Understanding the Big C in DMAIC

By | In Blog | March 27th, 2014

Control Is CriticalAs we’ve discussed previously in this series, a central Six Sigma tenet is define, measure, analyze, improve, and control, or DMAIC.

It has gained much wider acceptance and evolved into a scientific method for project development.

Six Sigma can absolutely be applied to database management as a means to improve data quality throughout a project, ultimately giving customers and users the quality data they need, when they need it.

Although the execution of each one of these steps is important in the process of completing a project satisfactorily, the control stage often is neglected. Control is a big part of the process, for without control, you can’t ensure the quality of a project.

Control is critical but, as Phillip L. Rybarczyk notes, “Quality practitioners often either neglect or poorly execute the control stage, jeopardizing the sustainability of any improvements acquired from the project.”

Testing and validation

Although his comments apply to manufacturing, they can equally be applied to software development. Testing and validation are key to remaining certain that changes are implemented and that any training or procedural changes — data entry processes or understanding the information that belongs in a given field, for example — are effectively adhered to by staff. Also key is testing, which ensures these changes are maintained. The control stage also is helpful in providing the team with benchmarks for ongoing improvement.

Ongoing? Yes. Control isn’t a one-off step in the process.

That bears repeating. Control is not a passing part of the DMAIC process. It is a critical step for Six Sigma practitioners, to be revisited on an ongoing basis. Control ensures an organization that quality metrics and improvements are lasting, embraced by everyone in the organization.

For example, the processes associated with improvements in a database project should be documented. Those improvements need to be sustained or increased over time. Without them, you may as well have never undertaken the project.

Quality metrics

What are those metrics? It depends on the project. What’s most important to the organization? Data quality? Availability? Uptime?

Once you’ve identified the quality metrics essential to your project, Rybarczyk suggests – again, from a manufacturing perspective that can easily be adapted to database management – implementing a checklist for documenting the controls, consisting of:

  • The quality characteristics to control
  • The tolerances to maintain
  • The frequency of inspection
  • The sample sizes needed
  • The instruments and analysis methods to use
  • Instructions on what to do if non-conformances are detected

As the Six Sigma Materials website notes, the control phase is critical:

There can not be enough emphasis placed on the importance of devoting the same high level of energy and commitment throughout this phase. Complacency and anxiety can set in for the sake of bringing closure and receiving some form of credit or bonus, etc. This can result in the process reverting to the former performance levels and loss of some or all of the gains. Rigorous follow-up and corrective action with comprehensive yet simple documentation can increase the likelihood that the gains are sustained.

Where have you used DMAIC? How important have you found the control stage to be in instituting Six Sigma for database management? Please share your experiences with us.

Image by Faramarz Hashemi.

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Chuck Ezell
Vice President – Data Integration Practice
With almost 20 years of development experience, Chuck Ezell leads the Development, Tuning and Automation Practice (DTA). Bringing his knowledge from supporting some of the world's largest infrastructures in database & application performance tuning. With a broad background in process automation, database application development, optimizing database performance and many DevOps tools. Although he and his team excel at optimizing and customizing Oracle systems, Chuck works with .NET, T-SQL, C#, PHP, javascript, Java, ANSI SQL, PL/SQL, APEX, and many other languages on systems by Oracle, HP, IBM, Linux and Windows. He uses SSMS, Oracle OEM, AppDynamics, Splunk, Visual VM along with many others, for performance tuning operations.

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