Two more V’s in Big Data: Veracity and Value

  • Volume: which considers the number of data points in a data set
  • Variety: or the number of features and parameters at each data point; more variety will often require complex data structures
  • Velocity: or a continuously updated data feed that is required to keep analytics updated in a ever changing world.


The entire environment of Big Data instruments once in a while sparkles without those three fixings. Without the three V’s, you are most likely lucky to be not utilizing Big Data arrangements at all and rather basically running a more customary back-end.

Despite the fact that the three V’s are the most broadly acknowledged center of characteristics, there are a few augmentations that can be thought of. The five V’s on Big Data broaden the three previously covered with two additional attributes: veracity and worth.


All in all, data veracity is characterized as the precision or honesty of a data set. By and large, the veracity of the data sets can be followed back to the source provenance. As such, many discussion about dependable data sources, types or cycles. Notwithstanding, when various data sources are joined, for example to expand assortment, the association across data sets and the resultant non-homogeneous scene of data quality can be hard to follow.

As the Big Data Value SRIA brings up in the most recent report, veracity is as yet an open test of the exploration zones in data examination.

Content validation: Implementation of veracity (source dependability/data believability) models for approving substance and misusing content suggestions from obscure clients;

It is important not to stir up veracity and interpretability. Indeed, even with exact data, misinterpretations in investigation can prompt some unacceptable conclusions. In any case, this is on a basic level not a property of the data set, but rather of the insightful techniques and issue proclamation.


Data esteem is a little more unobtrusive of an idea. It is regularly measured as the potential social or monetary worth that the data may make. Be that as it may, the entire idea is pitifully characterized since without appropriate aim or application, high significant data may sit at your warehouse with no worth. This is frequently the situation when the entertainers delivering the data are not really fit for placing it into esteem.

Notwithstanding, late endeavors in Cloud Computing are shutting this hole between accessible data and potential uses of said data. Amazon Web Services, Google Cloud and Microsoft Azure are making an ever increasing number of administrations that democratize data examination. Sadly, in flight, a hole actually stays between data engineering and flying partners. Luckily, a few stages are bringing down the section hindrance and making data open once more.


The issue of the two extra V’s in Big Data is the means by which to measure them. Veracity can be deciphered severally, however none of them are likely target enough; then, esteem isn’t a worth natural for data sets. Besides, both veracity and worth must be resolved a posteriori, or when your framework or MVP has been fabricated. This can clarify a portion of the local area’s aversion in embracing the two extra V’s.

Regardless, these two extra conditions are as yet worth remembering as they may assist you with choosing when to assess the appropriateness of your next big data project.