This is a significant departure from the traditional ETL world where a single vendor could extract and transform the structured or semi-structured data because the traditional ETL process is a linear ...

Data scientists today face a perfect storm: an explosion of inconsistent, unstructured, multimodal data scattered across silos – and mounting pressure to turn it into accessible, AI-ready insights.

When leaders think about data, structured data—such as payment amounts, invoice processing dates and customer names—likely crosses their minds first. Because structured data is objective, it’s ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Getting enterprise data into large language models (LLMs) is a critical ...

We've all heard the explanations for why AI projects fail: the models aren't advanced enough, they don't remember past interactions, they hallucinate answers — the list goes on. However, those ...

Unstructured data is now the constraint shaping how far artificial intelligence platforms can realistically scale. Enterprises are struggling to scale AI because unstructured data pipelines can’t ...

Roughly 80% of enterprise data sits in emails, contracts, call transcripts, and PDFs where traditional databases can't touch it. Much of this "unstructured" data isn't ignored because it lacks value, ...

This week LangGraph has introduced templates to simplify the creation of applications for common use cases, with a particular focus on transforming unstructured research data into structured formats ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...