What is data and database governance? Speed & safety through database DevOps

An organization’s approach to database governance can determine how quickly, safely, and reliably they can process critical structural updates to data stores up and down the pipeline. That’s because database governance covers:

• Establishing policies and standards to ensure data integrity and security
• Defining roles and responsibilities for data management
• Automating processes to enforce compliance and consistency
• Providing visibility and traceability across all database changes

Without these elements, teams either:

• Figure out how to deal with a manual approach, sacrificing speed and, likely, optimal developer UX
• Ease up on governance and risk the consequences, such as data breaches, compliance violations, and unreliable, untrustworthy, inefficient pipelines 

Database governance is close in concept of data governance, though it focuses on the storage technologies, structures, and metadata surrounding and housing every dataset. 

What is data governance?

Data governance is a formal framework for policies, workflows, use cases, access controls, protections, security measures, and monitoring tools – all meant to control, limit, and manage how teams and technologies use datasets. Properly governed data is:

• Accurate
• Secure
• Accessible
• Compliant
• Traceable

Data governance can set the tone for how an entire organization’s data is used, trusted, and valued. Business analysts, executives, data scientists, AI/ML engineers, application developers, and more all rely on – and benefit from the protections of – a strong, future-proof data governance framework. 

When defining a data governance strategy, the team should outline how to:

• Manage data properly 
• Define roles and responsibilities
• Control access to data and operations
• Enact safeguards that protect sensitive information
• Document and audit for compliance
• Track and monitor data and pipeline operations

This data governance strategy covers the entire data journey – from creation and storage to transformation, access, and eventual archiving or deletion, ensuring every step is secure, compliant, and aligned with organizational goals.

But “storage” has come to mean so much more than before, and to think of data stores having state for very long is a bit obsolete. In the data-centric world in which AI/ML and other advanced use cases are embedded in pipelines across every industry, data and data structures change more than ever before. 

Each database and integration needs to be considered carefully as part of the organization's database governance strategy. 

What is database governance?

Database governance is a focused element of data governance geared towards the data stores and management workflows up and down the pipeline. Managing database environments have distinct requirements and challenges that get even more nuanced across NoSQL databases, cloud data architecture, and AI-enabled pipelines. 

Managed by multiple teams and tools, database governance is a framework of policies, procedures, and tech-enabled workflows designed to ensure that the structures, systems, and metadata that store data are accurate, secure, accessible, and compliant. By embedding database governance into your workflows, you can manage risk, reduce inefficiencies, and enable teams to focus on innovation rather than troubleshooting errors or worrying about compliance violations.

A comprehensive database governance approach enacts a foundation of safety, security, and efficiency to underscore high-speed database changes, streamlined management, and the ability to trace, monitor, and audit every operation. Database, DevOps, CloudOps, DataOps, platform engineering, and other teams focused on reliable and optimal developer experiences look to governance programs to solve database hurdles such as:

• Setting clear standards for schema changes, data storage, and usage
• Defining roles and responsibilities for developers, DBAs, and other stakeholders
• Automating controls to enforce compliance, consistency, and scalability
• Maintaining audit trails to ensure traceability and accountability

In a world where AI/ML models, microservices, and real-time analytics depend on clean data and flawless database operations, strong database governance isn’t just a best practice — it’s a competitive advantage.

Database governance vs. data governance

Database governance operates at the intersection of structure and flow. It not only safeguards static data but also ensures that dynamic database environments can support the high-frequency development and operational demands without compromising quality or security.

While data governance focuses broadly on governing the data itself, database governance looks to the technologies, processes, and teams that manage the platforms storing and transforming that data. Data governance is about the “what,” while database governance is about the “how” and “where.”

Given the rising complexity of pipelines, abundance of data sources, and prevalence of transformations, database governance has a load of challenges in its way. 

What is a database governance platform?

A database governance platform is a comprehensive solution designed to manage, monitor, and enforce governance (compliance, security, internal standards, etc.) policies across database environments. It turns a data governance strategy into a consistent, repeatable, observable platform engineered for usability and continuous optimization. 

Liquibase Pro serves as a database governance platform within database DevOps pipelines by integrating tools and workflows to ensure data accuracy, security, compliance, and accessibility throughout the data lifecycle. These capabilities include:

• Flows that orchestrate database change workflows and best practices into Flow files for instant and consistent deployments 
• Policy Checks that define and adhere to code standards to ensure compliance and empower developers to write safe code, every time
• Automatic Drift Detection to detect database drift at scale as it’s happening, identify out-of-process changes, and take action to ensure data security 
• Targeted Rollbacks that can identify specific ChangeSets to undo without impacting surrounding changes, allowing for precise rollbacks 
• Structured Logging that enables database workflow visibility and insights for optimization, triggered automation, governance, security, and more 
• Observability that powers existing dashboards with the ability to track database change performance and drive continuous improvement

In combination, these capabilities enforce policies, access, standards, and compliance to improve, accelerate, and streamline governance and auditability while supporting high-speed, high-volume database changes.

Challenges of database governance

If database governance is essential for maintaining the integrity, security, and efficiency of modern data ecosystems, why isn’t it part of every IT team’s architecture?

Unlike the rest of the development and data pipeline, which is fairly well-served by control, compliance, and security automation tools, the database layer has been largely left on its own. DBAs tend to take on the governance burden through manual workflows, whether it’s part of a focused governance program or standard review, validation, and testing. 

What’s more, there is an intense growth of pipelines, data sources, applications, and embedded technologies throughout the data journey. These are the challenges that arise, so you can plan to address them with your database governance strategy:

• Siloed, fragmented data
• Complex regulatory compliance
• Secure, private data
• Maintaining data quality
• Slow, error-prone manual workflows
• Lack of standardization
• Fast-growing data pipelines
• NoSQL and other specialized databases
• Balancing accessibility with control
• Integrating with existing architecture
• Auditing and reporting

And on top of that, database governance tools and processes need to be continuously optimized.

Underscoring these database governance challenges is the siloed treatment of databases themselves – and so tackling database governance means unification. Bringing databases into the DevOps workflow to treat change as code allows for the integration needed for proper, reliable, and scalable database governance.

DevOps for database governance

Integrating database governance into DevOps frameworks enables organizations to balance agility and control without sacrificing speed, scalability, or security. Automating database changes by treating them as code, including version control and tracking, allows these teams to embrace smaller, more frequent database deployments and look back on workflows and operations to draw out insights for overall process optimization. 

These key database DevOps practices unite the broader data governance strategy into an automated, self-serve, observable, and – most importantly – integrated pipeline. 

Database CI/CD 

Continuous Integration (CI) and Continuous Delivery (CD) are the backbone of modern DevOps pipelines, so these principles need to be translated to data pipeline and database governance. That means:

• CI for the database – frequently merging smaller database schema changes to minimize integration conflicts.
• CD for the database – deploying schema changes to production quickly, reliably, efficiently, and frequently

What does it mean to embed database governance into a CI/CD pipeline? 

• Automated testing and validation within the CI/CD pipeline to ensure data integrity and structural consistency
• Detailed tracking of change operations and schema versions 
• Continuous compliance and quality checks to verify adherence to internal and external policies, security elements, access controls, and other possible misconfigurations

‍→ Check out our complete guide to database CI/CD.

Database change as code with version control

Treating database changes as code (Database-as-Code) brings the control, transparency, traceability, and accountability of DevOps practices to the database layer. Just as Infrastructure as Code (IaC) automates environment deployments, Database-as-Code extends the approach to structural data store updates. 

By managing schema and configuration changes as code, they can be included in version control systems, creating a git for the database workflow. This allows teams to apply the same rigor to database changes as they do to application code, while keeping the process self-serviceable and efficient.

Database change as code leads the way to version control for database changes, which helps:

• Traceability and accountability across the database change pipeline, with a version history that enables easy audits, troubleshooting, and optimization analysis
• Eliminate inconsistencies, redundancy, and duplicate efforts between developers, DBAs, and others in the database DevOps pipeline, instead anchoring them in a single source of truth 
• Scale database governance capabilities to reduce the risks of data pipeline expansions, mergers, and future complexities, while preserving collaborative touchpoints

Shift-left for database change management

Shifting left – or bringing testing, validation, and security controls closer to development than production to minimize downstream issues – is a philosophy that permeates IT organizations. So why would the database be left out?

In the sense of database governance, the shift-left philosophy involves addressing these elements as early as possible in the change management lifecycle. By moving database governance tasks to earlier stages of the pipeline, teams can identify and resolve issues before they propagate downstream.

Shifting left in the database deployment workflow means:

• Proactive checks for quality, policy, compliance, consistency, and integrity – well before production environments
• Faster, continuous feedback loops to fix and deploy changes faster, then optimize future releases and workflows

In the world of development and data security, organizations like OWASP, the Open Worldwide Application Security Project, provides roadmaps to effectively shift-left its security strategy. Yet since there’s no specific Top Ten Security Risks list for database deployments, Liquibase poses its own. 

→ Shift database security left by prioritizing these “Top Ten” database deployment security risks.

Obviously, if you’re bringing CI/CD, version control, and a combined “shift-left”/“as code” approach to database change, automation is imminent. 

Automating database governance: data governance with Liquibase

As the cornerstone of high-performing development and data pipelines, automation is logically the way to go for database change management – and governance of data stores, too. 

Approaching any of this manually is immediately fallible, slowing down workflows and opening up endless opportunities for errors and disruptions. All in all, it’s not conducive to a fast-paced, data-driven organization to begin with, let alone one prioritizing safety and governance. Automation locks in consistency, security, and scalability while embedding governance as “left” as possible in the change workflow. 

A complete database DevOps platform, Liquibase serves as a database governance automation tool by ensuring database operations are seamless, reliable, and fully traceable – with all the control and compliance the organization requires.

Liquibase enables database governance automation with traceability that also levels up optimization and monitoring opportunities through database observability. Consistent outputs from automated processes feed into observability platforms, providing real-time insights into database change pipelines.

By unifying DevOps and database governance, Liquibase empowers teams to scale operations efficiently, reduce risks, and maintain high standards for data integrity and security — all while keeping pace with the demands of modern development and data pipelines.