• Operations Analytics
  • Experfy Editor
  • MAY 02, 2014

Operational Analytics – Infographic from IBM Big Data Hub

In the big data world, we know the power of the descriptive, prescriptive, and predictive data analytics. Another kind of advanced analytics done with business data is operational analytics. Abundance of machine data, available from sensors, meters, or GPS devices connected to running machines—is a key driver in operational analytics.  The major difference between operational analytics and other kinds of big data analytics is that operational analytics targets “live data.”

Though machine data was always available to business operations, the absence of proper technologies or tools made it impossible to utilize this kind of data in big data analytics. With the emergence of new, big data technologies for collecting “live data” from running machines, businesses suddenly found themselves on the next level of big data analytics, that is operational analytics.

As opposed to the forecasting powers of predictive analytics, the main strength of operational analytics lies in taking streaming data from running machines connected to devices, and then conducting instantaneous analysis to aid reactive, decision making to operational problems as they happen!

Industry experts have correlated the term “operational analytics” to any informed, decision-making process that is done on the fly. The operational analytics planners monitor business processes on a daily basis to rectify problems and improve operations within the shortest possible time-frame.

Businesses generally pursue operational analytics in different ways, one of which may be using a software system—packed with smart models of actual business processes, and pre-built algorithms for solving probable problems. These systems come with visualization tools for displaying vivid graphics, for example, graphs or charts of detected machine or customer behavior.

In another type of operational analytics, an enterprise resource planning (ERP) software system may aggregate information across many functions enabling real-time communications between functional groups, or streamlining business processes. Here, analysts or decision makers are often provided with automated solutions for immediate action. 


Characteristics of operational analytics

  1. General goal: Increasing efficiency in business processes
  2. Live machine data, as opposed to dead, historical data used as source of solutions
  3. Agile interpretation & corrective action
  4. Reactive analytics as it happens (real-time)
  5. Deep reliance on pre-built, software algorithms (automated solutions)
  6. Root cause analysis for detecting system failures
  7. On the fly decision making done with full knowledge and understanding of all available data
  8. Problem detection and solution aided by visualization tools (real-time monitoring & alerts)


Source: IBM Big Data Hub

Source: IBM Big Data Hub


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