AI apps rely on quality data to minimize AI hallucinations and generate accurate, reliable results. Such dependence creates a great opportunity to point out the benefits of AI – in terms of both privacy and quality – to secure the necessary resources for continued improvement.
The Disconnect Between Data Lakes and AI
Many organizations use Extract, Transform, Load (ETL)/Extract, Load, Transform (ELT) to ingest multi-source enterprise data into centralized data lakes that are responsible for cambodia rcs data enforcing data governance. and LLM agents to write functions that queried the data lake to respond to all possible user prompts. The problem is the list of all possible user prompts is endless.
So, despite their advantages in scalability, accessibility, and cost, data lakes are a bad fit for AI data or RAG for the following reasons. First, sensitive data may accidentally be leaked to the LLM or to an unauthorized user. Second, the cost of cleansing and querying the data at enterprise scale is extremely high. Third, data lakes don’t jive with generative AI use cases that require clean, compliant, and current data.
Making Data AI-Ready and Governable
We were always taught to think big: big data stored in big data lakes. But the only way to make data AI-ready and governable is to think small – really small.
Imagine a data lake for one that continuously syncs a single entity’s data with your source systems, protects it to comply with your data privacy rules, and transforms it according to your data quality standards.