Veracity (Data in doubt).

Veracity is what is conform with truth or fact, or in short, Accuracy, Certainty, Precision. Uncertainty can be caused by inconsistencies, model approximations, ambiguities, deception, fraud, duplication, incompleteness, spam and latency.

 Due to veracity, results derived from Big data cannot be proven; but they can be assigned a probability.

Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is.

 Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.

Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.

The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Obviously, this is especially important when incorporating primary market research with big data. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and a without a method of identifying what data points are relevant. As a result, data should be analyzed in a timely manner, as is difficult with big data, otherwise the insights would fail to be useful.