One of the most common buzzwords in the marketing research world - and perhaps the most prominent one to date – is “big data”. No matter where you look, whether TV, print, or online; you will hear discussions about the usefulness, limitations, and implications of big data for the economy, politics, or society as a whole.
The ubiquitous quality of this term has both legitimized and discredited it in the eyes of experts and the general public. However, I suspect that if the question “What is Big Data?” was featured as one of Jimmy Kimmel’s “Pedestrian Questions” few people would be able to provide a clear answer.
So…What Is Big Data?
Most people would probably associate big data with digital content, and they would be right. In its simplest form, big data refers to the unprecedented rise in the quantity and quality of information available since the advent of Web 2.0. Some examples of big data content include online network interactions (such as Facebook likes, or Twitter mentions), geographical data (check-ins, geo tags), biometric data (Fitbit or Jawbone stats), and word relevance in time (Google searches, Twitter hashtags).
From an analytical perspective, big data is often portrayed as something that needs to be “harnessed” or “mined” for its usefulness by researchers and data scientists, who are the equivalent of tamers in a circus act.
Even if this may sound less exciting, it seems to me that a more adequate comparison to how analysts work with big data is that of someone trying to collect rain. Just like rain, big data is stored in “the cloud”, once we have access to it, it tends to surround us in a haphazard and incremental fashion, like pouring rain –which sometimes can be overwhelming. In order to make use of big data, analysts and data scientists need to spend time collecting and aggregating until it has enough substance to make an impact.
Big Data In Action
One of the best examples of big data analytics is documented by the October 2012 issue of the MIT Technology Review; utilizing anonymized cellphone location data, researchers were able to track the travel patterns of a large proportion of Kenyans for an entire year. Comparing the data against the incidence of Malaria in the same region, researchers were able to map the point of outbreak and consequently, adopt geographically targeted preventative measures, which were labor and cost-effective alternatives to previous approaches.
The buzz surrounding big data is no coincidence, it provides researchers large amounts of information with greater accuracy and speed. However, it is necessary to remember that big data needs to be contextualized, parameterized, and complemented by adequate analytics in order to be effective. The analytics team at TrendSource brings the right mix of creativity, innovativeness and dedication for any research needs; incorporating data in a manner that will provide actionable results.
