As organisations have access to more and more data both internally and externally, there is an increasing need to digest and understand the ‘what’ as well as the ‘why’ of this data.

Big data is a powerful asset – collecting, analysing and using the insights gained from big data can impact decision making in the private and public sphere to an extent not seen before. However, big data produces a massive amount of information and finding the right approach to use the data and fully embrace its beauty is a challenge many organisations face at the moment.

At the same time, as the focus is drawn to big data, thick data is often overshadowed by the loudness of the reliability of big data sets. But thick data can provide the in-depth, unforeseen connections between data points that can only be identified through direct interaction with end-users.

Integrative approaches can help to look at big data from both angles: the quantitative view allows for an in-depth understanding based on fixed models, while the qualitative view allows for understanding and identifying the unknown.

At Epinion we use integrative approaches as well as a variety of other innovative and traditional research methods to gather and analyse data, both, quantitatively and qualitatively, to ensure that the most valuable and actionable insights can be distilled from any given type of data – be it big data or thick data or everything in between.


Over the last couple of years Epinion has expanded in both ends of our methodological spectrum:

  • Epinion uses advanced analytics and processes big data for research questions that help our customers track and predict the behaviour of their customers.
  • At the same time, we are increasingly focusing on ethnographic methods of data collection as well as analysis approaches that are based on new advances in anthropology, behavioural economics and psychology to gain in-depth insights on the “reason-why” behind behaviour.

As strong as the two extremes along the method spectrum are independent of each other, combining the two methodological directions adds new value. To understand how the two methods can complement each other, it is important to understand each of the methods and their advantages.

Big data is a word that captures the fact that there is more data available, a higher volume of data and data from a bigger variety of sources (Laney, 2001). Advanced analytics is used to describe the ‘what’ – both in regards to what people have done in the past as well as what they will do in the future. Because of the large scale of data, we’re able to see and precisely predict future behavioural patterns that previously remained undetected. Epinion use a variety of techniques within advanced analytics to combine, describe and analyse these types of data.bigthick_picture2.png

Where big data offers width, thick data offers depth. Thick data is rich in meaning not because it is based on a large sample size, but rather because it is based on a thorough in-depth understanding of a small(er) sample.

Using an anthropological approach to understand thick data means we expand the scope to include aspects such as attitudes, experiences, opinions, emotions, behaviour, context, social dynamics and sensory information – in short, all the complexities of human life that cannot be reduced to numbers. Taking these factors into account means that thick data can help us understand not just what people do and have done – but also why they do it. This allows us to enrich big data with insights into what drives people – not just, e.g. as consumers, but as human beings.

Combining big data with thick data, then, ensures that we don’t just aim to foresee what will happen, but we understand why it will happen. We don’t simply have to settle anymore with identifying e.g. churners amongst a client’s customer base. We can also gain an understanding of the contexts and the motives that make certain customers likely to churn so this can be prevented in the future.

Contact our colleague Mette ( to get some inspiration on how you can get started using both big and thick data as a means for gaining strategic customer insights.

- By Helena Linde Pedersen
- By Jesper Mygind

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