Data Variety - The Ugly Duckling of Big Data

January 31, 2014 posted by Lennard

Tags: Non-Techie, Marketing, Beliefs, Big Data, Variety


Although, based on Gartner’s original framework, Big Data consists of three, some argue even 4 Vs (Trusty Wikipedia link), the majority of companies today seem to be focusing their Big Data efforts on Volume and Velocity, the first two easy Vs, making the third V, Variety, the neglected, harder to solve, ugly duckling of Big Data. 

It’s understandable, after all, the term Big Data itself refers only to Volume. Velocity, the speed data comes into and passes through an organisation, is also quickly understood and mastered - gaining real time insights from the Twitter fire hose is a great example.

A quick look at the Google Trends report below confirms the difference in awareness between the three Vs. Volume is clearly dominating searchers’ interest, with Variety placed third. Furthermore, it looks as though interest in both Volume and Velocity have levelled out, with Variety beginning to gain momentum.

Google Trends - Volume, Velocity, Variety

Advancements in hardware technology are mainly to thank for solving the Volume and Velocity challenges. Data Variety is a different beast though, needing a new approach.


So what is data Variety and why should you care about it?

Marketing Data in Silos

Data Variety is the challenge of having disparate data sets, from different sources in different formats, all in silos, thereby neglecting the benefits a unified view of data brings. A great example of this is marketing data: social media, sales and advertising data, all saved and kept separate. Facebook and Twitter data on their own can only tell you how well one part of your campaign did, but cannot give you an insightful and holistic picture of your whole campaign. To get to the bottom of how your different social media tactics have impacted your website visits or how your media spend influences your online sales, you need to combine various data sets. No marketing campaign is single channel anymore, so your data approach shouldn’t be either.

So don’t hold back from gaining the most from your data, just because the challenge of data Variety isn’t as easy to solve as the first two Vs. Start simple by finding out what you want to gain from your data. Then do a data audit, or as we like to call it Data Landscaping, and find out where it is and in which format. Deciding on which data to bring together is the final step, before you can start analysing it and gaining insights. We advise all our clients to start small and scale up, increasing the sources step by step.

DataShaka is an award winning data management startup, helping to put data at the heart of our clients’ decision making. We merge all and any data, no matter what source or format and provide our clients with a single stream of unified data. If you are interested in hearing more from us, send me a mail:

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