solutions and people aren't comfortable using them. And so we put in training on storytelling, making decisions, et cetera. As AI is advanced, we've opened up a community around AI where people are sharing best practices and you're just watching people take off and it makes me happy. Number one, I don't have an army of AI specialists, so I need all the help I can get. And then you're just watching people pick it up. And what's so cool about that is the people you see succeeding are those who aren't data and AI professionals. They are, you know, a learning and a develop person or marketing or this or trans change management. And they're finding ways to build agents and to use data and to use AI to make smarter decisions. And that, that hits home for me because I'm a huge believer. Everybody has a seat at this table, they just need to understand what that seat looks like and develop skills around it. And so that's just at my organization. I've been an advisor to the US army for years. I'll be back at the Pentagon next week teaching. They're thriving in areas around this. I went to Scotland to teach Scottish fire and rescue. They're thriving with this. And I'm still invited the world over to speak on data AI. And so, yeah, people should have a ton of hope because when you get to the meat of it and you turn the news off, there's a lot of good and there's a lot of people trying. There's a lot of people putting effort to use AI and data and do it right. So there's definitely more room for hope than there is fear around these things.

That's wonderful. And yeah, certainly, I think like any organization where they pick up on the idea, okay, maybe we should have data literacy throughout. You know, they're going to start thriving. Like you mentioned. Wonderful. So you mentioned AI there. And of course, in the last few years, data literacy has been joined by AI literacy. Do you want to talk me through what's the difference?

Well, what's super interesting to me is I remember when generative AI started, we're talking 2020, end of 2022, 2023. And I'm thinking, did I just become obsolete because of data literacy? Right. And AI could do certain things. And I had a very dear friend, she said, no, you just became more important. And as you take a step back, data literacy's definition, right? My definition is the ability to read, work with, analyze, communicate with data that can translate into AI literacy, right? The ability to read, work with, analyze and communicate with AI. Do we have the way I call AI literacy, I really focus in on the prompting side of it because most people with AI, it's prompt engineering and generative AI. And do you have the ability to prompt it? Well, work with that prompt, evaluate its response and then make a decision with it. What's very interesting though, there's. It's out in the news or out that it's AI literacy gaps that are hindering integration and success. So it's just like everything, right? You don't put a kid on a bike and the kid just bikes, right? You can't just put AI in front of someone and think it's gonna work. So data and AI literacy, they're siblings. You need to be doing them in parallel. We need to understand that the data that builds the AI needs to be good data. Data governance doesn't go away. Yes, the AI might be able to do analyses for us now, but do you evaluate the data and what the analysis says? So I think they're just siblings. I think it's that constant evolution that has been happening with data literacy that it is now data and AI literacy.

Wonderful. And so, I mean, you made the point that, like, a lot of AI literacy is really about, like, can you work with these, with these chat tools? So can you write a good prompt? Actually, do you have any good prompt engineering tips? I'm always keen to learn how to do better.

Absolutely. The first one I give people, which is really, really funny, is when they're like, well, I don't know what to ask. And I'm like, well, ask it. That literally go into it and say, what do I ask you? And then see what it says and then just go, so. But then the one number one thing that I would tell people with prompting is be yourself. Don't try and force fit your thoughts into someone else's way. Take their ways, make them your own and prompt it. And then some standard tips are the more clarity and less ambiguity you put in the prompt, you should expect better results when you get the results. If you don't know if it's real or a hallucination, ask it for its sources. If you don't like the response that you got, tell it and literally say, hey, AI, I asked you this. Your response was junk. I can't do anything with this. Please make it better. That's all. You should be interacting with the engines that are there. Thinking about it, that it has democratized intelligence. And I think that is key for people to understand that this is not like a Google search. This is democratized intelligence at your fingertips. So if you want to learn how to play rugby or badminton, you want a recipe, you want to travel, you want a new marketing campaign, you want to analyze a product, just ask it. The key is evaluating the response to make sure it's real. And number two, do you know how to make a decision with what you receive? That's it. It's not rocket science. Rocket science went into building the model so that 99% of people who don't need to know that could just chat away. I like that.

Well, certainly I really like the idea of telling it to cite sources because, yeah, you don't want it making stuff up. And then you believe in something that's not true because it's just fabricated it. And yeah, the other thing about just talk in your own language kind of works really well. I mean, there's lots of guides are like, okay, you got to use this. So kind of style of writing or whatever, it's like, well, it's probably not going to give you an answer. Yeah.

If you find your own way, that's great. It's even when I write a book or I give frameworks to people on let's say data storytelling, data driven decision making, I want them to make it their own. Don't force yourself to use everything someone tells you. Find what works for you, take notes, journal. I'm a huge journaler. And figure out what works for you so that then it is all about your relationship with the technology, not forcing yourself to use the technology.

I like it. Okay, so I mean that's the AI literacy side of things. Data literacy, slightly more complicated in terms of like the skills you need to learn, I think. So talk me through what are the skills you need to become data literate?

Yeah. So for me, I love to tell people you're already data literate. You just don't realize that like here's my phone, I'm traveling next week. I use the weather app to understand what I'm packing. We're in September, so temperatures and storms and stuff, they vary. So I love telling people it's already a part of you, you just don't realize it. What's key though is the business setting is different than using your phone. So if we go back to those four characteristics of reading, working with, analyzing and communicating. You know what I didn't say in there? Data science. I don't need you to learn statistics inside and out. You don't need to learn machine learning. You don't need to learn coding fundamentals. Yeah. Talking about, oh, coding does this. But you don't have to learn all the technical. So for people, you're exactly right. It is not a one size fits all. So any organization that deploys a data literacy initiative and everybody takes the same workshop, you're in trouble. Right. You want prescriptive plans, you want to assess where people are. Executives don't have time to sit through a, you know, five day, eight hour a day workshop. They probably only have an hour or two to learn everything they need to know about it. Then you have mid level managers. They have to make leaders happy and get their employees doing it. And then you have all the employees. So when you think about a data literacy initiative, it is truly an ongoing initiative to help your organization be more confident and comfortable with data. Don't force fit everyone into the same puzzle. If you are a data scientist, great if you are an analyst, great, a marketing manager, you name it, that's what you should be targeting is how does it fit into their world. Okay.

I do love the idea that, like, make sure that whatever you're learning about is going to be relevant to your job and relevant to working with all the people around you. So yeah, very good idea. Do you think there are any skill gaps around data literacy? So like, what are we missing at the moment?

I would say yes, because I think with. Even with me, right, Data literacy became a buzzword that shifted away from its. Its end goal is to empower everybody with confidence around data. Right, that's it. But it still misses things like change management. It still misses things like value driven metrics around it or value driven insight from it. So just like I can't buy a data AI tool and just expect results. You can't just teach people to read, work with, analyze and communicate with data and expect value. So I do think that a gap missing in data literacy initiatives is, number one, what does the change management look around this? How do we teach people these principles and then get them to deploy actual use of those things? And number two, how do we then define value that is derived from it? Like we can't just say did revenue increase? Because data is a part of a process. So I do think the other gap is how do we really measure value around this One that I created recently is with AI, I call it velocity to insight. Right. How long would it have taken you as a human to build insight? Now this is AI driven, but how long did it take you to deliver insight with AI? And that gap in the metric shows the more efficient way of doing it, but efficiency doesn't mean effectiveness. So I think that's why I get back to this value side is we can speed up effectiveness, we can have better visualizations, we can have more data, but in the end, if we're not using it and driving value, what good was it for?

Absolutely. So yeah, knowing everything is brilliant, but unless you're like getting some value from it, then yeah, it's not good. So, okay, maybe we'll talk about change management then. So I guess the first step is making sure that people actually care about data literacy, that they want to learn about it. And there's a lot of people who are a bit hesitant about data or AI. So how do you overcome that first hurdle, get people excited about it?

I think like some of the first steps of a change management within data AI, any of that is, number one, understanding what the true objective is. And the objective is not to teach data literacy course. The objective is to change people's behavior. That is hard, right? That is very hard. So the key, besides the right objective is what is your communication strategy around this? You can't just send people an email that says you have mandatory training. The moment you do that, you've lost. There needs to be messaging around it. They need to understand what's in it for them. They need to have ensure that leaders truly buy in. They need to understand how much time this is going to take. All these things add up. And so if you're not doing that communication property properly, they can feel overwhelmed. And the moment you've got them feeling overwhelmed, when 90 to 99% of people aren't data professionals, do you really think they're going to buy in fully? And the answer is no. Right. The world is already overwhelming. And so that beginning piece of the objective and communication strategy, and maybe you add a third piece in, which is leadership buy in. That's crucial. If you nail that, you can nail the program.

I love the idea of just making sure people don't feel overwhelmed because maybe there's essentially like you gotta try and turn them into a data scientist. Like you gotta do a PhD in your spare time. It's not really that at all.

It's not. And how many people I ask this question when I'm speaking sometimes. How many of you don't have a busy job that gets people to laugh. Everybody is busy. So the moment you say, hey, you're now gonna take part of this initiative. This is why leadership buy in matters. Let's say they're working 40, 50 hours a week and you're telling them you now have to dedicate four hours. Well, that's 44 to 54. They don't want to do that. So you have to then let them know, no, we're going to carve time out. Your deliverables are going to shift a little. That matters because people already are overwhelmed in the world. Adding another initiative is not necessarily the way to do it.

Yeah. So I think that leadership buy in is incredibly important. Do you have a sense of who needs to be like, accountable for this? Who needs to be running these data literacy or AI literacy programs? Is it your chief data officer, your CEO? Is it someone in L and D? Who does it need to be?

I would say yes to all that. Now, I know that's not a good answer. The other answer I share often is also not the greatest answer. But it is. It depends. Right? So an organization that doesn't have a chief data or chief analytics officer, that type of role, your CEO has to own this on top of it. Even if you have a chief data Chief Data officer, your CEO still needs to own this. Because if it is viewed as some separate thing over here and not a direct part of the business strategy, good luck. People have deliverables, people have targets, people have goals and things they have to achieve. And if they do not see this as fully bought in from the C suite and leadership, good luck. But to your point, then it cascades to who actually owns the program. A lot of time it will be L and D because it is a learning and development sort of program, but it has to be done in partnership with that C suite with the executive team, without executives really bought in. Good luck is one of the best terms I could use for them.

Yeah, certainly if your CEO is never making use of data, or at least never publicly making use of data for their decisions, then yeah, you got a bit of a problem there. Okay, so the other thing you mentioned around the change management is around finding time to learn because everyone's busy. Do you have any tips of it just like scheduling time to learn, like making sure it does actually take place?

Yeah. So one of the keys to this side of the change is letting people know it's not going to be 20 hours of your week. It's literally one to four hours a week is kind of, I would say the Max would be four and that's pushing it. Right. One to two hours a week of learning spread out over eight, 12, 16 weeks. Depends on how long the program is, what you're learning, et cetera. You've got to make them understand it's bite size and it's manageable and it's not going to overwhelm your day to day work. And I think that's always one of those ways of looking at things is how do we make this bite size manageable within the framework of what we're doing. And so I think that's the key is in that communication plan is helping people realize this isn't going to be like you said, you've got to learn a PhD on the job. First off, you don't need a PhD. But number two, it's only one to four hours a week. And like I said, when leadership truly buys in, they will make sure that your time is available for success.

Absolutely. I love that. Yeah, certainly you do. Like your 15, 20 minutes every day after lunch or hour on a Friday.

Yeah, that's it. And I will say this AI has become such a great tool to optimize things. Use AI to optimize your calendar. Right. One of the things that you can do in parallel to a data literacy initiative is use one of the generative models to say, look at my calendar, where did I waste time? What meetings were effective, what ones weren't, look at the notes. Because then it might be you find you have five extra hours a week. Because these meetings didn't need to be done in a meeting. They could have been done asynchronously. Poof, five hours, right. And so that is part of this is a lot of work that we do. We need to reevaluate in the age of AI to make sure it's the appropriate work we're doing.

That's a very good point. I mean a lot of this sort of the joy of AI at the moment is about like, okay, we can automate things, we can, you know, outsource our lives to technology. So but this involves a lot of process re engineering. So talk us through, like what processes should you be changing? How do you go about doing that?

I, I would, quite literally. Now again, this comes from, don't overwhelm yourself. I would put every process in the organization on the table. Now if you're in a fifty hundred thousand person organization, that's huge. That's why you start bite size and you start within your group and you say, I want to evaluate these three processes using AI. Evaluate efficiencies, evaluate where improvements can be made. Are there routine things that can be automated or built with agents? Because what you're going to find is, and I hate to say it this way, a lot of work done at organizations is inefficient. We know that. Number two, a lot of work could probably be kicked out, but because it's something people do and they're comfortable. Wonderful. Good, I'm glad you do it. But you got to separate yourself a little bit from this. So I am all about full reevaluation of all processes. But don't try and do it overnight. You're just gonna overwhelm yourself and bring yourself out. Understand that it is a three, five year process if you're a large organization to do this. But just start small, find a one process, evaluate it. Oh, we found 20 minutes here. Another one. Holy cow. We can get rid of this one. Another one. Oh, that's an hour time save. Who doesn't want time saved?

Yeah, I like that. It sounds magical. Saving time, free time to do stuff. So, okay, so maybe let's work through an example. So you got this process. You want to kind of save things. You didn't even mention AI or technology in there. I think to begin with it was just like, okay, well let's find out how we can do this process better. Yeah. Do you want to walk us through what this involves?

I will show you a direct thing that I did at my day to day job. Not my speaking stuff. They had a Microsoft Excel report. So all the publicly traded clients the company had, people would supply information, they'd put it into an Excel file. That would take time. And then in order to understand the Excel file, you have to know who you're looking for. Slide across. It was the perfect use case for a generative model that I trained. So what I did is I took a look and I said, okay, let's look at all the publicly traded companies that I can get information on. Poof. I took all that downloaded and you could either do this, scraping the web, downloading reports. I took earnings releases and annual reports. That was one mode of training I put into it. The second mode of training I put is I said, yes model, you can go to the web to get information. Boom. Second, third mode of training was yes, use your general knowledge from the LLM. Done trained that model and now they have a user interface. They just sit with it. Instead of having people having to supply information, they can go right in, ask questions, get information. They can literally use this Richie in a QBR meeting or in a meeting with a client on the spot and say they're talking about this, this, this, how could give me strategies to overcome this. Type it right in, have notes, taking it, put it in. Boom, there's your response. Went from a manual process of having to review every client, et cetera, to here's the use case for a generative model. Straightforward. Go, have fun.

That's brilliant because I know from speaking to colleagues, the quarterly business review meetings, it's like you put together an epic slide deck trying to work out what the customer's gonna care about this quarter. And then it's really tedious, it's time consuming, and most of the slides don't actually get used because the customer doesn't care that much about some things.

And it's just reciting metrics. Right. And versus a real strategic. A QBR should be a very strategic conversation. And when you use AI's power, you could say, tell me all the obstacles they've been going through for the last year. Oh my gosh, now I have a list. Tell me solutions to each one. Create Roadmaps create strategies and milestones. It's all there. Make sure you study it, obviously, because you've got to be knowledgeable on it. There it is. And so then it is not just some simple PowerPoint. Well, not simple massive PowerPoints. You're having strategic conversations with what they're trying to achieve.

I really like that. And so what do you think the measure for success is then? Like, what constitutes a successful AI initiative in this sense?

Oh, it's super interesting you brought that up. I did a post, I think just this week, a lot like there was a whole MIT article that was like 95% of corporate generative AI fail. But when you dig into it. No, they don't. That was just a catchy headline. When you dug into it, you found 40%, I think it is. 40% of the organizations had invested in like enterprise wide licensing. But 90% of people were using AI. That is a big discrepancy. So they might not have your license, they're using their own. So this undercurrent of AI success is happening right under the nose of C suite members. Yes, big massive projects might be failing, but why are you trying to tackle everything so massively when all you need to do is teach people how to use it well? So I find that to be super interesting. When you're looking at these projects, don't look at the big, big thing. Number two, please do not try and fit ROI of AI into old methodologies of trying to figure out return on investment. It doesn't fit because it is a different world. So I just started this week releasing new metrics that I'm generating and creating and building around. How do you measure value from AI? The one I released this week was Velocity of Insight, right? Because using AI can generate insight quickly. So you'd compare the measures and say, had I done this and you scale it, it is a math formula, it would have taken me X amount. So my velocity was really low in generative AI. It only took this fraction of amount. So velocity goes up. So you're like, that is a direct correlation. Insane. Here's the metric, here's the numbers. Our insight is coming through so much faster. The key though then is what are you doing with that insight? And if you're not doing anything with it, all you did was generate an idea. But if you're making decisions with it, even more impactful.

That's really interesting because I guess at the C suite level you generally think about, okay, we're making more money somehow, so you've got increased revenue or we're saving costs or something that's like really top line. But actually the stuff that's going to be more meaningful to your success is going to be like micro targets, I guess, more related to work. So you mentioned like velocity to insight. It's going to be things like, I guess, can you measure productivity gains some or similar?

A hundred percent? That, absolutely. That one is a direct. But we also have to remember that is it true just because you're faster doesn't mean there's a true productivity gain there. So we have to be cautious that just because you're more efficient doesn't mean you're more effective. It just means you sped things up. So we do, we do see in places where true productivity gains happen and you should measure it. Cost savings. Did this mean that you got in front of 20 more clients because you did this? And how many of the direct revenue attribution? 100%. But we do have to make sure that there is actual value coming from it, not just we sped up processes that weren't effective anyway. The other side that I think that you can measure from productivity is, and this is a metric I'm going to share next week. It's cognitive load reduction, meaning people are overwhelmed and busy. They're burning out all over the place. Can we use AI to get rid of some of that cognitive load and free their minds up to be more creative? I think that is a direct benefit because a happier employee obviously is going to be a better employee.

So you actually like the difference between efficiency and effectiveness? And actually I've got to tell a story because a hiking buddy of mine was saying his wife complains of him that he's efficient but not effective. And he was asking him why that was and he said, well, you need to get the chais changed on the car. So he took me for our Valentine's date at the restaurant next to the tire change place. So efficient but not effective. And yeah, I think it is why.

He probably thought it was super effective. Hey, two birds with one stone. Tires fixed, Valentine's Day, all done.

I mean it makes perfect sense of me. But yeah, his wife didn't think so. So yeah, I like that you got to focus on effectiveness rather than just pure efficiency metrics. I guess related to this. Do you have any more tips for just making sure that if you've got like a data literacy initiative or you've got an AI literacy initiative is going to run smoothly? Like how do you start going to scale these things out?

I think you need to. I don't like calling it proof of concept anymore. PoCs can be built but don't have any value. So I call it proof of value. So what I would say is, is twofold. Number one, you focus in your data literacy, AI literacy initiatives with objectives towards tangible value in the organization. Meaning what are we trying to achieve with this? Is it market growth, whatever it could be? You need a tangible use case around this. And that's the second side. So you're not building a proof of concept. Build a small proof of value that you can then sell throughout the organization and say, see here, look at this, this work. This group has saved X amount of time. They've made four extra decisions and they closed 10 more sales because of the data literacy initiative. But number two is you can't just focus on the ether. Oh, it's theoretical. No, you need to have tangible use cases. So when you do data, it's. It's that whole adage of when you're in mathematics, in school, where will I ever use this? In real life, if you ever have someone asking in your data literacy initiative, where will I ever use this? You're failing because it needs to be built in an applicable manner. I read data better to do xyz. I analyze data to find better insight. I communicate better because of this. Those are tangible outcomes. Don't just teach. Hey, if you analyze this way and they're like, I don't know where I'd ever apply that, then the program is missing the mark.

Okay, I like the idea of having good comebacks. Gordon, can you talk us through an example then of why data literacy is going to help you just in real life? Maybe an outside of the work situation.

When you take a look at, let's say buying a home, right? There's metrics everywhere. Everybody's trying to sell. When you use your own data literacy to buy a house, it is perfectly easy to just sit there and not try and study the data, not investigate the home, not investigate mortgage rates and all this, and taking a look at your finances, and then you might get caught in a situation that hurts you financially. But if you take the time to really analyze it, find out your finances, your mortgage rates, what will payments look like if I do xyz? What does that is just a data driven decision where that's not that that difficult, but you might not look at it as data literacy. It is data literacy. It's helping you make a decision. I'll use a personal example for my life. I was running an ultramarathon once where I struggled so hard. I don't know if I ever wanted to quit a race more in my life. I think I was probably thinking, miss a cutoff. Miss a cutoff. I never did. So I just kept going and I finished the race and didn't truly understand why I struggled so much until after the fact. It was probably 90 to 100 degrees in July. I was dehydrated. Even with all I was consuming, I was not hydrated enough. So climbing in the mountain, this is one of the hardest 50Ks in the world, and it's here in Utah, and one of my favorite races. I was not hydrated enough. I was crushed. Guess how much water I've got to remember the metric, but it was a straight metric point that kind of, I think, illuminated this for me. Guess. And not to be gross, but guess how much sweat loss that said I had.

Oh, man, it's gonna be like liters per hour or something like that.

It was like two over two gallons, maybe. It was nuts. And even though I'm consuming liquids throughout the race, it wasn't enough. You're exposed at times, the climbing is super difficult, and after the fact, you're like, oh, okay, I get it now. And so that was just personal data illuminating so that if I ever did that race again, my hydration strategy before the race could be much different.

Okay, two gallons, that's like modern. Eight liters.

That's.

That's a lot of sweat. That is really gross.

It was so bad. Thankfully, I didn't pass out or anything.

No, I have to. Everything about that story, it started off with, I'm running an ultramarathon. It went downhill from there. It's like, with the heat and the mountains, I'm like, that's just a. A crazy maneuver.

Yeah, absolutely. And, hey. But I finished. In fact, when I got to the last massive summit, over 11,000ft above sea level, I think it was, or 10,000, either way, I started to have a good race, started to just enjoy. And I'm chatting with people because mentally I'm. I'm. I had run it before. I knew what was ahead of me. And so there I am in just a much better headspace. And so that's a personal data point that once I hit that last peak, knowing what was ahead of me, that personal data point, you're like, okay, life is good.

Yeah. So, I mean, I guess even if you're not running ultramarathons, then, like, just. Or buying a house, they're like, shopping. You're going to use data at some.

Point in that traveling. Can I share a bad story? Once gone, I traveled to Finland at the end of November and did not pack very well. How cold is Finland at the end of November? It's frozen. And so there I am without a proper coat. And I remember thinking, I'll be fine, I'm good. No, it was freezing. And here's the dumb part. I didn't make a good data driven decision. I bought my wife a nice coat while we were there. I could have bought me one and I didn't. So there you go, poor data driven decision making right then.

Okay, yeah, it's a very simple like one data point, the temperature and it's going to affect your decision. Okay, I like that. That's about as simple as it gets. I feel like we got distracted. We were talking. Oh, we were talking about running data literacy programs, air literacy programs at work. So you've got some good combat now for like if someone says, what's the point of this? But so you mentioned sort of showing proof of value. Does that depend on which part of your organization you're trying this in? Like, I presume it's gonna be different for like sales or product or different parts of your business.

Absolutely, it could. Like let's say you're an HR department. You're starting to implement data and AI into the processes of hires. Can you measure the skills that you bring in? Create almost like a skills landscape, seeing where your gaps are, seeing predictions around which gap you need to fill. That's the HR side and that's actually a cost driven center if you think about it, because you're paying employees, so you're losing money. Every employee you bring into a degree and then you've got your investment side or your finance side and they're trying to figure out revenue side of things and cost savings. And then you've got sales and their data in AI literacy could be directly related to how many clients they close and targets that they're hitting. So yeah, I think you said it. Well, it depends on where in the organization you're operating and then how do you want to measure it and how do you create that value?

Okay, yeah, so I love that a lot of this is just about like being able to understand, like how close are you to hitting your targets. And that's like a real like, okay, well everyone should really be able to do this and you're going to have to use data at some point for that. So I guess the other side of this is around the cultural change aspects. We touched on this a bit before but do you have any tips for how to like, where you can have like good leverage for changing culture? Like, how are you going to make this happen?

Yeah. I've got six pillars of a data driven culture. The first one might be the most important. I call when you look at the definition of data literacy, reading is the most important characteristic to me because if you don't read it right, you can't do the others. Right. But the secret sauce to making data and analytics and data literacy initiatives success succeed is communication. It a hundred percent is. So the first pillar of a data driven culture is data fluency. And the fluency to be able to talk the talk, have conversations without getting people to look at you like they have deer in the headlights. Right. Another pillar of that is twofold. Number one, making sure the culture understands we're not changing you into a data scientist, something super technical. We're just trying to weave the DNA of data driven decisions as a part of what we are. Part of that means helping the culture understand that the gut feel and intuition still matters. So often we sit here and it doesn't matter. There are people think, oh, you're getting rid of my intuition, my experience. No, we're not. Those are personal data points. We're combining that with the data to make smarter decisions. Right. When how many people really pushed back against the Internet when it started to thrive? How many people really pushed back against the personal computer automobile? Yes, there was hesitancy to these changes, the smartphone, but now they're just ubiquitous in our life. And I think that's how we need to view data in AI is it's not to supersede us. It's another thing in our arsenal, if you will, to make better decisions.

Okay, Yeah, I like that he's dealing with the pushback. And I mean there's always going to be naysayers. You know, stupid new cars are ride my horse all day instead.

Well, Henry Ford is quoted as saying, and whether he said it or not, it's like if I listen to people, I, they would have wanted me to build a faster horse. Right. You can't build a faster horse, but you can build an automobile. Right. Now the pushback for a little while was electric cars. Now they're kind of dotting everywhere, you know what I mean? So it's, there is a natural evolution to these things and part of it is just helping people realize that what does their seat at the table look like? And helping them develop the skills to succeed in that seat.

Absolutely. And so just talking about sort of cultural Change. I always feel like there are, there are two big leverage points for genuine stuff is that one is like onboarding and the other ones are meetings. So actually, do you want to talk through like how might use data training as part of your onboarding?

I think it just needs to be a part of it, right? When. When a person is onboarded to an organization or switches roles, you get a whole list of training that is mandatory. Watch this video, fill out this form. Why don't we just make data learning a part of that? Quite literally because you know the organization and let's add AI in there is investing in data and it's investing in AI. And the moment you just stick it in front of someone, they're not just going to automatically succeed. And so they need to be prepared appropriately. And that doesn't mean teaching them where to point and click in a tool like click or tableau. It literally means teaching them how to use data to make better decisions. Qlik and tableau are just channels for that, right? Excel is a channel for that. So I'm 100% with you, Richie. Why is it not a part of onboarding when it should be okay?

Yeah, big fan of making that sort of widespread. And so the other one was meetings. I think, like everyone wants to learn how to do meetings better. Do you have a sense of how you can bring data or AI into meetings?

AI should be a meeting enhancer through and through, right? If I'm sitting here and I'm like, okay, I could have done it for this podcast. Now you and I know each other, right? But imagine someone I've never met with provides me information for the podcast. I could literally go right into generative AI and say, these are the key elements. You already know me because I use you all the time. What are the highlights I should bring forward with this? If I'm meeting with an internal meeting where it's a strategy discussion, okay, I'm going to go use generative AI and say, how do I make this more effective so that it's not an hour of people's time? And they walk away saying, what are we doing? They walk away saying, I know my call to action and I know my key takeaways, ways. Another one is clients. We talked about the QBRs. The other side of this that I think matters greatly for a data driven culture and culture in general is when negative meetings need to happen. Performance reviews, it could be reduction in staff. These are difficult conversations. I'm going to use a real world example of AI. My wife is a teacher. She Had a student that did not do the work and was like, doesn't that work count? And she used generative AI to come up with a nice, kind response to this student. She teaches at the junior high level. And her co workers were like, you should just, like, copy and paste this is like. And put it on your wall because it's going to do a very good job. It doesn't have empathy and compassion, but it can simulate it very well. She created this email for a more difficult conversation. She can then go in and modify it, make it more personable, and it helped to strategize a conversation that wasn't as strong, wasn't as easy to have. We should be doing that. Reduction in force can absolutely have damaging effects on a culture. And if you're saying, well, this is what the data told me, how many people are going to like data going forward? So there's ways to use this to strategize meetings to make them more impactful.

I do like that idea of helping you write difficult things, certainly. I mean, yeah, the reduction force idea, that's gonna be a hard conversation, whatever. Having a difficult conversation with someone who's not performing. But also, I guess even like, like, the salespeople, like, planning how to go about having a conversation, future conversation with the customer.

One. One thing. A salesperson, like, they're gonna get hit with, like, obstacles. Like, let's say, I'm trying to sell to you, Richie. And I'm like, and. And the web. You're all over the web. I'm like, okay, I've gotta sell a product to Richie. This is my personality. This is the stuff on the web. Tell me what potential obstacles in my sales presentation Richie, might bring forward. Now, obviously not all organizations have a lot of information, but a lot do. Why are you not going out there and using it to say, tell me the latest market trends that can affect this industry. They might push against this. It could be like this company just announced they're going through a large ERP shift. It's probably not the right time to sell to them. Right. You. You literally can use it to negotiations, obstacles, overcoming them. And if you're not, I just want to say, why so.

Absolutely. Yeah. I mean, there are so many great uses. I'm sure, like, anytime you're having an interaction with another person, practice on AI first, and it's. It's going to work well.

And you can use voice conversation with it. I remember waiting for a meeting once, using Gemini's voice AirPod in my ear, having a conversation about AI with it.

Okay, so I guess the one big fear of AI is that if you use it too much, you become reliant on it, Your skills are going to atrophy. Is that something that's a real worry or not?

Yeah, I worry about it quite a bit. I'm writing my fifth book right now. It comes out in March next year, close to the manuscript being done. I do like. And it's not just AI, and I hope people realize. Our brains, I think, have been atrophying with social media for a while. This is not new. I think AI can make it worse and maybe even kind of exponentially worse because, like, our cognitive muscles need to work out just like our physical muscles. You go to the gym, what happens when you stop going, your muscles get weaker, your body has issues, et cetera, et cetera. What do you think is going to happen if we're not critically thinking, we're not bringing the human element, we're not analyzing. I absolutely think it hinders us. That being said, it's all in the approach. I use it to write. I use a deep research function to write me white papers that I can then learn from. Right. That takes away two months of having to find websites and all that to 10 to 15 minutes. But then I need to be the one who goes in and reads it. I look at the sources. I like that article. I don't like that one. I'm pulling this in. So it can be just like going to a gym can bring enhancements. Like the right equipment can build this muscle different. A better bite can help you be more efficient. Allow AI to strengthen cognitive muscles rather than weaken them. This is. I have a term for this. It's called We're Engineering Intelligence or Engineered intelligence. Data +AI IQ EQ equals engineered intelligence. The human has to be a part of it. I do worry that we're going to weaken it more. Social media was already causing that. And so I do fear it. I absolutely do.

Okay, so I do have the idea that you still got to exercise your brain, even in the face of social media and AI that you know can help you do stuff without like really turning your brain. So go talk me through. What are your. What are your tips for expanding your.

Your brain then for me, just be human. Right. I. And much to my wife's consternation, I probably buy one to four books a week. That's not good. I could be addicted to other things much worse than that, but I'm addicted to buying books. The. The key for me is it doesn't take a ton of effort. 15 to 30 minutes a day. That's it. And everybody. I don't care if you say you can't. Everybody can find 15 to 30 minutes a day of prompting and evaluating prompts, getting better at prompting of reading books to get better at understanding data communication. 15 to 30 minutes a day. But the key to this is be consistent and create a routine, right? Just like going to the gym if I only work out two to three days a month, but I have killer days, that's like going like this, right? That's momentary greatness versus being good. Consistently. Don't have moments of greatness with AI where you spend a whole week studying it and then forget for three months. Instead, focus in on those incremental 15 to 30 minutes a day. Study, practice. I journal a lot, writing notes, writing my thoughts. You know what? One that my wife and I did to help our cognitive side is we bought adult coloring books. Like, meaning very detailed coloring pictures. You can get apps to do it. I don't recommend the apps. I want you to buy colored pencils. I have what I love, tattoos. And it's got a hundred different tattoos that are very detailed that I could just go in and color. Turn on a movie, turn on a show. And you're flexing your creativity muscles that quickly, right? I'm not telling you to go buy some Transformers coloring book, but buy the ones. If you just go into Amazon and say coloring books for adults to use, boom, then you can get a coloring book. There are so many things to do and. But it doesn't require a lot of time. Just like going and exercising. 30 minutes a day. That's great. 30 minutes a day of using your brain and exercising it.

I love that. Yeah, use your brain for 30 minutes a day. Also, the coloring book idea is really interesting. And I was thinking, I think my favorite book of yours is the one you wrote with Chandra Donaldson.

Oh, yeah. It's a children's book.

It's got Greek pictures, so.

Yeah, absolutely. And the artist did such excellent work. And. And for me, you're nailing it with the children's side of this. Children are the best data literacy detectives around. Do you know why? They're constantly curious, they're very creative, and they critically think, meaning they're trying to figure things out. We become adults, we see something on social media. Oh, it must be true. Or we get in routines at work. When was the last time we were like children? And I know that sounds funny. I'm all about trying to be a kid at Heart and having fun. And, hey, there's a topic I want to learn. Don't turn your brain off. Right. 1. And I don't know if I read this somewhere, but a mark of intelligence to me is you can have competing thoughts in your brain that contradict what you think is right, and you're comfortable with it. Right. And then you make decisions off it. It doesn't take much to work on your cognitive skills, but we're so distracted and our focus is so weak right now. I. Again, it's not just AI that does it. There's so many things that do it that just blocks 30 minutes a day. I'm not telling you. Get off social media. Totally. But instead of doom scrolling for 30 minutes, maybe pick up a new book.

Absolutely. That's wonderful advice is spend 30 minutes thinking, I like it. Pick up a new book, listen to it. Listen to it. Listen to an educational podcast. All right, so just to wrap up, I always want new people to learn from. So do you have anyone whose work that you're interested in? Like, who should I be following or researching?

Absolutely, two people. Sadie St. Lawrence is a friend of mine. I highly Recommend her on LinkedIn, her Instagram. She focuses in a targeted area. Well, in a lot of areas, but one area that I love that she focuses on is consciousness. With AI advancing the way it is, anyone who tells me, oh, it'll never achieve consciousness, couldn't they have said that about little humans 2 million years ago? Right. And so. And guess what conversation I had with AI once, Are we defining consciousness inappropriately or not inappropriately, but incorrectly? So Sadie St. Lawrence would be number one. Jepsen Taylor would be number two. His startup on AI is inventing new math. It is inventing algorithms that are more powerful than what humans can do. And I think that unleashes engineered intelligence in a very good way if we use it right. So Sadie St. Lawrence, Jebson Taylor. Two people to follow and listen to.

Wonderful. So actually, Sadie St. Lawrence was. She was a previous radar guest. So, yeah, people have to go back and check out the recordings on the Data Camp website. Nice, Jepsen. I don't know. So I'll definitely have to check out his book.

He lives not far from me, but no exaggeration, one of the smartest AI minds on the planet. And when you start to see what he's doing, you're like. And I love it because I think there's so many positive benefits that could come.

Wonderful. All right, I definitely want to check out. Okay, thank you so much for your time, Jordan.

Absolutely. Always good to see you, my friend. I hope you're great.

Likewise.

Wall.