A recent Gartner survey revealed that 63% of organisations lack confidence in their data management practices for artificial intelligence (AI). The survey, conducted in July 2024, found that many organisations are unprepared for the unique data requirements of AI, which could result in the failure of their AI initiatives. As such, Gartner predicts that 60% of AI projects lacking AI-ready data will be abandoned by 2026.
To address this, Gartner recommends the following five key steps:
- Align data sources with specific AI use cases, considering both internal and external data.
- Establish robust data governance requirements for AI to mitigate legal and ethical risks, involving close collaboration with legal and business leaders.
- Evolve metadata from passive to active, leveraging it to build intelligence and drive continuous improvement and automation.
- Prepare data pipelines to create AI model training datasets and live data feeds for production systems based on gathered requirements.
- Assure and enhance data quality through testing and monitoring, employing DataOps and data observability processes to track patterns and make necessary adjustments.
Gartner's findings underline the importance of data quality, highlighting that data with issues is fundamentally unsuitable for AI applications.
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