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Big Data: Exposing Value in Data with Big Analytics
IDC estimates that the Big Data marketplace is growing at a rate of 32 percent, seven times faster than the entire information and communication technology market, and will reach $23.8 billion in 2016. Similarly, Gartner estimated Big Data business to have been $28 billion in 2012 and is poised to grow to $34 billion in 2013.
Dan Vesset, vice president for IDC’s Business Analytics and Big Data research, said that “the Big Data technology and services market represents a fast-growing, multibillion-dollar worldwide opportunity. It is an important topic on many executive agendas and presents some of the most attractive job opportunities for people with the right technology, analytics, communication, and industry domain expertise.”
Dante Ricci, director of federal innovation at SAP, said that “For a while with Big Data, it was volume, velocity and variety, but now a lot of organizations are trying to marry those Vs with veracity and value. It’s not just about the data you have, it’s about getting something of value out of it.”
Increasingly though, people are associating analytics with Big Data. Huge volumes of data by itself is of no value. And the tools which are being developed to make sense of Big Data almost universally have the common thread of providing analytics, often in the form of dashboards and graphics which visually summarize the trends uncovered in the data. The term “Big Data Analytics” is being more commonly used, or sometimes just “Big Analytics“.
Colin White, president and founder of BI Research, said that “The data management piece is important, but what’s of equal if not more importance is the analytics. It’s what you do with the data that matters.”
In a An IBM survey report called “The Real World Use of Big Data” found that when analyzing Big Data, businesses are using the following analytics tools and technologies:
- 91 percent are using Query and Reporting
- 77 percent are using Data Mining
- 71 percent use some form of Data Visualization
- 67 percent use Predictive Modeling
- 65 percent use Optimization
- 56 percent use Simulation
