Organizations that are slow to adopt powerful and pervasive analytics may be hampered by legacy technologies, siloed data, or other blockers to innovation. These organizations may lose market share to disruptive or more efficient competitors, or because customers prefer doing business with organizations that know their preferences. This calls for a bold new approach in managing and leveraging information to enable decision-makers and decision-making systems to sense and respond as things change. Enter, continuous intelligence.

Achieving continuous intelligence requires an enterprise architecture that combines streaming technology with analytics and data science. Thus, continuous intelligence delivers insights-rich data to the applications, systems, or decision-makers as needed. By employing continuous intelligence throughout the organization, users are enabled to find insights on their own for more accurate predictions and anticipate future situations.

That said, there are 10 components that make continuous intelligence possible for operational excellence:

1. Streaming analytics

Streaming analytics can be applied to data to enable real-time views into business operations, providing insights for decision-makers and automated systems.

2. Embedded analytics

Analytics that are embedded within systems, applications, and devices provide analysts and managers with more detailed insights specific to tasks at hand.

3. Continuous queries

Continuous queries enable decision-makers to visualize queries about live future conditions so that they can see unfolding patterns and trends in the data.

4. Self-service

With self-service capabilities, business users outside of IT or data departments can quickly access data and generate reports on their own.

5. Visualization 

Visualization enables managers and analysts to quickly spot patterns and anomalies in the data.

6. AI-powered, search-driven analytics

Through natural language processing, a broad range of user queries can be understood, offering instant, on-demand responses and insights.

7. Machine learning 

An environment leveraging machine learning is constantly improving itself, offering refreshed data, algorithms, and applications to ensure that your actions and insights are always up-to-date.

8. Augmented analytics

The introduction of artificial intelligence (AI), natural language processing, and machine learning automate mundane and time-consuming tasks, leaving users the time to more deeply explore patterns and insights rather than on the mundane tasks.

9. Intelligent equipment monitoring

This enables a high level of visibility into the performance of equipment across the enterprise.

10. Anomaly detection

Immediate detection and remediation of anomalies that are spotted in the data can help your organization better optimize yield production, predictive maintenance, and more.

To learn more about the components needed for continuous intelligence and how you can leverage them to improve operational excellence in your organization, download the full report from RTInsights.