It’s been called the sexiest job of the twenty-first century and the best job of 2016. No, it’s not personal chef, software engineer, or Professional Millennial Consultant (if only!): it’s data scientist. This interdisciplinary profession has grown right alongside the proliferation of big data, recruiting employees well-versed in statistics and data analysis to clean and analyze all this passively collected info. But wait a minute: isn’t analyzing consumer data what market researchers are supposed to do? In order to better understand the roles of researchers and data scientists in deciphering consumer insights, let’s look at the differences and similarities in what each of them does.
Data is Data is Data
The lives of both researchers and data scientists revolve around data and the now countless sources it can be acquired from, as well as how the information it contains can inform smarter, effective decision-making. Data scientists can be thought of as a hybrid of computer scientists and statisticians. The data they’re concerned with is primarily derived from quantitative research of the massive amounts of raw information that are gathered through online activity and mobile interactions. They mine these statistics for meaning and trends, relying on cookies and digital transactions to glean patterns in consumer behavior. In this way, the broader function of data science is actually already part of market research, proving that the professions are really just two approaches to a similar goal.
Until You Interpret It, That Is
The key differences between data science and market research lie in the tools they use and their respective analytical philosophies. As online market research companies have already discovered, technology can certainly augment a researcher’s capacity, even improving upon the quality of the data collected by offering in-the-moment reactions and candid, anonymous feedback. But technology is absolutely essential to the job of a data scientist, who relies on parsing computational information to extract insights. And while both professions seek to learn about how people behave, the differing tools they use means that they end up analyzing and interpreting data differently too. While researchers turn to interviews, market surveys, and other forms of exploratory research to learn about and contextualize the customer’s story, data scientists use hypothesis testing, regression analysis, and data modeling to visualize and condense vast patterns in the customer’s activity.
Teamwork Makes the Insights Work
In order to ensure that a company is acquiring and analyzing customer intelligence to its optimal potential, data scientists and market researchers should absolutely be working closely. The only reason separate teams are even required is because the skills needed for each are pretty divergent. But as technology evolves and brands grow smarter, bringing the two teams closer together will only become increasingly imperative. A handful of ways to begin this merge today include
- Using qualitative research to understand and verify the insights of big data
- Suggesting data scientists’ hypotheses to test with market research methods
- Using researchers’ storytelling techniques to help contextualize data insights
- Integrating market research results into relevant data modeling
No business has ever achieved success by only looking at either past behavior or future predictions: a thorough understanding of both is needed to make the responsive and anticipatory decisions that help a company thrive. The combined insights of data science and market research will be a force to be reckoned with in the years to come, and brands looking for an advantage would do well to coordinate the two earlier rather than later. To learn more about just how effective combining quantitative and qualitative research can be, check out the eGuide below.