Shared Data: Reference and Master Data must be managed so that they are shareable across the organization. Ownership: Reference and Master Data belong to the organization, not to a particular …
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.
Data Quality – Parsing and Transformation
Data Parsing and Formatting Data Parsing is the process of analyzing data using pre-determined rules to define its content or value. Data parsing enables the data analyst to define sets …
Data Quality – Cleansing and Enrichment
Data Cleansing / Scrubbing Data Cleansing or Scrubbing transforms data to make it conform to data standards and domain rules. Cleansing includes detecting and correcting data errors to bring the …
Data Quality – Data Profiling
Data Profiling is a form of data analysis used to inspect data and assess quality. Data profiling uses statistical techniques to discover the true structure, content, and quality of a …
Data Quality – Causes of Data Quality Issues – Part-03
Issues Caused by System Design Failure to Enforce Referential Integrity: Referential integrity is necessary to ensure high quality data at an application or system level. If referential integrity is not …
Data Quality – Causes of Data Quality Issues – Part-02
Issues Caused by Data Entry Processes Data Entry Interface Issues: Poorly designed data entry interfaces can contribute to data quality issues. If a data entry interface does not have edits …
Data Quality – Causes of Data Quality Issues – Part-01
Data Quality issues can emerge at any point in the data lifecycle, from creation to disposal. When investigating root causes, analysts should look for potential culprits, like problems with data …
Data Quality – Business Rules
Business Rules are commonly implemented in software, or by using Document Templates for Data Entry. Some common simple Business Rule types are: Definitional Conformance: Confirm that the same understanding of …
Data Quality Programs – Guidance and Principles
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 …
Data Quality – Core Dimensions
A Data Quality dimension is a measurable feature or characteristic of data. The term dimension is used to make the connection to dimensions in the measurement of physical objects (e.g., …