With an increasing amount of people’s daily lives conducted digitally, there’s an enormous amount of data created that can be tremendously useful to individuals and businesses. In fact, businesses can leverage this data to uncover market trends, understand customer needs, and identify opportunities for operational efficiencies.

But because every aspect of our lives is represented digitally, for every meaningful data point about an individual’s preferences, for example, there are hundreds of irrelevant details that can easily cloud any picture created. This means that to get the true value of data, businesses need to trust the data they use. Building this trust and getting relevant, clean data from the mass of information available is the next big challenge for organisations wishing to make use of what many call ‘the new oil.’

Experian talks to its customers about building this trust through a three phrased strategy. This Three Pillars of Trust focuses on robust governance, a commitment to data quality, and using the right technology and tools. Without these measures, or by only focusing on one of the pillars, data can quickly turn into a liability for a business. This can lead to poor decision-making, compliance issues or even potential security breaches. In the field of finance in particular, access to and the processing of data is particularly important. In addition to stringent governance that applies to the sector, accuracy of data and the way it’s processed and stored is critically important.

We spoke to Laurie Schnidman, the CPO for Platforms and Software at Experian UK and Ireland, at the TechEx Global event that took place recently in London, UK, to find out more detail about the Three Pillars of Trust and why this strategy helps build trust in data and creates an opportunity for businesses to stay agile, respond swiftly to changes, and maintain a strategic edge.

“Robust governance [the first of the three Pillars of Trust], is the foundation of trustworthy data. We conducted research last year that found 83% of British businesses agreed that governance shouldn’t be an afterthought. Instead, it can provide a strategic advantage,” Laurie said.

“It’s […] also crucial within a data governance framework to define clear roles and responsibilities within teams. This may involve setting up a dedicated team.”

But even well-protected and verifiable data can sometimes be of little value if it’s not correctly formatted, complete, consistent and timely. Data formatting, de-duplication, and validation are all activities that are required to bring out the value of information and turn it into actionable insight.

It’s for this reason that Experian places Data Quality as the second Pillar of Trust. “Ensuring the quality of data includes implementing regular data audits, validation checks and data scrubbing. Having the right data partners in place that have the same focus on data quality and governance is also critical,” she said.

The Third Pillar follows on logically: Using the Right Technology and Tools. In 2025, it’s inevitable that AI and machine learning number among the most powerful components to the forward-thinking organisation. In addition to these cutting-edge developments, Laurie also name-checks the type of tech that’s still a common sight in the majority of businesses today: “Data management platforms, data lakes, and cloud-based solutions are key pieces of technology businesses need to prioritise as they provide scalable and flexible infrastructure to handle large volumes of data,” she said.

When it comes to AI and machine learning, in any sector, the potential impact for businesses is huge. In fact, according to techUK, generative AI specifically could contribute up to £120bn per year to the UK’s annual economy. This means trusting the data a business leverages for its AI and machine learning is especially important. “AI systems rely heavily on data to make predictions, automate processes, and provide insights. If the data is inaccurate or unreliable, the AI’s outputs will be flawed, […] leading to poor decision-making and potentially costly mistakes. That is why our research with techUK showed 80% of business leaders believed [that] the quality of their company’s data will determine the success or failure of GenAI tools.”

However, the combination of AI and human expertise is what is crucial for future innovation. AI can handle repetitive tasks and analyse vast amounts of data, while human experience and intuition are essential for strategic decision-making and creative problem-solving.

For businesses to truly take advantage of AI, they need to ensure any AI initiatives are aligned with their specific objectives. This will not only help prioritise AI projects that deliver the most value but support the sort of innovation that will give them a competitive advantage. Laurie spoke of an example within Experian with the global launch of Experian Assistant. This is a new generative AI-enabled solution that offers fast expert recommendations, coding, and technical support to clients using Experian’s Ascend technology platform.

Three areas where Laurie sees high potential applications for AI within a business are:

• Customer service and support in the form of AI-powered chatbots and virtual assistants,
• Fraud detection and cybersecurity which are reinforced by algorithms’ ability to sift large quantities of data, spotting unusual patterns and potential fraud,
• Automation of business processes.

On this last example, Laurie said, “Take data quality. Businesses can automate data quality processes, such as data validation, to make it easier for people with varying technical abilities to perform data quality tasks. In Experian’s Aperture Data Studio, for example, it’s simple to create data quality rules using natural language – but with the code produced available alongside for validation and customisation by employees.”

With a growing list of potential applications and use cases for AI in 2025, now is the ideal time for businesses to explore how AI and other advanced technologies can drive their success and growth. While there are several factors to consider, one thing is clear: the foundation of any AI initiative must be data. This data should be built on the three pillars of trust and centralised in one location to provide businesses with the ability to make clear and informed decisions. By leveraging such a robust data foundation, businesses can not only seize new opportunities and drive innovation but also secure a competitive edge in an ever-evolving market.

Find out more from Experian by watching the interview with Laurie Schnidman, UK&I Chief Product Officer, Platforms & Software at Experian who talks about their approach to governance, AI, and democratising data.