Joe McKendrick

Cloud is increasingly supporting faster and more comprehensive methods of data integration, including automated data pipelines, real-time data streaming, and data lakehouses that integrate cloud data warehouses and data lakes.

One factor that distinguishes a data-driven enterprise is a culture in which data, analytics, and cloud strategies are aligned with business objectives.

Artificial intelligence (AI) and automation—such as AI cameras and automated workflows—have become vital for physical operations.

While $1.1 trillion annually is being invested in digital factory initiatives worldwide, most companies, 64%, are still at an early stage of their digital transformation.

Harnessing the full power of real-time data streams delivers great value, but is difficult for many organizations. The top hurdle is integrating multiple data sources.

Data location is increasingly important as it has consequences related to governance, application performance, and latency.

The data pipelines and plumbing underneath efforts to support artificial intelligence and machine learning capabilities at the edge are critical.

The foundation of the industrial metaverse is built on digital twin technology where factories and facilities are represented and open to innovation and experimentation within a virtual world

Edge is going through a revolution or evolution, enabled by more connectivity, even in really low-end devices, that makes much more data available.

The integration and flow of information delivered by APIs is key to enabling long-term strategies that make any business work properly.

The challenge with embedded analytics is it remains costly and difficult to implement. But if done smartly, organizations can reap many benefits.

Hyperautomation is too compelling for businesses to ignore. They look to use it lower operational costs, reduce human errors, support governance efforts, and more.