Veracity is often mentioned as the 4th V of big data besides Volume, Velocity and Variety.
While veracity of course is paramount for a data quality geek like me veracity is kind of a different thing compared to volume, velocity and variety as these three terms are something that defines big data and veracity is more a desirable capacity of big data. This argument is often prompted by Doug Laney of Gartner (the analyst firm) who is behind the Volume, Velocity and Variety concept that also was coined as Extreme Data at some point.
As mentioned in the post Five Flavors of Big Data the challenges with data quality – or veracity – is very different with the various types of big data. If I should order the mentioned types of big data I would say that veracity has more challenges in this order going from some challenges to huge challenges:
- Big reference data
- Big transaction data
- Web logs
- Sensor data
- Social data
It’s interesting that you may say that variety has the same increasing order, but volume and velocity doesn’t necessarily follow that order apart from that big reference data is less challenging in all respects and therefore maybe isn’t big data at all. However I like it to be. That is because big reference data in my eyes will play a big role in order to solve the veracity challenge for the other types of big data.
Liliendahl on Data Quality 