DAMA-DMBOK2 - Data Management Body of Knowledge (2nd Edition)

Maintain an enterprise data architecture including conceptual models, data flows, and reference architecture aligned to business capabilities.

• conceptual model
• data flow diagrams
• reference architecture

• no maintained CDM
• drift between architecture and reality

Architecture changes follow review processes that assess impact on integration, quality, and compliance.

• ARB minutes
• architecture decisions
• impact assessments

• ARB bypassed for tactical projects

Define the overall structure of data and data-related resources as an integral part of the enterprise architecture.

• enterprise data architecture diagram
• reference architecture
• architecture review board

• fragmented architecture
• no ARB
• stale diagrams

Analyze, design, build, test, and maintain data models representing business requirements and system structures.

• conceptual model
• logical model
• physical model
• modeling standards

• incomplete models
• weak standards
• stale documentation

Maintain architectural standards and guidelines that direct data integration, data flow, and technology platform decisions.

• data architecture standards
• naming conventions
• pattern library

• weak standards adoption
• inconsistent naming
• no pattern library

Establish ethical guidelines for data collection, usage, sharing, and monetization that respect individual rights.

• data ethics framework
• ethics review board
• case register

• no framework
• weak review
• no register

Assess organizational data management maturity levels and establish improvement roadmaps for each knowledge area.

• DM maturity assessment
• improvement roadmap
• KPI tracker

• no assessment
• weak roadmap
• no tracker

Manage the unique requirements of big data environments including volume, velocity, variety, and veracity considerations.

• big data architecture
• data science governance
• model registry

• weak governance
• no model registry
• stale architecture

Establish policies, decision rights, and accountabilities for the proper management of data assets across the organization.

• DG strategy
• DG operating model
• council charter

• no strategy
• weak operating model
• unclear council mandate

Define data stewardship roles and responsibilities for oversight and accountability of data quality and usage.

• steward role description
• steward register
• data domain map

• no stewards
• weak mandate
• unclear domains

Develop and enforce organization-wide data policies, standards, and procedures for data management activities.

• data policy catalog
• standards register
• policy lifecycle

• stale policies
• weak standards
• no lifecycle

Establish a governance operating model with steering committee, data council, stewards, and clear decision rights aligned to business strategy.

• Charter
• RACI
• council minutes
• org chart

• no accountable executive sponsor
• stewards undefined

Define, publish, and enforce data policies, standards, and procedures covering classification, retention, sharing, and quality.

• policy library
• standards catalog
• exception register

• policies unpublished
• no exception process

Appoint business and technical stewards with documented responsibilities and engagement cadence.

• steward roster
• responsibility matrix
• meeting cadence

• stewardship in name only

Periodically assess maturity across knowledge areas and prioritise improvements.

• maturity assessment
• improvement roadmap
• investment plan

• no rebaseline after 2 years

Design and govern integration patterns (ETL, ELT, streaming, replication, APIs) consistently across the enterprise.

• integration catalog
• pattern guide
• interface contracts

• point-to-point sprawl

Document end-to-end data lineage from sources through transformations to consumers.

• lineage diagrams
• metadata tool output
• transformation rules

• manual lineage missing fields

Manage data acquisition, extraction, transformation, movement, delivery, replication, federation, and virtualization.

• integration architecture
• API catalog
• ETL inventory

• point-to-point sprawl
• weak catalog
• stale ETL

Define, monitor, and maintain data integrity through profiling, cleansing, matching, and continuous improvement.

• DQ rules catalog
• DQ dashboard
• DQ improvement plan

• weak DQ rules
• no dashboard
• no improvement plan

Collect, categorize, maintain, integrate, and deliver metadata to support data management and governance.

• metadata catalog
• business glossary
• lineage diagrams

• weak metadata
• no lineage
• stale glossary

Develop layered data models with traceability from business concepts to physical implementation.

• CDM
• LDM
• PDM
• model versioning

• physical drift from logical model

Apply consistent naming, abbreviation, and modeling notation standards across the enterprise.

• naming standard
• abbreviation list
• style guide

• inconsistent table prefixes

Establish a DQ program with dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness), KPIs, and remediation.

• DQ framework
• KPI dashboards
• issue log

• DQ owned only by IT, not business

Profile critical data elements regularly and monitor quality against thresholds with alerts.

• profiling output
• DQ rules
• monitoring dashboards

• thresholds not defined

Track quality issues to root cause and implement preventive controls.

• issue tickets
• RCA reports
• preventive control register

• recurring issues without RCA

Classify data by sensitivity and apply controls commensurate with classification levels.

• classification policy
• data inventory with classes
• labeling tools

• unclassified PII in shared drives

Implement role-based access, least privilege, and review privileged accounts regularly.

• RBAC matrix
• access review reports
• privileged account inventory

• no quarterly access reviews

Protect data at rest and in transit using encryption, masking, or tokenisation appropriate to risk.

• encryption inventory
• key management procedures
• masking config

• prod data in non-prod without masking

Design databases for performance, availability, and recoverability with documented operations.

• design docs
• backup schedules
• recovery test results

• DR tests not performed
• no documented runbooks

Monitor performance, plan capacity, and tune database environments proactively.

• capacity plans
• performance baselines
• tuning reports

• no capacity forecasting

Manage structured physical data assets through storage deployment, maintenance, and operations support.

• storage architecture
• capacity plan
• backup policy

• weak capacity planning
• stale architecture
• unverified backups

Ensure privacy, confidentiality, and appropriate access controls for personal, health, and private data.

• data security policy
• controls catalog
• monitoring evidence

• weak controls
• no monitoring
• stale policy

Control and monitor access to data assets through authentication, authorization, and audit mechanisms.

• access management policy
• access reviews
• privilege catalog

• stale access
• no reviews
• weak privilege catalog

Provide integrated analytical data via warehouse, marts, or lakehouse aligned to reporting needs.

• DW architecture
• semantic layer
• BI catalog

• multiple conflicting marts

Reconcile analytical outputs against sources and certify reports for trust.

• recon reports
• certified report register
• balancing controls

• BI numbers diverge from source

Manage unstructured documents and records with classification, retention, and search capability.

• ECM inventory
• retention schedule
• records policy

• legacy fileshares unmanaged

Store, protect, index, and enable access to data found in unstructured sources including electronic files and physical records.

• DMS configuration
• content lifecycle policy
• retention schedule

• uncontrolled documents
• weak lifecycle
• stale retention

Manage analytical data processing through data warehouse design, ETL processes, and dimensional modeling.

• DW architecture
• ETL design
• data marts inventory

• weak data marts
• poor lineage
• stale ETL

Enable access to decision support data for reporting, analysis, and data-driven decision making.

• BI tooling inventory
• analytics catalog
• KPI dictionary

• report proliferation
• weak KPI definitions
• no analytics catalog

Capture business, technical, and operational metadata in a managed metadata repository.

• metadata strategy
• catalog tool
• glossary entries

• business glossary stale

Maintain shared business definitions and link them to physical implementations.

• Glossary
• approval workflow
• term-to-column linkage

• terms approved but unlinked

Manage shared reference data sets to reduce redundancy and ensure standardized definition and use of data values.

• reference data inventory
• governance procedures
• change log

• fragmented reference data
• no governance
• weak change log

Establish authoritative sources for master data entities and implement processes to maintain data consistency.

• MDM architecture
• golden record policy
• stewardship plan

• weak golden record
• stewardship gaps
• stale architecture

Implement matching algorithms and linking processes to resolve duplicates and maintain a single view of entities.

• matching rules
• entity resolution platform
• review queue

• weak matching rules
• no review queue
• poor resolution accuracy

Govern reference data (codes, lookups, taxonomies) with authoritative sources and change control.

• reference data catalog
• change log
• authoritative source list

• multiple country code lists

Implement MDM for critical entities (customer, product, employee) with matching, survivorship, and stewardship.

• MDM hub design
• match rules
• survivorship rules
• steward workflow

• customer duplicates across systems