7 data quality management principles

The principles of data quality management are a set of fundamental beliefs, standards, rules and values that are accepted as true and can be used as a foundation for guiding an organization’s data quality management.

They have been adapted from ISO 9000 principles of quality management.

These principles are not listed in an order based on priority. Each principle’s relative importance will vary from organization to organization and can be expected to change over time.


1. Business-need focus

The primary focus of data quality management is to meet the data quality dimensions requirements of its business needs.

Why it’s important:
A data quality management program needs to ensure that the quality of the data meets the business needs as otherwise resources are wasted for no value gained. Understanding the current and future needs of the business is instrumental to a sustained improvement of data quality.

2. Leadership

Leaders at all levels convey the same purpose and direction and create conditions whereby the entire organization is committed to achieving its data quality objectives.

Why it’s important:
Achieving an organization-wide data quality management program requires for the leadership to align itself to a set of common strategies, policies, processes and resources. Otherwise, different units might pull in different directions and/ or double up on the effort to achieve their own data quality objectives.

3. Stakeholder engagement

Competent, empowered and engaged data stakeholders across the organization are critical to build sustainable data quality management.

Why it’s important:
Data quality is everyone’s responsibility, but for this statement to be true, all employees need to work in a framework where they are respected, recognized for their efforts, and empowered to raise issues causing bad data quality and have clear ways of fixing and preventing them.

4. Process approach

Good data quality is achieved more effectively and efficiently by understanding and managing all business and technical activities as interconnected processes that function as a coherent ecosystem.

Why it’s important:
A comprehensive and successful data quality management program needs to take into account all business and technical processes which acquire, produce, maintain, transform, disseminate, and destroy data. Understanding how these processes interact with each other and what results they produce will enable the organization to optimize its ecosystem and outcomes.

Learn how to use Pareto analysis and improve your data quality.

5. Continuous improvement

Successful data quality management has an ongoing focus on improvement.

Why it’s important:
Data quality management must always be understood as a program which needs to be continuously re-evaluated and adapted to keep up with internal and external conditions and gain incremental successes.

6. Data-based decision making

Decisions based on data and information analysis will generate the desired results more often.

Why it’s important:
Decision making can be challenging and complex as it always involves some uncertainty. Its different sources of inputs can often be interpreted and subjective. This is similar for decisions needed in a data quality management program. Facts, evidence and data analysis lead to increased objective decision-making.

7. Relationship management

For sustained success, the organization manages its relationships with its vendors of data management tools, as well as data producers, suppliers and consumers.

Why it’s important:
Data quality management doesn’t only cover internal stakeholders to be part of data quality improvements, but also its vendors of data management tools (ex: database management, data security, metadata management, etc.) , data producers and suppliers (ex: 3rd party data sources and systems, data cleansing services, and so on), as well as its data consumers (ex: business intelligence tools, service consumers, end-users, etc.).


These data quality management principles can be applied in many different ways. How the organization implements these principles will be decided based on the nature and specific challenges that the organization faces. One thing is certain, though, that the organization will find a lot of benefits setting up their data quality management program based on these principles.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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