Good morning, afternoon, or evening, or whatever time it is, wherever you are. I’m Malcolm Hawker. I am the host of the CDO Matters podcast.

I’m thrilled that you are joining us here, and I think this is our thirtieth episode, which is kind of cool. So if you’ve been along for the entire journey, my thanks. If this is your first time, also my thanks. We talk about all things data.

We talk about how to improve the tenure and lengthen the tenure of chief data officers. So I’m an an ex data leader. I’m an ex consultant. I’m an ex Gartner.

Analyst, whatever hat you’ve you could potentially wear in the data space, I’ve worn it, and my mission is to help data leaders or aspiring data leaders figure out how to optimize their investments in data, how to become better at what they do.

Along those lines, I am just I I’m excited to have this guest on on the podcast today. So today today, I I have a unique opportunity. I’m I’m actually talking to a coworker.

This is the first time that I’m actually will be speaking with somebody who works at the same company that I work with, but that’s really kind of just more of a coincidence than anything else because Laurence Young is, is what I would call the value engineer. I think at Profisee, he holds the title of of value consultant. But but but Laurence, his job is to help our clients understand how investing in our software, in this case, MDM software, can can help drive business value. But I but I know, Laurence, that you’re gonna talk in kind of more generality state beyond just MDM. We’ll we’ll talk more in general around investments in data, but why are we talking today?

Before we dive into that, Laurence, let let’s just hear a little bit more about you and your background and how you came to do what you’re doing.

Yeah. No problem. Thanks, Malcolm. So hi, everyone. My name is Laurence Young. As he mentioned, I am a value consultant at Profisee.

My background is, actually pretty broad. I’m I’m a data scientist. I also, have built, consulting practices, for different, systems integrators that are aligned with Microsoft.

A lot of my background is in systems management, AI, machine learning, and those sorts of things.

But, really, my role at Profisee is one more on the business side.

Having been somebody on the data side, and needing good data, I know some of the issues with what happens when your data is not necessarily where you need it to be. You’re not able to provide the insights that, are possible to run a business. And so what I do is I help people understand, okay, this is why it’s important from a business perspective.

It’s not just enough to say, okay. This is gonna make my job easier as a tech person, so this is why we need to improve the data, but we also need to talk about what’s in it for the business. And so that’s really my role, and and that’s my background.

The the way from I I I love it. So we’re we’re talking today because the number one topic that I had as my through my nearly three years as a Gartner analyst, talking to CIOs, talking to CDOs, VPs of data and analytics, the number one topic of discussion, easily, bar none, was how do I, as a data leader, connect, make it a tangible connection between investments and data? And when I say an investment in data, that that could be a report, could be a dashboard, could be MDM software, could be data catalog, could be data governance, could be a set of APIs, whatever it is. How do I when I invest in those things, could be again, could be software, could be invest could be a process, maybe even data stewardship. When I invest in those things, how do I show the value to the business?

That was the number one topic of discussion. And even if it wasn’t overtly mentioned as the topic, so often my conversations as an analyst would come right back to that because I would be asked questions like, well, how do I build a road map? How do I prioritize all of my competing initiatives? What do I work on? You know, what what do I focus on? How do I get stakeholder engagement?

Right? Often, these questions would come right back to, well, have you quantified the business benefit? Right? Because stakeholders, what’s in it for them?

This is this is what they care about. They care about outcomes. So we’re having this conversation today, and I’m talking with an expert in value engineering because this was the number one thing that I was asked as a Gartner analyst. And now that I’ve left Gartner, I continue to have conversations in with with AWS leaders, and it continues to be a number one challenge.

I just came back from the CDOIQ conference, the number one conference for CDOs on the MIT campus in in Cambridge, Massachusetts.

And, again, the topic comes up over and over and over again. So that’s kind of the the the the the why we’re we’re having this conversation. But when it comes to some of those high level whys, Laurence, I I’ve touched on a couple things. I talked about prioritization, road maps. I talked about executive stakeholders and getting alignment. What are some of the things that you’ve seen over the years about about why these why this connection is so important?

Well, I I think in many cases, technical teams and technical resources, they aren’t really thinking about things in terms of the macro, in terms of the business, and how this advances the business.

And, admittedly, I’m very much a process person.

And the reason to improve your data a lot of times is to improve processes, but it still needs to have some sort of translation, to why this is going to do something for the business, whether it’s saving costs, generating revenue, increasing productivity, and those sorts of things. Right? And so when I think about the why, I think about those sorts of things. Like, what are the the mid level things like improving processes, and where do those translate to the balance sheet of a company, or where do those translate to doing what you do better or doing something new? That’s kinda what I I I look at when I I start to help organizations figure out where where the value lies in in in data.

Now now something you said there is is is subtle, but I think it’s important. You actually mentioned balance sheet.

Mhmm.

Because I think it’s important when when we say value to the business, I would often that’s the first question I often asked my clients when I was at Gartner, and the answers that I would get back is, you know, how how I would ask something like, well, how how will you know if you succeeded? Right? How how will you know when you you’ve actually delivered? And the the answers I would get back was, well, the data will be better, or I will reduce the duplicate records, or I will eliminate null fields, or or the data will be better.

It’ll be easy to, you know, to to to manage the data or process the data. And I would stop and say, hey. Wait a minute. Business doesn’t care about that stuff.

So so what when I hear you say business and you mentioned balance sheet, that to me is like, You know? That that’s what we’re talking about here. You actually even said kind of the three things that matter, revenue, cost savings, and maybe even risk mitigation. It’s another thing that we could that we could that we could quantify here.

But I I would often hear and I I’d love your perspective on this. I I would hear when speaking about the balance sheet, you know, I would often hear, well, a day data, you know, isn’t the balance sheet item. It’s actually really not an asset. So we we we can’t quantify it as an asset because it’s really you know, our accountants don’t even think it is one.

What what would your response to that be?

Yeah. That’s a great question, Malcolm. You know, when you when you think about data maybe from a financial perspective not being an asset, Well, when you think about it, it is in the fact that you’re using that data to understand the hard assets that you have.

So when when when I hear that, like, my my first challenge is, okay, if data is not an asset, well, the the hard assets that you have, how are you keeping track of them? And the answer always comes back to data. Right? We’re using some sort of inventory system, or we’re using a CRM to take, you know, to keep tabs on our customers, or, you know, there are all these different systems that they’re using.

And what is the main component of a system that gives you that information that you need to work with or to understand what you have, it’s the data layer in that system. Right? It’s the data. And so that’s kinda you know, you you have to kinda boil it down to kinda lowest common denominator so people understand, yeah, well, it’s not an asset.

It’s how we recognize everything. You know, from a very human perspective, the way that we make decisions as human beings every day, it’s not from seeing an object and just thinking I’m going to do something with that object. We think about it in terms of, okay. Well, if I use this object, what’s it going to do for me?

So a good example is, a real life example.

My wife just had her, had her latest birthday party, and I I saw the balloons.

I’m gonna and I saw the balloons behind you a couple of days ago when we were meeting. That’s right. That’s right.

I’m sorry.

Oh, yes. That’s right. Yeah. The balloons. That’s right. Yeah. Yeah.

And and so, like, I’m an amateur photographer and I’m thinking you know, the first thing I’m thinking is, what is the best way for me to shoot this party?

Right? And so I don’t think about it in terms of, hey. This camera looks good. I’m thinking about it in terms of what are the inputs to the camera that are going to give me the best pictures?

What’s the shutter speeds that I’m going to use? What are the focal lengths I’m gonna use to determine the lens that I’m gonna use? And and those sorts of things. Right?

And all of those are data points. That’s data that I’m using to make a decision. And so that’s the that’s the thing. Like, we always use data when we make decisions as human beings.

It’s no different than when you’re in a business. Right? And and so that data is going to be important because when you listen to shareholder calls for public, for public companies, for example, yeah, they don’t really talk about the data.

But what they do is they talk about what the what the price points are, what the, price per share is. All these different financial metrics that you typically hear in in businesses. Right?

So those are all data points.

Got it. It’s it’s interesting. Listening listening to you, it’s it’s it’s interesting because the very justification you used for the importance of data and the importance of quantifying how it impacts business processes, right, is the very thing that I would actually hear from data data users as a reason why they can’t. Meaning that that that it’s an indirect connection.

Right? Like, I would hear all the time. Well, it’s you know? Okay. Yes. You know?

We have the data, but there’s an indirect connection between, you know, pipeline data and close book sales. Either there’s an indirect connection between data related to the supply chain and the price we’re we’re paying for our material our raw materials.

But what you just said is is that the data is an analog of the process. So it’s a representation of the process. And these things are intrinsically intertwined, and you can’t you can’t even you can’t separate them. So I love that.

I mean, this this guy has some deep philosophical things we could probably talk about here about data that’s describing this describing data that’s describing data. But, I mean, your your to me, your point is incredibly well taken. The thing I mean, I I would love to pick your brain on this given that you’re a data scientist. The thing that I I find a little maybe ironic is the word.

I I I don’t know if that’s the right word, but is that is that for companies that actually have data scientists and, you know, they’re in the business of building models.

Right? But data scientists will will take all these inputs, and they will build models to do things like maybe even predict future behaviors, right, or or model extremely complex processes, that would diff that would be that would be difficult to do otherwise. Right? Where the very people that would be in the business of maybe building a model to to quantify the revenue impacts of improved pipeline management, the very people that would be doing that are in the data and analytics organization, and and and they do modeling for a living because, ultimately, that’s kind of what I think we’re talking about here is is building a model.

Like, how do you model the connection between data and business outcomes? So I I I found it always a little bit ironic when I was at Gartner to have these conversations and and the very people and the very roles in the organization that’s in the kind of the business of building these models, sometimes often that are in the business of predicting the future. Right? Talk about indirect impacts that are in the business of of of predicting the future.

Well, they were they’re they’re right there working for data leaders. So, I mean, do do you see this as as a modeling exercise at the core?

Yeah. It it very much is. And so, you know, you brought up a good point about predictive modeling. Right?

Sometimes I I reach data folks, in in the process that I have to help them derive value out of improving their data. And they often say, hey, like, we’re having a real hard time connecting these dots. And so then what I’ll do since I am a data scientist, like, I’ll I’ll break it down into, like, a a sort of project that they might have. So I’m I might say something like, okay.

You know, for example, like, let’s talk about improving your customer data.

How is that going to perhaps net you more in cross sell and up sell opportunities?

Well, what if you were able to build a model, that basically predicted the success or likelihood of of marketing campaign if you gear them towards a certain amount of your current customers, that have spent a certain amount of money, by targeting them for a certain discount or a certain price point. And this invariably is some kind of predictive modeling that they’ve done before somewhere, whether it’s there at that company or at another company. And so I just try to boil it down into, ways that they understand these things to help them start to, formulate those ideas.

I I love the cross sell, up sell, and and even even the kind of the discounting example that you gave because that’s one where chances are pretty good those models already exist. Right? Or those metrics exist. Right?

Like, if I if I if I deliver you one if I delivered your marketing team one qualified lead, right, and, like, one sales qualified lead that just went into your sales pipeline, how much revenue would that actually equal? I I I know most marketing organizations probably aren’t can answer that question. Right? Like, there’s a twenty percent probability of a sale and our average sale is a hundred dollars, so that means for that one qualified lead, that’s probably gonna lead to twenty bucks in the bank.

Right? Like that like that level of, okay. That’s what it is. So that means that if we improve the data that that goes into creating that lead, that that that there could be twenty dollars at the end of the rainbow.

But I love I love the the sales and marketing across the Luxo because that’s that’s an area where a lot of data leaders can like, it kinda clicks, and those models probably already exist. Yeah.

I think it’s I think it’s a very obvious place for improvement for most companies.

Right? Because they sell something, they wanna sell more of it, and that’s that’s the easy that’s the easy lever to pull. Right?

Well, yeah. And it’s and it’s and it’s sexy. Yeah. Right? Like Yeah.

When you talk about drawing the line between, improving something like that and what a financial return is going to be, that’s a very quick financial return if you do it the right way, if you have the right data.

And that’s the message. Right? You have to have the right data so you’re able to make those decisions, and you’re not discounting too much or you’re targeting the right customers.

If you don’t target the right customers or you discount too much, that’s not gonna really translate as well on the balance sheet at the end of the year as opposed to having better data, deduplicated data, better quality data. Right? So that’s Yeah.

That’s the idea. Yeah. I mean, what what I mean by sexy is across the three levels. Right?

Revenue, cost savings, risk. I mean, all those being equal, revenue should should be what I always advise my clients. Revenue, all those being equal, is where you wanna focus because that’s what gets the most attention. Do you agree?

Yeah. I I definitely agree with that, because when you think about it, like I mentioned public companies before, revenue is gonna be one of the first things that they put in their yearly prospectus, for example.

Companies that are not public but are looking to grow, the first way they measure themselves is by revenue. So that’s that’s usually where I always start to.

Yeah. Okay. Well well, speaking of where you start, let’s let’s assume let’s let’s role play here. Mhmm.

And let’s assume I’m I’m chief data officer of Acme Incorporated. Man, I hope there’s really not an Acme. There probably is. Oh, you know there is.

Yeah. Oh, yeah.

I got the the phone book. Right?

I think they make the bricks. Right? Like, the red bricks. I think Acme is like a brick company. So My apologies.

Or something like that. Yeah.

Yeah. Yeah. My my apologies. Like, if you’re if if this chief data officer for Acme Corporation is listening to this and it’s just like for her, his eyes are just, like, rolling, like, you know, stop stop using as an example of all things broken.

Right.

My my apologies, CDO of Acme.

But but let’s assume I’m the CDO of of of of Acme. What’s the company then you use in, oh, NewCo. That’s in, like, case studies in, like, business school. New NewCo for Yeah.

You can use Contoso, though. Like, that’s the Microsoft standard. Right?

Like Oh, is it okay.

Yep. Yep. What are some other ones? Oh, Pied Piper. You can go back to Silicon Valley, the the the show at Silicon Valley.

If I’m the CEO of Pied Piper anyway, I’m sticking with Acme. I’m the CEO of Acme, and I’ve got a large scale data initiative. Maybe I’m I’m I know that I’ve got a governance problem or maybe that I know that I’ve got a customer data related problem.

Or maybe I’m being asked by my marketing team to build a customer three sixty. It doesn’t matter. I’ve got I’ve got a significant thing that needs some investment, needs money, needs people. I’m the CEO of Acme, and I’ve hired you, Laurence, as my consultant to help me, quantify and build an estimate of what sort of return I could expect to get out of that investment data. Break it down for me. What does that process look like?

So the the first question I always ask is why have you hired me in the first place?

Because that kinda gives you a good way good place to start. Right?

Usually, the answer is because I need to be able to justify this project to somebody. Right?

The way I look at the way I approach these, these sorts of engagements is by just talking to people.

When you think about it, my role is very much a culture oriented role. I don’t look at things, like, as a hardcore analyst like a lot of management consultants do.

And the reason being is because what I need to do is get folks within the organization talking to each other.

So that’s really that’s really core when we come into this. So the first thing I do is I talk to the first person to find out why he hired me. They tell me why they hired me.

And it depends on the answer that they give me. Right?

Why don’t you give me an answer, Malcolm?

And I’ll I’ll so Let’s go ahead and replay it.

Yeah. I’m I’m on the hook to deliver on this digital transformation.

And, you know, my marketing team is screaming at me for better customer data. Let’s let’s let’s let’s focus on that. So I need to I need to improve my customer data. So okay. And and and I’m I don’t have any money right now, and I need to go ask for some money. So, yeah, how do I do that?

Okay. So your marketing, your marketing org is asking for better data. What is it that they’re trying to do with the data? Or is is there somebody from the marketing org you want me to speak with?

Yeah. Well, I’m I’m hearing anecdotes, you know, when our customers call up. We’ve got, you know, four or five of the same customer record. Our customer service people don’t know what what, you know, which record to assign the ticket to.

Heard some some feedback from our logistics organization and that sometimes we ship stuff, we’re shipping to the wrong address. And I I I’m hearing, you know, from a marketing team that we’re having a hard time building kind of customer centric models and and and segmenting your customer data because the the the quality is really low and they don’t trust the report. So I don’t even know where to start. That that that seems like a lot, but those are some of the examples of the problems I got.

Okay. So let’s build a framework for this then, so we can work together to solve this.

What I’d like to do is I’d like to speak with somebody who’s close to the ground in the marketing organization who understands, the details of what these issues are. Somebody from shipping as well because it sounds like you all are spending a lot more money than you need to, when it comes to sending things out or maybe you’re getting things returned. So I’d like to speak with, those folks. But then also from you, I’d like to understand what are some of the, organizational initiatives that you all have to grow the company over the next couple of years? Because that’s going to be important too because if you want me to help you, solve this problem, we should be able to, connect the dots between what the marketing and shipping, teams are having problems with to whatever those organizational goals are. And if we are having trouble doing that, we need to speak to people between, the decision makers on those organization goals and the marketing and shipping teams?

So what I’m hearing so we’re we’re out of we’re out of the role, by the way. Yep. Cut.

Yep. Yep. So so what I’m hearing is is that there’s a top down. Yep. Like, let’s let’s let’s talk about the strategy. Let’s talk about where you wanna go, where you wanna be. But then at a functional level, when you’re talking to people who are actually doing this stuff, they’re doing and complaining about the data, there’s also maybe bottoms up.

That’s right.

Okay. So there’s both.

Yeah. Because when you think about it, you know, when we the reason why we go after what the organizational initiatives are in the first place is because to build alignment, for this data project, we wanna make sure that we find something to latch on to that everybody in the organization is, is going towards. So a common goal. Right? Now when you speak with folks, you know, kind of closer to the data, they’re not necessarily going to speak in those terms, but they’re going to give us a lot of the intel that we need to start asking more elevated questions, to the point where we can get up to having a direct connection between an organizational initiative and and the challenges that they’re having, from a micro level.

So I assume kind of where you’re going with this, kind of going back to my example, that from a higher level, a lot of these digital transformation initiatives and this is just one, by the way. It’s just one example. But, like, could loosely be tied to some sort of focus on the customer experience or customer satisfaction or make customer number one or just some sort of customer centric transformative focus. That’s kind of the top down.

Yep. And from the bottoms up, I suspect where what we could find here are are are we looking for KPIs about about how like, is is that is that the connection point from the top to the bottom? Or what’s what’s the connective tissue here?

KPIs do make my job easy when when they have them. It’s something that’s not always evident or not always known.

If there are not KPIs, then I just kinda get down to kind of brass tax with with folks that are closer to the data and understand how we can make their jobs, better. How can we help them, look better within the organization? How can we help the organization move forward? And, usually, like, when we look at those few things, there are going to be some answers there. Right? And so in lieu of the KPIs, you just have to ask a bunch of questions to really get a full, understanding of, well, what is it that data practitioners and data consumers are up against today?

Okay. But is it is it in your experience, is it common that there aren’t KPIs?

Is is that is is that something you’ve experienced often or maybe I wouldn’t say I wouldn’t say it’s super common.

I would say let’s say three quarters of the time there are KPIs.

Okay.

Probably more than that, but it is sometimes common for folks that are closest to the data to not have an intimate understanding of the KPIs.

And and so when that happens I mean, it it will happen. Sometimes what they’ll do is they’ll go back to, to a member of management in their area to understand those KPIs or they’ll bring them into the conversation as well, which is even better because then we can get another perspective on the business, the business challenges, that the data is presenting as well.

So I I that was kind of a leading question because I already knew the answer, or at least I knew my version of the answer. Answer. Because at Gartner, when I was having these conversations, I was I was shocked by the number of people that would ask me, well, we don’t have any KPIs. Do you have any you can offer?

I would I would hear that all the time.

Yeah.

Yeah. And and I’m like, I I this is your this is how you measure the success of your business. I’m not in the business of measuring your business. You’re in the business of measuring your business.

You know your business better. Right?

Right. Now Yeah. At at Gartner, we had I mean, I had spreadsheet after spreadsheet of sample KPIs that I would be happy to offer up. But, ultimately, you know, for a lot of data leaders, I think what they will find, this is not a normal that that they may hit a wall where their business partners don’t actually have success metrics for the thing that they’re complaining about or the thing they’re concerned about, which which I always find a little bit surprising, but it it at this point, it’s never truly surprising because it because it does happen. Do you I mean, to get to where you need to here, though, I assume you need some idea of what success looks like on the business side, which means you’ve gotta kinda pick something, don’t you?

Yeah. You do need to understand what success looks like on the business side on the business side. So here’s how I, you know, normally go about it.

I usually will speak with somebody a little higher up in the business that will have an idea, but I also do a lot of research too. So a good example of that is when it is a public company or it’s a private company that’s put out sustainability reports, oftentimes, it’s right there in black and white what their goals are.

That gives me something to drive towards.

In cases where I don’t have, you know, public documents or, or, things that are are put out in, like, the ten k, for example, for public companies. What I’ll do then is I’ll, and this is a plug for, the NYU School of Business, the Stern School of Business. They have a a good set of of kind of benchmarks that by industry that kinda tells you, like, what are things like their profit margins and their weighted cost of capital and and things like this that I can then use to say, well, based on your industry, here’s what I think you should do or let’s look at improving something that is industry based by five percent, so you’re a little bit ahead of the industry.

So I’ll go there too. And what typically happens is, when I I build a report and I give that report to the customer where I’ve made those assumptions based on industry level things, then all of a sudden, I start getting a lot of the extra data that they weren’t able to get as well or earlier because what’s gonna happen is they’re going to start pushing that data up that I provide to people. They’re gonna say, well, actually, here are our numbers. And so now we have a more accurate view.

And then that gives me the ability to, start to refine kind of what we think we can we can shoot at in terms of improving data.

So there’s a couple of different things you just touched on there. So let’s let’s assume that we we go through this process. We go through this interview process. We we’re talking to all these stakeholders.

Let’s assume that we get some idea of how success is measured for the priority areas. Right? So we already talked about them, you know, customer service and logistics and all these other things. And just spitballing here.

Let let’s assume that we come up with maybe twenty KPIs, like improving that promoter score, improve customer satisfaction, or customer retention, whatever. There’s a whole bunch that they could possibly be. Yep. But you but when you were talking about the the Stern School, what you kinda suggested in there is that figuring out the baseline is gonna be important.

Is that is is that is that kind of the next step of the process to understand where you are now?

Yeah. Yeah. Figure out a baseline that’s important. And then also the other thing is when you do have multiple KPIs, like twenty or so, like, what I really like to do is really focus you on somewhere between two and four KPIs.

Nice. So we prioritize. Right? And the reason we prioritize is because when we do finally have this report, that we can we we can provide you, I wanna make sure that we don’t overwhelm you with too much information.

You’re already overwhelmed with a lot of data, probably more data than you need because you’re looking to improve your data for some reason. Right? Well, I don’t wanna add to that when it comes to helping you justify your your report. So something simpler that you can easily communicate within the organization and get everybody aligned is key.

So you you prioritize, and I assume that that’s a function may may of those business priorities that you were talking about. Now I love the guidance there because I I used to do the same when I was a consultant and even when I was a gardener.

When I would hear my clients say, well, the business doesn’t tell me what their priorities are. I don’t know what the business priorities are. I’m not involved in those discussions. I would go right back to the ten q ten k’s, ten q’s, annual reports, all ends meetings, PowerPoints set up by people who are influential.

What whatever it is, there’s probably some bread crumbs out there to tell you what the priorities are.

And I love the idea of the two to four, whittling it down, and you whittle it down based on what you what you based on the input from your stakeholders and based on priority from senior executives. So you got the two to four. They’re whittled down. And now you’re trying to understand what your baseline is.

And let let’s assume that you can do that. You can understand that today, my net promoter score is seventy, and I want and and and that’s our baseline. Today, I I understand our customer retention rates are fifty percent baseline, baseline, baseline.

Next, I think, is the kind of this the the the the the magic.

Okay. Maybe. I may or maybe I’m getting ahead of myself. You you you tell me.

So I’ve got business metrics that I care about, and then I got a bunch of data. Right? I got I got data for customer. I’ve got I’ve got data for product.

I’ve got data data data, all this other stuff. What what’s what’s the what’s the magic to start to to connect these things? What what do you do?

Yeah. So here’s where the magic lies. You are going to have really two main parts of of this thing that you hand somebody to help them justify their project. There are gonna be the numbers.

Right? The financial, metrics that are going to give you the ability to see what a return is on this project. So that’s one, but I think the more important part is the alignment piece or the narrative. And so this is where you take all the information that you’ve gotten from the different groups.

You group that information into, like, into like sections essentially.

And then you tell a compelling story that satisfies, you know, as many of the groups as possible that, hey, improving our data is going to help. It’s going to be able to, help us do something better, do something more, improve these KPIs, improve these financial metrics.

But one thing I should point out though is when we think about KPIs and we start talking about, like, net promoter scores or we talk about being able to, increase customer retention. So we talk about customer retention. Right?

That’s right.

We still need to translate that into something financial to show the return because my CFO is probably not going to I’m I’m not gonna say they’re not going to care, but it’s not gonna be less resonant with them if, you know, we and and we increase our NPS, our net promoter score from fifty to fifty five. Right?

What they’re going to wanna see is what is the financial link to that, or the same thing with the customer retention rate. Right? So, like, let’s use customer retention rate as an example. Part of this magic, is translating that customer retention rate into some sort of financial gain, something that we either save or we generate in terms of revenue.

And so let’s say we have customers that, for a nice round number, let’s say we have a million dollars of ARR, or annual rate of basically, a hundred million dollars that we get from our customers every year, and we retain fifty percent of them, that means we’re only making five hundred thousand dollars off of renewals. Well, if we translate that to fifty five percent, that means now we have an extra fifty thousand dollars. And so we need to have a thought, basically, a thought exercise to figure out, well, if we improve our data based on the, based on the issues that we’re having as a company, what is the level of improvement that we could see on customer retention?

And then with that in mind, how much are we getting from our customers in the first place or per customer? And that’s going to help us determine okay. Well, if we improve by x amount x percent, then that’s going to be our return for that particular area that we’re looking to improve. So we have to go the full way, right, to, to translate even stuff like your NPS and your, customer retention rates into some sort of financial number to help us show what the return is gonna be for those things as well.

Yeah. So you you you touched on it. Like, that’s that’s the magic. That that’s where the magic happens. So to me, there’s two sides of this equation.

One side is the business metrics and the financial metrics. And and you and you draw a distinct an important distinction between the two because, you know, net promoter scores, that’s what marketing cares about.

Mhmm.

But the CFO is gonna care about money in the bank.

Right? And how do that’s but that’s that’s one side of the of of the kind of the KPI equation.

The other side of the KPI equation is data. Right? And the way that the the the way that IT people look at data and measure data, You could look at standard dimensions of quality, like accuracy, completeness, uniqueness, timeliness of data, some of the classic data quality metrics. Those are those are ones to start with where what I think I’m hearing you say is you need to find a way to model a connection between those two.

Right? Meaning Yeah. If if the desired outcome is is cross sell, let’s just stick with that one because it’s a good one and it’s a relatively easy one.

And, you know, today, you’re at a ten percent cross sell rate. You wanna get to twenty percent cross sell rate.

You got a theory.

Right? You got a theory that improving the quality of your customer data across those four four dimensions may sometimes there’s people argue that there are more, but the four classic ones that I’ve just mentioned. Mhmm. If you if you improve those four dimensions, there is a modeling exercise required to go from improving customer data quality to moving the needle on cross sells.

That that to me is is is where the lightning strikes. That’s the thunderbolt. That’s the magic, and that’s the modeling exercise that I’m talking about. Do you agree with everything I just said?

I do. And and you used a good term, move the needle. Right? And that’s the term that I use all the time with, like, fellow data professionals that I meet with. Right?

Because, yeah, it’s good to improve your data from a data perspective because you’re gonna be more efficient. You’re gonna be able to do things easier. You’re gonna be able to get stuff out faster. Right?

But does that move the needle for the business?

Probably not. Right? Because any good data professional is going to make it work even when the data is not great, which means you’re spending a lot of this extra time that they don’t see. And but they’re just gonna say, well, things are working well for us, so let’s just go ahead and and do things the way that we were going to do. That’s not gonna get your project justified. Right? And so you have to it’s really important for you to connect all those levels together so you have the the core problem and you’re able to, demonstrate graphically and through communication what it means for the business to to move that needle as you mentioned.

Yep. So I’ll I’ll give I’ll give the listeners and viewers a actual tangible example of what we’re talking about here.

It is through my experience in the past, and this is in no way, shape, or form an an endorsement of any third party data provider.

I just happen to use one here.

But in building a model like this, what I did in the past related to customer data quality is that I didn’t have any benchmarks on my data quality when it came to our customers. Now we just happen to be b to b, but this is equally relevant in b to c as well, and there are third party data providers that can help with this in a b to c world. But in a b to b world, what I did is I bounced my data against Dun and Bradstreet. K? They’re not perfect, and they don’t have a hundred percent accuracy. You know, know, like all data companies, they have some challenges, but they’re still pretty darn good.

What I found was that my my my data was and I’ll just I’ll loosely use a term of seventy percent accurate, give give or take. Thirty percent of the records that I shot over to DMP, they were like, what? We we don’t know what this is. So I came back with this this idea of the seventy percent data accuracy, and I and so that was my baseline from a data quality perspective for for customer data. Now on the cross sell side, we had benchmarks there. We we knew what our cross sell rates were across our various lines of business for given products and for given customers.

So what I did was is I said, okay. If we can go from seventy to seventy five right? Like, I’ll just we’ll just use this this metric of seventy to seventy five. We have a five percent data quality improvement in our customer data. Hey, business partners, stakeholders. What do you think that would mean to you?

And to me, that was that was the lightning connection because people were actually willing to have that conversation because I had actually found the right people to talk to within the business. I’ve I’ve talked to the people that worked in FP and A finance planning and analysis. They were doing the budgeting for marketing. They were doing the forecasting for marketing, and they were willing to have a conversation there. Is this is what I just described, is this does this does this pass the smell test to you, sir?

Yeah. I would. Yeah. And I I use I use personal stories too. So for example, I I started my career as a as a data engineer, and I worked with, k to twelve school districts here in the US.

And really what I was doing was I was taking library patron data, and putting it into a software. But the way that I did that was I had to get extracts from these school districts. And, I mean, these extracts had a ton of records. Like, it wasn’t it wasn’t out of the ordinary to see, like, a a ten school school district.

So a smaller school district have, like, twelve thousand records for all their students. Right? A lot of duplicates, a lot of data quality issues. And I had to build store procedures, to clean this data up in in SQL two thousand.

I had to build XSL style sheets. I had to spend a lot of time analyzing all this data. And I’ve I made that work. Right?

So, like, the school districts are happy. They didn’t see the amount of time that I was spending, but my company understood that it took me a lot of time to do it. And so the way that’s the way I look at it is, if we can automate a lot of that, through some data improvement methods, What is that going to mean? That means I’m going to be able to do more.

Right? That means I’m going to be able to take a lot of that time, like, say, twenty hours per week. So half of my time at at the time of those making, like, sixty thousand dollars a year. So I could offload thirty thousand dollars a year of of my effort to do other things to make the company more money.

The company could build more for my services, if I did more, like, SQL development sort of things. Right? And so that’s kinda what I that’s kinda how I do it too is I I just I relate it to a a real life, a real life story to say, hey. Like, this is what I did.

This is this is how I could have improved on it, and this is what it could have made the company. And that’s really what we’re doing for for, for people who need our help in saying, hey. This data is going to bring some level of value to this organization.

Yep. Love it. Now what I would advise to the data leaders who are listening is that all else being equal, you need to focus on the business side instead of internally within the data analytics side.

That’s right.

It’s it it if if that’s your only choice and that’s the only model that you can build and that’s the only metrics or maybe that is the desired outcome. Outcome. Maybe you are embarking on an operational efficiency play within your own organization, which is incredibly valid. Absolutely.

Every every data leader that I know is under increasing pressure. Certainly, every CIO I know is is getting less and less and less and less money every year. So that is a a a certainly a valid business outcome. I would just urge that all this being equal if you can focus on the revenue side or on the business side.

It is is what you do Yeah. Right as a company. Because, like, I was working for a software company. So that’s why Oh, there you go. Looking at it that way. Right? Yeah.

Yeah. Alright. Well, that’s that’s that that is how you work. That is your manufacturing plant. That was you who’s making the manufacturing more efficient. So that that makes total sense.

Yep. I was I was building the widgets. That’s right.

Right. Well, just before before we end off, I I do I do wanna touch on one last thing, which is the the idea that these models are never a one and done. Right? Like, this is a living thing that should be revisited because there’s no way you’re gonna nail it the the first time out of the gate. Right? There’s no way you’re gonna build the world’s best model out of the gate. Now what what do you advise to your clients in terms of, like, kind of an a programmatic approach here to to manage using managing these things over time?

So, one thing that I always recommend is make sure that you have somebody running point on keeping you true to what it is that you were looking to do in the first place. Right? Oftentimes, it’s a project manager, that’s a business leader. It’s somebody who understands, kinda what you were trying to get at by improving the data. And so building some sort of coalition or some sort of partnership with that person to make sure that the business and the tech teams stay aligned on that is key.

The other part of it is, you know, Malcolm, you mentioned that this is a living thing. Right? So we find all the time that organizational initiatives change. Right?

They change like the wind sometimes when you’re a public company because your shareholders demand other things instead, And so you might need to pivot. If you need to pivot, that’s fine. That partnership is key. Right?

Because then you all can kinda revisit what what we put together as kind of a value plan for you, and you’ve got even bring us back in to to kinda refine that towards what the new goals are. But it’s all about accountability in partnership within organizations. That’s why I look at this as very much a culture role. Right?

Because you you need to get people talking with each other and used to working with each other, and building that strong partnership, that strong relationship with each other. So when you do need to to pivot, it’s a much easier pivot as opposed to something laborious when you haven’t worked with somebody before.

Got the relationships. Easy easy to turn.

On that, love it. Thank you so much.

You know, I I’m I’m a huge, huge, huge believer in in the need to focus on building these, what could otherwise be called business cases. I I think you could you could call it an ROI analysis if you wanted to. A lot a lot of folks kinda push back on the idea of ROI, and that’s really a dog. But but what we’re really talking about here, folks, is quantifying quantifying, making it tangible the that, you know, the the value delivered to the business through investments in data or data data systems, data processes, tools, software doesn’t matter.

But if it’s not about business outcome and if it’s not about business value, then I think you’re right to ask why are we doing it. And this is a way to quantify it. So there are people out there, value engineers, analysts, data scientists, people like Laurence that do this stuff. I would strongly urge you to find these people.

If you don’t have one, it was it’s kind of the number one role that I would recommend for data and analytics teams in in now and beyond.

Often product leaders can do this. Often product managers are pretty good at when it comes to this kind of thing because product managers know how to build profitable products. So in the product development process, product people kind of know to look at all of the inputs and costs and then what would the revenue otherwise be. So product management is another area where you could be looking to kind of apply these types of roles or to glean these roles from. But the role of value engineer is absolutely positively critical to data and analytics leaders, to CEOs. I strongly urge our listeners to to think about augmenting the team with those types of roles. Laurence, thank you.

Hey, Malcolm. Anytime. No problem.

Alright. With that, I will tie off. My thanks to all of our listeners, all of our viewers. The CDO Matters podcast continues to exist because because of your patronage, because of your listening and your viewing. I’m thrilled to continue to do it every other week. Look for another episode of CDO Matters sometimes very soon. Thanks, everybody.

Bye for now.