Data Management
Transform scattered information into trusted, governed, and secure strategic assets that fuel decisions, analytics, and AI—while supporting compliance with national standards.
Why Data Management Matters
Data today is a national and strategic asset no less important than human or financial capital; it is the component upon which decisions, policies, and services rely in the digital age. According to the DAMA-DMBOK® framework by DAMA International, data management is defined as "a set of integrated practices that ensure maximum value from information assets through their management, control, quality improvement, and sustainability within an organization." DAMA studies show that poor data management can cause losses of up to 30% of an organization's annual revenue due to operational errors and poor data quality—highlighting the need for a disciplined organizational approach to data management. In Saudi Arabia, the Saudi Data and Artificial Intelligence Authority (SDAIA) and the National Data Management Office (NDMO) have emphasized in their official documents that "as a public sector asset, government data must be managed as a strategic asset." This is a fundamental principle in the national governance policies and Personal Data Protection Law (PDPL), ensuring government data is managed according to standards of quality, privacy, integration, and transparency. From this standpoint, data management in Saudi entities is a national governance and investment approach aimed at enhancing digital trust, enabling organizational intelligence, and supporting evidence-based decision-making—aligned with Saudi Vision 2030's goals of building a knowledge-based digital economy.
How We Build Effective Data Management
What You Get
National Data Standards Compliance: Practical implementation of national data management and personal data protection standards requirements, with documented policies and procedures easy to present in any assessment or audit.
A Data Management Office Ensuring Governance and Compliance: Establishment and operation of a DMO with clear structure, roles, responsibilities, and organized decision paths—making data decisions institutional rather than individual.
Data Architecture Supporting BI and AI: A unified data architecture connecting operational systems to data warehouses/lakes, providing a single source of truth for reports and analytical models.
Data Quality for Reliable Decisions: Data quality rules, catalog, data dictionary, and unified master entities that reduce duplication and errors while increasing KPI accuracy and dashboard reliability.
Personal Data Protection and Privacy Risk Reduction: Clear data sensitivity classification, access controls, and processing activity logs supporting Personal Data Protection Law compliance and reducing systemic and reputational risks.
Continuous Monitoring and Disciplined Data Management Improvement: Periodic measurement of data quality, compliance, and maturity allowing early gap identification and directing investments toward what truly raises data value—maintaining improvement sustainability over time.