Is making good, fast decisions in product strategy possible?

Last week, the OCDQ Blog, one of my favorites, ran a curiously named post: “Thaler’s Apples and Data Quality Oranges“. If you are familiar with behavioral economics, or the recent best seller “Nudge”, you might have heard about the apples story and why it helps to illustrate an important economic fact of life: people’s preferences can be time inconsistent.
What Jim Harris at OCDQ pointed out in this post is that the same time-inconsistency can very well appear in business decision making. Essentially, in an analogous situation to Thaler’s example, when faced with a long term choice between good data and great data, people typically prefer great data. However, when faced with a short term choice of getting good data today and great data tomorrow, under immediate decision making pressure, people typically opt for the lesser choice, revealing a similar inconsistency of preference.

The question here is not whether this inconsistency is right or wrong, but rather when it is appropriate. In many situations, this cannot be avoided. For example, for early stage start ups or companies with fewer large customers, there will never be “great” data, simply because of the dearth of data points, in contrast with more mature firms with perhaps, too much data to digest. Basically, in the early stages, management teams need to rely on “good enough” data rather than wait for “great” data, while in later stages, they can afford to move slower while collecting better data. Fred Wilson of Union Square Ventures has noted as much about this : “Early in a startup, product decisions should be hunch driven. Later on, product decisions should be data driven.” Indeed, in the early stage of a start-up, waiting an extra “day” for a better day might mean falling behind rapidly evolving competitors in the market.

This is why OCDQ also points out the impossible trinity of decision making, which I think applies squarely to expansion stage companies: Actually making a decision, making a good decision, and making a quick enough decision.

The idea here is that because of the dearth of data, you can realistically only achieve at most 2 out of those goals – either you make a quick decision, or you make a good decision, or you are quick and fast in analyzing data but do not make any decision at all. It is actually the very last case that I find is the largest obstacle to expansion stage companies looking to expand their market or improve their product and development. I have written about “analysis paralysis” before and I am even more convinced of the pernicious effect “data perfectionism” will have on a company’s agility. In fact, our recent forum, “Rapid Strategic Planning” embodies this principle: the management teams spend 1 single focused day to clarify and solidify their company’s annual plan. It was a focused, rapid process that did not make time for in-depth analysis or research, simply because we recognize that our portfolio companies will all need to be able to make quick decisions, with or without the supporting data.