Guiding Principles

The successful implementation of a data quality program in the enterprise is based on key guiding principles. These statements provide a solid foundation for a comprehensive data quality framework that addresses various aspects of data management, including data governance, ownership, profiling, cleansing, monitoring, security, and continuous improvement.

By following these principles, the enterprise can ensure that data quality management program:

  • Meets business needs,

  • Is aligned with the overall strategy,

  • Engages stakeholders,

  • Takes a process approach,

  • Focuses on continuous improvement,

  • Makes data-based decisions.

ADO’s guiding principles to build the data quality framework are aligned with the enterprise Data Office North Star:

  • The enterprise is committed to delivering high-quality data to our customers, partners, and regulatory entities, recognizing that the data we provide is critical to their decision-making, operations, and compliance. Specifically, any data delivered externally must meet the highest quality standards as defined as part of the Data Quality Framework. For further details, refer to Business Impact Matrix and Data Quality levels expectations and tolerance error ratios Matrix (Ref 1.)

  • Data Quality initiatives should be prioritized based on the most significant impact on our business goals and allocate resources accordingly. For further details on prioritization standards, refer to (Ref 1.)

  • Effective data quality management requires collaboration among various stakeholders:

    • Each data asset must have a designated data owner and data steward responsible for managing its quality and addressing data-related issues

    • The Data Quality Team within R&D will lead the investigation, prioritization, and resolution planning efforts

  • The enterprise is committed to continuous improvement of data quality through a systematic approach to planning, executing, and monitoring data quality initiatives throughout the entire data lifecycle. This includes:

    • Conducting regular data profiling exercises to understand data structure, content, and quality,

    • Cleansing and validating data,

    • Continuously monitoring and reporting metrics,

    • Reviewing and updating policies and procedures,

    • Conducting regular data quality reviews and audits.

  • To ensure that issues are resolved promptly and receive appropriate attention, we have established a clear escalation path for data quality issues. This escalation path involves multiple levels of responsibility, from Data Stewards to Executive Leadership. See (Ref 2.) for further details.

  • The enterprise recognizes that maintaining high-quality data is a collective responsibility. Therefore, we are committed to providing appropriate training and awareness to all employees involved in data quality management. See (Ref 3.) for more details.

(Ref 1) For further details on Business Impact Matrix and Data Quality levels expectations and tolerance error ratios Matrix, view Data Quality Prioritization.
(Ref 2) For further details on escalation path, view Data Quality Remediation.
(Ref 3) View list of stakeholders involved in Data Quality for details.

The following is a high level representation of the enterprise Data Quality Framework guiding principles:

Business Driven

Data Quality efforts should be adapted to the importance of the data for the business and the level of risk if data is incorrect.

Leadership

Data Quality efforts should be driven by a clear leadership that involves guiding and empowering stakeholders to achieve improvements.

Stakeholder Engagement

Parties that are involved through the Data Quality lifecycle management should be able to express their needs.

Process Approach

Data Quality should be managed across the data lifecycle from its creation to its disposal and as it moves between systems.

Continuous Improvement

Data Quality Management is supported by incremental and constant improvement approach.

Standardization

A unified approach should be shared across teams for Data Quality initiatives in a consistent and measurable way.

Sustainability

Beyond correcting errors, problems with the quality of data should be addressed at their root causes (including processes, skills and systems).

Proactivity

A proactive approach should be adopted whenever possible to help prevent data errors from happening, rather than implementing after-the-fact measures.

Data Quality Culture

Training and change management are provided to establish a Data Quality culture needed to drive continuous improvement across the organization.