Why Industrial AI Efforts Need DataOps
Industrial organizations that adopt DataOps principles early can accelerate AI deployment, reduce costly integration delays, and establish a sustainable …
Topic: Big Data
Big Data technologies and use cases for real-time analytics. Big Data technologies, market insights, and use cases for real-time analytics.
Warning Signs Your Organization Has Unreliable Data
Organizations that don't address the warning signs of unreliable data pay a steep price, and automated AI systems will drive that price up
Want to Make Your AI Investments Pay Off? Start with the Fundamentals
The ability to harness the potential of AI initiatives starts with a robust data infrastructure and a cohesive analytics strategy that tailors AI technologies …
What is the State of Predictive Analytics in 2025?
In 2025, predictive analytics is being widely applied across industries to anticipate events, optimize operations, and personalize experiences in real time.
How Synthetic Data Can Reduce Different Types of Biases Across Industries
Responsible AI development demands an ongoing commitment to mitigate bias throughout the system's life cycle. And synthetic data is an effective way to do …
Real-time Analytics News for the Week Ending July 26
In this week's real-time analytics news: AWS announced the open source release of Spark History Server MCP, an MCP server that enables AI assistants to access …
A Vision for Industrial AI and Data Solutions
Industrial AI won't transform an organization's operations unless its data is organized, understood, and
Real-time Analytics News for the Week Ending July 12
In this week’s real-time analytics news: Amazon Web Services (AWS) announced new capabilities in Sagemaker AI.
Why Layered and Agentic AI Demand a New Kind of Data Infrastructure
Agentic AI success requires platforms that can serve as immutable sources of truth while providing real-time streaming capabilities, contextual event …
The AI Data Protection Revolution: Why Your Current Backup Strategy Won’t Cut It
AI-ready data protection must deliver enterprise-grade security and compliance capabilities while maintaining the agility and performance that AI workloads …