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How Can Six Sigma Apply to Database Management?

Author: Chuck Ezell | 4 min read | March 24, 2014

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.

High-Quality Outputs

Emilio L. Cano and his colleagues, in Six Sigma with R: Statistical Engineering for Process Improvement, explain:

When we use Six Sigma for the improvement or creation of a new process, we apply the scientific method to obtain high-quality processes. The reason for this is clear: high-quality processes automatically lead to high-quality final outputs, usually referred to as ‘products.’

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.

Automatically, the database administrator will correct the wrong last name in the database, and the certificate will be generated again. In this way, the process will be improved and the outputs arising from this process will have the desired quality.

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:

Six Sigma is generally applied on a project level and typically deals with the development and deployment of the project. There are numerous other aspects to deploying and supporting applications, including system administration, database administration and network administration. In addition to studying the effect of Six Sigma upon the development and deployment of applications, Six Sigma effects should be studied on the other aspects of an IT organization as well.

Implementation Metrics

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:

First, sloppy habits and poor processes created thousands of duplicate client records. We would often find a customer like ‘ABC Consulting Services’ listed as either ‘ABC’ or ‘ABC Consulting’ or ‘ABC Services.’ Within each record we found outdated, inaccurate or often incomplete information. Then, we would find multiple orders for the same client cross connected across those records. No wonder orders kept getting mishandled. No one knew what ‘master record’ to follow.

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.

Dollar losses linked to poor data quality in the database decreased more than 53% from the time the project began to its completion. Within three months, dollar losses decreased by a magnitude of 10.

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