7 Data governance framework examples You Should Know

Discover the top 7 data governance framework examples strategies and tips. Complete guide with actionable insights.

Establishing a robust data governance framework is no longer a strategic option; it's a foundational necessity for any organization that relies on data to drive decisions, innovate, and maintain a competitive edge. Without a clear structure for managing data assets, businesses risk data quality issues, compliance failures, and missed opportunities. The challenge, however, isn't recognizing the need but choosing the right approach. A one-size-fits-all model simply doesn't exist, as the optimal framework depends on your organization's size, industry, regulatory environment, and data maturity.

This guide moves beyond theoretical discussions to provide a deep dive into practical data governance framework examples. We will dissect seven prominent frameworks, from industry-agnostic standards like DAMA-DMBOK and COBIT to vendor-specific models from IBM, Microsoft, and Collibra. For each example, we will analyze its core components, strategic advantages, and potential drawbacks. The goal is to equip you with the insights needed to evaluate which structure, or combination of structures, best aligns with your specific business objectives.

You will find actionable takeaways and replicable strategies that you can adapt to your own implementation roadmap. We’ll break down how these frameworks address critical areas like data stewardship, quality control, metadata management, and security protocols. For those operating in highly regulated sectors, understanding these models is particularly crucial. For instance, the principles discussed here are fundamental to specialized applications like Data Governance in Banking: Strategies for Success, where regulatory compliance and risk management are paramount. By exploring these real-world examples, you can build a governance program that not only protects your data but also transforms it into a reliable, enterprise-wide asset.

1. DAMA-DMBOK (Data Management Body of Knowledge) Framework

The DAMA-DMBOK framework, developed by the Data Management Association (DAMA) International, is one of the most comprehensive and widely respected data governance framework examples available. It's not a rigid, step-by-step methodology but rather a foundational guide that provides a standard industry view of data management functions, terminology, and best practices. Its core purpose is to establish a common language and understanding for all data-related activities within an organization.

The framework is structured around 11 distinct but interconnected Knowledge Areas, including Data Quality, Data Architecture, Master Data Management, and Data Security. Crucially, DAMA positions Data Governance as the central, coordinating function that orchestrates all other ten areas, ensuring they work in harmony toward strategic business goals. This makes it a powerful tool for building a holistic and enterprise-wide data strategy.

Strategic Breakdown and Implementation Insights

The following diagram illustrates the hierarchical nature of the DAMA-DMBOK framework, showing how Data Governance serves as the central pillar connecting strategy to the specific knowledge areas.

Infographic showing the hierarchical structure of the DAMA-DMBOK framework with Data Governance at the top.

This visualization highlights that Data Governance is not merely one component among many; it is the foundational strategy that guides the implementation of all 11 knowledge areas and establishes standard terminology across the enterprise.

Leading organizations leverage DAMA-DMBOK not as a prescriptive manual but as a strategic roadmap. For instance, major financial institutions like JPMorgan Chase have adapted its principles to build robust governance structures that meet stringent regulatory requirements. Similarly, IBM’s data governance consulting services are heavily based on DAMA-DMBOK, demonstrating its value in complex, large-scale enterprise environments.

Key Strategic Point: The power of DAMA-DMBOK lies in its flexibility. It provides the "what" and "why" of data management, leaving the "how" to be customized based on an organization's specific size, industry, and data maturity level.

Actionable Takeaways for Implementation

To successfully adopt the DAMA-DMBOK framework, consider these practical steps:

Start with a Pilot Program: Avoid a "boil the ocean" approach. Select 2-3 high-impact knowledge areas, such as Data Quality and Master Data Management, to pilot your governance initiative and demonstrate early value.
Invest in Certification: Encourage key team members, like data stewards and architects, to pursue DAMA certifications (e.g., CDMP). This builds in-house expertise and ensures consistent application of the framework.
Customize for Your Context: Use DAMA-DMBOK as a blueprint, not a rigid set of rules. Assess your organization's current data management maturity and tailor the framework's recommendations to address your most critical gaps first.
Focus on Business Value: Always connect data governance activities back to tangible business outcomes. Frame initiatives around improving customer experience, reducing operational risk, or enabling new revenue streams to secure executive buy-in.

2. COBIT (Control Objectives for Information and Related Technologies) for Data Governance

Developed by ISACA, COBIT is a renowned IT governance framework that provides a comprehensive structure for governing and managing enterprise information and technology. While its scope is broader than data alone, COBIT 2019 offers one of the most robust data governance framework examples by treating data as a critical enterprise asset. Its primary focus is on creating business value from data while effectively managing risks and ensuring regulatory compliance.

COBIT provides a set of control objectives, processes, and guidelines that help organizations align their data strategies with overall business goals. It achieves this through a series of governance and management objectives, with specific processes dedicated to managing data (under the "Align, Plan, and Organize" domain). This makes it particularly effective for organizations looking to integrate data governance directly into their existing IT and enterprise governance structures, ensuring a unified approach to risk and compliance.

Strategic Breakdown and Implementation Insights

COBIT is built on a principles-based approach, helping organizations design a governance system that fits their unique context. It emphasizes clear accountability, risk management, and value creation, which are essential for effective data governance. The framework's cascading goals mechanism allows organizations to translate high-level enterprise goals into specific, manageable IT-related goals, and ultimately, into actionable data governance processes.

This top-down approach ensures that data governance initiatives are not isolated technical projects but are directly tied to strategic business imperatives. For example, several European banks have successfully used COBIT's control objectives to build data governance programs that specifically address GDPR compliance. Similarly, global manufacturing giant Siemens implemented a COBIT-based data governance model to standardize data processes across its international operations, improving both efficiency and risk management.

Key Strategic Point: COBIT's strength lies in its integration with overall enterprise governance. It bridges the gap between business stakeholders and IT, ensuring that data governance decisions are driven by business needs and risk appetite, not just technical requirements.

Actionable Takeaways for Implementation

To successfully adopt COBIT for your data governance needs, consider these practical steps:

Align Data Objectives with Business Strategy: Use the COBIT goals cascade to translate enterprise goals into specific governance objectives for data. This ensures buy-in and demonstrates clear business value from the outset.
Establish Clear Accountability: Implement a roles and responsibilities chart (like a RACI matrix) as suggested by COBIT for key data processes. Define data owners, stewards, and custodians to create a clear accountability structure. A key aspect of this involves ensuring robust information security; understanding critical cybersecurity measures including firewall solutions is vital for protecting data assets under this framework.
Leverage Existing IT Governance: If your organization already uses COBIT for IT governance, extend its principles and processes to cover data governance specifically. This approach accelerates implementation and reduces the learning curve. For more guidance, explore these data governance best practices.
Start with a Proof-of-Concept: Select a critical business area, such as financial reporting or customer data management, to pilot your COBIT-based data governance initiative. Use the success of this project to build momentum for an enterprise-wide rollout.

3. IBM Data Governance Framework

The IBM Data Governance Framework is a highly practical, business-oriented approach that stands out among data governance framework examples for its emphasis on tangible outcomes. Developed by IBM and popularized by experts like Sunil Soares, this framework is designed to bridge the gap between high-level business objectives and the technical realities of data management. It focuses on establishing clear ownership, processes, and policies to manage data as a strategic enterprise asset.

The framework is structured around several core components, including establishing a Data Governance Office (DGO), defining policies and standards, and implementing data stewardship. Unlike more theoretical models, IBM's approach is deeply rooted in real-world application, providing a structured methodology that organizations can follow to build a governance program that directly supports business goals, from regulatory compliance to driving innovation.

Strategic Breakdown and Implementation Insights

The following diagram illustrates the key components of the IBM framework, showcasing how business outcomes are linked to governance processes, information management, and the underlying technology infrastructure.

IBM Data Governance Framework

This model emphasizes that a successful data governance program must be driven by the business, not IT. It starts with defining the desired outcomes, such as improved decision-making or enhanced customer trust, and then builds the necessary governance structures and processes to achieve them. This outcome-first approach ensures that governance efforts are never just an academic exercise.

Global enterprises have successfully adopted this framework to tackle complex challenges. American Express leveraged its principles to create a unified global data governance model, improving consistency and reducing risk. Similarly, Anthem Inc. applied the framework to manage sensitive healthcare data, ensuring compliance while enabling better patient outcomes. These examples highlight the framework's adaptability across different industries and regulatory landscapes.

Key Strategic Point: IBM's framework excels by forcing a direct alignment between data governance activities and specific, measurable business results. It shifts the conversation from "managing data" to "using data to achieve business goals."

Actionable Takeaways for Implementation

To successfully adopt the IBM Data Governance Framework, consider these practical steps:

Start with a Business Case: Clearly define the business problem you are trying to solve and the expected ROI. This could be anything from reducing compliance fines to increasing marketing campaign effectiveness.
Invest in Change Management: The framework heavily relies on new roles (like data stewards) and processes. Proactive stakeholder engagement and communication are critical to overcoming resistance and ensuring adoption.
Leverage Assessment Tools: Utilize IBM's data governance maturity models and assessment tools to benchmark your current state. This provides a clear roadmap and helps prioritize your implementation efforts. For a deeper understanding of how this ties into broader security measures, you can learn more about data security and compliance best practices.
Adopt a Hybrid Approach: Don't be afraid to integrate elements from other frameworks. IBM's model can be powerfully combined with the knowledge areas of DAMA-DMBOK to create a comprehensive and customized solution.

4. Microsoft Purview Data Governance Framework

The Microsoft Purview Data Governance Framework is a unified, cloud-native solution designed to manage and govern data across an organization's entire estate, including on-premises, multi-cloud, and SaaS environments. As a powerful entry in the list of data governance framework examples, its strength lies in automated data discovery, sensitive data classification, and end-to-end data lineage. Purview's core purpose is to create a holistic, up-to-date map of the data landscape, enabling organizations to understand what data they have, where it is, and how it is being used.

This framework is built around three main pillars: the Data Map, Data Catalog, and Data Estate Insights. The Data Map automatically scans and classifies data, the Data Catalog allows users to find trustworthy data, and Data Estate Insights provides a comprehensive overview of data risks and governance gaps. Its seamless integration with the broader Microsoft ecosystem (like Azure and Microsoft 365) makes it a particularly compelling option for organizations already invested in Microsoft technologies.

Strategic Breakdown and Implementation Insights

The diagram below illustrates how Microsoft Purview unifies data governance by creating a central map of the entire data estate, from operational databases to analytics platforms.

Microsoft Purview Data Governance Framework

This visualization shows Purview acting as a central hub, connecting disparate data sources and providing a single pane of glass for data discovery, classification, and policy management, which simplifies governance in complex, hybrid environments.

Leading companies are adopting Purview to accelerate their governance efforts. For example, Progressive Insurance leverages Purview for automated data discovery and classification to manage risk and ensure compliance across its vast data landscape. Similarly, H&R Block implemented Purview to govern sensitive tax data, improve data trust, and empower its data scientists with reliable information. These use cases demonstrate its value in highly regulated industries where data security and compliance are paramount.

Key Strategic Point: Purview's primary advantage is its automation-first approach. By automatically scanning and classifying data at scale, it dramatically reduces the manual effort traditionally required for data mapping and cataloging, accelerating time-to-value for governance initiatives.

Actionable Takeaways for Implementation

To successfully adopt the Microsoft Purview framework, consider these practical steps:

Start with Automated Scanning: Begin by connecting Purview to your key data sources (e.g., Azure SQL, Power BI, Microsoft 365) and run automated scans. This provides an immediate, high-level understanding of your data landscape and identifies sensitive data concentrations without significant manual effort.
Leverage Existing Azure Active Directory (AAD): Integrate Purview with your existing AAD for access controls. This simplifies security management by allowing you to apply familiar user roles and permissions to the data catalog and governance functions.
Implement a Gradual Rollout: Focus on Microsoft data sources first to build momentum and familiarity with the platform. Once governance processes are established, expand scanning and cataloging to other cloud providers (like AWS S3) and on-premises systems.
Use Built-in Compliance Templates: Accelerate your compliance efforts by using Purview's pre-built templates for regulations like GDPR and HIPAA. These templates include predefined classifiers and policies to help you quickly identify and protect relevant sensitive data.

5. DGI (Data Governance Institute) Framework

The DGI (Data Governance Institute) Framework is a practical, business-focused model that stands out among data governance framework examples for its emphasis on implementation. Developed by data governance pioneer Gwen Thomas, this framework positions data governance as a business-led program supported by IT, rather than an IT-centric initiative. Its core principle is that sustainable governance must be driven by business needs, value, and accountability.

The framework is built around 10 universal components, organized into three categories: Rules & Rules of Engagement, People & Organizational Bodies, and Processes. It provides a step-by-step, pragmatic methodology for establishing a governance program, focusing on defining roles like data stewards, establishing a Data Governance Office (DGO), and creating actionable policies. This business-first approach makes it highly effective for organizations struggling to get stakeholder buy-in and demonstrate tangible results.

Strategic Breakdown and Implementation Insights

The DGI Framework is designed as a practical roadmap, guiding organizations from initial concept to a fully operational, sustainable program. It helps answer the fundamental "who, what, when, where, why, and how" of data governance by providing clear templates and process flows for defining data rules and standards.

Its components directly address the common failure points of governance programs, such as lack of business engagement and unclear accountability. For example, Nationwide Insurance successfully implemented the DGI methodology to align its data practices with business objectives, improving data quality and regulatory compliance. Similarly, numerous healthcare organizations have leveraged DGI principles to govern sensitive patient data, ensuring it is managed securely and ethically by clearly defined business owners.

Key Strategic Point: The DGI Framework’s strength is its prescriptive, "how-to" nature. It provides concrete steps, role definitions, and templates that demystify the process of launching a data governance program, making it accessible even for organizations with lower data maturity.

Actionable Takeaways for Implementation

To successfully adopt the DGI Framework, consider these practical steps:

Focus on Quick Wins: Use the DGI methodology to identify and tackle a high-visibility data problem with a clear business impact. Delivering a quick, tangible win will build momentum and secure long-term credibility for the program.
Prioritize Stakeholder Engagement: Invest heavily in communication and change management. Use DGI's stakeholder analysis tools to identify key influencers and create a communication plan that continually reinforces the business value of data governance.
Establish Formal Roles and Responsibilities: Immediately use DGI's templates to define and assign roles like Data Owners and Data Stewards from within business units. This embeds accountability directly where the data is created and used.
Emphasize Business Value Over Technical Jargon: When communicating with executives and business stakeholders, frame every governance activity in terms of its contribution to business goals, such as risk reduction, cost savings, or revenue growth.

6. Informatica Axon Data Governance Framework

The Informatica Axon Data Governance Framework is a technology-driven, collaborative platform that distinguishes itself among data governance framework examples by focusing on business user engagement. It treats data governance not as a top-down IT mandate, but as a shared, community-driven responsibility. Axon provides a unified interface where business and technical users can collaboratively define data assets, establish policies, and manage data quality rules.

At its core, the framework is designed to bridge the gap between business context and technical data. It facilitates this by integrating a business glossary, data cataloging, and data quality dashboards into a single environment. This approach empowers non-technical stakeholders to actively participate in governing the data they use daily, ensuring that governance efforts are directly aligned with business needs and terminology.

Strategic Breakdown and Implementation Insights

Informatica Axon's strategy centers on making data governance accessible and operational for the entire organization. It moves beyond passive documentation to active, crowdsourced collaboration, where business users become the primary stewards of their data domains. This model is especially powerful for large enterprises managing complex data landscapes and stringent regulatory requirements like GDPR and CCPA.

For instance, global financial institutions like ING Bank have successfully implemented Axon to establish a common data language across dozens of countries, streamlining compliance and risk management. Similarly, Mercy Health leverages Axon to govern sensitive healthcare data, ensuring patient information is managed securely and consistently. These examples show the framework's effectiveness in industries where data accuracy and accountability are paramount. A successful implementation often underpins a broader digital transformation roadmap by building trust in enterprise data.

Key Strategic Point: Axon's unique value lies in its business-first orientation. By empowering business users with intuitive tools to define and manage data, it embeds governance directly into daily workflows, increasing adoption and ensuring policies are practical and relevant.

Actionable Takeaways for Implementation

To successfully adopt the Informatica Axon Data Governance Framework, consider these practical steps:

Start with the Business Glossary: Begin by collaborating with business departments to build a comprehensive business glossary. Establishing a common, approved terminology is the foundational step for aligning IT and business efforts.
Leverage AI for Initial Cataloging: Use Axon's AI-powered data discovery and cataloging features to quickly scan, identify, and classify data assets across your enterprise. This accelerates the initial setup and provides a comprehensive inventory for governance.
Promote Crowdsourcing and Stewardship: Actively encourage business users to contribute to data definitions, identify quality issues, and claim ownership of data assets. Use the platform’s collaborative features to create a community of engaged data stewards.
Integrate for End-to-End Governance: Maximize value by integrating Axon with other Informatica tools like Enterprise Data Catalog (EDC) and Data Quality (IDQ). This creates a seamless, automated workflow from data discovery to quality monitoring and policy enforcement.

7. Collibra Data Governance Framework

The Collibra Data Governance Framework is a technology-centric approach built around the company’s enterprise data intelligence platform. Unlike more abstract models, Collibra provides a tangible toolset designed to operationalize governance through the concept of data citizenship, where every employee is empowered with the right data at the right time. This framework is one of the most practical data governance framework examples because it directly links governance principles to a unified, collaborative platform.

Its core philosophy is to bridge the gap between technical data management and business user needs. The framework achieves this by integrating a business glossary, data catalog, data lineage, and policy management into a single, user-friendly environment. By emphasizing automation and business-focused workflows, it aims to embed data governance directly into the daily operations of an organization, making it an active, rather than a passive, discipline.

Strategic Breakdown and Implementation Insights

Collibra’s framework is designed to create a system of record for an organization's data assets, policies, and definitions. It establishes a clear operating model where roles like data owners and stewards are not just defined in theory but are assigned and managed within the platform itself. This creates accountability and streamlines governance processes like data quality issue resolution and access requests.

Global financial institutions like ING have successfully leveraged Collibra to create a unified data language across their international operations, simplifying regulatory reporting and risk management. Similarly, healthcare giant Cigna uses the platform to manage critical data assets, ensuring compliance with privacy regulations and improving patient data accuracy. These examples highlight Collibra's strength in complex, highly regulated industries where auditable data lineage and clear ownership are non-negotiable.

Key Strategic Point: Collibra's framework succeeds by treating data governance as a collaborative business function, not just an IT task. Its platform-driven approach democratizes data intelligence, making it accessible and actionable for business users who are closest to the data's context.

Actionable Takeaways for Implementation

To successfully adopt the Collibra framework, consider these practical steps:

Define Roles and Responsibilities First: Before implementation, clearly define your data governance roles (owners, stewards, custodians). Use Collibra’s workflow engine to automate the processes associated with these roles, such as approvals for new business terms or data access.
Leverage Out-of-the-Box Automation: Utilize Collibra’s automated data ingestion and lineage stitching capabilities to quickly populate your data catalog. This reduces the immense manual effort typically required and accelerates time-to-value.
Focus on Business User Adoption: Invest heavily in change management and training for non-technical users. Customize dashboards and views to align with specific business domains, ensuring users see immediate value in finding, understanding, and trusting their data.
Explore the Collibra Marketplace: Don't reinvent the wheel. Use the Collibra Marketplace to find pre-built connectors, templates, and workflows for common use cases like GDPR or CCPA compliance, significantly speeding up deployment.

Data Governance Frameworks: 7-Point Comparison

Framework	🔄 Implementation Complexity	⚡ Resource Requirements	📊 Expected Outcomes	💡 Ideal Use Cases	⭐ Key Advantages
DAMA-DMBOK (Data Management Body of Knowledge) Framework	High - comprehensive & theoretical	High - time and certification investment	Strong foundational standards and common language	Large enterprises seeking broad data management foundation	Industry standard; flexible; covers all data management aspects
COBIT (Control Objectives for Information and Related Technologies)	High - risk & IT governance focus	High - requires IT governance maturity	Risk management, compliance, and measurable KPIs	Organizations with mature IT governance & compliance needs	Strong IT integration; excellent for compliance and risk
IBM Data Governance Framework	Medium-High - business & process driven	High - investment in IBM tools and support	Business-aligned data governance with AI support	Enterprises leveraging IBM technologies & AI governance	Strong business alignment; AI/analytics support; proven track record
Microsoft Purview Data Governance Framework	Medium - automation-focused	Medium - Microsoft ecosystem integration	Automated discovery, classification, and lineage	Organizations in Microsoft ecosystem or hybrid cloud setups	Automation reduces effort; strong MS integration; cost-effective
DGI (Data Governance Institute) Framework	Medium - business-focused & practical	Medium - change management focused	Sustainable governance with strong business engagement	Organizations needing practical start with strong business buy-in	Practical methodology; scalable; vendor-neutral
Informatica Axon Data Governance Framework	Medium-High - collaborative with AI	High - investment in Informatica ecosystem	Collaborative governance with AI-powered cataloging	Large enterprises favoring collaboration and AI-driven governance	Strong AI and ML features; excellent user engagement
Collibra Data Governance Framework	Medium-High - role-based & workflow heavy	High - investment and customization needed	Automated and collaborative governance at scale	Enterprises focusing on data citizenship and automation	Intuitive design; automation; comprehensive workflows

Final Thoughts

Embarking on the journey of implementing a data governance program can feel like navigating a vast and complex landscape. As we've explored, there is no single, universally perfect solution. The right path for your organization is not about finding a magic bullet but about carefully selecting and adapting a model that aligns with your unique business objectives, technological infrastructure, and cultural maturity. The data governance framework examples detailed in this guide, from the comprehensive DAMA-DMBOK to the technology-integrated Microsoft Purview, serve not as rigid prescriptions but as powerful blueprints to inform your strategy.

The core lesson from analyzing these diverse approaches is one of intentionality. A successful data governance initiative is never an accident. It is the result of deliberate design, executive sponsorship, and a clear understanding of what "value" means for your data. Whether you are a large enterprise requiring the structured, audit-focused controls of a framework like COBIT or a nimble organization drawn to the business-centric, iterative model of Collibra, the principles remain consistent.

Key Strategic Takeaways

Reflecting on the frameworks we've examined, several critical themes emerge that should guide your implementation efforts:

Start with Business Value: The most effective frameworks, like those from Informatica and IBM, consistently tie data governance activities back to tangible business outcomes. Avoid the trap of treating governance as a purely technical or compliance-driven exercise. Always ask: "How will this rule, policy, or role help us make better decisions, reduce risk, or create new opportunities?"
Adapt, Don't Just Adopt: Think of these frameworks as toolkits, not instruction manuals. Your organization might benefit from combining the foundational knowledge areas of DAMA-DMBOK with the practical, technology-agnostic ten components of the DGI framework. The goal is to build a hybrid model that fits your reality.
People and Process Over Tools: While technology platforms like Microsoft Purview and Collibra are powerful enablers, they cannot succeed in a vacuum. The most crucial components of any framework are clearly defined roles (like Data Stewards and Owners), well-understood processes for data lifecycle management, and a culture that champions data accountability.
Iterate and Evolve: Data governance is not a one-time project; it's an ongoing program. The initial framework you implement should be designed to be flexible. Start with a focused scope, such as a single critical data domain, demonstrate value quickly, and use those wins to build momentum and secure broader buy-in for expansion.

Your Actionable Next Steps

Moving from theory to practice is the most critical step. Based on our deep dive into these data governance framework examples, here is a clear path forward to begin your initiative or refine your existing one:

Conduct a Maturity Assessment: Before selecting a framework, you must understand your starting point. Where are your biggest data-related pain points? What governance-related activities are already happening, even if informally?
Define Your "Why": Articulate the top 1-3 business objectives for your data governance program. Is it to enhance regulatory compliance, improve analytics accuracy, or accelerate AI development? This "why" will be your north star.
Form a Cross-Functional Council: Assemble a steering committee with representatives from business, IT, legal, and compliance. This group will be responsible for championing the initiative and making key decisions, mirroring the governance structures seen in frameworks like COBIT and DGI.
Draft a Pilot Framework: Choose one of the models discussed, or a hybrid of a few, and scope it to a single, high-impact business area. Define the roles, processes, and policies for just that domain.
Measure and Communicate: Establish clear metrics to track the success of your pilot. Regularly communicate progress, challenges, and wins to stakeholders to build trust and demonstrate the tangible value of structured data governance.

Ultimately, mastering data governance is about transforming data from a simple byproduct of business operations into a strategic, enterprise-level asset. By thoughtfully applying the lessons from these proven frameworks, you can build a robust program that not only mitigates risk but also unlocks the full potential of your data to drive innovation and secure a competitive advantage.

Ready to move from framework theory to seamless execution? The frameworks we've discussed highlight the critical need for collaboration between data experts, a challenge DataTeams is built to solve. Our platform helps you assemble and manage high-performing data and AI project teams, ensuring you have the right talent in the right roles to bring your data governance vision to life. Find the specialized data stewards, governance analysts, and data architects you need at DataTeams.

Blog

DataTeams Blog

12 Best free data visualization tools You Should Know

Speak with DataTeams today!

We can help you find top talent for your AI/ML needs

Get Started

DataTeams Blog

12 Best free data visualization tools You Should Know

Business Intelligence Software Comparison: Choose the Best BI Tool

7 Data governance framework examples You Should Know

Speak with DataTeams today!