Large enterprises in the UK and Europe want to incorporate generative AI technologies into their operations. Some 71% of the UK leaders surveyed believe that GenAI will significantly transform their organisations over the next three years. However, it’s critical to first clean up and optimise underlying knowledge bases and data lakes, as generative AI models are only as good as the data used to train them. So, getting that foundational data in order is a crucial first step. This article offers some critical tips for large enterprises to consider.
Audit and consolidate your data
Start by comprehensively reviewing all the data repositories across your organisation, from databases and data warehouses to unstructured data in file shares and content management systems. This way, businesses can identify redundant, outdated, or trivial data to purge. Finally, once you identify the most valuable, high-quality data assets, merge and centralise them into a unified data lake.
Improve data quality and governance
If you want to minimise accuracy issues, arrange regular data quality checks and processes. Also, establishing clear data ownership, stewardship, and governance policies helps ensure accuracy and consistency. As a result, you’ll be able to train more reliable and unbiased generative AI models.
Enrich and contextualise your data
Now that your data is well-structured, it’s time to supplement it with relevant metadata, semantic tags, and contextual information. Generative AI systems better understand the meaning and relationships within enhanced data. So, this step will guarantee more coherent and appropriate output.
Address data privacy and security
Two in five technology professionals flag data privacy as the enterprise’s top ethical worry surrounding generative AI. Carefully review your data for sensitive, personal, or confidential information. Before feeding the data to LLMs, implement the necessary data anonymisation, masking, and access controls. Besides, it’s vital to ensure compliance with GDPR and other regulations.
Modernise your data architecture with scalable storage, processing, and management capabilities. Leverage data integration, cataloguing, and lineage tools to simplify data discovery, access, and monitoring across your organisation.
By proactively cleaning up and optimising data foundations, large enterprises in the UK and Europe can make the most of generative AI technologies. When businesses invest resources and time in data preparation, this will result in more reliable and impactful AI-powered applications and insights.
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