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Why Companies Have to Trade "Perfect Data" for "Fast Info"

February 23, 2011 | Michael S. Hopkins | Data, AI, & Machine Learning
All companies collect data, and they want to act on that data, but they also want to make sure it is accurate. Better to wait on a decision until you have the absolutely correct information than act based on partial information.
That might make sense, but it's the wrong way to go, say the top two executives at Attivio, a privately-held enterprise software company. The problem with focusing on getting the numbers too right is that most companies sacrifice speed for accuracy. Companies have been trained to think about data all wrong, say Ali Riaz and Sid Probstein, CEO and CTO respectively of Attivio. Analytics don't have to be based on super-precise data, they say. "The report doesn't have to be perfect. It needs to capture the behavior, not the totality of it."
For analytics to work, companies need a new philosophy around leadership, decision-making, and performance management. One important element is the ability to consider a bigger picture and frame within which to consider the new kinds of data that is being gathered.
Riaz and Prostein spoke with MIT Sloan Management Review editor-in-chief Michael S. Hopkins about the stifling downside of the quest for perfect data, why "eventually consistent" is a concept every company should take to heart, and how to deal with the need for speed.
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