What are data governance principles?
Data governance principles are the foundational rules organizations use to manage and protect their data assets. These principles maintain data governance and accessibility while also complying with regulatory requirements. They give clear guidelines on managing data quality and usage to improve operational efficiency which reduces the associated risks. These principles form the bedrock upon which we build our modern information infrastructure.
In addition, these principles provide a consistent approach to managing data while remaining adaptable to your unique needs. As organizations navigate their journey toward sustainable growth, these principles become not just guidelines but best practices. Let’s understand how organizations can create a living framework for understanding how we create, protect, and derive meaning from our digital artifacts.
Why are data governance principles important?
Data governance principles are important because they solve main governance challenges, like:
Regulatory compliance: They ensure organizations meet industry and legal standards to avoid fines and reputational damage. These principles set clear policies on data handling and usage which creates a sense of trust in organizational practices.
Data security: They protect sensitive information from unauthorized access or breaches. They define access controls and security protocols so that organizations can build transparency around data use.
Data quality issues: They set standards for data consistency and completeness to resolve issues like duplicate records and outdated information.
Operational inefficiencies: They standardize processes and clear ownership to create a shared understanding of data across the organization.
Core data governance principles: a comprehensive breakdown
An effective data management strategy starts with setting data governance principles as the foundation. Here are 7 essential governance principles that you should keep in mind:
Accountability
Accountability assigns clear roles and responsibilities to team members for protecting data. Data stewards, owners, and users must understand their obligations to validate data usage appropriately and ethically. Beyond this, accountability ensures that designated members maintain data quality and address issues when they arise.
Transparency
Transparency refers to the visibility of data processes and decision-making across the organization. Transparent governance practices build trust by clarifying compliance with legal and ethical standards.
This promotes confidence in data usage, mainly when organizations deal with sensitive information. It reassures stakeholders that data practices are open and accountable.
Integrity
Data integrity means keeping data accurate and consistent throughout its lifecycle. It helps obtain reliable analytics from high-quality data for better decision-making and achieving regulatory compliance. However, you have to implement strict controls to prevent errors while making it accessible to designated users.
Data quality
Data quality means keeping data relevant and in a consistent format. High-quality data enables organizations to derive meaningful insights and increase operational efficiency. That’s why organizations must maintain data quality to reduce the risk of errors and create a solid foundation for achieving strategic goals.
Compliance
Compliance ensures that enterprise data governance aligns with regulatory requirements, such as GDPR, CCPA, HIPAA, and other standards. To achieve full compliance, establish policies and practices that meet legal standards for data protection and privacy.
Otherwise, non-compliance can lead to heavy financial penalties and reputational damage. Take Citigroup as an example — fined $135.6 million by U.S. banking regulators because it failed to address long-standing data quality management issues identified in 2020.
Security and privacy
Organizations rely on security measures, such as access controls and monitoring systems, to protect sensitive data. These measures, paired with privacy practices that ensure personal and confidential data is handled ethically, protect against breaches and misuse. Without such protections, organizations risk both — data and stakeholder confidence.
Collaboration and accessibility
Collaboration principles make sure data is readily available to authorized users so they can work together and share insights effectively. Accessibility, on the other hand, defines user roles and permissions so that data is available when needed without compromising security.
Combined, these governance practices encourage cross-departmental alignment and create a culture of shared responsibility for data.
Real-world applications of data governance principles
Here are some notable examples of companies that used data governance principles to address their governance challenges.
1) Procter & Gamble (P&G)
P&G‘s metadata management and master data management were not synced with operational reporting systems due to disjointed data systems. Their 48 SAP instances and decentralized processes caused inefficiencies and data leakages.
To address these issues, P&G deployed data quality software to standardize and synchronize master data across billions of records.
They eliminated outdated third-party technology and automated data integration processes to create dashboards for actionable insights. This improved productivity and gave them better control and visibility over their data ecosystem.
2) GE Aviation
GE Aviation had siloed data systems that hurdled decision-making. To solve this issue, they created a self-service data governance framework in 2016 that centralized data sources and made them more accessible to employees.
This way, their teams started performing data analysis independently which improved their decision-making processes.
3) Wells Fargo
Wells Fargo wanted to improve their data accuracy and security by creating a unified data source. So, they centralized data from multiple systems into a single, reliable catalog which streamlined data management processes. This reduced security risks caused by data discrepancies and provided more accurate reporting and analysis.
How to implement data governance principles effectively
Here’s a step-by-step guide to implementing data governance principles across your organization:
Assess current data practices: Evaluate existing data management processes to identify strengths and gaps in data quality and accessibility. This provides a baseline understanding and shows where improvements are needed.
Define goals and objectives: Set SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) goals that align with broader business goals. These objectives could improve regulatory compliance and increase operational efficiency.
Engage stakeholders: Involve key stakeholders across departments to get representation from all critical areas. When stakeholders from different departments are involved in the data governance process, they take ownership of the data in their respective areas. This promotes accountability and stakeholder buy-in.
Develop policies and procedures: Set clear policies and procedures for data management. Data quality, security, access control, and usage standards should be documented and easily accessible to employees.
Leverage technology: Use data governance tools and platforms that automate governance processes. These tools can be data catalogs and quality management software that support lineage tracking and compliance reporting to provide complete visibility into the data management lifecycle.
Iterate and improve: Collect feedback from stakeholders and adapt to evolving business and regulatory requirements.
For more insights, read this guide on data governance implementation steps.
Measuring success in data governance
After implementing your data governance strategy, measure its effectiveness to identify gaps. You can use the following KPIs to do so:
Data quality metrics
Accuracy: The percentage of data that is error-free and reflects real-world values.
Completeness: The proportion of data records with all required fields populated.
Consistency: Alignment of data across different systems and formats.
Timeliness: The speed at which data is updated and made available for use.
Compliance metrics
Security Metrics
Methods to improve your governance practices
Here’s what you can do to improve your data governance practices:
Conduct periodic reviews to evaluate policy compliance and governance effectiveness. You can even use the insights to refine processes and address gaps.
Gather feedback from stakeholders and end-users to understand pain points and identify areas for improvement. This could be done through surveys or interviews.
Compare governance performance against industry standards or peer organizations to identify best practices and opportunities for improvement.
Use advancements in governance tools, such as AI-powered analytics or automated workflows, to increase efficiency.
Educate employees on governance principles to consistently adopt and align with organizational goals.
The future of data governance principles
Rapid technological advancements and changing business needs are shaping the future of data governance. To stay ahead, you must adapt and refine strategies. So, here are the key trends redefining governance principles:
Agile data governance
Agile data governance represents a shift from rigid, top-down governance models to flexible and iterative approaches that adapt to changing requirements. It implements shorter cycles for governance adjustments so organizations can respond to new challenges or opportunities more quickly. This trend emphasizes broad stakeholder participation to align governance with diverse business objectives.
AI for enhancing governance practices
AI technologies are transforming how organizations implement and maintain data governance. AI-powered tools can identify anomalies in data far more efficiently than manual processes. Similarly, machine learning algorithms predict potential compliance risks and data quality issues. JPMorgan Chase developed an AI-driven system in 2021 based on ML models to identify fraud, anti-money laundering (AML) violations, and data governance issues.
In addition, AI-driven data catalogs enable precise tracking of data origins, which increases transparency and trust in data. It also provides semantic search features to simplify data discovery without spending several hours of manual effort.
Data democratization
Data democratization makes data accessible to a broader range of users so everyone can contribute to decision-making. This ensures that insights are not confined to only specialized teams for department decisions.
In 2020, Spotify introduced Lexikon, an internal data catalog that centralized data documentation and accessibility. Then, it became quicker and easier for teams to extract insights and make better decisions.
Empowering data governance with modern solutions
Organizations must adhere to data governance principles to ensure their data assets give valuable decision-making information. And we make this easier at data.world — a modern data catalog platform that simplifies data governance by unifying data assets and ensuring compliance through automated metadata management and role-based access controls.
data.world breaks down silos and integrates seamlessly with popular analytics tools so organizations can manage data while maintaining transparency and regulatory alignment.
We also provide the flexibility and automation organizations need to align their governance practices with modern challenges. Schedule a demo now to experience the future of data management with our agile data governance platform.
