Why Companies Have to Trade “Perfect Data” for “Fast Info”

Companies have been trained to think about data all wrong, say Attivio’s Ali Riaz and Sid Probstein. “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.”

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Does this sound familiar?

Your company collects data. You want to act on it. First, though, you really, really want to make sure that data are accurate. So you focus on getting it right. 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 Inc., a privately held enterprise software company based in Newton, Massachusetts. The problem with focusing on getting the numbers too right is that most companies sacrifice speed for accuracy.

Ali Riaz, Attivio CEO, and Sid Probstein, CTO, are “practically relatives” at this point, according to Riaz. “I think I saw his first child being born, the second child and the third child,” he says. They met when Probstein interviewed for and then initially “refused to work with” Riaz at FAST Search & Transfer, a company of which Riaz was then president and COO (it’s now owned by Microsoft Corp.).

Probstein “understood something I didn’t understand right away, that FAST, at the time, didn’t have its strategy right — I didn’t understand that because I’m kind of like a hopeless romantic,” says Riaz. “When I realized that he actually got it, he got that this company was not ready, I thought, ‘That’s a smart guy.’ I called him personally and begged him, and we’ve been together since.”

Riaz and Probstein 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

Where do you think tech-driven information and data trends stand in terms of how companies understand them? How has the capture and use of information changed most in recent years?

Ali Riaz: Let me go back in history. I used to work at Novartis Pharmaceuticals, and one of the things that was really bothersome for me at the time was that we could never agree on the data. We got to the management team meetings, and one system would say we have 17,500 employees and another would say we have 17,300 employees.


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Comment (1)
Bryant Avey
This was a great conversation. We hit up against these issues all the time when building data warehouses and business intelligence solutions for our clients. 

We often find that it just takes to much time and effort to dig through the final 10% -20% of "dirty" data. Great strategic decisions can be made with 80%-90% "clean" data.

If there's a good business reason to get parts of the data perfect, then make every effort to clean it. Otherwise decide to deal with it when or if it becomes important.