In a previous article we discussed how Six Sigma is a set of techniques and tools for process improvement, originally designed to address manufacturing defects, which has gained much wider acceptance and evolved into a scientific method for project development. So can the Six Sigma approach apply to database management? We think so.
Database management is often considered a process. Each step in the creation of a database and every process taken to ensure a database is operating efficiently can be broken down and analyzed. Because of this, the process is subject to improvement.
Emilio L. Cano and his colleagues, in Six Sigma with R: Statistical Engineering for Process Improvement, explain:
In their example, they cite information stored incorrectly in a database. To remedy future problems with a name misspelling on a document, for example, a process is created that automatically generates a problem ticket for action.
It seems simple — and the example does provide a very basic task — but any process can be broken down and subjected to such scrutiny.
James Ford, in a paper published on the Rensselaer Polytechnic Institute website, investigated Six Sigma’s application to database administration:
Ford ultimately looked at how implementing Six Sigma methodologies translated into database downtime. He found “there is no statistically significant difference in the amount of database downtime caused by the implementation of Six Sigma methodologies in an IT organization.”
And yet, there are many other potentially useful metrics within database management we could use to determine a project’s success. Ford notes some of those candidates, yet focuses only on uptime or availability.
In a 2012 case study, one firm was able to use Six Sigma to resolve database error problems that had already cost it more than $100,000. Ernie Arboles, a management consultant, wrote that the issues centered on one of his client’s sales ordering and processing databases. In the process of discovering the origins of these problems, which he says was extremely focused, the team finally found:
After working to resolve the existing issues, the organization was able to establish new policies and procedures to help the staff maintain the new data entry practices, preventing future problems.
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