For those who are employed in a business that "touches" data somehow - whether their company collects data, analyzes it, transmits it, or stores and protects it - "Big Data" is likely a term that has, over the past year or two, risen to the fore of their consciousness. Every component within a company that, for example, manufactures and sells ice cream in its own stores - from the R&D team that develops new flavors, to the marketers who decide which flavors to promote most heavily, to the financial analysts who determine the profitability of each retail location - is very likely a candidate to enter the world of Big Data.
And for those of us in market research, whose livelihoods are immersed an endless cycle of data collection, analysis, transmission, and storage, Big Data has become an inescapable vortex that promises to become more and more integral to the market research process as supplier familiarity increases.
Okay, so you've heard the term. Perhaps you've even thrown it into a conversation or two yourself. But just what IS "Big Data"? Is it more than just a buzzword? Why is it relevant to you and what you do every day?
At its very simplest level, Big Data is just a vast amount of data that has the potential to be "mined" for information. It can be structured, semi-structured, or unstructured. Stored in a "cloud" or a server. Culled from virtually any industry or topic of interest conceivable.
Obviously, quantitative parameters have not been definitively hammered out, so it's probably easier to determine whether or not a body of data is Big Data by how it can be used and what it "looks like" than trying to scope out its specifications.
Another way of looking at Big Data is as high-volume, high-velocity and high-variety sets of information that can be used for Business Intelligence (BI), especially when traditional data analytic techniques are lacking or insufficient. Big Data demand cost-effective, innovative forms of information processing but these efforts appear to be worth it. Why? Because they yield enhanced insight and decision-making.
By definition, then, if we accumulate sets of data from all of the research that we conduct over the course of a year, or five years, or more, and if we follow the Big Data "paradigm", there is nothing that says that we must keep those data sets hermetically isolated by client or specific study (assuming, of course, that our clients have been informed and have approved). If we have formatted these datasets to facilitate quick comparisons between the responses (e.g., making sure that questions about similar topics are worded the same, and that the same filters are used for each topic), we are able to combine the data into a single huge dataset that we can then filter, slice, dice, and puree however we wish.
Big Data, so far at least, appears to live up to the hype being heaped upon it by the market research industry. Its use will allow us to provide richer, more complex interpretations of why and how the marketplace operates that are not limited solely by our client's brand.
We can choose, for example, to look at data from all of our relevant 2014 pharmaceutical studies and compare OTC drug usage data across all pharmaceutical classes for studies conducted in the EU in 2014 with patients between the ages of 25 and 35 versus those who were 36 - 45 years old. Or we can project how film and TV preferences may help predict the winner of the next US Presidential elections by examining the findings of previous media research. We can even determine benchmarks for common questions that we use when we analyze findings for future studies, to see whether trends are beginning to shift in any meaningful way.
The possibilities for the ways in which the totality of our data can be holistically examined - in other words, sliced, diced, and pureed - are almost literally endless!
As exciting as it is to imagine the expanded possibilities that Big Data portends, it's also rather unrealistic to believe that there will be no "better mousetrap" coming at its heels. Big Data is likely just an intermediate stop on the data "transit system". But its use ushers in a new mindset for market research - one in which we become less study-centric (and, in a certain sense, less client-centric as well), and begin to focus more on the totality of a given market with true clarity, rather than almost exclusively just peering myopically at our client's small portion of that market. In this context, it's nearly unimaginable that this is an intermediate step - what do you think will be next?
For more on this topic, check out our next blog entry on Turning "Big Data" from the buzzword du jour to strategic way of thinking!
For more information pertaining to Big Data - or any other market research-related issue or technique - please contact us or visit nebu.com/nebu-data-hub.
Photo by Camelia.boban / Creative Commons
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