The 4 Steps to Business Intelligence (BI) Testing: How to Test Business Data

Business Intelligence (BI) involves repetitively amassing, scrutinizing, and transforming raw information into definitive, potent, and invaluable insights that serve to upgrade business strategies and influence data-driven decision-making.

BI equips businesses with predictive abilities based on certifiable evidence rather than suppositions or predictions.

Businesses leverage BI testing programs to derive more detailed and useful perspectives for decision-making based on solid data or evidence. 

The execution of this procedure has considerably evolved in the contemporary market. What was previously manual report generation has transitioned into real-time business integration.

This advancement benefits both corporations and end users because:

• Companies can efficiently identify successful strategies and ineffective ones.
• End users enjoy an enhanced software experience.

Suggested reading: => What is Business Process Testing (BPT)?

Accomplishing BI necessitates an array of tools, systems, and technologies that together constitute the complete setup.

To simplify the sequence of events:

User Transactional data (Relational database, or OLTP) flat file, logs, or other data formats etc. -> ETL processes -> Data Warehouse -> Data Mart -> OLAP additional sorting, categorizing, filtering, etc. to provide meaningful insights – BI.

Business Integration transpires when this analytical capacity influences the operation of a particular application.

For instance, your debit card might decline at a new spot because the BI notifies the app of an irregular transaction. This occurred to me when I attended an art exhibition featuring craftsmen from different US regions. I attempted a purchase with my debit card, but it was denied because the merchant was from a part of the US, where I had never used my card before. This exemplifies BI integration in action to prevent fraud.

Recommendations on retail platforms like Amazon or related videos on streaming websites are other instances of Business Integration in BI.

Considering the flow outlined above, it’s clear that ETL and storage systems significantly contribute to successful BI execution. Therefore, BI testing shouldn’t be an isolated event—it requires testing ETL and Data Warehouse tools as indispensable components. As test engineers, it’s essential to understand and learn how to examine these components.

Here at STH, we have prepared comprehensive articles that delve into these concepts. Links are provided below to help clarify these aspects and concentrate solely on BI.

• Data Warehouse Testing and ETL Testing – Methods, Procedures, and Challenges
• A Closer Examination of ETL and DB Testing – ETL Testing Essentials, Planning, and Tools

Another standard recommendation from Business Intelligence testing professionals is to examine the entire progression—from data extraction at the source to the very end. It’s crucial not to exclusively concentrate on the final reports and analytics.

The sequence should, therefore, proceed as follows:

Order of Business Intelligence Testing:

#1) Authenticate the Source Data:

Business data is typically diverse and originates from various sources in different formats. Ensure that the data type and source are coordinated. Also, conduct simple verification at this initial stage.

For instance, if student details are transmitted from a source for subsequent processing and storage, verify that the details are correct from the start. If the GPA displays as 7, which is beyond the usual 5-point scale, this data can be corrected or discarded at this point without further processing.

This generally corresponds to the “Extract” stage of the ETL process.

#2) Authenticate the Data Transformation:

This stage involves the conversion of raw data into industry-specific intelligence.

• The source and destination data types must be compatible. For example, a date cannot be stored as text.
• Maintain constraints such as primary key, foreign key, null, default value, etc.
• Verify the consistency, atomicity, isolation, and durability (ACID) properties of the source and destination, among other facets.

#3) Verify the Data Load:

(Applies to data warehouse, data mart, or any other final location):

The actual scripts that load the data and subsequent testing will indeed be a part of your ETL testing. However, the data storage system must be evaluated for:

• Performance: As systems grow more intricate, various connections between multiple entities are established to identify various correlations. While beneficial for data analytics, this often results in extended query execution times. Therefore, performance testing is crucial at this stage.
• Scalability: Data volumes are sure to expand over time. Thus, tests need to be conducted to ensure the existing setup is equipped to manage burgeoning data volumes. This also includes testing the data archival strategy. Essentially, the test should answer, “What happens to older data and how can I retrieve it if necessary?”

It’s also advisable to verify other aspects such as computational capabilities, recovery from interruptions, error logging, exception handling, etc.

#4) BI Report Testing:

Finally, we reach the reports, the last layer of the entire procedure.

These reports define Business Intelligence. However, as we’ve seen, the reports won’t deliver accurate, consistent, and timely results unless the preceding layers are fully operational.

At this point, consider:

• The generated reports’ relevance to the business.
• The potential to customize and tailor the report parameters, such as sorting, categorizing, grouping, etc.
• The presentation and intelligibility of the reports.
• If the BI elements are incorporated into the business application, include the corresponding application functionality in a comprehensive test.

BI Testing Strategy:

Having identified what to examine and with resources available for ETL and Data Warehouse testing, let’s discuss the procedure that testers should employ.

Essentially, a BI testing initiative is like any other testing project. Hence, the usual testing stages are relevant, regardless of whether it involves functional end-to-end testing or performance testing:

• Test planning
• Strategizing tests
• Test design (Your test cases will include more queries rather than just text. This is the primary difference between a standard test project and an ETL/Data Warehouse/BI testing initiative.)
• Test execution (To execute your queries, you’ll need an interface such as TOAD)
• Reporting defects, resolution, etc.

Wrap Up:

BI plays a crucial role across all sectors, including e-commerce, healthcare, education, entertainment, amongst many others. It assists enterprises in better understanding their transactions and enhancing the user experience.

We trust this article has given you sufficient information to delve deeper into the domain of Business Intelligence testing.

About the author: This article was penned by Swati, a member of the STH team.

If you’ve worked as a BI tester, we invite you to share your experiences or raise any queries or comments below.