6 Steps to Integrating Big Data & Market Research

Chris Martin

Is Your Sample Ample? Common Issues and How To Avo...

Good sampling doesn’t guarantee success, but a poor sample will guarantee failure.


Russell Elliot

    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.

    You might also like...

    Blog Featured Image Header

    How to Use Digital Ethnography and ...

    In one way or another, we’ve all encountered social media spaces. Whether you’ve had a Facebook account since it first landed on the internet, created different accounts to keep up with relatives duri...

    7 MIN READ
    Blog Featured Image Header

    5 Ways to Power Up Your Insight Pla...

    In case you missed it (which seems unlikely), ChatGPT, the Artificial Intelligence model trained for conversation interactions has been making waves in the last few months. But once you’ve finished as...

    7 MIN READ
    Blog Featured Image Header

    The Future of Market Research and B...

    Every time we interact with the online domain, we contribute to an ever-growing stream of information about our usage, behaviour and interactions with whatever sites or pages we use. A quick search su...

    8 MIN READ