Data governance tools enable an organization to build and manage data governance policies and processes. They improve the quality of data with validation, cleansing and enrichment. To achieve data goals, data governance programs involve tools and processes to help businesses manage data throughout the data’s life cycle, from acquisition to disposal.

Data governance relies heavily on people and processes, but successful data governance programs also require strong data governance tools. A variety of vendors and open-source communities provide tools to help businesses implement data governance policies that make sense for their business needs.

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Data governance impacts enterprises at strategic, tactical and operational levels by setting policies and processes for managing and controlling data assets. Data governance tools help in implementing and enforcing these policies and processes. These tools offer a comprehensive data governance framework and set of features and functionalities to manage an organization’s data life cycle.

Data governance tools catalog data assets; they collect data from databases, files, applications and other data sources. They then tag data assets based on predefined or custom metadata attributes and classify them based on their sensitivity, importance or relevance to specific compliance regulations. Data governance software ensures that data is accurate, complete and consistent by performing data quality checks and validations.

Data governance tools help businesses define and manage data ownership, roles and responsibilities as well as implement data security and privacy measures. They ensure data management processes meet regulatory compliance and quality standards. They also help automate the workflow and provide structure to large volumes of data.

Data governance tools serve several purposes, which include data quality management to ensure data remains accurate, complete and consistent across an organization. These tools can even be used to enforce compliance with regulatory requirements, such as GDPR and HIPAA. To maintain data integrity, data governance solutions take extra steps to track and log data usage as well as changes and access, providing a comprehensive audit trail.

Modern data governance tools are as concerned with keeping data moving to the right people or places within an organization as they are with data protection.

To make data more broadly accessible, data governance tools increasingly include policy and stewardship management features, thus simplifying access for a wider variety of user roles. Additionally, data governance tools often have artificial intelligence and machine learning functions built in.

For these reasons, companies looking for a strong data governance solution should consider those that have the following capabilities.

Democratize access to and collaboration with data

This includes providing accurate data for business decisions, thereby helping “organizations to become data driven at scale,” according to Forrester. Every business unit or department in an organization needs certain data to aid decision making or improve a product or service delivery.

Organizations usually have cross-functional teams that work together, share resources and collaborate on projects. Hence, it’s important to invest in a data governance tool that allows for easy access to data for all relevant parties, regardless of their location or role within the organization.

Allow the flexibility of ‘adaptive governance’ and balance demands

As Gartner has articulated, companies should be able to tweak their operating model through data governance tools to match the evolving needs of the business. There’s no “set it and forget it” in modern data governance.

This means that while data governance tools must do the basics well, they must also map well to business strategy. Companies should also balance the demands for greater access with continued requirements for privacy and control.

“The most advanced data governance solutions enable organizations to take accountability seriously and address increasing regulatory involvement and consumer demand,” Forrester said.

Tracing data sources and changes through automated data lineage

For better transparency, data governance tools should be able to trace data movement between different systems and processes. Automated data lineage helps users understand where data comes from, how it’s transformed and how it’s used in different processes and reports. This will help analysts understand the data flow from source to destination.

Given the rising importance of data to every business, the benefits of strong data governance extend to all. While not every company will have stringent data privacy needs, nearly all organizations benefit from the numerous advantages of using data governance tools:

• Improve data accuracy and consistency: Data governance tools allow businesses to set and enforce benchmarks for data accuracy and completeness across different systems and sources to guide data management efforts.
• Identify and rectify data errors: Data governance tools validate user inputs, cleanse data and use cross-referencing to identify and correct data errors.
• Enforce data access policies and control who can access what data: These tools enable access control mechanisms, encryption and data masking techniques to protect data from unauthorized access.
• Support various data sources and formats: Data governance tools support different data formats, such as structured, unstructured or semistructured data.
• Help businesses meet regulatory requirements: Data governance tools include the necessary frameworks and workflows to ensure organizations comply with industry regulations, such as GDPR, HIPAA and CCPA.

The best data governance tools break down silos between teams within a company and make it easier for them to securely share and collaborate on data to drive greater customer success.

Although they offer many benefits, they aren’t without challenges. One challenge most organizations encounter when implementing data governance tools is user adoption and change management.

Convincing stakeholders and end users to adopt data governance tools can be difficult — resistance to change, lack of awareness or perceived complexity can hinder adoption. Integrating a new tool into a workflow often requires a cultural shift within the organization. It might take some time for employees to adapt to new processes and workflows. This requires investment in knowledge and other resources to get employees up to speed with the new tool.

Additionally, implementing data governance can be a complex process, requiring careful planning and execution, hence the need to follow data governance implementation best practices when setting up your tool.

There are at least four key constituencies that will use or rely upon data governance tools. These include:

• Chief data officer: Helps set policies, strategies and plans for the business.
• Data governance manager and team: Establishes processes and tools to enable access, usage, storage, retention, transfer and deletion of data across organizational boundaries.
• Data governance committee: Monitors the implementation process and provides the top-down stamp of authority to facilitate data governance policies.
• Data steward: Oversees the quality of individual data elements.

Although not everyone in an organization will directly access a data governance tool, the population of users who rely on such tooling keeps growing. Depending on the organization, the primary users of a particular tool might be a business team hoping to put data to use in decision making, an IT taskforce seeking to improve operations or data scientists building ML models.

Businesses that are trying to choose between data governance solutions should consider their primary audience and fit the tool to the audience rather than the audience to the tool.

As companies seek to put data to use in newer business technologies, such as ML, it’s become even more important for organizations to pick tools that not only help to control the flow of data throughout an organization but also ensure data can be accessed securely and freely by an ever-growing population of users. Some of the top-rated data governance tools include:

• Collibra Data Governance: Best for complex data landscapes.
• IBM Data Governance: Best for strict regulatory compliance.
• Precisely Data360 Govern: Ideal for data analytics.
• Alation: Suitable for data governance workflow automation.

To help businesses find the best data governance tool for their use case, we analyzed the 11 best data governance tools, including their prices, features, strengths and weaknesses.