Big data and quantitative survey research have been pitted against each other as competitors in the world of data and research. Within organizations, the two types of data are often gathered, managed and analyzed by separate teams working with different vendors, further deepening feelings of competition. At Branded, we are often asked whether survey research or big data will rise to the top. However, the real question isn’t whether big data or survey research will win out in the battle for top data source. The real question is – how can these data approaches work together as compatible partners?

With so many varying perspectives in the marketplace, how does Branded define survey research and big data?

Quantitative Survey Research focuses on surveying smaller audiences to answer questions on narrowly defined topics that cannot be observed. This includes attitudes, intentions, preferences and motivations in addition to certain behaviors. Emotional response and opinions can be captured with a survey in a way that is not possible with big data. Big Data accesses a larger portion of the population than survey research and focuses on observable data that organizations or third parties collect about their customers and prospects in order to understand current or future behaviors. Gartner defines big data as high volume, high velocity, and/or high variety information assets.

The integration of survey research and big data is a robust combination

The integration of the survey research and big data disciplines give us the power to deepen and widen learnings, sparking a transformation from traditional research findings to competitive insights. Integrating the attitudes and motivations gathered through survey research with big data’s predictive behaviors is a powerful combination. To make this a reality, there must be a blending not only of the different research data sources but also the teams involved in the analysis. The skillsets of the teams focused on these two disciplines are different, with quantitative researchers experienced in study design, execution and project management, and data scientists experienced in engineering, statistics, and data visualization. This integration ensures a more well-rounded approach focused on insights with varying perspectives on analysis and interpretation.

The importance of data and analysis within organizations is clear.  Approximately 83 percent of executives in a recent Economist Intelligence Unit study indicated that increased use of data has made existing products and services more profitable, and allowed for more informed decision-making. Yet, only 40 percent of executives indicate they have been effective in giving employees access to the data they have gathered. Building bridges between quantitative research and data science teams helps solve for issues of data access across organizations.

The value of research is clear as more and more executives make data-driven insights a key component of their decision-making process. However, there is a persistent mindset that quantitative research and big data are competitors rather than partners in the quest for insights. The true competitive edge exists for organizations that integrate their quantitative survey research and data science teams and data assets, resulting in a powerful combination that drives the most robust insights.