The Data Suggest That I Am Right Again

Anyone with a basic knowledge of investing understands the concept of balancing risk in a portfolio. You want to put some money where there are safe and predictable returns, some in mixed funds and perhaps a bit in speculative growth stocks. You can go online and answer a few questions from your banker and readily get a measure of your risk tolerance—and based on that get guidance on building a portfolio.

A company can manage its product development in a similar way. A product portfolio should have a balance of mature profitable products as well as some that are breaking new ground and taking risks to uncover new markets and new customers. Consciously defining and tracking the right balance of innovation risk is a challenge. Companies may do it by tracking development spending across next-generation product evolution, innovative new features and new research into disruptive technologies or game-changing ideas.

It can also be managed by balancing people on a team and keeping the right number of risk-seeking innovators around to push into new spaces. These people—designers commonly among them—are generally the optimists and idealists who are fuelled by finding the next new thing and are never satisfied with how things are. If you think of them in terms of a risk index or scale, they are way off to the right: comfortable with uncertainty and often frustrated by conservatism.

So just as with our finances and product portfolios, there is a kind of risk portfolio of people as well: the risk takers balanced by those wired to seek more predictable, reliable outcomes.

At the team level, it is a source of fascination how decisions actually get made among this diverse mix of personalities. In product development research, we have all been on one side or the other of the debate between following intuition and listening to data. Smart people on opposing ends of the risk scale can easily poke holes in either of these approaches.

Considering this led me to the research of Tali Sharot whose work has uncovered what she calls an “optimism bias.” Her research reveals that we are wired hear data that point to a positive outcome for ourselves. In other words, we react more to new information that we perceive as good news rather than bad news. These findings seem to correlate with the idea of confirmation bias, which suggests that we tend to seek out evidence that supports our beliefs and to ignore or diminish the things that do not.

This all suggests that we are vulnerable when trying to accurately interpret design research involving innovation and risk options because we all have our own position on that scale. We will be more influenced by the research that reinforces our existing tendencies or beliefs.

But what if we first turn the research on ourselves? We may be able to identify our own risk biases before revealing the actual data and results. If we can identify our position on the risk scale and also identify the design option’s position on that scale, then we can predict what would be good news versus bad news and then correct against that before we interpret the results. This could help us to be more honest with ourselves and understand why we might be hoarding the data and anecdotes that support our position. We just may learn a bit about our counterparts and call them on their own biases as well.

Big decisions have to be made with sloppy information and all kinds of biases all the time, which can create a lot of dissonance on a team. Assuming, optimistically, that we all want to make good decisions that reflect the input we collect from our research, we should watch for the factors that appear to conspire against good decisions.

Trying to understand the risk we want to take, putting together the portfolio of people that reflect that risk, and then being aware of the biases we bring to the table seems like a way to get behind research and drive decisions we can believe in.

Published in IDSA's quarterly journal INNOVATION Summer 2013
Subscribe at www.idsa.org/innovation