Many companies have been struggling for years to stay afloat amidst the deluge of data created by internal systems.
But now, big data or extreme data is pouring into organizations in higher volumes, faster, from a wider variety of data sources, and in more formats than ever before.
Big data dangles a large expanse of promise, but it requires 
business analytics as the foundation of an extreme information makeover for organizations to exploit the potential it offers.
For example, companies that use predictive analytics achieve higher returns by tapping into big data, according to Nucleus Research. With an average ROI of 1,209%, companies achieve higher returns with projects such as web-based customer sentiment tracking and demand forecasting.
As the name implies, big data means a vast volume of structured and unstructured data that companies must decipher to bolster management decision making.
In a separate research report, Nucleus notes that expanding the volume of data that can be examined with analytics allows employees to detect conditions that impact a large number of transactions, but can’t be observed without automated analytics. For instance, an auto manufacturer could examine parts purchases in its servicing industry to detect flaws or quality problems before they escalate to a public relations or safety crisis, Nucleus notes.
In addition to volume, extreme data is characterized by velocity; it streams in as fast as a customer can Tweet a product complaint. By using analytics to quickly analyze large data sets, a company can uncover problems and get actionable insight while its employees can still do something to fix the problem.
“Many consumers are likely to Tweet or blog about a product long before they share their opinions with a call center representative,” Nucleus notes. “Customer churn prevention also requires timely reactions based on the accurate view of leading metrics.”
Research firm Gartner Inc. has described “extreme information management” as the concept that a company’s current information infrastructure needs to be managed, primarily because of the fast growth of data and the types of data that must be analyzed to fuel revenue and market share growth. By 2015, (continued…)
