Data disconnect stalling the UK's AI ambitions

More than two-thirds (67%) of UK IT leaders see disconnected, siloed data as preventing them from fully adopting AI and machine learning (ML) — making fragmented data systems the #1 biggest obstacle to AI success.

That's according to new research from Confluent, which surveyed 550 IT leaders in the UK on the maturity of their data infrastructure. When asked what the main challenges were to adopting AI/ML, siloed data topped the list for UK decision-makers:

• Fragmented ownership of data across disparate systems — 67%
• Ambiguity surrounding data lineage, timeliness, and quality assurance — 63%
• Insufficient skills and expertise in managing AI projects and workflows — 61%
• Limited ability to seamlessly integrate new data sources — 57%
• Insufficient infrastructure for real-time data processing — 50%

It's no surprise that these frustrations are so widespread given that 82% believe AI systems must leverage enterprise data "to realise their true potential." Without it, modern applications driven by AI are being undermined.

For example, 63% strongly agree that the use of AI for business applications and analytics will grow significantly; similarly, 41% strongly agree that there's an emerging role for AI-based agents within their business. Both are contingent on access to high-quality real-time data.

The same decision-makers have identified Data Streaming Platforms (DSPs) as the solution. Almost two thirds (64%) agree that DSPs simplify AI access to different data sources. 55% see them enabling data provenance and lineage tracking, while 51% say the DSP will ensure the quality, integrity, and timeliness of data.

"Data fragmentation has always challenged businesses, but in the AI era, it's never been more important," comments Richard Jones, VP Northern Europe, Confluent. "Historically, a lack of data might have meant an incomplete strategic picture, or a slow reaction to changing circumstances. Today, it threatens to derail AI initiatives completely. Without seamless, real-time access to high-quality data, mission-critical systems can't perform, and businesses risk falling behind. To maximise the potential of AI, businesses must prioritise breaking down data silos and modernising their infrastructure."