#84 Building High-Impact Data Teams at Capital One

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You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or or you're a data leader looking to scale data driven decisions in your organization. Join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in.

Hello everyone, this is Adele, Data science educator and evangelist at datacamp. What I absolutely love about hosting Data Framed is that I get to glean insights from the best minds in data science on how they've been able to build high impact, high leverage data teams. This is especially the case when it comes to building high impact data science teams in very specific regulated industries like financial services and banking. And it doesn't get a lot better from the data science team at Capital One. This is why I'm excited to talk with Daniel Kellett today, Chief Data Officer at Capital One. Dan has been with Capital one for about 22 years now and leads all the data functions for the bank across the uk. Throughout the episode we talk about his background and the hallmarks of a high impact data team. The importance of skills and background diversity when building great data teams, how to deliver impact with data and financial services, and much more. If you enjoyed this chat, make sure to subscribe and leave a review, but only if you like it. Now let's get started. Dan, it's great to have you on the show.

Thank you. It's great to be here.

I'm excited to talk to you about your background leading data at Capital One, building effective data teams, how to scale the impact of data science, especially in large enterprise banks, and more. But before, do you mind telling us a bit about yourself, your background and what got you to your current role at Capital One?

Of course, yeah. So I studied mathematics, so my background.

Is in mathematics and did my degree at the University of Nottingham in the.

UK, graduated back in 2000 and was.

Really keen to apply mathematics to real world problems. And so to kind of move away a little bit from that kind of academic bent and actually to really use what I'd learned from.

I joined Capital One as a graduate statistician back in 2000 and I've been here ever since. When I joined Capital One, the organisation had only been in the UK for about three years.

It was very kind of rapid growth, fast moving business and it was a.

Kind of a great place to join.

As a graduate, pick up all these skills and work on these different projects.

Initially my role was around building and maintaining the models that we use for marketing direct mail. But over the next five years I've.

Worked on a variety of different models and analyses across the whole customer lifecycle.

From marketing through acquisition and through to.

Customer management as well. Also kind of some operational use cases and even some people analytics work as well.

Stepped into a small team leadership role.

Providing statistical support for some test European.

Businesses we had at the time and back in 2012.

Then took overall leadership for the UK.

Statistics team as Director of Statistics, responsible.

For team strategy, delivery, recruitment and development.

About two years ago, moved into the UK Chief Data Officer role. Encompasses all the different data job families and the aim is bringing those different aspects of data together.

We're able to provide more focus and greater opportunity to leverage the great skills that we've got within the team.

That's really great and I love that story of ascending the ranks at Capital One. That must have been a very exciting journey. Capital One is often hailed as one of the most data mature banks out there and this is a credit to the awesome data teams working there. So I'd love it if you can break down in your own words what are the hallmarks of a high impact data team.

I think team is the key word here, right? I think having a really diverse mix.

Of skills and experience is what makes a great data team. But for me there are three real areas that I look for in terms of building that team. Firstly is a real consumer and end user focus. I think it is very easy to build statistical models or machine learning models that go nowhere. And they go nowhere because there is no actual consumer need or there's no business need for what the model's building.

And so I think a great data.

Team really has those consultant skills to be able to understand what keeps accountable executives awake at night, what are the worries of consumers and then how do I build a solution that meets that need. So I think that's one area.

The second is Technical excellence. And I think, you know, you see these posters that have the million and.

One different skills that a technical data scientist needs.

And I think for me you need.

All those, but you need those in your team. It's a team game very much. But you do need that technical excellence in terms of statistical rigor, in terms of your kind of software engineering skills, in terms of those machine learning, algorithmic knowledge. So technical excellence is definitely the second bucket and then the third is strong teamwork and communication.

Again, you could build the best model.

In the world that actually is pointed at solving a real world business problem.

But if you're not able to talk.

About that model and really discuss the benefits, then again it's not going to get used.

And so the ability to kind of work in a diverse team and then.

To be able to communicate results and understand different perspectives, I think they're the three areas that make the most impact for me.

So I'd love to unpack these elements even further. So let's start off with the skills component. So you mentioned here, one, subject matter expertise and user experience attention. Two, technical expertise and high level of skill density and three, communication and collaboration skills. So let's focus on that second element. You slightly alluded to this in your answer on the diversity of technical expertise of the team and how skills are distributed across the team rather than an individual. And I love that perspective since I definitely agree with with the notion that there is no such thing as a unicorn data scientist. So can you walk us through the different skills you hire for on your team and how they're distributed?

Absolutely. So if I think specifically about the data department, we have four key areas.

Within the department, each having their own team and kind of distinct skills.

Firstly, our data analysts. So our data analysts are great at.

Explaining what has happened. So, so they're the people we go.

To understand trends in the data. When something's gone wrong, how do we unpack that and make it right? But also they are the team that.

Implement the data driven customer decisions. So things like campaigns that we might look to run or the strategies that we might look to implement when we're thinking about SMS contacts.

So the skills that are most important for those data analysts is they need a really good understanding of the data domains. So what data is held, where and.

How do I use it?

Their coding level needs to be really good. But then also they need to have.

Those great kind of consumer stakeholder skills as well to be able to deliver the campaigns. For example, some of our analysts might have designed.

So they're the data analysts, data scientists are hopefully great at predicting what's going to happen next. And so for me, kind of they have to be experts in algorithms, uncertainty and predict. We place a lot of faith on our data scientists to be able to take historic data, roll those trends forward. So I think a great data scientist.

For example, I think about it like a bookcase. So you should have all these kind.

Of knowledge of different algorithms and approaches on your bookcase. And the real skill is knowing when.

To reach for which book.

In my mind, there's no one algorithm.

Is better than another. And actually sometimes an arithmetic mean is, is good enough.

And so being able to trade off.

Your complexity with your predictive power and.

Really understand the caveats that might happen.

For certain algorithms, that's kind of a big skill for data scientists. The third group are the data stewards. And really they're kind of the gatekeepers for making sure that we have high quality data.

And not only that we have that.

Data, but we can find it easily.

The right people have access, we know where it's stored and we know when.

It'S going to be deleted.

That's really important. It's a relatively small team, but very.

Highly skilled in terms of again, data domains.

But also that's probably where we have.

Our deepest coding skill in terms of data manipulation and also kind of that data governance aspect.

The final group within the department is a bit of a mixed bag, but it kind of encompasses both our data product side and what we call analytic engineering. And really this is probably where within.

My department we have the deepest software engineering skills.

And this team really look to help.

Design, build and maintain the platforms and tools that underpin all the analysis that we carry out within the business. These are the people who really understand the benefits of well designed, well maintained code in order to make everybody's lives easier.

That's really awesome. And I love how these skills cover the entirety of data projects, from ideation to deployment. You know, one common theme or skill set that emerges across these different roles is subject matter expertise. So I wonder, for technical roles, as you're finding candidates that come from different backgrounds and industries, how do you embed this subject matter expertise into your people over time? As you said, it's relatively easy to create statistical models, but not that easy to create great data products that have an awesome user experience within a specific data domain.

Yeah, and I think there's no substitute for experience here. And I think one of the things we try to do when we bring new people into the team is very.

Quickly to get them working on real world issues, real world problems to solve.

And so they're starting to build up.

That knowledge and that expertise from day one.

Key to that is making sure you're partnering people up with good mentors and.

Slowly looking to build out expertise and knowledge. Yeah.

So of course, beyond just hiring for diverse skill sets, hiring for diverse backgrounds and lived experiences is highly important for building a high impact data team. Do you mind expanding on how you embed diversity and inclusion as part of the hiring process and how you're able to leverage that diversity in the data products you create?

Yes, definitely. And the. There's a whole raft of things I think as an organization that you can.

Look to do to try and increase.

The diversity of backgrounds within your team. A big area for us has been to look at what our recruitment channels are to make sure that we've got a mix of different backgrounds within the team. I think it's fair to say if you've gone back five, 10 years ago, we had a very narrow range of.

Different channels and historically was very focused on mathematics graduates. But.

But actually over the last kind of four or five years, we've dramatically expanded where we get our great talent from. So one particular kind of example that I feel really proud of actually is.

We have put in place a program.

To bring people who are currently working.

For Capital One in the operation into data focused roles.

And so this has been a kind of great success. And one of the things I like about it was this is not leadership.

Led, this is an idea initiative that was built, brought to us by the.

Team with kind of a lot of passion and a lot of thought about.

How this is going to work.

And over the last 12 months we've had four people from the operations move.

Into roles within the data operation where we've looked to upskill them in technical.

Skills, whether that's data science or data analytics roles, but that's been really successful. And what we found is they have.

Brought with them a whole raft of knowledge about what it's like to be a Capital One customer, for example, because.

They have spent all their time working.

With or talking to our customers, but.

Also some of the challenges that might.

Be in place within the operation. So some of the kind of process challenges that might be in place or knowledge of how things actually work rather.

Than how we think they work, that's been really, really useful. It's definitely been great to firstly expands the diversity within the team in the.

Way of thinking, but also just the passion and the enthusiasm that these people have brought with them is infectious.

That's brilliant. I love the story around upskilling here. And this segues to my next question. Fantastically. So we discussed the importance of the skills, diversity and high impact data team. And given the difficulty of finding those unicorn data scientists with the right combination of skills, where does upskilling fit within the data team? And harping on that last notion, where does upskilling fit within the overall strategy of the company to level up the entire organization's ability to work with data?

Yeah, I'm a strong advocate of shaping.

Roles to individuals career development aspirations.

And I think that's something we try.

And stay true to within the team.

We encourage all the members of the team to take control of their personal development. A big aspect, especially at the start of the year, is to spend a.

Lot of time thinking about what do you want this year to look like, what are the things, things that you want to be working on, whether that's new skills, whether that's new areas of.

The business, and then how do we kind of shape your role to allow.

You to pick those things up?

Now a big part of that is.

How do you help people pick up those skills?

And for me that's around multiple things. We need to make sure that we.

Have good mentors in place. So it's really important that if you want to develop your machine learning skills.

For example, not only do you need.

To be in a role that gives.

You that opportunity, but you need to.

Be surrounded by people who are going to help you learn those things.

We kind of supplement that with training where appropriate. So if there's, there's some kind of opportunities to go externally and pick up.

Some of those skills, again that's something we look to try and do.

But then the other thing that we look to try and do is get that external inspiration and support.

And typically we do that with strong links with the university, with academia and.

Nottingham University in particular, given that's where we're based. We have kind of a long history.

Of strong links with the university, whether.

That is providing master's projects or guest.

Lecturing or PhD co projects.

And really that's a way for us.

To bring more of that outside thinking in and allow people within the team to almost fast track their knowledge on particular areas.

That's great. And I'm a huge fan of the attention to creating career goals and pathways for the people at the company. Now, outside of building high impact data science teams, I want to segue to discuss how to scale the impact of data science in itself within the organization data culture is obviously a huge element of creating a successful data driven organization. Do you mind walking us through the importance of data culture at Capital One and how you've been able to scale it?

I think it's not an understatement to say data and analysis is really at.

The heart of the key decisions that we make at Capital One in the.

Uk and it is part of the.

DNA of the wider organization as well. It very much kind of runs throughout the business.

I'll give you an example of that. I think one of the most important things to get right around your data culture is this concept of data registration and data ownership. I think too often I see examples.

Elsewhere of data transformation projects where everybody's.

Got really excited about technology and a data lake is set up, for example, and everybody piles in, throws in their data in a way that isn't necessarily.

Organized or structured, it just is there.

I've seen elsewhere examples of hackathons where.

Goal of the hackathon is to get as much data in as possible and.

I love that enthusiasm and I think that's great. But I think where that ends up is with a data lake where you can't find anything and you don't know who owns what and when something goes wrong or you need to find that.

Key piece of information, you're stuck.

And so having that really thorough data registration and ownership model, whilst not being the most exciting thing, it just saves.

You so much time and makes things so much better going forward.

And so I'm really happy with the.

Approach that we've taken at Capital One, which is to let's nail that data.

Registration process so that any data transformation.

We make in the future is much more organized and is more likely to succeed.

I think the, the other example is.

Actually just the role of the CDO itself.

I think just the acknowledgment that this is an important role, that there is a key financial business benefits in having strong data leadership and promoting individuals who value data.

I think is another kind of example there.

That's great. And following up on that, how do you as a data leader manage the conversation with the remainder of the C suite around data culture and its importance? You know, I'm definitely not asking for trade secrets here, but general best practices on how to approach this conversation.

I think that's all around really understanding.

What keeps them awake at night. I have this approach that I talk to with kind of new graduates around asking why.

And I think as a data scientist a big part of our job is to ask why and then to ask.

Why, and then to ask why, and then to ask why.

Because the first reason is never really the reason. And so if you can be curious and keep asking, why do you care about that?

Why is that important? Why do I need to solve that?

You're more likely to get down to.

The actual key, the actual thing that needs resolving.

And more often than not, that's something that data and algorithms can help with. And so if you are using data in a way that really solves the.

Things that matter, you're going to build that buy in, you're going to build that kind of culture across the C suite.

I definitely agree. And do you think organizations starting off in their data journey should opt for a low hanging fruit type of proof of concept when it comes to working with data, or go for a full fledged project and try to demonstrate value that way?

Yeah, it's tricky. I think it's all about a blend actually. I'm a big fan of quick wins.

So if there's things that we can.

Go out and get that builds that momentum, then that's great. Because I think what you don't want is an executive to stand at the front with a great PowerPoint and then.

Go, well, we'll be back in three years, it will deliver all these amazing things.

On the other hand, I think if.

All you do is quick wins, there's a real risk that you never get to some of that transformational stuff. So it is all about a really nice blend. And I hope that part of my role of CDO is to to try.

And find that balance, to help paint.

That longer term strategic view and make.

Sure that that continues to stay on track.

But also make sure that in the.

Weeks and months that follow, there's lots.

Of great news stories, there's lots of good stuff coming out.

And as a cdo, where does self serve analytics and empowering the rest of the organization to work with data fall into the team's priorities? What are the ways you've enabled people who are not necessarily data scientists to query and work with data that is relevant for their day to day?

This is a key challenge for a lot of organizations, definitely for us as well. It's something that we've spent a lot.

Of time over the last year or.

So grappling with and I think there's a few different approaches to tackling this. Firstly, I think you've got to have some training in place that put in place those kind of core coding skills and data knowledge. What we've found potentially historically is especially in that data analyst function, there's a.

Real risk of it becoming a bottleneck because they're the only people who is perceived can get that data or can pull that information.

And that just creates tension and frustration everywhere. So if you can get some basic training in, that's probably going to serve.

75% of your needs.

For me, that is all around knowing where to get data, knowing how to use that data, and then some really.

Core coding skills just to get people started.

The other things that we've tried to do is bolster that with some additional support. So we, for example, we have a range of different slack channels internally that.

Are staffed to really answer quick questions about data.

So if I'm working on this particular table and I don't know what this.

Particular field is, I can hopefully ask a quick question.

Some of those channels are kind of staffed intentionally.

Some of them actually are more community staffed and it's just a way of kind of unblocking some things.

The other thing that we found really useful is some actual in diary sessions. So we have an ask a data.

Analyst session twice a week that people.

Around the business can just sign up.

To come along, bring their query and our data analyst will help them out and hopefully make that run more efficiently or point them in the right direction if they're looking at the wrong place.

I think the other bit on the self serve analytics is to really think.

About what you open up to whom.

And I think the key here is.

Not to overwhelm people.

So I think there's a real risk.

If you go, well here is all.

The data we have across all our different systems and all our customers go for it. I have this view of it's really easy to add a lot of hay.

Without adding any new needles.

And what you really want to do is focus in on and say, well, okay, for these types of roles actually.

What you really need are these data sources.

Don't worry about all these other things. If it gets too complicated, we can.

Help you with some of those additional things. But actually the majority of your queries you're just going to need this table or you're just going to need these small number of sources.

I love how you end it here by making it simple for the user. Do you mind expanding what the iteration process looks like and how you continuously integrate feedback from your stakeholders?

Yeah, and again I think that's. I talked before about that consultant skill set. That's where that really comes in here actually. Because I don't want to get to.

A point where the Data team is seen as a blocker.

Whether that's because functionality doesn't exist or.

The data is in the wrong place, or there isn't the right access.

So we continue to try and actively.

Seek feedback from the end users of the data.

The other thing is, as we look to build tools and platforms, it's almost looking for.

NET promoter scores on those tools and.

Platforms as a great way of seeing are we making progress here.

Is this becoming more a tool or.

Platform that people enjoy, is actually solving.

Their problems or are there real issues? So definitely that feedback loop is super important.

You mentioned here the importance of training and upskilling. Where do you view the role of the CDO in scaling organizational data literacy? And can you comment on how it has evolved over the past few years from. From a role that's just been around leading the data team to now leading massive transformational projects?

Yeah, I think that area of data literacy is definitely one of my key concerns. And there's a real risk that you build a bit of an ivory tower.

If you've got a data team that goes, hey, these guys are the only.

People who can get to this data. They're the only people who can do this analysis.

They bamboozle us with their algorithms and.

Their charts, and if that's the outcome.

Then we're not winning. In my mind, I think that the.

Role is to help people better understand data and to demystify both data and machine learning. And I think the way to do that is to make it relatable, if.

Possible, to make it fun.

So one of the things that we've looked to do in the past is a bit of a roadshow around what is machine learning? Hey, I read about it in the newspapers. I see these articles about these things. What is it? How can we use it, what are some of the risks? And actually can we present it in a way that's fun, that doesn't make.

It scary, and really raises everybody's knowledge up of it.

And so we've looked to try and.

Do all kinds of stuff there. But I think the more you can.

Make it hands on and maybe even.

A bit ridiculous, then better people are.

Going to engage with that.

I love that, especially creating a community around data culture. Now, of course, given that we've been talking about data science at Capital One, I'd be remiss not to talk about some of the data work Capital One has done and the challenges in being impactful in the finance, financial services space. So I'd love it if you can walk me through the challenges in working with data within an industry that is extremely regulated. And how do you ensure that you're consistently innovating responsibly?

There's different ways to look at this and I think for me, the level of regulation in some ways really helps provide focus and actually ensures that innovation not only helps push the business forward.

But it does it in a way which takes into account consumer impact.

So in my mind, actually working within.

That regulatory framework really helps give that focus.

I think part of that is making.

Sure that you're building strong links with.

Both the regulators and with consumer groups.

To try and help shape the future. One of the initiatives that I'm most.

Proud of over the last couple of.

Years is working as part of the bank of England and the FCA's Artificial.

Intelligence Forum over the last couple of years has brought together a range of experts across finance to better understand the implications around data model management and governance.

When it comes to artificial intelligence.

Really helping to bring up the level of knowledge and have a really insightful debate about where do we want this to go next. And actually that's been a great forum, that's been a really good way of learning different perspectives, so getting different views.

From across the industry, across regulators, and.

Also helping to educate and engage. I think the links of academia also.

Really help here because again, again, that's another great way of building out knowledge. And I go back to my bookcase metaphor, but this is really helping us build another wing to that bookcase. The more you go out and you talk to different people, the more ideas you're going to bring back in.

That's great. And in terms of prioritization, how do you balance between quick wins and transformational outcomes? And how does the regulatory dimension of working in financial services impact that prioritization?

I think ideally you want to have a real mix of different things that you're working on.

There's a real risk that if so.

If I think about those three buckets.

Right, you've got your longer term strategic.

Delivery, you've got your quick wins, and.

Then you've got your almost must dos. Because of the regulatory nature of things.

You want to make sure you've got.

A real mix of those three things going on.

I think key to that is active and regular prioritization. And one of the benefits we've really seen of bringing all the different data families together is, is the ability to.

Prioritize across the data department.

That's given us a lot of flexibility.

To actually move quickly and to reprioritise if something Urgent comes in whilst keeping.

A focus on what is our longer.

Term goal because it allows us to build a strategy alongside delivering those rapid wins.

Of course, in financial services, the impacts of AI could make or break someone's ability to buy a home, receive credit or even accidentally lead to predatory behavior. For example, a loan recommendation algorithm giving a recommendation outside of a consumer's capability. So I'd love it if you can walk me through how you've embedded responsible and ethical use of AI in the development process and how you've been able to minimize harm.

Yeah, this has been a big focus for us over the last few years and I think has led to trying.

A lot of different things.

The framework I always try and use when I'm thinking about responsible AI or ethical AI is to think about the.

Different audiences that this is for.

And I think I end up with.

Three distinct audiences that we are trying to bear in mind when we're doing these things.

One is the consumer. And so we need to bear in mind that decisions we make have a real consumer impact, as you said, and we need to think through not only what is the business impact of making.

Some of those decisions, what's the consumer impact?

And so an ongoing push of how do you start to make your decision.

Frameworks more understandable to a consumer.

And I don't think that's necessarily a.

Really easy thing to do, especially as you look to increase complexity of your algorithms, But I think it is something that we always need to bear in.

Mind is that how do I justify.

The decisions that my framework is making in a consumer and the regulator's eyes?

The regulator is that second audience that we need to think about. So if the consumer is all around.

At the micro level, how do we understand those decisions?

I think from a regulatory perspective it's all around how do we justify the fairness of the systems and the understandability of the way that that's set up. And part of that is being very direct around the trade offs around making decisions. Especially if I think about your choice.

Of algorithm is a great example here.

Where you may choose a really kind of black box algorithm that's really complicated and actually will get more of your decisions correct, or something really simple that.

Is much more easy to understand and.

Perhaps not as effective. I think as a data scientist, as a cdo, you need to be working with regulators to understand whereabouts on that.

Spectrum is their appetite.

And then I think the third area to focus on is your key business accountable executives. So the people who are going to.

Be using the output of these models to drive their business decisions.

I think the area of interest there is perhaps around kind of bias.

How do you understand the potential biases that might creep in through your data.

Through your choice of algorithm, and how.

Does that play through into the decisions that are going to rely on those models that you build?

That's great. And I really appreciate how you break it down into multiple components between regulators and stakeholders. The last thing you mentioned here is how to manage the stakeholder relationship and AI governance. Do you mind walking us through what that governance model looks like at Capital One? What are the checks and balances embedded in the development process to avoid and minimize the harms of machine learning models and production?

Yes. Yeah. And we have really strong model governance processes in place throughout that model's life.

Cycle, not only in the model build phase where there's some really good structure around understanding what is the need for.

This model, where do you get your data from? How do you know your data is.

Correct, how do you understand the algorithmic.

F whole load of area of focus there, then moving on to the deployment. And to be honest, this is the area where actually typically you're going to find most of your model breakages actually.

Happen more at the deployment phase rather than the actual model build phase.

And so a real focus on how do you know that the data feeds.

Are going to flow through in the way that you expect?

How do you know that your algorithm.

Has been implemented in a way that you can evidence and test?

And especially in those first kind of few hours or days, what testing in.

Place have you got to make sure that you're getting the results.

But then it doesn't stop there. And once a model is deployed, you.

Need to make sure that you're continuing.

To monitor that usage, whether that usage changes. So you're going to expand the usage.

Or maybe the regulatory landscape changes.

So you need to make sure that.

You'Re continuing to reevaluate that model.

And so a really strong model governance end to end process is key there. I think another part of that is.

Clear roles and responsibilities as part of that governance process.

And so we make sure we're very intentional as to who is the person.

Who really knows this model, who knows.

The inside out, who is the person.

Who is on the hook for the decisions that these models make.

And then who is the person who's going to be validating that model in an independent way. And so by having really clearly delineated.

Roles and responsibilities, that's another way that you make sure that you've got those checks and balances.

So as a follow up, given the amount of interlocks and collaboration needed, would you say it's accurate that a data culture and common data language is needed to have a fruitful collaboration here?

Definitely, definitely. I come back to what I said.

At the beginning around the importance of team.

You've got these different roles across the.

Entire build and deployment lifecycle of a.

Model and if any of those roles.

Fails, then you don't end up with a model that's making the decisions you want. So yeah, really deep teamwork and collaboration is the key there.

That's great. Now, Daniel, before we wrap up, I'd love if we can think about the future for a bit. What are some of the trends and advances in data science that you're particularly excited about that you think will have a big impact in the financial services space?

So one of the trends that I'm.

Really interested in is the open banking initiative.

So the ability for consumers to allow financial organizations to access their banking trade line data. I think open banking initiative has been in place for quite a few years now. But what we are now starting to see are some really impactful real world applications of this. I think there's a lot of momentum there, whether that's in things like income verification or, or whether that is in.

Helping with credit risk. I think there's a whole load of opportunity in open banking.

The other trend that I'm really interested in is, well, more generally I'm very.

Interested in the hype cycle around data.

Science and machine learning. And I think we're at a point now actually where the rubber really hits the road. And I think people are needing to.

See kind of real return on expenditure when it comes to machine learning.

And for me the key to that is around simplification and focus. I think it's around doing a smaller.

Number of really well focused executions. And actually I think you'll start to see some real forward leaps there in the financial sector.

That's really exciting. Now, finally, Daniel, before we wrap up, do you have any final call to action before we end today's episode?

I think there's a couple of things that have popped up, especially over the last two years from one of the.

Lessons as we've gone through pandemic response.

And I think one of those is that short term versus long term investment.

In your foundations really shows at times of stress.

So going back to the quick wins, I think if all you're doing is quick wins and then the world changes, I think you find it really difficult to react because that investment in the foundations is not there you can find yourself behind. So I think that's been a really key lesson for me over the last couple of years. I think the other thing is this period has been a real reminder that changes that happen in the world will find their way into your data and your models, and that has to be.

At the top of your mind. If you're obsessed around customer impacting models and analysis, that's just got to be top of your mind.

Thank you so much, Daniel, for coming on Data Framed.

Great. Thank you.

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