Bridging the Qual/Quant Divide

Concern with “big data” have dominated conversations in the past few years. What does “big data” really means? What constitutes “big” versus “small” data? How does “big data” lead to real insights? The promise of data hasn’t played out as planned and we are starting to see a rethinking of how it should be used. Data has valuable uses, to be sure, but the belief that data would become The Thing that changes the world simply hasn’t manifested because it can’t provide meaning to the human condition behind the numbers. Quite simply, questions regarding people and markets cannot be answered by brute force, number crunching.

It’s important to note that the social sciences have a long-standing relationship with analysis of, and interpretation through, quantitative data at all scales and granularities. We know that data is neither good nor bad. It’s what one does with the data that matters. It’s how one understands and works with the benefits and the curses, the strengths and the limitations, of the data that makes the information useful.

Data is comforting because it is fixed, it’s solid, it is an object. It lends a veneer of scientific legitimacy to the things we create. But with the promise of data-driven creative not being fulfilled, we have an opportunity to resist taking data as given, an opportunity to bring an more expansive lens to the collection, management and curation of data. Not just agencies, but the companies for whom we work, as well. Only by looking for meaning in the data traces, the data “fumes”, will we be able to understand what is of value to people, and able to create messages that people value. To be able to do this well, to do this better than we are currently doing it, we need better tools for dealing with data at all scales and granularities—from collection to curation to manipulation to analysis to the drawing of defensible insights and conclusions.

I am a strong enthusiast for and advocate of data triangulation, of mingling data from multiple sources at many levels of granularity I’ve also always balked at the division of data into qualitative and quantitative, believing that behind every quantitative measure is a qualitative judgement imbued with a set of agendas. The distinction between qualitative and quantitative is of lim­ited use and creates needless barriers between input and outcomes. The cornerstone of a good strategic plan, campaign, etc. is the blending of the qualitative and the quantitative, and the embracing and connecting of very different representations from disparate sources at multiple levels of granularity.

That’s because in an industry that has to create ideas not just related to how and when people interact with a brand, but also why, an flexible perspective on multi-faceted data is the path forward to a creative spark.

As part of our evolution, we need to establish and foster deeper relationships with our colleagues, whether it’s planners, designers, data sciences, statistics, engineering, or developers. Data is a material for understanding, not a given from which we deduce that which lies latent within the data, waiting to be revealed. Data analyses should be more than incremental refinement on what is already known. We should work with data to challenge what we know, and to actively seek surprise. This is how we develop an understanding of what is meaningful by understanding people in context.

Published by gavinjohnston67

Take an ex-chef who’s now a full-fledge anthropologist and set him free to conduct qualitative research, ethnography, brand positioning, strategy and sociolinguistics studies and you have Gavin. He is committed to understand design and business problems by looking at them through an anthropological lens. He believes deeply in turning research findings into actionable results that provide solid business strategies and design ideas. It's not an insight until you do something with it. With over 18 years of experience in strategy, research, and communications, he has done research worldwide for a diverse set of clients within retail, legal, banking, automotive, telecommunications, health care and consumer products industries.

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