Information Governance vs Data Governance

Originally published on July 27th 2023 and updated on 12th December 2025

Grasping the contrast between information governance and data governance is essential for business executives who want to make better decisions, have lower risk, and face fewer compliance surprises. The terms get used interchangeably, but they solve different problems and operate at different levels. Not understanding the difference can be detrimental because it leads to gaps in ownership, inconsistent controls across systems, and increased regulatory and operational risk. 

Data governance is the strategic framework for how data is defined, created, maintained, accessed, and protected. It typically includes policies and controls for data quality management, security measures (like data loss prevention), and meeting regulatory requirements. 

Information governance, by comparison, is broader. As we explain in our overview of practical information governance, it spans the full lifecycle of information, created, used, shared, retained, and disposed across systems and formats (documents, emails, chats, files, records, databases, and more). It also clarifies decision rights: who can create, approve, classify, retain, delete, or produce information when needed. 

Key Takeaways 

• Data governance makes data trustworthy by standardizing definitions, improving quality, and enforcing controls. 

• Information governance manages information across its full lifecycle, from creation to defensible disposal, across all formats (email, files, chats, records, databases). 

• The two disciplines are complementary: data governance enables reliable analytics, while information governance enables compliant, defensible use of information. 

• A cross-functional Data & Information Governance Committee keeps policies consistent and resolves conflicts across IT, Security, Legal, Compliance, and the business. 

• Security tooling (DLP, access governance, monitoring, classification) helps operationalize governance and reduce breach and compliance risk. 

• Turning raw data into insights requires accuracy, consistency, and security first. Then lifecycle controls so insights remain usable, searchable, and compliant. 

• Regulations and standards shape governance requirements for retention, access, auditability, and protection. 

Understanding the Distinction between Information Governance vs Data Governance 

It’s crucial to recognize that data governance and information governance are distinct disciplines that solve different problems, but work best when they’re aligned. 

• Data governance helps ensure the data your teams rely on is consistent, high-quality, and protected. 

• Information governance helps ensure that the information your organization creates and stores is managed across its lifecycle and can be found, produced, retained, or disposed of defensibly. 

When these programs reinforce each other, organizations get trustworthy analytics and faster, lower-risk operations (especially in audits, investigations, and eDiscovery). 

The Role of Data Governance in Compliance, Risk Management, Security, and Business Goals 

Data governance is the discipline of managing the availability, usability, integrity, and security of enterprise data. In practice, that means establishing common definitions (What is a “customer”?), setting standards for quality (What’s an acceptable error rate?), and implementing controls for access and protection.

Strong data governance typically includes:

• Policies for data storage and processing (where data can live, how it moves, and how it’s transformed)

• Regulatory alignment (so data handling supports compliance obligations) 

• Security controls (to reduce the risk of unauthorized access, misuse, or breaches) 

When executed well, data governance reduces costly rework caused by inconsistent definitions and low-quality data. It can also reduce legal exposure and storage costs by minimizing duplicated or unmanaged data stores. 

The Role of Information Governance in the Decision-Making Process 

Information governance connects day-to-day information handling to business outcomes and legal defensibility. It guides how decision-makers use information assets to achieve corporate objectives while maintaining regulatory compliance. 

Information governance often covers: 

• Classification and retention (what should be kept, for how long, and why) 

• eDiscovery readiness and response (how quickly you can find and produce relevant information) 

• Defensible disposal (disposing of information you no longer need, consistently and safely) 

• Decision rights and accountability (who owns which information decisions) 

If your organization doesn’t currently have a plan or goals, start an information governance initiative today, especially if you’re dealing with rapid data growth, increasing regulatory scrutiny, or rising discovery costs. 

The Importance of a Data & Information Governance Committee 

Managing and protecting an organization’s data is essential not only for compliance but also for making faster, better decisions. Governance is hard to sustain without clear ownership, so many organizations formalize responsibilities through a Data & Information Governance Committee. 

This committee typically sets direction, resolves cross-functional issues, and ensures that policies are adopted consistently across departments (IT, Security, Legal, Compliance, Records, and the business). 

The Six Key Roles within a Data & Information Governance Committee 

A typical Data & Information Governance committee includes roles that work together to ensure effective governance from policy through execution: 

1. Data Owners: Define what data is collected, why it’s collected, and what “good” looks like for that dataset. They’re accountable for business outcomes tied to the data. 

2. Data Stewards: Ensure data quality, integrity, privacy, and appropriate usage. They translate policy into day-to-day standards and practices. 

3. Data Custodians: Manage the technical environment, systems, storage, backups, access controls, and operational administration. 

4. Data Users: Consume data and information for operations and decision-making. Their feedback often reveals where definitions, access, or quality break down. 

5. Data Architects/Analysts: Design how datasets integrate, model data flows, and identify improvements. They also help standardize definitions and reduce duplication. 

6. Legal/Compliance/Records Stakeholders (often a dedicated role): Ensure retention, regulatory obligations, and defensibility are built into processes, not bolted on after an incident. 

How Executives Influence Governance Policy Changes Based on Their Expertise 

Executive leaders influence governance outcomes because they can align policy decisions with business strategy and risk appetite. They also have the authority to resolve conflicts (for example, when one team wants to keep everything “just in case” and another needs storage and risk under control). 

When executives sponsor governance, the organization is more likely to: 

• Adopt common definitions and standards 

• Fund the tooling and staffing needed to operationalize policy 

• Enforce accountability across business units 

• Treat governance as a business enabler, not a compliance tax 

That leadership directly impacts how effectively organizations can leverage resources, whether raw datasets or actionable intelligence derived from them. 

Benefits of Implementing Robust Security Software for Successful Data Governance 

Security and governance are tightly linked: governance defines what should happen, and security controls help ensure it does happen. 

Importance of Robust Security Software in Preventing Breaches 

The Data Governance Institute emphasizes the importance of strong safeguards for managing enterprise data. Poorly governed data, especially inaccurate, duplicated, or unstructured data, can lead to misinformed decisions. When security controls are weak, a breach can cause major financial damage, operational disruption, and reputational harm. 

Robust security software helps operationalize governance through: 

• Data loss prevention (DLP): Reduces accidental or unauthorized exposure 

• Access governance: Ensures the right people have the right access for the right reasons 

• Monitoring and alerting: Detects suspicious behavior earlier 

• Classification and labeling support: Helps enforce policy consistently 

A chief data officer (or equivalent governance leader) plays a key role by ensuring that policies, processes, and controls are aligned across structured and unstructured data. 

Protection Offered by Such Measures Towards Company Assets 

Strong governance and security together enable safer analytics and better decision-making. With modern tooling, organizations can also use techniques like automation and machine learning to identify patterns in large data volumes, turning data into insights while reducing manual effort. 

Key areas strengthened by robust controls include: 

• Data Architecture: A clear architecture enables efficient reuse and reduces duplication, saving costs. 

• Data Quality: Higher accuracy reduces downstream mistakes caused by flawed analysis. 

• Data Security: Strong controls protect against unauthorized access and build stakeholder trust. 

To summarize: implementing robust security software underpins successful data governance by protecting sensitive assets, supporting compliance, and enabling confident use of data across the business. 

DG Foundations: Making Enterprise Data Reliable and Governable 

Organizations don’t benefit from raw data until it becomes reliable, usable, and aligned to business objectives. That journey usually starts with disciplined data governance (so inputs are accurate, consistent, and protected) and is sustained by information governance (so outputs remain searchable, policy-aligned, and defensible over time). 

Rather than treating this as a one-time “data cleanup,” most organizations see the best results when they operationalize a repeatable lifecycle, from collection through analysis, supported by standards, accountability, and the right controls. The next section breaks that lifecycle down in practical steps. 

Turning Raw Datasets into Meaningful Insights Through Effective Implementation of Information Governance 

Data is only valuable if it becomes meaningfully usable. Effective information governance ensures insights are accurate, relevant, and compliant, especially when information comes from many formats (records, documents, emails, files, chats, reports, and databases). 

Below is a practical way to think about the lifecycle from raw inputs to decision-ready insights.

Data Collection 

Collect relevant information from trustworthy sources, such as internal systems, customer interactions, logs, or external research. Prioritize reliability and tie collection to business objectives so you’re not accumulating data without purpose. 

Data Processing 

Convert raw data into a usable format by cleaning errors, resolving inconsistencies, and standardizing structures. Processing is where many “hidden costs” emerge, so clear standards and automation can pay off quickly. 

Data Storage 

Use storage and retention approaches that support retrieval, privacy, and security requirements. Storage isn’t just about cost; poor storage decisions can increase discovery risk and slow response times. 

Data Analysis 

Analyze processed datasets using analytics techniques (statistical methods, BI tooling, or machine learning) to extract insights that support corporate goals. The goal is not to have  “more dashboards”, it’s to make better decisions quantifiably more often. 

When information governance is implemented well, organizations often see: 

• Stronger regulatory posture 

• Reduced legal exposure (because retention and disposal are defensible) 

• Lower storage and operational overhead 

• Faster discovery and response when issues arise 

Beyond cost savings is the biggest value is: leaders can make decisions with confidence because the underlying information is governed, traceable, and policy-aligned. 

Regulations and Standards Related to Information Governance and Data Governance 

Having data governance policies and information governance principles is important, but many organizations are also impacted by external regulations and standards. The right approach depends on your industry, geography, and risk profile. Below are common examples that influence both data and information governance. 

General Data Protection Regulation (GDPR) 

GDPR applies to companies operating in the European Union (EU) and those processing personal data of EU residents. Decision makers must ensure compliance, including appropriate legal bases for processing, strong security, clear retention practices, and support for individuals’ rights. 

Sarbanes-Oxley Act (SOX) 

SOX is a U.S. federal law designed to protect investors and improve the integrity of financial reporting. For many organizations, SOX influences controls around financial data integrity, access, auditability, and retention. 

ISO/IEC 27001 

ISO/IEC 27001 is an international standard for information security management systems (ISMS). It provides a framework to establish, implement, maintain, and improve security processes, helping reduce risk and demonstrate commitment to protecting sensitive information. 

NIST Cybersecurity Framework 

The NIST Cybersecurity Framework provides guidelines and best practices to manage and reduce cybersecurity risk. Aligning governance efforts to the framework can improve resilience and clarify controls across identify/protect/detect/respond/recover activities. 

Challenges Faced in Both Information Governance and Data Governance 

Large organizations face persistent challenges in managing data and information, especially across global teams and multiple systems. Information management is rarely straightforward at scale. Here are two common challenges: 

Lack of Understanding of the Business Value 

Many organizations treat governance as a necessary evil rather than a business enabler. When leaders don’t see the value, governance gets underfunded, and the results show up as poor data quality, compliance violations, higher discovery costs, and security gaps. 

Data Silos 

In many organizations, data lives in silos across departments, tools, or regions. Silos make it harder to access and use data effectively, and they often lead to inconsistent definitions and quality issues. 

Conclusion 

Information Governance vs Data Governance: What’s the Difference? 

The simplest way to remember the distinction: 

• Data governance makes data trustworthy and secure enough to use. 

• Information governance makes information manageable and defensible across its lifecycle. 

Organizations that invest in both typically see better decision-making, lower compliance and security risk, and faster response when they need to find, preserve, or produce information. The most successful programs also share two traits: clear cross-functional ownership (so policy sticks) and enforceable controls (so policy becomes practice). 

FAQ 

What is the difference between Data Governance and Information Governance? 

Data governance is about making data accurate, consistent, secure, and fit for analytics and AI. Information governance is about managing information across its lifecycle so it can be retained, found, produced, and disposed of defensibly. They overlap but serve different purposes. 

Why do organizations need both Data Governance and Information Governance? 

Organizations need data governance to ensure reliable data for analytics and AI, and they need information governance to reduce regulatory risk, streamline eDiscovery, control storage, and ensure information is defensibly managed. One without the other creates gaps. Together, they create a unified governance ecosystem where data flows correctly, and information remains controlled throughout its lifecycle. Without both, organizations end up with blind spots in security, compliance, and decision‑making. 

What job titles are responsible for Data Governance vs. Information Governance? 

Data governance is typically owned by roles like Chief Data Officer, Data Architects, Data Stewards, Data Governance Managers, and analytics teams. Information governance is usually owned by Legal, Compliance, Records Management, Information Governance Leads, and sometimes CISOs. 

Do I need Information Governance and Data Governance if my organization isn’t regulated? 

Yes. Even unregulated organizations face risks from inconsistent data, excessive storage, security gaps, and uncontrolled information sprawl. IG and DG improve efficiency, decision quality, and operational resilience.