Catalog
concept#Data#Governance#Product#Security

Ethical Data Use

Guiding principles and practices for responsible collection, processing and sharing of data within organizations.

Ethical Data Use defines principles and practices for responsible collection, processing, and sharing of data within organizations.
Established
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

Data catalog / metadata repositoryConsent and preference management platformLogging and auditing systems

Principles & goals

Transparency: Traceability of data use and decisions.Purpose limitation: Use data only for clearly defined and legitimate purposes.Data minimization: Collect and store only necessary data.Accountability: Define responsibilities and control mechanisms clearly.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Reputational damage from improper data use.
  • Bias and discrimination in decisions and models.
  • Legal sanctions for non-compliance with data protection rules.
  • Apply privacy-by-design and data minimization from the start
  • Make DPIAs mandatory for high-risk data processing
  • Engage stakeholders and maintain transparent communication

I/O & resources

  • Inventory of existing datasets
  • Regulatory requirements and policies
  • Stakeholder and risk analysis
  • Ethical data policy and implementation guides
  • DPIA reports and audit logs
  • Training materials and role descriptions

Description

Ethical Data Use defines principles and practices for responsible collection, processing, and sharing of data within organizations. It covers legal requirements, fairness, transparency, purpose limitation, and risk assessment as well as governance measures, technical controls, and processes to reduce harm. The goal is trustworthy data use, compliance, and sustainable value creation.

✔

  • Increased trust from customers and stakeholders.
  • Stronger legal compliance and reduced fine risk.
  • Better-informed decisions through higher data quality.

✖

  • May slow innovation due to additional reviews.
  • Cross-border regulation can create complex requirements.
  • Ethical assessments can be subjective and context-dependent.

  • Share of datasets with DPIA

    Percentage of produced datasets for which a data protection impact assessment was completed.

  • Number of ethical incidents

    Registered and verified incidents concerning ethical concerns or misuse.

  • Data minimization ratio

    Ratio of actually used to originally collected data points.

Hospital: patient data for research

Established processes for pseudonymization, consent management and ethics review for research datasets.

E-commerce: non-discriminatory recommender systems

Auditing training data and implementing fairness metrics to avoid systematic disadvantage.

City government: open data with privacy

Anonymization and aggregation of open datasets and communicated usage rules for citizen data.

1

Take stock of data landscape and create stakeholder map

2

Identify and prioritize legal frameworks and risks

3

Define ethical policies, roles and controls

4

Implement technical measures (pseudonymization, access control)

5

Establish monitoring, audits and continuous reviews

⚠️ Technical debt & bottlenecks

  • Undocumented data provenance and transformations
  • Missing access controls on sensitive datasets
  • Ad-hoc scripts instead of reproducible data pipelines
data-qualitylegal-compliancecross-functional-alignment
  • Sharing personal data without valid consent
  • Using sensitive attributes for automated decisions without review
  • Selling aggregated datasets without adequate anonymization
  • Unclear or overly broad consents allow abusive use
  • False trust in complete anonymization
  • Missing audit trails hinder proof of accountability
Data governance and stewardshipLegal and privacy expertiseEthics assessment and risk analysis
Regulatory compliance (e.g. GDPR)Trust of customers and the publicData quality and traceability
  • • Legal frameworks and contracts
  • • Limited personnel and financial resources
  • • Legacy systems without adequate data controls