Good morning. Good afternoon. Good evening. Good whatever time it is, wherever you are in the world, I’m so thrilled you are joining us.

My name is Malcolm Hawker. I am the head of data strategy with Profisee Software and your host for the CDO Matters podcast. I am absolutely tickled, that I am joined today by Jordan Morrow, prolific author of at least three books, and maybe there’s more that I don’t know about. But there’s there’s there’s be data analytical, be data driven, and be data literate, which with second press, which is I mean, that’s impressive. We’ll have to I’d I’d if you can share what that actually means. I know that’s impressive, though, because not a lot of books even go to a second to do share second press. So that’s fantastic.

You are a thought leader. You’re a you’re prolific on LinkedIn. You’re a LinkedIn top voice. You’re even what they call a data vendor.

We’ll we’ll talk about that maybe a little bit more, but but but when it comes to data and analytics, when it comes to thought leadership, when it comes to sharing insights and best practices, there there are few far fewer that are that are less prolific than you, Jordan. So thank you for taking the time to join us today.

No. You’re way too kind in that intro, but thank you so much for having me. And and I hope all listeners understand how prolific you are too. So they need to understand that too.

You’re you’re you’re extremely kind, which which I’ve come to learn seems to be part of your DNA, which was just another reason why I I I just needed to speak with you because because you you you’re kind, and you you you clearly, are supportive of a lot of people out there, but you’re supportive of me, and I and I thank you for that. What does it mean for a book to go to a second edition? What is what is what does that mean? Like, I mean, that’s that’s a big deal. Right?

Well and and it was I don’t know if I ever thought one of my books would go to a second edition. And so I was chatting with my publisher, and and with AI being what it is, it was like, well, do you write a book about AI? Do you update a previous book and add AI to it? And that’s the route we chose to go was, like, look, with AI becoming as prolific and and asymmetrical and where it’s going and everything, it’s just so big and hyped that it was like, why don’t we update the very first book around data literacy to to have AI be included in there? So we went through, looked at places within chapters where we could update, and we included a whole new chapter. And so, like, I didn’t realize the whole process because it has a whole new cover, like, code for it and all this, and it’ll launch later this year.

Yes.

Awesome. I I saw it on the Cogut page website, though. They’re already advertising it. So they’re they’re they’re pre advertising.

I’m excited to read it.

You are a top voice in data literacy and interested to hear what you have to say related to AI literacy. Previous guest, actually, Bill Schmarzo. Are you do you know Bill?

I do.

Okay. He he he is a top proponent of AI literacy as well. I think it it’s very interesting. As as I see like, going back a year, I’ve seen awareness and knowledge of AI grow over the last year as we would expect it to, but I think we’re still not there. But what makes AI a little different is is it can touch so many of us in so many different ways.

And it it’s you know, I I I’m not a fan of the word literacy because because I’m because I know I I I I fear that that sometimes that that could be construed the wrong way. But the idea that we need to know how this stuff works, this need we need to know how it impacts our lives. We need to know when we’re being influenced by it.

Yeah.

Right? Like, all these things to me just seem like table stakes for modern life. What what what do you think?

Oh, agreed. And and you know my feelings on literacy too. I don’t like when it’s misconstrued. I’m the guy who helped pioneer the field, but I tell everybody you’re already data literate.

Right? Can we just enhance your skills more? And I you’re exactly right. I think this idea of awareness, knowledge, and ongoing learning are are now table stakes.

They are must haves to a degree because we’re looking at this wave of technology, and that’s not my term. Right? Gustavo Suleiman, cofounder of DeepMind. He wrote the book, The Coming Wave.

The reality is this is touching everything. Right? In prior industrial revolutions, you you had, I think, what we would call more symmetrical, more to the point Yeah. Evolutions.

Now AI is like, I saw just the other day, and I don’t remember where it was, but an AI driven or AI, a part of a toothbrush or whatever. And I’m like, this is ridiculous at this point. Like Yeah. Anger driven toothbrush. I mean, come on. But it shows that everybody’s trying to touch it. Everybody’s trying to get a hold of it, utilize it, put it as a catchy phrase.

But the reality is where does our knowledge really start and end and and how do we utilize it? That’s when we think of that term AI literacy. To me, it’s the effective and ethical use of AI. And what I mean by effective is do you know how to prompt it?

Do you have a sound understanding of what it does? And do you know how to evaluate it? Right? Question it.

Not just prompt it, but question the results and then execute upon them. Because the reality is these tools are gonna be everywhere. Do we have the ability to implement them and utilize them effectively? Ninety to ninety nine percent of people don’t need to know how to code within a large language model or vector database and do what you don’t need to know that, but you have to have a better understanding of how it’s gonna impact things and where you can apply it in both your life and your career.

Yeah. It’s boy. You know, there’s a lot of work ahead of us in in in the field writ large. I just made a post on LinkedIn today asking what is AI governance because I’m not entirely sure we know.

And maybe that’s normal and maybe that’s okay because this is so new and it’s evolving and it’s moving fast. And but we do need to start figuring out what some of this stuff means and how to get our hands around it. I don’t have anywhere close to all the answers, of of course.

But what’s different this time around is that I think we could be influenced in in ways that are meaningful that we just don’t know about, and and that that’s particularly children. Right? Like Absolutely.

Yeah. I’m what what what do you think? What what would what would you what if a CDO asked you, Jordan, what how do I get started on governing AI? Right?

Acknowledging that that’s like a mountain for a hiker. Where where do where do you start the climb?

Oh my gosh. Well, I I’m I’m one of those that it’s kinda funny to say it this way, but if your BI isn’t doing well, why are you even talking about AI? Mhmm. And what I find super interesting about AI, I agree completely, it’s exciting to think of all the possibilities that we don’t even know where these models and everything will produce things so quickly because of their computational power that we can’t.

But do we have that literacy, obviously, to grasp it? Now going back to your question, what I find super interesting about AI right now is that I feel like we are going back to the true principles of data governance, data management, implementation, doing it correctly versus yeah. Like like, we were talking about prerecording and and all that. You you visited Gartner, and it feels like the same thing is being talked about over and over again.

But this time, we have to do it right because the this might be the one time where the hype is gonna live up to reality or reality will live up to the hype around AI. And so to harness this power, I forget which guest it was you had. I believe it was you who had a guest that there’s no need, I think you said it, to write your own large language model. I don’t know.

Go get it off the shelf and modify it. Yep. Do you know the data that is feeding it and the lineage of that data? And and so your data governance principles matter greatly.

How how is your governance around that matters? And I think that helps translate into AI governance. To your point, we don’t necessarily even know what that’s gonna mean from ethics, regulations, and all these things. But I find it super interesting that this time, it feels like we’re going backwards a little in the sense that we have got to get our house in order from a data perspective.

It doesn’t have to be perfect. You said this in another episode. It might have been in one. You’re not gonna have perfect data, so please don’t try.

Just test things out.

But you’ve got to be working and moving. And and there’s a term in, like, ultra marathons, at least a book maybe around it, like relentless forward progress.

Are you relentlessly learning, adapting the agility of it, your AI readiness episode? It’s about mindset so that as these disruptions continually happen, your data’s in a good spot, your culture’s in a good spot, and your people can apply and adopt these things appropriately.

Yeah. I think you you touched on something that I’ve actually seen unfolding over the last few major conferences that I’ve gone to, which is the idea that that that unsexy is sexy again.

I love it.

Right? Like like, data governance, MDM, like, basics and getting that right. And, well, phase two, we’ll forget about that.

Or or or or we’ll get to it at some point. But, what I’ve seen a lot recently is is, like, an awareness, and this is good because it start everything starts with awareness, which is, you know, yeah, we’ve gotta get the data house in order, and we need to have some sort of idea of a framework. And I know that that’s that’s a bit of a kind of a catchall, but we we need need to at least start building some muscle memory here around how to do governance. And if we can’t do it on a customer record sitting in a CRM, how will we ever expect to do it for everyone in our sales organization using a chatbot to make recommendations on how to sell to our customers?

Yeah. You’re spot on, Mike. It’s the old adage, garbage in, garbage out. And the and the reality of it is and and this is I love the word awareness.

We could even use that for part of the literacy side. Right? It’s are we aware of the necessity? And and this goes to one of the things that’s, I think, on your mind is this awareness goes to non data and AI professionals to c suites everywhere.

Yeah.

Do you really understand what this takes? Do you understand the investment that is necessary? Do you understand the buy in that is necessary?

Do you understand how to utilize these things appropriately?

I think that’s one reason these things have not succeeded is I think the autonomy and authority and the investment dollar has not been put out. We we buy tools and think it’s gonna solve it. And you can’t buy AI and think it’s gonna solve things. That’s not how this works.

Yeah. Agreed. So you had mentioned, a some feedback that I had coming out of the recent Gartner conference, and I made a post on LinkedIn, and I think you had commented on it. You may you kinda briefly touched on it. I made a comment that said that there’s this this elephant in the room.

And and what I meant by that was was that, you know, I was an analyst for three years. I’ve gone to a lot of a Gartner events. I was a Gartner client before I was an analyst.

And I’ve been going to a lot of these events, and they’re awesome, and the networking is great, and the people are clearly very, very smart, and they’re very passionate, and they they care, and there’s all those things.

But the elephant in the room is we keep saying a lot of the same things over and over and over again. I’m guilty of that. Right? I I I do that.

I’m the one talking about, hey. Business value. Business value. Business value. And I know that my clients and the people that I talk to aren’t tracking it.

So what’s the what’s what what is what is the delta there, Jordan? Is this is this a, like, you know, I don’t wanna eat my greens? Right? Like, I I I I just wanna eat the sweet stuff. I don’t wanna eat my greens. What’s what’s what’s the delta there between what we know we need to be doing and what we really are?

Well, in in so I’ve had a theory, and this is one that’s in my mind maybe just a month or two. That if you look at a business and you ask, let’s say, a marketing person, what does sales do? Well, they’ll have a pretty good idea. You ask HR, what does the c suite do or marketing do? They have a pretty good idea. You ask non data teams and professionals, what does data do?

Yeah.

They don’t necessarily have that great of an idea. Because I think it is this obscure area where it’s been obfuscated and and and had issues with it because of hype and all these different things, when I speak about it, it really boils down to that data and AI are here to support the business strategy.

And I think that companies think you have to oh, the enterprise isn’t ready for this and that. It’s like, I don’t wanna hear that. What I wanna hear is, what is your business trying to do? And when I know what your business is trying to do, how can data and AI support these things?

I think that simplifies things for a business because it’s not the that that you can’t boil the ocean. Data and AI is massive. So if we can pinpoint down certain areas of the business, that’s a data strategy. But they don’t think of it that way.

We have so many terms and hype and machine learning and this and that. I’m just like, can let’s just take a step back. And that I think is part of it is this, disconnect between what is data and AI really meant to do? Forget the tools, the technology, the sweet stuff.

Let’s look at the greens. Let’s look at these vegetables on your plate and figure out these make the system go. And when the system is going well, you have your cheat meal. Right?

You have a big cinnamon roll and some cookies and all of this. But the reality is it is we’re we’re coming right back to it. Are your fundamentals in place? Now there is one key thing to me that matters, and I am.

I’m in my fourth book right now, and I’m doing the inverse. The fourth book is around how to like, it’s business one zero one for data professionals. Right? They didn’t go to school for these things.

And there’s an article that came out on CNBC about and and you and I both, I think, share this sentiment, quit creating new c suite titles. But the CAIO, we you know it’s gonna be there in all of this. But without the right autonomy and authority, they use the word authority given to them, and without that person having good nontechnical skills, is it just another figurehead? Is it are we just gonna be back to where we were?

And I think those are some of the fundamental things, and and you are doing such an excellent job of helping people this way. It’s can you understand where data and AI fit in to help a business succeed?

So the the answer to the question, at least partial answer to to the question and and the question really was is is that we know best practices. We know things there that there are things as data leaders that we need to do in order to prolong our tenure, in order to drive value for the business, in order to do these things.

Part of that answer, couldn’t agree more, is a relentless focus on business success and less focus on technology.

Yeah.

Could not agree more. I do think, though, to a certain degree, some some of the organized organizational incentives here have have a bit of a perverting effect because we’re incentivized to deploy technology. That’s that’s often how we’re that’s often how we’re measured.

So so do you see any opportunity there to maybe revisit how we actually our individual performance gets measured?

Absolutely. And and I’m gonna use a different example of this. I had a guy reach out to me, I think just last week, and he wanted some advice. He was interviewing for a role, and we talked about, like, KPIs and metrics.

And I brought up Goodhart’s Law. And Goodhart’s Law states when a measure becomes a target, it ceases to be a good measure. And so a measurement can be the implementation and adoption of software. Don’t make it a target, though.

Right.

Target it’s the old adage of leading versus lagging indicators. Right? You you can’t make implementation, oh, we put in a new software.

Wonderful. Do not incentivize on that. What does the usage look like internally? Are people applying it to business problems?

And what was interesting is the approach I gave this gentleman was different, and he recognized it. It’s different than the way sales have done it. He texted me when? Yeah.

Two days ago, mate. He got the job. He got the offer. But it’s it’s gonna be he’s gonna have to adopt this if and go at it in a different way.

And I completely agree with you that if we’re making things targets that should not be targets, we’ve perverted the incentives around this, and the incentive should not be implementation of a tool or purchase of a tool or even training on a tool. The incentive should be the outcomes that the organization has that the tool is driving. And that’s your wagon indicator. The leading indicator, sure, are usage maybe in this and that.

But, yeah, there is a whole reframing of these things that can take and and we are at this time when it’s almost perfect if we think about it because AI is changing everything. Why don’t we use this as a time? And I maybe I shouldn’t say a perfect time, but at a good time. AI is changing everything.

Why don’t we then take a step back and change things ourselves? Think about it differently.

Disrupt before you are disrupted. Yes. Absolutely.

Disrupt your job, basically, before before it gets disrupted. Right? Like, revisit how you get measured.

Maybe have conversations about adding additional KPIs to your compensation package that actually are tied to business success. Walk out on the limb a little bit will be a little risky, and it will be a little scary to be walking on that limb. But I think that’s what I’m hearing you say. I’m hearing you what I’m hearing you also say is be relentlessly focused on the why, not the how.

Technology is a how. Technology is an enabler. The why is the business success is the business outcome. But but I also did hear you say, you know, hey.

Before you are acted upon, you should probably act.

Absolutely. And and it’s one of this is where when we when we go with my more probably pure definition of data literacy, which I know you understand in AI literacy, this awareness, this ongoing learning, especially when the AI can be taking some of the how away from us, then it’s gonna become incumbent upon us to understand it took the how away. It’s given me good results. Do I then know how to execute upon this for our three year plan, our five year plan, the mission and vision of the company?

I like what you just said. Why don’t you be proactive and act versus living in a reactive phase? And we’ve heard it probably throughout all of our speaking engagements or working with companies that how many times are data teams putting out fires versus lighting fires.

And Yeah. I think if you get these correct and and it’s a it’s a big task. It doesn’t happen overnight.

But if you can light fires versus putting out fires, that’s I’d much rather be camping around a nice warm campfire than have a wildfire outside me that I’m trying to put out.

So what you just said could roughly be labeled as maybe leadership.

Right? I mean and I know that’s a bit of a catchall, and it’s it’s a bit of a cop out, you know, to say, oh, well, it’s a lack of leadership. But everything that you just described, if you start putting it together, really is about data leadership, I I would argue. Doesn’t have to be data leadership.

It could be leadership of any. We could be saying this about supply chain right now. Right? And and stop reacting and and and be proactive and build models to understand when your supply chains are gonna run.

We we could be applying it to anything.

When when it when it comes to leaders, how much of a leader do you think is is made versus how much of a leader is born? I know that’s a little bit of a of of an out there, but it also does start to get into the the discussion about business versus technology and where should data people come from. And if you’re a nontechnical person, let’s let’s transition a little bit to to to leadership. What what do you see as the secret sauce to leadership, and is this something that you can train?

So I I don’t disagree one bit that it is leadership. You know, I have this scale in my mind. On one side is the desire of a company to use data and AI, and that’s usually weighed heavily. On the other side is the leadership, the buying and investment, and that usually is not as strong as this side.

So the scale’s out of whack. Now you bring up something such an interesting point to me, and that is, can you create leaders? I do think you can, but I I put a caveat to it that you have to allow them be to be creative in their authentic selves. Don’t change them into something they’re not.

You know, I think CEOs and and new CEOs sometimes are not their authentic selves because they go through training and you have these leadership events that teach you how to be a CEO and stuff like that. And I get there are some principles you need to learn. You’re running a company. I get it.

But don’t we all think, like, you’re a genuine person, Malcolm. It’s easy to chat with you. What about fake leaders? What about those that struggle?

And so for me, yes, there are leadership principles. Learning how to communicate better. Learning businesses in and out. If you’re, in a c suite or senior leadership of a company, you need to understand your industry, your market.

That’s all learning. But I don’t like fake leaders. I want you to be your genuine self. That to me is one of the keys that I’m gonna believe in.

That’s one of the keys to me that I’m gonna go after. And I think we both know data people who are themselves, and I could say they have success. But it’s when I see inauthenticity where I’m like, I can’t work with that. Right?

You can’t just hire a data leader and hope for success. That’s not gonna work. And so there’s there I do think you can train leaders. I’m I’m I wake up like, right sitting next to me is my journal.

I’ve got two books to read. I’ve got books everywhere. I believe learning progression and development is key. But there there’s a piece that don’t lose who you are through that.

That’s that might sound philosophical and all these things, but that’s the kind of leader I like is that authentic one.

Well, I I I like where this conversation is going because I I think that, you know, there’s a lot of conversations online about unreal expectations of CDOs.

Right? And and and and one of the smartest CDOs I’ve ever had a conversation with, Sol Rashidi, she said she she thinks that, you know, big part of this is that there’s really unreal expectations and and poorly defined expectations of CDOs.

And CDOs are often walking into kind of some some bear traps and and kind of I don’t wanna say doomed, but has the deck stacked against them to a certain degree. But to the point about authenticity and and, of course, I can’t speak for everybody, and this is a sweeping generalization. But in my experience, most of the data people that I know or people who are passionate about data, let’s just say that, like, who are authentically passionate about data, Like like me, like you, like others.

Like our friend Scott Taylor. Literally authentically passionate about data.

If you are, what I tend to see is that most of those folks who who I’ve known with any sort of depth or where I’ve had the opportunity to work with or partner with are really good operators.

Yes.

But they’re less strong when it comes to strategy and influence and selling.

Yeah.

Well Right? It’s yeah. You’re spot on here, Malcolm. Like, I’ve been asked to mentor kids in college that are going through or I’ve been asked questions either to mentor them or or ask questions from them, and they ask me what classes should I take.

And and I think they would have wanted me to tell them what data courses to take, and it’s like, no. Your your degree will do that just fine. Please go take leadership courses. Go take communications.

A sales class is perfect. I you’re spot on. Like, I am not a technical guy anymore. If you ask me to code something, it’s gonna be a struggle.

I’m going to generative AI left and right. Strategy is my specialty, and I don’t I don’t disagree one bit with you. And these are the areas where if you are the operator on a data sense and this is what you do, you’ve got to be cognizant of what your strengths are and what your weaknesses are. And for example, let’s say you’re not good at operations or strategy.

Do you part who do you network and partner with that is? And you’re working with them hand in hand to get this done. Sure. You should be developing and learning how to do strategy better on a business sense in the data side, etcetera.

But, you know, every I I don’t like the adage of, you know, work on your weaknesses.

Yeah. To a degree, but your strengths are your strengths. Know where your weaknesses are and hire the right people to fill that in. And I think that is tough.

That that’s that’s that’s such a great point. Building the right team around you, knowing where you, have gaps.

Previous guest in the podcast, Veronica Durgan, actually said these these exact same things, and she says, I know where I’m weak and that I go and try to fill those gaps with the right people and put them around me.

When I look at the CDO role, to me, where is the the most friction is the idea of being a change agent Yeah.

In an organization. Like, an agent of change. I’m gonna go in here, and I’m gonna change.

Because I think some of the if you’re really authentic, you actually can sell. So I I take back my previous comment about not being able to sell because if you really, really are authentic, to me, that rubs off and people wanna buy what you’re what you’re selling because people gravitate towards authenticity, I think. But the the hard part is is is that being, like, an agent of change and I need you, CDO, to go change the organization. That’s where I think we may have the hardest part be be because when you if that is your stated goal, right, go change people or go change a culture or, you know, like, wow. That seems really, really, really hard.

Yeah. But you’re you’re spot on, and and it’s so interesting. This is my world. Right? When you think about how I approach data literacy and stuff, you’ll hear me say the biggest roadblock to data and analytics success is the culture and people of the organization.

And CDOs, that’s not what they’ve been trained on the majority of them. Right. They haven’t been trained on this. And what’s so interesting to me is this whole area of change management has essentially, I don’t wanna say fully ignored in the data space, but without it, it’s not succeeding.

And so for me, it’s I it’s interesting. I started a new role February first twenty twenty four is basically like you are for Profisee on senior vice president and leading data and AI transformation at my company, AgileOne.

I’ve got three main pillars of this year, and guess what one of them is? Change management and building a good change management program for around data and AI, and it’s already translated into another area of the business, which is automation. What areas can we automate in the business? And so this is hard.

This is not what data professionals signed up for. But without being a good disruptive and I sometimes, I like to use the word evolve or evolution versus change because change sometimes has that negative connotation, and it’s hard to change. But you have that side that needs to be a part of it from the CDO perspective. Then when you flip to the other side of the c suite, I think a better understanding, giving some grace to CDOs, I think some c suite members have to have a better understanding of how data operates and what it takes to do this.

This is my scale. So a little bit of grace for CDOs is the c suite better have a better understanding. You can’t just hire a CDO or whatever title you wanna give them and think it’s gonna solve it. It won’t.

There’s more to it than that.

Totally agree. I think we may be onto something, though, around change management. And I know others have mentioned this and talked about this and talked about the importance of it, and I get that. But it’s interesting.

If you look at the one area where we arguably have struggled the most over the last fifteen, twenty years is governance. And to me, that is that is the essence of change management, right, which is I’m gonna establish a baseline. Here’s some policies and procedures. This is what we need to do, and then we need to manage change against it.

Right? You you spun on. Right? Creating the creating the starting point. Like, you’re here’s a set of policies that we need to follow.

That’s the easy knock knock part. Yeah. Easy. But the hard part is, okay. How do we manage this change to that?

That? Because our business is evolving. Our requirements are changing. The macro economy is changing. Everything’s changing, and we need to evolve that.

And how do we do that?

So that that I think I think you’re right. Now the question becomes, if you’re not that CDO, right, if you are maybe more of an operator, shovel in the ground, let’s get it done. Right? Maybe a little more technology inclined.

Do you could you hire a you? Could you hire a Scott Taylor? Could you hire and I’m just I’m spitballing her of of of common friends, and and there are other people out there with these skills.

Kind of more of of a spokesperson storyteller, charismatic voice of a data program, could you have that be next sitting next to you as a CDO?

Absolutely. Because I might not be able to code, but I’m gonna have a better understanding of the technical than other senior leaders who are not, you know, our role. Right? Absolutely. But here’s the catch is that requires that CDO and leader to go out and network.

And you have you, you have me, you have Scott Taylor, k so many people who are so open on who they are on LinkedIn.

And go network with them. If you don’t know how to do it, all you have to do is say, Malcolm, I listened to the episode. Can you give me five minutes of your time to figure this out? Yeah.

I think you and I are both willing to say, you got it, or I’ll do an episode on it, or we could do it this way. There’s another thing out there. Generative AI is pretty good. Right?

And if you’re trying to figure out, oh my gosh. How do I hire a change agent or an evangelist or this or that or develop these things? What do I do, Chat GPT?

And then evaluate the response and then counsel off with people. So these are the things that they are not the technical. They’re not the the these prowess is like that. These are the the softer skills.

And it’s not where, per se, people have gone. And what’s interesting about change management, it rolls out. And let’s say you’re you’re building a whole new implementation of governance and management at your company. You better manage that change with the data professionals.

Because what if the data professionals have loved the old software and they don’t wanna move on? I’ve been in that situation, not managed very well. Right? Then you go on the flip side.

What if you’re the data professionals rolling out these amazing analytics? You better manage the change on the other side. And so it’s one of those that I love your question. If that’s not where your comfort is, that’s okay.

Talk to people and figure out, create a head count that says, this is your CDO. I’m hiring my lead evangelist, who their job is to know the know and talk to everybody. Okay. Great. Let’s do it.

I I I I love it. I think I think there’s something there. I think there’s something for CDOs who may acknowledge that they may not be that kind of that outgoing evangelist type. Yep. You know, we’ve talked a long time about data storytelling. We’ve we’ve we’ve talked a lot about the need to network and to interface and to understand the business and to be in front of the business.

There’s another aspect here around just plain old marketing.

Yeah. I would argue that we just don’t know how to do and just kinda just think of somebody else’s job. But you could I think you could even make a case. And this is interestingly where in the past where I think there actually is a tie back to literacy because I view literacy as as a product person, I view literacy very much as a go to market Absolutely.

Exercise. Right? Which is is is training and and awareness and understanding and making sure that users are comfortable with what you provided, if they got questions about what you’ve provided and the support, basically, what you’ve provided. But a part of that is also just pure brass knuckles marketing around what are you gonna get?

Why should you care? What are the benefits? How do I use this thing? And then we’re right back to literacy.

Well and what’s so interesting is there’s a term out there, thought leadership. And I think that we need to view thought leadership as both an external thing, and you can create it internal at your company.

Yep.

You can market internally. You can talk about what we’re doing. You can talk about all this. Get that why out there.

Right? Like, there I have no secrets that I am where I am today, Malcolm, because I can speak. I can public speak, and it I’ve traveled the world doing it. I would not be as successful without my ability to do that.

But what people might not understand is I’m an introvert. At the end of the day, I wanna be back in my hotel room. I want it to be quiet.

I remember when data literacy first was going, this might be back in twenty seventeen, and I had a coworker that basically was, no. You have to be out on LinkedIn. And it it’s like, really? Sometimes I don’t wanna write, but I know I need to. And so being able to craft messages to your point, the often people gravitate to authenticity.

People gravitate to storytelling.

These are the things that can make data come to life. How many people went to school for degrees in data and statistics? Right? I wish more would, but that’s not what they did.

So can I tell stories? Can I do this? I had an editor at Kogan page, and I know, you and I need to talk offline about your book proposal and all that, but I had an editor there who was basically, like, remember don’t something to the effect. I don’t remember the exact conversation.

Don’t use too many personal examples. And it’s like, nope. Not gonna happen. I I can use some business examples, but people relate to the personal.

That’s gonna draw them in. And so this evangelism, being good salespeople, if it’s not you, hire someone who can talk. Creating marketing materials internally at your company so that the change and evolution can happen because people get it. A joke I’ve used in the past is, do you like getting an email that says you have mandatory training?

Not very many people do. So why in the world, if I’m gonna run data literacy programs, would I ever say you have mandatory training? Versus what if we do an hour long webinar around what this topic actually is? What if we train the leaders to understand the program we’re gonna put in there? What if you train mid level leaders to understand how to deploy a program?

That is a different approach than here’s your new software. Go and use it.

As we talk, we seem to be slowly building out a new definition of a next gen data and analytics organization. Right? So what we just talked about was some sort of kind of lead storyteller, lead evangelist.

I would argue, as I as I have many times in the past, I would argue that there is a role for what I would just loosely call a value engineer, somebody who can actually start articulating real value here.

I think there is a role for a marketing person, like, not who’s not the lead storytelling, but who’s actually, like, do doing marketing or even maybe even internal training. Right? Who could be responsible for training programs and training development, user development, which would align to more of a traditional literacy role. So when you look at it, the these roles start to look a little I don’t think a lot of CDO organizations have these functions today.

No. Right? They’ve got architects and analysts.

This is the next gen of how to do this because some of those roles that the traditional CDO suite would hold, AI could obliterate.

And that’s an interesting thought process to be thinking of. So and I shouldn’t say obliterate. I don’t like that. I I like the idea of simplify.

Let’s put it that way. Right? Where it does the engineering more for you than needing twenty engineers. That is a different world that we’re gonna approach.

So That’s interesting.

Yeah. So when we think about the old way that we would set these data teams up, more of the soft skills, this I like how you describe it. This is a whole new thought leadership thing, Malcolm, that you’ve created, and that is your next gen data office. What does it look like?

And what are your most important roles? In the yes. You still need architects, engineers to do governance and management. Might not need as many, but they’re doing their job.

But you need people on the the the army on their feet going out and helping everybody understand what’s going on and why. The new tools, the it could talking about the marketing or the evangelist, Chat GPT went to the world November twenty twenty two. Imagine if every CDO office had someone to say, this is what generative AI is and did a training in January twenty twenty three, less than two months after it came out, to just be like, let’s be aware of what this is. We’re still learning, but let me tell you what I know.

What a wonderful thing to have.

It’s it’s an interesting pivot here because there’s still going to be a role and roles, plural, to manage and manipulate the data supply chain. Let’s let’s call it the the the supply chain. Right? The raw materials, the creation of the stuff that lives in the warehouse Yeah.

Like, our our digital shelf as it were. There would always be roles there, but that’s where you’re talking about there’s gonna be a lot of augmentation there. There’s got there’s gonna be a lot of augmentation Yeah. There.

But what you’re suggesting is a pivot away from creation of and management and optimization of the supply chain towards more of a pivot towards how that data would be used and how it would be digested and how it would drive value. So take a classic analyst. An analyst in the past would understand the data in the fields and, you know, be doing all the ERDs and and all of that stuff. But the analyst of the future could instead pivot and say, okay. Here’s how to best use this.

Yep.

Here’s how you’re gonna get the best value out of this.

I loved your value engineer. Right? Because essentially what a value engineer is was my thoughts around data and AI strategy is there just to support the business. And that value engineer, and call it a value analyst, is now saying, look, ninety to ninety nine percent of you who are not data professionals, this is how we use it and apply it to the business.

And we know that things aren’t perfect in data and analytics, so there’s the iterative mindset. I love the phrase learn fast. Like, Nelson Mandela is one of my heroes. I don’t know how many books I have behind me from him, but he had a phrase that went something like this, and I might mess it up.

I never lose. I win or I learn. I either win or learn. Something like that.

I would love to see a world in data where we’re not rewarding, talk about incentives, on the outcome of the data project. We are incentivizing on the learnings that come from the data project because it doesn’t always go the way that we are thinking. Now imagine you have a data suite that says, run your analysis, make the decision, and, obviously, we care about the outcome. But what I care most about is when we start to analyze the outcome, what learnings did we just get that we now apply back to our off the shelf LLM, to our own AI, to our analytics world?

We have our evangelists going around the company. We have our marketing selling it out. You have created a data data ecosystem that hopefully, truly at that point, is augmenting business success.

Yep.

I love it. So I think the balancing act for any CDL that is listening to this and is is maybe going, That’s interesting. Okay. That’s that’s cool.

The the balancing act to me is how do you and and this is rhetorical, and maybe maybe we talk about it. Probably not gonna answer all the questions now.

But how do you focus on foundations? We recognize that foundations are important. Right? K. We recognize these things are important.

You gotta get your house in order. You’ve gotta have a data governance framework. You’ve gotta understand what MDM is and data quality is and all this good stuff, and you gotta do that.

But how do we, as leaders, make sure that we’re not investing and focusing on a foundation that will not scale into the future, that will not represent that that next gen organization and the next gen business needs? How do we make sure that we architect a cell phone instead of a landline?

Oh my gosh. If you and I could answer this right now, we would invest, retire tomorrow. You know what I mean?

Right. Yes.

But one of the things that I would say is every CDO and data professional should be building into their schedule at some point, whether it’s once a month or once a quarter, what you call an iterative period. And what I mean by that is maybe you schedule the last Friday of every month to evaluate your work. Does it still align to the business needs, and what are the industry trend industry trends? What are they doing? Are we aligned? Still good? Okay.

This periodic evaluation of your work is necessary because I think one thing that happens in the data space, and and shout out to Brent Dykes for writing about this once, is we do a set it and forget it. We build something and we forget it. Three years later, we’re still doing it. We at this point in our in our economy right now, three years later might be too late. If we’re evaluating things once a month, and this could be another position in their data work evaluator. All they do on a regular basis is they’re studying the industry and the trends and evaluating our work. They’re a right hand person to the CDO.

But I think that’s so critical as I I think you said it really well in the AI Readiness episode where just get started. Please just get started. Don’t try and boil the ocean, but make sure you’re evaluating it as you go so that you’re pivoting when disruptions hit. Your mindset is we pivot.

So so you just touched on something. I’m I’m paraphrasing you now. But maybe one of the answers to my question, and you touched on this, and this has been touched on many things. Zomak even even touches on this in in the data mesh, which is the idea of of measure the usage of your products.

Like, simple stuff. Yeah. Right? Can you can you tell me how many reports are actually used and how many are not used?

And then and review that weekly, monthly, quarterly. Who who knows? Right? But but and then take action on it.

If you built, like, fifteen hundred dashboards and none of them are getting used, well, then that says something.

What a waste of time. Right.

But but but it doesn’t that’s not very trans transformational, but it does represent more of a slow and a baby step towards a value driven way of looking at the world and a different way of being incentivized.

Right? Yes. Instead of being incentivized to simply publish the dashboard, you’d actually be incentivized based on how many people used it. The next step beyond that yeah. Go ahead.

I I was just gonna say this touches upon one of your frustrations with data literacy.

I don’t want data people to blame the end user if it’s not being used. Right? You don’t wanna say, well, their lack of data literacy I think that’s one of your frustrations with it is lack of data literacy makes it so they’re not using it. What if it’s a bad product?

And I don’t want I for me, I don’t look at I I’m pretty positive as a human being. Right? And and I don’t wanna look at it and say that either of those are the thing. What if we just say, oops.

We missed it. We’re gonna learn fast. I didn’t win. I’m gonna learn. So I’m gonna study it out.

I’m gonna schedule two hours of my day tomorrow to study that report. I’m gonna ask some constituents, why aren’t you using this? What if they’re just too busy and you built a wonderful product and they’re just busy right now? So you don’t necessarily have to kill it, but maybe you don’t use it for three months.

This is that train that is moving consistently for success.

Well, it’s what you just touched on, another way of saying that is you you could argue that that is more of a product driven approach because that’s what a product manager would do. Here’s another person to include on your next gen gen data and analytics org chart is a product manager because that’s what a product manager would do. Now could you have this be an analyst? Could you have this be, like, you know, classic data and analytics kind of staff?

Yes. Of course. But a product manager would ask those questions. Hey. I noticed you weren’t using the dashboard anymore.

What changed? Oh, well, it was good when it launched, but we we introduced a new bit metric or a new supplier. Something changed, and the old dashboard isn’t, and I just didn’t have time to come talk to you about changing it. Who who knows?

Right?

But product management. Right? Life cycle management. Sunset the thing if it’s not working, if it’s not being used. Move, adapt. And, yes, that is all about a product, and that is all about being in tune with what your customers need.

And I would argue literacy is absolutely critically a part of that.

My my what you touched on about about about my concerns about literacy was was when I was an analyst, what I would hear customers, my clients my clients do is is that they wouldn’t do all those other things. Right? They wouldn’t be having regular interactions with their clients. They wouldn’t understand the usage of the dashboards. They wouldn’t have a clear definition of the strategy. All the it wouldn’t do all these other things, but they’d be spending millions on literacy.

And my point was, hey.

Right? If you if you’re only rowing with one or, you’re just gonna go in a circle. You need all you need both ors, metaphor.

Oh, you well, you’re you’re spot on. Like, anyone who doesn’t think and I’m I’m my nickname, I’m not sure I’m happy with. Right? The godfather of this space.

And the really reality of it is, don’t pipe things up. It’s a buzzword. If if you think data literacy is gonna solve all your problems, good luck to you. If you think data mesh like, I’m so with you on this, Malcolm.

It’s it’s I call it a jigsaw puzzle, and there are different pieces of the puzzle that need to be put in place. Now, what’s really cool about our day and age is we’re we’re talking about the next gen data team. Right?

Yeah.

The puzzle is not only different puzzle pieces, those puzzle pieces can morph and shift and do these different things and create a whole new picture.

But make sure we’re putting the right puzzle pieces together, not just putting one piece on the wall and saying, there it is. Because when you forget all the other pieces, it’s an incomplete picture.

Yep. And that that speaks, I think, to the general mindset of, of what we need CDOs to be in the future. Right? We I I’m saying we have of course, not all of us, but I know that I, individually, in the past, have been kind of prone to what I would argue is a little bit more deterministic thinking. Like, all or none thinking, like, it is or it is, and it’s high quality or it’s crap quality or or or whatnot.

We’re moving rapidly towards a world of of probability and not determinism.

Right, where everything exists on a scale, even truth. I’m an MDM guy, and for years and years and years, I was talking about single version of the truth, when in reality, there’s a lot of versions of truth. What’s true to a marketer may be different than what’s true to somebody in legal.

And I think that’s a part of where we need to get to here as well, which is a a slightly different way of of thinking. Right? Because in the world of AI, that whole thing is about probabilities.

Yes. Well and our minds aren’t designed very well to think probabilistically.

Right? I would probably argue that our minds are better at deterministic, like, you do this, this is the outcome versus you do this. Well, here’s seven possible outcomes, and how do we adapt this? Now I’m as nerdy.

I love that stuff, but I also understand that the majority of people aren’t it it’s like, if I eat this, I do you know? And so to your point, that mindset is huge, and it’s this ability to be continually evolving or changing in in light of a changing world. And and I think it’s exciting. I think you develop the right mindset and skills in this whole I’m my mind is probably gonna be swimming on it today, this next gen data team.

Because I think there’s something to that on who the right people are to lead out a data team to make sure there’s success from a data perspective.

Yeah. I I I think there’s a lot of reasons why we’re prone to deterministic ways of thinking. Maybe it’s hardwired into us. Is the tiger chasing us, or is the tiger not changing chasing us?

It’s convenient. It’s galvanizing. It allows us to go all in on one thing, and we know as humans, we’re not that good at multitasking. We’re certainly not that good at no nuance.

Right? Because nuance requires a lot of energy, I would argue. It requires a lot of energy, whereas deterministic ways of thinking does not. It just is or it isn’t, and I know, okay.

Well, then I can turn my brain off. I just go that direction. I turn my brain off. So many interesting things here.

I I Jordan, I could keep talking for hours. Love the conversation. Love the back and forth.

When are gonna when are people gonna see you next on a public stage? Is that data universe?

It is. You and I together there in New York. It’s gonna be awesome to meet you in person. Early May, I’ll be I’ll be in New Orleans. AgileOne has their customer forum. Look forward to that. And then still in May, I’ll be at in New York, hopefully.

And well, hopefully or hopefully not. Depends on my travel schedule. I like being home with the fam. I will say one of my interesting events coming up is PBS, the television station. I’ll be speaking at their annual meeting, in Las Vegas on my birthday, May thirteenth. So gonna be down there for my birthday for it.

Alright. You’re only two days away from my mother’s birthday. So, yeah. There you go. Well, listen.

Again, thank you so much. Such great insight, such great authenticity, such great passion. Yeah. I think we’re on to something here.

We’ll we’ll collaborate on the next gen next gen data and analytics organization. There’s something very interesting here. I hope your brain swims on that for the rest of the day because mine certainly will be. To all of our listeners, please take a moment to subscribe if you haven’t already.

I would love that. It would let me know that we’re on the right track when it comes to creating the content that you need to be a better data leader and a better CDO. Thanks for everybody for tuning in. Please look forward to another episode of CDO matters sometime in the very near future.

Thank you so much, Jordan.

Thank you so much, Malcolm.

Alright. Thanks, everybody. We’ll see you soon. Bye bye.