Fast Decisions Trump Perfect Data

The Forrester Muse
SEP 20, 2012 8:41am ET

When you last pulled up a chair to this blog we talked about data quality persistence and disposability for big data. The other side of the coin is, should you even do big data quality at all?

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Comments (4)
The title "Fast Decisions Trumps Data Quality" caught my eye because if you look at the decision process...especially a fast one...the quality of the decision is dependent on the quality of the data used to make the decision. Let's take what is generally considered the best model for making fast decisions - The OODA Loop, which stands for Observe, Orient, Decide and Act. This decision model was initially created for air force fighter pilots, who often have to make split second, life and death decisions. Although, in recent decades it has been adapted to many realms from business to even motorcycle riding.

Orientation is generally considered the most important mode. The Decision that emanates from it is wholly dependent on the quality of the data gathered during the Observe mode. If you buy this, then I guess it is possible to argue the reverse. The quality of data drives a quality decision.

In the end it is true that you can make a any speed...but the quality of it depends on the quality of the data, which when combined with the pilot's knowledge and judgement about his/her surroundings during Orientation dictates his/her Action and the ultimate outcome. For a fighter pilot, that means precisely nailing the target or killing off a wedding party in some village near the target.

I can't help but consider what my supervisor said to me in my first "professional" job as a research assistant in a lab at Children's Hospital in Boston: "Don't worry. If you make a mistake, no one dies." Now across the street in the main hospital where the care is provided, that was very much not the case. I think your premise is true within spheres, such as most business settings where no one immediately can die and there is time to recover from a poor decision resulting from poor data.

Posted by Peter P | Monday, September 24 2012 at 2:40PM ET
Excellent series and very thought provoking. Thank you for your insights, Michele!
Posted by Jeffrey T | Thursday, September 27 2012 at 2:57AM ET
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