Data Governance: Policies and Procedures
The business need for a strong data governance policy has become critical.
AI usage continues to grow, and the risk of data breaches increases every day. These two factors alone have raised the importance of data quality and security.
A data governance policy documents how an organization utilizes and manages its data. It establishes rules for data protection, defines roles and responsibilities related to data oversight, and sets standards for data quality and security.
While the specific content of a data governance policy may vary based on the organization’s size, business processes, and data complexity, its core purpose is to provide a single source of truth for data standards and practices.
This, in turn, helps create trustworthy data, improves data democratization, and increases regulatory compliance.
In this article, we examine the key components and best practices for data governance policies and procedures.
What Is Data Governance?
Google defines data governance as “everything you do to ensure data is secure, private, accurate, available, and usable.” It helps ensure a business’s data is safe, secure, private, usable, and compliant.
Data governance best practices encompass the processes and procedures everyone in the organization must follow, as well as the tools and technology that support them across the data lifecycle.
A data governance policy sets internal standards for how data is collected, stored, processed, and discarded. It defines the kinds of data being governed and specifies the types of data each role can access.
Data governance also involves complying with industry standards, federal laws, and other external standards.
Developing a Data Governance Policy
Data governance programs often begin by focusing on finite issues and then expand their scope to address additional concerns or data sets. Thus, developing a data governance policy is an iterative process.
Each iteration starts with gathering feedback. The data governance team initiates the process by collecting feedback on core data issues like access, security, and storage. This feedback helps them understand current data handling practices, existing problems, and potential improvements.
Based on the collected feedback, the team articulates the goals and objectives of the data governance program. These might include protecting data integrity, implementing data management software, establishing standardized processes, and fostering a data-driven culture.
They then define and document the guiding principles for data governance. The Data Governance Institute offers a set of universal principles that can be adapted to fit the organization’s needs. They cover areas like integrity, transparency, auditability, accountability, stewardship, checks and balances, standardization, and change management.
Key Components of a Data Governance Policy
An organization’s data governance policy is comprised of three core sections: purpose and scope, roles and responsibilities, and data policies.
Purpose and Scope
The policy should clearly define its purpose, including how data is handled and which stakeholders influence data infrastructure, policies, and procedures. This section should also outline how the policy’s success will be measured, including short-term and long-term goals and KPIs.
Roles and Responsibilities
This section specifies the roles and responsibilities of all data governance stakeholders, including the Data Governance Office (DGO), decision bodies, data stewards, and data custodians. A clear hierarchical structure and a data governance glossary can further enhance clarity.
Related Data Policies
This section addresses various facets of data governance, such as data quality, security, access, and usage, in separate subsections or linked policies.
Data Governance Procedures
Depending on the organization’s needs, a data governance policy may include some or all of the following procedures.
Creating Standards and Policies
This involves developing guidelines for consistent and appropriate data use and management, covering security, access, privacy, and quality. Establish clear data standards for formatting, definition, structuring, representation, tagging, transmission, use, and data management.
Establishing a Data Governance Framework
This framework, a subdivision of the data governance policy, should focus on rules and processes that ensure compliance with relevant laws and regulations. It standardizes rules and processes, ultimately improving data standards, business strategies, and data privacy.
Implementing Data Security Policies
This includes creating a robust data security plan to protect customer and business data from unauthorized access and use. This plan should include access restrictions, data protection measures, and protocols for preventing data breaches and malware.
Establishing Data Quality Controls
Data quality controls define the processes used to ensure data meets organizational standards for accuracy, reliability, precision, completeness, timeliness, integrity, and confidentiality.
Promoting Data Sharing and Integration
Facilitating data sharing among researchers promotes collaboration and fosters new discoveries. Implementing data integration through automation links records and enhances data accuracy.
Training for Roles and Responsibilities
Training ensures everyone understands the responsibilities of their role (data administrator, data steward, data custodian, data user, etc.).
Monitoring Progress
This involves tracking improvements and identifying areas needing adjustments as the policy is implemented. Regularly assess the program’s progress using metrics like sales figures, customer feedback, and website analytics.
Developing a Data Catalog
A data catalog stores metadata and provides users with tools to locate needed information. It acts as an inventory of the organization’s data, providing a clear description for human understanding.
Benefits of Data Governance Policy
A data governance policy provides a single source of truth for standards and practices to ensure data quality and accuracy, delivering benefits in improved data quality, wider availability of data across the organization, and tighter regulatory compliance.
Trustworthy Data
A data governance policy outlines clear standards for data collection, categorization, and storage. By adhering to these standards, organizations can prevent inaccurate data from influencing business decisions. Data quality controls, for example, establish processes to guarantee data meets standards for accuracy, reliability, precision, completeness, timeliness, integrity, and confidentiality.
Data Democratization
Data governance policies promote data democratization by outlining procedures for dismantling data silos and establishing centralized data hubs. This allows teams to independently and quickly access the data they need, leading to more efficient decision-making. For instance, promoting data sharing and integration enables researchers to collaborate and potentially make groundbreaking discoveries in their respective fields.
Regulatory Compliance
Data governance policies define controls for data collection, access, and usage. By adhering to these controls, organizations can avoid fines and reputational damage associated with the misuse of sensitive information. Implementing robust data security policies is a critical component in protecting customer and business data from unauthorized access and use.
Transparency and Accountability
A data governance policy makes governance goals, practices, and responsibilities accessible and transparent across the organization. It clearly defines roles and responsibilities related to data governance, ensuring everyone understands their role in maintaining data integrity.
Alignment with Business Objectives
A data governance policy aligns data management practices with business objectives. By ensuring data is accurate, accessible, and used appropriately, organizations can leverage data to make informed decisions and achieve their strategic goals.
Collaboration and Communication
The process of developing and implementing a data governance policy encourages collaboration and communication between different departments and stakeholders involved in data management. This fosters a data-driven culture within the organization.
Enables Continuous Improvement
A data governance policy is a living document that can be reviewed and modified as the business evolves and regulations change. This ensures that data management practices remain relevant and effective over time.
Governance Is Iterative
A comprehensive data governance policy can improve informed decision-making across the organization. To achieve that goal, however, it’s essential to involve and gain alignment with all stakeholders across the organization.
As we said at the beginning, creating a data governance policy is an iterative process. Laws around data security, use, and collection are changing. Business goals change. New methods of data collection emerge as AI and other technological innovations occur.
Therefore, it is critical to design your data governance policy with flexibility and adaptability in mind and revisit it regularly to ensure it remains aligned with these changes. Your Chief Data Officer must also stay up to date with relevant regulations and data governance best practices.
