Skip to main content

Information Governance vs Data Governance

Information Governance vs. Data Governance: Key Differences

While Information Governance (IG) and Data Governance (DG) are closely related, they focus on different aspects of managing organizational assets. Here’s a simple breakdown of their differences and how they work together:

1. Definitions

Data Governance (DG):

- Focuses on managing **data as an asset**.

- Ensures data is accurate, consistent, secure, and available for use.

- Example: Defining who can access customer data and how it’s stored.

Information Governance (IG):

- Broader than DG, focusing on managing **all forms of information** (structured data, unstructured data, documents, emails, etc.).

- Ensures information is used effectively, ethically, and in compliance with regulations.

- Example: Setting policies for retaining and disposing of emails and documents.

2. Scope

Data Governance:

- Primarily deals with **structured data** (e.g., databases, spreadsheets).

- Focuses on data quality, metadata management, and data lifecycle.

Information Governance:

- Covers **all information assets**, including unstructured data (e.g., emails, PDFs, videos) and structured data.

- Addresses legal, regulatory, and business requirements for information.

3. Goals

Data Governance:

- Ensure data is accurate, consistent, and trustworthy.

- Improve data accessibility and usability for decision-making.

- Example: Creating a single source of truth for customer data.

Information Governance:

- Ensure information is managed responsibly and aligns with business objectives.

- Mitigate risks (e.g., legal, compliance, security) related to information.

- Example: Ensuring compliance with GDPR for all customer-related information.

4. Key Activities

Data Governance:

- Define data ownership and stewardship.

- Establish data standards, policies, and procedures.

- Monitor data quality and resolve issues.

Information Governance:

- Develop policies for information retention, disposal, and archiving.

- Ensure compliance with laws and regulations (e.g., GDPR, HIPAA).

- Manage risks related to information security and privacy.

5. Stakeholders

Data Governance:

- Data owners, data stewards, IT teams, and business analysts.

Information Governance:

- Legal, compliance, IT, records management, and executive leadership.

6. Tools and Technologies

Data Governance:

- Data catalogs, metadata management tools, data quality tools.

- Document management systems, eDiscovery tools, compliance platforms.

Key Takeaway

- **Data Governance** focuses on managing data as a technical asset.

- **Information Governance** takes a broader view, ensuring all information is managed responsibly and aligns with business and regulatory needs.

- Together, they ensure that an organization’s data and information are accurate, secure, and used effectively.

Comments

Popular posts from this blog

Defensible Disposition - Purge; Don't splurge in legal costs!

Defensible Disposition: A Smart Strategy for Compliance and Security What Is Defensible Disposition? Defensible disposition is the legally sound, systematic process of discarding or destroying records, documents, and data that are no longer needed for business, legal, or regulatory reasons. By implementing this practice, organizations ensure compliance with legal requirements while reducing risks associated with unnecessary data retention. Why It Matters: Key Principles ✅ Compliance: Aligns with laws, regulations, and industry standards to avoid legal and financial penalties. ✅ Accountability: Establishes a clear audit trail to document every step of the disposition process. ✅ Security: Protects sensitive information by ensuring secure destruction, preventing unauthorized access or breaches. ✅ Efficiency: Eliminates outdated records promptly, reducing storage costs and streamlining data management. How to Implement Defensible Disposition 1️⃣ Identify Records – Determine which reco...

Parser Generator

If you have a need for a parser generator for your applications say a SQL parser or an expression parser to plugin somewhere in your application, your obvious choices are ANTLR or JavaCC. ANTLR  (ANother Tool for Language Recognition) is a parser generator developed by Terence Parr, it is extremely versatile and is capable of generating parsers in multiple programming languages, but requires a run time library. The Definitive ANTLR 4 Reference is an excellent book to get into ANTLR. JavaCC  (Java Compiler Compiler) was written originally by Dr. Sriram Sankar and Sreenivasa Viswanadha. It is only capable of generating parsers in Java but doesn’t require a runtime library and the parsers it generates are very performant for an LL(k) parsers. Useful References: 1.   https://dzone.com/articles/antlr-and-javacc-parser-generators 2.  Work on JavaCC