Many business intelligence systems offer exceptional reporting, best-in-class dashboarding and predictive capabilities. However, navigating among different systems and remembering where to find the information you need can be challenging. Hybrid analytics solves this problem by delivering business intelligence across multiple systems through a single web-based interface that includes links to the systems where the data and visualizations reside.

In addition, Hybrid Analytica can enable companies to make more efficient use of their existing infrastructure. For example, companies can deploy a centralized data repository to store both on-premise and cloud data, reducing the need for costly data duplication, while still providing access to cross-environment analytics and artificial intelligence/machine learning (ML).

Unlocking Business Insights: Exploring the Power of Hybrid Analytica

This hybrid approach to analytics is also key for ML-in-the-loop applications. In this case, traditional domain knowledge and control theory are combined with information in the physical world resulting in robust, semi-interpretable models for improved prediction and control.

Similarly, organizations that have invested in on-premise data infrastructure like SQL Server can now leverage the full potential of Azure cloud analytics through native connectivity bridges for security, querying, replication and DR. This allows them to maximize existing investments while accelerating the time to value of cloud capabilities in a hybrid analytics architecture that supports both legacy and modern platforms. In this way, hybrid analytics can help drive unified insights without disrupting day-to-day business processes. For example, an on-premise SQL Server database could be used to augment the monitoring and reporting of a hybrid digital twin workflow leveraging existing domain knowledge and control theory to improve modeling for better predictive performance.