As Big Data looms as a force to strongly influence decision-making and collaborative models of leadership emerge under the auspices of design thinking, it will be interesting to determine how these two trends will intersect within organizations that are facing decision that influence their feasibility or effect the communities that they work in and impact.
With the emphasis on the numbers among the data crunchers and the desire among design thinkers to start with people and take a more intuitive approach is there away these approaches can coexist or will they be mutually exclusive?
Thinking, Fast and Slow, Daniel Kahneman introduces the impact of Paul Meehl's research in psychology and makes that case that time and time again data can actually provide more valuable results or accurate information than the experts in a variety of fields. One outcome of this research is that the case can be made that formulas and data may be more reliable than intuition, undermining one of the key pillars of design thinking.
At the same time, there is the risk in taking too rational an approach in relying on the data at the expense of overlooking the subtleties or the integration of disparate parts or stakeholders that ought to be taken into account. Apart from that, there is the risk in taking the data as is without critically examining the information and determining if there are interpretations that are overlooked or, alternatively, overemphasized. For all the promise that various bits of Big Data can provide us, their is the risk of looking at the wrong microcosm or looking for closure or resolution in isolation rather than taking a step back from that entrancing bit of data about, for example, the distance a carrot travels and going after a bigger prize.
This is indeed where design thinking comes in, despite the reluctance that may be had about investing trust in intuition or an insight or outlook that is not grounded in the certitude that data can provide. The learning design approach, delineated clearly by the Kelley brothers from Ideo and the d.school at Stanford University in their book Creative Confidence outline an approach that takes a more artisanal approach to problem-solving or decision-making and establishes a process that strives to arrive at unique solutions that are the consequence of perhaps messy but ultimately handcrafted and organic solutions that strive for a more comprehensive view and involving participants or stakeholders as deeply as possible in the process of defining the problem and solution in a manner that draws on the broadest range of inputs and insights available.
Big Data and Design Thinking can coexist in situations where problems or questions are identified in the data and the careful, messy processes of design thinkers are brought to bear in pursuit of a solution that can be comprehensive and is grounded in engaging as many people as possible. If the Data speaks and the designers listen, then the chances of success is quite high. However, if a more traditional or monolithic approach to the data and the solution is given favour than there is a risk that design thinking may get relegated to the benches despite the potential that it has to generate lasting, elegant, and surprising solutions to the problems that the data identifies.