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Data Warehouse Testing for Strategic Data-Driven Business Decisions

Posted by Indusa Admin on November 17, 2015 12:37 pm

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Enterprise data consists of multiple data that resides in multiple systems. By bringing together data from heterogeneous sources, data warehouses offer a composite and collaborated platform that captures the entire data of an organization. Increased focus on data-driven decision making coupled with data center migrations and increased compliance regulations is compelling organizations to deploy top-notch data strategies.

According to Gartner, poor quality of data costs an average organization $13.5 million a year. Since organizational decisions are based purely on enterprise data, having top quality data is essential for business excellence, mandating the need for efficient data warehouse testing.

Utilization of incorrect data in making critical business decisions can prove detrimental for organizations. Therefore, comprehensive testing of data at every stage of data warehouse management is becoming increasingly important as more and more data is being collected, analyzed, and acted upon. Different types of testing are required throughout the data warehouse lifecycle. These include data quality testing, functionality and load testing, performance testing, UI testing, interface testing, and regression testing. Let’s look at some of the goals of data warehouse testing that enable organizations to make precise and timely data-driven decisions:

  • Accurate Data Loading:

    Accurate loading of data into the data warehouse is one of the primary goals of data warehouse testing. All records need to be validated to ensure that the full content of each field is loaded. Utilize data profiling tools to compare record counts between source and destination data to weed out possible data errors early in the life cycle. Test boundaries of each field to discover database limitations and devise strategies to overcome the limitations.

  • Efficient Data Validation:

    Validating that data is modified according to business rules is imperative for data warehouse testing success. Track and monitor input data and expected results and verify if they meet the required business goals. Create test data and testing scenarios and populate data sets to ensure mobility and versatility. Compare range and submission of values in each field and validate accurate processing of fields.

  • Ensuring Data Quality:

    Data quality testing throughout the Extract, Transform, Load (ETL) phase is of upmost importance. Any defect in the ETL phase will be very costly to rectify later. Setting various data quality rules and testing data across testing scenarios is essential to discover duplicate records, invalid data types or null records. Review detailed test scenarios to ensure all data quality rules are met.

  • Scalability and Performance:

    As more and more data is loaded into data warehouses, it is essential that the system is tested for load, scalability, and performance. During the setup of data, load the database with the maximum data to ascertain that the limits to the amount of data that can be carried. Compare these loading times with reduced data to anticipate possible issues with scalability. Monitor the entire loading process and consider how large volumes of rejected data will be handled. Formulate test queries and performance requirements for each query.

  • Integration Testing:

    Once the initial data load is done, verifying the data warehouse for end-to-end functionality, UI, and integration with other applications and software is an essential aspect of data warehouse testing. Bring various modules of applications together and test them against a set of inputs to check for integration. Design integration test scenarios, focus on touch points between several applications, monitor how process breakdowns occur at each and every step and plan how data will be restored.

Ensure Effective Communication

As the digital revolution progresses, more and more data will be used to make strategic business decisions. Evolving needs of the business will lead to incessant changes in the data warehouse systems, compelling organizations to create new information management roles to harness the several opportunities that big data presents. Gartner predicts that 25% of organizations will have a Chief Data Officer (CDO) by 2017. Moreover, while different types of testing is vital for every stage of the data warehouse lifecycle, good communication between project teams  of all systems involved is also crucial. By bringing team members from all systems together to create data warehouse test scenarios in the production environment, organizations can carry out end-to-end testing and ensure accurate data is loaded, stored and extracted, and is available at the right place, at the right time and in the right form.

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About the Author -Ahesanali Vijapura

Ahesanali Vijapura is a highly professional Senior Project Manager (QA Services) at Indusa. He has vast experience in managing manual and automation test teams, onsite as well as offsite. He is an expert when it comes to software testing in various environments, server/client testing management and integration of multidisciplinary software and hardware systems.


Contributing Writer: Neha Kumar