3 V’s or 7 V’s : What’s the Value of Big Data?

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The Original 3 V’s of Big Data

The 3 V’s of Big Data – Volume, Velocity and Variety – were coined by Doug Laney of Gartner (then META Group) in 2001, since these attributes aptly defined Big Data. Veracity has been added to the 3 V’s of Big Data, probably around 2012.

Ever since then several analysts, enthusiasts and researchers have played around with the V’s of Big Data but nothing seems to shake the original characteristics that differentiate Big Data from any other data and the 3 V’s seem to have stuck around.

Re-calibrating The V’s

Whereas this may be the best technical definition till date, management needs to extract Value from Big Data and from a usage standpoint, the V’s of Big Data may be re-calibrated as below:

The Big V – Value of Big Data

  1. Value – This is indeed the holy grail of big data and what we are allBig V - Value of Big Data looking for. One has to demonstrate value that can be extracted from big or small data in order to justify the investments, whether on big data or on traditional analytics, data warehouse or business intelligence tools, whatever may be the buzzing nomenclature. There seems to be an increasing interest related to the Value of Big Data, as indicated by the number of Google searches looking for similar terms over the last two years.
Increasing Search for Value of Big Data
Search for Value of Big Data (Google; Jun’13 – May’15)


The Defining 3 V’s

The most important and the end-goal of Value has to be followed by the three original attributes that define Big Data; Volume, Velocity and Variety.

      1. Volume – There is no doubt that the information explosionVolume - 7 Vs of Big Data has re-defined the connotation of volumes. There are several such staggering statistics going around and it has become increasingly difficult to keep track of the number and magnitude of the pre-fixes attached to “bytes”, while measuring the volume. Since there is a “helluva lot of data”, the term “Hellabyte” has been coined beyond petabytes, exabytes, zettabytes and yottabytes. However, since these measures will be superseded by the likes of Brontobytes, Geopbytes and more, lets move on!

3. Velocity – Similarly, velocity refers to the speed at whichVelocity - 7Vs of Big Data the data is generated. Some of the factors that exacerbate this trend are the proliferation of social media and the explosion of IoT (Internet of things). In the context of business operations that have not yet been touched by social media or IoT, the velocity arises from sophisticated enterprise applications that capture each and every minute detail involved in the completion of a particular business process. Enterprise applications have traditionally captured such information but the world has woken up to the power of such information largely in the big data era.


4. Variety – The last of the original attributes of big data is variety.Variety - 7Vs of Big Data Since we are living in an increasingly digital world where technology has invaded into our glasses and watches, the variety of data that is generated is mind-boggling. The computing power available is able to process unstructured text, images, audio, video and data from sensors in the IoT (Internet of Things) world that capture (almost) everything around us. This attribute of big data is more relevant today than it ever was.

The Operating 3 V’s

Having covered the journey of the original V’s that define the attributes of big data, let us look at another dimension. In order to be useful, any data, whether big or small, needs the support of the following operating 3 V’s:

5. Veracity or Validity of data is extremely important andVeracity - 7Vs of Big Data fundamental to the extraction of value from the underlying data. Veracity implies that the data is verifiable and truthful. If this condition is violated, the results can be catastrophic. More importantly, there are several cases in which the data is accurate but may not be valid in the particular context. For instance, if we are trying to ascertain the volume of searches on Google related to big data, we will also obtain results pertaining to the hit single “Dangerous” from “Big Data”.

6. Visible – information silos have always existed within enterprisesVisibility - 7Vs of Big Data and have been one of the major roadblocks in the attempt to extract value from data. Relevant information should not only exist, but should also be visible to the right person at the right time. Actionable data needs to be visible transcending the boundaries of functions, departments and even organisations, for value unlocking. Individuals might have believed that Information in their hands is power but in the age of Big Data, collective information available to the world at large is truly omnipotent!

7. Visual – we live in an increasingly visual world and the statistics ofVisualization - 7Vs of Big Data increase in the number of images and videos shared on the Internet is staggering. According to official statistics, 300 hours of video are uploaded every minute on YouTube. In a business context, appropriate visualization of data and dashboards is critical for the management to be able to extract value from their limited time, resources and even more limited attention span!

Various other V’s

In addition to the 7 V’s described above, there are several other V’s that may be considered:

    • Volatility – With more applications such as SnapChat and IoT sensors, we may have data in and out in a snap. Volatility of the underlying data sources may become one of the defining attributes in the future.
    • Variability – One of the cornerstones of traditional statistics is standard deviation and variability. Whether or not it makes to an extended list of V’s relating to Big Data, it can never be ignored.
    • Viability – Embedded in the concept of value is the need to check the viability of any project. Big Data projects can scale up to gigantic proportions and guzzle a lot of resources very quickly. Those who do not learn this fast and get fascinated with fads will funnel funds towards futility resulting in failure. In a nutshell, viability of any project needs to be established and Big Data projects do not have the liberty of exemption, whether or not it remains a trending buzzword.
    • Vitality – or criticality of the data is another concept that is crucial and is embedded in the concept of Value. Information that is more meaningful or critical to the underlying business objective needs to be prioritized. Analysis paralysis needs to be replaced with a more pragmatic approach. Technology allows marketers to create segments of one, but is such extreme segmentation vital or even aligned to the organizational strategy?
  • Vincularity – Derived from Latin, it implies connectivity or linkage. This concept is very relevant in today’s connected world. There is significant value arbitrage potential by connecting diverse information sets. For instance, the government has forever been trying to connect the details of major expenditure heads and correlating the same with the income declared in tax returns to identify concealment of income. The same purpose may now be achieved by drawing information from social media posts.


It does not matter whether we define 3 V’s of Big Data or 7 V’s of Big Data

It does not matter whether one set of V’s is Victorious (that’s another V) over another set of V’s

What will set your organization apart is the Vision of how insights drawn from diverse data sets, big or small, align and contribute to the organizational objectives.

The only thing that matters is the extraction of Value of Big Data from the exploding digital landfill !

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Few examples of how we leveraged the framework to create value using Artificial Intelligence, Machine Learning and Natural Language Processing on Big Data to extract value:

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  1. […] Once the veil is lifted, there is nothing artificial about AI and the magic is reduced to 1’s and 0’s. Even if you are not the skilled magician yourself, at least you are in a position to understand how the pieces fit together. This is when you can extract value from Big Data. […]

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