Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline. But, as organizations increasingly adopt automation in their governance processes, this perception is changing.

What is Automated Data Governance?

Automated data governance is a technology-driven approach to manage, protect, and optimize the quality, availability, and usability of data within an organization. Instead of relying solely on manual efforts, automated data governance uses reproducible processes to maintain data quality, enrich data assets, and simplify workflows. This approach streamlines data management, maintains data integrity, and ensures consistent data quality and context over time.

Related: Learn more about data governance.

Why Automated Data Governance Is Important?

Manual processes often lead to inefficiencies and errors, especially when dealing with large data volumes. Companies relying on manual approaches experience higher levels of human error, leading to delays in producing meaningful results. Also, manual processes consume valuable employee time, diverting resources from higher-value initiatives.

Conversely, automation streamlines data governance tasks, reducing errors and accelerating processes. Integrated data management solutions that automate data governance, quality, and lineage enable organizations to use data effectively across the enterprise. This automation adds scalability to the governance processes, supporting growing data ecosystems. Some key reasons for the adoption of automated data governance include:

• The growing volume, variety, and velocity of data
• The increasing number of disparate data sources
• The need for near-real-time data management

Benefits of Automated Data Governance

Data generates valuable insights that enforce trust in business decisions, which essentially means that the timely availability of accurate and complete data empowers businesses to make decisions. Automated data governance improves data accessibility and quality to help enable timely business intelligence. Here are some key benefits of incorporating automation in your data governance processes:

• Automation results in improved data quality: Automating data validation checks and metrics allows users to continuously monitor data quality across various sources. This proactive approach helps identify and fix issues, errors, and inconsistencies on time. As a result, data becomes more accurate and reliable, enabling better decision-making across the organization.

• Automation promotes data accessibility and collaboration: Data glossaries simplify data access for users across different teams. It helps translate complex data assets into consistent language, removing confusion and making data more accessible. This capability promotes cross-functional independence, allowing teams to analyze data and derive insights without barriers.
• Automation enables data enrichment: Modern data solutions use AI to generate relevant information, such as titles and descriptions, of datasets. Users can use AI-driven enrichment options to automatically add relevant metadata, making it easier for them to understand the context and relevance of the data.

Best Practices for Automating Data Governance

Here are some best practices that offer actionable steps for implementing automated data governance processes, ensuring data reliability, compliance, and optimal decision-making.

• Invest in a No-Code Data Governance Solution: Implementing a code-free solution enables users to create, manage, and automate data governance workflows without requiring extensive technical expertise. These solutions often feature drag-and-drop interfaces and pre-built templates, allowing business users to set up data governance rules, workflows, and policies quickly and efficiently. This approach democratizes data governance, making it accessible to a broader range of users within your organization.
• Enable Automated Workflow Orchestration: Workflow automation allows users to manage and coordinate data governance processes across various systems and departments. Search for solutions that simplify the data journey from consumption to analytics via automated workflows. In addition to improving data governance initiatives, automating workflows ensure consistency, reduce bottlenecks, and improve overall efficiency in data management practices.
• Implement Automated Data Quality Checks: Data quality always comes first. Integrate auto data quality checks into data pipelines to continuously monitor and validate data against predefined quality metrics such as accuracy, completeness, and timeliness. The system must automatically flag anomalies and suggest corrections without manual intervention. Automated data quality checks help maintain high data standards and reduce the workload on users.
• Review Performance with Automated Dashboards: Develop automated dashboards that consolidate data governance metrics and KPIs in near-real-time. These dashboards should provide insights into data quality, data usage patterns, and more, allowing users to continuously monitor the effectiveness of data governance initiatives. Look for a solution that offers connectivity to the analytics and visualization tools to ensure near-real-time reporting.

Key Features of an Automated Data Governance Solution

An automated data governance solution features several components, each playing a crucial role in your automation initiatives.

• Metadata Management: Metadata management is essential for effectively understanding and managing data assets. Automated data governance requires tools and processes for capturing, storing, and managing metadata, such as data definitions, lineage, and usage information. This approach enables users to make informed decisions about data usage and ensures data quality and integrity.
• Data Discovery: Automated data discovery is the process of identifying and cataloging data assets across different sources and repositories within the organization. This component lets users gain comprehensive visibility into data assets, leading to improved governance and decision-making.
• Data Lineage Tracking: Tracking data history with automation keeps a detailed log of where data comes from and how it changes over time. Automated data governance solutions provide tracking and documenting data lineage capabilities, allowing users to understand how data is used and ensuring data quality assurance and compliance.
• Integration of Governance Controls: Integrating governance controls with data workflows ensures that governance processes are a part of organization’s data operations. Incorporating governance controls directly into data workflows allows users to enforce policies in near real-time, promoting consistency and accuracy throughout the data lifecycle.

Conclusion

With data volumes, veracity, and velocity rising rapidly, automated data governance will likely serve as an essential defining factor for companies looking to successfully utilize data for analytics and reporting.

Astera Data Governance is a no-code solution with automation at the forefront. It’s a part of Astera’s unified platform that offers a range of automation capabilities, including workflow orchestration and more. It has customizable access controls and an intuitive interface, so you easily collaborate on data projects, ensuring streamlined workflows, strict compliance, and enhanced transparency.

Interested in learning more? Schedule a free demo personlaized to your use case or get in touch with us here!