Transcript
Speaker: If organizations are asking a question, how can AI replace what our engineers are doing? They're looking for the wrong end of a telescope, right? Because presumably you could get marginal advantages and and cut 30% cost out your business. But the more interesting question is what can an engineer who is AI enabled do? Yeah. and they can add a hell of a lot more than 30% to the top line. AI doesn't have an intelligence problem, it has a context problem. So the value is in the context, the context is in the semantic curated data layers. So enormous amount of value in that and making sure machines and humans can use that appropriately. Overrated is this endless circular argument around value, discussion, break the loop, go and talk to your CFO.
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Speaker: Check out them in the show notes or visit omni.co. That's O-M-N-I.co. Now, back to the show. Hello everyone, welcome to another episode of the Stats Data Podcast.
Speaker: Today, we're going to be discussing the evolution of the data leader and what that might look like in the new world we are moving into.
Speaker: I think if you asked 10 CDOs what their job titles were, you'd probably get 10 different answers, which definitely adds to the confusion. But my guest today has spent his career at the sharp end of that shift. And Richard leads data the data practice at Ant Digital.
Speaker: And he's built and reshaped data teams through every height scale over the last decade. So he's going to bring a unique a view to what's happening in the space right now and hopefully share some insights about where he sees the industry going and how data leaders can make sure they are being a profit center rather than a cost center. So Richard, great to have you on the show. Thank you for joining me. are you doing today?
Speaker: Harry, thanks so much for having me. Doing great and um yeah looking forward to this conversation. Excellent. Well, look, I gave a very high level overview, Richard, but yeah, let's so for the audience.
Speaker: who Who are you? What's your background? It'd be good to get ah a bit of a sense of yeah your your your career. Yeah, my background is um is not necessarily unusual.
Speaker: Station AI with a with finance twist in big four originally and part growing teams. that had the fortune of of of also working on the other side of the table, Jaguar Land Rover.
Speaker: ah growing a really fantastic um a team there, which which which delivered a lot of value and a lot of lot of impact. And God, we've got some stories from the time there. But now, ah yeah, I'm at and digital. We're at Tech Consultancy. The legacy of and probably when it was formed 12 years ago as in software development, I'm responsible for growing the previously undersized, dare and AI side of a business um and is known for kind of its it's its brilliant culture and and brilliant people. And and and and like even my job title represents that. So I'm chief of data and ocean enthusiasts. So we all have a formal job title that describes what we do.
Speaker: But we also have something that tells people we work with a little bit about ourselves. Mine's ocean enthusiasts. I love paddle boarding, surfing, swimming. I'm going to Cornwall next week. you know that's that That's my thing. ah We're all human. um And so that is part of our formal job title.
Speaker: ah The chief of data part is I'm responsible for what we do in the market, you know our skills, how we show up. wow you know, um and and and and and senior level guiding and steering of ah of our clients.
Speaker: Amazing. I like that with the job titles. A good conversation starter, imagine. Before we finish this, Ben, you're going to have to, won't put you on the spot now, but you're going to have to tell me what your and title would be.
Speaker: Yeah, it wouldn't be it wouldn't be far from from yours. Probably mountains. I i love yeah mountain biking or skiing or hiking. um Yeah, that's that's my my passion. That and that' my dog. so Doing them with doing bega in the mountains with my dog.
Speaker: as There you go. ah that's what That's what it's all about. That's what it's all about, getting to know the people as well as the professional. And it's so important, right? um As the ah data interconnects every every team of a business, um so I think that that personal touch and making things more human, it always helps build them relationships with stakeholders, with the business, um and that's where you tend to get the the most done when you' you're all collaborating and not siloed, from from what I hear. Spot
Speaker: but on So, that um Rich, you've obviously had been in quite a few different environments um over your career. What's your overall read on where the industry is right now and where it's it's going? um Yeah, like my read is relatively simple.
Speaker: like Massive inflection point, profound change, um acceleration of change, ah you know, like, quick history lesson, I guess. let's Let's cast our minds back. Obviously, Harry, you're far too young for the start of this part, but we'll we'll give it go anyway. um the you know Really, there's three eras of of um of data that I think CEO's recognized. The first era is the sort of big data and warehousing era where
Speaker: we realized that collecting and and and the collecting and storing data was was really powerful because people might need to use it to do things and that in itself grew a profession that the tools and skills got professionalized around that and you know we started to to to use that data in all manner of of applications.
Speaker: yeah The second era really evolved through the 2010s, I guess, where um we evolved from that to go, yeah, we need to deliver impact, we need to deliver value and methods and tools and um and and capability evolved towards that. you know we We have data products, data mesh, streaming, sophisticated governance frameworks, and so on and so forth.
Speaker: um And that is that that that is still ah very relevant and current and and where a lot of people are. Where we are now in truth is the agentec era where again everything has changed and and you're suddenly in a position where um the the gap that previously existed between insight and action that may have come from an inability of organizations to really use their data for impact is now closed permanently, right? If you if you do it right.
Speaker: So whereas in the second era, we may have um we may have produced their next best action model. Now you'd have an agentic agent that is able to put in place a correction or a discount from it for the for the for the person booking the but but but the hotel or restaurant or whatever it is before the problems happen and before they've even discovered that but they've yeah been changed rooms or or whatever it is. And um that's the that's the world which we are entering and organizations are starting to get to grips with over the last couple of years. What it does for the industry in terms of not just data for ai but also AI for data is is something we are still bottoming out.
Speaker: Yeah, I think that's fair to say definitely the what I see on the market. there's ah There's a lot of change. There's a lot of first movers who are trying to find find value in in these toolings, but also with the governance and the frameworks and not letting things get out of control. And um yeah, I think that's why it's such an interesting time to be to be in data, and more so than any of these other previous hype cycles because of the speed.
Speaker: um how How would you describe a modern data team today versus five years ago? What started to change um in, um I mean, tooling we obviously know, but more around, I suppose, the structure and the culture of those things?
Speaker: Yeah, so that you could you yeah you might hear me say this a lot, but this agendic era is a bit of a paradox. Everything has changed. but also nothing has changed. There's an enormous contradiction in that. and um the The people who are winning at a moment, as yeah as you as you put it, they're figuring out what has changed and what hasn't changed. so In terms of like what the team looks like,
Speaker: There definitely are arguments you know in ah in a growing data team that you don't need as many people necessarily as you might have done previously because you're able to do more. i think what we typically find is that you do, you simply ask to ah deliver far greater and expand the you know the sort of territories and number of use cases being asked and all that sort of stuff. So that that that's really important.
Speaker: um you know Engineers, ah but the core engineering skills and mindset is isn't change. What is changing is the ability to use agentic tooling on top of that in order to accelerate development of pipelines or to analyze architecture and optimize and so on and so forth. Then you know on the on the analyst side of things, yeah building of dashboards,
Speaker: you know, is becoming a thing of the past. You know, that is not the, you know, 10 years ago that was a ah very in demand skill that is but's not so much anymore as as you probably experienced. But there's something even more valuable there, which is the ability to coach the business into making decisions and to use the information appropriately.
Speaker: And so what you're just seeing is people um pivot to the highest value points on those of those value streams. um And I think that that is the the biggest change. So probably sort of moving up a value stream.
Speaker: And what do you mean? Where where is that? what what What are we talking about here what in the value stream? Could you be more specific? Yeah. so um i you know I'm going to caricature to bring from people might get upset with my description. I don't always mean this literally, but whereas previously an analyst would have spent the majority of their time building a dashboard because someone needs some information, a weekly report of the performance of the sites in in their organization, all of the branches in their organization.
Speaker: and They go and they sort of spec that and they work out the the KPIs that need to be shown on that and we want to show the the profit this week versus last week and how it compares to last year and things like that.
Speaker: and then we'll publish it and people will start using it. Presumably, I don't know. What do they use it for, Harry? Are they actually doing anything with it? Those decisions they said, they said they really needed this dashboard, but if they use it if they need it so much, why why are they not using it? And why are they not like, what what is it that is going?
Speaker: Now, You're perhaps in a situation where organizations can use agentic analytics. So you could simply go, tell me the greatest insight that I need for my business, for this area of branches this week, and tell me what to do about it.
Speaker: And it's likely that you know using agentic analytics, the the user can be told that. So what is the role of data within that? Well, first of all, it's the curation and creation of the the semantic data layers and in order to satisfy all of that because AI doesn't have an intelligence problem, it has a context problem.
Speaker: And so the value is in the context, the context is in the semantic and curated data layers. And so enormous amount of value in that and making sure that the machines and humans can use that appropriately. And then even a higher of value stream, it's coaching of business into asking the right questions.
Speaker: and understanding how to fundamentally make decisions. Right. So um I don't know. There's two examples of of where the role is really changing.
Speaker: No, i I couldn't agree more. And it's something that um I've seen. There's a lot of obviously scaremongering around. Yeah. AI is going to take your jobs. And actually the most, you know, the role so that that we work at Cognify and what i'm seeing in the industry and through speaking to people is more people are doing software engineering than ever, know, in data um as well. Everyone's moving further backwards in in that flow. We actually had a meetup recently where the the question was asked, um who who's doing more software engineering than before? and
Speaker: about 90 of the room put their hands up um because it's whilst the the front end of that output is um is yeah changed from a from a dashboard the mechanics behind the scenes it's not as simple as just chucking an lm on it there needs you every different data environment is is different so yeah i can definitely see see that and it's just i thought suppose goes against that narrative of ai is going to steal your jobs actually people are doing more If organizations are asking the question, well how can AI replace what our engineers are doing? They're looking for the wrong end of a telescope, right? Because i mean presumably you could get marginal advantages and and cut 30% of cost out your business. But the more interesting question is what can an engineer who is AI enabled do? yeah And they can add a hell of a lot more than 30% to the top line.
Speaker: um and and and and and And so, you know, success, the successful the winners in the Agentsic era will be the organisations who frame the question, as you're pointing out.
Speaker: And to your second point, um you know I think this is something we've touched on the pod quite a lot. um And again, see the very best people getting the very best jobs is that context, understanding the ability to really get into the the the the meat of what the the business needs. um And that softer skill, you know the learning and being the best of Python or SQL is is no longer a differentiator. um yeah you You are going to lose against an LLM.
Speaker: But asking the right questions, building relationships and getting, you being very commercially minded with that analytical lens is is the differentiator that we see. So I couldn't agree more with the the points that you've raised there, Rich.
Speaker: we We touched on it. You know, what where... Where are are organizations that you're seeing, you particularly as a yeah working in the consulting space, where are you seeing the things holding people back and and what are the the the key blockers for moving into this new world?
Speaker: yeah i think um I think there's a lot of really good intent out there. There's a lot of you know excitement, there is there's some nervousness, as you rightly described. And and um ah there's theres There's a lot of FOMO, but ah you know lots of organizations are roughly in the same space, which is showing ambition towards ah you know and starting on their bad AI transformation journey in in in truth. right um
Speaker: so Obviously, some are marginally ah ahead. and ah you know six months ahead. But but but the the difference between those that are miles ahead and those who are absolutely laggards actually hasn't entirely emerged yet. That's starting to be shaped. I guess what I'm saying is it's certainly not too late for organizations to start and jump um on that. What is what is kind of holding people back and what are we seeing?
Speaker: um Certainly when I'm working with boards, the thing that I really admire is bravery. because you need to be brave in two respects. Firstly, um you need to be brave enough to pick up the phone and go, we need bit of help with this because we're not the experts in AI and data.
Speaker: And maybe that's something we should really get our heads around. And so i think the sort of the bravery of um ah of of boards to show a little, you know, senior leaders to show a bit of vulnerability is is really um is is is is really impressive um ah but also then the bravery to act and do something and to recognize that they need to experiment and manage and take risks and and and so that bravery kind of sticks out out for me.
Speaker: Rich, as a data leader, how do you help bring boards, executives, other business leaders on that journey and and help inspire some of that that bravery? Because think that's something that yeah we're going to get onto really the role of a data leader and how it to hold. But I think first and foremost, and it's helping give that give that sense of security to be brave. So yeah, I It's a really good question. how How do you inspire that?
Speaker: We're used to inspiring change amongst you know the the wide workforce right as as data professionals. in In many ways, that's what you need to do in order to bring a use case to life. and and and You're kind of just taking it up a notch. I think you just recognize it first to recognise the human element. is like Just because they're on the board doesn't mean that you know make they they obviously know significantly more and and have this like brilliant ability to to to to manage trade-offs is is you know real
Speaker: um a real strength of of board members. but um ah i mean we We do a two-day course actually with boards and it goes right back to basics of of of AI and it goes right back to use cases and it goes right back to this idea of you know as we've already described everything has changed but nothing has changed and helping boards identify the what is going to change but what is not going to change gives an enormous level of comfort that drives clarity and clarity
Speaker: allowed them to to act. so yeah What is going to change? okay The skills and capability of these people and these people but you know is is going to need to evolve. What hasn't changed?
Speaker: What are your use cases, Harry? well What are your use cases? Oh, right. yeah it its Just like normal business problems. that we yeah yeah yeah yeah Yeah, those, right? Oh, God. Well, um actually, there are these use cases that we've never been able to crack because it's never had sufficient ah ROI.
Speaker: We could really look at those now, couldn't we? Because um actually it might be far more efficient and lower cost to to deliver against those. And therefore we might be able to go, yeah, there you go. So that's that's sort of a all part of the process. So, um you know, it's a very it's it's a very human thing still. Just because they're board members doesn't mean that, um ah doesn't doesn't radically transform the approach.
Speaker: Excellent. ah It's the humanisation again. um Right. So look, Rich, the weve said as we said, I think, um you know, I said it at the beginning, you asked 10 different CDOs what their title is, you'll you'll probably get 10 different answers. And in truth, I think there's a question of, you know, you're a CDO, but ah yeah does that role still still need to exist when we're we're creating these leaner, more embedded,
Speaker: embedded teams. So I suppose what I'm framing here is what, what does the new, how do you see the role of a data leader evolving as these teams change? Yeah, right.
Speaker: I feel very passionately about this. So yes, and if we get it right, if it did and I mean, as a data professional, it it's um increases the role of data within organizations. um let Let me put it this way.
Speaker: And we see a lot of AI strategies. OK, so zooming out for a second. ah An AI strategy which doesn't, which isn't isn't underpinned by data, by good data strategy and good data a good data team and good good practice around data isn't investable.
Speaker: like It's not investable. So a board shouldn't invest in AI strategy that doesn't have, isn't connected to a good data strategy because they'll be making a lot of mistakes. They'll be pushing it out in an ungoverned way where, as I said before, AI doesn't have an intelligence problem. It has a ah context problem. we have We have clients who come to us who have right, we've given everyone Claude and we've started using it and we start burning a load of tokens and and something's missing. We're not going, we've not got a bloody data platform. so um yeah This guy over here is coming up with something entirely different to the person over here. They've produced their own little weird spreadsheet of context that is entirely different to this person. and yeah You're not able to productionize these things. Suddenly, you're finding everyone's spending a lot of time maintaining and updating their widgets for the data context is core.
Speaker: In the agentic era, we're facing a whole bunch of challenges. We're facing a whole bunch of challenges around governance with ah yeah and and and and and ethics and so on and so forth. we We're um facing a whole load of technology challenges. We're facing a whole load of challenges concerning culture and behavior. All of these things are exactly the things that data leaders have been have been delivering for the last 15, 20 years, right through the three eras. It's just now that AI is involved too. But fundamentally, it is the same problem set.
Speaker: So I believe that data leaders are really, really well equipped, if not the best equipped people in the organization in order to actually answer questions and provide provide the answers to the organization as to what we should do in the agentic era. I think that data leaders should be pushing into the agentic layer, finding their role and taking more ownership.
Speaker: I think the mistake would be to become more subservient, to become more back office, and um and to kind of recede into ah into a cost-sense mentality.
Speaker: Interesting. I think um I definitely agree. I think one of the points with AI is that Sometimes I see people asking almost for permission.
Speaker: um Can I own this? Is yeah is this something that, the that yeah where does this fall? And I think actually a good leader leader, like a good data person ah at an IC level, they see a problem and they go and solve it.
Speaker: um And I think there's a bit of apprehension in the, you know do, do we need to build a prototype, build a, build, build something that shows some value and go and, go and sell it. Um, yeah. And, and if you were, if, you know, if you and I were, were were CEOs of, of, a mid-market organization, right. Of, uh,
Speaker: Yeah, of whatever organization it be. Harry and Richie's WidgetCo, right? And we were there going, oh, bloody hell. Yeah, we need to be doing this this AI thing. Yeah, we need an AI strategy. We were looking around and going, well, who in our organization should should we trust to lead that?
Speaker: i e I would believe strongly that whoever's in charge of data is probably best qualified to answer many of the questions because it's just a different version of the questions they've they've been having to answer previously and all the same challenges and and and trade-offs that they've been managing previously.
Speaker: So um yeah that's how I see it. Nice. Rich, I think one of the the things that's plagued, as we've said for for a long time, and there are plenty of exceptions as well, but data teams being a cost center rather than driving real commercial outcomes. um how How do you avoid that? um yeah How do you escape that trap if you're already in in it? What's your advice for poor leaders?
Speaker: Yeah, am very fortunate to have um really ah been at a sharp end of this when I when i was at JLR. We made a lot of money for JLR, right? A lot of EBIT. We had a process whereby we measured that and it was measured by internal audit and reported upon The organisation, in truth, and I think I'm being unkind, saying saying this, didn't really care about data, and and didn't know why, but they did care about money, and so we really lent into the the value that that that data could give, and and in many ways became the money people rather than the than the data people. right now It's just ah a way of of of of driving data culture, but perhaps in a slightly alternative way.
Speaker: we how you do it right um there are some there are some fundamental basics the first one is you need to have the right relationship with a business so you need to push yourself more into a coaching role than a receiver of of re requirements okay of course in this and this sounds very basic and it is is use cases use cases aren't always use cases And I see this as my pet hate. I see all the time. Sometimes use cases are just ideas.
Speaker: Sometimes use cases are just point problems for individual people. They have to go through a process of curation to turn them into something that actually resembles a use case and has some impact.
Speaker: It's at the right scale and size. Sometimes you need to zoom out and and and and and you know think about it. redesign of ah an entire process rather than trying to solve point problems within it.
Speaker: and Fundamentally, you need to you need the the capability in your your team to write a business case. and It sounds a little bit boring. Everyone goes, I need to write a business case. It's sort the afterthought. right but How you do that and agreeing the format with the business and agreeing the parameters of that and agreeing how you're going to measure it, whether it's money or otherwise, doesn't have to be measured. that you know Impact can be measured in in in other ways.
Speaker: um That is the discipline that is required and investing time in that discipline actually has a massive payoff Can we expand on that so point around building the business case? Because I think that's something which would be good to understand maybe your frameworks or how how do you actually know about building a business case? Because um there are plenty of very smart data people, ah but I think it was really helpful to hear
Speaker: how How do you actually go about doing that cause that's something that we hear a lot. we We want to work on this project, but we can't get the right level of investment. or I want to make these hires to build X and and yeah I can't get the investment to do that. so So how do you build a business case um that a CFO is going to give you the big green tick on?
Speaker: yeah so stuff So I'm going to caveat it. i I'll tell you might i tell you a couple of things that are important to me, but I'm going to caveat this with it doesn't matter what I think. It matters what your CFO thinks.
Speaker: So literally go and have a conversation with them or your or you know or the finance director or whoever relevant um and go, how do we do it in our organization? What is the context? What does a good business case look like in the context of this organization? What is important to people? Because it is contextual, right? It's it's a successful business case in one organization. You couldn't necessarily lift it straight up and put it in ah in a different one nevertheless right here are the things that i think in general um yeah i think that that you're going and are asking the question first off what what does this need to look like you need to know what metrics are important it's slightly flippant but honestly when when someone's right we need to write business case what's the first thing that people do they open up microsoft word or or powerpoint and they start writing a bloody business case no no no the first thing is going to go towards the person who's going to be
Speaker: receiving the business case to ask them what a good business case looks like and what the templates are or what, you know, when they receive a good one or when they receive a bad one, what they expect, how would they deal with the fact that measuring, you know there is uncertainty over the the outcomes of, you know, there's risk and so uncertainty. If we give the organization a better forecasting tool, let's imagine that's a use case.
Speaker: we we want to forecast our sales more accurately, right? If we do that, how do we value the the benefit of that and start to unpicking what would be acceptable to the finance organization? And some of those things may manifest in the P&L and some of them may not, but yeah but you some of them may be the time and efficiency savings, which I always think is kind of generally a fairly small, if you're relying on time and efficiency, to saying you're probably barking up wrong tricks. one form thing guy you know You want to be looking to me be upside and the um um um um the um and the benefit of things.
Speaker: um so what you know I would say make sure the they are a sufficient size. the The sort of, to a certain extent, the bigger the better. Individual, okay, well, you know, we're going to help um Sam save 15 minutes a week in the, you know, in the purchase to pay process by having this report available to them. I mean, you're into... yeah You're in the wrong territory there if you going to want to add all those bits up. so You need to zoom out. We are going to transform this process by automating steps A to Z like in in in one go and put a human in the loop to do this. um yeah du and du that Okay, cool. right What costs can we take out?
Speaker: Now, I think that in the data world, the data tooling now can replace a whole bunch of SaaS in the organization. So you should look to retire SaaS through this as well on other IT spend. So what can you what what what costs are they? you cut down Top line, how does it improve it? Simple P times Q of you know the price times quantity that you're going to, yeah, sure. But what are the risk factors around that? How are we going to mitigate those risks?
Speaker: And then fundamentally, who are we going to share that risk with business? So I would rarely put forward a business case that is that but has my name at the top of it, Harry. I would have my name,
Speaker: Plus, I would have the name of the the the the director of the business function that is going to use the forecasting tool, let's say in this instance. right And they are also underwriting the fact that they will use it. It means that they've got skin in the game. It means you've got kind of joints and several responsibilities for putting it forward. It means that they want to use it. They've got they've got you never got skin in the game. Otherwise, the risk is that they're getting something for free and and people generally mistreat and misre reuse things for free. So doing that joint sponsorship is is is absolutely vital.
Speaker: But showing that you're willing to measure and then come back and measure as well ah later on, showing that the results may not be instant and being transparent about that, but there's a two-year payback period or whatever it is that we will track and monitor and show that we can also, um ah if things aren't going well, we'll will'll we'll turn it off.
Speaker: we will happily turn this thing off um and and we will we will pivot and we'll do something else. I think that's a nice framing. I said, of going back, I particularly like the first point as well, right? um That understanding of of that context and yeah it it rings true of you don't receive a ticket and just go and build the ticket. um It's the the same mentality, the same mindset of you just need to be hungry, as ah yeah anyone be hungry for information to to really understand what the the why behind whatever it is you're doing.
Speaker: And what um and and this the the use case thing that I've i've spent part on talking about, but it's important. the The key thing to remember is want to need. Right. Yes, someone may want the report. does But what do they need?
Speaker: What do they need? And maybe did the good business leader or is able to differentiate and identify between want and need. they They're very often different and we should be doing what the business needs, what not what necessarily individual people think they want. And our job is to coach them towards what they need.
Speaker: So we've talked about the difference in how the teams are looking as we move into this agentic world. um We've spoken about, I suppose, what touched on much some of the points of what a good data leader looks like. and I think that's what something which I'm keen to unpick a bit more. think the role of a data leader and some of the responsibilities are changing.
Speaker: we The data leadership roles that we've worked at Cognify have, you know, needed people that are still hands on in the fact that they are not the you know these ivory chair data leaders that um don't don't understand really what's going on on the ground in their teams we we're we're seeing them um disappear that may be biased because we work with a a lot of high growth tech companies rather than your more traditional organization but yeah keen to get your view on it rich yeah what what does this new makeup of ah of a data leader look like in the agentic world what should people be aspiring to
Speaker: Well, let's let's let's let's talk about that hands-on thing, right? Because because you're right you're right. I don't think it's optional anymore. And the reason I don't think it's optional is because it's really easy now to get hands-on.
Speaker: I'll be honest. I've spent far too much time and ah my own money on Claude and other tooling, building things that could I have previously built could I have previously but I probably might have been able to eventually but I didn't try because it was too it would have taken me too long and it was too hard the paris country too high yeah my my my as you say my Python skills whether it would just really it would have been a very painful process for me guess what
Speaker: Ain't a problem anymore. I'm i'm building things that are, are you know, for for pet projects and so on um that ah that And and ah yeah I am able to get far more hands on than I than i um previously would have done. So actually, I think we've gone through a an arc or whatever, whereby I think it was bit of a trend that that um you you do have data leaders who could ah you know just ah be be wise. and um you know and there's There's no reason why they can't be hands-on anymore and you'll see far more of that. um so
Speaker: I think that's ah that's a That's a given now and and goes all the way through the um free preview preview for the stack. yeah Yeah, I mean, um yeah Cognify, ah Julian, has built an application that pulls live data from our CRM and uploads ah to DocuSign and that you stuff that we've yeah never been able to code before and we can build stuff. so The barrier to entry is is so so low. If you actually know what you're doing, the speed of which.
Speaker: And I think yeah AI is meant to increase efficiency and it increases efficiency. The fact that your data leader can be in the room, quickly pull out their laptop and prototype something in front of you in a yeah know in an exec meeting. Look, here's what it would look like.
Speaker: um Would you then go and get your team to build it properly? Yes. and I think that's a really important point, right? Because that does happen and it is exciting when you do something, either in a meeting or within 24 hours and it amazes people. But he's also the the role of the daily to exercise and balance and and restrain all of that because we all know the issue of then industrializing things, governing things, having things in a production environment, having
Speaker: ah yeah yeah know um a sharing of of tooling and models. and you know so Complexity is non-linear. As soon as you start to have you know an organization of more than 10 people, and yeah the complexity suddenly skyrockets.
Speaker: And i think the successful day leaders are able to excite and inspire as you're describing, but also at the same time, they're able to wear the hat of of of being realistic about the roadmap of capital investment that but may be required in order to ah do a bunch of the stuff that they actually need to do and to professionalize it, um build a fully, you know, build and own a semantic data layer.
Speaker: um and to ah you know um do the upscaling court ah program for the wider business so we can have AI and data champions throughout business.
Speaker: And yeah, for the for the for the for the cost of the tooling that is required in order to support that new world. Amazing. Well, look, Rich, we're getting to the end of the podcast. It's been great discussing. I suppose I've got a few quickfire questions for you to to reel off. So what's what's the most overrated thing in data leadership right now?
Speaker: um Overrated is this endless circular argument around value discussion. Break the loop. Go and talk to your CFO.
Speaker: I like it And what do you think is being most overlooked? um Taking advantage of the risks that other people are putting into the business ah by introducing AI.
Speaker: We need to seize that space and um and take ownership for it. And that is taking ownership for what when what you're talking about when other people are bringing AI tooling and stuff like that and plugging them in. Yeah, I think data people know best.
Speaker: And what should a data leader be doing differently in the next 12 months compared to the the previous five years? Yeah, i bravery, push into the agency layer, have confidence there.
Speaker: What we've learned as data professionals over the past 15, 20 years um actually positions us really well to solve the problems in the agendic era. We may not know all the answers today, but we're really well equipped to figure it out.
Speaker: um Establish your role in that space and have the confidence to lead. Excellent advice. Rich, it has been a real pleasure to chat today. i think there's been some real nice bits of of wisdom around some of these frameworks. I think my key takeaway is and context is king, not just for your AI, but for yourself and how you understand the business and the problem and
Speaker: what that leads to, whether that's a piece of analysis, data architecture, or building a business case. I think, yeah, that that context is king is is definitely my my takeaway. Thank you for joining us.
Speaker: Thank you, Harry. Thank you, Cognify. Thanks for having me. That was a really good fun conversation. Cheers. Cheers everyone, speak soon, bye bye. Hi everyone, just a quick one from me.
Speaker: If you've enjoyed today's episode, I'd be so grateful if you could hit that follow button or leave us a rating. Even better, pass the show on to a friend who might also get some insight from it. It really helps us grow the community and continue to share amazing conversations.
Speaker: I also wanted to take a minute to talk to you about Cognify. those of you that don't know, Cognify is the leading recruitment partner for modern data teams. We help some of the world's best organizations scale data and drive real value from the hires that they make.
Speaker: you're thinking about building a team or making a hire and you're struggling with talent or just want some insights on the market, then I'd love to jump on a call with you and tell you a bit more. Equally, if you're looking for a job and want to find your next dream role, then reach out to myself or any other Cognitify team. We'd be happy to see if there's anything on our books that we can help you with and give you general advice on the industry.
Speaker: Finally, big thank you to Omni, this season's sponsor. If you'd like to learn more about the AI analytics that Omni can deliver you, then check out the link in the show notes or come speak to me. i can happily point you in the right direction.
Speaker: Again, thanks for listening and look forward to seeing you a few weeks time.
