CDO Matters Ep. 34 | Modern Data Leadership

October 5, 2023

Episode Overview:

We live in a world of constant change and massive technical and social disruption, yet the way we manage data has largely remained the same.  A continued focus on the same old and tired approaches to data management are no longer serving the needs of data leaders and they must be revisited.  In this episode of the CDO Matters podcast, Malcolm explores what it takes to break free of old mindsets that limit the growth and tenure of data leaders, and to embrace more modern forms of data leadership.  

Episode Links & Resources:

Good morning, afternoon, or evening. My name is Malcolm Hawker. I am the host of the CDO matters podcast. Thank you for joining us today. I can’t believe this is our thirty fourth episode already. It could be the thirty, thirty fifth, maybe we staggered the release, but around there.

I can’t believe we’ve already done that many episodes of the podcast. I sincerely and truly hope that you as a data leader, as a chief data officer, CDAIO, CDAO, whatever the acronym is that you use, if you’re a data leader or aspiring data leader, data manager, data steward, somebody who lives and breathes in data like me, maybe you’re an analyst. Maybe you’re a consultant, welcome.

Thank you for being here. I sincerely hope you’re getting value from these podcasts. I launched this podcast over a year ago with the goal of increasing the tenures of chief data officers or people who want to be chief data officers, I want to empower data people. I want to change how we manage data. I want to drive help you drive value for your organizations. That’s why we did this podcast.

Just over a year ago, looking back, I had just joined Profisee, from Gartner, where I was a Gartner analyst, where I had the honor of talking to literally thousands, plural, thousands of data leaders about their challenges day in and day out about what was working, about what wasn’t working. That was Nah, that was just such an awesome job.

My thank you to to Gartner and all of my peers at Gartner for that amazing, unbelievable, incredible experience. It was it was something I will always always cherish. I did end up leaving the position. I I learned a ton about myself in the process. I learned a ton about what takes just kind of mentally and physically to be talking all day every day with senior business executives. It’s both an incredible, unbelievable honor, but but it’s also it was actually kind of kind of draining, especially for—brace yourselves on this one—a Myers-Briggs introvert.

I am a Myers-Briggs introvert, which means that interacting with others and interfacing with others and talking with others while I enjoy it, while I find it incredibly rewarding while it’s my job. It is literally my job and that’s what I get paid to do, but it does draw energy from me. That’s what, a Myers-Briggs introvert means is that it kind of draws energy from me. It doesn’t provide energy. To me. We probably all know. Most senior leaders are are are e’s in in Myers-Briggs parlance, the the ENTJ. I’m I’m more of an INTJ. They’re ENTJs, which means they they gain energy from interacting with people. And these are people who who want to be around others.

And if there’s a company event or maybe, you know, like a company meeting, the first thing they wanna do is go find people and interact with people. And the more time they spend with people, the more energized they get That’s not me. I’m the opposite. I need my fortress of solitude. I need I need to escape to my my, you know, my ice cave, at the end of the day. Wasn’t that Superman’s for it was made of ice? Was it not that that’s the metaphor here in case you’re wondering, like, what’s he saying ice cave? I I thought he was Canadian. Does he live in an ice cave? No. I live in Florida. But you get my point.

That was one of the bigger reasons why I left Gartner. Another bigger reason we’re gonna talk about today, in a lot more detail on this podcast. You’ll notice I don’t have any guests with me today. That’s cool because what I’m about to share, well, I mean, it’s not it’s not cool. It’s not uncool. It just is what it is. Because what I’m about to share, it really comes from right here, the noggin in here, and and I believe that I’m the best person to speak on the things that I’m gonna talking on today. So that’s why this is a solo effort as it were.

A couple more housekeeping things. By the time you watch this, it’ll probably be close to the end of September, if not the beginning of October. I’m on the verge of doing a lot of speaking this fall. My goodness. I am literally traveling the planet again. Did it in the spring, and I’m gonna be doing it again this fall twenty twenty three. A number of conferences. I’m doing the IDMA, the assurance Data Manages Association, Comfort of Philadelphia in October. I am doing DGIQ data governance and information quality. The East conference in DC in December. I’m doing the IRM enterprise data, conference in London in early November, I’m doing enterprise data world in late September in Los Angeles, smattering it between there.

I’m traveling through about seven or eight different cities, including some in Europe, where we are partnering with Microsoft, to hold these technical briefings related to MDM and the Microsoft Fabric. So we’ll be talking, with local data leaders in, Dallas, Toronto, Chicago, Boston, Munich, London, and forgive me if I’ve forgotten one. But I’m gonna be a busy, data evangelist this fall. And if you’re in any one of those cities that I listed off, whether it’s through a conference or one of these briefings, I would love to see you.

Checkout Profisee dot com. Within our website, we’ve got details on events. If any of the cities that I listed off, you’re there, and you would love to talk about data or data fabrics or MDM or data governance or data strategy spend a day in a Microsoft technology center. These buildings are unbelievable if you’ve ever if you’ve ever been to one is where Microsoft holds a lot of local client meetings. They hold their a lot of their technology briefings is exactly what we’re we’re gonna be doing this fall in partnership with profits or with with Microsoft, of course, Profisee of my employer. So that’s one of the housekeeping items. Would love to see you this fall. And maybe you’re gonna be attending one of those industry events, which would be cool as well. So that’s housekeeping, item number one.

The other housekeeping item. I don’t ask for this often, but if you like the content, if you’ve checked out the podcast before, please subscribe. Subscribe through, you know, if you’re if you’re catching us just on audio. Obviously, the, you know, Spotify and Google, and Apple podcasts of the world. That would be great or maybe just subscribe through YouTube. YouTube, we do stick these, online, obviously, where you get to see the full, the full experience here, as as it were, and and thank you for that. So it would be it would be it would be really neat if you could, subscribe. And I guess, like, all the usual like hitting like buttons and all and all the rest of it. I’m not here to be liked. I I’m here to help. And it, but if you do hit the like, that would suggest that you’re getting value from this. And that, that would be, that would be good to know because I would love to continue to do this.

So but again, I’m not here to drive clicks or, you know, drive, you know, add revenue through any of these platforms. That’s not my thing. My my thing is here is is to be here to help. Let’s talk about that a little bit more. Forgive me, it’s still coffee hour here, and I’m gonna be taking some coffee. If you’re checking me out on YouTube, you will notice a minor change to my studio environment here. I have a a splatter screen in front of my mic. One of my one of my coworkers said that I was really giving punchy Ps and it was wrecking our audio. So I got a splatter screen. Yeah, this this thing here. That’s what that’s supposed to do.

So hopefully, it, yeah, it does that. In the coming months, non sequitur. In the in the coming months, I do hope that we’ll improve some of the video quality here as well. This is my home office. It’s little tiny office here in my my home in our little beach hut, in in East central Florida. But, hopefully, I’m gonna be upgrading the video experience the video platform we use compresses this video significantly. And I’d, I’d, I’d love to get just slightly higher video quality.

But, anyway, so to today’s topic. And why did I opine at the beginning about, my experience at at Gartner? And and you may have heard in in multiple episodes in the past. I mean, I’ve shared some of my anecdotes about about why I left. Gartner and and what I’m trying to do here at at Profisee and and and the help that I’m trying to give. I think it would be appropriate to say that I did get slightly frustrated at Gartner, not because I was having to be extroverted all day every day. But part of my frustration at Gartner, and you may have heard this in in previous podcast episodes.

I know I expressed this recently when talking with, Bill Schmarzo and Alice and Stegraves, both was a certain degree of frustration? I think that’s the right word on on sharing what we know our best practices and sharing what we know works four data leaders, like things like focusing on value and things like prioritization and things like building out the team and and things like, you know, how to improve your culture and data governance and all these other best practices, the things that I talk about day in and day out on this podcast and on LinkedIn.

By the way, if you’re not connected with me on LinkedIn, please do. Please reach out. Would love to connect. I’m sharing anecdotes insights, best practices, advice. Almost every day on LinkedIn, four times a week, five times a week. So I would be sharing these insights, and and advice at Gartner, and and it was pretty rare, that I would have the ability to talk to data leaders who were actually implementing a lot of those. And and a lot of the things that we knew were working were not getting implemented. And and I kept wondering why. Why? Why? Why why do we still struggle with some of the things that we’ve always struggled with? And then I would step back and I would kinda look at broadly at the space. I’m waving my hands. You can’t see that. If you’re listening on on Apple Podcast, I’m waving my hands. Kinda goofily here about the space.

And what do I mean by that? Well, let’s, you know, the space, meaning the industry, software vendors, consultants, analysts, and and practitioners. That’s kind of the way that I would look at the space. Those those kind of four groups. And I knew what what I was saying as an analyst knew the advice that I was giving, and and I would look at the vendors and vendors were, for the, for the most part, basically, just saying what the analyst would say.

Sorry. It’s true.

Analysts can most certainly drive agendas because the more that analysts talk about things, the more vendors talk about things and the more that analysts and vendors talk about things, the more that customers will talk about things because that’s and the more that consultants will talk about things.

So there’s this kind of this self-fulfilling prophecy here that the more that the analysts are talking about certain topics or certain issues, the more they tend to become real. But from a best practice perspective, from an advice perspective, I took a step back and I and I and I asked myself, okay. We still have a lot of things that we’re struggling with. We’re still struggling with things like quantifying business value. We’re still struggling with governance. My goodness gracious. We’re still struggling even though we know it’s important. We’re still struggling with data quality, and and on and on and on.

So I stepped back and I just kinda looked at what the, you know, what others were saying from a best practice and guidance perspective. What I found was a lot of same old same old. I’ve been in this space a long time. Twenty plus years. And the things that we’re saying now, as true as they often are are the same things we were saying twenty years ago. And I look at how things have changed from a technology perspective, from a business perspective, just from a social perspective, heavens, across so many things, things have changed.

We live in a world of constant disruption. We live in a world now where never would have even imagined war in Europe. We live in a world of increasing amounts of debt and and devaluation currencies and supply chain disruptions and pandemics and so many things have changed. But so much of what we advise as analysts and consultants and as vendors hasn’t really changed that much in the data world. And I think there’s a a number of reasons for that.

And then I do think that we have a little bit of a self-reinforcing loop going on here. I talked about that before But I do think that there’s a little bit of self-reinforcing here in in that we keep trying to pound a lot of square nails into round holes. And we know that the advice we’re giving is good. We know that, right? Like, focus on value. That’s like, that’s like fortune cookie stuff. Right? Of course, we’re focused on value. That’s good.

And we can even go into more detail about how to do that and how to do things like build business cases and and ROI analysis, KAGR analysis, all sorts of other analysis, pick your pick financial flavor, how you have however you wanna do that, and we can go into details about what, you know, kind of good looks like from a from a recommendation perspective. And we keep recommending it. And we keep recommending it. We keep recommending it. And we know it’s good, and we get a little frustrated.

So we keep recommending it more. And more and more. And I started to see a few things. Evolve out of this. And I started to to to look at things a little bit differently while I was still at Gartner and certainly over the last year. Some of this frustration, I think, that I certainly was feeling was as an analyst and that I know that I was feeling as a practitioner Right? Like this idea of why don’t why doesn’t the business focus on data quality? Why they just they they and now we hear this all the time at Gartner. All the time.

They just don’t get it. Why are they trying to make our jobs harder? Right. Why don’t they care about data quality? We care so much about data quality. Why don’t they care? Why don’t they care? Why don’t they get it? Why aren’t they doing these things? Why aren’t they focused on the basics? Right? Why are data scientists wasting eighty percent of their time on data quality issues?

So these kind of sentiments that I that that I was feeling at Gartner that in many ways, in many ways, I believe now in retrospect, I was amplifying when I should have been squelching them. I think I had good intentions. I had positive intentions, but I think In many ways, some of these mindsets I was actually amplifying, and some of the advice I believe that we were giving actually amplified some of these mindsets of what I could be largely viewed as us versus them. Right, us being the data people, the people that get it, the people that care, the people that are working hard to allow us to be data driven and who doesn’t wanna be data driven?

It’s actually even part of our strategy. We talk about being data driven, who doesn’t wanna be data driven, and I’m the one that is trying to push this giant rock up the hill. And the business just keeps pushing the rock back down on me over and over and over again. And that’s certainly some of the frustration I heard when I was a Gartner analyst talking with literally thousands of CEOs. It’s certainly some of the frustration I was feeling you know, so you have practitioners frustrated.

You have analysts frustrated. I think the vendors are less so Right? Of course, because they just wanna sell software. And I think the consultants, to a certain degree, I know upwards of fifty percent of my audience is consultants. But I think it’s right for consultants to take a look back and say, okay. Well, how much are you trying to fix that or how much are you trying to exploit that? And what I mean by that is some of those frustrations, the us versus them. Put all of this together. Put it in a pot, make stew. And I’ve come to the conclusion that we’ve got some challenges related to mindset. I will just loosely call mindset.

And I think my dear listeners and viewers, I think that these problems and this mindset and the problems that the mindset causes have a lot to do with short CDO tenures. I think they have a lot to do with our inability to drive value. I think they have a lot to do with the fact that many of our customers do not view us in a positive light. And I want to explore this topic more deeply, and I wanna start talking about what it would mean to become a more modern data leader. What would it mean to to to evolve past the mindset of us versus them? What would that mean to us? How do we do that? If that is in fact a problem, I would love to discuss it more with you.

I’m gonna have lots of discussions on this on LinkedIn. You can certainly add comments onto, you know, YouTube and and, if that’s how you’re consuming this content, but I wanna explore this concept more because I I’m starting to believe that there are a number of things that are really holding us back and I want to eliminate those because I am sincere in my desire to assist. I am sincere in my desire to extend your tenure to make you a better data leader to the degree that this advice helps. I do know I don’t know if the advice helps. I hope it does. I don’t know if it helps, but I do know I do know with certainty the same old same old same old, the finger waving, the business doesn’t get it.

The, a lot of the old mindsets and a lot of the old ways of thinking, I do know with certainty, they’re not helping. They are most certainly not helping. So let’s talk more about what would it mean to be a little bit more modern. What are some things that we should maybe be thinking about as data leaders? I’ll list some of these off. This is this came off a LinkedIn post that I made, but but I think I’m gonna write a book here. Maybe it’ll start with a small kinda ebook and maybe evolve in time. Maybe I’ll have to clone myself in order to do it. Maybe I can use AI to help do that. Well, not write the book. I wouldn’t do that. Well, no. Probably I wouldn’t. Yeah. I just I just wouldn’t. I’m an artisan when it comes to this stuff. But maybe I can help AI can help me clone myself so I have more time to do it. But anyway, what would?

Topic of today’s discussion that was a long lead in. Man, I’ve been rambling for good twenty minutes. As you know, I do. If you ever watch me on our live sessions in the last Friday of every month or if you’ve ever listened to podcasts, I tend to ramble a little. What would it mean to be more modern when it comes to data leadership?

Number one, let’s talk about data quality. A modern data leader would see the refinement of data, and I like that term refinement. I like the oil metaphor here as well. Right? Taking it from some raw state or unusable state to to refining it. Because we know that that the the raw the raw stuff coming out of the ground has great value and we’re just gonna make it even greater. So it’s not a it’s bad and it’s crap. Which is kind of today’s perception of data. Because when we use the phrase data quality, the opposite of data quality is poor quality. Good quality, bad quality. Black white. That’s not really how data exists.

Data exists in various states of fit for purpose. Sometimes it’s really fit for an operational purpose as it exists in CRM system or an ERP system or digital marketing system or maybe as new some entry in a in a web log somewhere. Who knows? Right, as just one little chunk of transactional data coming off an IoT sensor somewhere. But anywhere, that data is fit for a certain purpose. Right? So sometimes it’s fit for that purpose and sometimes the quality is good. Often it’s fit for an operational purpose, but it may not be fit for an analytical purpose.

So think about that. Right. Right now, today, when we talk about data quality, we just kinda lump everything into this bucket of data quality. We that it’s poor data quality. But what does that mean? I think at a high level, often what that means for us as data people especially those who are focused on the analytical side of the house and building reports and data science and insights off of the data. Data quality for us means, okay, is it fit for an analytical purpose when the data coming out of the ground?

As it were using the the oil metaphor when the data is fit for and coming from the source is fit for an operational purpose. Right there, that split Right? That split for us to say, well, it’s low quality. What is perfectly good for the business? What is perfectly good for our customers? What is perfectly good for issuing an invoice or recognizing revenue or sending an email or any other business process that our customers are measured on. It is good for those purposes, but not good for an analytical purpose. Call it low quality.

Well, right there, when we do that, we put up a barrier. We put up a barrier that creates an us versus them. We put up a barrier that says we’re gonna make a judgment on this data and say it’s bad. When in fact, to our customers, it may be perfectly fine. It may be associated to successful transactions. Maybe delivered goods. Happy customers. Who knows? For our for our customers, for our internal customers, that data could be perfect. But if we have to spend a lot of time normalizing it or changing it or concatenating it or doing something else to it, maybe even steward it. Maybe even applying human oversight to it. We will say it’s bad. This divide folks, this divide related to data quality, I think is part of our mindset challenge.

So this idea that it’s bad. It’s bad. It’s bad. It’s bad. The data quality is bad. What the business is making it harder for me. The data sucks, the quality is low. When in fact, it may be perfectly good. This is a problem. It’s a problem.

So a modern data leader would say the opportunity to refine the data to put it in a place where it could be used cross functionally in analytics or data science or AI or my dashboard or my anything else, the opportunity to do that to refine that data, that’s an opportunity. It’s not a burden. It’s an opportunity not a burden today for a lot of data leaders. If you talk to them, it sounds like a burden. That’s what I heard over and over and over and over again at Gartner, and I hear it almost every day on LinkedIn.

The burden of low quality data, woe is me. Why is the business making my job harder? The business is trying to excite customers, build products, be more efficient, innovate, the business has positive intentions.

So this is number two. A modern data leader would build a team and a data culture and a team culture that views the business as having positive intentions. I don’t know. And I was on the business side for a number of years. I led a product organization. I was a chief product off or I didn’t know anybody that worked for me or anybody that I hired or anybody that I interfaced with that was purposely trying to make data bad. I just didn’t. I mean, it didn’t.

We were we were we were trying to build products. We were trying to support customers. We were trying to meet our SLAs. We were trying to deliver stuff on time. We weren’t trying to make data leaders’ jobs or lives harder. Our intentions were good. And often, not even with just a matter of good intention, often, we were killing it. We were delivering products. We were innovating. We were supporting customers. We were efficiently operating.

So again, this idea that that could yield bad that all of those things, right, like that working hard and and and delivering goods and supporting customers and everything that that are business partners do day in and day out. If we assume they have positive intentions, that’s a paradigm shift. Because today, I don’t I don’t hear a lot of that. I don’t, I honestly guys, I just don’t hear a lot of it. What I hear is it’s a burden they’re making my job harder.

So our customers, the people who consume our insights, our analytics, our data, have positive intentions. Modern data leaders will push that mindset within their organizations. That’ll be a foundation of the organizations that modern data leaders build because if you think that people have positive intentions, if you think your customers have positive intentions, you’re not gonna sit back and build this wall between you and them.

You’re not gonna say, why are you doing this to me? Why are you doing this to me? You won’t be a victim to what is perceived as poor data. You will be enabled to buy it. Because that’s job security. If you have spent a lot of time fixing data, it’s not a burden. It’s an opportunity. So notice.

Number three here. Notice I am trying consciously consciously to use the word customer. Because I believe that the modern data leaders put customers at the center of everything they do. And they use the word customer as often as they can. And trust me, folks, even I find myself falling back into old ways. We use the phrases stakeholders and users and and end users and the business. That’s my favorite. The business. This this this kind of nebulous, the business. That doesn’t really kinda humanize who we’re trying to support. And we should because these are people. These are customers. These are these are these are consumers of our insights, our analytics. Our data science, our reports, our dashboards. These are people, and they are our customers.

So I would advocate as much as you can as a daily leader to make and weave the notion of customer into everything you do and put customer at the center of everything you do. That would necessarily mean I think that you should start looking at things through the lens of products. Okay? Because customers consume products and or services.

Maybe a separate podcast to talk about the difference between products and services. Let’s not get hung up on that right now. I do understand that There is a fundamental difference between a product and a service. You own one, the other one you’re basically buying access to. Let’s just put that aside for now.

But your products are your customers are consuming products. Does this mean that you slap a data product label on something and call it good? No. That doesn’t mean anything. In and of itself. On the surface of data product, just doesn’t need anything. But I see a lot of people doing that. What I mean here is the integration of product management into data management, into what you do day in and day out as a data leader. Right?

Putting customers at the center of everything you do. This would include things like user centric design, customer centric design customer feedback, customer surveys. All the things that you experience day in, day out as a consumer of stuff. You know when you’ve had a positive customer experience and you know when you’ve had a bad one. What did that positive customer experience look like? How do you integrate some of those processes into your processes as a data leader?

You don’t have to remake the wheel here. This is not something net new. This is product management. Product managers do this stuff for a living. And notice I’m not saying product owners or data owners. We need to dispatch with the word ownership. You don’t own the data. You don’t. As much as you you could give somebody a a label of a data owner, they don’t own the data. What does data ownership even mean?

There are five if you ask me, there are five attributes of ownership. It’s people who create it. People who it refers to, people who govern it, people who manage it, people who sell it. Who who’s the owner? When you slap a a label of owner, again, this is an us and them. I own. Can you imagine you hire somebody or me?

Let’s just to say that I’m anointed the data owner of customer data at ACME Incorporated. And I’m the first day on the job, and I’m gonna go talk to by stakeholders. And I glibly use the word stakeholders and not customers. We should look at them as customers. But on day one, ACME Incorporated, and I am now the new data owner of customer data and I sit down with the chief revenue officer and say, hi, I own your customer data.

Is that a great way to start a conversation? No. It’s rhetorical. It’s not. It’s not a good way. You could maybe stay instead. Hey, listen. I’ve been given responsibility to ensure the governance policies that you and I collaborate on are enforced on this data because we have a mutually beneficial outcome when we do that. I’m here to steward the data or shepherd the data or support your business through the lens of better data. But I’m not gonna sit down and say, I own your data. Good luck with that, by the way. If you do. Right?

I’ve done that. I’ve said that. I’ve been the person at the other end of the table looking at a senior vice president of sale saying I own their data. It doesn’t make for a productive meeting. It immediately will put your customers on the defensive like, whoa, whoa, whoa. Hey, wait a minute. Hold on. You’re saying I own the data. You’re oh, you own customer. Oh, do you? I’m the one out there talking to them every day. Now you’re telling me you own them?

This is an example. The idea of data ownership. And, yes, folks, I know, Donna, data ownership. This is the same old same old. It’s not working. It’s not working. It’s not as anybody who’s are they succeeding at governance. Is this model working for you? Is the Domino wheel working for you? Maybe in some regards, but chances are no. Research after research after research shows that governance is a huge issue for companies.

So let’s try something different. Instead of saying data ownership, I own the data. Instead of saying in creating a wall and a division between us and them, we own you do what you do in the business side. Take a customer centric view. Take a product centric view.

How can I help you today? Customer? How do I help you be better? How do I help data enable better decision making? How can I do that?

I’m not here as an owner. I’m here as an enabler. I wanna build better products for you customer. Customers at the center of everything you do. And there’s example after example after example, there will be more content on this, by the way. I’m just picking on data ownership. That’s what that to me, that’s a layup. Of an example of a wall between us and them. We need dispatch with this. Stop using that phrase.

Product manager. I’m a data product manager or maybe a data steward or data custodian. It’s fine. We can use those words if we want to. But I’m an owner. Give me a break. And and if you are doing it that way, it’s not I I get it. There’s a lot of people who’ve been telling you to do it that way. But step back and ask, okay, is it really working? Which kind of dovetails when we start talking about product management?

A modern data leader would not see the low adoption of their products. The complaints about their products. They would not see the inability of their products to deliver business value. They would not see any of these things as customer failures. They wouldn’t see them as a result of low customer skills. They would see them as a result of a product failure. I failed to meet my customer’s needs. My product does my customer doesn’t like my product. My customer is returning my customer is complaining about my product. My customer is not using the product. My customer may be may even be going to somebody else to get a a comparable product from somebody else, even if mine’s free.

My customer may not trust. My product. May question it. May question its accuracy, its validity, its usefulness. If this is happening, and it probably is in some regard, in some level, if this is happening, is that a result of low user skills? Is that a is that a result of a skills gap? No. Probably not. It’s probably a fact that the product itself is problematic. That the data is weak. The data is incorrect. The data is worthy of questioning. The data is not consistent.

So if your customers don’t like your products, then there’s a problem with your product. Not the customers. Do we need to train our customers? Of course, we do. Do we need to make sure that they know what the right tool is for the right job? Of course, we do. But I challenge the very notion of this idea of data literacy because it turns everything around. It turns what should be a product problem and a product challenge product opportunity into something about the consumers, into their skills, into their knowledge. And we should look first inwards before we look outwards.

This is another attribute of a modern data leader. We need to look inside before we look outside. Have we done everything that we can to make sure this product is trust were the inaccurate and usable and well designed and intuitive and all of these things. Have we have we kept the customer at the center of everything we we do. Have we done robust interviews? Have we made sure the technology is correct? Have we done everything that we possibly can to make sure that is the best possible product that is the best fit for the need that it’s easy to use all of these things.

If the answer is no to any of those questions, focus there first. Look inward. Look at what we do, how we do it before we look outward. If we exhaust all of that, if we exhaust all of that introspection and we are one hundred percent confident, that this is simply a lack of training, well then focus on training. But don’t make that the primary thing. Don’t start with the assumption that the training is the issue that user skills are the issue. Don’t start with that. Look in before you look out.

I’m passionate about this stuff guys. I really, really am. And I’m gonna keep talking about this because I think there may be something here. If you disagree, please let me know. Other attributes of a modern data leader. This is maybe gonna be a long podcast and coming up on forty five minutes, but, anyway, shocker. Other attributes of modern data layer.

Well, they measure value. And and they measure value because it’s important because that’s why we’re here. If we’re not here to drive business value and business outcomes, then what’s the point? A modern data leader would measure that would make a connection between data and business value. And they would even go so far as to say, I would like to be compensated based on the business outcomes that I drive. I would like to find a way for my business KPIs or my KPIs in the data world to be linked to business KPIs. That’s how I would like to be compensated. That’s what a modern data leader would say is tie my performance to business performance.

Now I can hear you saying, Malcolm, why would I do that? Cause I can’t control that. Why would I do that? Cause I can’t control that? You would do that because that’s what’s best for the company. I know this takes leadership. I know this takes bravery, but you gotta do it. I made a post to date on LinkedIn talking about the sixth annual CDO survey that Gartner produced that showed the data leaders that focus on data quality, CDOs that focus on data quality are forty three percent less likely to drive value for their business. Yet more than half of CDOs are actually measured on data quality KPIs. Rock, hard place.

This is a perfect example. This is the answer of why you want to focus on business outcomes. CDOs succeed and drive value for their organizations when they focus on outcomes not on data quality. Yet so many CDOs are measured on data quality.

But how do we pivot that? Well, you change it from the measure of being data quality. And when we chase data quality, it’s less effective for the entire business, but that’s why we’re here. When we chase data quality, forty three percent less likely to drive business value. Instead, chase business value, chase business outcomes. Have that be the metric. Have that be the way that you’re measured as a modern data leader.

This is a win-win. Better for you because it’s driving value for the organization. So I would argue in many ways that the fear of being tied to things you can’t control is actually shortening your ten years. Step out on that limb and be willing to tie your performance to business performance. When you do that, so many good things will happen.

Instead of being focused on data quality and all of the things that go into creating data quality frameworks and data clean ups and all the data governance related to data quality with poor definition of scope because if you’re not measuring value, how do you prioritize and we know that most don’t measure value?

So instead of being kind of just broadly focused on this nebulous idea of data quality, which we also know is not benchmarked well at all. Focus on the outcomes. Use the outcomes to limit your scope. Use the outcomes to limit what governance you’re focusing on and what governance you’re not focusing on. It makes you more efficient. It increases the likelihood that you will produce good outcomes. It ties you to specific high priority, high visibility business initiatives. And it’s good for the company. Win win, measure, and be willing to be compensated on those measures as they relate to business outcomes, not data quality outcomes and on and on.

Speaking of governance, a modern data leader would view and manage data governance as a business enablement function. Not a change management function, a business enablement function. Again, I can hear many of you say, well, data governance is in essence change management. Okay. Fine. You’re gonna set some baseline rules, and then you’re gonna measure, you know, you’re gonna monitor against changes to anything. It’s I get it. It is a change management’s function, but the mindset. Remember, we’re going back to mindset, the us versus them.

What we need to do is to consider governance as an enablement function. Measure the business impact of governance and focus on governance through the lens of enablement, business priorities, business outcomes, business enablement. Pivot governance from a from a must do.

And I’m hearing this every day. It’s how do we shift from the defense to the offense? The way you do that is to look at governance through the lens of a business enablement function. More content to come here. More content to come. Lastly, challenge the status quo. Ask Why? When you keep hearing the same best practices over and over and over yet, you also see that things aren’t changing that much. Start asking why? Challenge the status quo, be a leader, go break some stuff. Question the notion of data first. Or being data driven or data led.

Being data driven was never about the data. It wasn’t. Being data driven was about the business, our customers, using data to make decisions, fact-based decision making. That would that’s what it’s about. So for our customers being data driven, I think that’s a reasonable aspiration.

But for us, The problem the problem with being data driven is that we took it. We took it as not only the guidepost, the north star, for our customers, we took it as our north star. Meaning, we put date at the center of everything. Data is important. Data is the most important thing. It’s not. For us, It’s not. It’s the customer. It’s the customer.

You don’t hear a lot of manufacturing companies out there talking about being ingredient or material or part focused. Data is a raw material to our manufacturing plan. Yes. Quality matters. Yes. Process matters. All those things matter. Excellence and those things do matter. But you don’t hear manufacturing companies or product centric companies talking about how they are ingredient driven. You will hear some people talk about the quality of the ingredients. That’s a separate thing. But it’s not about the ingredients. It’s about the customers. It’s about the customers.

I see a lot a lot more, you know, like the AI and the garbage in garbage out. We need to focus on data folks and data folks on data. Focus on the customers. Focus on creating a product that customers want to use and would otherwise be willing to pay for. Focus on customers. Focus on companies. Don’t focus on the data. Focusing on the data means we tend to be a little introspective. Instead of starting with a business need, we start with an inventory of goods.

I’d see this all the time at Gartner. I would see data leaders start with this bottom up process. And I see this today when in discussions about data products. Instead of starting the conversation with business needs and business outcomes, what do our customers want? We start and this bottoms up thing by looking at every nugget of data that we’ve got everywhere. And we do detailed profile and discovery, and we try to understand what’s like the lowest level and most minute level of kind of where can we apply a SKU to?

And is that a is a field a product, a record, a attribute, a, whatever table. Is that a like, when we get in these drawn out conversations about what’s a product? That happens when we take a date of first approach. These time-wasting academic enterprises and undertakings where we spend days and hours and weeks trying to understand what is and what isn’t a product is a function of a data first might set.

Take a customer first mindset, ask them what they need. Ask them what their problems are. If that’s your north star, you’ll work your way down. You’ll figure out what data is important, what data isn’t important. You’ll build a solution to solve that problem. That’s the product. It’s that simple. It’s that simple.

This is not an ontology driven exercise. This is not an inventory driven exercise. Understanding what our customer needs does not happen by looking at our inventory of goodies. It doesn’t. It doesn’t. It starts by asking them what they need and what the problems are and how can we help.

Clearly, I’m passionate. I’m nothing but full of passion on this. I think there’s something here. And what I mean by that is I think we have a significant opportunity maybe even an obligation to change how we think as data leaders.

Do we see data quality as a burden? Do we see product creation as this bottom up inventory driven enterprise? Do we see value as a metric tied to data quality or do we see value as a metric tied to business outcomes? Do we question the status quo?

If we are ever gonna get past having half the tenure of CIOs, If we’re gonna get past, our customers doubting the quality of our output, doubting the importance of data quality, doubting the importance of the things that we do, we need to start thinking a different way because the way we’ve been thinking for the last twenty years isn’t working.

It’s partially working. But it’s not working to the degree that we needed to work. Our businesses are looking to us as change agents our businesses are looking to us to drive the digital transformation.

So when you start talking about data culture, you start talking about data culture. Look inwards before you look out. The data culture is not something that happens outside of us. And outside of the data analytics function, it starts at home. Look inwards. Data culture starts with taking this customer centric mindset. And all the other things that I’ve talked about so far in this podcast today. Starts at home.

Look inwards before you look outwards. Build the culture. Model the behaviors you want others to model before you ask them to model them. Be the change that you want to be, that you need to be, that you’re you want your organization to adopt if you model those behaviors. If you deliver the value, if you put customers at the center of everything that you do, you will be more likely to see them model those changes than you would if you were simply waving your finger at me.

Here ends another episode of the CDO Matters podcast. I’m so grateful.

If you made it this far, wow. Thank you. I’m honored that you have. Please join me on LinkedIn where we can continue these conversations day in and day out. Again, my thanks for your patronage, my thanks for listening. And I will see you on another episode of CDO Matters sometime very soon. Thanks, all.


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.


Complete the form below to request your spot at Profisee’s happy hour and dinner at Il Mulino in the Swan Hotel on Tuesday, March 21 at 6:30pm.


MDM vs. MDS graphic
The Profisee website uses cookies to help ensure you have the best experience possible.  Learn more