In 2012, Gartner updated its definition for BigData as high volume, high velocity, and/or high variety information assets. Volume, Velocity and Variety are called as the 3 Vs of Big Data.
Many researchers have introduced many Vs to the list of 3Vs, saying 3Vs are not enough.
Bernard Marr in an article in linkedin describes 5Vs: Volume, Velocity, Variety, Veracity and Value.
An article in dataconomy.com says about 7 Vs: Volume, Velocity, Variety, Veracity, Variability, Value, Visualization.
Lynda.com’s Techniques and Concepts of Big Data with Barton Poulson, mention around 10 Vs: Volume, Velocity, Variety, Veracity, Validity, Variability, Value, Venue, Vocabulary, and Vagueness.
Below is a quick summary of most of these additional Vs:
- Veracity (conformity to facts; accuracy)
- Refers to the trustworthiness of the data. Does the data actually contain enough information to make accurate conclusions.
- Is the data clean and well-managed, meeting the requirements for the data processing.
- Data or its meaning can change over time and even place, due to many uncontrolled factors; you need to measure and account for them.
- Does it have enough value for the time and effort you invest in them.
- Once data has been processed, presenting the data in a manner that’s readable and accessible.
- If the venue or location affect access to the data.
- Vocabulary refers to the metadata that describes the data and is significant especially when combining data from very different sources. Different sources may call the same thing with different names.
- If the researcher is clear on the goals and purpose of the research; or else he will be wasting a lot of time.
- Volume, Velocity and Variety are usually called as the 3 Vs of BigData and was introduced by META Group (now Gartner) analyst Doug Laney in a 2001 research report and related lectures.