Big Data: A Guided Tour

Look at its power. Imagine the potential. It often feels like Big Data is omnipresent. Not least in the workplace, where it has become a feature of so many current discussions about strategies and business plans. As the US President’s office points out, it can be a force for good: saving lives and making the economy work better. But Big Data also brings with it risks and responsibilities – as witnessed by growing consumer concerns about data protection and information security.

Big Data may now be famous. But it can be hard to pin down – a little mysterious, even. This Ipsos paper sets us off on a guided tour of the subject matter – taking us from the all-important definitions through to questions around how it should (and should not) be used.

For those of us involved in the research industry, there are some clear rules we need to follow if we are to make sense of it all. And that’s before the role of “traditional” market research techniques come in.

What is "Big Data"?

“Big Data” is a very broad term, used often - and for a variety of different purposes. But it can be simply defined as any collection of data or datasets so complex or large that traditional data management approaches become unsuitable (Cielen and Meysman 2016).

This definition provides a framework for categorising:

• The types of data that exist
• The business problems the data can beapplied to
• The methods used for capturing data and extracting insight from it.

And, in turn, this framework helps us to understand:

• Opportunities for leveraging new sources of data
• The risks that co-exist with the opportunities
• Where there are connections to more traditional market research approaches.

The characteristics of big data

The term Big Data is often applied to datasets simply because of their size and/or their complexity. Some definitions are so broad that they lack clarity - for example, in 2014, the Executive Office of the President of the United States issued a report on Big Data that defined it as: “Large, diverse, complex, longitudinal, and/or distributed datasets generated from instruments, sensors, internet transactions, email, video, click streams, and/or all other digital sources available today and in the future”. The typical industry description of Big Data rests on the “Three Vs”, which has its origins in META Group (now Gartner).

The three Vs are the elements that fundamentally define Big Data:

• Volume
• Variety
• Velocity

While some commentators say all three elements should be present before data can be considered to be Big Data, other researchers and clients believe that only one of these elements may be enough.

Some definitions of Big Data are so broad that they lack clarity.

We do not see Big Data as a simple alternative to traditional methods like surveys. Our view is that the different types of data possess distinct merits for strategic and tactical questions. Depending on the question, these can be employed separately or in coordination.

As discussed in the paper, Big Data includes mostly ‘passive’ data where individuals are not explicitly engaged to answer questions or otherwise interact with researchers. While this does provide new opportunities to examine what individuals are doing and saying; often pairing this with more traditional active and interactive sources provides the deepest and most actionable insights.

We do not see as a simple alternative to traditional methods like surveys. Our view is that the different types of data possess distinct merits for strategic and tactical questions. Depending on the question, these can be employed separately or in coordination.