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SEP 29, 2011 10:26am ET

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Aristotle, Data Governance and Lead Rulers

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Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, and, perhaps most notably, policy enforcement.

But sometimes this emphasis on enforcing policies makes data governance sound like it’s all about rules.

In their book “Practical Wisdom,” Barry Schwartz and Kenneth Sharpe use the Nicomachean Ethics of Aristotle as a guide to explain that although rules are important, what is more important is “knowing the proper thing to aim at in any practice, wanting to aim at it, having the skill to figure out how to achieve it in a particular context, and then doing it.”

Aristotle observed the practical wisdom of the craftsmen of his day, including carpenters, shoemakers, blacksmiths, and masons, noting how “their work was not governed by systematically applying rules or following rigid procedures. The materials they worked with were too irregular, and each task posed new problems.”

“Aristotle was particularly fascinated with how masons used rulers. A normal straight-edge ruler was of little use to the masons who were carving round columns from slabs of stone and needed to measure the circumference of the columns.”

Unless you bend the ruler.

“Which is exactly what the masons did. They fashioned a flexible ruler out of lead, a forerunner of today’s tape measure. For Aristotle, knowing how to bend the rule to fit the circumstance was exactly what practical wisdom was all about.”

Although there’s a tendency to ignore the existing practical wisdom of the organization, successful data governance is not about systematically applying rules or following rigid procedures, and precisely because the dynamic challenges faced, and overcome daily, by business analysts, data stewards, technical architects, and others, exemplify today’s constantly changing business world.

But this doesn’t mean that effective data governance policies can’t be implemented.  It simply means that instead of focusing on who should lead the way (i.e., top-down or bottom-up), we should focus on what the rules of data governance are made of.

Well-constructed data governance policies are like lead rulers – flexible rules that empower us with an understanding of the principle of the policy, and trust us to figure out how best to enforce the policy in a particular context, how to bend the rule to fit the circumstance.  Aristotle knew this was exactly what practical wisdom was all about – data governance needs practical wisdom.

“Tighter rules and regulations, however necessary, are pale substitutes for wisdom,” concluded Schwartz and Sharpe. “We need rules to protect us from disaster. But at the same time, rules without wisdom are blind and at best guarantee mediocrity.”

This blog originally appeared at OCDQblog.com.

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Comments (6)
Hi Jim

Great article. This is similar to Hans Monderman's thinking - less is definitely more.

My mind also wanders to Cluedo, where a common scenario may play out as Colonel Mustard kills Mrs White with a lead pipe in the study.

Posted by Adrian B | Thursday, September 29 2011 at 11:53PM ET
Hi Jim, I appreciate your emphasis on collaboration and not "clobberation" to empower data consumers, stewards et al and flexibly respond to the needs of data/information management. An excellent primer on data governance is the 1985 movie "Bazil" with Robert De Niro. Should be required viewing before embarking on data governance. The whole "Buttle" and "Tuttle" data error and the "Ministry of Information" are wonderful commentary on how data governance can go astray. http://www.youtube.com/watch?v=nWbIxFKtTmE
Posted by Peter P | Wednesday, October 05 2011 at 11:02AM ET
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Blog Archive for Jim Harris

Pondering a Big Data Philosophy
Galileo, the Hubble and Clear Data Insight
When Poor Data Quality Lands on the Ledger
Poor Data Quality That Kills
Data Quality and the OK Plateau

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