Data governance is a comprehensive system of policies and procedures for managing data effectively and in compliance with relevant regulations. It establishes rules and responsibilities for how data is created, stored, accessed, used and disposed of, ensuring its quality, consistency and reliability.

However, data governance remains the No. 1 data integrity challenge, cited by 54 percent of respondents to the Precisely survey. Part of the problem is that organizations rely on traditional data governance practices, with static frameworks that lack the speed and agility they need.

Automation can help organizations modernize and streamline their data governance practices. By automating data governance, organizations can improve data quality, meet regulatory requirements and lay the foundation for sustainable growth.

Drawbacks of Manual Data Governance

Traditional data governance practices rely on labor-intensive manual processes that emphasize a top-down, control-oriented approach. A governing body defines data standards and sets rules for ensuring that data-related activities adhere to those standards. The governing body also establishes clear roles and responsibilities, designating individuals or teams to manage specific data domains.

This model works well enough with a limited number of data sources that remain relatively static. However, most organizations have data scattered across multiple on-premises and cloud environments and updated in real time. Rigid, rule-based controls and manual classification cannot keep pace with today’s requirements.

Manual processes are also prone to human error, leading to inaccuracies and delays. Typos and incorrect entries compromise data quality. More significantly, manual processes mean that employees have less time to focus on higher-value tasks.

Streamlining Manual Processes with Automation

Organizations can eliminate these roadblocks with automation. Automated tools can quickly scan and identify data assets and classify them based on predefined rules. They can also capture and manage metadata, providing the context users need to understand the origin, evolution and relationships of data.

Monitoring is a key function of automated data governance. Automated tools can monitor data quality against defined rules and standards, flagging any anomalies or inconsistencies. This ensures that data governance policies are applied consistently across the organization, regardless of data source. Automated tools can also monitor data handling practices and enforce security policies.

Automation facilitates regulatory compliance by identifying where sensitive data is stored, who’s using it, how it’s used and who’s responsible for it. Organizations can then ensure that the right processes are in place to prevent unauthorized access. Reports and audit trails provide insight into data governance activities and provide accountability.

How Automation Improves Data Governance

By automating data governance workflows, rules and scripts, organizations can maximize data accuracy with minimal human intervention. Automation eliminates manual, repetitive tasks and minimizes the risk of human error for more consistent and reliable data governance. Ultimately, automation builds greater trust in data quality and fosters its effective use across the enterprise.

Automation also facilitates collaboration by providing a centralized platform for data access and management. Organizations gain a 360-degree view of their data assets, with insight into their lineage and their relationships to business processes.

Implementing and managing automated data governance tools requires specialized knowledge and expertise. It can be especially difficult to integrate these tools with legacy systems while ensuring security and scalability.  Technologent has specific expertise in both data governance and automation, and can help you take full advantage of automated tools while avoiding pitfalls and minimizing risk.