Validating Data Integrity Across 50 Source Systems with a Qlik Sense BI Datahub

Overview

Our client, a leading global motor industry organisation, engaged Prolifics Testing to support the User Acceptance Testing (UAT) of its newly implemented Qlik Sense Business Intelligence (BI) Datahub for UK operations. The programme required independent validation that the Datahub accurately consolidated, transformed, and stored data extracted from 50 live source systems, creating a single trusted source to support business-critical decision-making. Applications varied in complexity but included those vital to business operations, such as Vehicle Registration, Finance, Warranty, and CRM.

Prolifics Testing worked alongside the BI solution vendor to deliver consultancy-led assurance, verifying data integrity, completeness, and consistency across all ingested applications. Following a detailed proof-of-concept phase, Prolifics Testing committed to a fixed-price commercial model for the UAT of all 50 applications, delivered by our UK testing team.

To optimise speed, traceability, and documentation quality, the engagement incorporated targeted use of GenAI to enhance the testing process:

• Recording and transcribing meetings with application SMEs

• Generating draft test plans based on initial scoping discussions

• Capturing and analysing execution sessions for rapid validation

• Analysing data to validate source vs target dimensions

• Producing concise, summarised test completion reports

This approach reduced manual effort, strengthened evidence capture, and ensured a consistent and repeatable assurance framework.

Approach

The testing approach was led by Prolifics Testing in close collaboration with subject matter experts (SMEs) from each source application contributing data to the BI platform. For every application in scope, the team established:

• The data dimensions extracted from the source system into the Datahub

• Any transformations applied during the data flow

• The level of test coverage required to validate each target application, including the number and complexity of scenarios — the main driver of effort for each system

Prolifics Testing completed a detailed assessment of all in-scope dimensions across the 50 applications and ran a proof of concept on one high-complexity and one medium-complexity system. This informed a structured and repeatable delivery method, ensuring consistency across all applications.

Once the approach was agreed, delivery followed a clear and collaborative process:

• An initial session was held with the SME for each source system to gather inputs, discuss expected data behaviours, and identify the data ranges needed for validation. Following this, a tailored check sheet was produced outlining dimensions and required data for execution and shared for SME review.

• Execution sessions were completed jointly with SMEs, who witnessed the validation of their data sets within the Datahub. Testing progressed once relevant test data was confirmed as available.

• GenAI was used to record and transcribe meetings, analyse execution sessions, and generate structured summaries, strengthening evidence capture and reducing administrative overhead.

• Defects were logged in Jira and managed through coordinated triage with the client’s project team. Fixes were re-tested with full traceability from identification to closure.

• Weekly progress updates and a central backlog tracker provided visibility throughout, with each application signed off once all validation steps were met. This delivered a consistent, transparent, and scalable UAT cycle across all applications.

Conclusion

The UAT phase was delivered within the planned four-month timeline through effective collaboration between Prolifics Testing, the client’s SMEs, and the wider project team. The engagement provided a structured and reliable validation of the data flowing into the Qlik Sense Datahub, ensuring a trusted foundation for downstream reporting and visualisation.

Key benefits achieved include:

• Full verification of the consistency, completeness, and integrity of data extracted, transformed, and stored in the Datahub

• Confidence for the client to progress with BI reporting for all signed-off applications, knowing decisions are based on accurate and reliable information

• A robust and repeatable UAT framework that not only validated the current implementation but also established a scalable model for future development, data enhancements, and ongoing alignment with source systems