Total Data Quality Management (TDQM) Framework: A Systematic Approach to DRL 7 Maturity
Introduction: The TDQM Philosophy
The Total Data Quality Management (TDQM) Framework provides a comprehensive, lifecycle-based approach to systematically managing data quality across all organisational stakeholders. Developed by Wang (1998) and refined through extensive research, TDQM represents a fundamental shift from reactive data quality firefighting to proactive, continuous quality management embedded throughout the data lifecycle.
TDQM operationalises the achievement of DRL 7 maturity by providing structured processes that ensure data quality is engineered into systems, continuously monitored, and systematically improved. At DRL 7, organisations don't just have quality data, they have sustainable processes that maintain data excellence as a natural outcome of how data is designed, created, managed, and consumed. TDQM provides the methodological framework for embedding these quality processes across the organisation.
The TDQM framework has been carefully aligned with the 10 Root Conditions of Data Quality (Lee and Pepino, 2009), ensuring that the systematic processes it defines directly address the fundamental challenges that prevent organisations from achieving data maturity. By following TDQM principles across all root conditions, organisations create the sustainable quality foundation essential for Predictive People Analytics and AI enablement.
TDQM as a Systematic Toolkit for the DPM:
The TDQM framework serves as a structured toolkit that enables the Data-as-a-Product Manager to operationalise quality management across all stakeholder groups. Rather than describing what stakeholders need (as the DPM responsibility frameworks do), TDQM prescribes how to systematically achieve and maintain quality through four integrated lifecycle phases:
Engineering: Define information and quality features, production processes, and input quality standards for consumers, manufacturers, and suppliers respectively
Monitoring: Measure information quality as perceived by consumers, measure process quality for manufacturers, and measure input quality for suppliers
Improvement: Provide feedback to consumers, evaluate and prioritise process improvements for manufacturers, and evaluate/implement input improvements for suppliers
Continuous Cycle: These phases operate continuously, creating sustainable quality management rather than one-time fixes
The framework operates across three critical stakeholder dimensions:
Data Consumers: Those who use data for analytics and decision-making. TDQM ensures they receive data products that meet defined quality standards
Data Manufacturers: Those who build and operate data infrastructure. TDQM ensures their processes systematically produce quality data
Data Suppliers: Those who create and input data. TDQM ensures quality is built in at source through defined standards and controls
The TDQM Lifecycle Approach:
What distinguishes TDQM from ad hoc quality management is its systematic lifecycle approach. Each stakeholder group progresses through Engineering → Monitoring → Improvement phases continuously:
Engineering phase establishes what quality means and how it will be achieved for each stakeholder group
Monitoring phase measures whether quality standards are being met in practice
Improvement phase systematically addresses gaps and evolves quality standards as needs change
This lifecycle thinking embodies the shift from data by-product (where quality is addressed reactively if at all) to data-as-a-product (where quality is engineered, measured, and continuously improved). The TDQM framework provides the operational playbook for maintaining DRL 7 maturity once achieved.
