CDO Matters Ep. 27 | Keys to CDO Success with Renée B. Lahti

June 29, 2023

Episode Overview:

Wouldn’t it be great to have a playbook for how to be an effective data leader?

In this episode of CDO Matters, Malcolm has an in-depth conversation with an extremely successful IT and data leader, Renée B. Lahti. With CIO experience spanning multiple industries for some of the best-known brands on the planet — including SC Johnson and Symantec — Renee’s depth of experience shines in this conversation about the current state of the role of CDO, including Renee’s insights on the key factors of CDO success.

Failing fast, redefining value, hiring for diversity and being more agile: These are just some of the many fascinating topics discussed in this episode, which could be considered a playbook for any data leader looking to emulate the success of an accomplished data leader.

Episode Links & Resources:

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Good morning, afternoon, or evening, everybody.

I’m Malcolm Hocker, your host for the CDO Matters podcast.

I am honored to be joined today by Renee Lotti. Renee, hi.

Hi. Aloha.

Aloha. Yes. Renee is, lucky enough to live on the big island of of of Hawaii, with her feral chickens and and dogs and and everything else that you get when you live in that wonderful, amazing, idyllic, paradise like place.

I met Renee we met online, which I I guess is increasingly common these days.

I was struck by a comment that Renee made to one of my many LinkedIn posts.

We were we were bantering about, AI and how AI is gonna take over the world. And and and Renee says something that really, really struck with me, which which was, I think you said it’s it’s actually tagline in your in your LinkedIn post. You said, AI will help humans be more human.

And I I was just I was like, oh, yes. Yes. Yes. Because we’re gonna have to be more creative, and we’re gonna have to figure out more innovative and novel ways to solve difficult problems and and be more human.

But that that that’s that’s kind of really how we met. I’ll I’ll intro Renee a little bit because, her background is truly, remarkable. It’s spectacular. Renee’s been the CIO of SC Johnson, a little company called Symantec, another company called, Hitachi Vantara.

When I was doing a little research, I had known Hitachi as more of a services company, but they’re also an infrastructure company and has almost ten percent of the kind of the overall kind of data infrastructure and storage market and is out there competing with the with the Amazons and EMCs of the world and and whatnot. So to make a long story short, Renee has been a CIO for a long time with some pretty big companies and knows the world of the CDO. Although you’ve never had that title yourself. Right?

No. And I think it’s the early days before all those titles get sorted out. The I and CIO, sometimes it’s innovation, sometimes it’s information. I think we have an identity crisis or at least a role and responsibility crisis among those those particular I call them suite. CIO, CDO, CAO were yeah. It’s a it’s a little bit of a struggle, but, no, I’ve never held the CDO title per se.

Did you have any that worked for you?

I did, but it was, you know, early days, you know, not as far back as the dinosaurs roaming the earth, but close. And I had head of analytics teams. I had had dotted line, hard line, and then some of the responsibilities actually fell under a CIO’s role in the early days. So it was Right. It was before it all became labeled as it is today, which I still don’t think is completely crystal clear for everybody.

Well, why isn’t it?

I think that it’s an identity crisis because of really understanding or lack of understanding of context. When people say, I’m applying for a CDO role or a CAO role or a CIO role today, especially today, Alpro CIOs in there too, you better understand who you report to. You better understand how the role is defined as success. Some places, it’s just cleanse the data and get the infrastructure working and let people work remotely all over the world. Or it’s, oh, no. We have a very discreet business problem, and we need AI and ML to do x y z, or others are we want a culture of data scientists all over the company. It’s very disparate, and I think because of that, I think the role is actually set up to be to fail or not be as successful as it could be.

And just the fact that we’re saying CAO, CDO, CAO, CIO, and we’re CDIO.

We’re all in the same bucket, It’s, yeah, it’s very it’s not as, cut and dry. The CFO, we’ve known what that acronym’s meant since the dawn of time.

CHRO, chief revenue officer, COO, CEO.

That those are all pretty tried and true KPIs to measure their success. I’d say the the suite of these things that we just are talking about, the KPIs change, context changes, tech technology reinvents itself every twenty twelve months now or so or faster, which means these roles have different ways of measuring success and probably have a very schizophrenic, context by which they’re trying to do their jobs.

So one of the things that certainly unites us is our desire to help these people succeed and CDOs, CDAOs.


Given what you just said, do do you see there being kind of an important role for people who are evaluating these positions to can just step back and and redefine the role or push for I mean, it goes without saying that you should if if you’re being hired into a c level role or even a SVP role or VP role, that you should push to have clarity on what your deliverables are. But but how do we get to a point where the the role is better defined? Because it it seems like it’s a recurring theme. I’m hearing a lot of that, research that that, people that you and I both read. Right?

Randy Dean, Tom Davenport have have printed they said that, you know, role ambiguity is part of the problem. So there’s agreement there. How do we get around that?

Well, I think there’s a couple. It’s, you know, it’s it’s it takes two to to get a the right person in the right role to help the the entity be successful and define success in in the the best way possible. And I think one thing is, yes, let’s get to the leaders who are writing these roles, or the the agency or whoever to better understand the role. But I think that means you gotta take even a bigger step back because it means do you whoever’s posting this role thinks they need this role.

What real discreet business problem are they trying to solve?

And that question, as simple as it is, is super hard to get a a a a discreet answer from because the CDO role just is supposed to cleanse the data, bring it together, and then money will fall out of this hyperconverged storage device that the CIOs help them set up in their own data center or up in the cloud. It just it’s so, it’s so nebulous. And now we put JEPT on top of it. It’s like, now they want this other thing too.

I would say there’s a couple pieces. One, the CDO needs to recognize they need to ask some tough questions through that process and suggest. Well, let me suggest maybe how this role might start out and could evolve. So there’s a little more of a proactive.

Let me tell you a little bit, because it’s a nonlinear role. If you think about design thinking and all of the things you know, it’s a non linear. You rapidly explore. You fast fail.

Does the culture of this company have a patience at the c suite level to allow failures at that level? We say it. We’ve said it for years, but are you walking the talk on that? I think the other piece is really challenging is, there’s some really what used to be the soft skills.

They call them soft skills, but I call them leadership skills.

These individuals incumbent upon the CDO and and the like have to understand that their jobs involve organizational change management.

They have to understand that there’s pieces of this for success. And, again, I think the the research even says that.

You have to be an embracer of moving the needle on data literacy, a data and AI culture.

And if you look at what that is, that isn’t showing up every day and doing. It’s inspiring.

It’s talking over the back fence, if you will, to your peers and creating buzz. It’s in empowering your teams.

Those aren’t necessarily skills you’re gonna see written in a job description, but you have to believe that or have to hope or you have to confirm that those are those are things that the entity that’s hiring you understands, and that’s how success is gonna be measured.

So do you think a part of it here is you know, if I look across the rest of the c suite, I I think you can make some logical conclusions about what highly definitive kind of nonsquishy, measurable KPIs would naturally be. Right? If you’re the chief product officer, it’s better products. Right? It’s more rev if you’re the chief revenue officer, obviously, it’s more revenue. If you’re chief operations, it’s, you know, making sure that the factory runs efficiently.

When it comes to the chief data officer, you you used a couple of different examples, data literacy and a and a data driven culture.

And in my conversations with CDO while I was at Gartner, those were those were big ones. Right? Digital transformation was another one. Right?

I’m I’ve been I’ve been given a digital transformation mandate. But then when you push on that, it it’s it’s like the balloon half filled with water. Right? Where where the water just squeezes out the top and you really can’t pin down what that actually means or how to even measure it.

So so there’s the soft skills couldn’t couldn’t agree more, of course. But but I’m wondering how much of this is a problem of these these expected deliverables that are really, really hard to measure.

Well, I think it goes back to, and I did I think I said this signed a colleague the other day that I heard the word value. And, again, value immediately mentions someone holding a bag of money. Right? That’s just kinda the brain that we’re hardwired.

Well, you know but if you think about CDOs, you think about generative AI, you think about the chat g p mania that’s out there, value has you gotta step it back, and we’ve gotta redefine value. And wherever that comes from, and I think it’s this, you know, the executive committees of companies or the CEO or somebody or the board if it needs to be, value has to be redefined and as well as the KPIs of how we measure it.

So and, again, that’s that will give the platform for those CDOs to to do what you kinda says. Oh my god. All these these these variables or how do you measure it squishy. Don’t be squishy.

You know, back to the old adage, measure what matters.

So when we talk about value now in the world of AI and machine learning and the chat GPTs of the world, we’re talking about environmental impact value.

Mhmm. We’re talking about social impact, or we’re talking about, ethical and, impact. And the reason I say that is I foresee very shortly in the world of companies with boards that risk subcommittee of an audit committee of a board, one day is gonna say, so tell me about this algorithm that you use to release this product to market, and what unintended consequences had you planned for, mister or miss c CDO?

Now that’s a very technical conversation, and I’m not sure it would come out exactly like that. But how come we miss this thing that has environmental or social or human rights impacts we didn’t know about?

That’s a value conversation. It’s an economic value, not a financial, I’m holding a bag of money conversation.

Okay. Fascinating.

I I I tend to agree, but I’m not sure we’re prepared to answer many of those questions, if if any of them.

Right? Like, societal value as a whole. I I had on a previous guest a woman named doctor Cheryl Flink where where she was talking about kind of human centered design and where where the value to society should be how we determine, or how we least kind of fuel the decision making process at organizations. Some of the things that you just tossed out, sustainability.

Right? I mean, that could be a that DEI could be a part of this. Sustainability could be a part of this. There’s so many things that could be a part. But you were yeah. Okay. Here’s one.

I was I was reading some some research recently that that said anywhere up to this is your previous business. Anywhere up to seventy percent of data in data centers is dark, meaning it’s just sitting there collecting dust.


And I also saw recent research that suggested that the data center industry produces more greenhouse gases than the airline industry.

So let’s assume that those are off by a factor of two. Even if they’re off by a factor of two, those those things sound kind of bad.

Right? Like and we got a data hoarding problem.

But what you just said what you just said is you could foresee a world where the board says, hey. Their The New York Times has published something about us and how we’re killing the environment because of all of our because of our data footprint.

Did you think about that CDO?

And I guarantee the answer right now is no. I didn’t.

So Well, and in fairness, the CDO will need to be locked arms as best buddies with the CIO because they may or may not be always fully in control, but those two are joined at the hip on that.

But it’s a valid question. I agree.

Here here would be my answer to that one. Role play with me for a second.


In the world of AI, granularity matters, not volume. So if I was a CDO or coaching a CDO or coaching someone to coach a CDO, I’d say, look. Granularity, not volume matters, having learned that the hard way myself. Everyone wants to put the world’s hoarding of data, shadow IT data all in one place and then go touch it and then throw magic fairy dust on it and becomes valuable.

That’s not necessarily true, especially now. Example being, do you want a hundred thousand pictures to throw into an AI algorithm that are vague and not very discernible, or do you want a hundred really sharp images where you can see the background, the foreground at the pixel level to give to that same algorithm if the algorithm is meant to identify objects and images?

You want the granularity. You don’t want the volume.

So my CDO, I’d say, look. Go tell them how you can save money, reduce your storage, or at least take it down a bunch of tiers, and have a conversation about and in the future, it’s granularity, not volume, so stop asking me to and the CIO, to hoard all this data for thirty years. We don’t need it all.

What I think And here’s your here’s your carbon footprint calculation going down by x.

Well, I I firmly believe that board boards will ask CIOs for sustainability targets very soon very soon. Very, very soon.

Anyway, what what I think I just heard you say, and I and if and if true, I completely agree. Like, so this I this idea of granularity, granularity to me would be a function of knowing what your desired outcomes were.

Yeah. Okay. So you’re nodding. You you you agree.

This was the number one recurring theme of my conversations with CDO when I was at Gartner, which was I’m having a hard time getting stakeholder engagement. I’m having a hard time getting funding. I’m having a hard time building up my road map or prioritizing my road map. And and the list of of of of knock on effects here is long, but the common theme of all that was not knowing what the expected outcomes were supposed to be and not having a specific KPI that you’re going after.

One of the first things that you said today as part of our conversation as as a part of the kind of the recruiting process is to be razor focused on what you want that person to do, what you want out of the role. So this is this kind of the second time we’ve we’ve come back to this idea of knowing exactly what you’re delivering and be focused on on on outcomes. But, Renee, I can tell you, so few CDOs actually do that. And I’m I’m trying to figure out why.

Like, why can’t they build business cases? Why can’t they tell you how they’re driving business value?

Well, I think part you know, and in fairness, I think I mean, I think it was, Davenport and Bean wrote the article. I can’t remember. But they talk about the seven personas of the CDO. Yeah.

I mean, it’s schizophrenic. It it goes from everything from being a a governor of the data that no one can touch unless they ask permission to it. So almost like a just a, you know, just say no and then I I grant you access to, you know, the strategic thinkers. It’s this whole spectrum, and you can’t do all of that.

That’s setting somebody up for for failure. So you may you may not know that because you are being asked to do all of it. I think it comes back to just saying, what first business problem? Maybe it’s all true.

Maybe that’s a road map of a journey. But, clearly, if someone said, I need a CDO, something, somewhere was there was an immediate business problem that you gotta double click, triple click down to find that first thing that said I need this role and why. And it may be the why might be, well, but that’s not really the what this role does. But let’s talk about how we fix that, and then let’s move on to these other things.

I think the other piece is CDOs need to kinda get engaged in that value data value engineering. Like, you have to actually push a bunch of stakeholders. You harness them in a room that are disparate across customer, employee, executives, the frontline workers, and say, let’s talk about what data means to us as a company, and let’s get very discreet. We can have ten of these, ten use cases, twenty use cases.

The problem is usually not enough that we don’t have enough use cases is having too many and which one to pick. And you gotta pick one, and you gotta double down on it. And that comes back to that granularity. You can’t have granularity in a conversation if you’re trying to solve twenty five use cases at the same time.

Be granular.

Make that’s get that success, and I guarantee that success will be faster than you think if you laser focus on it. Harvest that and the diversity of who else at your company and in your own team, empower them. That becomes that culture change. So indirectly, you’re creating change agents. You’re creating change champions.

This is how we do it going forward, and then you can start tackling one at a time. But the granularity component is not just about the data. It’s how you solve those problems.

Sounds very MVP ish to me. Minimum viable product ish.

It sounds a little agile ish to me.

It is.

Okay. Alright.

It is.

You just you just described a role that I I make a lot of posts on LinkedIn about here’s what here’s how you should build out your data and analytics organization. Here’s some of the roles that you should be thinking about. Here’s where you should be looking for or a or b.

And you just said something that I haven’t included in any of those recommendations, but I think I should.

You you mentioned the value engineer.

And I’ve actually worked with people who are value engineers, who who call themselves value engineers, and they’re brilliant and they’re amazing. Often, they are sitting in FBA, FP and A type roles, financial planning and analysis type roles. And maybe they’re often in product type roles. But in the world of the CDO, that could be an important right hand person.

Like like, literally a direct report to a CDO who was there helping maybe as part of an FP and A process, but helping the CDO figure out what is the value of a, b, or c.

I I’m I’m having a little bit of an moment here because that would be brilliant to have that person be be be right there next to a CDO helping make those decisions.

And and you mentioned FP and A product, and there are value engineers having worked for, you know again, Hitachi is more than just inventory. It’s a a at the time I was there, it had nine hundred subsidiaries. So we had IoT. We had manufacturing.

And so we had value engineers and product and FP and A of all walks of life. Imagine the KPIs, the interesting conversations. Wow. What’s what’s your KPI for value?

How do you measure it? And it’s usually, you know, p times q. It’s either volume of something or the probability of something. So putting that value engineer under a CDO and and understanding that value actually may be defined differently as a result.

The others aren’t wrong. You just need another dimension now is absolutely the right way to go with this. And the exercise that that value engineer should be involved in is bringing all of those diverse right. Diversity solves problems best.

Hands down. Bring all those diverse opinions to the table to figure out what is it we’re trying to do with our data. Right? One’s a product person, another person’s a profitability or stock for the shareholders or whatever.

What’s the CDO trying to do? They’re trying to reuse that data, that that resource that never expires.

It’s a renewable resource. You can use it over and over and over again. It’s not like it’s gonna go away if we don’t do this particular use case now. It’s gonna stay there. But what’s the next best what is the best one we do next for the benefit of the company?

Well, what you just said, I’m paraphrasing again, is kind of road mapping. Right? So it it it’s being it’s being very strategic in terms of helping define a data strategy, but it’s also the a, b, c’s, one, two, threes of of a of a road map.

Because for me story.

Across the company. As a whole, you get everyone aligned, not agreeing, but aligned to say, we’re going this way, and mine’s there. Mine’s in the road map, but I’m okay that it’s third and not first.

Still, what’s the difference between an alignment?

Agreement, everyone’s still gonna say, yeah. The sky’s blue.

Alignment is, like, we’re gonna agree to disagree. I think it’s green. You think it’s blue, but we’re all directionally correct. And we all know that whatever the color of the sky is, we’re all moving in the right direction. Like, we’re all pointed up into the right in the magic quadrant speak.

Does that make sense?

Got it. Okay. So we we’ve stacked hands, and we’ve agreed, and now we’re gonna go yes. It makes it makes total sense.

We so we’ve agreed. We’ve stacked hands, and now we’re gonna go charge the bill.

Yeah. Or or a different one is, hey. Well, there’s a little bit of a delay. I don’t know.

Yeah. I’m I’m seeing some oddities as well.

Hopefully, it’s just, through the recording platform and not Okay.


In reality. But, yeah, I’m seeing some delays as well. Hopefully, we can figure it out.

Okay. Keep going.

You made a mention of Chicago, your hometown.

Well, I was gonna say the other one I was saying alignment is saying, Malcolm, you and I are aligned.

We’re we’re gonna be in Chicago for, the jazz festival. I’m gonna take a bus. You’re gonna take a train. As long as we’re gonna be there or playing, as long as we’re there by three PM on Sunday, we’re good. That’s alignment.

Awesome. Got it. So you have been dropping nuggets of six CDO success since we started talking.

The the first one was kind of know your role, be clear on the role, have clear expectations with your with your senior senior leadership. We were just talking about the importance of what you call granularity, and be razor focused on specific goals and specific outcomes.

You were also talking about getting alignment. You were talking about road map. You were talking about quick wins, being agile, being MVP.

Have we missed anything in the in the keys to CDO success? All those things, by the way, bang on. Everything I couldn’t agree more, and, obviously, it’s working for you. We miss anything?

Reporting structure Yeah. Higher up the better is critical, and I’ll use an analogy.

And I I have good CFO friends, so apologies in advance my colleagues who hear this. But for the most part, if a CIO role gets put under the CFO, it is the kiss of death of transformative, innovative role. There’s a few. There’s asterisk exceptions to all rules, but for the most part. I would say the same thing applies if the CDO is reporting under the CIO. Again, an apology, CIO colleague, same.

Reporting structure and up leveling that conversation to be on the executive committee or having access to that board when that board does say, hey.

You just released this service, and this service had an unintended consequence in Europe of x. What the heck? You don’t want that coming through your boss. You wanna have that conversation firsthand with whoever’s having that asking that question. So I think that’s super important.

And then I think the last one is really understanding having that IQ, sorry, EQ or EI capabilities. And I’ll be biased for a minute in a good way that women research shows women have more of a strength in that naturally. Everybody can do it, but that tends to be a superhero strength for women. And I think that’s a big one in the world of CDO, which is working in data and maybe a little more of the zeros and ones on a data side. You’re really asking things like empathy, problem solving, rinse and repeat, put your your leadership of diversity there to maybe have a little little disagreement in a in a closed room to figure out what is the better answer. Things like that, I think, also are part of what’s gonna make that role successful.

So getting back to the CFO, CIO.

I’m gonna be in so much trouble, but No.

It’s it’s okay. I mean, the CFOs are inherently conservative because that’s how you want them to be.

That’s their job.

That’s their job. Right? That’s that’s that’s their job, and they’re inherently conservative. And if you’re a CDO working for a CFO, I think your primary mandate is gonna be don’t blow up the business.

Right? CIOs on on on the other hand are are a little bit of both. Right? A little bit of the inherent conservatism that goes in with IT, a little bit of the innovation or a little bit of the r and d, but what I’ve seen under the CIO is is a lot of what I’ve called in a few previous roles kind of run run the business, not grow the business. Right? Where the Right.

Business BI. If they’re business insights or analytics, it’s not Right. The other stuff.

Right. Yeah. It’s not operational applications of the data. It’s not the data sitting in the ERP or the CRM or those other systems. Maybe it’s the infrastructure sitting underneath them, but the business processes that are fueling them are completely divorced from the data, which is which is problematic.

So, yeah, I think what you said makes makes complete sense. Now you were talking about the inherent advantages of women in leadership roles. You used a couple acronyms. I think the was it a tiebacks of the DEI acronyms?

And and Well, it was EI it was EI and EQ. So emotional quotient or emotional intelligence, you know, depends on Oh, okay.

Okay. Okay. Thank you. So I didn’t I didn’t I I missed it. Okay.

That’s okay. And and as I said, this is not to disparage my the other gender. We all can do this, but research shows that those, quote, soft skills Yep. Horrible word, needs to stop we need to stop calling it that. It’s what we need more than ever. I call them leadership skills, are just a little bit more, of a natural tendency for women.

And right now, when we talk about change enablement, data and AI literacy, teaching people to be citizens of data science, bring disparity and diversity to the table, and then get out of the limelight, not be the smartest person in the room, empathetic leaders are more patient and aware, all of that. That’s a strength that women should double down on as CDOs.

That is their superpower, and it may not necessarily be there in that job description they’re looking at. But if you read between the lines and recognize how the company can be successful with a role like this, especially in the world of AI and chattyPT and all the pandemonium and chaos that’s going on in social media about it, that is a skill that or a, it’s almost a competency or an asset for women that they should be using.

So are you saying that well, I’m hearing, as an undertone here, kind of you you talked about empathy. You talked about a few other super skills. I I also heard indirectly kind of consensus building. Yeah.

Right? And being able to drive two levels of alignment in a in a highly effective way, which is quite obviously one of the key skills of the CDO because it’s it’s it’s it’s horizontal. You need to go and talk to the the revenue officer and and your chief procurement officer and the CFO and and drive consensus across them. Often, when you don’t own any of those individual processes, you’re still gonna be heavily influencing them.

So that makes total sense. Now you’ve made, three references now to to to chat GPT, and I and I haven’t taken the bait yet, but I I I can’t I can’t resist. Yeah. I I get the sense that that you’re seeing, just a lot of hype.

What what I mean, is this is this just just way too much hype right now, or is or is or is should we be worried, or should we be excited, or what what do you think?

So Renee’s opinion, if social media was around during in my Symantec days, you had the Stuxnet event.

Or if if social media was around during y two k.

Y two k or Sarbanes Oxley or any of those things, we’d be seeing it just like this. So let’s just put that as kind of the table set the table. So that’s part of it.

However, it also is in the world of reframing what are tools. So back in my day, when I went to math class and a math exam or a science exam, I could walk in, I’m gonna date myself, with my HP scientific calculator, and I could have that sitting next to me. Technicians for me. My test was not about, can Renee do the square root of a ten you know, a ten digit number fast? That wasn’t the point. I gotta get that thing figured out so then I could go apply it and do my math and do my exam.

ChatGPT’s saying, Renee’s opinion, humble opinion, is if if we frame it up right and we do a number of things we’ll talk about in a couple minutes, it is just another tool. And it’s actually a tool to make us like you said, my tagline is make humans be more human. Use our higher order brain.

A trait that chat GPT will never have because it’s a human trait only. It’s not a machine trait. Machines will never have this, and I very rarely say never in the world of technology.


I don’t turn on my laptop in the morning and chat TPT has a bunch of curious questions of me. I go to it with curious questions. Curiosity is what helps us reinvent and be human.

AI, generative AI, those are tools that optimize. They optimize the hell out of stuff faster than our brains could even possibly imagine.

So we need to be at the forefront of transformation, like you said, transformation, disruption, being curious, and reinventing while that tool, and that’s all it is, it’s a cool tool, and you can interact with it and it makes you laugh, is optimizing the heck out of something.

The other piece to that, and then I’ll pause once you kinda say what the heck, Renee, is we are responsible for the ethical use of it. So we may have we’re gonna have to codify ethics, which is my it blows my brain. Right? Right? And then we gotta codify it. We gotta codify it because if we don’t codify ethics for the likes of generative AI tools like chat GPT, it’s not gonna do the right thing. It’s gonna optimize on the not right thing, not even knowing that it’s not the right thing.

And I’ll pause there for a second.

What the heck, Renee?


Sorry. You said you’re gonna say, what the heck, Renee? I’m like, okay. What the heck, Renee?

So our our our common friend, the highly intelligent, well spoken, and much learned Bill Schmarzo, would would completely and totally agree with you on the codification of ethics. But I’ve actually had some interesting conversations recently online about this very topic, fancy formulas need specific direction on what a successful outcome looks like, right, then then we have to figure out a way to to codify ethics.

How in the world do you do that? It for something that is arguably maybe it shouldn’t be. I I don’t know. A separate conversation, but that is arguably highly subjective.

What’s ethical to me may not be ethical to somebody else.

And there’s a lot of conversations going on around AI regulation.

I know. You got people like Elon Musk saying we need to regulate this. Right? And I I okay.

Honestly, I’m kinda generally for smaller govern government even though I’m I’m Canadian. I’m generally for smaller government, but I’m not concerned about the size of governments here. Our government government and its involvement. I’m just concerned about the competency of government and its involvement because I I think inevitably that’s where things would go.

But to the task of codifying ethics, that sounds hard.

I well, it so back to my remember? Curiosity and reinvention, that’s a human trait. I’m confident that the brilliance of our human higher order brain it’s a muscle Just like every other muscle in your body, more you use it, the better it gets. That’s where we need to be focusing.

It blows my brains as well. I can’t I can’t imagine it either because I’m not I’m not that smart in that space. But what I do know, and I’m gonna quote one of my my all time favorite heroes. Her name is Cathy O’Neil.

She’s a mathematician from UC Berkeley, also a author of a great book of probably ten years ago now, Weapons of Math Destruction.

Love it.

Yeah. And the book, it’s again, it’s so relevant now as it was ten years ago or or so. But her quote was really great. Algorithms are only codified opinion.

Algorithms are only codified opinion. It’s it’s opinions in math. And she does a great YouTube video. I don’t mean to promote you know, I’m not promoting her specifically. She’s a great YouTuber Ted Talk on hers her and her son’s definition of a successful dinner and how you would go about writing an algorithm for that. And I’ll just do it there.

Reporting back. Yeah.

But but okay.

It you can write the algorithm. That’s not the challenge. It’s it’s codified opinion. So if we’re already doing that, it’s incumbent upon us to then somehow and I don’t even know what it looks like, Malcolm, so I’m saying it without knowing it.


Like, going to the moon and back in the JFK era. I don’t know how it’s gonna work, but I’m pretty sure we’re gonna have to figure it out is, we gotta codify ethics somehow. Like, the basic I don’t know. It’s human rights and I mean, there’s some basic things I would like to think that as humans, being more human humans, we could actually agree on.

Because if we don’t, what’s gonna happen is they’re gonna we’re gonna optimize for all that opinion that we’re not even know we don’t even know what that opinion is in that code, and it’s gonna get optimized really fast. And we’re gonna create products and services and solutions and machines and tests and jobs and you name it, not even knowing the the, you know, the the unintended consequences, the false positives and false negatives that we’ve created with our eyes shut because we put our heads in sand.

It’s it’s already happening. The the the horse is out of that barn. I was just watching I was just watching a video where where where somebody had figured out where ChatGPT actually, like, hired TaskRabbit to get over a CAPTCHA process. So somebody had given chat g chat ChatGPT a task of doing something that required getting past a a CAPTCHA.

Right? The click here to prove you’re not a robot. Right? And ChatGPT successfully fulfilled the task, and and I and I don’t I I can’t dive into the details because I just saw the sound bites.

But the sound bites in and of itself were were just like, oh my gosh.

Where Chad GPT actually hired somebody, like, on on TaskRabbit or Fiverr, whatever, one of the, you know, Yep. Of those services to actually do the CAPTCHA and lied in order to get the person to do it. They said chat g p t said they were visually impaired person and needed help to do this. Like, so that the horse is out of the bar.

ChatGPT. That’s not a ChatG I mean, ChatGPT optimized because that’s already out there in the world of the Internet and stuff. Yeah. We created that. Oh, man. SEO nets, we need that.

For hours about this. This this this it’s just it’s just it’s just fascinating. I wrote a blog recently where I I I used a metaphor. It said that the horse is out of the barn, and we’re racing we’re running after it with a bunch of ropes.

Yeah. Yeah.

But anyway Well, you know, I think the other challenge in back to how the CDOs can be more successful, there’s slow adoption to this data driven culture, that buzz phrase.

And so we’re trying to race with a rope behind a racing horse of, you know, chat GPT or generative AI optimizing faster than our brains can possibly imagine. And we’re trying to do data driven culture change management. It’s highly iterative.

It’s it’s collaboration. You go across, you go down, you you empower your team to rate reach across and hold the arm or hand of the person across that’s normally the mortal enemy of the business or whatever.

And then have to come up with metrics to measure that and say it’s successful. We’re trying to do that while you got this thing rapidly optimizing. So it’s it is a a little bit of a pressure cooker. I think recognizing it, just saying it out loud, putting it out there in the universe through a podcast to say, look. This is what we’re up against, and we still have we still own this thing. We do reinvention, and we are still curious. Those two things, not yet anyway, not till we get to, you know, that next generation that we don’t quite have only in fiction novels, will we be able to say some some entity besides human beings can do those two things?

Lots of plates fitting here.

On on that note, I I’d love and and and my last question for you. I would love to hear from you.

Well, let let me set the stage. Failure has been a huge part of my professional development. And and and having some massive failures along the way has been incredibly helpful for me, believe it or not. I think I I’m I’m a little concerned that that we’ve got a culture now that is that thinks failure is a bad thing. But for me, it’s been incredibly important, and I’ve learned a ton from it.

As as a as a CIO responsible for data and analytics, what what is kinda maybe one of your biggest and and and what failures, and what did you learn from it?

Well, I think, you know, I’ve always been the fail fast mindset. You’re right. It is not we can say it and put it print on T shirts, but failure is just never, it’s that big f word that second worst f word in the world. Right?

My failure was actually, kind of what I described earlier, foreshadowing a little bit of this probably gonna be asked this question. I was that CIO that used to be, in the early days, an analyst and data person, and then you get you know, you move your path of your journey, your career, and it winds like this, and you end up in the CIO world. And in this particular case, I was a CIO of a subsidiary of nine hundred entity, multinational company out of Japan.

And it was, we need to get all this data. Think of all the data, the global data. Let’s put all the data together.

Renee san, do something. So we got this funding, and it was a big number of funding. And the the, you know, the failure on me is what I just said. I’ve learned this the hard way. Like, wait. What business problem we’re trying to solve? We’re just trying to put all the data and clean it and put it somewhere.

Because once you put all the data together, something awesome is gonna happen because we have so much of it, and we’ve kept it we’ve ordered it, like you said, for so long because we love to hoard data.


Because there’s a might moment somewhere in that data that might make us the next widget or insight or something. So I did it, and we finished it. And you check the box off of all the project go live KPIs, and you you have all the success criteria that was established, and then you’re done. And then it was crickets.

Like, okay. Where are those data stewards? Where are those where are those logins to this thing that you all are gonna be inquisitive and then figure stuff out? And and all of a sudden you realize it was a giant business you know, BI tool.

It was rearview mirror stuff. It wasn’t, you know, it wasn’t a data literacy or an AI literacy.

And I didn’t have those words back in that day, but now, you know, hindsight’s twenty twenty. No one knew how to have a conversation differently. They used this data in the same way they’ve always done it. It was a failure from the standpoint of lots of headcount, lots of people all in, total cost to invest and operate, probably forty million dollars.

Wasn’t a small number. It was advertised. It was depreciated. It was on my books, and every other business unit that bought into it’s on their books as a brick on their spend until it went away, until its value went away or we write it off.

Well, we figured it out and we redid it, but it wasn’t about the technology, and it wasn’t about keeping that data clean, which was only clean for about a nanosecond, and then it was dirty again.

It was back to that granularity. I’m like, pause, time out. Let’s go figure out something. And, ultimately, we turned it into a win, an moment saying, alright.

What are this? What’s happening in the business? What’s what do we want? We’re getting ready to sell this brand new product.

It’s an m v a minimal viable product. M v right. MVP. We We wanna go find who will most likely wanna buy this from us, and the salespeople wanna go sell just to the people that probably would buy it because that’s how they’re compensated.

And so we’ve created a a propensity to buy algorithm out of a very small set of that forty million dollar investment, but it paid back. And it continued to pay back at ninety percent plus propensity to buy for multiple years. I eventually handed over the keys to the to the role to someone else as part of the succession plan, but, it was very successful. So that was the moment, and that was the lessons learned the hard way by not more than just me, just what are we doing?

Like, what’s the business problem we’re trying to solve? And if I was a CDO, I don’t think it would have been any different. Right.

Because I had an I had datas data mini CDOs in all the lines of business that were so invested in building this thing that was very expensive.

It didn’t didn’t pay.

So you built you built you built the data and analytics or at the very least, the business intelligence field of dreams. You you built it in in dot com. The the the the the multimillion dollar Tableau self-service dashboard and and nothing against Tableau. Awesome product.

Could be anything. Could be the click. Could be the burst. Yeah. That you Einstein, whatever.

Yeah. Cell service dashboard and nobody came. Okay.

I would say the oh, go ahead.

Please finish.

Well, I’m just gonna say the other part of that is build it. They won’t come. I mean, that just makes it just we learned it the hard way. Don’t know everyone else listening don’t do that. But the other one that we got caught up in a lot, and I call it the Coke versus Pepsi debate.

In my early days, is it Oracle or SAP? Or before that, is it, is it Lotus Notes or Outlook? And now it’s Kubernetes versus Hadoop. We got so stuck on, like, it’s it’s an opinion.

Is it Azure or is it AWS?

Yeah. There you go. And it it had no bottom line value add whatsoever. And I’m gonna get price loaded for the statement. But they all kinda do the same thing. Sorry. At the end of the day, they kinda do.

It’s kinda undifferentiated products. Yeah.

There there’s opinion and there’s price discreet business cases, but you gotta invest. And it’s gonna be sticky, and you gotta make it work. And the the quicker you align back to alignment, not agreement, align and say, let’s move on with this thing so we can get to this upper right magic quadrant thing that we’re trying to do together as the first use case of many that will will actually be successful regardless of Azure versus whatever or Hadoop versus Kubernetes. So that was my other learning.

Well, it it’s funny.

The recovery from my biggest failure was finding a sales use case. And in your in your case, it was finding the sales use case, right, which is make salespeople money and you’re a hero.

Right? Like, it’s that simple. Right? Like, it’s it’s don’t you don’t have to go hoard all of this data and and build the giant, you know, Mount Everest of data, or or the analytics field of dream to help salespeople make some more money as a as a v one, and you’ll and you’ll be a hero for you’ll be able to ride that wave at least six months, probably a year.

Well, then they’ll ask you to do more. So if you’re bringing money and making profitability for the shareholders or if you’re a nonprofit, you’re you’re bringing in that value that that the company is expected to do do good with and, you know, you’re gonna ask me to do another one, and they’re like, how did you do this? And then you can start that buzz, that data literacy campaign, that that change management culture that unfortunately is incumbent upon this role, that CDO role. If you’re gonna get to being a data driven culture, which is, you know, a def you know, definition of success for this role, you gotta do it in a storytelling way that’s relatable to your audience.

You gotta come over the proverbial wall or that fence and talk in their language, and salespeople wanna make money. Shareholders wanna get profitable dividends.

The audit committee of the board doesn’t wanna be at risk of x y z. Like, figure out that, and then you, CDO, will have the secret sauce for success.

With that, wiser words were never said. Thank you so much, Renee, for joining us today. It’s it’s it’s been a real thrill. It’s it’s great to hear success stories from somebody who’s been there.

So my thank you for joining.

Oh, welcome. Thank you. This was a blast. I thoroughly enjoyed it. Thank you.

Awesome. To our viewers, our listeners, our subscribers, thank you so much for joining in to another episode of CDO Matters, and I look forward to seeing you on another episode sometime very soon. Bye for now.




How can today’s Chief Data Officers help their organizations become more data-driven? Join former Gartner analyst Malcolm Hawker as he interviews thought leaders on all things data management – ranging from data fabrics to blockchain and more — and learns why they matter to today’s CDOs. If you want to dig deep into the CDO Matters that are top-of-mind for today’s modern data leaders, this show is for you.

Malcolm Hawker
Malcolm Hawker is an experienced thought leader in data management and governance and has consulted on thousands of software implementations in his years as a Gartner analyst, architect at Dun & Bradstreet and more. Now as an evangelist for helping companies become truly data-driven, he’s here to help CDOs understand how data can be a competitive advantage.


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