NOV 18, 2011 12:41pm ET

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The Speed of Decision


In a previous post, I used the Large Hadron Collider as a metaphor for big data and big analytics where the creative destruction caused by high-velocity collisions of large volumes of varying data attempt to reveal elementary particles of business intelligence.

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Comments (2)
I think the relationship between decision speed and data quality needs to consider many other variables beyond speed; eg decision scope, time horizon, consequences of being incorrect etc. I agree organizational ability to recognize and adjust to errors is an important factor. An incorrect decision based on poor data could still get you to the best answer sooner if a constant continuous improvement mindset (as you point out, fail fast but learn). Individuals, especially children, operate that way but it is much more difficult for organizations. Organizations need to see the entire cycle, from data to decision, in order to find and address the constraint to being faster and better decision makers. Cliche, but I think the issues this blog raises really illustrate that partnership across functions, connecting the white space, asking "when" instead of yes or no questions, is important to getting data driven decision making, BI and data quality to the next level.
Posted by Ed U | Sunday, November 20 2011 at 6:22PM ET
Thanks for your excellent comment, Ed. Especially your point, "an incorrect decision based on poor data could still get you to the best answer sooner if a constant continuous improvement mindset" is being used. I believe that the biggest obstacle to taking data management and business intelligence to the next level is that the organization is inherently biased against believing that poor data quality and poor decision quality are prevalent. Instead of a constant continuous improvement mindset, most organizations prefer a constant continuous blissful ignorance mindset. Which is why so many organizations complain when they are blindsided by data management disasters and business intelligence blunders. Although the business world will never be totally predictable, we can not turn a blind eye to either the need for data management and business intelligence best practices, or the reality that no best practice exists that can completely eliminate the potential for poor data quality and poor decision quality. A key concept of statistical process control and continuous improvement is the importance of closing the feedback loop that allows a process to monitor itself, learn from its mistakes, and adjust when necessary. The importance of building feedback loops into our decision making is too often ignored. Best Regards, Jim
Posted by Jim H | Monday, November 21 2011 at 9:52AM ET
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