Data-related challenges are impeding AI adoption in the enterprise, with 76% of organizations citing five or more challenges.
In fact, 66% cited inconsistent data sources as a major or frequent challenge, followed by uncertainty about timeliness or quality at 65%, and 64% saying data is spread across separate silos.
This is according to a report titled “Data Streaming 2024 Report” prepared by Confluent, which surveyed 4,110 IT leaders across 12 countries including Singapore, Australia, Canada, France, Germany, India, Indonesia, Japan, Spain, UAE, UK and US.
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Data governance is a business imperative to safely scale and share data, but
When it comes to organization-wide data, 60% of respondents noted governance-related inconsistencies as another major or frequent challenge.
Other challenges cited included fragmented data ownership, data being too old, and owners being unwilling to share it.
Moreover, the explosion of AI, particularly generative AI, makes access to the right data even more important, the report adds. As more companies accelerate their AI adoption, new data challenges are emerging, including those due to a lack of in-house AI expertise.
According to the report, 70% of respondents cited three or more challenges that are preventing them from scaling their AI and machine learning (AI/ML) initiatives.
65% of respondents cited a shortage of AI-related skills as a major or frequent challenge, followed by challenges with data lineage and data fragmentation at 64% and a lack of infrastructure for real-time data processing at 63%.
Addressing these challenges
That said, the survey revealed that 51% of IT leaders say their data streaming platforms (DSPs) are helping their organizations become more agile and address these challenges. In fact, 63% say their DSPs have facilitated AI advancements extensively or significantly.
This comes in the form of breaking down data silos (93%), accessing existing data (88%) and helping people discover existing data (86%).
Finally, 84% said their DSP helped them resolve governance-related inconsistencies.
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