Common cause are events, occurrences and points of reference that are considered normal variation. Uncommon cause are unusual events that aren’t common. The reaction to common vs. uncommon causes are different.
Traditional wisdom would tell us to work on improving common causes of errors while simply noting uncommon causes of errors. It has been proven that to change processes because of uncommon causes of errors is costly and can actually increase the number of common causes.
Let’s say you drive the same road to work every day and on average it takes you 30 minutes to get to work. Then one day it takes you one hour to get to work because of an accident on the road. The common cause is normal traffic flows. The uncommon cause is abnormal traffic flows as a result of an accident. Would you permanently change the route you drive to work because of one accident that occurred on one day? Not likely. However if the road closed because of a sink hole, uncommon cause, you would have to permanently change the route you drive to work each day.
Today’s business environment is rapidly being changed because uncommon causes of change have become common. As a result the mental models of the past and all the related rules of thought no longer apply.
Evidence of Uncommon Becoming Common
Because of the internet more data has been created in the last ten years than since the beginning of time. The quest to create meaning from the data has increased the demand for minds that understand data. More importantly mind that understand the increase in uncommon causes becoming common. Consider:
- Over 30 Billion pieces of content shared on Facebook every month
- Over 40% projected growth in global data generated per year
- More than 235 terabytes of data has been collected by the US Library of Congress by April 2011
- 15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress
- The explosion of data in U.S. Health Care represents over $300 Billion potential annual value—more than double the total annual health care spending in Spain
- The increase in data represents a 60% potential increase in retailers’ operating margins from the insights gained about consumer preferences
- 140,000–190,000 more deep analytical talent positions are needed in the US today to create value from all this data
- The demand for analytic skills is growing; 1.5 million more data-savvy managers are needed in the United States by 2015
Data can be manipulated to mean anything but not everything has meaningful value. To gain meaningful value from data one must understand common vs uncommon causes of variance illustrated within the data. However the real value is in understanding that uncommon causes are now the common cause that reflects the current market dynamics.
When common becomes fueled by uncommon causes of change that means our entire mental models must change. Get it?