This glossary is a list of six popular V's of Big Data terms that may be unfamiliar to the reader.

Value

 — The evaluation of the usefulness of gathered data for business. Data in its raw form isn't useful unless knowledge and insights can be harnessed for productive application.

Variety

 — The handling of structured, semi-structured, and unstructured forms of data for effective storage and analyses. Inconsistent data formats and data structures cause data and software development teams to find insufficient work-arounds to manage data translations.

Velocity

 — The increasing rate in which data is created, stored, processed, analyzed, and visualized. In one Internet minute, Google conducts 5.7 million searches. The speed of data is lightning fast.

Veracity

 — The assessing of data's quality and making informed judgments distinguishing between noisy data, erroneous data, degree of uncertainty, misinformation, and disinformation. Data with high variety, velocity, and volume will not be 100 percent accurate, so all of us need to account for imperfection.

Volume

 — The enormous quantity of data entering digital systems. Managing the scale of data requires different approaches, processes, and systems.

Variability

 — The degree of change in outcomes when the same data is applied to the same circumstances repeatedly. Ideally, data workers want the algorithmic-based outcomes to be consistent, especially when performing experiments. It provides evidence that generalizable observations can be stated with confidence and sets a precedent for making predictions. In practice, however, data is much more heterogeneous than the data industry would like to admit.