What is data governance in ERP?

Data governance is the overall management of data availability, usability, integrity, and security. It includes policies, standards, processes, and roles (data owners, stewards) that ensure data is trusted and used effectively across the organisation.

Why is data governance important for ERP?

Poor data quality leads to wrong decisions, operational inefficiencies, and compliance failures. Data governance ensures that master data (customers, vendors, products) is accurate, consistent, and secure – the foundation for a successful ERP.

What is the difference between data governance and data management?

Data management is the practice of collecting, storing, and using data. Data governance is the strategy, policies, and oversight that guide data management. Governance sets the rules; management follows them.

Data governance

From ERPEDIA, the independent ERP knowledge base

Data governance is the framework of policies, processes, and roles that ensure data is managed effectively, securely, and in compliance with regulations. In ERP, strong data governance is essential for data quality, master data management, and reliable reporting. This article covers the pillars of data governance – with links to compliance, data migration, and cybersecurity.

Contents

1. Why data governance matters
2. Pillars of data governance
3. Roles & responsibilities
4. Data quality management
5. Master data management (MDM)
6. Data security & privacy
7. Data lifecycle management
8. Best practices

1. Why data governance matters

Poor data governance leads to:

Inaccurate financial reports
Duplicate customer/vendor records
Compliance violations (GDPR, SOX)
Operational inefficiencies
Bad business decisions based on bad data

Stat: Gartner estimates that poor data quality costs organisations an average of $12.9 million annually.

2. Pillars of data governance

Data policy

Rules and standards for data creation, maintenance, and usage.

Data ownership

Business roles accountable for data quality and decisions.

Data quality

Accuracy, completeness, consistency, timeliness.

Master data

Core entities: customers, vendors, products, chart of accounts.

Data security

Access controls, encryption, privacy.

Data lifecycle

Creation, retention, archiving, deletion.

3. Roles & responsibilities

Role	Responsibilities
Data owner	Senior business leader accountable for a data domain (e.g., customer data owner). Approves policies, resolves issues.
Data steward	Day‑to‑day management of data quality, standards, and processes. Works with business users.
Data custodian	IT role responsible for technical environment, security, backups.
Data governance council	Cross‑functional group that sets strategy, resolves conflicts, oversees governance program.

4. Data quality management

Dimensions of data quality:

Accuracy: Data reflects reality.
Completeness: All required fields populated.
Consistency: Same values across systems.
Timeliness: Data is up‑to‑date.
Uniqueness: No duplicates.

Typical data quality score after governance program: 85-95%.

See data migration for cleansing techniques.

5. Master data management (MDM)

MDM ensures that master data (customers, vendors, products, etc.) is consistent across the organisation. Key activities:

Data standardization: Same formats (e.g., phone numbers, addresses).
Deduplication: Merge duplicate records.
Golden record: Single source of truth.
Data enrichment: Add missing attributes.
Governance: Who can create/change master data?

Tip: Start MDM with the most critical domains: customers and products.

6. Data security & privacy

Data governance includes security policies:

Access controls: Who can view/create/edit data? (See cybersecurity).
Data classification: Public, internal, confidential, restricted.
Privacy: GDPR compliance – consent, right to be forgotten, data minimization.
Audit trails: Track changes to sensitive data.

7. Data lifecycle management

Stage	Activities
Create	Data entry with validation, approval workflows.
Store	Secure storage, backups, encryption.
Use	Access controls, data quality monitoring.
Archive	Move old data to cheaper storage, retain for compliance.
Delete	Secure deletion when no longer needed (GDPR).

8. Best practices

Start small, think big: Pilot with one domain, then expand.
Get executive sponsorship: Data governance needs top‑down support.
Define metrics: Measure data quality (completeness, accuracy).
Automate where possible: Use data quality tools, MDM software.
Communicate: Make data governance part of company culture.

Key Takeaways

Data governance ensures data is accurate, consistent, secure, and compliant.
Key pillars: policy, ownership, quality, master data, security, lifecycle.
Roles: data owners (business), stewards (daily mgmt), custodians (IT).
Master data management (MDM) is a core discipline – create golden records.
Start small, measure quality, and automate.

Who should be a data steward? Someone with business knowledge, attention to detail, and authority to enforce standards. Often a senior analyst or manager.

How often should data quality be measured? Monthly for critical data; quarterly for others. Automate where possible.

What is a data governance tool? Software that helps manage policies, workflows, data quality, and MDM (e.g., Informatica, Collibra, Talend).

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