The use of appropriate and high-quality test data is essential for comprehensively testing any system, especially complex systems like staff, payroll and rewards platforms. However, sourcing, managing and using test data comes with many challenges that organizations continue to struggle with.
1. Our test data becomes unusable after a certain number of uses
One of the biggest challenges with test data is that it has a short shelf life. After using the same datasets multiple times to test different scenarios, the data loses its ability to produce reliable and consistent test execution results. Also, test data decays over time as attributes like dates and statuses fall out of valid ranges required for current testing needs.
When test data expires in this manner, new datasets need to be sourced and configured. If this data provisioning is done manually, in a non-optimized way, it takes up valuable testing time and delays test schedules. Lengthy data preparation also increases cost of quality.
2. Testing customer and staff systems with production data is risky
Given the vast complexity of their data, many organizations view production data as the most reliable for test validation and coverage. However, sourcing live data from staff and payroll platforms for testing comes with significant challenges and risks.
First, using real staff personal information (PII) and payroll details for testing raises ethical as well as legal issues related to privacy, consent, and data protection. Second, cloning large volumes of data from production environments strains infrastructure and impacts performance. There is also the risk of data leaks in masking and copying processes, which jeopardizes security. Third, refresh cycles with production test data are infrequent. So, the relevance of test scenarios suffers due to using outdated data.
3. We don’t know what data we need to satisfy the tests we want to run
Even after getting access to production-like test data, teams face bottlenecks in aligning test data with desired test coverage.
Staff, payroll, and rewards platforms involve complex rules, dependencies, and entitlement calculations. To comprehensively test all such logic and process variations, the underlying test data needs to contain the right mix of attributes like grades, levels, locations, employment types, payment tiers, benefits choice, deductions, reimbursements, absence history, awards and more. Figuring out all the vital data patterns and combinations requires significant analysis effort. Missing key data linkages in test data leads to incomplete testing and escapes.
4. our automation runs fail because test data is invalid
Another common data-related challenge in testing staff platforms happens while running automation test suites. Even with test data, automation scripts fail frequently because the test data is incorrect. Issues like duplicate identity codes, invalid references, and data that violates system-enforced rules and constraints cause automation scripts to error out.
Such data anomalies and exceptions throw off automation tools that are optimized to run using clean master data. Diagnosing automation failures caused by bad test data and fixing defective data to re-run tests takes huge amounts of tester time and slows down releases.
5. Test data takes too long to source and is tightly coupled to large integrated environments
Staff data for testing comes from multiple upstream sources like HCM, ERP, and financial systems. Setting up end-to-end dataflow processes and pipelines to pull relevant subsets of data from these systems into test environments demands heavy IT and infrastructure efforts. This delays testing tasks, despite urgency to keep up with aggressive release timelines that business demands.
The considerable resources and lead times required for test data preparation also stems from the fact that test data is often tightly coupled to source systems. Modifying test datasets breaks referential integrity and system rules, leading to more defects. Facilitating overrides requires invasive customizations just for testing needs. This increases technical debt and code quality issues, that take long to remediate after testing is complete.
6. We get false test execution results because the data we have used is invalid
Unreliable and inadequate test data leads to incorrect test scenarios and verification results. When staff platform features like salary calculations, deductions rules, and awards eligibility criteria are tested with data that does not realistically represent edge cases, exceptions and system limits, then defects escape into production. Data-related defects that impact accuracy of pay and benefits calculation are especially serious.
Left undetected during incomplete testing, data issues lead to inappropriate rewards or under/over payment. Such problems severely impact staff trust, cause financial losses and risk legal penalties for the business. This risk could be avoided if test data quality and data validation receive more priority and investment during testing.
7. Other teams who we share environments will steal our data
Staff, payroll and rewards platforms frequently leverage common integration and infrastructure environments used by other applications. In such shared ecosystems, test datasets need to be carefully secured from modification and deletion.
But test data stored in such shared environments often gets unexpectedly corrupted or replaced. Other project teams with data refresh needs end up mistakenly or intentionally overwriting test data belonging to staff systems, without any notifications. By losing vital test data in this manner to end use conflicts, testing staff platforms suffers delays and cost overruns that could be avoided by test data protection.
Test data solutions for critical employee systems
In summary, provisioning high-quality test data specific to validating complex staff systems like payroll and rewards, continues to challenge testing success, productivity, and coverage.
Prioritizing test data management with solutions for sourcing, masking, validating and protecting data can help address chronic bottlenecks like lengthy preparation, distortions affecting automation reliability and incomplete testing. Otherwise, these, problems risk letting defects escape into staff production systems, creating enterprise quality risks and overheads.
Speak to a Curiosity expert today to learn how we can help you overcome test data challenges!
