Five years from now, what will the world of data look like?

More data? Almost certainly.

More AI? Quite possibly.

But will organisations actually be ready for it?

In this episode of the Data in 10 Podcast, we're doing something a little different. We're looking ahead to 2031 and asking what might change in the world of data over the next five years.

Host Andi Jarvis is joined by James Blake, CEO of Binary10, Steve Smales, Chief Operating Officer of Binary10, and two special guests: James Wheeler, former CIO of Le Creuset and CTO of Permasteelisa Group, and Ian Tilsed, Director of Digital Technology and Operations at the University of Surrey.

Bringing together perspectives from retail, higher education and data consultancy, the panel discusses the future of data management, the growing influence of AI, the need for better governance and regulation, and how organisations can prepare their people for technology that keeps changing.

And yes, there are a few predictions about robots and football along the way. 😅

We had so much to discuss that Data in 10 became Data in 15 for this episode!

In this episode, we cover:

00:00 – Introduction and predictions for 2031: Introducing the panel and kicking things off with some light-hearted predictions about the future.

02:08 – The future of data in retail: Why the volume, variety and demand for data are expected to keep growing.

03:27 – Managing growing volumes of data: The challenge of making information accessible, dealing with legacy systems and delivering more live data.

04:42 – Data standardisation and governance: Why consistent datasets, stronger governance and the right technology architecture could become increasingly important.

05:37 – The future of data in higher education: How financial pressures, AI, legacy systems and changing student expectations could reshape universities.

06:46 – Data strategy and future regulation: Why organisations need to align data with their long-term goals and why stronger data maturity requirements could be on the horizon.

08:58 – The data skills of the future: The importance of continuous training, keeping pace with technology and maintaining fundamental data knowledge.

11:14 – New roles and capabilities: From AI engineers to knowledge managers, how the skills organisations need may evolve.

12:18 – What can we learn from the past? Why some information management skills are becoming relevant again in the age of AI.

13:23 – Will AI replace people? Why people will remain central to data and how organisations should use AI to support their workforce.

14:43 – Final thoughts: Wrapping up the predictions and inviting listeners to share their own views on the future of data.

AI, data governance and the next five years!

The game has changed! AI is now changing how organisations think about data. But what happens as its use becomes more widespread?

Will we see better data standards?

Stronger regulation?

More accountability for how organisations manage and use their information?

Steve Smales predicts a move towards greater data standardisation and governance, while James Blake raises an interesting possibility: what if organisations were required to demonstrate a certain level of data maturity? The panel explores why stronger governance, clear data strategies and better-managed information could become increasingly important as AI adoption grows. The technology may keep changing, but the need for trustworthy data isn't going anywhere.

How will data skills change by 2031?

Another big question: how do you train people for jobs and technologies that might not even exist yet? AI engineers, prompt engineering and knowledge management all come into the conversation, alongside the challenges of keeping training relevant when technology evolves so quickly.

Ian Tilsed shares the challenges facing higher education, where universities need to plan for future capabilities while dealing with funding pressures, recruitment processes and changing expectations from students. James Wheeler also highlights how younger generations are entering the workplace with different expectations of what technology and AI can do.

But there's an important point here. While learning new tools will matter, understanding the fundamentals of data and technology will remain just as important. Because the fundamentals are going nowhere! It's not simply about keeping up with the latest technology. It's about understanding how to use it responsibly and effectively.

From retail and higher education to AI, regulation and future skills, there's plenty to think about in Episode 8. Listen to the full episode and see whether you agree with our prediction and let us know your predictions in the comment section, please!

Need help getting your data ready for the future? Whether you're reviewing your data strategy, preparing for digital transformation, improving data quality or managing data across different systems, Binary10 can help.

We're the data people, and we help organisations make data-driven decisions they can trust.

EPISODE TRANSCRIPTION:

Disclaimer: This transcript was generated by an AI tool that did its best, but it's never met different British accents it could fully decode. Expect a few funny mistakes. Enjoy!

[00:00] - Andi J

Hello, and welcome to the "Data in 10" podcast. Or today, is it the "Data in 5" podcast? Because we're talking about predictions, challenges, where we're going to be in five years with data. To have this discussion with me, I'm joined, as always, by James Blake, CEO of Binary10, Steve Smales, chief operating officer,

but also we've got two special guests. James Wheeler, a retail CIO, and Ian Tilsead, the director of digital technology and operations at the University of Surrey. So before we get into the data challenges, Steve, you tell us, give us a prediction for five years' time. Nothing to do with data, just a prediction. Okay.

Where are we going?

[00:37] - Steve S

It's going to be slightly under five years. Okay. I'm going for May 2031, Everton Premier League champions.

[00:44] - Andi J

Wow. [all laughing] We asked for predictions, not delusions.

And by Premier League, do you mean the Northwest Counties Premier League?

[00:53] - Steve S

Ooh, cutting.

[00:53] - Andi J

Yes, cutting, cutting. But, it's possible. Nobody would have said Leicester would win the title.

[00:59] - Steve S

Well, exactly, yeah, and look how well they've done since.

[01:01] - Andi J

Yeah, well, they are [chuckles] the best team in the Championship next year. Jamie, any predictions for 2031?

[01:06] - James B

I won't be as bold as that, but I reckon, I honestly think that maybe 25 to 50% of us will have a robot in our homes.

[01:15] - Andi J

Ooh. Now, do we already have robots in our homes?

[01:18] - James B

Well, you could say that, but I mean the- Like a humanoid with- ... a humanoid robot that you can high-five and get him to do the dishes.

[01:25] - Andi J

Okay. Well, we're on dodgy ground with a five-year prediction, and let me give you a story from the 1970s. There was a survey done in the US in the 1970s, and 25% of respondents predicted that by the year 2000, 25% of commuters would be using flying

machines to get to work. We are not, dear reader, using flying machines to get to work, unless you consider flying to a different country, but then having to get in a taxi. So, who knows what predicting the future will lead, but do you know what? We're going to give it a go anyway. So, let's welcome our panel. We've got James and Ian with us who are going to talk about our predictions for the next five years. So, let's throw James under the bus first of all.

[02:07] - James W

Thank you very much. [laughs]

[02:08] - Andi J

In retail, a sector that is moving fast and doesn't feel like it's going to stop moving fast at any point, where do you think we're going with data in the next five years?

[02:19] - James W

Oh, five years is tough. Eighteen months is probably quite difficult to predict at the moment.

I mean, look, the amount of and growth of data that we've seen over the last few years has been unbelievable, particularly when you start thinking about digital platforms and how retailers are now obviously embracing those and the ways that they are selling. So the amount of data that they are now accumulating and have access to is significant. And that is only going to grow, and it is going to grow at a far quicker rate than I think we've already seen as well. I think there's going to be more data sources, types of data available, and I think, particularly with AI coming as well, that is also going to be data hungry and be crucial to what's going to be needed as well. So, I think from a challenge point of view, just managing the sheer amount of data that is coming through and making sure that you are processing that in a responsible way as well, I think is also going to be really important as well. And I think for a company and technology departments, I think a challenge is going to be how you architect your systems to be able to cope with this as well.

[03:27] - Andi J

Because data's only useful if you can find the information you need to answer the question that you have. And having more data actually sounds more problematic.

[03:36] - James W

It does, and making sure that you are choosing what is the right data to be processing as well.

And particularly in retail as well, and legacy retailers as well, we've got archaic architecture and still legacy systems as well.

It needs a lot of thought to think about how you're going to get that data available and make it accessible.

And businesses are going to be expecting to have live views of data much more than they would have done in the past, I think.'x

[04:02] - Andi J

I suppose the last question from me, but the good news is in retail, because you're working in a sector that sells products that comes out the other end, you must be working with enormous budgets to manage all of this data challenge.

It must be easy for you to deal with. [all laughing]

Money just, you open the window, money falls from the sky, and you just put some more stuff into the data.

[04:18] - James W

Yeah, maybe some retailers are in that space. I mean, maybe, but look, retail is a tough environment to be operating in at the moment as well.

And people are getting pressed on margins and so inevitably, we're going to have to find clever ways of making sure that we've got the right tools and resources to be able to build the infrastructure and architecture we need to do to ensure that the business are getting the most out of the data.

[04:42] - Steve S

Yeah, and I think one of the things, it's a good point, they've got more and more data coming in, there's obviously then you've got the danger of you've got the same data in more than one place or the same type of data but in different standards.

So, I think from my point of view, in five years' time, I can see a lot of a move towards more standardization of data sets, and a lot more governance as well around data and how it's stored, how it's kept, how the quality is kept in line.

[05:08] - James W

Yeah, I think that's true. I think that's right.

And I think the technology partners have a really difficult thing placed on their hands as well, a situation, because you can see even with the technology vendors at the moment, a lot of strategy changes from them and the real big ones as well.

A lot of new tooling coming in, a lot of tooling that's come in that's been decommissioned.

So, I think there's still a lot that we need to be guided for by the technology vendors as well as what the right environments and architectures are going to be built to really get the best out of the amount of data.

[05:37] - Andi J

But for you, Ian, this must be really easy predicting five years' time because you only really have five intakes of students before, maybe a smaller intake at Christmas, but you've only got five lots of students coming in.

It's quite a slow, predictable environment, isn't it, higher education?

[05:50] - Ian T

I wish it was easy to predict. [all laughing]

[05:53] - Andi J

You're just like kicking them in.

[05:57] - Ian T

I think given the sector conditions at the moment in the UK, they're very challenging with the financial side of things.

I think in the next five years, we're probably going to see a lot of a leaner sector than it is now.

I think they're realizing that there's a lot more they could do, and I think data is at the core of that alongside AI.

And I think students will also drive that too, because if you think now, five years' time, we've got 11, 12-year-olds coming to, 12, 13-year-olds coming to university, they're going to be much more IT literate, much more AI literate.

And they will be demanding an experience which equates to what they get from commercial.

So we need to get ourselves into a position where we can meet that head-on, but we have a lot of legacy systems.

So I think, yeah, better governance, better data, enterprise data maturity will be needed in five years' time.

[06:45] - Andi J

Jamie, how do you manage that, running a consultancy that helps companies with data challenges?

But ever-increasing pots of data, ever more difficult to access legacy systems, constantly evolving new systems with vendors shifting bits as it goes and as it grows.

Sounds terrifying. Where are you going to be in five years' time?

[07:06] - James B

Yeah, it's a really good question.

I think one of the frustrating things I think we have is that we have some great tools, we have some great capability and knowledge.

We go in, we're a strategy-first company, so whenever we work with a client, the first thing we do is look at their data strategy or their digital transformation strategy to really understand where that organization is going over the next five years so that you can make sure that the data is aligned to that journey, all right?

Because it's super important.

Now, one of the things I wanted to say and sort of bring up in terms of my prediction in the next five years, and it's a bit boring, and I might have said this before, but actually, let me flip it, and I think it's important, and that is regulation.

So today, organizations are risk-assessed when it comes to data. GDPR, DPA 2018, all good, but actually, it doesn't press on the organizations, I don't think, hard enough to take data seriously.

So I can see, and I'd actually champion it, that in the next five years, organizations will have to prove a level of capability, I believe, to whether it's internal government departments, whatever it might be, and that will be fantastic because actually, from our perspective, it'll be great because the knowledge, the skills, the tool sets we have, organizations will want more of because they'll need them.

And actually, if we think about our fears around AI and will they take jobs away, there's a great opportunity around the regulation of AI and similarly data, that if we can get some kind of formal level of maturity that organizations need to act in their particular sector, that is massively going to drive lots of great things around data, make AI more efficient.

So, while it sounds a little bit boring, I actually think it's super important, and I hope that that level of regulation starts coming in because it will drive us.

[08:48] - Andi J

I'll just tell our American listeners, by the way, regulation is the thing that governments do, and they can't, because you're probably not aware of what that is if you're listening in the US.

But what about skills, Steve?

Lots of things changing, and we don't really know where it's going, but we have an idea of direction of travel.

But how do we skill up a workforce to be able to take advantage of all these challenges coming down when we don't really know what the challenges are?

[09:12] - Steve S

Yeah, it's a difficult one. You can only move with the technology and train people up. It's all down to training, isn't it?

And I think, as I mentioned earlier, I think standards, I think things in five years' time will be a lot more standardized. It has to be, and that's where that regulation will come in to make things more standardized.

But yeah, I think the main thing is just going to be training, but because technology moves so fast, you've got to keep that training up to date constantly.

You can't use a course that was good four years ago.

And you actually, I mean, we do Microsoft certification, don't we? And we've seen it quite often that courses are retired and replaced on a regular basis.

So, it's a constantly moving goalpost.

[09:46] - Andi J

Yeah. How do you two fit into that challenge?

[09:49] - James W

Well, I was going to say, picking up on the point that Jamie and Ian made actually about, and Ian said this, about people, their expectations, the younger people coming through about how they can use technology.

And I think people coming into the workplace now are so much more savvy about what they can use technology for, what they can use AI services for, and what they can do with data as well.

So, I think that training point and having the right guidelines as a business as to how you should be responsible and operating in a responsible way is really important.

[10:18] - James B

Yeah. And I think on skills, I think it's also important to not lose the origins of everything that's happening.

So, of course, the latest tool sets, the latest capability, a lot of the training courses will be around that.

But I think it's so important for young people to understand those sort of foundational things around data, binary, ones and zeros, positive and negatives, and how microchips feed that within computers, and just to get a full understanding of how this operates.

And so when they're using these modern GUI-based tools and technologies, they actually understand how they're being compiled and built because we mustn't lose that understanding.

So, I think that's-

[10:56] - Steve S

Yeah, it's fundamental.

[10:57] - James B

It is, yeah.

[10:58] - Andi J

And Ian, you work in an educational institution, but how do you stay on top of making sure the people who work for you are right at the cutting edge of this, so you're making the right decisions for the next 10 years without actually knowing what that 10 years looks like?

[11:12] - Ian T

Yeah, it is a challenge, and we look at other sectors like retail and commercial. And to pick up the capabilities point, it's very difficult to try and work out what a typical IT department or a university needs in five years. So, I think we're having to look ahead and try and think about what skills we need in IT, whether it be AI engineers. I've seen an advert for a chief prompt engineer that now exists. And if you look at things like knowledge management, that used to be very vogue back in the '90s. Now it's coming back because we need knowledge managers who can help curate an institution's knowledge, which is kind of allied to that data. So, what new capabilities do we need in five years?

And I think that's really difficult to try and work out at this stage, especially with how long it takes universities to decide to provide the funding, then secure the posts, and so on.

[12:04] - Andi J

Yeah. So, this is the "Data in 10" podcast. We are going to make it the "Data in 15" podcast today. So, we're just going to roll through because we've got such a great discussion going on. We're just going to maybe ask one more question, and then we'll wrap up at about 15 minutes. So, you mentioned, though, that knowledge managers, which were popular in the '90s, are coming back. Is there any element of trends that you're seeing now?

Yes, the technology's different. AI is different to anything we've seen before. But are the patterns and the trends the same from the past?

Is there anything we can look at from the last 20, 30 years that will help us know where we're going in the next 5, 10, 15 years?

[12:39] - Ian T

And I think if you were now sitting in a library, and if you think about the library and the sort of information management skill sets that people have, and my wife is a librarian in a law faculty.

It's as much now about understanding where to find the data rather than the books.

And teaching the students on how to use AI to find the right piece of information at the right time.

So, I think there are skills like that that are changing and adapting and evolving to meet the new needs.

[13:09] - Andi J

Yeah. James, any thoughts on that?

[13:11] - James W

That's a great point, actually. Yeah, I completely agree with that.

I think that the ability to understand where the data is and get to it doesn't change, doesn't go away.

It's going to be even more important.

[13:23] - Andi J

And Steve, any thoughts on maybe what isn't going to change in the next five years?

[13:29] - Steve S

Ooh, now that's a really good one. What isn't going to change?

I mean, people aren't going to change. So, data might change, the technology might change, but you've still got a workforce and people who are using data, consuming data, people who are working with data.

Yes, AI might change their job role, but the people themselves are going to be the same, aren't they?

So, what we need to do is make sure that we use AI to help people, not replace them.

[13:56] - James B

I think that's a really good point, isn't it?

That we've got to educate people to kind of educate AI to make it work for us.

I think there's a problem at the minute that people just think that it's already built and it's already there to give you everything that you need.

[14:09] - Ian T

It's already perfect.

[14:10] - James B

Yeah, but it's not.

You made the spot-on point, Ian, around we have to feed AI, this is where the data is, and give it some knowledge so that when further down the line other people are asking, "Oh, where can I find this data?" It already knows because it's been fed that intelligence.

So, I don't think, especially in, I'm sure AI will get, I hate to use that phrase self-aware, I'm sure that was in a movie somewhere, but that it will be able to do it itself.

But in the next five, maybe not that long, but definitely in the short term, it's really important that we educate AI to work for us properly.

Super important.

[14:43] - Andi J

Well, thank you. The Data in 10 podcast is the Data in 15 podcast today.

It's an interesting discussion, future predictions. Everyone always has their own thoughts on where it's going to go.

If you agree or disagree with anything that's been said, there are comment sections around the podcast, whether you're watching on YouTube or whether you're listening on Spotify.

Leave a comment, leave your prediction. We'd love to hear from you.

But for now, we'd like to say thank you to James, Ian, Steve, and Jamie, and chaps.

[15:09] - James B

See you next time.

[15:10] - Steve S

See you next time, chaps.

FAQs.

1. What is data maturity?

Data maturity refers to how effectively an organisation manages, governs and uses its data. It considers areas such as data quality, ownership, accessibility, processes and the ability to use data for reliable decision-making.

2. What is a data roadmap?

A data roadmap is a plan that outlines how an organisation intends to improve its data capabilities over time. It identifies priorities, projects, resources and milestones to help align data improvements with wider business goals.

3. What is a data catalogue?

A data catalogue is an organised inventory of an organisation's data assets. It helps people discover what data is available, where it is stored, what it means and who is responsible for it. Data catalogues can make information easier to find and understand across different systems.