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6 Steps to Integrating Big Data & Market Research

Christopher Martin

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Grayling Ferguson

    Big data and market research are two information gathering tools that businesses must employ to succeed in the modern age. In terms of how they function within an organisation, there are two main schools of thought: competition and collaboration. In the competitive model, both are carried out in tandem, seeking independent insights. Each must present their ideas to management functions and convince decision makers to invest in their proposals. It’s an easy practice to put into place but comes with one major problem – waste.

    Combining Big Data and market research is a much more difficult proposition, but the potential benefits are immense. The most noticeable improvement is in efficiency. Tasking both functions with solving the same problem means that complementary insights are more likely to be discovered and proposals must never be ignored due to a lack of funding alone. Second, Big Data solves the challenge of scale which market research faces, and market research solves the psychological and behavioural questions that data alone cannot answer.

    To achieve synergy between the two functions is a cause of much debate – but our Insight Engine model has been crafted to enhance the attributes of both and ensure maximum efficiency is achieved.

    Tweetable Big Data Ideas

    1. Big Data is an ideal tool for selecting the perfect research candidates (click to tweet)
    2. Qual research provides the psychological understanding that Big Data can't (click to tweet)
    3. Quant allows for scale required to test Big Data hypotheses in practice (click to tweet)

    Introducing the Insight Engine

    Integrating Big Data and Market Research

     Why Data Integration Matters

    Ultimately, the crucial point of our Insight Engine model is that when used correctly it is circular in nature. Qual and quant research look to build upon the patterns and predictions of Big Data, first seeking to understand why and then scaling the understanding. In turn, these findings are fed back into the database, alongside real-time data to help the data analytics team make more accurate and reliable predictions.

    This can be developed further into an agile model of insight development. Every team should always be working at a different stage. While data analysts are seeking a new idea, the research team should be testing and understanding the previous. Over time, this creates a lean, continuous insight generation machine that delivers timely insights when they are needed. It keeps costs down while increasing output – helping you make the most of your valuable resources.

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