Operational Intelligence

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In the era of big data very often we see companies storing a vast amount of data without having a real business benefit out of it. Moreover, the large variety, velocity, and the volume of the data make it almost impossible for human analysis and therefore machine analysis is required. One of the Nebb's services that address the problem of extracting valuable information out of data is Operational Intelligence (OI). Operational Intelligence is a set of best practices in big data and state of the art machine learning algorithms. It focuses on providing analysis of business processes, as they are executed within computer systems, and in assisting in optimizing these processes by identifying and detecting situations that correspond to interruptions and bottlenecks. We divide Operational Intelligence (OI) activities into three groups:

  • Offline Operational Intelligence
  • Online Operational Support
  • Logging Infrastructure Improvements¬†

Offline Operational Intelligence

Offline Operational Intelligence is applying Operational Intelligence on historical data. In general, algorithms are applied to historical data being logged while your system is being executed in a specific time frame. Based on the patterns extracted from the historical data, we can help you increase the understanding of and the ability to communicate how your system is used.

Online Operational Support

The output of the Offline Operational Intelligence in combination with the real-time execution events can be combined to create prediction models of your process. Each new event of your process can be compared with the existing models from the Offline Operational Intelligence and future behaviour can be foreseen. This can help you in optimizing your process on the fly, but also can help you in preventing equipment failures and unplanned stops that will lead to lower overall equipment effectiveness.

Logging Infrastructure Improvements

As a part of the Operational Intelligence very often we do screening on the information system logging infrastructure. Quite often we find cases when the collected data is mostly unstructured and without context, which makes it a tough candidate for further machine-based analysis. In that case, we suggest improvements on the existing logging infrastructure that will benefit in less data but more business information.