We value the power of humans supported by machines over human-only research or big data analytics
Our third manifesto item addresses the conflict between human-led insight and the world of big data which appears to be a very visible debate in the world of research.
In one sense there has always been a friction between the worlds of qualitative analysis and quantitative understanding. Most people see them as complimentary but very different. The growth of ‘Big Data’ as a concept and the proliferation of tools, AIs and technology based systems for helping mine and understand big data has been impressive over the last few years – but it hasn’t really addressed the issue of how the two world views interact.
At the heart of the conflict lies the fact that qual research is very much about human-to-human contact. It’s about interviews and understanding the nuances behind the words. Most of the time a qual researcher is alert to the ‘unsaid’ in an interviewee’s answer and will follow up with further questions. Qual research can reveal a great deal about human desires, needs and motivations.
On the other hand, analysis of big data shows trends and correlations that a human researcher couldn’t hope to find. AIs can make links between apparently unconnected events to build a picture of what is happening in a macro environment. Both approaches can be vital to good research, but often they are not used in conjunction.
We believe that there is a way the two disciplines can be brought together. Through atomising evidence – that is breaking it down to well defined smaller chunks – and making it easily discovered, humans can crate an overlay of big data results and combine those insights with the results of conversational or qualitative research.
Our view is that this can only lead to better research and a breaking down of one of the traditional disconnects in research.