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May

The big data equation

Much ado has been made about big data and the desire to “have” big data has extended from being just the domain of very large organisations to organisations of all sizes. This “big data” wave is increasingly desired but what, exactly, is big data?

The big data equation

Do a quick search on the internet and you’ll find a number of different definitions but they will all pretty much start with the standard three Vs; Volume, Variety and Velocity. Others have added a fourth V: Value. 

The starting point of the three Vs addresses the data itself, how do we deal with it? None of those Vs have anything to do with actually getting the fourth V, value, out of big data. From the business owner’s perspective, they don’t need to know what big data is, they need to know what value or ROI they will recognise from their big data investment. With that in mind, I have developed my own big data equation for providing value from a big data investment.

3V + A(Q2) = V

As you might imagine, the final V in the equation is Value. The first three Vs are Volume, Variety and Velocity. But what is the A(Q2) component?

The big data umbrella can be broken out into two distinct components, big data storage (the traditional three Vs) and big data analysis. Value is not derived from big data by just storing the data, it is derived from the analysis of all the data. Thus the A component of the formula is Analysis.

However, as all are likely aware, simply doing analysis does not provide value, the analysis needs to have quality and be timely. Thus, the Analysis must be combined with the two Qs: Quality and Quickness. 

Many times I’ve all had someone present analysis that, upon investigation, proved to have problems.  Coming from a Business Intelligence background, the only thing worse than no BI in my opinion, is bad BI – BI that generates the inaccurate information. Thus, in order to have value from your big data investment, the information generated from the system must have good quality.

The other Q, Quickness, is important as well. Some insights come with an expiration date, they are only valid for a limited time, and thus the quickness with which those insights can be generated is important or order to maximise the time in which they are valid.  For example, if you can only assess the risk of someone abandoning their web shopping cart after they abandon it, has that insight provided any value? The value comes when a risk is recognised in real-time and actions are taken and the frequency of the risk occurring is reduced.

The example above requires real-time quickness, but that immediacy is not always required. A long-standing BI mantra is “Get the right information to the right people at the right time”. A big data solution should be able to not only meet the right information and right people aspects of the statement, but also the right timing component. 

Contrary to what the name may imply, the value of big data does not come from the data itself, but from the proper analysis of that data when it is needed. If there is no value being generated from your big data investment, you may want to consider calling by its real name, a data graveyard.

Posted by: Mark Worthen, Senior Consultant, Enterprise Applications | 30 May 2013

Tags: Data, Big Data, big data equation, big data investment


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