Data governance

What is the difference between data governance and data management?#

Data governance decides who has authority over data and which rules apply to it; data management is the day-to-day work of carrying those rules out. The NIST glossary describes a data governance model as establishing authority and the parameters for management and decision making, and defines data management as plans, policies, programs and practices that protect and enhance the value of data and information assets throughout their lifecycles.

DAMA International places data governance at the center of its data management framework and ties it to accountability, policies and related controls. In short: governance sets the decisions, management executes them, and governance checks that execution matches the rules.

What are the 4 pillars of data governance?#

There is no single official list of four pillars, and the pillar names vary by author. DAMA's DMBOK instead describes 11 knowledge areas with governance at the center, so treat any pillar list as a planning shorthand rather than a standard.

The checklist below turns a common four-pillar framing into questions a team can answer for any one system, such as a CRM or a ticketing tool.

• 1Ownership and accountability: is a named business owner accountable for decisions about this system's data?
• 2Data quality: are the fields that matter defined, and is someone checking that they are accurate and complete?
• 3Protection and access: is access limited to the people who need it, with sensitive fields identified?
• 4Lifecycle and compliance: are retention and deletion rules written down and followed?

Is data quality part of data governance?#

Yes: data quality is one of the outcomes governance is accountable for, but the quality work itself is a separate discipline. DAMA lists data quality as its own knowledge area, alongside data governance at the center.

A practical way to split them: governance defines what good data means for a field, who owns fixing it and what threshold is acceptable; quality management measures the data against that definition and corrects it. A governance and data quality framework therefore needs both a written standard and a recurring check, or the standard is never tested.

What does operational data governance look like day to day?#

Operational data governance is the routine application of governance rules to the records a business creates every day, such as tickets, work orders, invoices and call notes. It shows up as small, repeated decisions rather than policy documents.

Typical activities include approving a new field before it is added to a CRM, reviewing who has export access each quarter, applying retention rules on schedule, logging why a dataset was shared and with whom, and resolving disputes about which system is the source of truth for a customer record.