RTInsights Team

It’s no longer enough to simply ingest streaming data. Companies need to transition to developing insights that help them understand events in real time.

Cathy Reese, Partner, IBM Advanced Analytics Global Practice Leader, discusses continuous intelligence in the chemical industry, including how one chemical company is transforming customer service by analyzing historical data for context and streaming data to make up-to-the-minute recommendations to their customers.

Companies are increasingly generating huge volumes of data at the network edge. Massive volumes of data are flowing from smart meters, Internet of Things (IoT) sensors, autonomous vehicles, health monitors, and industrial automation devices, to name just a few sources.

Continuous intelligence (CI) produces insights from streaming data to take actions in milliseconds to minutes. It frequently makes use of artificial intelligence (AI) and machine learning (ML) models to perform the real-time analysis and make connections between different events as they are happening.

Cathy Reese, Partner, IBM Advanced Analytics Global Practice Leader, discusses the benefits and inhibitors her clients are experiencing as they implement continuous intelligence strategies. In this video, Cathy provides examples from the transportation, utility, and retail industries to provide practical lessons learned.

AI everywhere has the potential to improve operations, enhance customer engagements, and speed the time to insight. Learn how AI embedded into business processes enables the greatest benefits and how to identify and overcome the challenges.

DataOps enables modern applications by making the transition to the cloud easier.

SQL remains the de facto standard for operational applications to interact with back-end databases.

Not long ago, nearly all payments originated from a relatively small number of point-of-sale terminals.

IBM’s Mike Beddow describes his experiences with clients using historical and streaming data to improve patient care.

The traditional approach to processing data at scale is batching; the premise of which is that all the data is available in the system of record before the processing starts. In the case of failures, the whole job can be simply restarted.