Members of a data governance team review policy documents

Across the private, public, and nonprofit sectors, organizations rely on data to evaluate the progress of their initiatives, allocate their resources, and make data-driven decisions. Government agencies use data to assess the impact of their new policies, while health care organizations analyze data to find ways to improve the quality of the care they provide to their patients. 

Without accurate, reliable information, organizations cannot achieve their goals, and they face a greater risk of experiencing data breaches, regulatory penalties, and other harmful outcomes. 

Data governance tools continue to evolve, as new data management resources and security challenges emerge. By following data governance best practices for how data is collected, protected, and used, organizations can maximize the value of their data while managing the risks. 

What Is Data Governance?

Data governance refers to the way an organization manages its data as defined by a specific framework that outlines the organization’s data quality and security standards, data collection and storage policies, and guidelines for processing and using its data.

Organizations tailor their frameworks based on their needs, data sources, and systems. Industry standards and regulatory requirements also influence these frameworks. 

Topics covered in a data governance framework often include:

  • Organizational goals for data assets
  • Data standards and policies
  • Roles and responsibilities
  • Audit and oversight processes
  • Data governance tools

Often used interchangeably, data management and data governance are distinct but closely related functions. Data management refers to the storing, processing, and maintaining of data, while data governance is about the rules and procedures for how data should be handled and protected. 

Benefits of Data Governance Frameworks   

Investing in the establishment of a data governance framework offers many benefits to an organization. In addition to protecting its data from unauthorized access, having well-defined data standards can improve the organization’s efficiency and compliance. 

The benefits of employing a data governance framework include:

  • Increased efficiency: Overly complex or burdensome data policies can slow operations and create barriers. Improving the visibility and consistency of the data across systems can minimize inefficiency, while effective data governance programs can ensure that stakeholders have access to the data they need. 
  • Improved data protection: Implementing data governance policies can help reduce the risk of data breaches and protect private information. Data systems that restrict access can improve security and regulatory compliance, while also building trust with stakeholders. 
  • Stronger regulatory compliance: Global and public sector organizations often face complex regulatory compliance challenges. Adhering to strict data governance policies can improve an organization’s ability to maintain compliance by systematizing its data security procedures and supporting its audit and compliance reporting. 
  • Enhanced data analytics: Employing effective data governance strategies improves the accuracy and completeness of an organization’s data, which bolsters the analytical value of that data. When organizations have trustworthy data, they can identify areas for growth and improvement.  

Resources on Data Governance   

The following foundational resources examine how data governance frameworks are designed, implemented, and applied across sectors.

Data Governance Tools and Assessment Models

Organizations use a range of tools and assessment models to develop, implement, and refine their data governance frameworks. Countless platforms offer data governance tools that can be customized to an organization’s specific needs. 

Grouping these tools by function — such as automation and security — can help organizations assess their data governance priorities and select tools that support their frameworks. 

Automation 

Automating their data collection and processing helps organizations apply their data governance principles consistently and at scale. In particular, data automation can improve efficiency and reduce human errors. Automation tools can discover data sources, classify data, and detect anomalies, while still operating within the governance rules as defined by the organization.

Other applications of automation include:

  • Assigning metadata tags that identify sensitive information
  • Identifying incomplete or duplicate data to improve the quality of the data
  • Documenting the data’s life cycle to track its movements and access
  • Generating audit logs to support the management and oversight of the data

Data Catalog

Organizations use data catalogs to inventory, analyze, and standardize their data. Governance tools that compile data from across departments can improve the visibility of the data and help ensure that all stakeholders are working from the same trusted data. For example, a data catalog can serve as an organization’s single source of truth, reducing inconsistencies and allowing for real-time data analysis. 

Similarly, data lineage tools allow organizations to track information as it moves and changes throughout its life cycle. These tools help organizations improve the accuracy and transparency of their data. 

Data Security

Data security is at the core of effective governance frameworks. Organizations can employ robust access controls to minimize the risk of data breaches while avoiding unnecessary barriers to legitimate use of the data. In short, data governance policies provide a way to balance access and security.

Data governance tools that implement role-based permissions, endpoint security measures, and data loss prevention procedures can strengthen the security of their data while allowing stakeholders to have enough access to it to be able to collaborate. 

Maturity Model

A governance maturity model evaluates an organization’s needs and the current state of its data governance and management procedures. This assessment examines the organization’s data policies, quality, security procedures, and compliance to provide the organization with a road map to improve its governance practices. 

Maturity models encourage organizations to implement best practices and track their progress against benchmarks. They also offer valuable insights for resource allocation and risk management. 

Research on Data Governance

Recent scholarship on data governance practices underscores their importance to organizations’ quality management, innovation, and risk management. The following academic studies illustrate how data governance concepts are examined and applied across sectors.    

Public Sector Data Governance

In the public sector, data governance is about more than implementing best practices for managing data. Governmental organizations often have a legal obligation to develop, implement, and share their governance standards.

For example, U.S. law requires federal departments and agencies to establish data governance programs to oversee their data management practices. State and local governments also have a legal responsibility to comply with data standards. 

As in other types of organizations, governmental organizations often struggle with limited resources, siloed data, and a lack of data leadership — challenges that can be magnified in the public sector. As a result, public administration professionals need to be proficient in data governance practices and their strategic applications. 

Public Sector Data Governance Resources      

Public sector organizations have unique needs and responsibilities when it comes to data governance. The following resources focus on governance requirements, implementation guidance, and policy considerations specific to government organizations. 

Data Governance Best Practices

Data governance standards determine how organizations manage their data. But what are the best practices for data governance? 

Developing standards for their data quality and privacy can set organizations’ expectations while also meeting regulatory requirements. Employing a strategic approach that considers the life cycle of the data and invests in continuous improvement also benefits organizations. 

Approach Data Governance Strategically

Putting an effective data governance framework in place can improve an organization’s operational efficiency, mitigate its risks, and enhance its internal communications. To achieve these outcomes, organizations need a clearly defined strategic approach to data governance. 

This starts with clear goals. An organization’s data governance goals may include reducing inconsistencies and errors in its data, improving the quality of its data, or managing its data-related risks. Tools such as maturity modeling can provide the organization with a road map to follow in developing and implementing its data governance policies. 

Standardize Data Quality

Data comes in many forms, including numeric data, text, audio, and visualizations. Organizations cannot standardize the forms of data they use, but they can standardize their data’s quality. 

Having high-quality data allows organizations to make reliable, data-driven decisions. The quality of their data is high when it is managed with consistency and security. Data governance frameworks can set standards for the accuracy and completeness of the organizations’ data, ensuring that their expectations align with regulatory requirements. 

Prioritize Data Safety

Storing and managing data creates risks for organizations, particularly those with legal obligations to keep the data private. Data governance frameworks help organizations mitigate the risk of data loss or unauthorized access. 

Effective data governance requires clarity in policies related to data usage, security audits, and access controls. By setting clear standards for data safety, data governance frameworks help organizations comply with data privacy regulations and increase trust with their customers, constituents, and other stakeholders.

Consider the Data Life Cycle

Organizations create, clean, analyze, and store data. Effective governance policies consider the data throughout the stages of its life cycle, with customized procedures for each stage. 

For example, organizations can specify which employees are authorized to modify data at different stages to maintain a standardized and comprehensive data catalog. Audit logs and data checkpoints can help track data throughout its life cycle. 

Invest in Continuous Improvement

To maintain their usefulness, data governance frameworks require regular updates as technology and requirements change. For example, the rise of AI has introduced new opportunities and challenges for organizations. Data governance policies can be designed to adapt as the data landscape evolves.

Organizations can plan on regularly assessing their governance metrics. Audits and reports can help organizations identify gaps and guide improvements. Regular needs assessments also encourage organizations to modify their governance framework as their priorities change. 

Data Governance Best Practices Resources

While data governance strategies vary depending on each organization’s needs and goals, many best practices apply across organizations and sectors. Learn more about the best practices in data governance with the following resources:

Investing in Data Governance

Across industries — including health care, manufacturing, finance, technology, government, and nonprofits — data governance plays an increasingly important role in organizational success. 

Data governance frameworks provide a strategic foundation for organizational change. Employers increasingly seek professionals with data analysis, risk management, and strategic planning skills. Building their technical, change management, and data security proficiencies can benefit professionals working in every industry. 
 

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