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Let’s define Big Data

Over the last couple of years Big Data has been rising like a tsunami wave of new tech catchwords. But how many really understand what this really means, and most important of all, where did all this big data come from? Like all other new tech catchwords, the term has been overused and misused and this can cause confusion. So let' try and shed some light and seek some clarity in the words of the knowledgeable. What is big data and how is it best defined?

In the words of Lisa Arthur, [Forbes Contributor] Every company has its own (more specific) description of Big Data however on a more generic way of describing Big Data is

“a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.”


What we love about this description is the fact that it hones in on the fact that there is no Big Data without analysis – without analysis it is just data. Disorganised data has been around for many years, what’s changed is the size of the data due to improved technology that can provide more and more data each day. As a result of this the size of the data has increased exponentially hence the term “Big Data” came to life. This however has brought about the requirement and the technology to organise data and extract useful information from it.

It is especially interesting to note how the traditional has not been forgotten in this definition. It comprises different types of data handled in new ways. Many articles (and some books) about Big Data stick to digital simply because it is easier to collect and make sense of the data using automated processes. However traditional processes and human observation can sometimes be an important element of Big Data too.

Contact Centre feedback is a good example of such traditional data. The voice of agents is an important element because they are the front liners, they actually get to hear the “voice of the customer”. The rest of the Company can only speculate about what it’s like to be a customer, whereas agents know exactly what the true customer concerns are, they know what language they use and what their fears, disappointments and desires are.

Their contribution to creating ideal target personas for the marketing department is one way of harvesting traditional information that can then contribute to writing strong copy on the company’s digital touch-points which then effects conversion and generates even more data to analyse. The Big Data cycle is a cycle that never ends, it is circular and constantly in motion, a process of continuous self-improvement.

Betsson’s very own Big Data definition sounds something like this “The data available from our website and contact centres that allows us to improve our products and especially our customer experience” in Ulrik’s own words “We need to let the data come alive and work for us”.

The value in the data can only be harvested if the data is well organised and used for a real one-to-one personalised customer experience, relevance is key to Big Data analysis which pushes any organisation forward. Since Ulrik joined in 2012, this has become a strong part of the Betsson Group’s vision and strategy.