Puzzling out big data [Information Technology Analytics]

Big data comes in many forms. It comes as customer information and transactions contained in customer-relationship management and enterprise resourceplanning systems and HTML-based web stores. It comes as information generated by machine-to-machine applications collecting data from smart meters, manufacturing sensors, equipment logs, trading systems data and call detail records compiled by fixed and mobile telecommunications companies. Big data can come with big differences. Some say that the `three Vs' of big data should more properly be tagged as the `three HVs': high-volume, high-variety, high-velocity, and high-veracity. Apply those tags to the mountains of information posted on social network and blogging sites, including Facebook, Twitter and VouTube; the deluge of text contained in email and instant messages; not to mention audio and video files. It is evident then that it's not necessarily the 'big-ness' of information that presents big-data applications and services with their greatest challenge, but the variety and the speed at which all that constantly changing information must be ingested, processed, aggregated, filtered, organised and fed back in a meaningful way for businesses to get some value out of it.