Introduction to Data Quality Improvement Lifecycle

The data quality improvement lifecycle covers the various stages involved when dealing with data. Few areas covered are:

• Capturing the data
• Validating the data
• Cleansing the data
• Adding and improving the data etc.

Pictorial representation of data quality improvement life cycle:

1. Plan

Data Quality team assesses the scope, Impact, and priority of known issues and evaluates alternatives to address them.

Activities involved:

• Identify issues
• Priorities them
• Root cause analysis

2. Do

Data Quality team leads efforts to address the root causes of issues and plan for ongoing monitoring of data.

Activities involved:

• Manage non-technical Issues with business team
• Manage technical Issues with BI/IT team

3. Check

Actively monitoring the quality of data as measured against requirements. 

Activities involved:

• Data Quality Rules Setup
• No action in case of results are in thresholds. 
• Take additional action in case the results are not in thresholds. 

4. Act

Act stage is for activities to address and resolve emerging data quality issues. The cycle restarts, as the causes of issues are assessed and solutions proposed. Continuous improvement is achieved by starting a new cycle. 

The quality of data improves with more iterations.