Marketers may be embracing AI tools harder than a new love interest but those at the sharp end believe disconnected data is preventing businesses from fully adopting AI and machine learning, making fragmented data systems the biggest obstacle to success.
That is according to new research from Confluent, based on a survey of 550 tech leaders in the UK to discover on the maturity of their data infrastructure.
When asked what the main challenges were to adopting the technology, siloed data topped the list for UK decision-makers (67%), followed by 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%); and insufficient infrastructure for real-time data processing (50%).
The report maintains that it is 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, they argue that modern applications driven by AI are being undermined.
Even so, nearly two-thirds (63%) strongly agree that the use of AI for business applications and analytics will grow significantly; similarly, 41% strongly agree that there is 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, while over half (55%) see them enabling data provenance and lineage tracking, and a similar proportion (51%) say the DSP will ensure the quality, integrity, and timeliness of data.
Confluent VP Northern Europe Richard Jones said: “Data fragmentation has always challenged businesses, but in the AI era, it’s never been more important.
“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.”
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