How to Develop Data and Data Governance Policies

This is week 5 in an 8-week series on “How To” do specific efforts within the framework of Data Governance starting with how to utilize GenAI and working through data inventory, assessing risk, developing policies, implementing capabilities, driving change management and measuring for success.

What is Data Governance?

Data governance refers to the framework and processes organizations use to manage data availability, usability, integrity, and security. A well-structured data governance policy ensures that data is consistent, trustworthy, and does not fall into the wrong hands.

In today's data-driven world, managing data effectively is not just a matter of technology but also of policy. Data governance policies ensure that data is handled in a way that is secure, compliant, and valuable to the organization. Here’s a step-by-step guide on how to develop robust data and data governance policies:

Define Objectives and Scope

Clarify Goals: Begin by defining what you aim to achieve with data governance. Objectives might include improving data quality, ensuring compliance with regulations like GDPR or HIPAA, enhancing decision-making, or reducing risks associated with data mismanagement.

• Ensuring data accuracy and consistency
• Maintaining compliance with regulations (GDPR, CCPA, HIPAA, etc.)
• Reducing security risks
• Improving data accessibility and usability

Scope: Determine which data assets fall under governance. This includes personal data, financial data, customer interactions, etc., across various departments.

Stakeholder Engagement

• Identify Stakeholders: Include representatives from IT, legal, HR, finance, and business units. Each has unique insights into data usage and needs.
• Workshops and Interviews: Conduct sessions to understand how different teams use data, their challenges, and what governance they believe is necessary.

Establish Data Ownership and Responsibilities

A governance policy must clearly define:

• Data owners: Individuals responsible for specific datasets
• Data stewards: Those who oversee data quality and ensure policies are followed
• Data users: Employees who access and utilize data for business operations

Assigning these roles ensures accountability and consistency in data management.

Assess Current State

• Data Audit: Review existing data practices, systems, and policies. Identify gaps in data quality, security, and compliance.
• Compliance Check: Ensure current handling aligns with legal requirements. This might involve consulting with legal experts.

Craft the Policy Framework

• Data Classification: Develop criteria to classify data based on sensitivity, criticality, and regulatory requirements.
• Roles and Responsibilities: Clearly define who does what in data governance. Roles might include Data Stewards, Data Owners, and a Governance Council.
• Data Access and Lifecycle Management: Detail policies on who can access data, how long data should be retained, and how it should be disposed of securely.
• Security and Privacy: Establish protocols for data encryption, access controls, and privacy protection measures.

Develop Standards and Procedures

• Data Quality: Define standards for accuracy, completeness, consistency, and timeliness of data.
• Change Management: Procedures for updating policies or systems should be clear to accommodate new data types or changes in law or business practice.
• Incident Response: Create protocols for handling data breaches or policy violations.
• Develop Data Standards and Guidelines

To maintain data quality and consistency, organizations should establish:

• Data classification rules (e.g., public, internal, confidential, highly confidential)
• Data retention and disposal policies
• Data security protocols (encryption, access controls, authentication)
• Data integrity measures (regular audits, error detection, correction processes)
• These standards should align with industry best practices and regulatory requirements.

Implementation

• Training: Educate all employees on new policies, emphasizing their roles in governance.
• Technology Tools: Implement or upgrade tools for data management, like data catalogs, master data management systems, or data quality tools.
• Pilot: Start with a pilot project to test governance in a controlled environment before full rollout.

Implement Security and Compliance Measures

To protect sensitive data, organizations should implement:

• Access controls (who can access what data and under what conditions)
• Encryption standards for data in transit and at rest
• Incident response plans for data breaches or policy violations
• Compliance monitoring to ensure adherence to regulation

Monitor, Report, and Adjust

• KPIs: Establish key performance indicators like compliance rates, data error rates, or time to respond to data queries.
• Regular Audits: Conduct audits to ensure adherence to policies and identify areas for improvement.
• Feedback Loop: Use feedback from stakeholders to refine policies continuously.

Data governance is an ongoing process. Organizations should:

• Conduct periodic data audits
• Gather feedback from stakeholders
• Update policies based on new regulations, technologies, or business needs
• A continuous improvement approach ensures that governance policies remain effective and relevant.

Documentation and Communication

• Policy Documentation: Keep comprehensive, accessible records of all policies, changes, and reasons for those changes.
• Transparent Communication: Regularly inform stakeholders about governance policies, updates, and the importance of compliance.

Even the best policies are ineffective if employees do not understand or follow them. Regular training sessions, documentation, and internal communication ensure that staff members:

• Understand their roles in data governance
• Know how to handle sensitive data properly
• Are aware of the consequences of policy violations

Cultural Integration

• Promote Data Literacy: Encourage a culture where data is seen as an asset that needs protection and proper management.
• Leadership Commitment: Ensure that governance is championed from the top down to embed it into the organizational culture.
• Create a Data Governance Committee

A dedicated team should oversee the governance strategy, review policies, and address emerging risks. This committee should be responsible for:

• Updating policies in response to regulatory changes
• Addressing data-related challenges and conflicts
• Ensuring company-wide awareness of governance policies

Leverage Technology for Data Governance

Various tools can help automate and enforce governance policies, such as:

• Data management platforms (e.g., Collibra, Informatica, Talend)
• Data security solutions (e.g., DLP, IAM systems)
• Audit and compliance tools to track adherence to policies
• Automation minimizes human error and enhances efficiency.

Developing data and data governance policies is an ongoing process that adapts to new business strategies, technologies, and regulatory landscapes. By following these steps, organizations can not only protect their data but also leverage it as a strategic asset. Remember, the best governance policies are those that are practical, well-communicated, and integrated into the daily operations of the business.

Be sure to check out more material from Sogeti on Data Governance and AI at:

Sogeti Labs LinkedIn? https://www.dhirubhai.net/showcase/sogetilabs/posts/?feedView=all

Directly at Sogeti Labs Blogs https://labs.sogeti.com/

I published my first book, it happens to be on Data Governance. it's my take on doing data governance and keeping your sanity. I hope you enjoy reading it.

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