Guide: Best Practices in Data Management for the Financial Services Data Professional

The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more resilient operations.

AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive operational performance.

Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.

The enterprise data stack is at an inflection point. Storing and streaming data is no longer enough. Businesses need a combination of streaming technologies, high-performance in-memory databases, and adaptive intelligence to support real-time enterprise operations.

Organizations that treat AI as a standalone initiative see incremental results. Those that treat it as a data architecture transformation, however, unlock sustained performance improvements.

Drone programs have already proven their value as a data collection tool across construction, agriculture, energy, and infrastructure. The next frontier is making that data work harder.

For AI to work in production, models are not enough. Systems need access to current, external information that can be used directly in decision-making.

For AI workloads operating at scale, implementing storage solely based on performance benchmarks does not reflect real-world operating conditions.