Thoughts of an optimist
Can "Small Data" profit from Big Data? Edward Appleton takes a client's eye view of the role of qualitative research in this age of machine intelligence.
Hardly a day goes by that we read about new advances in algorithms, predictive analytics, Big Data, artificial intelligence. A recent, if alarming example: Tesco's ambition to introduce facial recognition software at the cash desks of its 400 or so petrol stations in the UK to facilitate "targeted" advertising.
Some researchers actually predict a limited future for human skills in MR. At a recent Mobile Market Research Conference in London, TNS's Jannie Hofmeyr heralded the age of "machine-everything" (my phrase) for market research from the use of predictive analytics to automated read-outs.
What space is left for qualitative research in a world increasingly dominated by numbers, measurability and predictability? Is qual in danger of being marginalised, trivialised even?
I'd suggest that there's a good chance that it will actually profit from Big Data, that multiple new opportunities will be thrown up in its wake.
<li>Mega-trends are invariably followed by a counter-trend, before a point of synthesis occurs. I predict 'Small Data', effectively qual and a refocus on the psychological, will follow and complement Big Data.
<li>Big Data throws up as many questions as it answers, something stated increasingly often by quant experts. Answering the question "why" people do what they do is as important as ever, and it's unlikely to go away.
<li>Behavioural Economics (BE): the findings of numerous academic studies highlight the primacy of the social, contextual and intuitive-cum-irrational in explaining behaviour.
<li>Big Data can certainly help drive efficiencies, save money but how often does it help drive a top-line growth agenda? Or an innovation strategy? Expertly executed qualitative research ethnographies, immersions, groups at the beginning of any innovation project is of immense value.
Added to this are increasingly authoritative voices pushing back against a totally rationalised worldview. A recent Economist article stressed the elements of uncertainty and imprecision in scientific approaches (<a http://href="http://econ.st/1cxVU7V">Economist, October 2013</a>); the economist professor <a http://href="http://www.johnkay.com/books">John Kay</a> has written cogently about un-plannability, or "obliquity" in business success. The role of the un-quantified, the microscopic is effectively given higher credence.
What could all of this mean for qualitative research? If we are, indeed, witnessing the first signs of a rehumanisation agenda, a stress on the individual, on social context, psychology, emotions, then qual is extremely well positioned. It will, however, mean that quallies need to maybe think and behave a bit differently, playing to but building on strengths.
Some thoughts on what's needed
Embrace digital opportunities
Nothing against groups and IDIs whatsoever but a toolkit that embraces digital is so much more powerful. Online communities (to take just one example) are extremely valuable for all sorts of research projects. They offer so much: people contribute over a period of time, in their own time, upload pictures, comment on them, react to others" views, build on them Whenever qual can sensibly use new technology to help enrich understanding be it Smartphone-enabled selfethnography, tapping into sites such as Pinterest or Instagram it should look to do so.
Expand from moderation into facilitation
Qualitative research can and arguably should play a much more active, if not a leadership role at the front end of an innovation process. Here the key question is often: what are the unmet needs? What pain points occur when, in what context, for whom, and why? Get this right and much that follows in the path to launch becomes easier. Who better to do this than a quallie?
Work on manageable ethnographic tools
Traditional ethnographic techniques tend to be lengthy and costly. Modified ethnographic approaches that allow a "bite-sized" warts-and-all picture far faster than was traditionally considered acceptable are in my view immensely valuable. If you understand a context well, you're half way to understanding a decision making process.
Join qual with quant
If qual can link more tightly and without antagonism with quant, then it has better chances of being more influential. So when developing a proposal, including mixed methodologies with many different tools and viewpoints is useful. A quantification of the qualitative is for many clients the Holy Grail.
Qual needs to raise its voice, improve understanding, create a compelling and resonating narrative by writing articles, case studies, blogs, engaging on social media, and penetrating magazines outside the narrow confines of market research to reach people making budgetary decisions. We stay within the narrow but cosy confines of our own disciplines at our peril.
Qual often comes up with insight nuggets that others clients, agencies, quant researchers run with. Recognition and value attribution can easily get lost. Qual needs to track success down, document it, and loop it back to budget deciders to ensure they plan accordingly for next year's financials.
All the above sounds, perhaps, overly optimistic. There are indeed multiple headwinds an ongoing bias to quantification in many countries, notably the US and Germany plus a clear preference at C Suite level for facts to be "hard", with qual often regarded as soft, unreliable.
If, however, if there is an appetite among quallies to step into such new arenas as innovation workshop facilitation, and offer themselves as recognised interpreters of behavioural complexity, essentially as the partners of Big Data, I think the opportunities will be there. The skill sets may well be complementary, psychology and sociology picking up where technology leaves off.
Copyright © Association for Qualitative Research, 2014