In Analytics, Use Cases

In the first part of this post, I drew a bright line between the world of BI dashboards and the situational intelligence analytics that industries are now deploying to derive value from the variety of data sources at their disposal.

Here are some examples of situational intelligence applications that exemplify these points:

  • Optimizing workforce scheduling—an operator of wind farms faces multiple variables in scheduling crews to perform maintenance and repairs. Assigning the day’s work depends on crew availability and skills, weather conditions, part availability, safety constraints, travel time to and from a turbine, climbing time up and down the turbine, and much more. It used to take managers many hours each day to build work schedules using traditional tools and those schedules had to be manually revised when weather conditions, for example, suddenly changed. Now optimization software automatically builds the most efficient schedule in minutes, making adjustments on-the-fly as conditions demand.
  • Predicting failure—a utility faces mounting pressure from regulators and ratepayers after catastrophic failure of power distribution equipment. How do they determine the true risk inherent in their millions of assets spread over thousands of square miles? They had charts showing the historical performance of their assets, but all assets age differently based on geographic location, relationship to other assets in the network, workload, maintenance record, and more. With predictive analytics, asset planners see the likelihood that an asset will fail plus the consequences should it fail. These two measures given them an accurate gauge of risk for each asset and group of assets. Risk-based decisions (as opposed to gut instinct and incomplete data) drive choices about maintenance and capital expenditures.
  • Detecting anomalies—a railroad has tens of thousands of miles of track to inspect and maintain. Wear and tear, weather conditions, and natural disasters continually affect track conditions. Their visual dashboards showed them on a monthly basis which routes had slow throughput and which sections of track were overdue for inspection or repair. This data is significant since an increase in system-wide train speed translates into millions of dollars of revenues. Using anomaly detection, they now pinpoint sections of track that warrant inspection and possible repair before they fail or cause train delays. The data analysis is presented on maps that highlight problematic sections of tracks.
  • Streaming analytics—a construction company needs to know where tens of thousands of vehicles, tools and pieces of equipment are and how they are being used (or abused). By using streaming analytics on the data from sensors placed on trucks and tools, the company pinpoints equipment that is delayed in transit, reassigns unused equipment to other nearby sites, prevents tool theft and loss, and audits vehicle movements to support applications for fuel tax rebates, to name a few.

These “real” analytics systems may sound like they require highly sophisticated and educated users to operate. In reality, not only are these regular business users, but they do not require specialized training and they have the ability to interact with the analytics to execute what-if scenarios, for example.

Dashboards still have a role for many users in many scenarios. But as computing and communications technologies continue to connect the world into an Internet of Things, true analytics systems for prediction, anomaly detection, optimization, and streaming data will take their place at the head of the table.

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