Morning, afternoon, or evening, whatever time it is, wherever you are, however you are consuming this content. Hello. I’m Malcolm Hawker.

I am the host of the CDO Matters podcast, and you’re listening to that today. I’m thrilled that you are here. Thank you for subscribing. Thank you for listening. Thank you for being a part of our growing community of data and analytics leaders.

Here on your podcast provider of choice on LinkedIn on YouTube and on lots of different places where you consume content related to your favorite topic. Yes.

Data.

It’s actually my favorite topic. So, it has been for years. I’m I’m fascinated by data. I’m fascinated by the way it can be used. I’m fascinated by the management of it. We’re gonna talk about that today.

As you may notice, I am without guests today. I’m going solo as it were.

I think you’re gonna see more of that in in the coming year. I’ve got some feedback that that says that I I tend to be a little more maybe maybe authentic is the word. When when I’m flying solo, I certainly tend to rant. More when I’m flying solo.

That’s that’s for sure. So, not that not not that I don’t enjoy my guests, because I certainly do. And I don’t know everything, shocker, Nobody does. And that’s really the value that a lot of my guests can bring to the table is such a diversity of value or of of input or diversity of perspective and experience, and I really enjoy asking questions.

I do. And I enjoy learning from others But I think we will maybe in 2024. It’s late 2023 now as you’re listening to this, you’ll be seeing or hearing more of me solo a few logistics before we dive into our topic today, which is all about data culture.

A few logistics.

This podcast will launch on December 14, 2023. So if you are watching this like most do, or listening to it within a day of its launch just a heads up the next day Friday, the fifteenth of December 2023. I’m doing a live event.

As I do every month on LinkedIn, just FYI told the listeners out there, on the, typically the last Friday of every month, this one will be the fifteenth because of the holiday season the US.

But typically, the last Friday of every month, I will do a live event on LinkedIn where I am generally joined by guests or me alone or whoever, and and we talk about data stuff live. And you can ask your So the cool thing about being on LinkedIn is, you know, just fire off your questions and ask anything you want. I’m gonna be joined on Friday, December 15^th by the CTO of Microsoft. Eric’s wife.

I am honored that Eric’s gonna be joining.

I know Eric. We’ve had, had a couple of lovely meals together. Had a couple of great conversations just to an awesome guy.

Knows his stuff been around the block. Obviously, when it comes to Microsoft, a really interesting brain to pick given their, place in the world these days, especially when it comes to things like Open AI, and and therefore, forty nine percent investment in OpenAI, things that are going on there. So we’ll certainly be talking about AI. I don’t know how you can talk to somebody from Microsoft and not we’re gonna be talking about the Microsoft fabric. I’m a believer.

If you’ve tuned to any of the previous episodes of the podcast, particularly one titled the date of fabric demystified, five, five, five, five.

I’m a believer in the date of fabric writ large. A year ago, I wasn’t. I was a contrarian.

You know, I contributed to some of the kind of a data fabric narrative while I was at Gartner analyst. Contributed to some of the research certainly reviewed. Peer reviewed a lot of the research being done around the data fabric, primarily by one of my, my friends, Mark Beyer, Gartner, and and others And I believed in the concept of the fabric. I certainly believed that it as as as a fear theoretical construct.

But I at the time, this would be a year, year and a half ago. I I thought the technology just wasn’t there yet, to do what what Gartner would call the activation of metadata. So this this bulk analysis of a lot of data, and applying AI ML graph and a lot of fairly advanced technologies to data to allow it to start to classify its own governance policies, to classify its rules of use to classify itself, to classify its own data quality standards where where data can actually start to inform how it is used and and and and how it can be optimized within organizations.

That’s kind of a higher level. I think of the highest level manifestation of the data fabric. There are kind of iterative implementations of a fabric that kind of start with a common infrastructure, which is what and talk to Eric about it, from Microsoft.

To make a long story short, I I think we’re there now from a fabric perspective. So if you’ve heard about it, you wanna learn more tune back to previous episode of CDO Matters, called the Data Fabric demystified, tune in on Friday, December 15, 2023 on LinkedIn live. We’ll be certainly talking about with Eric, and it will be an ongoing conversation, going going forward, on this podcast. So that’s the logistics. That’s number one. Live event December 15. Hope you can join.

Well, beyond that, season’s greetings, happy holidays, Merry Christmas, happy Christmas, all of it.

Happy Hanukkah.

Happy whatever you celebrated this time of year with your family and friends, I will be making a rather brief trip, to my homeland of Canada.

Western Canada. I’m originally from Edmonton, Alberta, and all my family is still there. So I’ll be making the trip up there actually on late on this on Christmas Eve.

Man, I don’t know if you guys have traveled recently, but airfares. My goodness. The prices of which have gone through the roof and I can afford to travel home to see my my parents, at Christmas time, was to fly like late on Christmas Eve. I have to stay at an airport hotel when I land on Christmas Eve. So ho ho ho. But anyway, yeah, first world problems.

I I will be take taking a trip home to Canada and spending some time, up in the cold. And, hopefully, there’s snow on the ground because there isn’t hearing in Florida.

But that that’ll be great. And I wish the same for you. I wish, a really, really wonderful holiday season for you, spending time with friends, and family. And I wish peace.

You know, the holidays and and and particularly Christmas time, you know, you know, peace is is a common message there and and boy oh boy. Do we need it?

I won’t go into a lot of details there, but it just it just pained me. The things that are going on around the world. And, I I just wish I just wish we could find more peace in the world.

So data let’s talk about data. Let’s talk about data culture.

So I made a post on LinkedIn today Today’s date, for late November ish.

I make a lot of posts on LinkedIn as you as some of you may know.

But made a post on LinkedIn today about data culture. It’s it’s going to be something I talk a lot about in 2024. And and the reason why I’m gonna talk a lot about data culture is because I think I think we need to look at culture differently.

And I I hope you are tuning into the podcast because by now maybe, you you recognize that I tend to take a bit of a provocative stance on a lot of things.

And the reason why I take a provocative stance, and that includes on data culture, by the way. And we’re gonna talk about that today. The reason why I tend to take a provocative stance is is not for clicks and not for likes and all of that stuff. I I I really I I don’t care about that. I’m just here to help. I really legitimately am here to help. My role and my mission here is to extend CDO tenures, and to pro and to kind of improve the standing of of of the data and analytics role and the data and analytics function at most organizations.

The reason why I’m being provocative is because I spent three years as a Gartner analyst.

Talking day in and day out with, CDOs and CIOs spoke to literally thousands of data leaders over the last five years both within my role at Gartner and as a distinguished architect down at Brad Street and and other data leadership roles, including running an IT function. I’ve spoken with literally thousands of people.

And I’ve I’ve been lucky enough to play a consulting type role, particularly while I was a Gartner, and and and being kind of what we could call maybe a thought leader in this space. I know that that that rubs a few people the wrong way. I think it’s an okay phrase. It’s fine. I’ve been around a long time. This is real gray hair, and and I know some stuff and I’m here to share what I know.

And while a thought leader, particularly at Gartner, I would share insights on the things that that we know.

Through through research, like, through quant research, right? Study after study, serve after study, I would share insights in the things that that we know work when it comes to getting data or getting value out of data when it comes to, you know, building out of data analytics function. When comes to getting stakeholder engagement to it comes to building out governance functions. Like, things I could go into a lot of detail. It’s gonna it’s these are these are topics that we that we that we talk a lot about here on this podcast about the things, you know, best practices, the things that we know work. And I was sharing those day in and day out as a garter analyst and they were so rarely implemented.

And I I would be talking about the things that need to happen yet it wasn’t moving the needle. And even often when these things were getting implemented or partially implemented, the the the needle still wasn’t moving. We’re still struggling with low CDO tenors, we’re still struggling to show and articulate the value of data within organizations. And we we’re still struggling to to to really kind of solidify shall we say, you know, the the the value of data writ large within organizations?

So I was having these conversations and and I really felt like the message was good. And and I and I felt like I had data to show that the the recommendations were solid, but we still weren’t making, and I wasn’t making much of an impact. Part of the reason why I joined Profisee. Part of the reason why I’m having this podcast, but I’ve been on a bit of a journey over the last couple of years.

Where I’ve been forcing myself to think about things differently because I think that’s really what’s needed. I’ve I’ve had a bit of a Eureka. Eureka.

Moment, recently when it comes to issues of mindset When it comes to issues of how we as data and analytics leaders think about problems, think about flips out of problems is an opportunity. Right? How we how we kind of approach problems?

What we think about the organizations that that we exist in? What we think about people’s tensions within those organizations. What we think about people’s, you know, struggles, how how we approach them, right, and how we approach the role how we deploy, resources, how how we motivate our teams. These are all kind of things related to mindset, and I would argue actually maybe even culture, right, which is kind of brings us back to today.

One of the things that was consistently cited in a survey that we did at Gartner every year called the CDO survey. And I would argue that Gartner CDO survey is is the kind of the polo an ultimate, it’s it’s the top of the heap when it comes to insights about what CDos think.

When I started there, the the n as a Norman for the participants in the CDO survey was around four hundred. And I think last year, they got over eight hundred.

Survey responses from, like, legit real CDOs.

So there there’s really great data here.

And there are other data sources as well. The new Vantage partners Randy Peen, Tom Davenport, puts together a survey every year CTOs. There there’s other surveys out there as well.

And at Gartner and these other surveys, by the way, what is consistently cited as as a top barrier, if not typically the top barrier for CDO success, the ability to deliver value within organizations, the ability to succeed.

One of the top barriers that is consistently cited is something I will just loosely call a lack of a data culture.

Within organizations.

Right? And and we’ll talk more about that today and what that actually means. But this is something that is consistently cited as like the number one roadblock. Okay?

You’re hired as a CDO. Hooray. Congratulations. You’ve got an awesome job. It really is an awesome job.

It’s a hard job, but it’s an awesome job. So congrats. You’ve been hired into this role, and now you have to go deliver you have to go solve some problems. You have to implement a governance foundation and governance function. You have to, you know, support and and manage a data and then function. You have to define a data strategy. There’s all these things I could keep going.

But there’s all these things that we would typically associate with the CTO role. Right? And and Ultimately, we’re here to drive value. We we are here to drive benefit for the organization. And when it comes to value delivery, again, what we would hear over and over and over again is I can’t do it. Or we’re struggling to do it or we’re not doing it because there’s no data culture within the organization.

And for years, this is something that I would advise. My clients is you need to go find ways to change your influence. The culture.

I would say this all the time. Right? And it said widely within our space. You need this is like a deliverable.

Right? It’s like, oh, go hire data scientists and go hire some analysts and and, you know, deploy governance framework and change the culture, boop boop boop, like, like, like, wow. I mean, I was saying this day and day, we need to go find ways to change the culture.

And I’ve been thinking about this a lot recently. And survey after survey, nope. Still got a problem with culture. Still got a problem with culture. Now I guess it isn’t shocking because culture change is hard. Any CEO will tell you culture change is really hard.

Right?

Because it takes time, right, becomes it it’s it’s it’s a hearts and minds issue. It’s it’s not a checkbox issue. It’s not a, you know, like, well, I turned this on or I deployed the soft where it has to do with the way people think and it has to do with mindset, which is hard to change.

Even though I think we need to change ours.

But we would see people kind of embarking. Okay. I got this deliverable. It’s I’m gonna go change the culture and two years ago by and I haven’t changed the culture.

And what I started to see while I was at Gartner was this this idea that CDOs were kind of handcuffed.

Right? And where where it got to the point where I would I would hear a lot of resignation Not not actually like I quit, but, like, I would hear, like, defeat.

Right? I would hear I would hear, yeah, well, you know, I’ve done everything I can, but, it’s the culture.

Right? And what what can I do? I’m I’m I’m just the VP of data and analytics. You know, I’m doing my best, but I can’t change the culture of the company that’s beyond me.

Right? It’s out of my hands. I can’t I can’t change this. So, you know, it it it is what it is, and maybe I just need to move on because this company doesn’t value data.

But I I’m hearing that a lot out there now guys. Right? And maybe maybe you’re in that position.

May maybe you’re in the position where you feel handcuffed, where you feel like you are for lack of a better word powerless when it comes to, you know, affecting change within your organization, getting people to really value data.

That’s what today’s podcast is about because I want I wanna turn that around. And if you do feel that or if you’re starting to feel a little deflated, if you’re starting to feel a a little resigned to the fact that that that that there’s there’s some cultural barrier out there that you can’t get around We need to talk about that some more because I think I think we need to start thinking differently about this very, very issue.

But let’s let’s define what what’s what’s culture.

I’m not an HR expert.

But chances are pretty good with if your company is relatively big enough and you’ve got a relatively well defined HR function, HR people are really focused on this. Right? Like, so so culture are the kind of the shared values behaviors norms ethics that that personify the desired that that personified the desired behaviors of an organization, the the behavior thought that the desired thought of an organization that the the the the shared ethics of an organization. Right? So it’s things like, you know, collaboration.

We value diversity, we value collaboration, we value teamwork.

It’s all of those things together the kind of that kind of define define what the organization is, the things that matter most to the organization.

Right? This this is the culture. So your HR department will probably have a few slides about this and will they be talking about trust? Will they be talking about things like growth mindsets and talking about things like we value innovation, we value creative thinking, we value we value all these things. These are the things that are important to us, right, as an organization as a cumulative whole. And it’s important to the organization. It needs to be important to the individuals within the organization.

It was interesting. I was in a, a series of CDO roundtables in London a few weeks ago where I was having some discussions around this topic of of culture. And and somebody within the group noted And and and they gave the quote to like a a rock star. I I and I forget the name of whoever said it.

But they said that, you know, the the corporate culture at culture is all the things within an organization that we don’t need to talk about. And I and I found that a a kind of an interesting, slightly provocative view on culture, but it’s it’s just the kind of the the ways that we operate or at the very least the way we aspire to operate as individuals and as a collective. It’s the things, it’s the values, it’s the behaviors that we care about the most, and we’re trying to to promote And I do think that often it’s the things that we don’t really kind of need to talk about. It’s the is the connective tissue that bind us all together that we don’t really need to necessarily talk about Right?

We we value teamwork. We value diversity. We value, in we we we value integrity.

Right? We value honesty. We value and and are aiming for creating a trusting environment.

All these things sound great. They’re all wonderful. Your HR department is focused on them. There are examples of kind of high functioning cultures where we are working well together. Right. We will trust each other when we think that everybody has positive intentions, we’re all working towards a common goal. There are many examples I’m sure you’ve experienced in your professional careers where the culture’s not so or maybe there is a culture of distrust where people don’t have positive intentions or well, where we we don’t work well together or where there are organizational barriers that they create competition between groups and and and on and on and on, where there’s maybe a lack of diversity or lack of opinion sharing or may maybe there is a, you know, there’s more of a fear based culture where, you know, people don’t feel like they have space to to to, you know, to share their opinions or to share their thoughts.

And those types of cultures, I mean, like, that that’s that’s bad. Right? Cause we we’re not gonna be a high functioning organization. Right? If we can’t share our opinions, if we can’t be creative, we can’t do the things that we need to be doing as human beings, So when it comes to data, let’s talk about what does it mean to be a data culture?

I I think to a certain degree that that may be a little bit of overkill. Right? Do we really need to add data hyphen culture to it?

Maybe, maybe not. A separate conversation. I think we could have interesting, you know, theoretical conversation about whether that’s even needed.

But but I think At least from a high level, there is a fairly practical perspective here that that I think has value.

And what that that perspective is is is I think a data culture is simply one where an organization values the use of facts to make decisions.

Right. I I think I think literally that’s it, guys. I I don’t think there’s a lot more we need to think about here. I think I think if if if you want to use a word, or a phrase of data having a data culture and creating a data culture, I think I think just simply saying, okay, an organization where they decisions right, are are made based on facts and not intuitions.

Pardon me, I’m getting over a cold.

You hear me sniffly.

So so I think I think I think there’s a practical use there, and and I think that that’s okay.

Right? And and that and and we we don’t need to necessarily get into the argument whether or not a data culture is a real thing or not. Or is that overkill, it may be the culture is just enough. Maybe simply saying that we value facts, that we’ve and that that we’re not driven by intuition is is something that we could say with, you know, inside or outside of data function, Right? But suffice to say, I think as a practicality and I think as as a way of thinking, I think it’s okay to say, okay. Data culture equals one where an organization where people value facts to make decisions, maybe not over, but at least equally with intuition.

But let’s talk about intuition.

I don’t think all intuition is bad.

Right? I I think, you know, when when when ChatGPT could have sprang onto the scene.

I started to feed it a lot of the the questions that I would get when I was a Gartner analyst, and there was a lot of the same questions over and over again.

And when I got back from Chai GPT was pretty good. It wasn’t great, but it was pretty good. It was seventy to eighty percent of the answer. Right? Then twenty to thirty percent of the answer, it just wasn’t there. And the twenty to thirty percent would come from kind of the school of hard knock the twenty to thirty percent would would come from the things that would be really, really hard to train a large language model. Right?

Having been there, done that, been in the position, gone through thirty years of being in business and making decisions, and and and all of the intangibles as it were, that have value.

Right? That would be really, really hard to model in data, whether that model was a regression or whether that model was simply a a dashboard or or any form of predictive analytics that, you know, today, the kind of the gold standard arguably is is chat GPG and and LLMs, right? That are using literally billions and billions of parameters.

The lamma two model uses seventy billion parameters.

You know, and and and and took six thousand GPUs to to create over twelve weeks.

Think about that. I mean, that’s, like, are you really gonna be building your own models? Do you have six thousand GPUs just laying around that you can that you can use for twelve weeks straight? To build your own model.

No, you probably won’t be building your own model. Anyway, this is my way of saying, you know, you can get to seventy eighty percent with relatively high confidence, I think you could say. Right? Using today’s predictive analytics advanced analytical models, I think that when it comes to, forecasting or providing data to provide confidence on the outcome of a decision.

I I think we could consistently, generally, depending on the use case, of course, and depending complexity of of of what’s being decided. And the date of this being provided, as as a rule, we could probably get to sixty to seventy, maybe even eighty percent. Consistently.

Right? Just just using hard data.

Then, of course, there is this other world of intangibles.

The twenty to thirty percent of the question that wasn’t answered by ChatGPT that I that that that, you know, when I was asking about, you know, best practices for data and analytics for data governance.

So this this is a way of saying that intuition is not without value. It certainly has value. And we see this today in highly advanced analytics. There’s still a place for gut.

K? So that doesn’t need to go away nor can it fully go away until we get to this world of called AGI. You know, artificial generalized intelligence, where where it’s like, this is the skynet stuff, right, where it’s machines are smarter than us, are solving novel problems, are, you know, applying, thought patterns that didn’t exist as a result of the training of the data.

So when it comes to a data culture, we can say we want the date we want the organization to use data to make use facts to make decisions. That’s fine. And that’s great.

And we can also kind of recognize that there’s still a place for intuition.

Right? I I think that’s okay. So this is not an all or none. It’s not an all or none. We we pay executives a lot of money because they have that experience that can fill in the extra twenty to thirty percent on a decision.

So those things are all fine.

Now let’s talk about where things get a little bit sticky.

And when it comes to data culture, when it comes to you as a data leader, trying to say, you know, I’m trying to promote a data culture.

Right? And I want the organization to embrace a data culture. I want the organization to embrace facts. To make decisions.

There’s a couple of problems.

One of them is that we, as data and analytic and analytics leaders, we as CDOs, don’t value the data that we provide.

We don’t.

Survey after survey after survey.

Shows that we as data leaders do not quantify the business outcomes of the data that we provide.

We don’t.

In the last magic quadrant survey that I did to support the MBM magic quadrant in, at at Gartner, we we did a survey four hundred data layers, only ten percent of them actually quantify the business impacts of the use of MDM within their organizations.

And this is something that we would see over and over and over again through all those surveys that I mentioned. Right? Can you quantify the business impact when I meet my business impact, more revenue less cost, less risk in dollars.

Because that’s the biz that’s how the business performance is measured. It’s not measured in fewer null fields. It’s not measured in, you know, standardized table formats. It’s not measured in, you know, you know, whatever data quality measure that you wanna use.

Right? It’s measured. Business impact is measured in dollars. Are you measuring this?

Answer?

No.

Consistently.

They’re not. Data and analytics leaders cannot show the value of the data they provide.

We can look at it really kind of two ways.

We can look at it through the lens of a forecast, expected value, an actual value.

When you provide that dashboard, right, when you provide that report, what what you’re basically providing is something more of an expected value. Use this data and we will expect that good things will happen.

Right? You’ll sell more Right? Your customers will be more happy. If you do these things, then your your net promoter scores will increase.

But it’s it’s it’s it’s a forward looking anticipation of of of of future outcomes. Right? So so there’s that angle and we don’t model that. We don’t we we don’t model what we think the expected business outcomes are gonna be on average.

We could. We could. This is not this is not an impossibility folks. This this this is being done and can be done and I can I separate podcast conversation, I can tell you how to do it?

We can do it. And by the way, If we as data leaders who are the ones that are building predictive analytics, we’re the ones that are building all of the models to predict future behaviors. Right, to predict supply chain shortages, to to predict customer demand in the future. These are future predictions.

If we’re the ones that are responsible for doing that, if we’re the ones that are responsible for deploying AI in organizations, I think we can build models to predict the impacts of better data.

But that aside.

So we don’t we don’t model right, the expected impact, nor do we model the actual impact?

How many of your organizations are following decisions?

Right? This kind of falls into an area known as decision science. I think it’s a largely ignored, competency shall we say within business I don’t know of any companies that are doing it.

We were certainly talking about it when I was at Gartner, and I think we need to be talking about it a little bit more. But we don’t measure the expected impact and we don’t measure the actual impact.

And we actually could. This is one of the things that I that is kind of exciting maybe about AI is we can actually start capturing more of this data and modeling more of this data. Mean, and what what I mean by this is the actuals.

Right? Like, I looked at this report. I was provided this data based on this data, I took this and this action had this many outcomes. Right? Like, we don’t we don’t model that.

We don’t. We could. We could. I mean, we do model actuals Right? We we can we know how many units be sold.

We know what our net promoter score was. We know what our sales pipeline looks like. But we don’t model how we don’t attribute that back to a decision, an individual decision that was based on data.

In theory because we’re data driven. So we don’t do these things.

I’m gonna have some coffee.

Even though we could, there’s investment there, of course, but we don’t do them.

So this leads me to an a bit of an aha moment that I had.

I don’t know yesterday. Just one of my coworkers here at Profisee.

Would would call it perhaps a fever dream. I had a bit of a fever dream.

And I came up with this pithy impact statement.

I’m sharing it with you now.

Data people don’t have data on the value of data.

We don’t have data on the value of data.

We don’t.

We we we we just we just don’t. Maybe you do individually if you do My hats off, you’re an anomaly.

You you are not the norm. Most organizations are not can’t, most CDos cannot tell you.

What’s the value of data? Right. What is the value of one customer record?

Right? Like like what whether even just even just what that record is worth or or or or the flip side of value is cost. Right? Can you tell me how much it costs to store one customer record in my CRM per year?

Anyway, I’m ranting here, but but no. We don’t.

We don’t. Data people have no data on the value of data.

Okay.

So let’s circle back to data culture.

Well, If you’re telling me that you want the company to be data driven, but you’re not, What does that say to the rest of the organization?

Think about it.

I want you to be data driven.

I, CTO, want you business people to be data driven.

And if you don’t use my report, if you don’t utilize the data or the models that I build, if you if you if you don’t care about the data, if you are just throwing the report away and completely using intuition, then you’re not data driven, and I can’t help you.

K? Well, if that’s the behavior that you want, if that’s the culture, that you want from the rest organ organization, but you don’t model that culture.

Do you see a problem?

I do.

The to me to me that’s a lack of integrity.

If you’re saying you need to be data driven, but I don’t.

I think that’s a bit of a problem. And I think, frankly, guys, I think our stakeholders see it.

I think our business partners see it.

Right? And they and they see it.

I think way more than than we’re than we’re ready to admit.

But like all human beings, I think some of that frustration with our business stakeholders, it just I think it just bottles up and then it then it then it then it just kind of explodes.

Right? Where where where where you will where you will hear a CEO just, you know, express ultimate frustration and, you know, amount of money that’s being spent spent on, like, data science, experiments here about the amount of money that’s being spent on data and analytics, or maybe it’ll explode where the data function gets moved under the CFO or some something else, and I’m hearing all these things happen, by the way.

But I think our data state our our business stakeholders are customers.

For customers, I think our customers see this I think our customers see us doing things like acting one way and saying something else. I think our customers see us focusing on things like data literacy and blaming the customer for a lack of skill when we can’t even tell our customers how much our products are worth.

Think about that.

If you walked into a store and the price tag on everything was I don’t know. Your guess is as good as mine.

Yet you wanted somebody to value something you can’t put a price on?

What what does that say about culture?

What does that say about the culture of your organization versus the culture of others?

Maybe perhaps just perhaps The organization is far more data driven than you think they are or give them credit for being.

Maybe the organization around you needs data to tell them What the value will be of something?

Maybe the organization needs data to tell them what the value of the thing is that you’re asking them to consume.

That sounds to me like being data driven.

If I’m a business stakeholder and you’re asking me to do something, which is what you are. Right? I’m I’m I’m the chief revenue officer, and you’re asking me to do something. You’re asking me to add a field to Salesforce to make sure that the the data quality improves.

Right? You’re asking me to to use this dashboard. Right? You’re you’re asking me to change how I capture account information in my CRM. You’re asking me to do something. You’re asking me to value the data.

At a high level, that’s what you’re asking me. Whether that is the creation of the data or the consumption of the data. You’re asking me to do something.

And I’m the CRO. I’m like, okay.

Well, what’s in it for me? First question I ask, okay, you can’t quantify that. What’s in it for rest of the organization? Can’t really quantify that. You’re just gonna say the data quality is better.

Well, that that doesn’t mean anything to me.

Now you can say In response to that, you could say, uh-huh. See? They don’t care about data quality. They don’t value data.

And they’re not data driven.

Is that really the truth?

I don’t know a chief revenue officer that isn’t driven by dollars.

So maybe the metric is just off.

Right?

So you care about data quality.

They care about dollars.

And if they don’t care about data quality, is it right for you to say aha?

That’s a lack of a data culture.

And that’s a cultural impediment to me.

When in reality, they do care about data.

Their metric is just different than yours.

Their metric is dollars. And until you can speak that metric, And until you can show in that metric, they’re not gonna they’re not gonna change. But this is what I saw over and over and over and over again at Gartner guys. I would see data people saying you don’t care about metrics. You don’t care about data.

And then in reality, having been in business for thirty years, I know darn well.

That a chief revenue officer cares about revenue and deeply cares about metrics used to measure it.

And deeply cares actually wants to be more fact driven wants to have somebody tell them if you add this field to Salesforce, this is the benefit you get If you do that, if you do that, well, then I’m more like I’m more likely to say, heck, yeah, we’ll do it. Of course, because you’re speaking my language. You’re talking about metrics that matter to me. And you’re talking a bit in in in in in a way that’s gonna make my wife better.

But instead, what we say is, well, you there’s no data culture here. They don’t get it. They don’t care. They don’t care about data.

When the data that you care about is dollars and the data you care about, data quality, data quality metrics.

The the list is is is long.

So do you see the point I’m trying to make here?

Mindset shift mindset shift.

Maybe your data culture is better than you think it is. And you’re just not speaking the right language.

Maybe you’re doing yourself a disservice by saying one thing and doing another.

Saying that you want your organization to be data driven, but not actually producing any data on your own to show the value of your products, your data, your insights.

So these are the types of kind of provocative thoughts. I I I I’m I’ve been having.

And and I think it may help explain a lot of why why there is still far too short CDO ten years. Why CDO and data and analytics functions are are still kind of struggling to get funding, to get support, to get admired, for lack of a better word within organizations.

If we can change our mindset and we can start to say, okay, you know what? They are data driven.

The data that they use just happens to be maybe a little bit different than the way that we look at the data.

Right? And finding a way to embrace the fact that maybe your data culture is way better than you think it is.

I don’t know, guys, I’ve been business thirty years.

I don’t know any business that doesn’t want better data. I I’ve yet to see it. I have yet to see it.

Yes. There are certainly situations where you can provide the data and intuition still roos the day. Certainly happens. It certainly happens. And maybe sometimes there’s a lot of biases because we’re all human beings folks Bias can play a a a big role here and where, you know, that that that amount the weighting of intuition, it gets disproportionate where we put too much weight on intuition and not enough weight on the data. I suspect this, the situation certainly happen a lot because we’re human, because we’re biased, but not because we have negative intentions, not because We don’t see the value of quality good data, but just because we’re human.

So I would I would welcome you to start thinking differently about data culture.

Is there really a culture a lack of a data culture in your organization.

What I would see and what I what I when I kind of pressed often when I was a Gartner, around this is often, not always, but often a data leader saying there’s no data culture here was kind of code, for The business isn’t doing what I want them to do.

That that’s what I would hear. Because when I would ask, okay. Well, what what what are you trying to get them to do? Well, I’m trying to get them to value data quality. I’m trying to get them to use my products. I’m trying to get them to you know, be mindful of downstream impacts of doing two bit stuff in CRMs and on and on and on, I’m trying to get them to do these things and they won’t do them.

Bergo, we have a bad data culture. And to me, The opportunity here is one of leadership.

Right? Maybe maybe there is a strong data culture.

Maybe within your organization all they want is some data that shows.

If I do those things that you’re asking me to do, how does it benefit the organization? How does it benefit me? How does it benefit our shareholders? How does it benefit our customers? Can you quantify that?

If you can do that, if you can quantify that, if you can quantify the value of your data. You can even quantify the value of the decisions made on that data, I think that’s a little bit harder. Maybe a separate podcast for that in and of itself. I think decision science is the intriguing field.

But if you can if you can quantify at high level, the aggregate impacts of the things that you were asking your business stakeholders to do, I suspect they’re gonna be far more likely to do it.

And if that happens, you provide data to show your business stakeholders the benefit of doing what you’re asking them to do To me, that is exactly what we want. That is a data driven culture.

But if you can’t provide data, about the value of your data, well, then you are embodying a lack of a data driven culture.

So some food for thought here. I would love your comments you know, I I know there’s opportunities to comment certainly on YouTube. There’s opportunities to comment through all of the, through all the podcast platforms. But But but I think that there’s an opportunity here folks for us to model to model the behaviors that we so desperately want others to model.

And if we don’t model those behaviors, if we don’t use data, to prioritize our road maps.

And what I mean by data, quantifiable business outcomes dollars.

That’s the language of business dollars.

If we’re not using data to make decisions about what we work on, If we’re not using data to justify the things that we ask our business partners to do, then we’re not modeling a data driven culture.

And we’re acting out of alignment with the very, very things that we are asking from our business units. So food for thought.

Thank you for tuning in today. Thank you for listening. There’s gonna be more on this in 2024. I’m gonna be speaking on this at a few different conferences well.

I think I think we need to keep peeling this onion because I think it is kind of the tip of the spear when it comes to a lot of things related to ways we could improve and change our mindset to finally finally kind of get over the hump of businesses seeing the value in what we do for a living. So I hope you tune in next time. I hope you tune in to previous episodes of the podcast. I hope you subscribe I’m grateful, eternally grateful for you listening today and your community and your partnership and your comments on LinkedIn and everything else.

I hope you continue to tune in. We will see you on the next episode of CDO Matters sometime very soon. Thanks, all.