- Criticality: A Data Quality program should focus on the data most critical to the enterprise and its customers. Priorities for improvement should be based on the criticality of the data and on the level of risk if data is not correct.
- Lifecycle Management: The quality of data should be managed across the data lifecycle, from creation or procurement through disposal. This includes managing data as it moves within and between systems (i.e., each link in the data chain should ensure data output is of high quality).
- Prevention: The focus of a Data Quality program should be on preventing data errors and conditions that reduce the usability of data; it should not be focused on simply correcting records.
- Root Cause Remediation: Improving the quality of data goes beyond correcting errors. Problems with the quality of data should be understood and addressed at their root causes, rather than just their symptoms. Because these causes are often related to process or system design, improving data quality often requires changes to processes and the systems that support them.
- Governance: Data Governance activities must support the development of high quality data and Data Quality program activities must support and sustain a governed data environment.
- Standards-Driven: All stakeholders in the data lifecycle have data quality requirements. To the degree possible, these requirements should be defined in the form of measurable standards and expectations against which the quality of data can be measured.
- Objective Measurement and Transparency: Data quality levels need to be measured objectively and consistently. Measurements and measurement methodology should be shared with stakeholders since they are the arbiters of quality.
- Embedded in Business Processes: Business process owners are responsible for the quality of data produced through their processes. They must enforce data quality standards in their processes.
- Systematically Enforced: System owners must systematically enforce data quality requirements.
- Connected to Service Levels: Data quality reporting and issues management should be incorporated into Service Level Agreements (SLA).
Data Quality Programs – Guidance and Principles
Master’s degree in computer science with Specialization in Software Engineering and Earned PGD in Management Information System. Accumulated 20+ years’ Information Technology, Data Governance, Data Management, and Compliance related consecutive hands-on experience, possess diversified business and technical background across the Aviation, Healthcare, Investment, Education, and Construction sectors.
45+ Online Professional Certifications, which includes:
• CDMP – Certified Data Management Professional
• CDPO – Certified Data Protection Officer
• GDPR – General Data Protection Regulation Practitioner
• EDRP – EC-Council Disaster Recovery Professional
• AWS Certified Cloud Practitioner
• Oracle Enterprise Data Management Professional
• Oracle Database Cloud Certified Professional
• ITIL – Information Technology Infrastructure Library (ITSM – IT)
Experience includes leading data governance and management initiatives, ensuring compliance with NDMO, GDPR and KSA PDPL, and implement/improve data security, privacy, & protection. Expertise spans a wide variety of platforms, tools, technologies, hardware, software, and operating systems.
Served in leadership capacities such as Manager – Data Management, Sr. Team Lead – Database Specialist, Sr. Team Lead Consultant, Sr. Applications DBA, Sr. Resident Consultant, and Manager Systems.