8. Data Quality Management Plan (Draft)

Introduction
The Data Quality Management Plan outlines our strategies, processes, and responsibilities for maintaining high-quality data. It ensures that data is accurate, consistent, complete, and timely.

Objectives

• Accuracy: Ensure data correctness.

• Completeness: Avoid missing or incomplete data.

• Consistency: Maintain uniformity across data sources.

• Timeliness: Keep data up-to-date.

Roles and Responsibilities

1. Specify the roles responsible for data quality:

   1. Data Stewards: Responsible for specific data domains.

   2. Data Owners: Accountable for data quality within their areas.

   3. Data Governance Committee: Oversee overall data quality efforts.

Data Quality Dimensions

• Accuracy: Validity, precision, and correctness.

• Completeness: Presence of all required data elements.

• Consistency: Uniformity across systems.

• Timeliness: Freshness of data.

Data Profiling/Assessment (identify anomalies, outliers, and patterns)

1. Detail how data profiling will be conducted:

   1. Use automated tools to analyze data quality.

   2. Identify anomalies, outliers, and patterns.

Data Quality Rules
Document specific rules for each dimension:

• Example (Accuracy):

  • Rule: Birthdates must be valid dates.

  • Description: Validate format (YYYY-MM-DD) and range.

Data Cleansing

1. Outline procedures for data cleansing:

   1. Correct inaccuracies, missing values, and inconsistencies.

   2. Define processes for manual and automated cleansing.

Monitoring and Reporting

1. Explain how data quality will be monitored:

   1. Regular checks, audits, and exception reports.

   2. Metrics (e.g., accuracy rate, completeness percentage).

Remediation (steps for addressing data quality issues)

1. Escalation paths for critical issues.

2. Corrective actions and timelines.

Communication

1. Detail how data quality efforts will be communicated:

   • Training sessions for data stewards.

   • Regular updates to stakeholders.

Review and Improvement

1. Schedule periodic reviews of the plan:

   1. Assess effectiveness and adjust as needed.

   2. Continuously improve data quality processes.