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. Good afternoon. Good evening. Good whatever time it is, wherever you are in the world.

I’m Malcolm Hawker, the host of the CDO Matters podcast.

Thank you so much for joining. If this is your first time checking out the CDO Matters podcast, welcome. I would invite you to check out, of course, this episode, which is gonna be awesome, Angelie. No pressure.

I would welcome you to check out this episode, but previous episodes talk to data leaders like Jordan Morrow, p ten strength hold, Joe Reese, on and on. This is our fifty fourth episode, and every other week, we share insights that chief data officers and people who want to be chief data officers can help to improve the way they excel at a data and analytics function within their organization. So with that said, thank you.

Angelie Bansal is my guest today. Angelie, you are the head of governance at Cervelo. Yes?

Correct. Correct. Yes. I am. So, yeah, been there about two years, and just loving loving the experience.

Awesome.

Thank you for taking time out of your day. I I I wanna talk about our origin story very, very briefly. But to give our listeners and to give our viewers a heads up about what to expect today, because I appreciate that when I’m listening to podcasts. We’re gonna talk today about data culture.

But we’ve got a nice little surprise for our listeners and viewers today. When you watch or listen to this, it’ll drop on July twenty fifth. Whenever you listen to it, it could be it could be the year twenty thirty when you listen to it. But on July twenty fifth, it’ll be the week after the CDOIQ conference in Boston.

CDOIQ, I would argue, is the preeminent event for chief data officers. It is attended roughly by about a thousand chief data officers, well attended by by some amazing thought leaders, people who are doing amazing things at their organization, sharing best practices, sharing insights, do’s and don’ts, you name it. It’s a great conference. And, Angelie and I are co presenting at that conference on the Wednesday of the conference.

What what time are we, Angelie?

I believe we’re about eleven thirty.

Okay. Thanks. It doesn’t matter. Actually, I was just thinking because it’ll already have happened by the time this drops.

It’s eleven or eleven thirty. We I just got an email on that yesterday. I should pay more attention to email. But we’re presenting at the conference, and what you’re gonna hear today is is what we presented at the conference.

So if you didn’t have a chance to go to the CDOIQ conference, but you wanna get a taste for what it’s like, you’re gonna get a taste for that today because two of the presenters will be sharing insights with you. So I’m I’m kinda I’m kinda stoked about that. We’re gonna talk about data culture, the the title of our of our joint presentation. And by the way, Angelie, thank you for including me in the presentation.

I I’m humbled.

Thank you for accepting you. You know, one of our our early conversations was actually the inspiration for the talk. So we’ll get to that.

Our origin story.

I was thinking about this today. I was like, when did I actually meet you? I I knew that you were kind of in similar spheres, and I think now you keep me honest on this. I’m pretty sure I met you at a bar in a hotel in Los Angeles. Am I wrong?

Or am I wrong? You are actually right.

It sounds a little bit more scandalous than it actually was, but, yes, we met in a bar.

Well, it’s a it’s a hotel like lobby bar, bar. So it’s it’s not like we were inching out at the club or anything. It’s it’s a fact we were staying at a hotel. It it was enterprise data world, I do believe, in Anaheim in what year that would have been twenty twenty two?

No. No. It was early twenty twenty three.

Okay. Alright. Yes. Yeah. Yes. And and anyway and we we hit it off. We had a conversation about a lot of the things that we’re gonna talk about today, and we’ve been pals ever since.

And we we keep seeing each other at all these events.

Angelie is an angel walking the earth because she once bought me a Coffee Crisp bar.

Now for those of you who don’t know the magic of Coffee Crisp, I feel sorry for you. But beyond that, Angelie and I both share Canadian roots. And Coffee Crisp is like a thing. It’s a chocolate bar, but they’re so good. And you bought me one once, and I will forever hold a place in my heart because of that.

Oh, well, thank you. I’ll remember that, and I’ll bring you in in Boston, too.

Alright. Angely, share share the share the premise of of of the presentation.

Share let let’s set the stage about what we’re gonna talk about, what led to the like, what was the genesis of this conversation about data culture?

Set set the stage for us.

Yeah. Absolutely. So I know we’ve been been chatting quite a bit about just like hearing about data and culture and and trying to, you know, make this distinction about what is a culture and what is a data culture when in reality we didn’t really see a difference from culture, an organization’s culture versus a data culture. So when we, happened to cross paths at a conference in London late last year, you were hosting a series of round tables with data leaders that you you so kindly invited me to and there was one CDO that was there that I think really struck a chord with both of us. She was from Belgium, I believe.

Yes. The justice the justice department in in Belgium. She was their CDO, and I will scream out her name in, like, five minutes when you’re when you’re trying to talk. Yeah.

No worries. But she was just phenomenal. She had this energy about her. She was making progress and we really spent some time afterwards like downloading on what what was the special sauce that she was bringing that was really moving her organization, moving, her people forward to really embrace data as just something they do.

And I think that the session that she sat in on started with, one of the contributors actually saying that there’s a great quote about data being the things, culture rather, being the things that we don’t think about, we just do, Right? And so it just kind of turned into into this ongoing conversation, actually, Malcolm. I think we spent a good couple of hours, just talking about the topic of how do we drive data as the culture. We don’t think about the things that we need to do.

It just happens and it happens organically. It happens naturally. People are upskilled, but we’re failing to do that. We’ve been talking about it for so long, but we’re failing.

And why are we failing?

Well well right. And and you touched on a couple of things that that that that that struck me as well and really have have reinforced some of the things that I’ve been thinking about over the last year. Coming out of my experience as a as a as a Gartner analyst, I used to hear all the time, when when when the conversation came up about the delivery of value or or broadly success as a chief data officer.

The issue always came up around culture, almost always. And in in CDO survey after CDO survey, particularly the Gartner survey, which I would argue is kind of the preeminent survey, when asked the question of what is your number one blocker as as to success as as a CDO, consistently, this idea of a lack of a data culture was was towards the top of the list.

And that always kind of perplexed me because I view culture the same way as you just described it. Right? As as as the as the way the business operates just cause. Right?

It’s how we operate. It’s the things that we hold dear. It’s the things that we care about. It’s the thing it’s the it’s the shared, beliefs and and behaviors of the organization, which stood in stark contrast to the idea that culture is some sort of deliverable.

Right.

Right. Right.

Right. Right. Like, it’s like it’s it’s like it’s something you need to do. It’s like a line on on a requirements doc.

And I would hear that often talking to CDOs. It’s like, okay. Well, what are you focused on? Well, I need to deliver on culture.

It’s like, wow. Okay. Holy cow. That’s a tall order.

I mean, but what’s what’s what how do you feel about that? What is your response to that?

So my my immediate reaction and I I mean, I’ve read the survey multiple years and and seen that same challenge come up. And I’m like, what do you mean? Like, is your organization not using data?

Because if if they’re using data, there’s some sort of culture around it. There’s some norms that are happening in pockets. People know how to collect that data, gather it, use it in a way that’s meaningful to them. Otherwise, why on earth are they collecting it?

I mean, there’s a separate governance story around why people collect collect data and pack right it away. But, you know, how how can you say that there’s no data culture? You’re using data in, probably, in pockets, but you’re just not using it in a way that necessarily fits that particular data leader’s vision.

So instead of saying, like, I I don’t have the culture that I’m looking for or a culture that fits my agenda, we’re hearing data leaders say, well, there’s no data culture here.

Well, right. And I’ve been on the business side of the house. I I’ve been on the software development side of the house where it was my job day in and day out to talk to process owners.

And having having been in that situation, I know there’s all sorts of data. There’s generally most companies, there’s no shortage of data.

It’s it’s out there, and data is used to make decisions day in and day out. It’s used to make decisions about how to build software. It’s used to make decisions about how to optimize supply chains. So so it’s out there. So what I’ve struggled with for years now is this gap between people saying data leaders saying, we don’t have a data culture yet knowing, okay. Wait a minute.

Data is being used widely, and you could even argue that intuition is a form of a data driven decision making process because intuition is a function of everything you’ve been doing for the last x years, which is which is a data collection process. So there was this gap. So in our presentation in Boston that happened last week, yet still hasn’t technically happened, we we came up with and and this is largely under your tutelage.

Kind of the idea one of the one of the premise one of the kind of the key, ideas that we are positing in our presentation is that data leadership is is where we need to be focused as as a key deliverable, as as something that we can own and something we control as data leaders, and that if we focus on the leadership aspect of it, from that culture can be transformed.

So what are some of the key aspects from a leadership perspective that you see we need to double down on?

Well, I think one of the things that, that really has has been, I think, effective and we we saw I wanna call her net her Inez.

Yes. From my Bosch. Inez Bosch. Yes. That’s it. Yes.

Yes. So I don’t know where I came from. It’s so kind of in the in the cobwebs of my my brain.

But, I think And if you search your LinkedIn, it’s I n e s I n e s.

She’s she’s brilliant.

She she very much is. But one of the things that she talked about was actually sitting down with the hands on keyboards and watching their process to understand how are they using data and then coming back and saying, I see you have a problem and I think I can help you, would you like my help? And that to me was really this eye opening expression of both collaboration and servant leadership where she wasn’t trying to push an agenda that would benefit her, but more so upskill and uplevel people that would ultimately be more productive and be more effective in their functions by learning what they were doing.

Yep.

So, anyway, that was a big key.

That that’s something that that that’s something that I cannot stress enough and that I share often in insights that I post on LinkedIn and every and everywhere else, which is this idea of understanding your customer’s business as well or better than they do. And I know that’s a tall order because to understand it better than they do, then obviously you’d be better fit to do their job. But but it’s it’s a pithy quip and it’s a way of saying you really need to understand how they work.

And what you just shared, Anjali, like the stand over the shoulder, understand their challenges, understand what works, understand what doesn’t work. And maybe it’s not all applicable to what you do. The Pareto principle will apply here. Eighty percent of what you watch probably won’t be, but the twenty percent that is will be the Oh, I had no idea that’s why that field keeps getting left blank.

Yep. Exactly.

Yeah. It’s that kind of stuff that’ll lead to other things like empathy and and other things that we talk about in our in in our presentation. So servant leadership collaboration. What else do we need to up our game on from a leadership perspective?

Well, we talk a lot about taking a risk.

Right? So what we’ve seen, you know, myself on the consulting side, I know you you saw it at Gartner as well, was these data leaders coming and saying, well, what’s my competition doing? What are organizations of the same size and scale and same industry?

Where are they struggling? You know, and it it almost feels like a, a therapy session, right, in that, okay, they’re struggling in the same way we are, as opposed to, you know, a way of of differentiating the pain that, pain and challenges organizations are experiencing around their data. So, you know, instead of taking that input and saying, okay, we’ve gotta do things differently, a lot of times what I’ve experienced is organizations and leaders take comfort in the fact that their key competitor is having the same problem or set of problems. And then things change very slowly because everybody else is dealing with the same thing.

When we were when we were talking in prep, I had this massive moment when you were sharing that insight with me. And it’s it’s it’s something really interesting.

I’m not sure it’s profound, but it’s meaningful.

And that’s this idea that kind of I would argue all technology leaders. This isn’t a problem problem unique to data, but we really, really don’t want to feel like we’re the only ones doing something.

Right? Right? Like like, I I can’t tell you how many times I’ve been sent or I’ve gone or I’ve chosen to gone to industry events just to go there to make sure that what I’m doing is not wackadoodle.

Right. Right. It’s like I’ve been seen. You see me. You understand me?

Well, well, well, well, right. And that feels good. Right? Because nobody wants to think they’re they’re wackadoodle. But but at the same time, what you told me, what you shared with me is that, hey. This has a reinforcing effect on the status quo. And just because you’re not the only one doing it doesn’t mean it’s the right thing to do.

But a light when you shared that with me, a light switch went off in my head, which was going back to my experiences at Gartner, going back to to to all I kind of embrace from a best practice perspective.

If you really peel the onion there, how much of that is a function of the fact we’ve just always done it and everybody does it that way? And how much of that is a function truly, absolutely, positively of quantifiably backed research that tells us it’s the right thing to do? That’s a kind of rhetorical statement, but what how do you respond to that?

Well, I mean, I think that, you know, if maybe we take an example, right, of, you know, the Blockbuster and Netflix days, where Netflix is one of those organizations that when we think about their data processes and what they’ve embedded in those processes are a lot of the things that have driven their culture. Right? They they just automatically, you know, build in the data quality capabilities that they need for their business. They automatically build in the analytics and insights that are required for them to make their decisions.

Now, if Blockbuster at that time, when they were deciding whether to stay as brick and mortar stores or really lean into the the mail order digital type of, viewing experience, Well, you know, I I think that there there would have been a moment where they would have to decide whether or not their data, you know, their data approach would have been to operationalize a lot of stuff that we don’t like to do or just continue with the status quo and guess what? They continue with the status quo. So I think, you know, when we kind of look at where things could go differently and where we’ve seen organizations that have taken that risk to do something radically different or even slightly different where they’ve landed versus how how their competition has failed in the same space.

I love it. And I really like talking about the companies that could loosely be called the digital natives.

You mentioned Netflix. Uber is one. There I think Amazon is is is arguably another, but there are examples out there, and they’re not all startups, by the way. Some of these are the biggest of the big companies where data governance actually doesn’t exist.

Right? Bad thing to hear for a consultant that focus on data governance. But but but data governance doesn’t necessarily exist. It’s just interwoven into everything they do. You you could call it governance ops. Could you not?

Exactly. Exactly. You know what? It’s not fair to say that governance doesn’t exist. It’s just governance is part of their culture.

There isn’t a data culture. There isn’t a governance culture. It’s just their culture to have healthy data habits that fit their needs. So, and that, you know, as a governance consultant, that’s what you wanna see.

You don’t want to see these massive failures of, of behavior where you go in and I think one of the most painful things that that as a consultant you can do is go in, set up this amazing program, have your clients say, yep. I get it. You you transition the ownership back to the clients with all this hope and energy that they’re going to move forward with it. And then in six months time, you get a phone call where the client says, oh, you know, we need your help again.

You know? So whatever we help them with just didn’t make it into their fabric.

If you had a client say, hey, Angelie. I’m I’m interested about putting my governance lead into or putting one, not maybe not a lead, but maybe a who knows a steward? Just somebody who does governance, who knows governance, into the agile teams of every software development function in our group, in our organization, what what would you say? Good idea, bad idea?

I would say good idea as long as there’s collaboration. Right? I I I would not wanna see this army of stew stewards being deployed across these agile teams Yeah. Without having that common connection of what is our lowest common denominator for data standards, for data expectations, and, you know, those healthy data behaviors. So if we’re an organization that believes in the value of, say, cataloging your data and having visibility into what data exists so that we can ultimately create data products for our organization, well, every team better be cataloging their data in the same way. So we need to have that common definition of what, what good looks like across these data teams. So as long as we have that, I think that, you know, it’s probably the best idea you can, you can deploy out to your teams.

So as as a data cop, probably bad idea, but as a business enabler, great idea. Right. Love it. Okay.

Right.

So we’ve touched on a lot of aspects of of leadership that we’re touching on our we touched on yesterday in our present or last week in our presentation in Boston.

You talked about active collaboration. You talked about servant leadership. I love that. That doesn’t necessarily by the way, folks, when we say servant leadership, it doesn’t necessarily mean that you have to have sir done a tour as a data steward or as a policy setter or as an MDM analyst.

It doesn’t necessarily mean that you need to do that. What it what it means is that you have empathy and insight and knowledge of how those processes work. Right? You need to be as a CDO, I would argue you you need to be t shaped.

Right? You need to be very wide and you can eat go go to a certain level of depth on all of those subjects and then always really good to go deep on one or maybe even two, maybe even what would that be? H shaped? I don’t I don’t know.

I’m I’m I’m going off the rails here, but but servant leadership why? Yeah. Yeah. Maybe.

Yeah. Maybe. Servant leadership, active collaboration, engagement.

We just talked about innovation and risk taking, and and and don’t do that. I think a big one we need to touch on that we that we we touched on in the presentation as well is this idea of mindset.

What what does it what what sort of mindset do you think CDOs need to embrace in order to to to be the agents of change their companies expect?

Yeah. I mean, I think one of the biggest challenges that I’ve seen, especially with mindset, is really ensuring positive intent and really viewing every, you know, failure.

You know, not as a failure but as an opportunity.

So when we think about things like data quality, yeah, we measure the quality of data, but a lot of times our teams see that as a report card exercise and they’re being graded on how well they’re managing their data. We know it’s never going to be a hundred percent. But instead of looking at it as, you know, an opportunity to end up in summer school, maybe we look at that as an opportunity to really upscale our people in terms of what what the expectation of their behavior with the data actually is. How do you create data and ensure that the way that you’re creating it services the need of the organization as well, you know, as well as your own personal or, you know, functional requirements.

I love it. So assuming positive intent.

Huge. So so important. One of the messages that I’m giving in my presentations this year and have been giving for a while now is the everywhere on the idea of positive intent. But if we want to peel that onion a little bit and get into the detail, One of the examples that I like to give is around data quality.

And a lot of us complain a lot about about data quality. Right? We like to complain about data quality.

But I would urge you to, within your teams, to to start asking questions about why is data structured differently in different business applications?

Why do we need to transform data into some into some sort of standardized format? Why is the definition of customer for marketing different than the definition of customer for finance?

Right? Yep. Why?

So if I asked you those questions, Angelie, what would you say?

I’d say context matters. Right? So what, you know, finance is looking at from the term customer is likely somebody that’s engaged and exchanged services and goods with your you and your organization versus marketing, looking at customer as somebody that’s either actively engaged or somebody that we would like to to cold call prospect, or ultimately convert into a paying customer. So context one hundred percent matters, but two things can be true at the same time. We just have to honor that and understand who’s using what and what their definition of the truth actually is.

Yes.

Two things can be true at the same time. And when we say the data quality is bad, that is a far reaching deterministic, arguably reductionist view of something that’s probably a little more complex than that. Right? If we can all agree that it makes common sense that the way marketing looks at the world is different than the way the finance looks at the world, and we generally can agree on that, then then we can naturally conclude that the data in the in the applications built to serve those processes, marketing versus finance, is naturally different, which means it’s different by design.

Yep. Right? It’s different by design.

There’s nobody in in your marketing organization who is creating requirements with the purposeful intent of making your job as a data person harder.

Right? They are trying to make their job easier, and trade offs are being made every day in every work situation, including the software development process between speed and quality.

And and some and sometimes so even if you put aside the fact that data is structured differently by design because these are different applications with different contexts and different truths, but even when that’s the case, in the case of marketing, I can say this from personal experience, there are tough choices that are made between quality and speed every day. Marketers will tell you, we don’t wanna necessarily slow down the lead capture process, but we fully acknowledge that the things that we do to increase the velocity of leads coming in may negatively affect their quality. We’re sorry about that, but it just is what it is.

Right. Right. And we’ll we’ll fix it later. Right?

So Well, that’s part of the engagement aspect that you were that that you touched on before, which is if you’re engaged in marketing, you’re gonna know that.

You’re gonna say, hey. They made a tough choice They didn’t necessarily wanna make, but they had to make in order to meet their lead lead quota, and there’s gonna be downstream impacts on us, but that doesn’t mean they have negative intent.

Right. Right. And I think with, like, uncovering that, that discrepancy, right, between marketing and, finance and then communicating and understanding what that impact truly is, I think there’s also an opportunity for our data leaders to simply tell it like it is. Instead of dumbing down the message for one group or changing the the way in which that message is delivered, which I you know, I’ve seen time and time again when when when we see what the communication to a a technical team is versus the business, well, I mean, the technical team gets all of this detail about what, you know, what’s going on and really has a clear picture of why is it so hard and it’s gonna take so long to get something done whereas the business gets a gloss over and they’re left asking, why is it so hard? Like, why why why do I need to wait this long and then ultimately spin up a shadow IT, which nobody loves, to get to what, what they were looking for?

Well, if shadow IT exists, I I I would urge the data leader in that organization to take a hard look at the customer satisfaction of the people who have who have created the shadow IT organization.

One hundred percent.

Just just just something to share. Okay. So getting back to our our our our conversation about data leadership. Let let’s talk about some of the ways that we work in in in the date in the data function. Right? Like, putting shovels in the ground, kind of our operating model. What are some of the things that you would would urge our our CDO friends and other data leaders to to think about when it comes to working differently, about how they do their jobs maybe a little bit differently?

Well, I think one of the big things that that we, we spent time talking about was very much putting, data at the center of business or at the service of the business And with that, taking more of a data product mindset to determine the value in delivering the, the datasets that matter to the business in a way that works for them as opposed to, you know, what what we batted around was the data that seemed to make sense to to the data organization in a silo. So I think it, you know, it but again, we come back to that theme of collaboration to one, understand what exactly the business is looking for, the tough decisions that they’ve had to make, and now delivering out the right products to them.

You and I are both believers in data products, but we’re also both believers in data product management as as an enabling capability and not just focus on this reusable governed shareable discoverable nugget of something that sits in a data catalog. These are two very different worlds. What does a data product management discipline from from a data perspective? What what what are some of the things that I would need to to be doing as a CDO to start making that real?

Well, I mean, I would one I mean, once you understand what exactly is, required, what kind of that base common denominator of information and standards are, you start delivering. Do something. Deliver it out. You know, build out that product and test it.

This is the best way to fail fast and truly understand what matters to your organization. How are they using the data that you’re providing to them? If you’ve built it, failed, checked it out one time, and have moved on to the next thing, well, guess what? You didn’t deliver the value that you were hoping to.

So let’s make sure that it’s the right thing and we’re measuring for success so we can then refine what we’ve built and continue to make sure that we’re we’re actually meeting the business, you know, business where it matters and that they’re satisfied with what they’re getting.

You just touched on something that’s so important.

I I I I saw a meme on LinkedIn a couple of weeks ago, and it was I don’t know why it’s always, like like, this all Star Wars theme meme where it’s the the the guy and and and the gal and one the guy has, like, this quiz or the girl has a quizzical look and the guy has, like, this kind of this awestruck look. I don’t know. Whatever. This meme and it was the meme basically said, you and I’m paraphrasing the meme, of course.

You you know, three hundred data dashboards, three hundred reports, three dash three hundred dashboards that aren’t being used. Therefore, you’re not data driven.

Right? Right.

Like but it was it was an indictment of the business Right.

Because they weren’t using the three hundred dashboards.

And and and all the comments were, like, basically, man, the the business doesn’t get it. They don’t care about data. They’re not using data. They they’re not there’s no data culture. They’re not data driven, finger wavy, silly business stuff.

And and and I’m looking like, I’m like, am I only one in this room that thinks that if if you put three hundred products on the shelf at Walmart and not one of them moves, then that is probably the the the product?

It’s probably the product problem.

It’s not the consumer’s fault.

Right. But I’m watching this on LinkedIn. It’s like like, what in what other world would you put three hundred products on the shelf and not nobody ever bought them? And you and by the way, you’re you’re the only store in the town.

You’re in a little town. Right. And and and you’re, like, you’re running the only bakery in the town. And not all bakery is a good metaphor because you need that to to survive.

You literally need this to survive. And you’re the only one baking the bread, and you put it on the shelf every day, and nobody buys the bread. And and there was this litany of comments talking about how how silly the consumers work because they didn’t buy the bread.

And this, my CDO and data leader friends, is exactly the mindset that Angelina and I are talking about.

Right?

Turn turn turn it around. If three hundred if you if you created three hundred dashboards and nobody are using them, then you need to look at why people aren’t using them. And chances are pretty good they’re not meeting their needs.

Right.

I don’t know how I got on that on that on that rant. But but you were talking about product management And and you were touching on some of the important things about product management. We’re both believers in product management. But you also touched on the idea of agility.

Get something out there. Right. Now what what what would what does that mean? What does agility mean to you from the perspective of actually executing against the governance framework or executing against a a big lift like becoming AI ready?

What does that mean? What does agility mean?

I mean, I I continue I’ve been a firm believer, and I continue to preach start small, but think big. So take meaningful steps early, often, quickly, deliver something, figure out if it’s working for people. And if it is, then evangelize it and tell people and, like, scream it from the rooftops that we built this thing. Look at how great it is. So you generate that excitement, and you can then start to articulate the value of it. So now, not only are you bringing along your data users that probably gonna ask them to do something a little bit differently than the way that they’ve been doing it for n number of years, So you gotta bring them along, but then also, you’re starting to create that that talk track and that message for your leadership as well. So you can continue to fund and invest and build that flywheel to, you know, continue to have the the resources that you need to build out your culture with data in it.

I love the flywheel metaphor. I think that that that’s an effective one.

Okay. One other thing that that is to me is most often the elephant in the room.

And I think one of our biggest opportunities to improve as data leaders, and that’s around the idea of quantifying the value of of what we do. And it’s and it’s something that we certainly touch on in the presentation.

And and I and I think that is it is the root cause of a lack, I should say, of a failure to quantify the value of what we do is the root cause for a lot of our evils.

What would you say to a CDO who said, well, I I I I kinda get it, but that’s really hard or maybe it’s impossible or it’s difficult. What what would you say in order to convince them of a different perspective?

Well, one, it’s not impossible. It’s just a matter of finding the right use case to tie your your tie tie your value statement to.

Organizations are using data to make decisions.

Now if they have the right data or better data, how much better is that decision actually gonna be? How much more effective is your organization going to be? We have anchored ourselves to these metrics that I don’t believe mean a whole lot, to organizations.

We’ve talked about how many attributes have we cataloged and how many owners have we identified and how many trainings have we completed without really saying by doing this, by putting in a new solution and spending all of that the time, effort, and, dollars around that, we’ve actually improved the way in which we’re able to access our data, which cuts down our decision time because we get to the data faster. It’s right, so we’re not chasing down, you know, chasing down the variances.

So we’re actually, you know, communicating what is the business value. I think right a lot of the metrics that we’ve been, attuned to to using don’t really talk about the business value. They just talk about these, you know, stats of of your data performance.

So my my, you know, plea to to data leaders is really think about the business value that means the most to your leadership and communicate your changes, challenges, and improvements based on how those, those, value drivers are performing. And then when you’re talking to your data users, the people that are actually creating, managing, and handling data, those stats around how much has actually been cataloged, how many, data quality, issues have been remediated in the last month, those become valuable to them because that actually shows that their behaviors are improving the data as well.

Love it.

There’s a there’s a few things I think that are worth unpacking there and reiterating.

One is it is not impossible.

I think that as data leaders, if we are out there saying it is impossible to measure the value of we of what we do, that is tantamount to saying it’s impossible to measure our individual value as contributors in this organization.

That’s not a hill I think I would want to die on. If I am unable to articulate my individual value or our group value as a team, as a data and analytics function, I think that’s a very, very dangerous perspective to be forward in the organization, particularly since the the pithy quip that I like to to say is we’re in the modeling business.

And I’m not and I’m not talking blue steel here. Right?

I I’m I’m I’m talking about we in the data analytics function, we are in the business of building models. We build models all the time, particularly if we have a data science function, which about forty to fifty percent of us do, maybe centralized, maybe decentralized, maybe both. But a good half of us ish have a data science function where we’re doing things like building models to predict whether we’re gonna run out of materials for our for our manufacturing process, to predict what bio behaviors are going to be in the future and how much goodies people will buy of our goodies people will buy.

So we’re in the business of looking forward. We’re in the business of tying indirect factors to direct outcomes. When when you kind of peel the onion, that’s what people always come down to is that it’s impossible to to to model causal relationships between indirect and direct factors. Well, that’s not absolutely true because there’s a lot of different ways to do it.

And if there’s anyone in the organization who can, it’s you, the data leader.

So I would argue, yes, you can measure these things. Thank you for reiterating that. And and I would just invite you to take a fresh look at the issue of value quantification.

Because at the end of the day, if you are engaged and you are actively collaborating and you understand how your your customers businesses work, Angela, you talked about drivers. Mhmm. Right? Now, if your business, if your line of business partners don’t know what their business drivers are, your CFO probably will.

And these are actual things. These these are things like cost of goods sold.

Leads marketing leads converted. There’s, like, literally thousands of drivers that that within an organization.

Sitting under those drivers are literally formulas.

Numerator, denominator that are, guess what, made of data.

They’re made of data. So if the data gets better, the formula is gonna change. So sorry. I’m on a bit of a rant, but value value is is, I I think, a great place to kinda tie off on on on the broader conversation.

So if you were to tie this all together, Angelie Mhmm.

Put a bow around it and and and summarize some of the key things that we would want our listeners, viewers to walk away from this presentation with, what would they be?

Yeah. I mean, I think, you know, I love the, you know, the the analogy that that we’re in the modeling business. Because aside from these models, I, you know, expect and and, you know, see the best data leaders truly modeling the behaviors that they expect their organization to follow. Right? So, you know, I think my biggest takeaway, that I would I would hope that, people have from this is, you know, model what what you want your organization to be. Do the do the hard work, walk the walk, and then bring your people along. Upscale them, give them the resources, show them the value of the changes that you’re introducing, and truly give them the data that they need, not the data you think they want.

Wow.

That’s awesome.

The only thing I could I could possibly add is couldn’t agree more.

When it comes to being data driven, it’s a bit of the pithy platitude. At the same time, however, if you want others to use data to make decisions, then you need to use data to make decisions and it starts with that value conversation. Right? You need to be able to articulate the value you’re driving for the business.

If you can’t articulate that and if you’re not using data to make decisions about priority, to make decisions about what to focus on, not to focus on the, obviously that’s priority, where to invest your resources, What software you should be investing in and not investing in. Processes you should be in. If you’re not using data to make decisions about that, I think that’s the place where you need to start and model those behaviors and become data driven internally within the data function, then others will follow. And that’s my key takeaway for this presentation, which is be the exceptional leader your organization needs you to be.

Embrace some of the things that Anjali and I were just talking about across the board from whether that is active collaboration, whether it is talking about value, whether it’s having a positive mindset, assuming positive intent, a lot of the things that we just talked about. If you do those things and you do them day in and day out, culture will follow.

You will build a data culture within your team, and then you will build a data culture outside. And slowly over time, it will come together, but it starts with value and it starts with leadership. So well, that was fun. I I hope we do it.

I I we hope we do, like, half that well when we’re actually presenting to a few hundred people, which is in almost a month, but it’ll have been last week. But, Yeah. Yeah. That was that that was fun.

It’s it’s we are birds of a feather.

Yeah. Yeah. For sure. No. That was a that was a good time, Malcolm. Thank you so much for inviting me to to join you on this, this fun chat and, overview of what we what we talked about, and we’ll continue to talk about in the future as well.

Absolutely. What’s a good way for people to find you, Angelie?

So I’m on LinkedIn.

You just look me up by by first name, last name, Angelie Bansal.

And, you know, I’m happy to happy to connect with all of you.

Wonderful. Alright. With that, if you made it this far and if you enjoyed our content here on CDO Matters, join the community. How do you do that you would ask? Well, subscribe to the podcast.

Follow me and connect with me on LinkedIn. I will happily address any of your questions, concerns coming out of this episode or any other one. I have a live event the third Friday of every month on LinkedIn where we do basically this, but instead of Angelie, it’s you. So if you’ve got questions that you wanna ask me about data governance, data strategy, data culture, master data management, data quality, you name it, nothing is off the table, third Friday of every month on LinkedIn, you’re free to join me there. But, again, thank you for all our listeners, all of our viewers, all of our subscribers.

I will see you all again in another episode of CDO Matters sometime very soon. Thanks again, Angeline. See you soon.

Thanks, Malcolm. See you. Bye.