Skill Hub — Data & DAMA
DAMA / Data skills
Practical working skills for data governance, quality diagnostics, metadata, master data, lineage, reference data, and integration interoperability. Not framework summaries. Real methods, decision rules, and artifact templates.
What this skill group covers
This group covers the data management work that happens between "we have a data problem" and "the data problem is fixed and won't recur." It focuses on operational skills: diagnosing why data is wrong, deciding who owns it, tracing where it came from, and building controls that survive the next project cycle.
These skills are designed for enterprise data consultants, SAP data stewards, integration analysts, and AI agents that need to produce structured data artifacts instead of generic explanations.
When to use this group
- A business process fails and the root cause is data, not code or configuration.
- Master data changes in one system but does not propagate correctly to another.
- A report is wrong and no one can explain which source system the number came from.
- A data quality initiative stalls because ownership, rules, or enforcement are undefined.
- An integration fails and the data payload is malformed, incomplete, or mapped to the wrong target field.
- You need to document data lineage, metadata, or reference data for compliance, migration, or AI readiness.
Skills in this group
Data Governance
Diagnose missing ownership, undefined rules, and unenforced policies. Produce a governance action plan with named owners, decision rights, and enforcement mechanisms.
Data Quality Root Cause
Trace a data defect from symptom to entry point. Classify the root cause type. Produce a correction plan and a prevention control.
Master Data Management
Map master data domains, identify duplication and fragmentation, define golden record logic, and design replication governance.
Metadata Management
Catalog business, technical, and operational metadata. Identify gaps that block reporting, integration, or AI readiness. Produce a metadata inventory with ownership.
Data Lineage
Trace data from source to consumer. Document transformations, hops, and ownership at each stage. Identify lineage gaps that create audit or trust risk.
Reference Data Management
Manage code lists, status values, and classification schemes. Prevent drift between systems. Design distribution and change control for reference data.
Data Integration & Interoperability
Diagnose integration failures caused by data mismatch, schema drift, or mapping errors. Define data contracts and validation rules at interface boundaries.
Recommended paths
Data quality incident path
- Data Quality Root Cause
- Data Governance — if ownership or rules are missing
- Master Data Management — if the defect is in master data
- Data Lineage — if the source is unknown
Integration failure path
- Data Integration & Interoperability
- Reference Data Management — if code values mismatch
- Data Lineage — if the data path is unclear
AI readiness path
- Metadata Management
- Data Lineage
- Data Governance — if ownership is unclear
Status and limitations
This skill group is a public working interpretation of data management practice. It is not official DAMA-DMBOK documentation. It draws on DAMA knowledge areas but translates them into operational skills for enterprise consultants and AI agents.
Some skills are more mature than others. Data Quality Root Cause and Master Data Management have been tested in SAP support contexts. Metadata Management and Data Lineage are more conceptual and may need adaptation to specific tool landscapes.
All skills assume you have access to systems, stakeholders, and data samples. They do not replace vendor documentation or specialized data quality tooling.