a Efficient Testing of a Business Analytics System

It’s a proven fact that effective integration of testing component into the implementation cycle builds confidence among business users who make crucial decisions based on the analytics data generated. Testing of business analytics application takes a different approach from the regular transactional applications as there is a demand for data-centric approach rather than an individual feature testings.

Challenges of testing an analytics system:

Major challenges include:

  • Data volume and complexity
  • Data loss during data integration
  • Data irregularities from different data sources
  • Time & effort consuming
  • No audit trials
  • Special skills to execute data validation on demand

To make sure of data completeness, consistency, reliability, accuracy, and consistency throughout the data lifecycle, it is absolutely vital to test all the aspects at each data entry points in the architecture.

Business Analytics Testing Strategy

The prime intention of testing business analytics application is to achieve credible input data. A proven test strategy is the first step of an effective test cycle. Normally this strategy would cover test planning for each state, data mobility and the responsibilities of each resource involved. To empower a test readiness the key areas that the testing strategy should focus on are:

  • Define a testing scope with testing techniques and types to be employed
  • Setting up of a test environment
  • Make availability of relevant test data
  • Data quality and performance acceptance criteria

In an enterprise business analytics system normally there are four key data testing stages – Data Collection, Data Integration, Data Storage and Data Presentation.

Data Collection:

During the data gathering phase, it is highly essential to keenly understand various data sources, time boundaries of selected data and any special cases considered. Key focus areas in this phase include:

  • Validation of the data required and the availability of the data sources
  • Profiling of data in the expected lines. Identifying errors at this stage will reduce the cost of fixing it at a later state

Data Integration:

In this phase, testing plays a crucial role as all data transformation takes place here. It is important to make sure that the transformed data complies with the expected transformation logic. Key areas of focus include:

  • Validation of the data model
  • Reviewing the data dictionary
  • Validating the source to target mapping

Data Storage:

The data storage phase includes loading of data within the system. The data loads can be one time, incremental loading or in real time. Key areas of focus include:

  • Validation of data loads at regular time intervals
  • Performance and Scalability testing
  • Parallel loading execution check
  • Validating data archival policy against data loads
  • Handling error logs, exception handling and data recovery from failure nodes

Data Presentation:

Data Presentation phase enables to have a graphical interface to the test data. Key areas of focus include:

  • Report model validation
  • Report layout validation as per business requirements
  • End to End Testing

Key Benefits of Analytics Test Strategy Include:

  • 100 % traceability throughout the system
  • 100% test coverage in test cases
  • Test case automation
  • Saving up to 50% time and resources by automating regression testing
  • Effective bug tracking mechanism
  • Improved test cycle time through reusability components
  • Overall test process improvement