The term big data was first used to refer to increasing data volumes in the mid-1990s. In 2001, Doug Laney, then an analyst at consultancy Meta Group Inc., expanded the notion of big data. This encompassed increases in the variety of data being generated by organizations and the velocity at which that data was being created and updated. Those three factors -- volume, velocity and variety -- became known as the 3Vs of big data, a concept Gartner popularized after acquiring Meta Group and hiring Laney in 2005.
Separately, the Hadoop distributed processing framework was launched as an Apache open source project in 2006. This planted the seeds for a clustered platform built on top of commodity hardware and geared to run big data applications. By 2011, big data analytics began to take a firm hold in organizations and the public eye, along with Hadoop and various related big data technologies that had sprung up around it.
Initially, as the Hadoop ecosystem took shape and started to mature, big data applications were primarily the province of large internet and e-commerce companies such as Yahoo, Google and Facebook, as well as analytics and marketing services providers. In the ensuing years, though, big data analytics has increasingly been embraced by retailers, financial services firms, insurers, healthcare organizations, manufacturers, energy companies and other enterprises.
