Developing Data Science Solutions Developing Data Science solutions involves the iterative inclusion of data sources into models that develop insights. Data Science depends on: Rich Data Sources in Data Science …
As an accomplished professional with a Master Degree in Computer Science (Specialization in Software Engineering) and a PGD in Management Information Systems, over 20 years of hands-on experience in Information Technology, Data Governance, Data Management, and PDPL plus NCA Compliance. Expertise spans diverse sectors, including Aviation, Healthcare, Investment, Education, and Construction, allowing me to effectively bridge the gap between technical and business needs.
Leadership roles include Manager – Data Management (DMO), Manager Systems, Sr. Team Lead – Data Specialist, Manager Applications, and Adjunct Faculty Member among others. I am also deeply involved in applied research, focusing on Big Data Governance, Privacy, Security, and Compliance. My work has been published in prestigious international journals, offering valuable insights into solving industry challenges.
Possesses extensive expertise spanning a diverse range of platforms, tools, technologies, hardware, software, and operating systems, effectively leveraging this knowledge to address complex challenges in Data governance, security, privacy and compliance.
With 45+ professional certifications, including Certified Data Management Professional (CDMP), GDPR, CCPA, Certified Data Protection Officer (CDPO), ITIL, EC-Council Disaster Recovery Professional, AWS Cloud Certified, Oracle Technologies including e-Business, Databases, and Oracle Cloud Infrastructure (OCI). I have continually enhanced my expertise in the ever-evolving field of data privacy and security. My competencies also include Disaster Recovery, Business Continuity, Regulatory Compliance (GDPR, NCA, CCPA, NDMO), Data Architecture, Metadata Management, and more.
Numerous initiatives in Data Governance, Data Management, Data Privacy, Data Protection, ensuring compliance with international, and local standards such as NCA, GDPR, KSA PDPL, and NDMO.
Big Data and Data Science – A Glance
Big Data and Data Science: Driving Innovation Through Insights Big Data is produced through email, social media, online orders, and even online video games. Data is generated not only by …
Data Quality – Policy and Metrics
Data Quality Governance and Policy Integration Data Quality efforts should be supported by and should support data governance policies. For example, governance policies can authorize periodic quality audits and mandate …
Data Quality – As a Part of Data Governance
Data Quality as an Essential Part of Data Governance Data Quality Management A Data Quality program is more effective when part of a data governance program. Often data quality issues …
Data Quality Program – Readiness Assessment / Risk Assessment
Importance of Readiness Assessments in Data Governance Findings from a readiness assessment will help determine where to start and how quickly to proceed. Findings can also provide the basis for …
Data Quality Program – Implementation Guidelines
Data Quality Implementation Guidelines Typically, a hybrid approach to executing data quality initiatives works best – top-down for sponsorship, consistency, and resources, but bottom-up to discover what is actually broken …
Data Quality – Audit Code Module and Metrics
Data Quality Checks and Audit Code Modules Create shareable, linkable, and re-usable code modules that execute repeated data quality checks and audit processes that developers can get from a library. …
Data Quality – Preventive and Corrective Actions
Data Quality Improvement: Preventive Actions How Does Metadata Improve Data-Quality The best way to create high quality data is to prevent poor Q.D from entering an organization. Preventive actions stop …
Data Quality – Tools
Data Quality Tools in Enterprise Planning Tools should be selected and tool architectures should be set in the.planning phase of the enterprise Data Quality program. Tools provide a.partial rule set …
Data Quality – Incident Tracking System
Data Quality Incident Tracking System Overview The incident tracking system will collect performance data relating to issue resolution, work assignments, volume of issues, frequency of occurrence, as well as the …