Data Warehouse Implementation – Guidelines and Principles

Posted on February 6, 2022 by Muhammad Rawish Siddiqui

Aligning Data Warehouse Projects with Business Goals

Focus on Business Goals: Make sure DW serves organizational priorities and solves business problems.

Importance of Defining End Goals for Data Warehousing

Start with the End in Mind: Let the business priority and scope of end-to-end data delivery in the BI space drive the creation of the DW content.

Global Data Warehouse Implementation Design with Localized Execution

Think and Design Globally; Act and Build Locally: Let end-vision guide the architecture, but build and deliver incrementally, through focused projects or sprints that enable more immediate return on investment.

Optimizing Data Summarization Strategies in Data Warehousing

Summarize and Optimize Last, not First: Build on the atomic data. Aggregate and summarize to meet requirements and ensure performance, not to replace the detail.

Enabling Data Transparency and Self-Service Analytics

Promote Transparency and Self-Service: The more context (Metadata of all kinds) provided, the better able data consumers will be to get value out of the data. Keep stakeholders informed about the data and the processes by which it is integrated.

Metadata Management Best Practices for Data Warehouse Implementation

Build Metadata with the Warehouse: Critical to DW success is the ability to explain the data. For example, being able to answer basic questions like “Why is this sum X?” “How was that computed?” and “Where did the data come from?” Metadata should be captured as part of the development cycle and managed as part of ongoing operations.
Collaborate: Collaborate with other data initiatives, especially those for Data Governance, Data Quality, and Metadata.
One Size does not Fit All: Use the right tools and products for each group of data consumers.

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Muhammad Rawish Siddiqui

Master’s degree in computer science with Specialization in Software Engineering and Earned PGD in Management Information System, in addition to 45+ Online Professional Certifications.

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.

Engaged in Applied Research with a focus on Big Data Governance, Privacy, Security, and Compliance, resulting in the publication of numerous articles and research manuscripts in prestigious international journals, including the Journal of Sensor Networks and Data Communications, the Journal of Electrical Electronics Engineering, and the Innovative Journal of Applied Science. These works contribute practical insights and solutions to address industry challenges effectively.

Experience includes leading data governance and management initiatives, ensuring compliance with NDMO, GDPR and KSA PDPL, and implementing/improving data security, privacy, & protection.

Served in leadership capacities such as Manager – Data Management, Adjunct Faculty Member, Sr. Team Lead – Database Specialist, Sr. Team Lead Consultant, Sr. Applications DBA, Sr. Resident Consultant, and Manager Systems.

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.

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)

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