Catalog
concept#Governance#Data#Analytics#Security

Data Governance

A framework for managing data quality, access, responsibilities and compliance across an organization.

Data governance defines policies, roles, and processes to manage data quality, access, and compliance across an organization.
Established
High

Classification

  • High
  • Organizational
  • Organizational
  • Advanced

Technical context

Data catalogs and metadata repositoriesIdentity and access management (IAM)Data quality and monitoring tools

Principles & goals

Define clear data role responsibilities (RACI).Treat data as a product with owners and SLAs.Introduce automatable controls and measurable quality targets.
Iterate
Enterprise, Domain

Use cases & scenarios

Compromises

  • Governance perceived as bureaucracy and ignored.
  • Unclear ownership leads to diffusion of responsibility.
  • Insufficient technical implementation causing gaps in controls.
  • Start iteratively with pilot domains.
  • Aim to automate controls and reporting.
  • Treat data as a product and assign product owners.

I/O & resources

  • Data inventory and metadata register
  • Legal and compliance requirements
  • Organizational roles and responsibilities
  • Governance policies and process documentation
  • Classified data catalogs and authorization models
  • Metrics, reports and audit logs

Description

Data governance defines policies, roles, and processes to manage data quality, access, and compliance across an organization. It establishes clear ownership, classification, and controls across the data lifecycle. Implementation requires organizational alignment, role definitions, and technical enablers to ensure trustworthy and value-driven use of data.

✔

  • Improved data quality and trustworthiness.
  • Reduced compliance and liability risks.
  • Better data access and more efficient usage by business areas.

✖

  • Effort and cost for adoption and ongoing maintenance.
  • Possible slowdown of innovation with excessive centralization.
  • Dependence on clear roles and organizational acceptance.

  • Data quality score

    Aggregate value from completeness, accuracy and consistency.

  • Compliance deviations

    Number of detected breaches of governance rules per period.

  • Time-to-access

    Average time for authorized users to gain access to data.

Enterprise-wide governance policy

Implementation of a central policy for data classification in an international corporation.

Domain-oriented governance model

Decentralized rules with clear domain ownership to accelerate product delivery.

Governance for self-service analytics

Balancing data security and usability for business users via templates and guardrails.

1

Define governance goals and scope.

2

Establish role model (owner, steward, consumer).

3

Implement policies, classification and technical controls.

⚠️ Technical debt & bottlenecks

  • Unstructured metadata storage without stable IDs.
  • Manual processes without automation paths.
  • Outdated classification and missing synchronization.
missing ownershipmanual processestechnical integration
  • Policy without clear owners leads to no implementation.
  • Only technical controls without organizational rules.
  • Governance initiative as a one-off project without operations.
  • Introducing too many rules at once and risking overload.
  • Not involving stakeholders early enough.
  • Treating technology as a substitute for organizational decisions.
Data management and domain knowledgeLegal and compliance understandingProcess and change management skills
Data classification and sensitivityTraceability and auditabilityAutomatable access controls
  • • Legal frameworks and data protection laws.
  • • Existing system landscape and interfaces.
  • • Limited personnel and financial resources.