4 Easy Ways to Perform BI Testing

Timothy Joseph
Timothy Joseph | March 28, 2024

4 Easy Ways to Perform BI Testing

Business Intelligence reports and dashboards are crucial to a business's success, as they provide valuable insights and facilitate data-driven decision-making. They are essential tools for collecting, analyzing, and presenting data to enable businesses to adapt to changing circumstances, identify opportunities for improvement, and maintain a competitive edge in their industry.

Report and dashboard testing is crucial in the realm of data analysis and decision-making. They serve as essential tools for organizations to track key performance indicators, monitor trends, and make informed business decisions. Ensuring the accuracy, reliability, and functionality of these tools is imperative, as errors or inconsistencies can lead to flawed decision-making and financial losses.

Testing helps identify issues related to data accuracy, visualization, responsiveness, and security, ensuring the information presented is trustworthy and accessible. Furthermore, it enhances the overall user experience, enabling seamless and efficient data-driven decision-making.

In today's data-driven world, the need for rigorous report and dashboard testing is paramount for businesses to thrive and maintain a competitive edge.

What is Business Intelligence?

Business intelligence (BI) is data that companies use to create effective strategies and make informed decisions. BI is crucial for companies because it helps them obtain detailed insights into essential business information that allows the leadership to act effectively.

Business intelligence comprises of various components, applications, and technologies rather than a single tool or system. It involves a sequence of events including

  • Data records or formats
  • ETL
  • User transactional data
  • Datamart
  • Data warehouse
  • BI
  • OLAP

In short, BI testing verifies BI reports, staging data, and ETL processes to ensure all implementation is correct. In this four-part sequence, we will dedicate four blogs to four different aspects of BI testing: business intelligence testing, ETL testing, Data Warehouse testing, and OLAP testing. This is the first part dedicated to BI.


BI Testing Methodology

As mentioned, ensuring that the reports are supplied correctly is important in business intelligence testing services. If there is an issue within the report, then the root cause of the problem can be traced to the data pipeline.

The BI testing methodology can be divided into two unique stages.

Stage 1: Data Processing and Storage

  • Source data: The information in the source system might have data problems because of how it was entered. BI teams have no control over their source data, which can lead to problems that affect the business reports. That is why it is important to validate the integrity of the data source to ensure precision.
  • Data warehouse/Database: Even if no errors are found in the source testing, the data warehouse could be the problem. There is the possibility that some orders could be missed in the data warehouse, leading to these issues. It could also be that the data for these orders has been accidentally misplaced.
  • ETL: Once the data has been obtained from the source system, it is then converted and uploaded to the data warehouse. This transformation is vital since it involves business rules, which is also why there is a high chance for mistakes, miscalculations, and errors at this stage.

Stage 2: BI Testing

  • Reports: Each BI report is made up of SQL queries, prompts, and filters. Issues could arise in any of these items due to technical or developmental mistakes. Generating these reports is an important development activity, which is why it must be tested to ensure that all information is accurate.
  • Dashboards: The dashboards in BI testing combine several reports with different data and visuals. These two may or may not be connected. In most cases, the dashboards are the final informational pieces used by businesses, which is why testing them is of paramount importance.
  • Data layers: Also called the metadata layers, data layers provide high-level objects with easy access for business users. The information here is obtained from databases and is considered soft data transformation.

How to Formulate an Effective BI Testing Strategy

You can ensure BI test readiness by evaluating the following:

  • Test scope: This should describe all of the testing techniques as well as the types that were used.
  • Test environment: This ensures that the testing environment has been set up and is ready to perform the tests.
  • Test data availability: Experts recommend that testers have their own test data available, such as information that covers all business scenarios.
  • Data quality: Testers should list down the business intelligence quality assurance and performance acceptance criteria for their BI testing.

4 Steps Involved in a BI Testing Sequence

Here are four checkpoints to consider for each stage in this testing approach:

  1. Data Acquisition

    The main aim of data completeness is to make sure that all of the required information has been obtained for loading into the target. In this phase, it’s important to understand the different data sources, along with any deadlines and other special cases that need consideration.

    Two key areas of this stage are:

    • Confirming the required data as well as the availability of the data sources. Example: validating the connection strings, analyzing sample top rows of tables
    • Data profiling helps understand the given information, particularly in identifying the different data values, issues, and value conditions during the initial stages. By identifying problems with the data early on, testers can significantly reduce the cost of having developers fix it later in the cycle.
  2. Data Integration

    The testing performed during the data integration stage is highly crucial, as this is the phase where data transformation takes place. All business requirements are converted into transformation logic, which is why thorough testing is necessary to make sure the information complies with the designed transformation logic.

    The key areas of this stage are:

    • Validating the data model involves ensuring that the data structure is in line with business specifications.
    • Data dictionary review to confirm the metadata that is used in the project.
    • Source validation for target mapping ensures traceability throughout the process. Example: calculated business fields mentioned in the Mapping document.
  3. Data Storage

    This stage involves loading business data within the warehouse or OLAP cubes. Depending on the preference, the data can be loaded one at a time, in real-time, or incrementally.

    The key areas for this phase are:

    • Data load validation, wherein information loads are validated according to the given time intervals.
    • Performance and scalability, wherein the first and subsequent loads are tested to ensure that the system is within acceptable limits.
    • Parallel execution verification could directly impact the system’s performance and scalability.
    • Archival and purge policy validation to ensure the data history is based on business specifications.
    • Logging error verifications and recovery from potential points of failure.
  4. Data Presentation

    The final step in this testing cycle is presenting data. Testers can use a graphical interface to perform this testing.

    The key areas of this stage are:

    • Validation of the report model to check if any errors were missed. Example: Validating the KPI’s against the database query
    • Report layout validation according to the mockups and business requirements set forth.
    • End-to-end testing is done as a guarantee that the whole system is going to behave according to its design.

Advantages of BI Testing

The following are the main advantages of business intelligence testing for companies:

  • Informed Decision-Making: BI tools and reports provide a clear and comprehensive view of an organization's data, helping decision-makers access critical information quickly. This informed decision-making can lead to more effective strategies, improved operational efficiency, and a better competitive edge.
  • Data-driven Insights: BI allows companies to turn raw data into actionable insights. By analyzing historical and real-time data, organizations can identify trends, spot opportunities, and address challenges, helping them make data-driven decisions that drive growth.
  • Improved customer experience: Business intelligence directly impacts customer satisfaction and customer experience.
  • Enhanced Operational Efficiency: BI reports can reveal inefficiencies and bottlenecks within a company's processes. Organizations can streamline operations, reduce costs, and enhance overall efficiency by identifying areas that need improvement.
  • Competitive Advantage: BI enables organizations to gain a competitive edge by understanding market dynamics, customer preferences, and emerging trends. This insight helps develop products and services that cater to customer needs and stay ahead of the competition.
  • Real-time Monitoring: BI dashboards and reports often offer real-time or near-real-time data updates. This feature is invaluable for monitoring key performance indicators, ensuring timely responses to issues, and adapting strategies to remain agile and responsive in a rapidly changing business environment.

BI Testing Tools

These are the most common tools that are used in business intelligence testing:

  • Microsoft Power BI: This business analytics service from Microsoft provides companies with interactive visuals and in-depth business intelligence capabilities. Its interface is easy enough for Microsoft to allow its users to create reports and dashboards for their needs.
  • Microsoft SQL Server: Another Microsoft product, SQL Server, is a database management system that primarily stores and retrieves data according to the requests it receives from other applications.
  • Oracle BI: Oracle Business Intelligence, or OBI, is a compilation of business intelligence tools. It includes the offerings from former Siebel Systems business intelligence and Hyperion Solutions business intelligence.
  • Apache Impala: This open-source parallel processing SQL query engine is used for data stored within a computer cluster that runs Apache Hadoop. The software has long been categorized as the equivalent of Google F1, which is why it was developed in 2012.
  • Pentaho: The business intelligence software Pentaho provides users with features for data integration, reporting, data dashboards, OLAP solutions, data mining, load capabilities, and more. Hitachi data systems acquired Pentaho in 2015 and became Hitachi Vantara in 2017.


Business intelligence is a vital part of all companies that want to make better and more informed business decisions. Many organizations rely on business intelligence testing to gain more knowledge of themselves while providing their users with a better experience.

If you are looking for business intelligence testing services, then QASource can help. We have specialists who can perform the steps in the BI testing process effectively to ensure that your organization or business has the data it needs to succeed.


This publication is for informational purposes only, and nothing contained in it should be considered legal advice. We expressly disclaim any warranty or responsibility for damages arising out of this information and encourage you to consult with legal counsel regarding your specific needs. We do not undertake any duty to update previously posted materials.