In response both to the growth of data privacy regulations and to an increasing desire to leverage data for business insights, effective data governance tools are a must-have for organizations across all industries. Below, we’ll take a look at some key benefits of data governance software, and we’ll examine 11 leading solutions worth considering.
What is Data Governance?
Data governance is the process of consistently and effectively managing an enterprise’s data, with regard to both security and usability. The wider the range of stakeholders involved in doing so — and the more consistent the rules are under which those stakeholders participate — the more likely you are to ensure that your data is not only protected for compliance reasons, but also remains useful and accessible for business operations.
“If companies are going to be agile in their decision-making, they need their data to be similarly responsive and agile,” says Alation CEO and co-founder Satyen Sangani. “They also need to drive down the cost of compliance and regulation. A strong data governance program accelerates strategic decision-making and drives efficiency by putting governance capabilities into the day-to-day workflows of every employee.”
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What are Benefits of Data Governance Tools?
Data governance software offers a wide range of benefits, from regulatory compliance to improved efficiency, enhanced privacy and security, and better data quality and control. Different solutions have strengths and weaknesses in each of these regards, making it crucial to ensure that any data governance tool you’re evaluating meets your specific needs.
Beyond ensuring compliance with an ever-growing range of data protection and privacy regulations like the EU’s GDPR, data governance software also helps enterprises manage and leverage their data as effectively and efficiently as possible, ensuring data quality and availability for all stakeholders within an organization.
Any data governance solution you’re considering should include the following functionality at a minimum:
- Security and privacy features to ensure policy compliance
- Collaboration features to involve all stakeholders in the process
- A constructive balance between ease of use and depth of functionality
- Automation to increase performance, accuracy, and efficiency
Also read: Designing Valuable Data Governance Frameworks
Top Data Governance Tools
A wide range of powerful data governance software is available, with extensive documentation, demos, and free trials available for each offering. It’s crucial to take the time to study and trial a range of options before making a selection.
To help in your search, what follows is an alphabetical list of 11 solutions worth considering.
Alation’s platform merges machine learning with human insight to automate and optimize data stewardship, data classification, business glossary, and data quality documentation. Engaging end users in the process is a key focus, with an approach that makes data governance, collaboration, and communication capabilities part of users’ daily workflows to encourage accurate, compliant data-driven decision-making.
People-first approach is designed to get the entire business engaged in data governance, guiding users toward compliant use of data by making data governance an intuitive part of their day-to-day workflow.
Data consumers are alerted to quality at the point of data use, (i.e. in wiki pages where the data is described), in Alation’s intelligent SQL editor where queries are written, and in lineage diagrams where upstream data sources and transformations are visualized.
Behavioral Analysis Engine (BAE) leverages AI and machine learning to improve the platform by enhancing data discovery, supporting natural language search, and emphasizing the most active data sets in empirical usage.
Available as a full cloud or hybrid solution, Alex prides itself on the low total cost of ownership for its technology-agnostic data catalog, leveraging automation from its inception to improve efficiency and scalability. As a single, out-of-the-box complete platform, Alex is focused on ease of deployment across a variety of use cases.
- Capable of harvesting and ingesting metadata from a wide range of platforms and technologies, on-prem or in the cloud, with connectors for all major cloud providers.
- To improve consistency and performance, all platform components, features, and functionality are built by Alex rather than leveraging disparate solutions under the hood.
- Automated data lineage allows users to switch context easily between business lineage, technical lineage, user lineage, application lineage and technology lineage with a few clicks.
- Flexible ontology configuration allows the platform to speak the language of any domain, any industry, or any customer-specific requirements via simple configuration settings.
Ataccama ONE automatically calculates data quality and classifies data to help companies prioritize and focus, with a “self-driving” approach designed to automate as much as possible in order to improve efficiency and ease of use. Security and privacy policies can be automatically enforced for all relevant data assets, making data available to those who need it when they need it.
- Automation is based on zero-effort AI learning and metadata-based automation, meaning that for any new data source, nothing needs to be reconfigured — the solution automatically understands the contents and quality of the data inside.
- Flexibility is a key strength due to the solution being both data source agnostic and environment agnostic — all configurations can be reused for any environment and data source, rather than requiring a separate configuration for each one.
- Data profiles, data lineage, data quality, anomalies and relationships are automatically generated for all data in the catalog. Self-improving AI suggests business rules, assigns business terms, and detects new relationships.
Collibra Data Governance automates key governance and stewardship tasks to ensure that data governance stays up to date as the enterprise evolves, leveraging active metadata to understand an organization’s data across all sources and environments. Desktop and mobile apps provide quick access to data, reporting, and tasks, regardless of location.
- Contextual search helps users quickly find the data they need, with lineage information to clarify history and context of search results.
- Pre-built templates speed implementation and provide an essential framework for cross-functional collaboration.
- Data and metadata can be analyzed within existing tools, without needing to adopt new systems or processes.
- Automated business processes increase efficiency and accuracy.
- Data Helpdesk lets any user submit a ticket to flag incorrect data, then intelligently routes issues and escalations to the right people.
- Wide range of APIs enable integration with legacy data ecosystems while ensuring security and compliance.
With a fully cloud-native data catalog, data.world maps an organization’s distributed data to consistent business concepts to generate a unified body of knowledge. An online catalog of pre-built integrations, connectors and APIs is designed to speed deployment and increase functionality, and data.world’s open data community helps users connect and share datasets.
- Patented knowledge graph technology allows users to use natural-language search with a data catalog that automatically understands relationships between data assets, business concepts, and people.
- Fully managed SaaS solution has been cloud-native from the beginning, with a continuous release cycle promising no lengthy installs, migrations, upgrades, or downtime.
- Open and flexible solution enables connections to a wide variety of DataOps solutions including data warehousing, observability, lineage, and business intelligence.
- Provides complete context for all data, in the cloud or on-premises, including metadata, dashboards, analysis, code, docs, project management, and social collaboration features.
- Transparent pricing is listed on the data.world website. A variety of enterprise tiers, discounted pricing for education and nonprofits, and community tiers start with a free option.
Acquired by Quest Software in December 2020, erwin automatically consolidates metadata from a variety of data sources into a central data catalog, then makes it accessible via role-based, contextual views, including a fully configurable On-Demand Impact Analyst Dashboard that consolidates important insights.
- Automated, configurable, and schedulable metadata connectors, developed in-house, enable harvesting and documenting of metadata from databases, file systems, data movement code, third-party metadata repositories, and data consumption endpoints.
- Forward and reverse data lineage information is viewable with a single click from any point in the data governance framework. Lineage views are created on-demand from harvested data without requiring manual creation or maintenance.
- Enables users to create rich visualizations of all metadata, business terms, policy and rules associated with any keyword, navigable with a single click.
- Configurable Business Glossary comes out of the box with three business asset types (Terms, Policy, Rules), with an intuitive interface to create and associate additional business asset types as needed.
- Artificial intelligence/ML-based matching of technical and business assets, combing through the metadata repository and business glossary to provide ranked matches between any metadata and/or business data terms.
The IBM Watson Knowledge Catalog is a machine learning catalog for data discovery, data cataloging, data quality, and data governance, allowing organizations to access, curate, categorize and share data, knowledge assets and their relationships, wherever they reside.
- Automation is key, with AI-powered data discovery providing a profile of all data ingested into the catalog, along with augmented data management, auto-assigned business terms, auto-applied quality rules, auto-generated industry-specific and business-specific content, and auto-enforced data protection policies and rules.
- Data quality is automatically calculated, with the score included in an intuitive data lineage view along with other key information such as data owner and business terms to help users better understand the data and its trustworthiness.
- Integration with IBM Cloud Pak for Data enables Watson Knowledge Catalog users to deploy critical data and AI services on public or private clouds or on premises while integrating with proprietary, third party and open-source capabilities for data governance and AI lifecycle tools to provide an end-to-end data and AI solution.
Precisely’s data governance, catalog, and metadata management solution Data360 uses automated workflows to help businesses improve efficiency and ensure compliance. The platform is designed to extract, aggregate, and analyze large volumes of data at any point in a business process without disrupting or changing existing applications.
- Capable of analyzing data on any platform in any business process and in any format.
- Connects data across compliance, analytic, and operational governance use cases.
- Data360’s “3D lineage” delivers deep context by visualizing the intersection of data, process, and business lineage.
- Embedded value-based framework provides a single view into how data impacts business goals, objectives, and metrics.
- Flexible metamodel and no-code workflow enable quick deployment with minimal setup, implementation, and training.
- Automation eliminates manual processes and embeds analytics into day-to-day operation.
Informatica Data Governance incorporates data catalog, data privacy, and data quality capabilities in an end-to-end governance solution. The company recently introduced Cloud Data Governance and Catalog, a comprehensive SaaS offering that combines data cataloging, data quality, data governance, and AI governance with unified metadata-driven intelligence in the cloud.
- Integrated platform covers definition, discovery, quality, privacy, and data delivery in a comprehensive solution that leverages AI and machine learning to automate manual tasks.
- Modular platform is designed to be extended and modified over time without requiring custom integration, while leveraging a common metadata foundation.
- Enterprise Data Catalog Advanced Scanners provide detailed data discovery and automated end-to-end lineage with no black boxes, including the ability to extract deep metadata and lineage from complex data sources.
- Using patented AI, offers data privacy risk analytics to understand risk exposure, track proliferation/use and anomalies, and prioritize automated remediation.
OneTrust DataGovernance automates the discovery and classification of data to improve accuracy, efficiency, and compliance. Because DataGovernance is part of the broader OneTrust platform, privacy and security are built in, enabling organizations to integrate data governance into their privacy, security, third-party risk, GRC, ethics and compliance, and ESG programs, and more broadly encouraging a holistic approach to data governance.
- Via the OneTrust DataGuidance regulatory research platform, 40 in-house researchers and a network of 800 lawyer partners provide daily insights and regulatory updates that provide greater context to data discovery and data classification.
- The data discovery functionality from OneTrust’s 2020 acquisition of Integris has been integrated into the solution, enabling scanning and classification of all types of data, including structured, semi-structured and unstructured, in the cloud or on premise.
- The OneTrust platform is designed to allow customers to grow into it by starting with one module and adding others later, while continuing to leverage the same source-of-truth data set.
SAP Master Data Governance (SAP MDG), available on premise or in the cloud, is designed to simplify enterprise data management, increase data accuracy, reduce risk, improve compliance, and reduce total cost of ownership. As a single application for enterprise master data management, SAP MDG consolidates master data; automates the replication and syndication of master data throughout the system landscape; and measures, monitors, and improves master data quality and processes.
- Supports master data consolidation and central governance as well as data quality management, and is designed specifically to process analytics on large volumes of data.
- Offered as a complete, out-of-the-box application to speed implementation, with predefined data models, user interfaces, workflows, and business rules.
- Provides extensive data domain coverage with flexibility to customize and extend, along with a broad partner network to expand that coverage further and meet industry-specific needs.
- Built on a unified architecture with integrated capabilities across all domains, enabling customers to reuse components and manage cross-domain intersections with an integrated data model.
- Cloud edition enables customers to minimize initial investment with a subscription to a highly standardized SaaS solution, then grow into a platform approach.
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