Concept
Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. OI solutions run queries against streaming data feeds and event data to deliver analytic results as operational instructions. OI provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions. The purpose of OI is to monitor business activities and identify and detect situations relating to inefficiencies, opportunities, and threats and provide operational solutions. Some definitions define operational intelligence as an event-centric approach to delivering information that empowers people to make better decisions, based on complete and actual information. In addition, these metrics act as the starting point for further analysis (drilling down into details, performing root cause analysis — tying anomalies to specific transactions and the business activity). Sophisticated OI systems also provide the ability to associate metadata with metrics, process steps, channels, etc. With this, it becomes easy to get related information, e.g., "retrieve the contact information of the person that manages the application that executed the step in the business transaction that took 60% more time than the norm," or "view the acceptance/rejection trend for the customer who was denied approval in this transaction," or "Launch the application that this process steps interacted with." Different operational intelligence solutions may use many different technologies and be implemented in different ways. This section lists the common features of an operational intelligence solution: Real-time monitoring Real-time situation detection Real-time dashboards for different user roles Correlation of events Industry-specific dashboards Multidimensional analysis Root cause analysis Time Series and trend analysis Big data Analytics: Operational Intelligence is well suited to address the inherent challenges of Big Data.