Operational Intelligence

Why does your business need Operational Intelligence?
Operational intelligence refers to the use of dynamic business analytics that provides insights into the business processes that help the management in real-time decision-making. Every business needs to make tactical and strategic decisions from time to time and having operational intelligence in place can be game changing.

• The key driver for organizations in the service-based industry is customer interactions. The data patterns generated by ‘how, when and why a customer interacts’ and ‘the tone of the interaction’ provide real insight into the future products and services to be offered and help improve the current customer satisfaction level.
• To incorporate operational intelligence in business, a multifaceted product needs to collect, correlate and predict the future conversations/issues with the help of AI algorithms. In our research to build and implement strong products for operational intelligence driven by AI, we are working with some advance platforms. These platforms offer reactive and proactive intelligence engines to reduce revenue leaks, detect problem centers and improve overall operational efficiency. The proactive and reactive methods of intelligence are driven based on the principles of anomaly detection.
• Anomaly detection techniques work on multidimensional, multilayered data patterns to bring right benchmarking, detection and forecasting for possible issues. This makes it possible for a business to take the appropriate action in time to avoid losses and failures or to anticipate risks and opportunities using the data.
• The approach to anomaly detection for operational intelligence is broadly divided into two categories — proactive intelligence and reactive intelligence.

Proactive intelligence

The proactive method is the new way of anomaly detection and exploits AI.

Working

It is important to fully understand the technological framework of the dataset being used. Based on the understanding of the data patterns, AI can draw certain benchmarks to define “normal” patterns of the data. Predictive analytics and machine learning in real time are used in this method. Based on metrics that are most significant to business, anomalies are auto identified. AI works on dynamically updating the algorithms that define “normal” data patterns vis-à-vis anomalies.

Advantages

• Minimizes the occurrence of false positives or negatives due to the dynamic nature of anomaly detection.
• Performance anomalies are identified before they affect the end-user. This is a very significant feature for the service industry.
• Analytics in the proactive method can predict future traffic levels using AI which is not possible in the reactive method.
• Benchmarks that define “normal” data patterns may be revised or fine-tuned for parameter-based anomaly detection.

Reactive intelligence

The reactive intelligence method is a traditional approach and is one of the tried and tested methods for anomaly detection. It is used by businesses across various industries.

Working

Identifying the issue nucleus as defined by the business management. Defining AI based on data models that represent the acceptable data pattern. Continuous learning by accumulating acceptable patterns. Working to continuously learn and improve the algorithms used. This leads to better accuracy and efficiency. Discover problem clusters among the dataset. Focus on hierarchical model and reporting of anomalies for immediate corrective action.

Advantages

• Time tested method of anomaly detection and has been known to give the best results.
• Continuous updates to the acceptable data patterns help improve the accuracy.
• Algorithms are created based on acceptable patterns of data defined by the stakeholders. This ensures clarity of objective.
• Immediate corrective action may be taken as soon as the anomaly is detected.