RTInsights Team

As the speed of business continues to accelerate, organizations need to find solutions to keep up and more.

As enterprises embrace the potential of data science and machine learning, they face the challenges of operationalizing these processes in their technical environments and choosing investments according to their strategic objectives.

According to Gartner research, 60% of IT organizations have an open-source software (OSS) policy of some kind. However, there’s a real gap between having a policy and actually instituting a program to ensure best practices and compliance with that policy.

As the digital economy matures, the way data is processed is changing rapidly. Mobile applications, sensors, and other always-on technologies are constantly sending seemingly infinite streams of data. And enterprises need to process that data instantly—or as close to real time as possible.

The web is the greatest repository of knowledge humankind has ever created, and it’s about to unlock some previously unheard-of innovations for enterprises who are willing to see quality data amid the chaos.

When a machine or system breaks down, technicians often do not know the root cause, and may need to spend hours or days probing and investigating the issue.

Digital transformation is more than just automation or process management.

Digital transformation requires agility that may be hindered by necessary legacy ERP systems.

Modernizing applications as a part of digitization presents a challenge for many businesses, who may undertake a process that becomes more fundamental than transformational.

At the heart of all continuous intelligence (CI) applications is the ability to perform analytics on real-time data streams.

Cathy Reese, Partner, IBM Advanced Analytics Global Practice Leader, discusses the benefits and inhibitors her clients are experiencing as they implement continuous intelligence strategies. Learn how an insurance company is analyzing historical data for context and streaming telematics data to provide personalized service and pricing.

Most organizations concerned with event streaming are leveraging this modern data movement pattern for streaming analytics—analytics computations on real-time data streams from applications, social media, sensors, devices, websites, and more.