7 principles of data quality management

1. 7 Principles of Data
Quality
Management
By George Firican

3. • 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.

4. • 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.

5. • 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.
•
• 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. • 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.