Good morning, good afternoon, good evening, whenever you are, wherever you are. I’m Malcolm.

It’s good afternoon here, Malcolm, and good morning to you, I think, in floor if you’re in Florida. Yes. So so yeah.

It is. It it is. So, but but we do record these, of course, and people are replying back whenever I you know?

But, yes, good morning to me here in in Florida and and, of course, good afternoon in the UK. And good day, good evening, good whenever to all of our listeners and viewers and subscribers.

I’m Malcolm. I’m the host of CDO Matters podcast. And, joined today by the esteemed Eddie Short.

Esteemed Eddie Short. Yeah. Well Yes.

Thank you. Yeah. I well well well, you are. We’re gonna we’re gonna talk about your background in, in a little bit.

But I mean, I I’ve known you, Eddie, for probably two years now. We we trade a lot of content on LinkedIn. We comment on each other’s posts.

I think we’ve I think we’ve had a couple of one on one sessions in the past.

I I I I look through past guest of the podcast. I was like, I can’t believe I haven’t talked to Eddie.

So I’m glad we’re getting a chance to to to to be together today and have a chat couple of, couple of data nerds like ourselves.

You’ve you’ve got a long, history in in the world of data and analytics. You’ve been a CDO multiple times. You’ve been a consultant. Tell us just kind of a little bit about your professional evolution, Eddie, and where you are now.

Yeah. So very quickly, I started life as a chemical engineer for Mobil Oil, which is now ExxonMobil.

Worked for them for four years.

Let’s say, one of the cancerous feature of my life back in early in the nineties. It was like, if you want to get involved in our large SAP program, you need to have twenty years in the business. I’ll become a consultant. So I went and became a consultant. So I worked for KPMG the first time, did a lot of the classical end user computing stuff, and then going to and then it became, became, early days of the dot com era. So I became re morphed as an e business, e e commerce strategy consultant, and did a number of things with them. And then I was poached by what is Sema Group, which is now Atos, to basically help build their UK e business practice.

Built that up in the late nineties.

And a couple of attempts to make some money myself in the dot com era failed miserably.

And in two thousand and one, I ended up at Ernst and Young as Ernst and Young Consulting was bought by Capgem. So when I arrived, it was CGUI, Capgem and Ernst and Young. And all that done I mean, I built my first AI system or expert system, at university nineteen eighty nine, to design distillation columns as an engineer. But, and I did a number of things around data, but I became went mainly full time into data in the early two thousands, because I was a director in their technology strategy practice. Eighteen months in, they asked me to a number of us to do strategies for what we were gonna do, and I got data.

They seemed to like the strategy, and they said, would you like to run the business?

And I inherited initially six data migration guides.

Six data, and they were some crazy people. And we went basically back through the value chain over the next two or three years to build out, you’d say now, what what we called at the time, business information management.

Because I picked up quite early on some unstructured guys, and we did document and records management.

I built a thing which is seminal to this. Two thousand and four, I became a vice president, and I built a point of view called creating the intelligent enterprise. And then originally, it was from business intelligence to intelligent business, but my boss at the time said, Eddie, half our clients are in the public sector. If you call it intelligent business, they won’t get it at all. So let’s call it intelligent enterprise.

And that was I would call it then the an early data and information strategy methodology. But, yeah, we were doing structured and structured data to data. We called it mashups in those days, not big data, bringing together different types of data.

And I went from being UK to being global leader for Camp Gemini, what is now their data and analytics practice.

We largely served CIOs.

And at that time, I remember you were probably a Gartner analyst at that time.

It’s kind of saying in various Gartner meetings and trying to get into the magic quadrant for services and saying Oh, yes.

Can’t we get a different title for this? Because, you know, CIOs, they don’t do data.

And, actually, data we we’re trying to exploit data and turn it into information. Can’t we be the c I the CIOs?

Of course, that was a futile conversation, but, I didn’t really like working for CIOs. And I at the time, they weren’t making it it was kind of a virtual global practice, it wasn’t a global business. So at that point, I was approached and that became my first global, what you call it now, CDO role. But I went to British American Tobacco, Never smoked in my life.

You know, company grew up with people who smoked, but, yeah, I never smoked in my life. But it was, it was another top ten companies in the UK. I joined what was then called business services, and I was helped them to build a global business. They were a classic multinational with lots of reporting, and the only thing they had really centrally was a Hyperion financial management system.

Oh, I know.

Well And, and we spent over three or four years about a hundred million pounds, not all new, but I basically consolidated a bunch of investments, presented to the board, sponsored by the COO, and we were with finance marketing, operations, supply chain, as well as IT to build this infrastructure.

I remember that little SAP thing, so we had good great things. And, our global CEO for the time said that we want to do global SAP. We actually had seven instances.

And I said to him in a classic Eddie way, why do you want to do that? There’s what’s the benefit? And he said, well, everybody else is doing global SAP.

By the way, they still are. They they still are. I I had many conversations last week at the Gartner event in London where that is still a thing. It’s still a huge focus. Consultants making bags of money on SAP consolidations.

And and I’ll just pause for for breath there. There’s kind of two things.

In that journey, so, at the time this program is priced at five hundred million, I said we can do every to CFO, I said we can do everything you want if we just basically extend what we’ve done on the BI data program.

Not the answer they wanted to hear.

I went over to Waldorf, headquarter of SAP, and gave them the benefit of my wisdom, which was why is this HANA thing, at the back end of everything? Because at the time, I kinda get to this point here. All of us data guys are sitting there at the exhaust of everything to do with process.

And we’re picking up all this exhaust stuff, and, effectively, as we spend tens tens and tens, if not hundreds of millions, we’re now cloud based systems, actually reassembling that exhaust there to tell you what happened.

But going back to my old enough days, when I first was taught about, computers, it was input, process, output. And, actually, input is data. But nobody was thinking about input data and kind of that became part of this intelligent enterprise concept of kind of how do you make the data part of the input to become, I would call it, predictive by design.

How much do you what you describe though, like, from it sorry. Wait. Okay. How how much of what you describe as data as exhaust is really because of the dominance of ERP in the manufacturing space?

And and and the tendency to look at data through a manufacturing lens, a pipeline lens, this linear begin and end.

Yeah. Well, exactly that, Malcolm. I think you’re spot on. But what and what my construct was was about intelligent enterprise was, you know, say people process technology, but it was simply, actually, it’s really process defines technology.

People are the business case. How many people can we save? And data is kind of the the forgotten child, which at the end is like, shit. We need some reports to tell us what’s going on.

And it’s kind of like bolt the end of the end. But that was kind of the present. And when you actually think about it, having done my MBA and all these things, it’s kind of like, well, it’s this process thing. As you say, it’s a nineteenth century construct for how you basically scale a manufacturing business.

And, yeah, I worked in BAT at the time, but now increasingly eighty in the UK, it’s eighty five percent of the economy is services. In the US, maybe it’s eighty two, eighty three.

It’s like it’s not it’s not fit for purpose model. And when we super forward ahead, you know, you think about multichannel, omnichannel.

If you try to map out process in the traditional way and design systems, this does not work. You know, basically, we have different channels. We have phone, we have apps, we have web, we have social.

And what you’re talking about over the in a modern system is journeys of customers through all those different channels.

And and actually that zooms you into, yeah, you really need data, a data driven or data enabled model and you often increasingly need AI to work out which is the right routing through that.

And and actually, you don’t need process. What you actually need is kind of real time orchestration.

You need for some regulated industries, you need to be able to spit out what were the steps. There’s process steps to tell the regulator how you actually got from a to b. But process is that kind of structure. And what what’s happened in many businesses is you what you and I go again, go back to the spot in my career, Michael Hammer reengineering the corporation.

That was all about, you know, let’s obliterate processes. But, actually, that was the generous of the ERP revolution, where largely what we did was hard coded processes, and we did some automation and some efficiency. And in the new world, we can genuinely obliterate processes. So my construct now is kind of zooming forward.

It’s kind of people, data, AI with process and technologies. I call them second order. The pro the the process is basically you you’ve gotta have some things within system. And technology, clearly clearly, you’ve gotta have it.

But for most businesses, for most organizations, technology is the table stakes.

It’s not a source of competitive advantage.

And that’s You touched on something.

I I I I’d love to continue to drill on this, and maybe take the entire time to do it because I think you you’re touching on something that is really foundational here.

This manufacturing centric approach, let’s just loosely say that.

It the the artifacts that it creates, I I think, are ill suited for a digital world, and this is basically what you’re saying. I would argue that how we approach data management is a reflection of that manufacturing centric approach, and it doesn’t work very well. I would argue governance for sure is a a reflection of that.

I I work with a lot of incredibly smart people, and and we have discussions about, governance as a control mechanism.

And in a manufacturing world, it most certainly is because you have specifications, you have guidelines, you have you have standard operating procedures, and these are the controls. It has to be here and it has to be here. Otherwise, you’re gonna produce a product that is suboptimal.

And I think that’s been applied to data, and I think that does make sense to a certain degree. But if you’re an Uber or a Netflix or or one of these digital natives that has a completely different operating model, you don’t really operate you don’t work that way. Yeah. And you don’t and and you don’t need a governance function because governance is inherent to kinda what you do. Okay. So you were at you were at a KPMG.

You you have this moment Yeah.

That it that is is aligned to what you are now calling the intelligent enterprise. You move from KPMG into a couple of CDO roles, Aon, virtual media. Did you did you apply some of the some of the things?

Yeah. Yeah. So, basically, Tia, what about governance? Actually, as we passing, when I was in KPMG, one of my little things was risk and performance, two sides of the same data.

I was not a popular partner when I went into one bank, and said, why are we doing the same project five times to the lead partner? And he said, what do you mean?

I said, well, we’ve got a pro a program in the front office. We’ve got a program in the middle office. We’ve got programming compliance within risk, and we got somewhere something in IT building stuff. They’re all basically solving the same problem, and he is, and he said, well well, he said, if we if we basically collapse these into one project, which will cost basically about the equivalent of two of the five, we will do everything the customer needs.

It’s like, but that’s not what the customer the customer we’re doing what the customer wants. I said, we should be doing what the customer needs. That way, we’re meant to be advised. It’s like, this is, well, this is not what they wanna hear.

It’s like but when you actually think about it from a data perspective, certainly in terms of data protection and data governance, the the data you need to treat your customers fairly, to know your customers, if the same data you actually need to manage regulators is the same data you need to market and sell. And if you’re basically doing this pipeline and thinking not in the linear process of I need a compliance process and in a marketing process, I need a sales process, but actually, we can do many things. You can say risk is two sides of the same coin and you can to use an in line, buy one, get one free almost.

You still have to have the outputs that are different. But and so zoom that forward.

Yeah. I took that to Aon where I redesigned the data business. I I was the CTO for Aon Hewitt, which is the people people data people analytics. So we were I was at the vanguard of the creation of people analytics in twenty fifteen, twenty sixteen.

We collected all sorts of data, pay day, assessment data, psychometrics data, and that was a three, four hundred million dollar business.

We used, but one of the things we had was it was a survey business.

And clients said to us, this is this is terrible. The surveys are awful. We should be you should be paying us to fill in the surveys.

In the end with, and again early stages, two data scientists, Google TensorFlow, we basically reengineer we, I, reengineered the survey process so we could just suck data with with client’s permission out there, Workday, SuccessFactors, and all these other systems and normalize it ourselves, take all the shit away from them, and spit them back the answers. And suddenly, with after two people in a pilot over three months within a year, we’ve got information increased net promoter score, better value, and we’re actually starting to drive more intelligent insights.

Also, important is people. When you start to combine all these people data, you can start to look at, you know, what drives people, what motivates people. Now sadly for us in Aon Hubert, our main client was the CHRO, not the people you need to do to manage people, unfortunately.

They are a compliance function.

But when you sell to the business, you can look at what the drives and motivation. So as an example, in a private banking situation, actually, with a couple of US banks, we we kind of say, we’ve got all this data about you. And we sat with the COO of this of this division, and he said, well, my problem is I’ve got some star bankers. Everybody else is may median or below. And he said, is there anything you can do to help us look at who are the median bankers that we can move up?

Combining all the driver psychometric data that we got from all these different things, we can start to build models which are not AI, but they are AI, but basically say, effectively stratify your people and say, right, actually, with with the right training and development, these people can be moved to eighty percent of their Star Banker. These can be improved and and in the nicest of where these people probably can’t be.

That was a nice interesting service.

Zoom forward to Telefonica o two.

So that’s a ten billion dollar business. We became Virgin Media o two, but and this is a seminal moment for me. And kind of I went in there initially as a consultant, as an independent to do a a data strategy.

And after two months, I’d done the data strategy. I’d worked with the chief commercial officer, chief marketing officer, chief operating officer, CFO, presented to the CEO, and he said to me this guy called Mark Evans, he said to me, Eddie, great strategy, but I’ve seen five of these, and I’ve only been here for seven years as CFO and then CEO. They’ve all failed.

And I look around in our world. There’s quite a few people who are looking on their CVs. They were at Telefonica in those periods. I’m going, alright. Their trading has been but all failed.

And and they said, well, you know, what’s your confidence in doing this? I said, well, I’ve done it three or four times.

But it’s like, well, but you’re a consultant. We need somebody who can, deliver this. Well, then they came down. Would you would you deliver it? You gotta come on board as as an employee again. And so I came on board as an employee.

What we did and what I did initially was mapped out this journey, and and we I asked for five million pounds, about six million dollars, and they said, well, you’ve got no chance of doing this journey with six million dollars. I said, well, yeah. You’ve got no confidence in this journey. So what I’ll do is I need five million for six months. Let me go away, and we’ll work on a number of the use cases. And we worked on things like basic personalization, churn management. Interesting in telecoms, particularly mobile, the the key message was still how many ads, how many new contracts we’re gonna get.

Yep.

Some basic things around stratifying the customer base and identifying what we call red customers.

These are customers we’re going to get rid of. They cost us money. Every time there’s a contract renewal, they’re getting a discount. They’re asking for more. They’re asking for free.

They’re asking, threatening to leave, the concept of changing people’s mindsets that some people we can get rid of because actually Heresy.

Yeah. Firing customers, that’s heresy.

Gold customers who actually make us two thousand pound two thousand dollars customer lifetime value, silver customers and bronze customers. Let’s move these guys because actually we’ll drive more revenue and more profit and actually save money by stopping trying to keep these guys who basically are exploit in the system. It’s kind of mindset shift. And after six months, funnily enough, they sign up to a three year program for twenty, thirty million pounds dollars a year to deliver what we call the time omnichannel decisioning or in fact, we called it marketing speed, campaign management and journey orchestration.

But what it turned into was a revenue and customer transformation.

And me, the CDO, was the senior responsible owner. We’ve doted into the board and the CEO of you are the owner of this revenue customer transformation. So on one side, we built we and that’s we built a Pega centric platform powered with Telium with some, data enrichment from a company called Intent HQ, which are used by a lot of telcos and banks, link that into our migration from legacy to cloud based with a Teradata system. We were trying to migrate off that to a cloud based environment. But what we also did, we basically built an agile business model where we brought together marketeers, technologists, salespeople, and customer service to reengineer the way we worked and the people were in order to exploit that. So we were gonna be doing sprints and weekly team meetings and looking at how we’re gonna orchestrate things.

So it was a business complete business transformation.

Fast forward to twenty twenty one, we merged in the UK with Virgin Media to form Virgin Media o two, the second largest telecoms business in the UK.

And at that point, in this new platform, we had about half a million customers on our new platform.

Some guys from Virgin Media saying we’re building a new digital business. What we’re gonna do over the next three years is basically build this new platform, and we’re gonna fold the business into this platform.

This new platform, the answer is Google GCP, and we’re gonna put some technologies around it. But we’re gonna build this state of the art digital platform.

And and I’m look and they said, we like what you’ve been doing, but, frankly, it’s old school. It’s old school. This is new, new, new.

Suffice to say, I survived about six months and then left the beep left the building.

Zoom forward two years later, and I’ll come back to why we didn’t last two years. My platform is now running thirteen and a half million customers despite the fact that all development was cut off when I left. So it was never properly finished.

I I won’t tell you how much they spent, but it was somewhere between one hundred, million and one billion pounds.

On the GCP migration, you mean?

GCP, but all Yeah.

Not just the GCP migration, but all the The whole thing.

Okay. Okay.

Yeah. Suffice to say, everybody, over the next two years, they spent hundreds of millions.

Subsequently, everybody’s been fired because yes, they’ve done the migration of the data to GCP.

Value creation, Nilpua. Business change, Nilpua.

That’s because basically it’s like, we’re building a better mousetrap and you guys are gonna use it because it’s so good. It’s so great. It’s so fantastic. So everybody’s going, but it’s what is it?

What’s in it for me? Where what what what’s in it for me? It’s this kind of epic fail. I know that kinda takes you to where we first started talking because in parallel to that I thought I need to do I need to do I’ve been talking my time for time about doing a doctoral program and I need to codify what I’ve been doing all this time.

And so I signed up to do a Doctor of Business Administration in twenty twenty one which is kind of a professional PhD. The theory goes, it’s not an MBA. It’s not a tall you they spend a year teaching you to be a researcher, but you’re really what you try what they try to do is combine. You’ve got twenty five years of business experience.

We wanna bolt on effectively half the research that you’re doing a PhD, but bring that professional and research thing together to give you a PhD level, but a more practical view of that. I started to look at the exam question of how do you deliver sustained, and I added it then sustainable, sustained and sustainable so it can fit the ESG green circular economy piece, competitive advantage with data analytics and AI. I I add in AI, but then really, I describe AI as just sophisticated analytics.

So it’s gotta take an Completely agree, by the way.

And I’m looking at that and and and so day day one, they kind of they start saying, these are this is what teachers teach your research. I’m starting looking for all the research papers and I’m looking for all the luminaries that we know. And I’m not going to name them. But the luminaries who are writing in, Forbes and HBR and writing great articles about fantastic work with data and analytics. I’m looking There’s quite a few of these are US professors.

Then I’m looking for where are their research papers that they put in. It’s like, again, none.

Zero academic research. I was like, this is kinda like doesn’t compute. I’m good.

And what I found is that the people who’ve done actually the academic research are mainly actually in in Europe and in Asia, and there’s there’s still not an awful lot. Right? Because when you you’ve been in the analyst space, the number of real definitive piece of how much value you create with data is is pretty appalling given how long we’ve been doing this. It’s still but there are there is some research.

And what research there is is based on, centrally, a thing called resource based theory, which is a stop is a strategic, theory, kind of an alter ego to everybody knows Michael Paul’s comparative strategy and spy forces and value chain. Kind of externally facing, you know, your the environment you find yourself, your power of customers, suppliers, etcetera. Resource based theory developed in the US really in the eighties and nineties. It’s about what you what how do you build a competitive capability inside?

And and what resource based theory says, for something to be a real competitive advantage to you, it’s got to be valuable. Well, no brainer. It’s got to be rare. That means, yeah, kind of different to what other people have got to be done. And then the flip side of rare, it’s got to be inimitable, difficult to copy.

And then, fundamentally, it’s got to be embedded in the organization. So and and when I look at all the people trying to train businesses, and I give you my Virgin Media two one, they’re focusing on in the nicest possible in data and AI, GCP, and other technologies, which are neither rare nor difficult to copy.

So you’ve gotta have the technology. As I said, you gotta have it, but you got rare and difficult to copy.

You’ve got to focus on the value and embed it in the organization. So you see in that example, no embedding in the organization, a massive organizational resistance. So lots of expenditure here, no benefit.

And then there are some other theories that we can come into but it’s kind of like looking at that as as a basis. And so I’m looking and then say, well, when you do research we’re talking about what we talk about day to day big problems but when you’re trying to get a doctoral thesis signed off you have to solve a narrow problem. So yours truly then works on the basis of, okay, this resource based theory, they’ve done all these things right. What have they haven’t they looked at? And they hadn’t looked at the role of a CDO in this, you know, they basically looked at, you know, what data do people have, how much money do they get, what’s the culture, because they’re data literacy.

But they hadn’t actually looked at the rules and responses really contribution of the CDO. So that’s the bit that I took on.

And I started the research in anger last year, and I conducted some surveys with the support of the data leaders group, NICE international data organization.

We got about a hundred CDOs to fill it in. And, then the results came in, which was a bit like what you would say that, yes. We could start to see strong correlation between data analytics and AI and company performance.

And then you actually look at that there isn’t a lot of positive correlation between the contribution of CDOs and positive company performance in that in that model. We’re just not hitting the bars because too many of us are focusing on the the VM question of the, yeah, data pipelines, data mesh, data tools, which again, as I’m saying, you have to have, but they’re not focusing as we’ve seen on debates.

Half of them can’t describe what the value is. I mean, my question to you guys is, like, why don’t you just quit? Because if you can’t describe the value of what you’re doing, you shouldn’t be in the job. And they’re not doing anything on the organization, and and that’s basically why we’ve we’ve held.

Well, gosh.

Okay. So much to unpack there.

So much to unpack. My my first one my first really meaningful question would be, did you ever meet sir Richard?

No. I didn’t. I I never met him because I was in the telephonic a bit, and and Okay. We kind of step back. We we were like Virgin Media and Virgin Media too. We they pay about a few million pounds a year to license the brand name, and and there there are some involvement, but I wasn’t.

I saw I saw Virgin. I was like, hey. You know, maybe he’s he’s on the list to go to space with sir Richard at some time at some point in the future. Okay. That that was me being glib. But, man, okay, so much to unpack. Where where to start?

But the the GCP field of dreams at at at a previous employer, and we don’t need to pick on the previous employer because there are plenty of examples. I’ve had multiple guests on this podcast who’ve who’ve explained the exact same scenario. Renee Lotte, who who who was the CDO of of of Hitachi Vantara, explained the exact same thing, shared the exact same anecdote. I’ve had other conversations with other CDOs where they said the exact same thing, where they have tried the field of dreams, spent literally hundreds of millions of dollars to get to the exact same point that you just previously described. So we don’t need to pick on any one company, because it keeps happening over and over again.

Why?

Like, we know these things, these galactic, gigantic initiatives are high risk. We know we know when we focus on technology, it’s a bad idea.

We know when we can’t articulate business value and we can’t tie the span to to to specific business. We know these things, but we keep doing this stuff. Like, why?

Well, I think if certainly, in if you’re in a bunch of industries, say, financial services, telecoms, retail, you know these things, but then you see Amazon, you see Google, you see Meta, you see Microsoft, and particularly with Amazon, it’s like the Pac Man. It’s a technology driven business, but he’s eating my lunch. So as much as I know, it’s like they they kind of it’s like rabbit in the headlights. I need to try and copy them, and they’re doing the latest technology.

It’s but they are you’d say they’re the classic digital natives. And what what I find is that they you you can import to people from Amazon or Google, but they’re it’s like red blood cells. There it’s a virus invite, and the white blood cells resist massively. So you kind of, you have to think differently and and you don’t. It’s kind of statementally obvious, but people revert to that. It’s like, as I gave you the BMO two, when we’ll build a new digital business, we’ll effectively create our own Amazon. We’ll create our own and, therefore, we need the people, the technology to basically do that, and then everything else will be great.

It’s it’s not. It’s it’s it’s but it’s also a way we we actually fund and manage these things. So we there’s a lot of challenges, I would say, in the finance world because these guys are looking to say, when can I capitalize an investment? And looking at technology, we capitalize an investment when it’s when it’s switched off. It’s kind of like, but that’s actually that’s only the beginning.

It’s not the value delivery.

It’s just on.

Yeah. And they’re suddenly saying, yeah. Right. You’re a year in. We’re capitalizing it. You won’t get any benefits.

It’s like, well, we we don’t we didn’t say we were gonna get any benefits until a year in. It’s like, but you you’ve chosen to go that way. So there’s kind of that that other mentality that we’ve got to basically you gotta get the technology the technology is an asset and all this other stuff is intangible. We can’t how do we oh, we gotta expense that.

Well, we’re not used to doing that.

So it’s kind of like Oh, use or lose.

You use or lose. That was every every year. CapEx, I had use or lose, and it it creates these perverted incentives to go buy stuff.

Yeah.

Just And and and then and then there is this culture, which I’ve seen as being a partner in the big four, and it’s particularly acute in this country that I live in the UK.

It’s not so bad in the US, but it’s not we have developed a culture of risk avoidance as opposed to risk management. Yes. And that is in bay in some way to do with over the over engineering of the finance capability.

And so you’ve got whereas you might in the US, you still do a lot of experiments, fail fast, all those things. It’s still a bit alien over here, and people are not good at managing it. And then you also look at there there are many things at fault. The Shrek firms, the hedge funders, they they haven’t working up to this.

They still and it it comes back to what I was saying twenty years ago when I talked to Gartner. By calling it the chief data officer function, and just to a CEO sounds that sounds like some technical thing. Right? It’s like kind of it’s like in the tech so they’re gonna build some tools.

I get that, and I can understand that. I’m gonna buy some tools. I’ve heard about this OpenAI tool. I heard about this Google tool.

I can buy they can buy that, and then we’re gonna be great. It’s like so even though we you kind of institutionally know, there’s still that the the people at the at the top, they need simple storytelling messages.

And one of the massive failures in the CDO community and I say, because I’m old enough and ugly enough, I said, fifteen, twenty years ago, I was known as mister Spock by a lot of people because I’d be bringing my data, bringing my evidence, bringing my fact.

Mister Spock, you’re beating us to death with logic again. We it’s kind of like, we get that. We get that. But we in secretary, the CEO wants a story, you know? And they need a story. They need a need and, Antonia, you look at most boards.

They get the the core finance numbers, but most of the conversation is still not really data driven at the board level. It’s kind of still it’s still storytelling. And so we’ve got to get with that program.

And, you know, when you look at the CFOs and others, they’ve gone through twenty years of leadership programs. We’ve gone through twenty years people have gone through twenty years of, alright, data mesh training, MDM training. I’ve got now I’ve got to use some Python. I’ve got some SQL. But they haven’t learned the leadership skills, which means you they can’t communicate and operate at that executive committee level.

You start to see the same thing in another side. Now suddenly, CSOs are really pop pop pop pop important, but people who who can talk about cyber and risk have suddenly been whacked three three levels up the organization. It’s like, we need a CSO, but we don’t understand what they’re talking about.

But but but I’m afraid of all of our data getting into the wrong hands, so let’s find it.

So so that that’s what I see. It’s kind of this leadership challenge, and and and there has been this jockeying for position of where where where do you where do you sit and and kind of I look at the banks and then kinda CDOs, the sec second class person in the COO organization, secondary to a lot of CIOs, CTOs.

It’s just because so we we’ve kind of got to the limit of our incompetence as they would say.

And people then revert to, yeah, I need to I’ll need some pipelines. I need some tools.

And and as opposed to really just go and talk to the business, you know, that’s what I said, the power of Excel. Even with Excel, you can do some great prototyping. Everybody can understand it. And then it’s like saying, well, if we we’re gonna take this a bit further, we’re gonna see this we might need something like Tableau or Qlik or Power BI.

And then behind that, we go a bit further. We prove some more value. We need a more sophisticated data capability beyond there. But so it’s taking people on a journey is what’s gonna go back to my telephonica thing.

The five billion to do the basics churn, blah blah blah before the fifty, hundred million omnichannel decisioning because you’ve taken people on a journey. And that’s that’s all those things I kind of just call it and it’s not a management one zero one leadership training development and we’re just we’re just missing that from a lot of the people in in our in our world. And so I then compose and try to codify that in a doctorate which is hard because I want to solve big problems but then Right. As I as you probably touched on yeah I then created one so how do you do this and I’ve created seven habits of I think I called it originally of the highly effective CDU stolen with pride from Stephen Covey Covey but I called it seven habits of the highly effective analytics and data enabled AI powered business.

So it’s kind of because you’re trying to get capabilities that you need to develop and they’re not I need to be this, this, that, and that other AI or this data tool, but it’s the capabilities that and that goes back to my research and just say things that things that business leaderships will understand.

So so let let’s go back to something you said a while back.

You’ve been using the phrase, data driven. And as it goes, I’m fine with that. As as a kind of a pithy marketing, kitschy, I just I I get it. Right? I I I think I I understand the underlying meaning of using data to make decisions, and I and I’m okay with it.

But but I think we use that phrase a lot and I think it’s to the point where often it lacks meaning. And you gave us a very specific example of that where you were questioning, for lack of a better word, academic research in the field of data and analytics.

Yeah.

And this is something that I stumbled on while I was at Gartner, which is we talk a lot about being data driven. Right? We talk we, data people, talk a big game. Yeah. It’s interesting because we almost talk about it like it’s somebody else’s deliverable.

Yeah.

Right? Like, it’s not ours. That’s that’s what they need to do. We’re already data driven because it’s in our title. Right? And we’re data people and we get it. But that’s something that they go and need to go and do.

But what I stumbled on is when you peel the onion on some of the data that we have on our own performance Yeah.

What you find is really I would I hate to use the word shocking because that sounds like click baity and and, like, just just too incendiary. But what you find is that there’s really not a lot of data there. I’ll give you an example.

Gartner does a an annual CDO survey, and I would argue as surveys go, this one’s pretty good. They they had the last time they did it, they had an n, well over seven hundred and pretty darn good and came up with a stat that says only forty four percent of CDOs feel like they’re delivering meaningful value to their organizations.

Yeah.

But who are they asking? CDOs.

Yeah.

And and and and what question are they asking? Well, value. Like, let me okay. So first of all, the Dunning Kruger effect is very real.

Yeah. We like to think we are a lot better at just about everything than we really actually are. So the forty four percent is a self assessment. And if that’s a self assessment, in actuality, it’s probably, a lot lower than that.

Yeah.

And let’s press on value a little bit. Oh, okay. Do we have metrics around value? Can you actually say, oh, well, I do or I am or I’m not.

I I’m delivering business outcomes. I put money in the bank. No. We don’t. Because data people have no data on the value of data.

We know this. When every time we ask, if you can actually quantify it, answer’s no. So in one simple little stat that that is being used widely to describe our performance, and I could keep going. Right?

I could I could list for an hour. I could talk about studies and surveys that aren’t real studies and where the surveys are vendor commissioned.

Yeah. Right? Which are which are specious from the get go. Absolutely. But but but that aside, right, there’s not a lot of real data here.

Right? Like, I had to defend, a master’s thesis, but, you know, and I had to defend my my my conclusions. I had to show data. I had to show real and we don’t there’s not a lot of that what in what we do.

I mean, it’s not that hard. I mean, and that this is one of the this is the crazy thing. It’s I started in this in the era of business intelligence and then it’s kind of like, yeah, we had to report before we were helping people report the performance of their business.

Come back in there, working at KPMG in twenty eleven, twelve, the data science thing, machine learning arrived.

Now these people who are suddenly PhD physicists in math basically told us, you you guys you guys are old school. You don’t know what you’re talking about.

And suddenly, we created these new analytics things where they didn’t measure. They didn’t measure.

They were trying to do cool really cool stuff. But those are the people who stuck along with the kind of BI is kind of like, I’m in the believing of practicing what you preach, and you have to measure your performance. Now precisely to your point, at the same time, there were lots of other people doing data monetization. We could sell our data.

Yeah. I describe that mainly as like panning for gold where there’s an awful lot of shit and there’s one or two nuggets, and and often quite a lot hardly any nuggets.

But That assumes the lawyers will even give you a pan.

That assumes your lawyers will even give you a pan.

But, yeah, lots of people spend a lot of money trying to monetize cell data. But I look at this leadership thing, and I’ll give you the thing which we did at Telefonica and I just do consistently, which is you sit down as a leader with other leaders and you say, right. Okay. We wanna do this personalization solution.

We want to do this supply chain solution. We wanna do this financial solution. Right? You can’t do this without me.

I I’m working with you. We’re working together. Our teams are working together.

We get a combined business case, and then we have a leadership discussion. Are you thirty percent of this, seventy percent of this, sixty percent of this? But you get a a leader’s agreement. You but rock up to the CFO and say, we’re gonna deliver twenty million a year per annum annualized to the bottom line.

We’re both claiming fifty percent of that. So and that when you delivered it and everybody looks back, yeah, we you’ve delivered twenty five. Okay. Great.

Oh, we’ve only delivered fifteen. But you’ve got evidence that says, we know you can argue about whether it’s fifty five, but actually, right, okay, I’ve delivered twelve and a half of that twenty five. That was my that was my measure.

And by by going to the board with I’ve got the director of customer service, I’ve got financial controller. I’ve got the director of sales.

We’ve together have comb comb comb comb comb comb comb comb comb comb combing this, business case.

We’re signed up to deliver it, and then six months, twelve months, eighteen months down the track, we’ve delivered it. We’ve measured it. We’ve measured it. We’ve measured the outcome. As leaders, we share the benefits, and then that’s that’s all you have to do to measure the results. And the the the organization as a whole is a success.

But that’s my point. It’s this leadership act lack lack of leadership skills and ability to basically share and negotiate, which is which is lacking because then I built a dashboard and it said, okay. Well well, we built this and we built well, we’ve got we built some data products. Great.

Fantastic. But it’s like, who’s using them out? Who’s measuring the value of that? You you’ve gotta have other leaders.

As I was gonna say, one of my habits is you have to be the ultimate servant leader. You cannot deliver any value on your own unless you work for a pure data business. You work for who just sells data like Kantor Research or maybe Bloomberg? But, you know, for ninety nine percent of you, your success is predicated on doing something with somebody else.

Your data plus their function, delivers this value to the customer or this compliance saving or this cost adjustment.

And again, as as leaders, you agree. And that and that’s for me, that again, that’s my point about I come back to my VRIO, resource based theory. Yeah?

If in doubt we can’t measure the value, we’re not doing the change management, what do we default to? The technology which is not rare or difficult to call. And that’s it’s that’s that’s institutionalized failure but that’s what’s happening and and it’s it’s it’s it’s it’s it’s challenging. It’s worrying. It’s disturbing. And I’m I’m writing about this, and and now I just feel like I’m constantly bad cop out there. Yeah.

You’re you’re not the only one. You’re you’re you’re you’re not alone.

People people are afraid to ask for help. I mean, I think I’m just here. I’m I’m now back to being an independent, I’ve got a lot of gray hair. I’m here to help the c level.

That includes the CEOs, but also includes CIOs and CDOs with how do you drive performance with data and analytics. I’m not gonna be your guru on Python. I’m not gonna be your guru on where the the sorts of nuances of whether whether GPT four o is better than Gemini one point five. But, you know, I kind of understand all that stuff, but it’s really how do you get that value and the organization embedded.

You guys have got the the technology bit coming out of you, but I’m gonna challenge you because I understand all that. But bringing it all together, which is really where where where we what we’re gonna make a difference on because, fundamentally, we’re in this cross I would call it a crossing the Grand Canyon moment. We had Geoffrey Moore crossing the chasm. I look at what’s happening out there right now.

The level of volatility, uncertainty, complexity in the world, uncertainty was interest rates, economics.

Then you add in the sustainability green net zero agenda, and then you add on top of that the technology, but particularly AI and generative AI. And my simple expression is that most chief executives cannot cross this chasm alone. They need support. They need they need assistance. They’re not getting it from a lot of scrap firms. It’s not about PowerPointing this to death. It’s about people who can actually help you cross the build the bridge and actually help you get across the bridge.

And that’s what I’m about in trying trying to help people and is bringing all that experience. So we submit research and and and being that person who just says, country two, that doesn’t work and and just turn together. Now I’ll give you the, I know we’re running out of time, but my, when we merged together with as VMO two, my former boss, Mark Evans, didn’t become the CEO. He he stood down and, the guy from VM got the CEO role. And he Mark said to me, he said, Eddie, I really appreciated working with you because you were the only person in my leadership team whoever told me I was wrong.

Whoever challenged me, everybody else just got that. Yes, Mark. Oh, whatever you want, we’ll we’ll deliver. And you the honesty, transparency, and getting the story across. And I think it comes back to what I was saying earlier.

Too much happens at the board and the executive level, which is still in great storytelling with and and we we rock up with our data and evidence, but we can’t tell good stories. We can’t operate that leadership level. We’ve got to we’ve got to cross our chasm as data in the data community to be leaders, truly leaders, and we need to bring that evidence to the guys who are just sitting there with the one liners.

And there’s a great book that I recommend everybody to read which is called May Contain Lies. It’s written by a guy called Professor Alex Edmonds of London Business School and it pops the challenge. You know, we pick up many different ones. I mentioned the academics in our space who didn’t write any academic papers on the subject, but the easy one which everybody talked about is Malcolm Gladwell. You know, ten thousand hours gets you mastery in any subject. Yeah.

That’s a classic one which went through boards in the late two thousands. Everybody’s got, yeah. That was fantastic.

It wasn’t based it was based on a study of, a conservatory, basically, a music study in Germany where they just happened to know that people who who, win the conservatory had done many thousands of hours of training, but there wasn’t any it was one paper which wasn’t even talking about ten thousand hours of mastery. And from that, we built an entire because Gladwell was a brilliant storyteller.

We’re here with all the evidence.

If we can get to be ten percent as good we got we we got we better than one percent or ten percent better storytellers to get that across, operate the leadership team to help our CEOs get through the BS into crossing this difficult journey, which we’re all on.

It it is it is mind boggling to me how one isolated data point from one survey I I this is kinda something that I’ve been telling a lot for through the perspective of data literacy.

One survey, one data point, and a handful of incredibly effective storytellers can can can be the genesis of a trend.

Right? A a hype worthy thing, a shiny object that makes us look over here What what and and I’m not saying this is always the case, but, there are plenty of of trends or plenty of hype stories in in our space. And the story that you’re telling is circle back to value, business value, measure what you were managing, be a leader, be brave, be willing to take some risks, buck the status quo. The things that we’ve been doing aren’t working.

Status quo, same old, same old. These manufacturing line approaches to everything that we’re doing not working. Eddie, I I love it. You’re not alone.

I’m I’m telling the same stories out there. I know there are others like us that are telling these stories.

Where can people reach you to get more information?

So, they can reach me on LinkedIn, which is, I’m Eddie Short or, LinkedIn, in Eddie s.

They can get me at my new venture, which is Intelligent Enterprise Partners. So I’m Eddie, e d d I e, at I e hyphen partners dot co dot u k, or they can look at our, website, which is ie partners dot co dot u k. And, and, and if if you you can also find what I’m doing on a research with through, Aston University, which is a UK university where I’m doing my doctoral program. But, you’ll see my blog on LinkedIn, which is called the Intelligent Enterprise blog.

And there you’ll see so follow me on LinkedIn. Drop me an email. I’m, I’m happy to basically speak to anybody and everybody about, the challenges of basic as as you say, driving value value and transformation in the world of, an AI powered data enabled world.

Wonderful. Good stuff. Eddie, thank you so much for your time. We are on the cusp of a bank holiday long weekend for the UK and Memorial Day here in the US.

So happy long weekend to us all. Of course, this episode will air long after that, but we’re all in good moods because of the weekend coming. Thank you so much for your time. Really appreciate it.

Your insights, awesome stuff. To all of our listeners, if you are not subscribed to the CDO Matters podcast, we’d be thrilled if you joined our growing community of data practitioners, data leaders, data managers, data stewards, data governors, you name it. Please subscribe.

We will check you out on another episode of CDO Matters podcast sometime very, very soon. Thanks all. Thanks, Eddie.

Thank you. Thank you, Malcolm.