Key Takeaways
- Data governance establishes policies, standards, and accountability for enterprise data assets.
- Master Data Management (MDM) creates a single source of truth for critical business entities like customers, products, and vendors.
- Data quality is measured by accuracy, completeness, consistency, timeliness, validity, and uniqueness.
- Metadata management catalogs data definitions, lineage, and relationships to ensure understanding and trust.
- DAMA-DMBOK provides a comprehensive framework covering 11 knowledge areas of data management.
Data Governance and Management
Quick Answer: Data governance is the framework of policies, processes, and standards that ensure data is accurate, secure, and available for business decision-making. It covers data quality, master data management, metadata, data lifecycle, and compliance with regulations like GDPR and SOX.
Why Data Governance Matters for Management Accountants
Management accountants rely on data for budgeting, forecasting, performance analysis, and strategic decision-making. Poor data governance leads to:
- Inaccurate financial reports due to inconsistent data definitions
- Compliance violations from poor data handling practices
- Inefficient operations when employees can't find or trust data
- Failed analytics initiatives built on unreliable data foundations
Data Governance Framework Components
| Component | Description | Example |
|---|---|---|
| Data Policies | High-level rules governing data management | "Customer data must be encrypted at rest and in transit" |
| Data Standards | Specific formats and definitions | "Date format: YYYY-MM-DD" |
| Data Stewardship | Assigned accountability for data domains | "Finance data steward owns GL chart of accounts" |
| Data Quality Rules | Measurable requirements for data accuracy | "Customer records must have valid email addresses" |
| Access Controls | Who can view, edit, or delete data | "Only Finance managers can modify budget data" |
Data Quality Dimensions
High-quality data is the foundation of reliable analytics. The six primary dimensions of data quality are:
| Dimension | Definition | Example Issue |
|---|---|---|
| Accuracy | Data correctly represents reality | Wrong customer address in system |
| Completeness | All required data is present | Missing cost center codes |
| Consistency | Same value across all systems | Customer named "IBM" in one system, "I.B.M." in another |
| Timeliness | Data is current when needed | Month-end close data available by Day 5 |
| Validity | Data conforms to defined formats | Phone numbers with correct digit count |
| Uniqueness | No unintended duplicate records | Same vendor entered twice with different IDs |
Data Quality Metrics
Organizations typically track data quality using scorecards:
| Metric | Formula | Target |
|---|---|---|
| Accuracy Rate | (Correct Records / Total Records) × 100 | > 99% |
| Completeness Rate | (Records with All Required Fields / Total Records) × 100 | > 98% |
| Duplicate Rate | (Duplicate Records / Total Records) × 100 | < 1% |
| Timeliness Rate | (On-Time Data Loads / Total Data Loads) × 100 | > 99% |
Data Lifecycle Management
Data has a lifecycle from creation to disposal. Effective governance manages each stage:
| Stage | Activities | Controls |
|---|---|---|
| Creation/Capture | Data entry, system integration, IoT collection | Input validation, standardization |
| Storage | Database management, data warehousing | Encryption, backup, access controls |
| Usage | Reporting, analytics, operational use | Audit trails, usage monitoring |
| Sharing | Internal distribution, external transfers | Data sharing agreements, anonymization |
| Archival | Moving inactive data to cold storage | Retention schedules, accessibility |
| Disposal | Secure deletion when retention expires | Destruction certificates, compliance verification |
Master Data Management (MDM)
Master Data Management creates a single source of truth for critical business entities. Without MDM, organizations struggle with:
- Multiple customer IDs for the same customer
- Inconsistent product descriptions across systems
- Conflicting vendor information in different applications
Key Master Data Domains
| Domain | Description | Example Attributes |
|---|---|---|
| Customer | Organizations and individuals who buy | Name, address, account number, credit terms |
| Product | Goods and services offered | SKU, description, pricing, category |
| Vendor/Supplier | Organizations providing goods/services | Vendor ID, payment terms, tax ID |
| Employee | Workforce data | Employee ID, department, job title |
| Chart of Accounts | Financial classification structure | Account number, type, hierarchy |
| Location | Physical and logical locations | Address, region, warehouse codes |
MDM Implementation Approaches
| Approach | Description | Pros/Cons |
|---|---|---|
| Consolidation | Data copied to central hub | Read-only; doesn't fix source |
| Registry | Links to sources without moving data | Low impact; complex queries |
| Coexistence | Central hub syncs with sources | Balanced; highest complexity |
| Transactional | Hub is the single system of record | Full control; major change management |
Metadata Management
Metadata is "data about data." It helps users find, understand, and trust data assets.
Types of Metadata
| Type | Description | Examples |
|---|---|---|
| Technical Metadata | Physical data characteristics | Table names, column types, indexes |
| Business Metadata | Meaning and context | Definitions, owners, business rules |
| Operational Metadata | Processing information | Load times, row counts, error logs |
| Lineage Metadata | Data origins and transformations | Source systems, ETL processes |
Data Catalog Benefits
Modern organizations use data catalogs to:
- Enable self-service analytics
- Document tribal knowledge
- Track regulatory compliance
- Support data democratization
Data Governance Frameworks
DAMA-DMBOK Framework
The Data Management Association's Body of Knowledge covers 11 knowledge areas:
| Knowledge Area | Focus |
|---|---|
| Data Governance | Strategy, policies, oversight |
| Data Architecture | Structures, integration, data flows |
| Data Modeling | Conceptual, logical, physical models |
| Data Storage | Databases, warehouses, lakes |
| Data Security | Access, privacy, encryption |
| Data Integration | ETL, APIs, data movement |
| Data Quality | Profiling, cleansing, monitoring |
| Master Data | Golden records, matching |
| Reference Data | Codes, classifications |
| Metadata | Catalogs, lineage, glossaries |
| Data Warehousing/BI | Analytics infrastructure |
Regulatory Compliance Considerations
| Regulation | Data Governance Impact |
|---|---|
| GDPR | Data privacy, consent management, right to be forgotten |
| SOX | Financial data controls, audit trails |
| CCPA | California consumer privacy rights |
| HIPAA | Healthcare data protection |
| PCI-DSS | Payment card data security |
Management accountants must ensure data governance programs support these regulatory requirements, particularly SOX compliance for financial reporting.
Which data quality dimension is violated when the same customer appears in the database with two different customer IDs?
What is the primary purpose of Master Data Management (MDM)?
Which stage of the data lifecycle involves moving inactive data to cold storage while maintaining accessibility for compliance purposes?
A company's revenue figures in the CRM system show $5.2M while the ERP system shows $5,200,000 for the same period. Which data quality dimension is primarily affected?