Data governance policies are the guidebooks of best practices and boundaries of data management activities. No big organization can manage data assets across multiple teams without unified governance.
Modern data platforms are vast and complex, often managed by numerous, decentralized teams. This necessitates clear boundaries and guidance on essential data management practices. These boundaries are defined by data governance policies, which provide a framework for handling crucial aspects like data quality, security, lifecycle management, and architecture across an organization’s data platforms. A comprehensive set of policies ensures consistency and alignment among data teams, guiding them in building new platforms, migrating existing solutions, and improving overall design.
Importantly, adherence to these policies helps maintain regulatory compliance, a critical aspect of data management. Regulations like GDPR, CCPA, and HIPAA mandate specific requirements for data handling, and robust data governance policies help organizations meet these obligations. By establishing clear procedures for data access, storage, and processing, organizations can minimize the risk of non-compliance and associated penalties.
However, effective data governance extends beyond mere compliance. Good policies also address the cost of operating a data platform, a factor that has become increasingly important with the rise of cloud-based solutions. Well-designed policies can lead to significant cost reductions by promoting the reuse of best practices across different teams. Consequently, this prevents redundant efforts in solving common technical challenges.
Furthermore, policies focused on cost optimization, such as data retention policies that guide the decommissioning of obsolete datasets, contribute to lower operational expenses. By setting clear guidelines for data retention and deletion, organizations can minimize storage costs and reduce the complexity of data management. Ultimately, this allows for the development of more cost-effective data products.
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Data Governance Policies Definition
A data governance policy is a formal documented statement that provides clear and concise guidelines for how data should be managed throughout its lifecycle within an organization. It establishes mandatory rules, processes, and standards for various aspects of data management, including data quality, security, access, retention, and lineage.
These policies serve as a framework for ensuring data consistency, integrity, and compliance with relevant regulations. They are designed to promote responsible data practices, minimize risks, and support informed decision-making. By defining clear expectations and accountabilities, data governance policies help organizations achieve their data objectives and maximize the value of their data assets.
List of Data Governance Policies
Data governance policies should prioritize areas where standardization offers the greatest benefits or where regulatory compliance is paramount. Here are some critical domains typically addressed by data governance policies:
- Data Quality: These policies define how data quality is measured and maintained. They specify which data assets require monitoring and provide clear definitions for quality dimensions like completeness, accuracy, and consistency. This ensures a shared understanding of data quality expectations across teams.
- Data Privacy: With the increasing importance of data privacy regulations, these policies outline procedures for identifying, classifying, and handling sensitive data throughout its lifecycle. They are essential for ensuring compliance with regulations like GDPR, CCPA, and HIPAA.
- Data Classification: These policies establish a standardized process for categorizing and defining data assets. This includes creating data glossaries and mapping data assets to shared categories, preventing duplication and promoting consistency.
- Data Lifecycle Management: These policies provide guidance on managing data throughout its entire lifecycle, from creation and storage to archiving and deletion. This helps organizations avoid unnecessary costs associated with managing obsolete or unused data.
- Regulatory Compliance: These policies translate legal and regulatory requirements into specific organizational guidelines. This simplifies compliance efforts by providing clear instructions for data handling practices.
- Data Architecture: These policies establish frameworks and standards for designing new data products and ensuring they align with other data governance policies, such as those related to data quality and security.
- Metadata Management: These policies regulate how metadata is created, stored, and maintained. This includes guidelines for registering new datasets in data catalogs, ensuring data discoverability, and promoting collaboration among data users.
Beyond these core areas, data governance policies can also address data ownership, data ethics, security training, and other practices that foster a strong data culture within the organization.
The Examples of Data Governance Policies
Effective data governance policies strike a balance between establishing minimum requirements and avoiding excessive restrictions. They define both lower and upper boundaries for data management procedures. The lower boundary ensures that essential requirements, often driven by regulatory rules, are met for all relevant data assets. Conversely, the upper boundary prevents applying the policy in ways that create too much work or extra costs.
- Lower Boundary: Data stewards must review data quality health scores of critical data assets on a monthly basis. This ensures regular monitoring and proactive identification of data quality issues.
- Upper Boundary: Quality checks should be configured only for critical columns to avoid wasting resources on testing for problems that do not significantly impact business operations. This promotes efficiency and prevents unnecessary overhead.
This approach ensures that essential data quality checks are performed consistently while preventing excessive testing that could strain resources. By setting clear boundaries, data governance policies promote both effectiveness and efficiency in data management practices.
More examples of data governance policy are shown in the following infographic, which also shows how policies differ from data governance standards.
Best Practices for Creating Policies
Creating effective data governance policies requires careful consideration of several factors:
- Clear Data Ownership: Establish clear data ownership for each data domain. This ensures accountability for policy implementation and enforcement. Data owners should be responsible for understanding, communicating, and upholding the policies within their respective domains.
- Address Regulatory Requirements: Thoroughly research and understand all relevant data compliance regulations applicable to your industry and business activities. Incorporate these requirements into your policies to ensure compliance with legal and industry standards.
- Prioritize Reusability: Design policies with a focus on reusability across different data teams and technologies. Avoid creating policies that are overly specific to a single domain or technology, as this limits their applicability and effectiveness across the organization. Strive for general guidelines and principles that can be adapted to various contexts.
What is the DQOps Data Quality Operations Center
DQOps is a data quality and observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and automatically detecting data lineage to enable root-cause and impact analysis of data quality issues.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at a table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
