McKinsey's Bias Busters Collection covers how are decisions are often affected by cognitive and/or organizational biases.
These include being unwilling to cut back on an under performing asset or activity, getting too distracted by shiny new initiatives, sticking with numbers or projections that are out of date or likely incorrect.
But our favorite was what they call taking an "outside view" of a project or forecast. They describe this as:
"... building a statistical view of your project based on a reference class of similar projects."
They say this is important because if you do the analysis based only on in-house data, you're likely going to miss important information that can be learned from other, similar projects.
This is what we do in our forecasting projects here at Small Business Labs.
Because we're forecasting things that usually have little or no historical data, we can't use traditional forecasting models. Instead, we use diffusion of innovation models that incorporate growth rate data from similar industries or products that are more mature.
For example, we built our coworking forecasts in part by using the growth and expansion rates experienced by premium coffee shops and boutique hotel chains in their early years.
This approach helped us in creating forecasts that have been fairly accurate. It also helped us forecast how coworking's industry structure would likely evolve, and in particular, the emergence and coexistence of big and niche coworking spaces.
We learned about this approach long ago while working at a consulting firm (not McKinsey). We were unaware of McKinsey's "outside view" method, which was developed by Nobel laureate Daniel Kahneman and his colleagues in the 1990s until we read the article.
But it's nice to know that we are using an approach similar to one designed by Kahneman and used at McKinsey.
While McKinsey focuses on the impact of biases on corporate decision making, the same biases impact small businesses and independent workers.
The one we see most often with smaller businesses and independent workers is an unwillingness to cut back on a activity that clearly is not adding value.
Regular readers know we often write about biases and our efforts to avoid them.
These are some of our least read articles. But we'll continue to write about biases.
It's too important a topic not to.
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