Big data is one of those topics that seem to crop up cyclically in the news circuit, sometimes with scary-sounding article titles proclaiming the demise of traditional primary marketing research. While big data won’t kill traditional marketing research, there is overlap between the two fields, which present a great opportunity for collaboration in order to develop more meaningful insights.
What is big data?
In simple terms, it is a large dataset that can grow quickly (often due to continued input or creation) and requires specialized analytic techniques to uncover findings. This encompasses a staggering assortment of information and sources including emails, social media, search engine records, business/financial transactions, machine/energy usage, and much, much, more. All this data, however, is useless without a way to synthesize it. Analysis is where big data earns its reputation as an industry disrupter. With a vast amount of information to pull from, the right analysis can uncover correlations, identify trends, and even predict behavior. Sounds powerful, right? In the right application, big data can be an indispensable tool to most any business.
However, big data does have some drawbacks:
- Big data is only as good as its data source. While a large number of data points may generate high levels of statistical confidence, inaccurate inputs or a biased sample can lead to invalid conclusions. When utilizing big data consider the quality/reliability of the data source, and make sure it is representative of the population you wish to analyze. For more information on the potential biases of big data and how to address these issues, give Big data and the danger of being precisely inaccurate1 a read.
- It can be difficult to sift through the noise. The beauty of big data is that there is a whole lot of data to synthesize. The catch is that there can be so much data that it is hard to interpret what is actually insightful, and what is junk. Remember, not all patterns are meaningful. (For some entertainment value, check out this list of spurious correlations). Recognizing insights can be difficult, and sometimes big data analysis may lead to more questions than answers. At times, additional methods of research may be needed to deliver understanding.
- It may not be appropriate for all applications. Want to develop a product, or launch a new brand? Big data might point you in the right direction, but it can’t tell you what the consumer is thinking. In these types of scenarios, learning from the consumer themselves is usually the best course of action.
How can big data and marketing research work together to bring greater insight?
Despite some of the inherent drawbacks in big data analysis, it is still an incredibly powerful tool, especially when paired with primary research. Combining big data analysis with primary research can foster greater depth of insights. In order to best utilize big data and marketing research for your organization, consider the following:
- Use marketing research to explore trends found in big data. When used effectively, big data can uncover new trends before they appear in traditional survey data. This allows for increased agility, as marketing research can address significant market changes earlier, and to greater effect.
- Confirm marketing research findings using big data. Sometimes learnings from marketing research can go against an individual’s or even a company’s expectations. It’s part of the reason for conducting research in the first place. Utilizing big data to find supporting data for these controversial findings can earn leadership buy-in, and provide opportunity for additional insight.
- Combine big data and marketing research for more insightful tracking. In tracking studies, let big data augment the story when it comes to data with clear metrics (i.e., sales, customer returns, complaint logs, etc.). The data’s already there, might as well use it.
As big data analysis continues to grow in importance and analysis capability increases, primary research design needs to adapt to incorporate the power of larger, more varied data sets into the analysis process. Learning to integrate the two disciplines can create better insights, richer stories, and even lead to competitive advantages. After all, everyone is working towards the same goals – better understanding of customers, markets and the betterment of your organization.
- McFarland DA, McFarland HR. Big Data and the danger of being precisely inaccurate. Big Data & Society. December 2015. doi:10.1177/2053951715602495