Passing the buck; Business or Data?

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This is a podcast episode titled, Passing the buck; Business or Data?. The summary for this episode is: <p>Is business the problem or is it the data? Business science hasn’t changed since Peter Druker. But, it needs to change. It was designed in an era of certain technologies and data limitations. We had to work in isolation.&nbsp;</p><p><br></p><p>Those days are long gone.</p><p><br></p><p>So who’s to blame for inefficiencies, unsolved problems, mismanaged processes and other barriers? Join this weeks episode of Catalog &amp; Cocktails with hosts Tim, Juan and special guest Benny Benford to find out where the buck stops.&nbsp;</p>

Speaker 1: This is Catalog& Cocktails, presented by data. world.

Tim Gasper: Hello, everyone. Welcome. It's Wednesday once again. Welcome to Catalog& Cocktails presented by data. world. We're coming to you live from Austin, Texas, and a couple other places you'll find out in a minute. It's an honest, no- BS, non- salesy conversation about enterprise data management with tasty beverages in hand. I'm Tim Gasper, long- time data nerd and product guy at data. world, joined by Juan Sequeda.

Juan Sequeda : Hey, Tim. I'm Juan Sequeda, the principal scientist here at data. world, and as always, Wednesday, middle of the week, end of the day. Time to take a break. I'm actually in San Francisco today, so it's 2: 00. I started my day really early, so even though it's 2: 00, I deserve a cocktail right now.

Tim Gasper: Always fine to have a cocktail.

Juan Sequeda : Always, always. It's 5: 00 somewhere, right? I'm here in San Francisco because we're organizing these really cool, Honest, No- BS dinner series. We're actually taking them on the road and having really small, intimate dinners with data leaders. Next week, we will be, I think, in Boston. Then at the end of the month, we'll be in Atlanta. If you're interested around that, just ping me on LinkedIn. With that, today, oh my gosh, today's going to be so much fun because today's one of those episodes that you realize that we could probably speak for hours and hours. I'm super excited to introduce our guest, Benny Benford, who is the former chief data officer at Jaguar Land Rover. Benny is somebody who, I think in the last couple months, taking a break and he's been taking all the stuff that he's learned and dumping them out on LinkedIn. The amount of knowledge that he's been putting out there has been literally priceless. The stuff that I share with people, and people don't believe it. I'm like, " Dude, just go look at what he's saying. He's a million pounds per head. That's what you should be producing for value for data." Anyways, we'll talk about all this stuff. Benny, it is such a pleasure and honor to have you here. How are you doing?

Benny Benford: I'm doing really well, thanks. I'm joining from the UK. It's 10:00 p.m. here, so I'm very happy to join you with my cocktail.

Juan Sequeda : Cheers. Well, let's kick it off with that. Well, what are we drinking? What are we toasting for today? Kick us off.

Benny Benford: I'm drinking an old fashioned. Despite someone who likes disruption, there's some things that just shouldn't change, and so let's drink to that as well. Despite all the large language models in AI, let's keep having humans playing a role in data. They know what the context is, so to humans having a role in data.

Juan Sequeda : Well, I'm having an old fashioned here right now too. This is a beautiful old fashioned at the bar. They made it here at the hotel. I'm going to cheers to that. Let's not forget that this is all a social, technical phenomena that we need to understand and study. We live in this world where it's humans too. It's not just technology. Tim, what are you going to be toasting for today?

Tim Gasper: I feel a little bit out of vogue here. I don't have an old fashioned, but I do have an old- style cocktail with a twist. Y'all may know what a Tom Collins is, which is gin, lemon, simple syrup, and some club soda. Well, this what I call a Tim Collins because instead of gin, it's got whiskey in it, so kind of old- style, just a little different. I'll toast to that as well. Let's keep the humans in data too. The fancy stuff is fun, but don't forget about the humans.

Juan Sequeda : Yeah, we got whiskey keeping it old. Cheers.

Tim Gasper: Yeah.

Benny Benford: Cheers.

Juan Sequeda : All right. We got our funny warmup question today. What's your favorite buck to pass?

Tim Gasper: Hmm.

Benny Benford: Anything that's admin. I hate admin, so if I can get anyone else to take on the admin, I'm very, very happy. I just can't get motivated about anything I don't see the purpose in it. Filling in time sheets, doing anything admin- related, yeah, I hate it.

Juan Sequeda : I'll follow up with doing the dishes. If I can figure out someone else to go do the dishes.

Tim Gasper: Yeah.

Juan Sequeda : I'll cook. I love to cook, but somebody else can do the dishes.

Tim Gasper: Yeah. I like cooking as well. For me, it's why the cats weren't fed. Luckily, I've got the kids to blame for that.

Juan Sequeda : All right. We got a lot to go through, so let's kick it off. Benny, honest, no- BS, why is business the problem and not the data?

Benny Benford: Business has not changed at the rate that data and technology have. Fundamentally, look at the last 10, 20 years. We've gone to the cloud. We've gone serverless. We've brought in data science. We've brought in data engineering, we've brought in analytics engineering. The rate of change is phenomenal. Business science has not moved on, the art of management science, how you manage a business, how you structure an organization. Peter Drucker, phenomenal individual who picked up from Ford to look at how you could learn from how Henry Ford changed manufacturing and bringing those ideas to the business world. His ideas are still top of the pile. We haven't changed how we run the organizations, despite the fact all of the stuff that runs organizations, the data and the technology has moved on, so business needs to change. Business needs to keep up,

Tim Gasper: Man, Drucker. I haven't heard that word in a while. I remember in management school learning Drucker. I think his fundamental principles were put together, what, in the'60s, or when was it?

Benny Benford: Well, he had a very long career because he literally picked up from Ford, so would have started, I guess, in the'50s. I think he went towards the end of the century. Most people will have heard of the phrase, " Culture eats strategy for breakfast," and will have seen that put on some management presentation at some stage. That's Peter Drucker. Phenomenal, phenomenal individual. Yeah, some of his ideas, one of his ideas was you do not put process around the knowledge worker. It's out of date, and we can pick up on that one a bit more. There are some things that have just moved on.

Juan Sequeda : Let's take that one, for example. If I step back for a second, I think the foundations and the fundamentals of different areas and different disciplines, it doesn't matter if it's old. Old is not bad if you just lay this foundation, and then you build upon it.

Benny Benford: Yeah. inaudible

Juan Sequeda : Foundation is, there was a foundation. That foundation from the past is not the right foundation for today, or we need to be evolving, or there's something else that's missing? Because I'm going to be stuck and say, "All that stuff from the old is bad. Oh, we got to throw that away." I'm like, " I don't know."

Benny Benford: It was designed for the past. I did a post on LinkedIn to compare where data is with manufacturing pre- Ford, where Ford described the era that he inherited that the average manufacturing worker spent more time finding parts, and understanding the validity, the trustworthiness of the parts and getting them to work than actually doing the job, which is manufacturing, which perfectly describes how data is today. The average datas person spends more time trying to find data, trying to wrangle it because it doesn't fit the purpose, it hasn't come through proper quality assurance processes, than they do actually doing their job in processing and working with data. If we look at how businesses have been designed, they were designed for an era where technology and data brought such limitations on how you could run a business that you designed around them. If we really go back pre- computing, almost pre- ERPs, everything was designed around paper forms, and you had massive systems designed to get paper around the world, and all of the processes, the complexity of a process was limited to how complex you could make a paper form, or how complex you could pass it around. Then we had this wonderful thing called ERPs come in, but it was so hard to do computing, so difficult to do computing that the idea of changing an ERP from one company to another company was horrendous, so you designed a company around an ERP. It's insane. You designed how an organization should function around the capability of SAP, Oracle, others to design computer systems. Those processes were designed not for the organizations and what they're trying to achieve, but around a computing process. Getting even closer to data, individuals work with data on their PCs. You couldn't have the idea of collaborative datasets, individuals working together on the cloud. Really, really dangerous giving individuals access to servers because you give too many people access, they'll bring the damn thing down, so by nature, businesses are siloed. The KPIs that every function has are different. Everyone's, most of the KPIs are done outside of systems. They're done inside spreadsheets because they didn't have a collaborative environment to work in, so businesses were, by their nature, siloed. You solved problems in functional silos, and then you brought everyone to integrate together. All of that can be rewritten. There's no reason for silos. We can work in collaborative environments. You can look at areas where you can own your systems rather than actually... An insane conversation I remember once, that will remain anonymous, in my consulting days. Very senior level in an organization, someone said, " The first thing we need to consider when designing our organization is the fact that we're an SAP company." No insult to that software company, amazing company, but the first thing you need to consider when you design your entire organization is a software stack? That's insane. That's completely the tail wagging the dog. Yeah, we've got the ability to do things very, very differently now, but the world has not caught up in the slightest. We're still running siloed teams with different metrics and different functions with processes that change the world very, very slowly when we've got the ability to move on. If you look at those who are real digital disrupters, they're not working like that. They're working very differently.

Tim Gasper: This feels like, Benny, a very big frame shift. I think it's very fascinating to hear you set up this whole, that business is the problem, not necessarily data. Because I think we are such in a frame of mind of technology first around data, and, " Oh, well, it needs to be bigger. It needs to be faster. We need better tools. We need..." It's always about, how do we create the right technology to help us unlock things? Then on top of that, when we do start to talk about process, let me just name something that's been buzzy lately, the concept of the data mesh. Even that tends to think a lot around the data, and the technology, and how we then map the business to that. It sounds like you're going further. It sounds like you're saying that we should actually look at the business itself. Can we change the way that we're doing business, or organize our business in a way where... To your point of that businesses, they were designed around technology and around data. How does it actually become more of something that works symbiotically together? Is that the right way to think about that frame shift, or is it a little bit different than that?

Benny Benford: It is. One of the ways to, I guess, open up that mindset shift, if you encourage everyone who's listening to think of some of the most transformative data solutions that they've seen in their actual lives, rather than the Netflix algorithms that get talked about a lot. How many of them have actually used the latest cutting- edge technology, or actually needed to use it? Very, very few. We therefore definitely don't need what's coming out this year, and next year, and the year after to achieve radical business change because most of the transformative solutions have used technology that was available five, 10, if not more years ago. Therefore, that really emphasizes that what's been holding it back is the shift in thinking and changing how you run and drive your business. It's a really big shift. It's an easy statement to make, but I think this is so ingrained, it's almost hard for me to find a way to overstate how big the change is going to be. Radical statement, radical belief, but I think there's a significant percentage of people who are at senior levels who don't understand the purpose of the function that they're running. They understand that they execute a process, and they understand how that process has been designed. You can list random organizations. It's kind of like, and there are processes that are archaic is what you see in Moneyball. Moneyball, you see scouts who choose their players based on all sorts of archaic, outdated things that existed pre- data. That's how you see organizations price their products, decide how many volume of whatever product they want to be producing next year, deciding what they're going to launch onto the market. They have very outdated processes for doing that that come through decades of, often coming off the back of systems and ingrained knowledge. They think their purpose is to run their processes neatly as possible, but they've never stepped back to say, " What's the purpose of a process for the organization?" and now that we're in the new era, can rethink that entirely. If you're looking at pricing, actually, pricing may have been the way to maintain competitiveness with your peers. Now, you can run dynamic pricing. Maybe it's a way to enter entirely new markets, and the whole purpose of the team that you've run has changed massively. We've got the opportunity now to where you shouldn't just be redesigning a process to improve what the purpose of that current process is, but to change your business model. We should be visiting that on every major project that we look at. That's a really, really fundamental way of looking at the world.

Juan Sequeda : This just sparked an interesting thought in my mind here. You said something very critical here. It's like the actual senior folks in the organization, they probably don't even understand how the business works. They're almost like, they're running, "I got to do this function. I got to do this thing. I got to do it. Why? I just got to go do that. I got to go do that," so they're not actually, they're not being curious themselves to understand why we're doing that. Like, " We got to migrate to this ERP system. It's going to migrate from work, oh, to SAP, or SAP whatever. It costs$ 2 million, $ 3 million. Going to take two years, whatever. Yeah, we got to go do." People know how to go do this very well, but why are we actually doing that? How is this actually improving the business, and so forth? I think, so your point is that there are senior folks who actually have been there for so much time that they really don't understand the bigger picture. I can't buy that. I think people have just been in their silo. They understand their silo. They understand the people around them, but not the bigger picture. I always talk about like, wait, the data folks need to understand how the business works, and also this younger, newer generation because data is a newer thing. Data wasn't a thing 10, 20, 30 years ago. If the younger or newer generation who's focusing on data, and we're telling them, " You need to understand the business," but at the same time, they're looking at their senior peers and they're like, " Well, they don't even understand the business, or they think they do, and they're making it so complicated." It's like, in reality, who actually knows what's going on? Somebody needs to know because a company is making money, and they're still out there, so I think that's like a complete disconnect of what's going on. I think this is where I believe that we're going to go see the true leaders stand out. I think it's based on empathy, and passed on curiosity to say, " I'm going to go the extra mile to really figure out what's going on, and why, and try to connect the dots across all these silos."

Benny Benford: Completely agree. I'd say not just to... It's a real nuance, but maybe I'm nitpicking here. Not just understand what the business is, what it can be. The businesses who are doing best are the businesses that keep evolving. I'm constantly on the lookout for examples of really interesting data transformation. One occurred to me. I'm getting back into cycling again, and I got some kit. I'm techy. I got some kit for my bicycle that measures all sorts of data from Garmin, but Garmin were the GPS guys, and so were TomTom, and TomTom, kind of wear a TomTom. Now they still exist. I had a Google, but they're a software company that do a little bit of mapping. Garmin now, they're not just the GPS guys. They take data off my pedals. They take data off my heartbeat. They do all sorts. They've got shipping forecast data coming in because they've just kept expanding their business and going, " Well, this is where we are today. What if we had new data? What services can we spin up? What else can we do?" It's not just looking at what you are. It's looking at what you can be and expanding your business. That's where the value is today. The companies who move the fastest aren't just trying to expand their penetration into a certain market. They're redefining markets all the time. That's where data is at its most disruptive and most value- adding is by redefining things. It doesn't mean it's the only thing you should be doing, but I think it's where you're going to get most of your value.

Tim Gasper: I think this is hard for a lot of companies because it doesn't feel safe. I go back to your comment about just running the playbook of the process, as opposed to really thinking about your purpose and letting everything else pivot. There's a joke that, at some point, somebody told me at one point. They said, " A lot of companies like to do R& D, ripoff and duplicate." I think that's true across every industry. It's safe. " Just copy what everybody else does and your job is safe." This seems hard. It seems like few people want to do this, and if everybody wasn't doing it, wouldn't it be chaos?

Benny Benford: It's really hard, and it's really vulnerable. There's a joke that if you don't want to get fired at a senior level, then hire some of the biggest brand name consultancies and choose technology on the Gartner Magic Quadrant and they will never fire you for those decisions. You're also going to really stand out. You're going to do the same thing as all of your competitors.

Juan Sequeda : You're a follower.

Benny Benford: You're a follower, exactly. There's lots of safety in that, and you can lead a nice, comfortable existence maybe, but maybe there's going to be a disrupter who views things differently and really questions what the market is, what the customers need, how the world's evolving. It goes back to fundamentals, does a SWOT analysis. What are the strengths? What are the assets of our company? What are the opportunities and pivots? You've got organizations, Lego is a fascinating example of an innovative organization. They make plastic toys, and they now make films, and have playgrounds, and theme parks. Organizations that continually revisit their purpose based on where they are, and for us data people, we should be not just questioning how the data that we have in our organization can support what the organization does today, how it can support new services, and how the organization we have today is in a better position than others to capture new datasets, and what we could use those datasets for. We need to constantly be on the cycle for innovation and pushing things forward.

Juan Sequeda : I think when we were talking before, you said something that really opened my eyes. Companies don't have the right to exist in the next five years. I think a lot of people believe that they're in this, we just take it for granted is what we do, but yeah, we need to be ready for change. Because I mean, we'll talk about AI later on, but AI's coming around and there's always something coming around that's going to go dramatically change things, and if you're not prepared, either ready to be that change or be that change or be able to go adapt to that change, your competitors will. Then that's probably why companies close down and they don't exist anymore.

Benny Benford: Yeah, agree. The reason I said that, I had a brutal start to my career. My first job was... I mean, brutal's the wrong way, but it was a very good grounding. My first job was working in business transformation, not in technology. I started in'08, so I saw... I worked on the insolvencies of more companies than I care to remember, and did more personal redundancies. It left me with this understanding that, yeah, the organization has the right to survive, and the rate of change... I think one of the biggest things people miss out when they talk about strategy and they talk about, "Can we go this way or that?" they forget the cost of indecision. People don't realize that not making a decision today is actually a decision to not make a decision, and probably the most expensive decision you can make in your business is to not do anything. All of this procrastination about, " Which direction do we move?" it's really costly. I can't remember the statistic on what the average age businesses last is. I know it's something that Jeff Bezos supposedly used a lot at Amazon, regularly quoted to people the average age of a company in the FTSE 100 or equivalents around the world, and it's not... New entrants come in all the time.

Juan Sequeda : I'm Googling this right now. A recent study... Well, this is the first thing that showed up. A recent study by Mackenzie found that the average lifespan of companies listed in the S&P 500 was 61 years in 1958. Today, it is less than 18 years.

Benny Benford: Wow.

Juan Sequeda : Mackenzie believes that in 2027, 75% of the companies currently quoted on the S&P 500 will have disappeared. This is reading the top thing that showed up here on Google for an average company.

Benny Benford: Those statistics really hit home, don't they? Because I was at a conference-

Juan Sequeda : I actually never thought about ... This is fascinating.

Tim Gasper: Gone are the days of just working at IBM for 35 years and getting your Rolex watch.

Benny Benford: Yeah, exactly.

Tim Gasper: Companies are like a disposable construct now.

Benny Benford: And the skillsets, the roles are all disposable. The world moves on fast. It's both, I think, in companies to need to do a lot more to support their workforce through this, but individuals need to be looking at this and questioning, " Is this company going to support my route to retirement?"

Tim Gasper: Mm-hmm. There's a lot of frameworks that try to adapt to change. Agile, lean, safe, things like that. Are these frameworks providing value to be able to respond to some of the disruption and be a part of some of that disruption? Or is that just not... That doesn't even scratch the surface?

Benny Benford: I think agile is, I think with a debate, small A or big A. Agile, some people just run off and think of kanban boards and what have you, and there's more to it than that, but agile with a small A, I think, has a huge role to play in that. I think that's been recognized at a lot of board levels now. A lot of companies that I've spoken to and the last organization I was in, the board recognized the need to move towards, to an agile organization where there's, one of the changes is the teams within the organization are much more empowered to drive changes, as long as they drive towards outcomes that are agreed at the senior level. Then there are mechanisms in place to agree to new outcomes as well, which is very different to how organizations used to work where, top- down, you decided what needed to be done, and you received your orders about what to do. Instead now, you're told what direction you need to take people in, and then you've got a local empowerment to do that. I think data's got a huge role to play in that. There's an article that I keep meaning to write, and keep feeling I've not done enough research on, so I'll say something relatively incoherent now, perhaps, but I think agile and data culture are two sides of the same coin. If you look at the articles on, what does it mean to be an agile with a small A organization, and what does it mean to be data culture, kind of data with a small D organization, there's autonomy, autonomy of teams to drive in their own direction, clarity of purpose and objectives. The agile talks a lot about transparent access to information, and metrics, and backlogs so everyone can actually hae a common understanding of what's going on. We've got this concept of stream- aligned teams coming up, so you're not just autonomous, but you've got the ability to drive through a whole value process, rather than earning just a section and learning organizations. I think data's got as large a say in how we create these organizations as agile does, and that they're two... Maybe along with how you create learning organizations is an interesting one. They're really big concepts that we need to push towards. That's my take on data, which is back to our toast at the beginning. It's very much around people and what people do with data, rather than just the records themselves.

Juan Sequeda : Now this goes back to something we were discussing before about data teams themselves. They need to go after the business organizations. They need to shift from that, " No, because" to the, " Yes, if." Like, " Oh, we don't have buy- in from the executive." Like, " Well, because why not? Have you actually gone in? Why? You just asked once and they said, 'No,' you stopped?" No, I think there needs to be more of this interaction. Business traditionally has lived in, these lines of business have been in these silos, which I think that's how it traditionally has been set up. Data is coming around saying it doesn't fit in its own silo. It's something that traverses all the different silos, so that's why you get all this different friction. That means that that's a good thing. Friction is energy there that we're like, " Let's go use this for the positive." I think sometimes, then we just end up in the easy space, " Let me just go then and work on this line of business," and then you just end up being an ad hoc report to generation, which is nothing wrong with that, but you're a follower. You're not a leader. Then you end up doing the same thing we've been doing before, except with a fancier tool.

Benny Benford: There's lots of... Like cloud migrations. I have no idea why a cloud migration is a project in and of itself, but you can justify it. You can do it. You can spend some time on it. There's lots of work that you can justify doing, but that doesn't really push you outside of your comfort zone and move forwards. I think to that maybe call it, " Yes, if" mindset, I think the world for data people has changed during COVID, but we haven't fully seized the opportunities yet. What do I mean by that? If you go to conferences pre- COVID when the room were polled on what your biggest challenge is, exec support was always listed as the biggest challenge from data leaders that I spoke to pre- COVID. It's not anymore, but yet people are still listing some of the same blockers as to why we can't drive changes, or we can't push a common data platform across the organization. " It's too big a change. We're going to have to reeducate everyone. We're going to have to change everything everyone does." Well, why not? The chief commercial, chief customer officer forced CRMs in at one stage. The chief finance officer forced ERPs in at one stage. Surely the chief data officer can force and achieve a common data platform across the organization. Surely, that's not too big an ask, and data is now at the table. I think the board are open for those conversations now, but we're used to shadowboxing ourselves and going, " Oh, we've been told so many times no on X that we're going to get told no again." I think it is time for data leaders to make very bold asks and to justify their seats at the C- suite and at the board level,

Tim Gasper: Make your bold asks. Push those things forward. I think this is fascinating. One thing that would be helpful, Benny, is if you could talk a little bit about your own journey that you've gone through and whether, as your role at CDO, at Jaguar Land Rover, or at some of the other experiences you've had, how you've faced these challenges and what you've seen work well and not work well.

Benny Benford: I'm going to have to admit that I've learned a lot of lessons along the way. All of the stuff I'm saying now, I don't necessarily do along the journey, but this is how I've got there. My background, I have a technical background. I studied, operational research university, so I always had an interest in how you could use math to shape the world. Did business transformations, as I said. Came across data when I was working in consulting. Loved it, but got frustrated as a consultant. You're limited on what change you can drive, so I tried to find an organization where I'd get the opportunity to test, how far can we go with data? I always had this question in my head of, why is everyone using Excel? It just feels so out of date, so how can we change that? Excel's older than me. It's probably older than the both of you as well. Excel is as old as the Sony Walkman. Who still uses a Sony Walkman? This question of, how can we completely change everything? I'm not interested in cars still and wasn't then, but got a lucky career break joining Jaguar Land Rover because of who I got to work for, the person who's now CEO. They became chief transformation officer. They'd been given the remit to set up a data team, and that was my big career break. They were deputy CFO at the time, so we got, which you've alluded to, Juan, given the target. When I came in and started the team, we were given the target to get a million pounds returned per person per annum because it was a CFO moving into a chief transformation role, which I'll be honest, sounded insane. It really pushed those of us in that team at the time, a number of people in the leadership team. I think it's really important to have a diverse leadership team to take on these type of broad tasks, to whether that was possible or not, but really pushed us to think about, " Well if you are going to get those types of returns, what are the value- drivers in a business?" Unsurprisingly, it's understanding revenue. It's understanding management of your working capital, which goes to forecasting. Some of the biggest returns early on came from improving forecasting, improving the margin by looking at the mix of vehicles. You really push on where does it make sense to spend time? Some of the things that people speak about are projects you get to eventually, but they're not going to be those massive, massive hits. Like predictor maintenance is a great project, but on one machine is not going to change a business, so it really pushed us to look at that. My role then shifted to an interesting challenge. I had a bit of a career setback. I didn't get the promotion I was looking for at the time and said, " What do you want me to do now?" The response came, " Well, you've shown you can get value from central data teams. Can we get value from everyone who's doing data work in the organization?" which was a really interesting challenge. Didn't really know what I was doing to start with, but formed a team to do that. The biggest recommendation I'd give to everyone is to survey your organization. If you are in charge of data strategy, you need to be really, really humble and assume you know maybe 2% of what's happening with data in your organization. To get a really wide community base and get them to tell you the problems, that was fascinating. Then we had data to tell us around why the rest of the organization was struggling to get value from data, and could prioritize what we solved. Worked on that for a while. It was mostly an evangelist role, and then got the career break to move to CDO. Some of the pushes that I speak about now, some of them were things that I advocated for. Some of them were feedback from the board. I talk about data platforms. I remember going to a presentation to make a business case for more investment in data platforms and the response came, " We get it. You've spoken to us about this many times before. When are we going to finish rolling it out?" That was sort of manna from heaven, so we then approached things differently and looked at how you can complete a data rollout. I don't think that conversation happens enough in the data sector. That's my background. Then recently, yeah, decided to move on to... Well, we'll get to that later on to what I'm working on now, but that was my background at JLR.

Juan Sequeda : Yeah. I would love to go into the million pounds per person. You wrote this post a while back about how you were actually calculating that. This would be great for our listeners to see like, " All right. This sounds crazy," which by the way, this was another thing that we chatted about before is, we need to have these crazy things because the moment somebody says they can't do it, somebody says, " Oh, I'm going to go do it," and they do it, then suddenly, everybody starts to go do that, right?

Benny Benford: Lots of the constraints are artificial than what we think they are. I think you were alluding to this. I don't know if people have heard of it, but the four- minute mile. The first time someone ran the four- minute mile, everyone said it was impossible, and then someone called Roger Bannister ran the four- minute mile. He wasn't the elite athlete. He wasn't the one winning all the competitions, but he ran a four- minute mile. The year after he ran a four- minute mile, so many other people ran one because it was suddenly, " Oh, it's possible," and so it's a mental switch. I think, most of the time, we work with what's possible. If you're given a ridiculous target, you get to challenge, " Well, what is possible?" The calculation, it's simple. At the end of the day, it's business benefits. Broadly speaking, all of the large ticket items either came from cash was saved, so you reduced the amount of stock that was stored in the company, either finished vehicles or stock in terms of inventory by improving processes. That's measurable. You know the stock level before and afterwards. Then there was an attribution, which I'll come back to in a second. Or, it came from improving profit, profitability by taking expense out or looking at margin of things that were sold. Can speak about this because there's an awards-winner for this. The largest project, which I wasn't the one leading, someone else was leading, was a recommended order bank that looks at specifying all of the vehicles in different markets. It helps you to set exactly how many vehicles should be the premium vehicle, exactly how many should have all of these features. You can get the margins on the vehicles right, and you can measure the margin before and afterwards, and see the value. Then there was an attribution, which was if the analytics, the data products alone had delivered all of the results, which is rare but does happen, then the team, we were allowed to claim 100% of the benefits. If it was the case that the data analytics products needed a significant business change off the back of it, it was seen as 50-50. If the data analytics project was seen as an enabler amongst a bunch of other things... There were new systems brought in. There was also business change, so there were other things going on, then we got 20% of the benefits, if I remember right. It doesn't have to be scientific. You're looking at a business lever that changes profit, revenue, the amount of stock that you've got and using that attribution mechanism. The phrase we often used when we had debates agreeing this at the beginning... You will find arguments of, " Well, the profit's improved, but the market's changed as well." The phrase we often use is, " Do we want to weigh the pig, or do we want to feed the pig?" We can spend all of our analytics time measuring our impact, because that uses analysts up, or we can spend all of our analytics time trying to drive impact, and you want to be somewhere in- between the two. Let's go with crude measurement and lots of impact over very precise measurement and not much impact.

Tim Gasper: I love that. Sometimes, we get too obsessed with the details about things that we lose the bigger picture, and the goal is to drive behavior.

Benny Benford: Yes, yeah.

Juan Sequeda : I'm curious. Did you have an accountant look at the stuff, or this was just your team's calculation?

Benny Benford: I am an accountant.

Juan Sequeda : This is something I've-

Benny Benford: For my sins, I am an accountant.

Juan Sequeda : Well, oh, so I've been bringing this some a lot. There's a podcast I listened to a while ago who brought about, he said like, " Oh, you should bring an accountant to the team." I've been bringing this up with a lot of folks and they're like, " What?" Is it your evidence that you need to have accounting on your teams?

Benny Benford: As it scaled, there were a couple of people on the team who were accountants, and it really helped. We actually ran training on accounting for the whole data team. Yeah, I think it's essential. I think you should train anyone who's trying to get value from a business to at least understand the accounts and to understand the difference between things like profit, and cash flow, and what it means to have depreciation. Just basic things like this. As we scaled, the finance team became really important. There was a team in finance that actually audited our results, that gave validity. They helped with some of the more complex analysis so they could take on some of the work. Yeah, I think it's really important to train teams that are trying to drive value on the science of value, which in some ways, is accounting,

Juan Sequeda : Understand the science of value, which is our compass. All right. We got so much more stuff to cover before we wrap up. You are doing something new. What is interesting is that it's a non- profit startup. Non- profit. What does the world need right now that you are trying to go change with this non- profit?

Benny Benford: What did I get frustrated about that caused me to leave? Because it was a lovely role. My experience was that there is no natural partner on the market for data transformation, and talk through that, then there's a couple of other problem statements that come off this. You've got lots of tech companies out there and they're brilliant, but no technology alone supports data transformation. You need a suite of them, and it's not the role of any technology company to understand how you put your whole suite together, and you don't need just technology. The technology companies aren't in the position to be ideal transformation partners. There's also this really weird thing that because the world is run by VC and investment these days, your value multiplier is much higher for a SaaS company than a services company so therefore, SaaS companies don't want large services arms to help for transformation, so they can't help. The consultancies, little controversial opinion maybe, but I tested this one on a LinkedIn poll. I think 87% agreed the consultancies, a lot of them have become conflicted because they want to sell data services. Therefore, to try to help you on a data transformation where your company's very good at doing data services itself and is self- sufficient, there's a conflict of interest. They're not natural partners for helping data transformation, so this was a gap that I noticed. I initially thought, " Well, I'll leave and I'll start up a consultancy to help provide that service." Then I very quickly realized, as a I looked at the market, oh my god. The market is overwhelmed with noise. We have so many data startups outcompeting each other with new terminology. Again, did a LinkedIn poll on this, and the vast majority of people agree the amount of constant new terminology in data is a problem. It's making it harder for people to move into this area. It's making it harder to have stable strategies, and you need to run through a long- term strategy, and that's the result of an overcompetitive market. Then the final problem statement I realized looking at this is, well, data's not yet a profession, and this is part of the problem. All of the thought leadership's coming from tech companies and consultancies. Tech companies, they do produce some thought leadership. They also produce marketing and sometimes, you can't tell the difference. The consultancies, similarly, they're trying to sell their products. It's not a profession. If you're going to try and set up something that's going to change this dynamic, no one is going to collaborate, and it is a collaborative effort with a profit- making entity, so the gap is there needs to be a non- profit that starts to drive collaboration in the market so that we do have common standards so that companies can accelerate the rate of their transformation. I've got a white paper, which I've shared with yourself, but I'm happy to share with people that reach out, that I was sharing last quarter that goes through a number of these problem statements and others that fall off it. Essentially, setting up a non- profit to start to standardize not the technical aspects. There are non- profits looking at things like new standards around graphs. Great. Don't question it. Need it, superb. There are entities looking at some of the ethics, increasing ethics in the data sector. Great. Again, superb. There's no one looking at business management and how it changes, and how do you manage a data transformation? How do you support organizations to do that? At JLR, we worked with a couple of other companies who were doing similar things. There was so much that we were doing in common, and there was no mechanism for sharing this knowledge, so develop the body of knowledge for data transformation and then running data- driven organizations, and do that in a non- compete way as a professional body would do. That's what I'm looking at setting up next.

Juan Sequeda : Yeah. What I find that is important with the work that you're doing is that this is the repeatable playbook in a way that... The playbook itself is not competitive. That's not competitive for a company. People should be looking at this, and it's not one playbook. There's probably, depending on the industry, depending on your size, what you're trying to go do, there's different ways of doing that. If we all are heading in the right direction, then we won't be wasting our times doing the next migration to the next SAP thing, without knowing what is our business function. Otherwise, we're going to keep reinventing the wheels, but with, I don't know, data's going to be not in the cloud, but I don't know, the space or whatever, right?

Benny Benford: Agree. I think the transformation needs to be led by people within a business. There's a lot of analogy with something like Lean Six Sigma. Lean six Sigma was a standard. Lots of organizations were set up to train Lean Six Sigma experts within organizations and then they drove through the continual improvement changes. One of the gaps I saw on the market is anything... I know lots of people from business backgrounds who want to know how to make their function more data- driven. There's nothing to help them do that in a way that is tech agnostic, and then gives them access to a continuing, evolving body of knowledge, which is what a profession is. That's starting to professionalize data itself, which is another thing we spoke about, Juan.

Juan Sequeda : Yeah. Well, we can continue talking just on this topic for another hour or so forth, but we're going to start doing this brand new segment. Brand new segment starting today. First time. We just decided yesterday we're going to go do this. Before, we did the mesh minute. I think we're done with data mesh. Let's go do AI is on everybody's mind. GPT, LLMs, all this stuff. One minute to rant about AI. You got your AI minute. Ready, set, go. One minute.

Benny Benford: In my view, large language models are kicking off the largest theft in human history. It's fascinating. IP is kind of weird anyway. Could spend 10 minutes on this. IP is very rigid in things like music, less rigid on things like written text. Our large language models have just gone out there and said, " It's all ours. We're not telling you whose data we've consumed, but we've consumed it and we're going to repurpose it. We're not going to credit you." That's not what happened previously with ideas. If you wrote an academic paper, if you wrote a good book, you had credit where it came from. Okay, maybe it's changing. Bard says it can credit some of its sources, ChatGBT can't, but there's certainly a lack of accountability and transparency in there. It's centralizing ownership of this in a way that's really quite scary. What do I think's going to happen next? Because I got to spend a minute on this, and I could keep talking. There's going to be legal battles. Organizations need to look at how they can start to run their own LLMs and maintain their own data. I'm done.

Juan Sequeda : Beautiful. This was an awesome kickoff in this first segment. I did not know where you were going to go with this. I agree with your inaudible Yeah and ... inaudible

Benny Benford: Yeah. It feels that way. It feels that way. There's no accreditation for all of the IP that they've taken to create their assets.

Juan Sequeda : Excellent point. Well, let's see-

Benny Benford: They're planning for it.

Tim Gasper: Yeah. We talked a lot about disruption today. Obviously, there's a massive disruption here, and some folks don't know how to react to that. Italy just banned GPT, right?

Benny Benford: Yeah. That's interesting. That's really interesting. See how that evolves, because if it does produce productivity enhancements, that's a brave thing for a country to do.

Juan Sequeda : All right. With that, let's-

Tim Gasper: TBD.

Juan Sequeda : Let's kick it off with our lightening round presented by data. world. I'll go first. CDOs. Shift more resources and power to them and their org?

Benny Benford: No. No, the resources should be away from the CDO's org. The org should be decentralized. Data should be in the functions. The CDO should be an orchestration role.

Tim Gasper: Hmm. Okay, nice. Strong opinion there. Second lightening round question. The Management Bible, Peter Drucker inaudible rewritten?

Benny Benford: Yes, 100%. We need to find a brave CEO that's going to experiment with how to run an organization, and then retest how to run organizations.

Juan Sequeda : All right. Third question. Are all companies becoming data technology companies?

Benny Benford: No. They don't all need to. I had an interesting conversation with someone the other day around we need to stay confidential, but around a kitchen organization, a kitchen fitting organization that couldn't care less about its data. It's massively profitable, it has great carpenters, it's expanding vastly. It doesn't need to worry about data. There's plenty of industries that don't need to worry about their data.

Juan Sequeda : Just explain more of that one a bit. You said, " Plenty of industries that don't need to." I'm curious. Can you expand on that? What other industries you would say like, " Yeah, no"?

Benny Benford: I mean, does a hotdog van need to worry about its data? There's lots of places. Anywhere... I mean, hot dog van's a weird one, choice. Maybe it does. Anywhere where you've got high margin and it's craft, and you're expanding, and you have demand from what you're doing, you don't need to refine your process, so if this is a craft area, it does not need to worry about data at all.

Juan Sequeda : That was interesting, very insightful. You're a very-

Tim Gasper: I'd like to unpack the economics of different industries, at some point, and just think about how disruption affects them differently, but that's the data nerd in me. All right. Fourth lightening round question for you. Fast- forward five years from now. We like to do some of these futurecasting questions sometimes. Five years from now, are businesses more successful at being data- driven and channeling this disruption, or are they more in chaos and disrupted?

Benny Benford: Both. The ones with the higher growth and higher margins are more successful. The ones with the higher mental health problems of their workforce are less successful. This is why you see so many people are battling it out right now because their organizations are trying to do everything. I've spoken to CEOs who go, " Oh my god. My CEO has just heard about ChatGPT and AI, and now wants to do these 15 things." You need focus. Organizations that have focus will succeed. Those who stay scattered will end up stressed and chaotic.

Juan Sequeda : I think that's a statement that will transcend the test of time, so that one we'll focus-

Benny Benford: It's just a totality, I suppose.

Tim Gasper: Well, it will be included in the newly-written management book.

Juan Sequeda : I'm sure Peter Drucker talks about that, so that part wouldn't get rewritten.

Benny Benford: I agree. I agree. There were plenty of Drucker that stands the test of time.

Juan Sequeda : All right. Well, we have so much notes here. Takeaway time. Tim, take us away.

Tim Gasper: All right. Such a great conversation today. We really started things off with this honest question about business versus the data, which one's the problem? You really started us off, Benny, by saying that the business hasn't changed nearly as much as the rate of technology. The art of running the business continues to be largely the same for 70- plus years. You don't put process around the knowledge worker, and all these things that were developed as part of the original management methodologies by Peter Drucker and others. They haven't evolved to the new times. They don't all apply to the new times, and many of these ideas don't fit into a world that we live in today where technology, data, and society is evolving at such a rapid pace. It was designed for the past. Ford described that the average manufacturing worker spent all their time looking for parts. You talked about they're worrying about finding the parts, the quality, amusingly, somewhat to the world of data today. Businesses were designed for an era where technology and data would be designed around instead of in conjunction with, and so you mentioned paper. Everything revolved around the paper processes, and then ERP came around and the business just did everything around the ERP. You designed your company around the technology, but things are changing. Business lines used to be more siloed and run independently. Now there's new metrics, new processes, and a lot of disruption. How many of the most successful companies are really leveraging bleeding- edge technology or algorithms? Less than we might think. Actually, they're leveraging it to disrupt as opposed to really... It's not the technology that's the factor that's driving their success, it's the disruption in the business model and how they're applying those disruptive approaches. You mentioned that purpose needs to be at the center, that a significant percentage of people at the senior level probably don't actually understand the purpose of their function. All they're doing is they know what the process is, or at least what they've been told the process should be and how it should operate, and they're just following out that process. They're just running the playbook they know instead of thinking about truly what is their purpose and trying to disrupt themselves and disrupt the market. You mentioned Garmin. They're not just a GPS company. They're like an IoT data company. Look at Lego. They're not a plastic piece, plastic toy manufacturer. They're an entertainment company. It's really thinking about how you can change the game around these things. Really, this centers around not the technology and the data, but around the business itself. Then you mentioned about there's all these strategy problems. The average life of a company is reducing, and people often... The worst decisions that a company or that people could make is actually indecision. Analysis paralysis is probably the greatest cost of the business. Agile and data culture are really two things that can help a lot, and they're two sides of the same coin. Then you mentioned autonomy, clarity of purpose, transparent access to information, stream- aligned teams. Which being from a product background, I'm very passionate about, so I'm excited to hear you mention that. Learning organizations. Shifting from, " No, because" to, " Yes, if." I think there's a lot of opportunity here for us to change the game around strategy, company strategy like you talked about. So much more, but Juan, I'm going to pass the baton over to you.

Juan Sequeda : Yeah. We talked about your experience at JLR. I like, " Excel is as old as the Sony Walkman, but who's using the Sony Walkman anymore?" I think those are the types of things we need to be looking at. Again, I love this whole million pounds per person. Anybody who's listening, go find the post about this, or just ping me and I'll link it to you. How do you do this? Well, you got to understand how this cash is being saved, how to reduce... How do you save cash? I'm going to reduce the stock. You could do the before and after. You can see how much of that. If I'm going to prove profit, I could take expenses out. I could look at the margins around this. You have this whole, these metrics, and you take 100% of the credit there if it was all done by the data products. 50% if it was done between half- and- half between the data and the business. 20% if you know that you're part of that change. Don't get obsessed with all the details around this stuff. We've got to be very practical about it. We should have, accounting should be part of the team. In your case, you're actually, accounting is your background. We need to understand profit, understand cash flow, understand the science of value, which is accounting. Have a diverse leadership team. Understand, what are the value- drivers? Understand how revenue, understand how to improve forecasting to understand where you should be spending some time. Survey the organization. I love this one. I bring this up time and time again. My broken record. A data catalog is not just about cataloging data. It's cataloging data and knowledge. Understand how people are doing things in the organization. Let them tell you what the problem is. The status quo is to focus so much on what is possible. People said the four- minute mile was not possible till somebody did it, and then everybody started to go do that. Then what's next for you? Not just for you, but I think what's next for the entire data world in general is there is no natural partner in the market for a data transformation. The majority of the people believe that consultancies are conflicted. They want to advise you how to do a transformation, but they also want to go implement it for you, so they're not a natural partner. Another issue is that data is not a profession, that we're still such a young thing, and that is a problem. We have this big gap of collaboration. I think you need to be able to have a way to bridge this gap, and it needs to be in this non- profit way. This is not to go standardize technical aspects. People are not looking at how to ... It's called standardize the playbooks around how to do business data transformation. This knowledge is really not being shared, and we need to be able to make sure that's repeatable. How did we do? Anything we missed?

Benny Benford: I'm exhausted by listening to that, so if we got through all of that, that was impressive.

Juan Sequeda : Well, that was all you.

Benny Benford: It's been a good chat. It was really, really good fun.

Tim Gasper: We've hit a lot of good things.

Juan Sequeda : Yeah, so let's just throw it back to you to wrap it up on advice, so throw three questions. What's your advice about data, about life? Who should we invite next and what resources do you follow?

Benny Benford: Advice. Diversity, diversity, diversity. It's so simple. I think it's so easy to become absorbed in your profession, particularly an area that's moving as fast as data, that that's all you read about. Read about everything. Read about history. Read about other domains. Speak to people from other areas. I learned so much from reading about other areas. One of the things that fascinates me is reading about other transformations like the Japanese uptake of technology after World War II. Fascinating thing to learn about when you're looking at technology change today, so diversity there. Diversity in your teams. The biggest lesson I learned at JLR was not just to recruit data people in the data team. Recruit people from change backgrounds with no data experience because they're going to teach you so much more about change than someone from a data background. Diversity is the advice. I'm going to cheat on people to invite because I couldn't narrow down to one. I think both of these people, Juan, but Bethany Lyons, superb. My brain always explodes when I have a conversation with her around data.

Juan Sequeda : She's already been on the podcast.

Benny Benford: Well, then I'm not cheating. I need to listen to her podcast. In which case, I'm going to flatter my former boss and say Harry Powell, a phenomenal data science mind who also understands business very, very well. He was the guy in charge when we had the million pound target, so he'd be good person to bring in. He's thinking very, very deeply around the world of graphs at the moment. In terms of what resources, I read a lot. I love reading books. I still think there's so much value in books that you don't get from online articles. That's read medium. LinkedIn, my biggest learning so far this year is LinkedIn is a two- way medium. As I've started putting stuff out there, people have reached out to me and I've created time each week for half an hour conversations, and I think that's priceless, so comment on LinkedIn. Reach out to people and find that time to just start chatting to people.

Juan Sequeda : 100% with you on the LinkedIn, the two- way medium. I've met so many amazing people with that. With that, actually, next week, one of the people, one of our guests next week is going to be Veronika Durgin, who's a VP of data at Saks, who I've also met through LinkedIn, and we've chatted through that.

Benny Benford: Yeah.

Juan Sequeda : inaudible that. We're actually going to be live from Boston because next week we're going to be in Boston for our Honest, No- BS dinner over there. She's one of our guests, so we're going to figure that one out, how to do the whole live thing. Benny, thank you so much for this amazing, valuable conversation. I agree with Michael who just said this was an incredible conversation. Thank you, thank you, thank you so much.

Benny Benford: It's been a lot of fun. Thank you for the opportunity.

Tim Gasper: Cheers.

Benny Benford: Have a good afternoon, I guess.

Speaker 1: This is Catalog& Cocktails. A special thanks to data. world for supporting the show, Carly Berghoff for producing, John Williams, and Brian Jacob for the show music, and thank you to the entire Catalog& Cocktails fan base. Don't forget to subscribe, rate, and review wherever you listen to your podcasts.


Is business the problem or is it the data? Business science hasn’t changed since Peter Druker. But, it needs to change. It was designed in an era of certain technologies and data limitations. We had to work in isolation. 

Those days are long gone.

So who’s to blame for inefficiencies, unsolved problems, mismanaged processes and other barriers? Join this weeks episode of Catalog & Cocktails with hosts Tim, Juan and special guest Benny Benford to find out where the buck stops.