The sheer volume and complexity of big data create an immense challenge for Big data testing and data verification experts. Big data brings together a huge amount of unstructured and structured data captured from a wide variety of sources, some of which may be erroneous. Validation professionals must apply a variety of techniques to isolate and check potentially problematic data. While traditionally each data processing task would have been independent of each other, today's data requirements often require data automation to ensure that all tasks are performing as expected.

There are two approaches to managing big data. The first is known as the business rules approach and the second is known as the data model approach. The business rules approach involves applying reasonable business rules to each piece of big data and then using this information to build a validation set that rules out the possibility of data inconsistency, redundancy or inaccuracy. The data model approach is based on the assumption that each piece of big data will have a different, unique data model that can be used to create a statistical relevance test.

Business intelligence (BI) applications are designed to facilitate business decision makers. Today, there is a wide variety of BI applications ranging from customer relationship management (CRM) to enterprise resource planning (ERP) to the supply chain management applications (SCM). In each of these cases, data is the key to making smart choices, and data integration testing is crucial to ensure that business intelligence developers are able to reliably extract, analyze, and use the information contained within the data sets. BIS and test data management tools help data integration developers achieve this goal.

Testing for business intelligence requirements usually involves two approaches. The first approach, known as the two-step validate, validates the selected business intelligence data sources against previously identified performance issues. The second approach, known as the two-step validate and test data sources, applies the framework of the two-step validate to data sources that have not been validated against performance issues. This approach is more challenging and typically requires more time and higher costs. To mitigate the risks associated with the two-step validate, some BIS software packages include an optional third strategy known as the real-time synthetic test data sources (RSTS), which run in response to changes in the BIS data sources and validate the selected sources against known true data patterns.

There are several benefits of functional testing with RSTS as opposed to BIS. First, RSTS can test for missing or duplicate data at the same time as it validates the database in question. Secondly, it allows for the simultaneous detection of invalid data and the correction of such errors before the data has time to impact operational decisions. Finally, functional testing along with data warehouse tools that automatically consolidate and normalize the data across multiple databases can greatly reduce the complexity of the database-aside from the obvious complexity of the collection of relevant metrics.

As BIS continues to evolve toward greater integration and greater accessibility, data warehouse tools have been developed to better support this evolution. However, validation of these tools is still needed at times because some of the newer tools simply do not have a way to distinguish validating data from null values. For this reason, validation is still a critical component in the selection of a tool. Without validation, the data warehouse will become a repository of inconsistent and erroneous information, rather than of true information that is needed for strategic decision making.