Testing is a standard practice in the world of software development and maintenance. It provides huge benefits for the entire development process that allows teams to communicate better, improve their project completion rates, and enhance user experience.
However, companies have to deal with many reports and dashboards to make smart business decisions. Creating, maintaining, and testing these dashboards correctly is important as the reports they generate significantly influence these decisions and the outcomes.
To perform these tests properly, organizations need to have the right business intelligence.
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 take effective actions.
Business intelligence is a collection of components, applications, and technologies and it cannot be done through one specific tool or system. It involves a sequence of events including:
- Data records or formats
- User transactional data
In short, BI testing verifies BI reports, staging data and ETL processes to ensure that 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 earlier, 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 preciseness.
- 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 have 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 to business users. The information here is obtained from databases and is considered soft data transformations.
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 the 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 the BI 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 to 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.
- 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 this 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 which 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 to target mapping ensures traceability throughout the process.
3. Data Storage
This is the stage that involves the loading of business data within the warehouse or OLAP cubes. Loading the data can be done one at a time, in real-time, or incrementally depending on the preference.
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 testing of the first and subsequent loads are done to make sure that the system is within acceptable limits.
- Parallel execution verification could have a direct impact on the system’s performance and scalability.
- Archival and purge policy validation to make sure that 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 the BI intelligence testing cycle is the presentation of data. Testers involved here have the privilege of using a graphical interface to perform the testing.
The key areas of this stage are:
- Validation of report model to check if any errors were missed.
- 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 that business intelligence testing can provide for companies.
- Faster analysis and intuitive dashboards: The primary advantage of using BI dashboards is that they allow for faster and easier data analysis while empowering even non-technical users to leverage data without the need to study code.
- Data-driven decision making: With accurate data as well as the ability to provide quick reports, companies can make better and faster business decisions compared to those that do not leverage such information.
- Improved customer experience: Business intelligence has a direct impact on customer satisfaction and customer experience.
- Improved organizational efficiency: Business intelligence provides leaders with access to data quickly while giving them a holistic view of their operations.
- Well-trusted and governed data: These BI systems improve data analysis and organization.
- Increased competitive edge: Companies can compete at a higher pace within their target market thanks to the insights they possess from BI testing.
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. It possesses an interface that Microsoft believes is easy enough 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 works to store and retrieve data according to the requests it receives from other applications.
- Oracle BI: Oracle Business Intelligence, also called OBI, is a compilation of business intelligence tools. It is made up of the offerings from former Siebel Systems business intelligence as well as the Hyperion Solutions business intelligence.
- Apache Impala: This is an open-source parallel processing SQL query engine that 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 also the reason why it was developed in 2012.
- Pentaho: The BI business intelligence software Pentaho is an app that provides users with features for data integration, reporting, data dashboards, OLAP solutions, data mining, load capabilities, and more. Hitachi Data Systems acquired Pentaho back in 2015 and became a part of Hitachi Vantara by 2017.
Business intelligence is a vital part of all companies that want to make better and more informed business decisions. Many organizations rely on BI to gain more knowledge about 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.
Get in touch with us at QASource and get a free quote for your business needs.
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