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Best Practices in Data Warehouse Validation - Test Planning Phase

The complexity and criticality of data warehouse testing projects is growing rapidly each day.  Data warehouses need to be validated for functionality, quality, integrity, availability, scalability and security based on the defined business requirements by an organization. Based on my experiences as a testing practitioner, I believe the following best practices in the test planning phase can significantly contribute to successfully validating the data warehouse.


1.    Comprehensively Understand the Data Model

The data architecture and model is the blueprint of any data warehouse and understanding it helps comprehend the bigger picture of a data warehouse. It's also important to understand the methods used for the key relationships between the major and critical data sources. The relationship hierarchies and the depth of data throw light on the complexity of transformation rules. The quality of data and size of data warehouse determines the functional and non-functional testing requirements for validating the data warehouse.


2.    Understand the Business Requirements Clearly

It's important to understand the complete business context of the need and implication of data warehouse testing. Mapping the business drivers to the source systems helps increase the testing quality, effectiveness and coverage. Getting the test data early during the test planning stage itself decreases risk and increases the predictability of testing.  The data transformations mapping document is the heart of any data warehouse testing project. Hence, understanding the business rules or the transformation mapping document and running books, early in the testing life cycle, helps the testing team reduce rework and delays in the test preparation and execution phases.


3.    Plan Early for Data Warehouse Testing Environment

Planning for test environments based on the quality, size, criticality and complexity of the data warehouse helps reduce the testing cycle. Also, the option of shared environments for the testing teams can also be explored to help reduce costs. Planning for test environments from the following perspectives help decrease the possibility of potential defects:


·        Reverse planning from the release date and preparing a high level test execution plan

·        Understanding and documenting requirements, mitigating the risks/constraints in preparing and running the tests from specific geographical locations - such as time zone, availability of environment or systems & access constraints

·        Planning for different types of functional and non-functional test requirements and their test data. Test data plays a vital role in Data Warehouse testing. Planning and preparing for test data early in lifecycle helps avoid the subsequent cascading delays during execution.

These best practices can largely contribute to a successful data warehouse validation. I shall definitely be blogging more on the best practices in the Data Warehouse test preparation and execution phases in the coming weeks. I do look forward to your thoughts, inputs and best practice ideas.

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