Hi. I’m Malcolm Hawker, and this is the CDO Matters podcast, The show where I dig deep into the strategic insights, best practices, and practical recommendations that modern data leaders need to help their organizations become truly data driven.

Tune in for thought provoking discussions with data, IT, and business leaders to learn about the CDO matters that are top of mind for today’s chief data officers.

Good morning, afternoon, or evening, everybody. This is Malcolm Hawker. I am your host for the CDO Matters podcast. I’m thrilled to, have everybody joining today, whether that is through Apple, Spotify, Google Podcasts, or on YouTube, on prophecy dot com, or any other channel that we distribute this content.

It’s my honor to be talking with you today. It’s also my honor to have Alison Sagraves who is joining today to discuss some very interesting topics.

I I I think you’re gonna be interested by the topic. I’m interested by the by the topic. We’re gonna dry dive into that a little bit. I got interested interested.

I got, I don’t say introduced. I think I reached out to you, Allison. I just sent you something on LinkedIn, which I do all of the time. Mhmm.

But I became to know I came to know of you, through an article that you had done and collaborated with recently with Randy Bean on Harvard Business Review. And the title of that article was why are CD AIOs? I got that right. We’re maybe we’ll talk about that a little bit as well.

But why are CD AIOs set up to fail? And I was like, I need to know more. And immediately after reading that article, I was like, okay. I have to talk to this person.

And that’s what led to the conversation today. So, Allison, thank you so much for being here. I do wanna touch on one thing. I wanna make sure, your background.

You were over thirty years in M and T Bank, and you were a found you were the founding chief data officer for the bank. So that’s no small feat at all, particularly with a with a major bank. So congrats on a great career at M and T. And I’m I’m really looking forward to our channels, and thank you so much for being here.

Well, thank you. I’m really excited to talk with you, especially after we after our chat last week.

Yeah. Well, exactly. I I knew we were birds of a feather because it was like all of a sudden, we were twenty minutes in, and we had been ranting on some of the same topics. And I was like, okay.

We we need to put this, you know, I think I used the word maybe on tape, which is, like, really showing my age. I don’t think anything exists on tape anymore. Maybe maybe archives of data, but but, yeah, show showing my age. But so so, Allison, why don’t you instead of me summarizing, why don’t you summarize what you or Randy wrote about and what kind of led to this conversation and and why you think in many ways that CDOs or CDAIOs or CDAOs analytics.

Oh, no. I’m I’m gonna set up. But, anyway, why CDOs are are kind of designed or destined to fail? What what did you find?

Yeah. I think that, so, you know, I was really part of the kind of the early wave of this role from I was in the role from two thousand fifteen to two thousand twenty, and especially in banks, this was set up primarily as a initially, as a, excuse me, as a defensive role because there are so many compliance, financial, you know, reporting. All of those types of things are obviously essential critical to banks. So data, you know, trusted data is essential, you know, for regulated industries. So there there was a an iteration of CDOs that were set up in in health care and and, the regulated industries like finance in in the era that I became a CDO.

So, you know, we sort of helped in a way, like, frame the role.

But I think that, ultimately, you know, there’s just Randy does a survey. I used to take his survey every year. And, you know, every year you look at the survey and and, like, the scores the the the reported results would would start to decline, like, in terms of, you know, do we have a data culture? Do we think the role is successful?

And, like, the the scores were not improving. And so it’s like, clearly, something is amiss despite the fact that I in my view, a lot of progress had been made in in companies, and I could talk about that because I I interviewed some really significant, CEOs of very significant companies in Silicon Valley last year. And, it was very interesting to hear them talk about how they thought they were their perception of of what success looked like. And then I said, well, give tell me about a win that you’ve had, and then they would tell me something.

I’m like, well, why are you saying you don’t have a data culture? You just described something really significant that you’ve accomplished. So I I I actually feel that in a way, CDOs kind of have a complex and, that the job is never done, but, like, focus on the fact that you’ve delivered some real value. Focus on the fact that you’ve had some successes, and and don’t be focusing on the fact that you haven’t enabled every possible group in the company yet.

Like like, New York City is still under construction. This is a function that’s always gonna be under construction, but focus on some of the buildings that are up and and useful.

I think there’s a a perception problem is one thing, but but there are also structural problems as well that that we can get into. So, really, the article was meant to, you know, I’ve I’ve been out of, corporate America for a little over a year and, have been reflecting a lot and and working at Carnegie Mellon, teaching in their chief data officer officer program and and working on boards and working with CDOs. And I I really wanted to, you know, work with Randy who collects the data on industry, the surveys and, like, apply the real lessons learned on the ground, the real scars that CDOs have have suffered, and and and really try to document those and and try to create a bit of a path forward.

So shout out to Randy Bean and the survey from new Vantage Partners that he’s been doing the last, I wanna say, twelve years. May maybe even longer. Forgive me if I get that if I get that number, incorrect. But, like, I wanna say, like, a public service, but even it’s way beyond that. Like, the invaluable data there to the CDO community, fantastic stuff.

I would also, give a nod to my previous employer, Gartner, that does an annual CDO survey. I think they’re on their eighth now. I was involved in the fifth and sixth and seventh iterations of it. Also amazing data there.

That all basically says the same thing, right, which is we keep investing in data. We’re investing in data. Data’s a priority. Right? The role’s a priority.

Increasing numbers of companies year over year over year that have a CDO or CDO equivalent going from ten percent, over six years ago, seven years ago to now where I think we’re over sixty percent.

Fantastic stuff. But then when you when you ask, you know, hey. Are you are are you succeeding? Are you feeling like you’re succeeding?

Do you have a data driven culture? Are you driving business value? There’s there’s a lot of really depressing numbers there. So the the the it’s actually what you just said, Allison, is partially why I’m doing this podcast, because I was a Gartner and seeing, kind of a lack of traction.

Yes.

And and the conclusion I came to while I was a Gartner, not not in not in entirety, but a partial cause here was that, you know, I was giving best practices and sharing insights and talking about driving business value and talking about building teams and the importance of governance and dah dah dah dah dah. All this stuff we know it’s important, and it wasn’t happening. And and I came to the conclusion that, well, maybe maybe it’s the messenger, so maybe it’s me. And I’m still open to that possibility, by the way.

And then maybe it’s it’s the message. And I was like, no. Don’t think it’s the message. This stuff makes sense. Right? Like, connecting to business value and, you know, focusing on on on leadership and building the team and and all these the competencies.

And then I said, well, maybe it’s the medium.

Right?

May may maybe the Gartner model is just kinda getting a little old and tired, and maybe I need to find a different way to deliver the message, so we’re here today.

But to your point about the conversations that you had with with these CDOs in Silicon Valley that that that said that they that they weren’t succeeding, but they kind of were. Is that is that is that just excess humility? Is is it having overly aggressive goals? What what what do you think that was?

I think there’s two things. I think that I I feel like there’s a lot of apologizing for this role.

Mhmm. And and, like, some somewhat of a complex that if you haven’t fixed everything, you have somehow failed. So so there there there’s one aspect that I think is just what is expectations, what is realistic progress, are we good at actually capturing the value and communicating what that progress is worth? I’m not sure that we are. Mhmm.

And so some of it is just is is perhaps, is accounting and marketing, but and that’s superficial. But I but I do think there is some truth to that.

But, also, I think, that I I I feel like this is a this is a a field where urgency is required, but so is patience.

And I we have urgency, but we don’t have patience.

And and we’re we’re patient, like, for the wrong things.

Like, we’re how can I describe this?

As consumers, as people, as citizens of the country, it’s it’s obvious. We don’t need to preach this to people.

Yeah. So I think, on the patience and urgency front, if I if I really think about it and I actually reflect on one of the things that we said in the in the HBR article is, I think one of the challenges we have is being able to, like, cross pollinate an organization. So the urgency point, the patience and the urgency is, you know, we we are able to get these successes, but they’re very hard to replicate in other groups.

Even though in many cases, you’re using the same data, and we gave that example in the HBR article that, for example, if you’re a bank and you’re analyzing fraud data and you’re looking for patterns, that same data is gonna show you what customer behavior is doing from from looking at it from the if you if you’re looking at it from the customer viewpoint of, you know, what services might this customer need, it’s the exact same data. But if you’re successful in using that data to identify fraud patterns, but you have not been successful in using that data for marketing, you you’d say, well, you know, we’re not data driven.

We’re a failure when, in fact, it’s just that we haven’t been able to cross pollinate that discipline and that that skill set into another group and and deploy the that insight generation and, being able to to convert these insights into action in another context. So I feel like we somehow kinda lack the patience for that, kinda knowledge transfer to another context. And, I guess when I talk when as I reflect on it, when I talk to these CDOs, I I would hear these, like, really meaningful stories about substantive, demonstrable, like, business value they were creating in in area x.

But then in area y, maybe they hadn’t yet achieved it. And I thought, well, that’s just like train that’s a transfer problem. That’s not a capability problem. And so, you know, we’re beating ourselves up when when we’ve demonstrated that we actually have the capability to do significant things in a in a in these companies, but we’re just not necessarily able to repeat them across context.

And I think that’s that’s a different kind of skill set, and so maybe we’re labeling the problem wrong. I don’t know. I’m throwing this out there. I’d love to hear your thoughts about this.

What do you think?

Or or the metrics of success are askew.

Yeah. Right? Before before we came on, then we were talking about how inflation is measured.

Yes. Right?

Because that’s what people like us, we we think about this stuff. We’re talking about inflation. Maybe the metric is wrong, and maybe in this case, I I’m not questioning the question in in the survey, around things like, you know, are you driving business value? Are you are you succeeding in a data driven culture? But that’s a pretty all encompassing question that could mean a lot of things to a lot of different people.

May maybe maybe the problem’s in the question or maybe the problem is in in the scope of the question.

Because what I’m hearing you say is the data is value is being delivered, and we are making baby steps, shall we say, in in cultural change.

But we haven’t made this galactic angels are singing unbelievable. The whole company is now data driven change, and that that’s what’s driving this perception that that CDOs are, for lack of a better word, failing to meet expectations as it were.

Right? Yeah. I mean, it’s it’s not a yeah. I think that perhaps that it not to say that it’s the the question itself, but I think it’s just maybe human nature, is that you can never declare victory because you’re always trying to get better. And maybe we’re not good at communicating what better looks like.

And and, I think in some in some ways, there is a a mismatch between the the progress that has been made and our ability to quantify it, market it, and repeat it Yeah.

Like I said, in in other context. So, you know, I I I there is a part of me that thinks that, that people are are too hard on they’re they’re a bit too hard on themselves.

It it it appears that’s why.

Say that there aren’t there aren’t real problems and that there aren’t things that need to change. I do not wanna say that it that that’s the only reason.

Well, let’s let’s pick on two that you’ve picked on twice, accounting and marketing.

Mhmm. These are two areas where I know CDOs can do better, most certainly.

I had a post on LinkedIn a couple of weeks ago where I where I said, if there was one role that I would suggest CDOs look at for twenty twenty four, if there was one, it would be what I would call a value engineer, which is basically the accounting piece of the puzzle that you just mentioned, which is how do you quantifiably prove that you’re delivering business value? And that’s something that we used to see at at Gartner all the time. Like, if that if that if you asked, are you driving value for the organization? Answer is sixty, seventy, eighty percent. Yes. Can you prove it? No.

That’s and so that’s an accounting problem to your point. Agreed?

Yeah. So I think there is a component to this of, you know, do you have a way to, you know, I hate to kinda use the word scorecard because Yeah. Yep. It sounds like simplistic. But but on the other hand, you know, do you because we had a scorecard. We had we measured various dimensions of value.

I mean, there are ways that you can do this to, have the discipline of, of quantification, and it needs to be, you know, you need to partner with finance so that you’ve got independent, you know, groups looking at these numbers and they have credibility. But, yeah, I do think that, I think in in increasing the discipline around, as you called it, value engineering, it would be a really good step to to adopt in a in a more broad based way. I think people do this in, at the in certain organizations with certain levels of sophistication, but there isn’t really a standard, and it’s not, can you know, I’m not aware of an industry standard, and and I’m not saying there needs to be one, but I do think this is a weak point in in the field.

Yeah. That well, I mean, there’s standards around the metrics that are used. Right?

TCO, ROI, CAGR. I mean, look, there’s there’s there’s a lot of different measures, high level that CFOs prefer.

But in terms of industry standard models for valuation, there may be value engineers that are listening to this and and, like, screaming at their TVs right now saying, of course, there is. I don’t know of any because anytime I’ve ever talked to CEOs about it, they they seem to think that this is a snowflake problem with with in terms of how each company values things, because ultimately, that comes down to kind of financial policy that I don’t know an awful lot about per se.

But problem so so so we agree. Value engineering.

What I would recommend to CDOs is if you don’t have one of those roles, you certainly could consider one.

Mhmm.

And and if you’re having a hard time justifying the funding for it, then go find those people. If your if your company is big enough, they’re probably sitting in FP and A roles, financial planning and analysis roles that may be under a c probably under a CFO, maybe under some PMO type organization that does some project cost accounting, that kind of thing. I guarantee you there are people out there. If you are bigger than, let’s say, a two billion dollar company ish, there will be people in your company that do this stuff for a living, that that know how to do value engineering in some shape or form. So that’s a problem number one, accounting. Problem number two, marketing.

I think in the data world, we dance around this one without actually using the word. We say things like data storytelling, and everybody say, oh, that sounds good, and can’t argue with that. Everybody loves a good story. I would even argue data literacy, which I kind of have an antibody response to that phrase and may we can talk about that.

We’ll have fun with that one.

Oh my goodness. It’s it’s the gift that keeps giving as far as I’m concerned.

But but but the underlying premise there is what I would call as a go to market function. Right? If I’m launching a product, if I’m serious about data products and I’m launching products, well, I have to have a support model for that product. I’ve gotta have, you know, a way of helping customers, help training customers, making sure that they know what the right tool is for a job. Arguably, that’s a marketing function as well. Go to market, market training, product product training, user education, user adoption, arguably marketing problems.

We could keep talking, but what what would you you know, looking back on your your career at M and T from that that marketing perspective, what are some things that maybe you did right or maybe you wish you had done?

Yeah. So, I mean, I’ll I’ll I’ll, like, speak more broadly just about the industry, some containing my my generation of CDOs would say, hey. You hey. You know, if we if we all had it to do over again and again, we started out kind of in the defensive era.

But if we had it to do oh, had it all to do over again, what would we do? We would start with the business. We would really start with, partnering with the business. In fact, I had this exact conversation with another with several CEOs, but one in particular who said, I would just, you know, build relationships with the business.

And then he proceeded to talk about, a really successful, deliverable that that he helped create with, with part of I think it was the commercial bank where they used, their their problem was in identifying opportunities for cross selling to their commercial clients. They ended up deploying a machine learning algorithm, and, ultimately, the the usage of this was was so successful, that you couldn’t, you couldn’t separate when the algorithm from at least the salespeople standpoint, you couldn’t separate when the algorithm said, hey. You should talk to this customer about x y z product.

They would say, well, I was gonna do that anyway. So it it turned out that that the, the machinery behind the the the you know, whatever. The the algorithm itself became so embedded in the process that it became I don’t mean to say invisible, but it became organic to how the salespeople ended up working with their customers. And in that case, the measure of success, which could have been, like, the profitability or the revenue generated by those relationship managers, you could have just measured, alright.

Well, have they improved their profitability? And And you would have had a hundred percent alignment between the value of of the data work that was done to to help improve the business and the business results. A hundred percent alignment. So, you know, in in we my husband’s always saying I mean, this is a this is a catchphrase.

Measure twice, Scott wants. You know? He he says that to me.

When we’re doing various projects around the house.

So so when when the CDO told me the story, I I said, I think the answer is we need to measure once. We need to measure once, and we have to have a hundred percent overlap between a business the business outcome and the data outcome. So, you know, something but I’d love your feedback on that.

Well, so so that that actually kind of to me that touches on both because marketing is is or I should say, the accounting is the measuring. But there’s also an aspect here that you were kind of touching on, and and this goes back to the our our our the kind of our first thread is you you as a CDO, you need to figure out how to ring your own bell.

Yes.

You you need you need to be out there shouting from the mountain tops, look what we did.

Right? And and there’s not nearly enough of that. I don’t I don’t think there’s, like and I think it’s maybe just IT people just, like, you know, we’re we’re we’re not into a lot of self promotion. We’re not into a lot of hype or although we we fall prey to it all the time. But in terms of, like, being generators of hype, I don’t I think we we we’re generally not like that.

But but there’s room for that in in a CDO organization. Right? There’s room for marketing. There’s room for promotion.

There’s room to be out there. And and the metaphor I’ve always used is is pounding the drum. You need to keep pounding the drum. You’re gonna once one one arm is here’s what we’re gonna do.

Here’s what we’re gonna do. And the other is here’s what we’ve done. Here’s what we’ve done. And and and I think both of these things I think there’s room for both, and and this is what what you just suggested in your last response.

Yeah. And I think it’s important that you’re not the one that’s always the being the one pounding the drum. You can be in the drum chorus, but the most effective pounders of the drum are your customers.

And I’m just trying in this case in in a sense, your internal customers Good point.

Successfully have deployed deployed solutions. When they can be the messenger, there’s there’s just such a a snowball effect to that. So I think, really finding the right allies in the business, having them, pound the drum, you know, having a having a drum circle. You need a drum circle.

I’m I’m I’m I’m all for it. I’ve actually I went to a powwow once, and and if and if you’ve never gone to a powwow, just phenomenal. Total non sequitur, but it included these these drum circles. And with this music, this is just so powerful. It’s unbelievable.

Anyway, before we move off to the HBR No.

Let me share. You know, on okay. I have to say, I know this is more of a guy thing, these drum circles.

Although, I’d love to Oh, is it? I didn’t know it was a guy thing.

Forest where I am today.

I do think there is something to having a little fun with all of this and having a camaraderie and having competition and having some sport and having not so having not so much seriousness around it. Of course, this is a serious topic. I’m not looking in any way to to minimize the the the types of things that we use data to solve in in any way.

But there is just, I think we need a little more, spark, enthusiasm, inspiration, to get people fired up. I mean, we’re the work is important. The work is exciting, and it’s intellectually stimulating, and it’s challenging, and we can really make positive change in in so many ways. And, I I I think there’s there is room for a little pizzazz factor in in this whole business.

Sizzle. That’s yeah.

That’s maybe later sizzle in a way that, sizzle in a way that is, that that has that isn’t salesy sizzle.

Yeah. Meaningful. Yeah. It has some depth and substance.

And and that, snake sizzle, I I kind of I get it.

Yeah.

A bit snake oil.

You know what I mean?

But, like, they they think Yeah.

You’re right. Yeah. Yeah. Something substantive. Yes.

But but we but I having more fun. Absolutely. So before we move off the HBR article, so we had CDOs, then then Gartner I I would argue Gartner kind of really pushed the CDAO, the chief data and analytics officer, and separated data and analytics. And I’m not a fan of that, for for because I because I happen to think analytics is is a data use case, and and maybe I’m a little data management centric and okay.

So be it. Because I think that creates kind of the data versus analytics creates these false divides. It’s analytical use cases versus typically more operational use cases. We’re gonna find a way to to bring them together to your point, find a way to synergize across use cases.

Mhmm.

And cross pollinate is the word you use.

But in the HBR article, you use the CD AI, meaning artificial intelligence.

And I want I wanna poke on that a little bit. Was that when you were talking with Randy about about the article, what how how did the how did that come about, the use of that? Is that is that or am I just behind the times and everybody knows this and I don’t?

Or Yeah.

No. I think it’s more recognition that, the way the way the way I would see it is that the capabilities that the the the first waves of CDOs set up, in terms of, some of the governance structures, the committee organizations, bringing people across the enterprise together, from all walks, from whether it’s the business, whether it’s compliance, legal, privacy, ethics, all of those things that seems perhaps a bit a bit heavy handed when this topic was only data, data quality, and so forth.

Now these topics in the world of AI, these are headline news. And people in daily and cocktail conversations and with conversations walking their dogs in the park are talking about AI and worrying about algorithms and bias. This is now this is part of conversation. So I think it’s interesting that we we CDOs kind of anticipated the the need for this kind of, cross functional dialogue to to work through these issues. And those structures, in a sense, can be, I think, heightened and and now applied with I think we’ve got the, like, the right construct, I guess, to apply to to the world of AI. You know, whether whether in any given company, it’s the right people, it’s been the right level of visibility, the right level of authority, That’s another topic. But, I think the idea that, having having these disciplines come together, that that’s the idea of the CDAIO.

Got it.

Is that, it’s leveraging the the those constructs and applying them in a in a in in a world we live in today.

I’m all for the public at large knowing typically the data science and AI roles and data science and AI as to the degree that they’re individual organizations within a broader IT function. I I’m all for them knowing that these would typically tier under some sort of chief data officer. So so I’m all for that. It just it just doesn’t flow off the tongue very easily.

That’s Yeah.

I mean, I I don’t think that anyone has you know, there’s no standard as to what how this is all gonna shake out. It was more, I think, to say that in the world of AI, here are some things that need to change.

Okay. I don’t need to change the name of the podcast is what I’m hearing.

That’s good.

That’s good. Alright. Let let let’s pivot. I I teed one issue issue up. I’d love your perspective on it because you do mention data literacy in the HBR article. So I guess we haven’t fully migrated off of that.

I’ll tell you my concern, and I I would love to hear your your your thoughts on that.

The whole to me, the the the data literacy movement, as it were, really picked up steam from two things. One, a book that Jordan Morrow wrote called be data literate.

Jordan calls himself, one of the nicest people he’ll ever meet, by the way, calls himself, the godfather of data literacy. That was one force. And another force is right around the same time, Gartner started hyping it.

And and those things together so data literacy got onto the hype cycle, and Gartner has this wonderful ability to to to create self fulfilling prophecies, in that they put things on the hype cycle, and then analysts like me start to talk about them, and then more people are talking about them, and all of a sudden they’re becoming really popular. And I think that’s what happened with data literacy, two maybe starting three years ago ish, and and here we are. It’s it’s a household name now.

And if you kinda go back to the genesis of of of data literacy, at least from the perspective, I would argue, of of Jordan Morrow’s book, the underlying premise goes back to a study he did while he was the head of data strategy or I can’t remember his exact title, but he was at Click.

Was at Click. We we were we were both keynote speakers at a conference in May.

There you go. And and he was at Click, and he commissioned a study that that said that, people don’t know how to use data and that they’re not getting any any value out of data.

And he drew the conclusion that they’re not getting any value out of it be because it’s a skills gap. There’s a the the the underlying premise of data literacy says that there’s a lack of skills in the user community in in in the their ability to use data. And and and Jordan describes it even in more detail than that. He says use, interrogate, question.

There’s a there’s a few attributes that he states in his book about, like, what does it actually mean to use. It it’s this interactive thing. It’s interrogating the data. It’s it’s operationalizing the data.

It’s it’s testing the data. All good stuff. But the but the premise is is that that is a function of low, for lack of a better word, low user skills.

And I looked at that and I said, okay. First of all, I had I had to I had to stop my my my thought process, which is, okay, the opposite of literate is illiterate, and that sounds a little mean.

And who wants to be mean? I’m trying to gain stakeholders and followers and acolytes in an organization, not not not alienate people, but but well, I’ll put that aside. Put it aside.

Okay. But what about the data?

Right? But what about the data? The metaphor that I use, Allison, again, I’d love to hear your your perspective here, is that if data is a product and let’s say I’m I was building a product. This this thing, this is my phone. This is three forty four.

And nobody used it. Everybody hated it. The the the the Yelp reviews were were horrible. Nobody could log in.

People complained about it. Maybe they even went, and and I was giving it away knock knock for free within my organization. You could use it. There’s no cost to use it per se, or maybe there’s some budget allocation every year.

Year. But let’s go assume somebody else goes by buys another one from somebody else, pays money for it. As a product manager for that, what I naturally conclude, the inability to use or get value from my product was because of a training issue?

Yeah. So I have a lot to say on this topic.

So first of all, I, you know, I I I don’t love the I don’t love the idea of of calling this, whatever this is, data literacy.

I would like to say, by the way, that, I am actually concerned about actual literacy.

And I think that anybody who wants to talk about lack of data literacy, I would like them first to understand, the literacy levels in the US public schools.

Because right now, fourth graders and eighth graders, I believe it’s something like less than a third of our fourth and eighth graders that when they’re tested, are achieving, grade level standards in, reading and in math. So I feel like this is a bit corporate hyperbole problem versus real problem. And if we’re gonna use a loaded word like literacy, I’d like it applied in a context where it really matters.

And I would like people to educate themselves about literacy and education in this country, and let’s talk about a real problem because that’s a real problem.

In a company, I’m not saying that there aren’t skills gaps in in various areas that need to be addressed, but I think those are role and context based. And I think that using a word like literacy seems a bit, vapid to me.

And I, you know, I I I think you lose credibility as a CDO when you walk around and and talk about, you’re not literate. When when people are functioning in a in a digital society at this point where, virtually every interaction you have, whether it’s, monitoring your own health, dealing with your doctor, doctor, shopping, whatever it is, you are engaged in the digital economy, and you’ve gotta be fairly savvy just to just to be a successful citizen these days. So I think you need to assume that people come to work with a fairly, you know, sophisticated level of, awareness of the digital economy, and you need to kinda meet them where they are. And I think talking about things in terms of data literacy is is, like, a little simplistic and insulting if you ask me.

Well, my biggest, agreed. But but if but I think my biggest concern is that that perspective is is blaming the users for a product failure.

Right. Yeah. I yes. I I think exactly. I think, it’s it’s this I think it’s this tone. I think we’ve talked about this word, the scolding tone of this is a you thing.

You’re not enough of this. You know? You’re not literate enough. You’re not focused enough on your data quality. Gold. Gold. Gold.

Instead of this is a we thing. What’s the we thing? What’s the problem we’re trying to solve? What are the things we need to do for our customers? What are the things we need to do to be successful as a company?

Do we have gaps that we need to address? How do we address those gaps? I think a more collaborative, more collaborative spirit as opposed to a compliance and, finger waving spirit is is what I think the next generation of of leaders needs to adopt. And and that’s not to say that that’s what everybody has been doing, but but I think in general and and and take this out of the context of CDOs.

Take put this in any leadership role.

People compliance cannot be compliance is not a sustainable competitive advantage.

You’ve gotta win people’s hearts and minds. You’ve got to be people want to be associated with a higher cause, and you can’t finger wave people to death, whether it’s to improve their data quality, whether it’s to be more so called data literate. Like, the the nature of the role needs to change to to be more how do we get people together, to attack real problems, in a in a a more intellectually rigorous way. And I think we just have a very we use simplistic hyperbolic language that to me is, is a bit of a turnoff.

Couldn’t agree more and and incredibly well said.

We need to have a collaborative, productive, supportive relationship with our customers. I love the fact you used the word customer, not stakeholder, not business. Customers because that’s what they are. They’re our customers. And and and kinda starting a conversation with, you know, well, I have concerns about your literacy is not a great way to start the conversation.

And and I think that I think I mean, I saw this at Gartner all the time because six hours out of every day, I was having one on ones with CDOs asking questions about challenges and things that they’re trying to overcome. And, frankly, what I saw was a lot of, for lack of a better word, maybe animosity towards towards towards customers.

Right?

And I because I would hear phrases like, well, you can lead a horse to water.

Well or they just don’t get it. Or, well, they’re the business, and they’re never gonna get data quality. They don’t understand that, you know, they don’t understand the downstream impacts. They just don’t get it.

They’re making my job harder. And and I kept thinking about it and think about it. And I this is something I think about literally every day. We have successful businesses.

Our business partners are doing a good job. The systems that they implement to support their operational day to day tasks are implemented the way they’re implemented for a reason Mhmm. To be as efficient as absolutely possible.

And what I think I’m seeing is is that maybe maybe that means that sometimes we are less efficient in the data and analytics world, but it doesn’t mean they’re out to get us. And it doesn’t mean they don’t get it, and it doesn’t mean they don’t care.

Right? Like, I know our job is hard. I’ve I’ve led data teams. I know the job is hard, but it’s the kind of the us and them, the animosity that that we really need to do away with.

And I and I think that taking a different perspective on on data literacy is a great start. And I would also I would also welcome a different perspective from around data quality. And and this can be our last topic today because I I you and I were talking about this. I made a post a little while ago citing this completely ridiculous stat, about how eighty percent of all data scientists’ time is wasted due to low data quality.

And my head wanted to explode when I read that, and then I did a little more Google searching, and my and my head was had separated from my body and was spinning clock counterclockwise above my body because what I saw over and over and over again was article after article after article after article saying kind of similar incendiary things about the the absolutely dismal state of data quality. And then when I peeled the onion on some of these things, I was like, okay. What does this mean? What are they spending this eighty percent of their time doing?

It’s like, well, they’re they’re they’re transforming data. They’re normalizing data. They’re they’re wrestling with integration across systems that that have disparate database management systems. They are, maybe dealing with semantic challenges between the way that different systems or applications define things.

And I was like, wait a minute. Most of this actually isn’t data quality at all.

It’s it’s it’s it’s it has to do with the fact that we’ve got kind of the specialized business functions that all work largely independently and do things slightly differently, but that’s by design.

Mhmm. That’s not because they’re out to get us. That’s because that’s not because they don’t get it. That’s not because they don’t care about data quality.

By the way, data quality issues are real. Right? Like, they’re they they are real like, duplicate records, incomplete records, records that are that should have been archived years ago that are no good. Like, there there are real data quality issues, but then the stuff that was quoted in these articles just were not.

But I talk with data leaders every day for three years who who who were complaining about what the business was doing to make their jobs harder.

Yeah. I think, you know, these it is amazing how, you know, these these things, like, eighty percent of their time. Well well, first of all, so I so I agree. I mean, there are there are data quality problems, but but a, you know, a mature data function, you know, should have tolerance levels measuring the proper dimensions that we all talk about, for your most critical data, and you should have the mechanisms or build the mechanisms to put them in place to to be able to speak to data quality in quantitative terms.

I mean, there’s a whole there’s a whole we all we all know the structures and and, you know, governance mechanisms around this. So so when we talk about data quality, we should be talking about it, with some specificity to, like, what is the actual, you know, what is the actual problem? You know, I I would hope that people working with data, if they’re using the raw material, that they are, that they are helping inform, the the collectives, the, you know, the the organization of when there are issues. And and and, I I wouldn’t expect a data scientist to to sit down and, assume that that that they should just start, like, generating insights.

It’s ridiculous. Part part of part of your learning is familiarizing yourself with the data, is actually looking at the data and and and get I I mean, I can’t I I don’t even know what what to say on this on this topic other than again, these are just such simplistic notions when, when we should be talking much more about, a spirit of inquiry, a spirit of learning, a spirit of communication of saying, hey. Why am I seeing these types of things? What is this suggesting there might be a deeper problem?

Do we have, redundant systems that might be the cause of this? Is is and then maybe that was by design back, you know, fifteen years ago. But is that now a problem that we need to address? So instead of throwing up our hands and saying I can’t do anything, it it we should use this as, whatever, material to be able to make decisions about where we need to be investing.

There’s just like, we don’t have the right feedback loops. We have complaint loops, but we don’t have feedback loops in the spirit of how do we really improve things. I I I, you know, I think we need to get into that, more of a more of this kind of collaborative, how do we build things together better for the future instead of I can’t work with this? I mean, it’s it’s just a mindset shift that I think is needed and and, you know, perhaps has been achieved in some organizations.

Perfect. It’s the mindset shift, how to be more collaborative, how to be more supportive, how to be more customer centric and and assume that, typically, the business probably has positive intentions because we’re all we we all have similar goals when it comes to at least the high level goals. So couldn’t agree more. Well stated. Allison, with that, we we have reached our our time. I know you and I could keep going, and I and I could just keep feeding the fire with these additional topics because I I know we think in many ways, similarly about, in many things.

Look forward to meeting you in person one day. Be awesome. But in the meantime, thank you again for your time today. Thanks all to our listeners and to our viewers and to our subscribers. If you’re not already a subscriber of the CDO Matters podcast, I would be thrilled if you subscribe.

But other than that, I will look forward to having another conversation with another brilliant data leader sometime in the near future. Thank you, Allison. Thanks, everybody.

Thank you, Malcolm.

Thank you.