LLMs allow enterprises to be more nimble in leveraging data, and make better business decisions, to gain competitive advantages. They have the potential to change the architecture of data stacks and have implications on data lineage, data quality, data security and observability. While this poses a threat to companies who address these issues using “pre-LLM” technology, it creates an opportunity for founders to re-think solutions leveraging the power of LLMs.
A modern data stack is a set of tools and technologies used by organizations to store, process, and analyze data. It typically includes cloud-based platforms, databases optimized for specific data types such as No-SQL databases, graph databases, and more recently, vector databases. These databases are complemented by tools for cataloging, lineage and quality, governance, and observability. Analytical and business intelligence tools use these databases and tools to deliver insights.
LLMs, on the other hand, are pre-trained artificial intelligence models capable of understanding human language at a large scale and complexity. They have enormous capabilities in terms of automation of tasks, drawing inferences, and generating documents. They can be used to drive business decisions. Combining the power of LLMs with modern data platforms, therefore, has huge promise.