Introduction

Data and information governance are among the most commonly used terms in the records and information management industry that are often misinterpreted and even confused with one another.

In this article we will look into the key differences between information governance and data governance as well as provide some foundational definitions that will enhance your understanding of the two terms. Let's start with what each term means in isolation.

Contents

What is information governance?
What is data governance?
Information governance vs data governance - what are the differences?
How does master data management relate to information governance and data governance?
What role does data quality play for efficient information governance and data governance practices?
Overcome Information Governance challenges by leveraging eDiscovery technology

What is information governance?

Information governance is a set of principles, policies, and procedures that organizations use to manage, protect, and control their information assets. This includes ensuring the accuracy, completeness, and reliability of data, as well as complying with legal and regulatory requirements, and maintaining the confidentiality and security of sensitive information.

Information governance implies a cross-functional approach that involves stakeholders from different areas of the organization, including IT, legal, compliance, and business departments, to establish a comprehensive strategic framework that ensures the effective and efficient use of information.

The key features of information governance include:

1. Policies and Procedures: Information governance requires the establishment of policies and procedures that govern the collection, use, retention, and disposal of information assets. These policies should be consistent with legal and regulatory requirements and reflect the organization's risk tolerance and business objectives.

2. Data Quality Management: Information governance also requires the implementation of processes and controls to ensure that information is accurate, complete, and consistent. This includes the identification of data sources, data cleansing, and data validation to ensure data quality.

3. Information Security: Implementation of security controls to protect ESI from unauthorized access, use, and disclosure is an essential element of information governance as well. This includes the use of encryption, access controls, and monitoring to prevent data breaches and unauthorized access.

4. Compliance Management: IG calls for the implementation of processes and controls to ensure that the organization complies with legal and regulatory requirements related to information management. This includes the identification of applicable laws and regulations, the implementation of policies and procedures to ensure compliance, and the monitoring of compliance activities.

5. Stakeholder Engagement: Information governance requires the involvement of stakeholders from different areas of the organization, including IT, legal, compliance, and business departments. This involves the establishment of roles and responsibilities, communication, and training to ensure that stakeholders are aware of their responsibilities and contribute to the success of information governance initiatives.

6. Continuous Improvement: Last but not least, information governance demands the implementation of processes and controls to monitor and continuously improve information management practices. This involves the use of metrics, data analytics, and feedback mechanisms to measure performance and identify opportunities for improvement.

The primary objective of information governance is to ensure that information is managed in a way that supports the organization's business goals, regulatory compliance, and business risk management objectives.

What is data governance?

Data governance refers to the overall management of the availability, usability, integrity, and security of the data used within an organization. It involves the establishment of policies, procedures, and standards that govern the collection, storage, use, and dissemination of data.

Data governance defines roles, responsibilities, and processes for ensuring accountability for and ownership of data assets across the enterprise.

The key features of data governance include:

1. Data quality: Data governance ensures that data is accurate, consistent, and complete. Data quality is essential for making informed business decisions and reducing associated risks related to inaccurate data.

2. Data security: Data governance ensures that data is protected from unauthorized access, modification, or deletion. This includes implementing access controls, data encryption, and monitoring to detect potential security breaches.

3. Data privacy: Data governance ensures that data is managed in accordance with applicable laws and regulations related to data privacy. This includes implementing policies and procedures to protect sensitive data and providing transparency to individuals regarding how their data is being used.

4. Data stewardship: Data governance assigns ownership and responsibility for data to specific individuals or groups within the organization. These individuals are responsible for ensuring that data is managed appropriately, including maintaining the quality of data, security, and privacy.

5. Data architecture: Data governance ensures that the organization's data architecture supports its business processes and objectives. This includes defining data models, data dictionaries, and metadata standards to ensure that data is consistent and easy to find and use.

6. Data lifecycle management: Data governance ensures that data is managed throughout its lifecycle, from creation to disposal. This includes defining retention policies and procedures, data backup and recovery, and archival strategies.

The primary goal of data governance policies is to establish a framework for managing an organization's data assets in a way that supports the organization's objectives and helps to mitigate risks associated with data management.

If you don't have a defined data governance program within your organization yet, you might want to consider adopting one in case of the following scenarios:

• Data is critical to business operations;

• Data quality issues are affecting business outcomes;

• You must comply with regulatory requirements, imposed by GDPR or HIPAA;

• There is no clear ownership or accountability for data within an organization, data can become siloed, duplicated, or lost;

• Your organizations have encountered increased data security risks, such as data breaches and cyberattacks.

Data governance programs can help tackle the aforementioned challenges and generally help organizations get a better control over their data.

Information governance vs data governance - what are the differences?

Information governance and data governance are related concepts, but they have distinct differences in scope, granularity, purpose, stakeholders, and approach.

1. Scope: Information governance covers all types of information assets, including physical and digital records, documents, emails, and other forms of content. Data governance, on the other hand, focuses specifically on data assets, such as databases, data warehouses, and data marts.

2. Granularity: Information governance is typically more granular than data governance, as it deals with all types of information assets, including unstructured data. Data governance, on the other hand, focuses on structured data that can be organized in a specific way.

3. Purpose: The purpose of information governance is to ensure that all types of information assets are managed effectively to support the organization's business objectives, while data governance focuses specifically on managing data assets to ensure their accuracy, security, and compliance with applicable laws and regulations.

4. Stakeholders: Information governance involves a broader set of stakeholders than data governance, including legal, compliance, and risk management professionals, as well as information technology professionals. Data governance is typically the responsibility of data management and information technology professionals.

5. Approach: Information governance takes a more holistic approach to managing information assets, while data governance takes a more technical approach to managing data assets. Information governance focuses on policy development, compliance, and risk management, while data governance focuses on the quality of data, metadata management, and data architecture.

How does master data management relate to information governance and data governance?

Master data management (MDM) is a discipline that involves the management of an organization's critical data assets, often referred to as "master data." Master data includes data that is shared across multiple systems and business units, such as customer data, product data, and financial data.

MDM is closely related to both information governance and data governance, as it requires the establishment of policies, procedures, and standards for managing master data throughout its lifecycle. In fact, master data management is an important component of information governance and data governance because it facilitates organizations to manage their critical data assets effectively, supporting their business objectives while ensuring compliance with applicable laws and regulations. This is how it benefits information governance and data governance exactly:

• It ensures data accuracy and consistency across all systems and business units, which is essential for making informed business decisions and avoiding errors and inconsistencies in reporting.

• It supports regulatory compliance by helping to ensure that master data is managed in accordance with applicable laws and regulations, such as data privacy regulations and financial reporting requirements.

• It improves operational efficiency by helping to reduce the time and effort required to manage master data, as it provides a centralized repository for storing and managing master data, which can be shared across systems and business units.

• It facilitates data integration by enabling data from different systems and business units to be integrated and used together.

• It provides a consistent and reliable foundation for data analytics, enabling organizations to gain deeper insights into their business operations and customer behavior.

What role does data quality play for efficient information governance and data governance practices?

Data quality is a critical factor for efficient information governance and data governance practices, as it underpins accurate decision-making, regulatory compliance, operational efficiency, customer experience, and advanced analytics.

More specifically, high quality data is essential for accurate data analytics and consistent regulatory compliance, as poor quality of data leads to difficulties in maintaining accurate and complete records, which in turn might increase the risk of non-compliance and associated penalties. That said, poor quality of data can also lead to inefficiencies in operations, as employees may need to spend time correcting errors or reconciling inconsistent data.

Overcome Information Governance challenges by leveraging eDiscovery technology.

Modern eDiscovery technology can help organizations to effectively handle large volumes of data, identify, classify, and analyze the data, as well as clean up ROT (redundant, obsolete and trivial) data. More so, end-to-end eDiscovery platforms, such as ZyLAB ONE, can improve how organizations control their informational assets and helps in a number of cases, including Retention and Archiving, Data Access Governance, and Privacy, Compliance and Risk.

How can ZyLAB ONE help you govern data more efficiently?

• It helps secure sensitive information, identify who has access to what, and prevent data breaches (by tightening permissions to least privilege access).

• It enables quick detection of sensitive information and data loss prevention, ensuring that it stays in secure locations, thus reducing risk.

• It facilitates identification and management of ROT data, reducing storage costs and improving security.

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