Experian and Solidatus unite to tackle AI's data trust problem

Experian integrates Solidatus data lineage into its Aperture platform as AI governance demands force firms to trace data from source to decision.

By Disrupts Editorial Team Wed, 4 November 2026 AI level 2 of 5

AI level 2 of 5: drafted by our AI editorial assistant from the company's announcement; fact-checked, edited and signed off by Mark Walker, Editorial Director. What the levels mean

A brightly lit data center aisle with two rows of server cabinets displaying blue and green indicator lights, and yellow data cables on overhead trays.

Experian, the FTSE 100 data and analytics group, has struck a partnership with enterprise data lineage specialist Solidatus to give organisations a joined-up view of where their data originates, how it moves through their systems, and whether it can be trusted to underpin AI-driven decisions. The integration embeds Solidatus' lineage mapping directly into Experian's Aperture Data Studio platform, combining data quality scoring, governance metadata, and end-to-end traceability in a single environment.

The timing is deliberate. Financial regulators across Europe are tightening requirements for data provenance: BCBS 239, which sets principles for risk data aggregation in banks, and the Digital Operational Resilience Act (DORA), which comes into full force across EU financial entities, both demand that firms can demonstrate exactly how critical data has been sourced, transformed, and used. For regulated institutions managing sprawling data estates across dozens of legacy systems, closing the gap between "we know there is a data quality issue" and "we know where it came from and what it has contaminated" has historically required stitching together separate toolchains.

Lineage as infrastructure, not afterthought

Andrew Abraham, Global Managing Director of Experian Data Quality, framed the partnership as completing a circuit that has long had a break in it. "Together, that gives organisations a much more complete picture: whether their data is reliable, what it means, where it came from and where it is used. Crucially, it also helps them demonstrate that they understand and have control over the data on which important business, regulatory and AI decisions depend."

British Business Bank, which already uses both platforms independently, is cited as an early advocate of the combined approach. Mark Willems, the bank's Associate Director of Metadata and Data Management, said a more connected approach has "the potential to reduce complexity and give our teams a clearer understanding of the data they rely on across the organisation." The bank's endorsement carries weight: as a government-owned development bank, it operates under heightened scrutiny of its data controls and reporting integrity.

Stewart Bond of IDC added independent analyst context, noting that data quality, governance, and lineage have historically been siloed disciplines, but that organisations now need a connected view to understand whether data can be trusted and where failures could propagate.

The AI governance multiplier

The deeper strategic signal here is not the partnership itself but the infrastructure layer it points to. As enterprises accelerate AI deployment, the question of model trustworthiness is increasingly inseparable from the question of data trustworthiness. A generative AI system trained on poorly governed or inadequately traced data carries regulatory and reputational risk that is difficult to quantify and harder still to defend before a regulator.

This convergence is reshaping investment priorities. Data lineage platforms, once a niche concern for financial risk teams, are being repositioned as foundational AI governance infrastructure. Solidatus counts HSBC, Deutsche Bank, Bank of New York, and M&T Bank among its clients, suggesting the category has already found its core constituency in the most data-complex regulated sector on earth. The question for cross-sector investors is whether the same demand curve is about to replicate in healthcare, insurance, and public-sector AI deployments, all of which face their own emerging regulatory frameworks for algorithmic accountability.

For Experian, the move also signals a strategic evolution from data quality vendor to a broader data intelligence platform play. With 25,200 employees across 33 countries and existing reach into financial services, automotive, healthcare, and agrifinance, the company is well positioned to carry the combined lineage-plus-quality proposition into verticals where AI governance pressure is building but tooling remains fragmented. Capital allocators watching the AI infrastructure stack should note that the compliance-driven floor for this category is rising across every regulated industry, not just banking.