Six Sigma Data Quality
Six Sigma data quality is the discipline and methodology to reduce data errors to 3.4 errors per million opportunities. In other words, Six Sigma Data Quality is about reducing data errors to 34 or less for every ten million opportunities.
A six sigma data quality opportunity does not translate into one record but the number of times a data error can be introduced into that record. For example a Six Sigma Data quality record that has 6 attributes and each attribute is sourced from a different system translates into at least 6 opportunities if not more.
Six Sigma data quality is about improving data quality standards to improve process quality and productivity.
Six Sigma Methodologies
The two commonly used Six Sigma methodologies are DMAIC & DMADV. Both methodologies incorporate the philosophy of continuous improvement using a feedback loop
DMADV (Define, Measure, Analyze, Design and Verify) is used to develop new processes or make radical changes to existing processes.
DMAIC (Define, Measure, Analyze, Improve and Control) is used to provide incremental improvements to existing processes.
Data Certification Program Challenges
Six Sigma data quality implementations usually encounter the following issues /challenges:
• Understanding the needs and fitness of data use
• Reference standards to measuring the data accuracy
• Finding the root cause of defects
• Data volumes and size of databases
Six Sigma data quality, once successfully implemented can save large organizations 100 of million dollars as the Global economy becomes more data intensive.
Our Expertise
Our team has extensive experience implementing Six Sigma data quality programs. We start with analysis of the process that generates the data as fixing data quality at source is cheaper than scrubbing data downstream and then drill deeper to identify the root cause of the issue.
Contact Us to get answers to your specific Data Challenges and find out how we can help you achieve your business objectives through better data management.