The secret to benefiting from Big Data lies not in accessing all the data, but in identifying, analyzing and acting on the right subset of data.
The primary goal of situational intelligence is to simplify access to high volumes of heterogeneous data and transform it to actionable information. With situational intelligence, users have the flexibility to hide or display the data they want to see on-the-fly, view performance over time, get a birds-eye view of a situation and drill-down to the details of specific assets to troubleshoot root causes, interface to related documents and applications to follow defined procedures, examine diagrams and asset documentation, or take corrective actions.
For instance, it is difficult to identify infrequent errors or combinations of factors within millions of data records by using traditional display formats such as tables and charts. However, the combination of geospatial visualizations, temporal displays and anomaly detection models can alert users immediately to the fact that a problem occurred and pinpoint precisely where and when it happened (and might happen again).
How would this work in the real world? Consider the 2+ million vehicle fleet of the United States Postal Service and the associated need for tires. Simply tracking all those vehicles simultaneously on a GIS application, waiting to identify vehicles with tire problems, is pointless and inefficient. Similarly, searching through a tabular report on all 2+ million vehicles looking for evidence of old or risky tires is so overwhelming as to be useless.
What if a USPS district purchasing manager wanted to avoid towing bills and downtime from tire failures by building a forward-looking budget for purchasing new tires for the coming 12 months?
Correlating and analyzing data on the types of vehicles, dates of tire purchases, miles traveled per month, typical weather and road conditions or other location information can give more precise information about current tire wear. This combination of data can identify and rank the vehicles that will potentially need new tires.
Analyzing this information with contracts and price lists from tire suppliers and budget projections for the coming year, the district purchasing manager can easily make data-driven decisions about which few vehicles out of thousands in her fleet require new tires. Plotting those vehicles and their routes on a map shows which delivery areas will be affected by tire replacement, and how many substitute vehicles might be needed to cover for those out of service for tire replacement. That keeps mail arriving on time and might even postpone the next price increase for stamps.