How to Automate and Improve Your Data Governance Processes

Data governance defines how data is gathered and used within an organization. It establishes policies, processes, and standards to ensure that data is consistent, trustworthy, and aligned with business objectives. As organizations collect and generate vast amounts of data from various sources, effective data governance becomes crucial for maintaining data quality, ensuring regulatory compliance, and enabling data-driven decision-making. 

Yet these processes are often cumbersome and can gobble the time of data stewards. As data volumes scale, organizations need a means by which to scale up the automation of these processes, as well. In this blog, we’ll 

Data governance plays a vital role in:

1. Data quality: It establishes processes and controls to maintain data accuracy, completeness, and consistency across the organization, ensuring that data is fit for its intended purpose.

2. Data security and privacy: By defining and enforcing data security policies, data governance helps protect sensitive information from unauthorized access, misuse, or breaches, ensuring compliance with data privacy regulations.

3. Regulatory compliance: Many industries are subject to strict regulations regarding data handling, retention, and reporting. Data governance frameworks help such organizations meet these regulatory requirements and avoid costly penalties.

4. Business alignment: Data governance ensures that data management practices align with the organization's business objectives, enabling data-driven decision-making and supporting strategic initiatives.

5. Data democratization: By establishing clear data ownership, access controls, and metadata management, data governance facilitates data democratization and self-service analytics, making data accessible to authorized users across the organization while maintaining proper governance.

Effective data governance is not a one-time effort but an ongoing process that requires collaboration among various stakeholders, including business leaders, data stewards, and IT professionals. By implementing a robust data governance framework, organizations can unlock the true value of their data assets, drive operational efficiency, and gain a competitive advantage in their respective markets.

Key data governance processes and how to automate them

Data governance encompasses several critical processes that ensure the effective management, security, and quality of an organization's data assets. Automating these processes enables organizations to streamline operations, improve efficiency, and ensure consistent compliance with data policies and regulations. 

Data quality management: This process involves establishing standards, policies, and procedures to maintain the accuracy, completeness, consistency, and timeliness of data. It involves data profiling, cleansing, and monitoring to identify and resolve data quality issues.

Automated data quality monitoring tools can continuously assess data against predefined rules and metrics, identifying and flagging issues such as inconsistencies, duplicates, or missing values. These tools can also generate reports and alerts, enabling data stewards to take prompt corrective actions.

Data security: Data security processes ensure the protection of sensitive data from unauthorized access, modification, or destruction. This includes implementing access controls, data encryption, and monitoring for potential security breaches. Compliance with data privacy regulations, such as GDPR and CCPA, is also a crucial aspect of data security.

Features in a data catalog can automate some of these key security processes. For example, data anonymization, role-based access control, and business glossaries (which house critical information about regulations like the GDPR) are all features of modern data catalogs that automate data security at scale. 

Data lifecycle management: This process focuses on managing data throughout its entire lifecycle, from creation and acquisition to archiving or deletion. It involves defining data retention policies, implementing data backup and recovery strategies, and ensuring proper data disposal when it is no longer needed.

Understanding the lineage of data, or its journey from source to consumption, is vital for data governance. Automated data lineage tracking tools can map the flow of data across systems, applications, and processes, providing visibility into data transformations, dependencies, and origins. This information is invaluable for impact analysis, root cause analysis, and ensuring data integrity.

Metadata management: Metadata, or data about data, plays a crucial role in data governance. Automated metadata management tools can capture, organize, and maintain metadata from various sources, providing a centralized repository for data discovery, understanding, and governance. This metadata can be used to enforce data standards, document data lineage, and enable better data governance decision-making.

Workflow automation: Data governance processes often involve multiple stakeholders and complex workflows. Automated workflow management tools can streamline these processes by defining and orchestrating tasks, approvals, and notifications, ensuring that data governance activities are executed consistently and efficiently.

By leveraging these automation tools and technologies, organizations can significantly reduce manual effort, minimize errors, and improve the overall effectiveness of their data governance programs. However, it's essential to carefully evaluate and select the appropriate tools based on the organization's specific needs, data landscape, and existing technology stack.

The role of data catalogs in governance automation

Data catalogs are centralized repositories that store and organize metadata about an organization's data assets. They serve as a comprehensive inventory of all available data sources, their characteristics, lineage, usage, and relationships. By providing a single source of truth for data discovery and understanding, data catalogs play a crucial role in streamlining data governance processes.

Data catalogs streamline data governance processes by facilitating collaboration and communication among data stakeholders. They provide a centralized platform where data stewards, data owners, and data consumers can document, discuss, and resolve data-related issues, such as data quality concerns, access requests, or policy violations.

Data catalogs can automate various governance processes by integrating with data governance tools and workflows. For example, they can trigger notifications or workflows when new data assets are added or when data quality issues are detected. This automation ensures that data governance policies are consistently enforced and that data assets are properly managed throughout their lifecycle.

A key benefit of data catalogs is their ability to enhance data discoverability and accessibility. In large organizations with siloed data sources, it can be challenging to locate relevant data assets or understand their context. Data catalogs solve this problem by indexing and cataloging metadata from various data sources, making it easier for users to search, browse, and find the data they need.

Data catalogs also promote data transparency and trust by providing detailed information about data assets, including their definitions, owners, quality measures, and usage policies. This metadata helps stakeholders understand the data's lineage, quality, and potential risks, enabling them to make informed decisions about its usage and ensuring compliance with data governance policies.

Overall, data catalogs are essential components of a comprehensive data governance strategy. They enable organizations to gain control over their data assets, promote data literacy, and ensure data quality and compliance, ultimately driving better decision-making and business outcomes.

Case study: How Discover Financial Services automates pipeline creation

Discover, a leader in financial services and digital banking, faced a growing challenge: how to keep up with the increasing demand for fresh, governed, high-quality data while mitigating risk. With hundreds of petabytes of data scattered across on-prem systems, including Snowflake and AWS S3, creating data pipelines was a labor-intensive process, often taking up to 30 days. Discover’s manual pipeline creation involved curating metadata and implementing quality control measures, all before delivering the data to internal users.

To streamline this process, Discover turned to Alation to activate its metadata, automating and accelerating pipeline creation. “We needed to cut the pipeline creation timeline from 30 days to one or two days,” says Prakash Jaganathan, Senior Director of Enterprise Data Platforms at Discover. By leveraging Alation’s ability to capture and enrich metadata, Discover’s data engineers can now quickly find, profile, and build pipelines based on trusted, governed data.

This automation has dramatically reduced the time it takes to create a pipeline—from up to 30 days down to as little as 15 minutes. Over 2,500 users across Discover’s global workforce can now access and enrich metadata, enabling faster data discovery and improved data insights. This has saved the company an estimated 200,000 hours, which they can reinvest into innovation and delivering personalized customer experiences.

Jaganathan highlights the impact of these improvements, noting, “By improving the speed at which we are able to acquire and use data, we can turn that saved time into product innovations.” Discover’s experience shows how automating data governance processes can fuel both operational efficiency and innovation while ensuring data integrity remains intact.

The future of data governance

The future of data governance is not just about adapting to new technologies—it’s about embracing automation to achieve unparalleled efficiency and precision. Advanced tools like artificial intelligence (AI), machine learning (ML), and workflow automation bots are transforming how organizations manage their data assets.

AI and ML are automating critical tasks such as data quality management, classification, and lineage tracking by analyzing large datasets and uncovering patterns, significantly reducing manual intervention. However, the next frontier is workflow automation bots, which revolutionize active data governance by streamlining repetitive tasks.

With automation bots, you can:

• Automate metadata quality checks to monitor data such as PII and ensure completeness, compliance, and accuracy.

• Enforce data policies and access controls seamlessly to maintain regulatory compliance.

• Centralize metadata management, providing clear lineage and contextual understanding of data.

These bots enable data stewards to focus on strategic, high-value initiatives rather than manual, time-consuming tasks. Customizable workflows and rule-based auto-curation processes further enhance operational efficiency, allowing teams to quickly adapt governance processes to evolving organizational needs.

As the adoption of automation continues to rise, tools like Alation’s Workflow Automation Bots—designed in collaboration with users—are becoming essential for modern data governance. They don’t just improve processes; they drive adoption and ensure sustainable governance frameworks for years to come.

Cloud adoption is another key trend reshaping data governance. As more businesses move to the cloud, they face challenges like securing data and ensuring compliance across multiple cloud environments. In response, cloud providers offer advanced governance tools for managing data across hybrid and multi-cloud setups.

Data democratization is also transforming governance. As demand for data-driven decisions grows, organizations must balance broad data access with maintaining security and compliance. This shift requires empowering users with self-service access while safeguarding data integrity.

The rise of new data sources, including IoT devices and social media, adds further complexity. Organizations must develop strategies to govern these unstructured sources while ensuring data quality, privacy, and compliance.

Adapting to these trends is essential for organizations looking to unlock data’s full potential, drive innovation, and maintain a competitive edge in the digital age.

Curious to learn more about how Alation can help automate your key governance processes? Book a demo with us today.