Frequently Asked Questions

This site helps you understand how well your organization manages data and where improvements will create real business value—using a globally recognized framework.

Measuring your data capabilities reveals where data is limiting performance today and where focused action will deliver the greatest impact.

It helps you reduce data-related risk, improve decision-making, increase trust in data, and avoid investing in initiatives your data foundation cannot support.

You receive a clear, comparable view of your data management maturity, prioritized insights, and exportable data you can reuse internally.

The results show which data capabilities are blocking progress, helping you prioritize investments that deliver measurable outcomes.

DAMA International is a not-for-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management. The vision & purpose of DAMA International is to be an essential resource to those who engage in information and data management. DAMA International's primary purpose is to promote the understanding, development and practice of managing data and information as key enterprise assets to support the organization.

The Organization Goals of DAMA-International are to:

• Help practitioners become more knowledgeable and skilled in the information and data management profession.
• Influence practices, education and certification in the information and data management profession.
• Support DAMA members and their organizations to address their information and data management needs.
• Form alliances with other organizations with similar principles to strengthen the profession.

The 11 DAMA disciplines define the full scope of data management capabilities needed to ensure data is trusted, secure, and usable for business, analytics, and AI. They are:

• Data Governance – Decision rights, ownership, policies, and accountability for data
• Data Architecture – How data is structured, stored, and flows across systems
• Data Modeling & Design – Logical and physical data models that support business needs
• Data Storage & Operations – Databases, platforms, and operational data management
• Data Security – Protecting data confidentiality, integrity, and availability
• Data Integration & Interoperability – Moving and sharing data across systems reliably
• Document & Content Management – Managing unstructured data such as documents and files
• Reference & Master Data Management – Consistent, shared core data across the organization
• Data Warehousing & Business Intelligence – Analytics, reporting, and decision support
• Metadata Management – Understanding what data exists, where it comes from, and how it is used
• Data Quality Management – Ensuring data is accurate, complete, timely, and fit for purpose

Data Governance is at the core because it defines who decides, who owns, and who is accountable for data. Without governance, data remains unmanaged—even if tools, platforms, or analytics are in place.

• Aligns data with business priorities
• Enables consistent data quality and standards
• Clarifies ownership across all data disciplines
• Reduces regulatory and operational risk
• Makes analytics and AI initiatives sustainable

Because data challenges are systemic. Assessing all disciplines ensures root causes are identified—not just visible symptoms.

Yes. You can select focus areas for deeper analysis while still maintaining a holistic baseline across all disciplines.

Chris Bradley is a recognized DAMA leader and co-author of the DAMA-DMBOK, contributing to globally adopted data management standards. Learn more about Chris.

It is standardized, DAMA-aligned, faster to execute, and data-driven—delivering objective results without long consulting engagements.

Yes. Many organizations use the results as a baseline for data transformation, governance initiatives, or platform modernization.

It means your data is reliable enough to support your business goals, from reporting and compliance to analytics and AI.

It shows whether your data foundation is ready for AI and highlights weaknesses that would otherwise undermine AI investments.

Industry research shows that many data and AI initiatives struggle to achieve measurable value without strong foundational capabilities. For example, Gartner predicts that 80% of data and analytics governance programs will fail by 2027 unless they are tied to prioritized business outcomes, and research linked to MIT’s NANDA project shows that 95% of generative AI investments produce no measurable financial return for organizations. These findings underscore the importance of strong data management practices and business alignment before scaling analytics or AI efforts.

Yes. The standardized structure supports benchmarking, trend analysis, and cross-organization comparison.

Yes. You fully own your data and can export it at any time. Example output files are available on request.

Yes. The data is designed for reuse in your existing analytics, dashboards, and reporting systems.

Yes. Consistency is maintained to ensure comparability, with updates only when required by changes to the DAMA framework.

We provide multiple security and deployment options to meet organizational, regulatory, and national-level requirements.

Yes. The solution scales from single organizations to multi-organization and national-level assessments.

Yes. The assessment can be tailored to specific industries, regulations, or strategic objectives.

Because reliable data underpins efficiency, compliance, innovation, and AI-driven growth. Without it, digital initiatives fail to deliver value.