In Analytics

Situational intelligence plays a role in every phase of a business’s operations, from strategic planning through day-to-day execution to crisis response. Some departments use it to better understand the current or expected performance of assets and resources under normal operating conditions, or to quickly determine when and why abnormal conditions occur. Other disciplines might use situational intelligence to respond more quickly to real-time events to avoid or reduce service disruptions.

The need for situational intelligence has evolved because earlier generations of software were not designed to meet current needs such as these:

  • To be highly responsive to customers and remain competitive, businesses need to consider not only what happened in the past, but more importantly what’s happening right now and might happen in the future
  • The consolidation of data from IT (business), OT (operational) and XT (external) sources is needed to ensure decisions are based on a complete understanding of what has or might occur, and not just on the (potentially incomplete) data from a single system
  • The Internet of Things is driving a massive increase in the volume, variety and velocity of data every day.  New approaches are needed to correlate, analyze and visualize this big data in terms of its scale and complexity
  • Determining that a situation exists or might exist is only part of the challenge; understanding what actions to take, and how and when to take them, must be an integral part of responding to a situation (as opposed to a disconnected set of unproductive and error-prone processes)

Here are just a few examples of how situational intelligence is being used today:

  • Anticipating and responding rapidly to the infrastructural impact of a severe storm to reduce service disruptions
  • Balancing the variability of customer demand with supply in real-time to optimize asset and resource utilization
  • Identifying performance issues over time and taking proactive steps to maintain assets or rectify situational threats
  • Correlating machine data with external systems and events (such as fire, weather and vegetation) to identify system failures before they occur
  • Performing root-cause analysis to determine why assets malfunctioned and triggering remedial actions as a result
  • Prioritizing tasks based on greatest financial impact or risk reduction
  • Predicting the extent of service outages and feeding that information proactively to customers to reduce service inquiries
  • Optimizing service delivery routes and times to reduce operational costs and improve customer service
  • Understanding the downstream or upstream network and service level impacts of an equipment failure

Future posts will examine these examples in more detail.

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