OVERVIEW
The Certified Data & Analytics Tester (CDAT) program is a professional certification designed to validate participants’ competency in testing data-driven systems, analytics platforms, and reporting solutions. As organizations increasingly rely on data for decision-making, ensuring data accuracy, integrity, reliability, and usability has become critical.
This program focuses on the principles, techniques, and best practices required to validate data pipelines, analytics applications, and business intelligence (BI) systems. Participants will gain structured knowledge in data testing fundamentals, data quality validation, analytics testing approaches, test data management, and governance considerations. The certification ensures that candidates are capable of validating data and analytics solutions in alignment with organizational objectives and regulatory requirements.
By the end of this program, participants will be able to:
- Understand the fundamentals of data and analytics testing
- Apply data validation and data quality testing techniques
- Test analytics reports, dashboards, and insights for accuracy and consistency
- Validate data pipelines, transformations, and system integrations
- Apply governance, risk, and compliance principles in data testing
- Support reliable and trustworthy data-driven decision-making
4 Days (9.00am – 5.00pm)
The duration and delivery schedule can be customized based on certification or client requirements.
- Data Testers
- QA Engineers involved in data and analytics testing
- BI and Analytics Testers
- Data Analysts involved in validation and quality assurance
- Professionals supporting data-driven systems
PROPOSED OUTLINE/AGENDA
Day 1: Foundations of Data & Analytics Testing
- Icebreaker
- Introduction to trainer and program objectives
- Overview of data and analytics systems
- Role of testing in data-driven environments
- Differences between application testing and data testing
- Types of data (structured, semi-structured, unstructured)
- Databases, data warehouses, and data lakes
- Data flows and end-to-end data pipelines
Day 2: Data Quality & Validation
- Recap of Day 1 learning
- Accuracy, completeness, consistency, timeliness, and validity
- Defining data quality rules and checks
- Source-to-target validation techniques
- Transformation logic validation
- Data reconciliation methods
Day 3: Analytics & Reporting Testing
- Recap of Day 2 learning
- Testing dashboards and analytical reports
- KPI and metric validation
- Managing aggregation and calculation logic
- Test data requirements for analytics testing
- Data sampling techniques
- Managing large datasets for effective testing
Day 4: Governance, Risk & Practical Application
- Recap of Day 3 learning
- Data privacy and protection considerations
- Regulatory and compliance requirements
- Auditability and traceability in data systems
- End-to-end data testing scenarios
- Defect identification, management, and reporting
- Best practices for continuous data quality assurance
- Certification readiness guidance
- Program wrap-up
PROGRAM METHODOLOGY
- Certification-Aligned Instructor-Led Sessions – Structured and professional delivery
- Case-Based Discussions & Scenarios – Real-world data and analytics examples
- Practical Data Validation Exercises – Hands-on testing techniques
- Guided Analytics Testing Walkthroughs – Applying concepts to BI use cases
- Reflection & Knowledge Validation Sessions – Reinforcing competence and certification readiness
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