How Data Operationalization Can Create 'Digital Twins' For Data

Data operationalization, complemented by the pragmatic deployment of AI use cases with said data, is, at its core, a move ...

Five Steps Leaders Can Take To Integrate AI Into Complex Workflows

One of the great ironies of the AI revolution is that some of the most technologically advanced companies are having the ...
HackerNoon

How Float Runs an AI Energy Company on a 3-Person Team with Tiger Data

How Float achieved 99.3% compression on 1Hz smart meter data with Tiger Data, enabling real-time AI energy analytics, lower ...
Semiconductor Engineering

From Data Accumulation To Data Activation: AI-Driven Data Feed Forward For Chiplet-Based Test

For most of the industry’s history, the lever for semiconductor performance gains was process-node scaling. That is no longer the whole story. As one recent industry analysis put it, advanced ...
The Cipher Brief

After the Intelligence Cycle: A New Schema for AI-Native Intelligence Analysis

Recent discussion of artificial intelligence in intelligence analysis has consistently framed the technology as a means of accelerating an existing process. The intelligence cycle (collection, ...
diginomica

Five startup lessons for data streaming enterprises

Confluent's Tim Graczewski on what enterprises can learn from startups - solving problems before fixing architecture, ...