January 5, 2023

By: Ashwin Devendran, Associate Consultant, LTIMindtree

Time-to-market (TTM) is one of the critical business KPIs for any organization, regardless of the industry or domain. Insurers look to accelerate product development through agility in the product development lifecycle, which also includes effective test coverage. Insurers devise Test Data Management strategies (TDM) that help them effectively manage test data management at an enterprise level, ensuring comprehensive test coverage, robust data quality, and security.

With rapidly evolving automated DevOps and privacy laws, insurers need secure data across global teams of employees, contractors, and customers. Enterprises must expand their traditional Test Data Management strategy to meet the need of the development and testing teams. Regulatory changes and technological advancements demand a robust test data management strategy. Insurers look for modern test data management that offers standardization, reusability of data, etc.

Decoding Test Data Challenges

The traditional TDM encompasses the creation of test data in a siloed, unstructured fashion. The rise of developmental methodologies demands fast, iterative release cycles, which have paved the way for a new set of challenges.

• Slow and manual environment provisioning
• The testing team lacks high-quality data
• More friction is added to release cycles due to data masking
• Increase in storage costs for test data requirements

These challenges impact the effectiveness, coverage, quality, and timeliness of any new product development and release.

Slow and manual environment provisioning: the most common method used by the developers and testers is the ‘Request – Fulfill’ method, in which the testers and developers find their requests getting queued behind one another, which can take weeks to provision test data. This leads to time sinks during test cycles, delays in application delivery, etc.

Testing teams lack high-quality data: testing teams often lack access to high-quality data. They are forced to work on the incomplete production data due to the complexity of setting up new test data. This results in decreased productivity due to the time spent on resolving data-related issues. The traditional TDM deprives the development and testing team of consistent quality data.

Data masking adds more friction to release cycles: insurers face challenges in processing and maintaining sensitive information like patient health records, financial information, etc. Anonymizing sensitive data is critical in ensuring regulatory compliance and protecting against data breaches. Insurers are consistently facing challenges in masking production data, and handling enormous amounts of sensitive personally identifiable information (PII), which directly impacts the launch of the product and test release cycles.

Increase in storage costs for test data requirements: with the increase in data and customers, enterprises create multiple and redundant test data, which results in its inefficient storage. Development and testing teams often face challenges in shared environments. Traditional test data management fails to meet the concurrent demands of the testing teams. This impacts the availability of the test data.

Imperatives for the Test Data Management Solution

The modern approach should seek to improve test data management in the following areas.

• Data Distribution
• Data Quality
• Data Security
• Infrastructure Costs

Data Distribution: cascading the copy of production data to a downstream testing environment is a time-consuming and laborious process that involves multiple handoffs. Insurers must look to improve TDM with a solution that streamlines the process. The modern TDM streamlines data distribution with software tools and technologies. The modern software tool automates the build process and eliminates manual integration. Some of the features that test data managers look for in the modern solution are:

• Automation
• Toolset Integration
• Self-service

Data Quality: test data managers must put in great effort to make the right type of test data available across the teams. The masked production data or synthetic datasets must be available to the software development teams. The modern accelerated TDM handles multiples large datasets and improves the data accuracy. The TDM must ensure that data quality is preserved across three key dimensions.

• Data Age
• Data Accuracy
• Data Size

Data Security: data masking tools are considered to be an irrefutable standard for protecting sensitive test data. However, to make masking more practical and effective, insurers must consider the following requirements. The modern TDM enables integrated workflows for both masked and unmasked data. This standardization of masking ensures data security for insurers.

• End-to-end repeatability
• Simplifying developmental expertise
• Integrated masking and distribution

Infrastructure Costs: with the increase in applications and systems, IT enterprises must build a solution that maximizes the effective usage of infrastructure resources. An optimized and modern TDM helps in eliminating the contention used in the environment utilization. Some of the conditions and criteria that the modern TDM solution fulfills are,

• Data Consolidation
• Data Archiving
• Environment Utilization

LTIMindtree’s offering on accelerating product releases to the market, MindPronto, brings differentiation in product ideation, design, configuration, and testing process using a ‘Lego’-like building block approach. The solution works on the principle of decomposing products, coverages/benefits into features. These features are brought to life through business rules associated with them and are rolled into templatized definitions of products and coverages, referred to as templates within the tool. These templates are utilized to instantiate the product (plans) and coverages that are sold into the market.

Prominent features of the tool include standardization, reusability, generating synthetic data for testing end-to-end test scenarios, and effective test data management. The tool helps in associating business process and test scenarios to product and process hierarchy, defining test scenarios, and generating test data, thereby ensuring comprehensive test scenario coverage, rapid scaling on business knowledge required for testing, and adopting a reusable approach towards test strategy definition for every product launch/update.