If there’s one takeaway from The Data Crowd’s gathering hosted by Oakland at this year’s Leeds Digital Festival, it’s this. Whether we’re talking AI, data governance, or privacy concerns, the core challenge remains the same.
That challenge?
Organisations need to figure out how to manage their data in an efficient, innovative and trusted way, all while staying on the right side of privacy laws and regulations.
Next, we unpack key themes from the session and how they’re shaping the future of data usage.
Data maturity: The same conversation, different applications
Kicking things off was a candid talk by the ICO (Information Commissioner’s Office), which framed data maturity for government as more than just an operational necessity.
Leaders need to build a culture that places data quality front and center.
Sure, we’ve all been caught up in the AI conversation lately, but the truth is, this is just the latest chapter in the ongoing saga of data.
AI is still somewhat the shiny new toy and being peddled by everyone under the sun, but it needs the same solid foundation that we’ve been discussing for years.
As Joe from Oakland rightly put it: “Don’t get lost in the idea that AI demands a totally different approach. At its core, AI is just another layer in the broader data discussion. The principles that help build trust in data, accuracy, governance, and quality, are the same ones that need to be applied here. In fact, AI governance should slot seamlessly into the frameworks companies have been using all along to manage data effectively.
Innovation: Encouraging risk to drive change
However, AI does ask of teams to go beyond existing data strategies. The University of Leeds brought an intriguing perspective on AI governance frameworks, certifications, and the role of intellectual property (IP) in data innovation.
Their takeaway?
You can’t innovate without getting a little risky.
In other words, companies need to stop being so risk-averse when it comes to doing new things with data.
Being innovative means pushing the boundaries. But there is the need for solid principles and buy-in from everyone in the business.
It’s about creating a culture that says, "Yes, we know this is a little out of our comfort zone, but we’re committed to doing it right."
That’s where data literacy comes in, especially when employees are signing contracts with tech companies.
They need to understand what they’re signing up for, not just in terms of the tech itself, but also the data behind it.
The freemium factor: Data and future products
Flutter made a compelling point when discussing how freemium products impact future data usage. In a world where products are evolving constantly, and are often free, organisations need to think carefully about the data they’re collecting and how it will shape future business decisions.
It’s easy to get caught up in offering something for nothing. But that data has to be stored, governed, and protected. And more importantly, everyone needs to consider the long-term implications. What happens when this freemium data becomes the foundation for future AI models? How do you ensure that it’s accurate and secure?
These are the questions businesses should be asking now, not after the fact.
Data privacy: Legal, ethical, and practical implications
Of course, no discussion on data is complete without touching on privacy.
A particularly heated moment during the panel came when discussing the role of data privacy in companies. With AI models like GPTs becoming increasingly central to decision-making, privacy concerns have hit a new high.
One of the biggest issues?
The lack of accountability when AI systems, like Facebook’s automated response models, make mistakes, whether it’s shutting down accounts without human intervention or responding incorrectly to data subject requests.
At the moment, there’s still a disconnect between legal requirements for data privacy and how companies actually apply them. Staff data, for instance, needs to be treated with more caution than customer data. Employees are more risk-averse and, quite frankly, less trusting. They study their employers in the same way their employers study them, and building trust here requires more than just lip service. They want to see concrete examples of how data is being used properly and ethically.
The role of the ICO and national-level influence
Thankfully, the ICO has been doing its part to help navigate these choppy waters. Research into privacy concerns has been published online, and they’ve even created an AI sandbox to test out new applications safely. But there’s always a catch. often companies are only start to realise what they’ve signed up for after the fact. As companies rush to innovate and bring AI into the fold, many are waking up to the legal ramifications that should have been considered back in procurement.
This is where cross-organisational power comes in.
It’s all good and well to get one department on board, but real change comes from government and industry working together to develop stronger frameworks that will prepare both for the future.
The more we can work together at a national and global level, the better equipped we’ll be to handle the AI and data challenges that lie ahead.
A final thought: It’s all about flexibility
Wrapping up, the panel agreed on one critical point: flexibility is key.
Data, AI, and innovation aren’t static. They’re constantly changing, evolving, and iterating.
Building flexibility into people’s expectations and frameworks is an enabler, not a constraint. The future of data usage depends on how well we can adapt to these changes while maintaining a strong governance and privacy backbone.
So, where does that leave us?
The conversation may have started with AI, but the themes of data quality, governance, privacy, and innovation have been with us all along.
The tools may change, but the principles stay the same. When everyone gets on board with those principles, this is the first step towards a data-driven future we can all trust.
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