In our previous blog, 5 keys to nailing a BI Implementation, we focused on achieving strategic success in implementing Business Intelligence applications. In this blog, we turn our attention to a tactical, but important aspect – Testing of Business Intelligence Applications.
Effective integration of testing in the implementation process builds trust and confidence among business users as they make crucial strategic decisions, based on the BI data generated.
Testing of Data Warehouse/Business Intelligence (DW/BI) applications is a little different than testing traditional transactional applications as it requires data-centric testing approach.
The typical challenges an enterprises faces while testing DW/BI implementations include:
To ensure data completeness, accuracy, consistency, security and reliability throughout the life cycle, it is important to test all these aspects at each data entry point in the BI architecture and not just at the end through reports or dashboards.
The goal of testing BI applications is to achieve credible data. And data credibility can be attained by making the testing cycle effective.
A comprehensive test strategy is the stepping stone of an effective test cycle. The strategy should cover test planning for each stage, every time the data moves and state the responsibilities of each stakeholder e.g. business analysts, infrastructure team, QA team, DBA’s, Developers and Business Users. To ensure testing readiness from all aspects the key areas the strategy should focus on are:
The below diagram depicts the data entry points and lists a few sample checks at each stage. – Data Collection, Data Integration, Data Storage and Data Presentation.
The primary aim of data completeness is to ensure that all of the data is extracted that needs to be loaded in the target. During the data acquisition phase it is important to understand the various data sources, the time boundaries of the data selected and any other special cases that need to be considered. The key areas this phase should focus on are:
Testing within the data integration phase is the crux as data transformation takes place at this stage. Business requirements get translated into transformation logic. Once the data is transformed, thorough testing needs to be executed to ensure underlying data complies with the expected transformation logic. Key areas this phase should focus on are:
Data Storage: The data storage phase refers to loading of data within the data warehouse/data mart or OLAP cubes. The data loads can be one time, incrementally or in real-time. Key areas this phase should focus on are:
This is the final step of the testing cycle and has the privilege of having a graphical interface to test the data. Key areas this phase should focus on are:
While above considerations are given, one important aspect that still remains to be addressed is the issue of ‘Time’. BitWise has created a platform based on DW/BI Testing Best Practices that automates and improves the overall effectiveness of DW/BI Testing. If you’re interested in learning more about this platform, please contact us.
With the features and benefits of this platform, the intention is to address most of the DW/BI testing challenges:
Testing BI applications is different than testing traditional enterprise applications. To achieve truly credible data, each stage of the lifecycle must be tested effectively – Data Collection, Data Integration, Data Storage and Data Presentation. If you’re not comfortable with your internal capabilities to test your BI applications, turn to the BitWise DW/BI Testing platform and lean on BitWise’s expertise and experience gained through testing business intelligence applications for clients over the past decade.
Radhika leads the BI Practice at Bitwise as Delivery Manager. Her key responsibilities include consulting clients during BI Implementation initiatives and ensuring per plan execution of projects by delivery teams. Being part of the Delivery Excellence Group within Bitwise she shares her experience and knowledge to come up with innovative solutions and helps implement best practices in the BI Delivery Model