Data Governance Framework: Best Practices & Strategies

A critical component for the long-range success of your data governance framework, a data strategy is a comprehensive plan for how your organization will collect, manage, analyze, and leverage data to achieve your business objectives. It involves a vision and objectives, data management principles, data lifecycle management and data integration. It’s also worth pointing out that it tends to be an extension of your organizational culture. For instance, the level of general trust you have of internal users will probably impact the level of data access you allow. 

Data processes are systematic activities in data management throughout its lifecycle, including but not limited to data collection, data storage, data security, and data integration. Within the data governance framework, these are essential for ensuring data security, data quality, and effective utilization.

Data policies are formal guidelines and rules that specify how your data should be accessed, managed, and used across the organization. They ensure consistency, quality, security, and compliance in your data management, making them a fundamental component of your data governance framework.

Like data policies, data standards are formal guidelines, but these dictate the format, structure and meaning of data elements to ensure quality, consistency, and compatibility across your organization. Data standards can establish formal requirements and standards for elements like data format, naming conventions, data quality, lifecycle management and regulatory compliance. These are essential within a data governance framework for enabling efficient and effective data management, data quality, and data sharing.

Your data governance framework needs to adequately protect your data from unauthorized access, theft, or corruption. Developing and implementing the right policies, practices, and technologies can help ensure that your data is handled responsibly and securely. A proper data governance framework will address matters like access controls, data encryption, data backup and recovery, and employee awareness and training.

One of the primary objectives of any data governance framework is to produce high-quality data for better decision-making processes. Your framework needs to have the features in place to define, measure, monitor and improve data quality across your organization. It should go beyond keeping data timely and consistent but should include root cause analysis and remediation when quality issues are discovered.

A proper data governance framework includes clearly defined roles and responsibilities, adequate documentation of data policies, formalized workflows, and regular meetings. All this can help produce one of the biggest advantages of a data governance framework: improved alignment and stakeholder engagement across departments.

Access to data tends to be irrelevant if people in your organization can’t make sense of it. In response, a data governance framework ensures that people across your company can engage with available data in meaningful ways through provision of appropriate data skills, proficiency with the right tools, and feedback mechanisms for continuous improvement.

You will want key performance indicators to show the effectiveness of your data governance framework. Typically, these fall into one of several areas, such as data quality, data security, operational efficiency or data usage. Adoption of the right KPIs can help you track organizational progress, identify areas for improvement, and ensure alignment with business goals.

Implementation of a data governance framework generally involves acquisition and implementation of a range of technologies to manage, enforce and monitor the framework’s policies and processes. Some of the typical technologies involved include data ingestion, data visualization, data integration and data privacy solutions.