Automatic data extraction with AI speeds up workflows, improves accuracy, and enables smarter decision-making across multiple industries.

Traditionally, AI progress was constrained by one thing above all else: access to data. Not enough volume. Not enough ...

1. The "Data Trash" Problem: AI models are only as good as the information they ingest. For most enterprises today, data is ...

Data analytics studies existing business data to identify patterns, trends, and insights that support better decisions.Data ...

Most businesses today collect a huge amount of data, but many struggle to turn that data into useful insights. This is where predictive analytics becomes valuab ...

Despite mandatory medical examiner reviews, the state has no publicly accessible database for in-custody death causes, with ...

Government agencies have gotten rid of gender identity and sexual orientation data elements from over 360 data collections over the last year.

Federal agencies are requesting access to state and local government data for immigration enforcement purposes. Some experts ...

As AI systems grow more autonomous, observability becomes essential. Learn how visibility into AI behavior helps detect risk and strengthen secure development.

The generative AI models used in classified environments can answer questions but don't currently learn from the data they ...

Engineer Ravi Teja Thutari proposes distributed event-driven data mesh enabling real-time contextual intelligence, faster decisions across connected physical and digital systems.

The automotive and industrial markets are undergoing rapid transformation, driven by Advanced Driver Assistance Systems (ADAS) adoption, Industry 4.0 automation, and the rollout of 5G infrastructure.