Browse by year

July - 2015 issue

Big Data- Quality still Scores Over Quantity

By Vignesh.A

Although there is extensive usage of the three Vs of Volume, Variety and Velocity as a basis for defining big data for over a decade, uncovering the facts reveals that each of these Vs actually require different technologies to be solved, which mirrors to the certitude that there is no one universal solution to solve all of the Vs. This conflation of big data characteristics, along with Value, structarbility and scalability still pose a confusing scenario since the phenomenon began in 2011.

Due to the commotion it is easy to find users who define big data in terms of petabytes/ gigabytes according to their need for performance, the analysis of data types and the requirement for speed in processing, analyzing with data driven decisions. Regardless of the scale of data that we currently consider big data, the only constant is that we should expect big data to grow by in magnitude over the next few years as environmental data and the Internet of Things creates vast amounts that will be used to contextualize almost every interaction and transaction in our lives.
So rather than following Gartner’s starting point for success with a three V’s approach, there needs to be no nonsense focus on the actual technological value gained. This also translates to stop viewing big data in terms of qualifying the data, but instead to make the data more accurate, efficient and accessible faster.

Hence to get more out of big data we need to place less stress on the quantity and repetition to determine the true value data possesses. If the same was true, then every tweet given out by Kaamal R Khan or Salman Khan would hold more value than the combined works of Shakespeare. It stands to be accepted that we are only starting to get to a point where we are truly able to focus on the quality of big data. In this regard, pure programmatic automation efforts to make data "better" need to improve on their nuances and contextual knowledge to improve recommendations. In conclusion, until enterprises and B2C organizations shift the power of data quality to the masses, big data is poised to struggle to become better.

Do let us know what you think.