The Instant Intelligence of Big Data: Improving the Analytical Value Chain

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In business, the rear view mirror is clearer than the windshield, said the sage of Omaha. And that is particularly true of business intelligence, composed almost entirely of such retrospectives. 

Consider this: Business intelligence proffers neatly organized historical data as a potential source of hindsight. Of course, there are also the dashboards of happenings in the "now" but precious little in terms of prompts to timely action. The time required to traverse that path from data to insight to intelligence to ideas to implementation to results is often the culprit. It’s nowhere near quick enough, especially for businesses like banking, telecommunications and healthcare that set great store by the time value of information and the money value of time.

But technology is transforming everything; the windshields of the near future will provide real-time information about the world around us. And the analytical value chain will be about mining vast amounts of data, as it is generated, from a variety of sources, to reveal patterns that may not even have been expected, that lead to actionable insights, and trigger appropriate business decisions.  This is a clear evolution from business intelligence as we know it and a definite move towards big data analytics, dealing with more data, more types of data and more current data processed in real-time to deliver immediate actions and outcomes. Patterns from historical data will continue to forecast tomorrow, but, in tandem, data from the ‘now’ will provide vital lead indicators and direct best course of action.

Business Intelligence is Evolving

Let's look at a scenario from the healthcare industry to understand the true real-time potential of big data. Patient monitoring systems in intensive care units (ICUs) generate huge volumes of running data that is visually available on monitors prompting immediate and contextual action. These systems can be programmed to respond to changes in vital parameters by sounding an alarm or triggering another type of alert, but not much more. Now what if the big data consisting of vital parameters gathered from a universe of ICUs could be stored in a central repository on the cloud and analyzed in real-time? Perhaps it would reveal undiscovered patterns, such as, let us say hypothetically, a set of five sequential anomalies generally leading up to a heart attack. Because the big data solution is capable of creating as well as acting upon insight, it can be deployed to track patients at risk for any sign of anomaly, and trigger an appropriate action long before the attack becomes imminent.

Big data analytics can also help recognize causal patterns and relationships that have not been previously recorded. For instance, it has enabled a hospital in Texas to link a distended jugular vein in inpatients with congestive heart failure to a likelihood of re-admission. 

Real-Time Capabilities of Big Data 

Apart from healthcare, the real-time capabilities of big data are extremely relevant to businesses that depend on time-critical condition-based interventions, like network monitoring in telecoms, sensor network management in energy and utility operations, and fraud detection in banking. Let’s look at the last one more closely. Big data caught the attention of the financial community by emerging as a proactive, preventive alternative to existing fraud detection and risk management applications, which, like business intelligence, were essentially post facto solutions. Today, the financial services industry, with its entrenched "data is value" belief, is beating a path to the door of the big data opportunity; according to a leading information technology sector analyst, this is the industry making the most big data inquiries. 

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