How real is big data? A new industry survey of 255 business intelligence and analytics professionals finds more than a third, 36 percent, already building projects around big data, and 35 percent planning to do so soon.
The survey, conducted by Enterprise Management Associates (EMA), in association with 9sight Consulting, finds most organizations at the threshold of big data, and even cites the insurance industry as a leader in big data analytics.
“Reinventing business processes through innovative use of sensor-generated data offers the possibility of reconstructing entire industries,” say report authors Dr. Barry Devlin, Shawn Rogers and John Myers. “Automobile insurance, for example, can set premiums based on actual behavior rather than statistically averaged risk. The availability of individual genomic data and electronic medical records presents the medical and health insurance industries with significant opportunities, not to mention ethical dilemmas.”
The report also notes that big data isn’t necessarily about huge volumes of files. “Big data isn’t as big as the market buzz indicates. Less than 10 percent of our respondents are managing 750 terabytes or more within their overall system. The most common enterprise size data environments are 50-100TB. Of that data, most companies have 10-30TB in their big data environments indicating that big data analytics can be served on a variety of platforms, not just Hadoop.” Much of this data is not only structured operational data, but also human-generated documents and deep operational transaction data. Of course, device and sensor logs are a huge source of big data files—exemplified by the adoption of telematics sensors among auto insurers.
Along with telematics, Devlin, Rogers and Myers also identified the key opportunities seen in big data:
Consumer behavior analysis: “Revenue generation and business model development, where there is direct or indirect interaction with large consumer markets, moves to a new level. Marketing uses social media information, both content and relationship, to move from sampling to full data set analysis, from demographic segments to markets-of-one, and from longer-term trending of historical data to near real-time reaction to emerging events.”
Cost containment in real-time: This “becomes viable as electronic event monitoring from automobiles to smartphones, fraud detection in financial transaction data and more expands to include larger volumes of often smaller size or value messages on ever-shorter timescales. Big data analysis techniques on streaming data, before or without storing it on disk, have become the norm, enabling faster reaction to specific problems before they escalate into major situations.”
Real-time forecasting: “Value arises as consumption peaks and troughs can be predicted and, in some cases, smoothed by influencing consumer behavior.”
Tracking of physical items: This also “drives drives deep optimization of operational processes and enables improved customer experiences. People, as physical entities, are also subject to tracking for business reasons or for surveillance.”
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
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