Beyond Just ‘Big’ Data

by Paul McFedries, IEEE Spectrum

When Gartner released its annual Hype Cycle for Emerging Technologies for 2014, it was interesting to note that big data was now located on the downslope from the “Peak of Inflated Expectations,” while the Internet of Things (often shortened to IoT) was right at the peak, and data science was on the upslope.

As new big data technologies advance and the challenges of extracting meaning from these massive data sets shift, it seems likely computer scientists will need an entirely new vocabulary to define these various trends.

For example, big data enthusiasts sometimes categorize storage units as “brontobytes,” each of which adds up to 1,000 trillion terabytes. A unit of 1,000 brontobytes is called a “geobyte.” Accompanying the new lexicon of big data storage units are new terms for data professionals, which include specialists in building data models, or data architects; managers of data sources, or data stewards/custodians; translators of data into visual form, or data visualizers; and those who change how a company does business based on analyzing company data, or data change agents.

Moreover, a new kind of journalism, data journalism or data-driven journalism, is emerging to apply statistics, programming, and other digital data and tools to generate or mold news stories. In addition, big data is being sub-categorized into finer definitions such as thick data, which refers to data combining quantitative and qualitative analysis.

There also is long data–which extends back in time centuries or millennia—and hot data, which is used constantly and therefore must be easily and quickly accessible. Meanwhile, cold data can be less readily available, as its use is relatively infrequent.  Read the article