Diagnosing research and strategy error in hindsight is far easier than cultivating institutional self-awareness and tools to preempt its occurrence in the first place. As an industry, we have become obsessed with the powers of big data, more data, automation, and speed. Yet Brexit, the US election, Uber, United Airlines, and many other examples show that, while important and valuable, data alone does not overcome the fundamental challenges facing our industry. Bias, stereotyping, and a lack of empathy and deeper understanding are still rampant. Great efforts are being made to address these issues, for example, through better sampling and better quantitative tools. But events like the above still surprise us and are cause for plenty of finger pointing, criticism and social media outrage. Hindsight bias is real and a perfect example of the fact that even as we are pointing a finger at bias, three fingers are pointing back at us.
This paper is a call to action to think about bias in a different way – by going back to basics. It is an outline about how to develop greater empathy from the beginning of a project to the end. Far from a touchy-feely or "soft" luxury, empathy is a critical tool that must be carefully cultivated in order to provide the understanding and explanation of data that yields truly meaningful and effective consumer insight and thereby helps overcome bias and stereotyping.