We look at how to gain structure from unstructured data, via AI/ML analytics to create new records, selecting object data via SQL and storing unstructured files in NoSQL formats.

The differences between structured and unstructured data mean that traditional database systems and modern AI database systems handle information in different ways.

In the dynamic world of data science, the conversion of unstructured data into structured data is a key process. This transformation is crucial for enabling more efficient data analysis and ...

English language querying on both structured and unstructured data Automated data quality assessment and improvement Automated business glossary generation, enrichment, and standardization.

We look at alternatives to relational databases that have emerged to help bring some structure to unstructured data and gain valuable insight by making it semi-structured.

Discover best practices that allow data pipelines to scale and support both structured and unstructured data.

By linking structured & unstructured data we can get recommendations for new leads, process improvements etc. Structured data will be in the form of product offering details & customer database where ...

“Properly curating unstructured data [in this way] is essential because it allows AI models to access and process the information accurately, leading to better insights and results,” Petrov said.

Harnessing the power of unstructured data allows companies to discover hidden insights, uncover trends and make data-driven decisions.

Let's start by examining those two broad-brush data categories: structured and unstructured. Structured data refers to the data resident within relational databases, often presented via customer ...