Business Intelligence (BI) Testing: 4 Easy Steps

business intelligence bi testing

In the field of software development and maintenance, testing is a common activity. It has significant advantages for the entire development process, allowing teams to communicate more effectively, increase project completion rates, and improve user experience.

Companies, on the other hand, must deal with a plethora of information and dashboards in order to make informed business decisions. It’s critical to create, maintain, and test these dashboards appropriately because the reports they provide have a substantial impact on these decisions and outcomes.

Organizations require the correct business intelligence to adequately conduct these tests.

What is Business Intelligence Testing and why is BI Report Testing Important?

Business decisions are frequently made via reports and dashboards. These choices would later determine the company’s growth and success. If BI reports are incorrect, then judgments made on the basis of these reports will be incorrect as well. Inaccurate reports undermine the organization’s credibility and put it at risk of legal and compliance concerns. It can also result in significant fines, thus businesses cannot afford to disregard BI Testing.

What is a Business Intelligence and How Does it Work?

Companies utilise business intelligence (BI) to develop effective strategies and make educated decisions. BI is critical for businesses because it allows executives to gain deep insights into critical company data, allowing them to take effective actions.

Business intelligence is a set of components, applications, and technologies that cannot be accomplished with a single tool or system. A series of events are involved, including:

  • Records or formats
  • ETL
  • Transactional data
  • Datamart
  • BI
  • OLAP
  • Datawarehouse

In a nutshell, BI testing ensures that all BI reports, staging data, and ETL processes are correctly implemented. We’ll devote four blogs to four separate areas of BI testing in this four-part series: business intelligence testing, ETL testing, Data Warehouse Testing, and OLAP testing. This is the first section dedicated to business intelligence.

Methodology for BI Testing

As previously said, it is critical in business intelligence testing services to ensure that reports are delivered appropriately. If there is a problem with the report, the root cause can be traced back to the data pipeline.

There are two distinct steps to the BI testing technique.

Stage 1: Data Storage and Processing

Source data: Because of how it was input, the data in the source system may contain data issues. business intelligence teams don’t have control over their source data, which can cause issues with business reports. To ensure precision, it is critical to check the integrity of the data source.

Database/data warehouse: Even if no problems are discovered during source testing, the data warehouse may be the source of the issue. It’s possible that some orders in the data warehouse were overlooked, resulting in these complications. It’s also possible that the information for these orders was mistakenly misplaced.

ETL: After getting the data from the source system, it’s converted and posted to the data warehouse. This change is critical since it incorporates business rules, which means there is a greater risk of errors, miscalculations, and blunders at this stage.

Stage 2: Reports from BI Testing

BI report: SQL queries, prompts, and filters make up each BI report. Technical or developmental errors could cause problems in any of these items. Because creating these reports is a critical development task, it must be thoroughly verified to ensure that all data is correct.

Dashboards: In BI testing, dashboards include numerous reports with various data and visualisations. These two might or might not be linked. Dashboards are frequently the final informational items used by firms, which is why they must be thoroughly tested.

Layers of data: Data layers, often known as metadata layers, give business users simple access to high-level items. The data in this article is derived from databases and is classified as soft data transformations.

How to Create a Successful Business Intelligence Testing Strategy?

You may make sure your BI test is ready by looking at the following:

The scope of the test should include all of the testing techniques and types that were employed.

This guarantees that the testing environment has been prepared and is ready to conduct the tests.

Data availability for testing: Experts advise that testers have their own test data, such as the information that includes all possible business scenarios.

Data quality: When conducting BI testing, testers should establish a list of the business intelligence quality assurance and performance acceptance requirements.

Steps to Business Intelligence Testing:

A BI Testing Sequence has four steps.

For each level of the BI testing process, consider the following four checkpoints:

1. Data Collection

The basic goal of data completeness is to ensure that all of the essential information for loading to the target has been received. It’s critical to understand the many data sources, as well as any deadlines and other unique instances that must be considered during this phase.

This stage has two major components:

  • Confirming the essential data as well as the data sources’ availability. That includes all possible business scenarios.
  • When conducting BI testing, testers should establish a list of the business intelligence quality assurance and performance acceptance requirements. Data profiling aids in understanding the given data, particularly in recognising the various data values, difficulties, and value situations early on. Testers can considerably reduce the expense of having developers resolve data errors later in the cycle by finding them early on.

2. Integration of data

This is the stage where data transformation occurs, and hence, testing completed during the data integration stage is quite important. Because all business requirements are transformed into transformation logic, comprehensive testing is required to ensure that the data matches the transformation logic.

The following are the main areas of this stage:

  • Validating the data model entails ensuring that the data structure adheres to the business requirements.
  • Review the data dictionary to ensure that the metadata used in the project is correct.
  • Traceability is ensured throughout the process by mapping source validation to target mapping.

3. Information Storage

This is the stage at which business data is loaded into the warehouse or OLAP cubes. Depending on the preference, data can be loaded one at a time, in real-time, or progressively.

The following are the important areas for this phase:

  • Data load validation is the process of validating information loads based on time intervals.
  • Performance and scalability, in which the first and subsequent loads are tested to ensure that the system can handle them.
  • The system is operating within acceptable parameters. The performance and scalability of the system may be impacted by parallel execution verification.
  • Validation of archival and purging policies to ensure that data history matches business requirements.
  • Error verifications and recovery from potential failure sites are recorded.

4. Data Visualization

The display of data is the final phase in the BI testing cycle. The testers in this case have the option of performing the testing using a graphical interface.

The following are the main areas of this stage:

  • Validation of the report model to ensure that no errors were overlooked.
  • Validation of report layout according to mockups and business requirements.
  • End-to-end testing ensures that the entire system will perform as expected.


All businesses that want to make better, more informed business decisions need to invest in business intelligence. Many businesses employ BI testing to learn more about themselves and provide a better experience for their customers.


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