Test Data Management Services (TDM)

With inaccurate & inadequate data comes poor testing, results you can’t trust, and a whole lot of wasted time, money, and effort. Some of the key challenges faced in a TDM strategy include:

• Lack of processes to dispose of the test data when not needed
• A huge volume of data for validating the business processes
• Managing the multiple sources & formats of data as required for testing
• Managing the changes required to share data that may affect multiple projects
• Availability of the test data range required for end-to-end testing & coverage for all possible combinations
• Techniques to handle PII related data for being compliant with regulatory bodies such as HIPAA, PCI, GLBA, SOX, etc.

Test data quality management is highly challenging due to data sensitivity, complex infrastructure, heterogeneous data sources and of course, limited availability. The successful outcome of any business testing is driven by the ability and availability of quality as-close-as real ’test data’. A robust Test Data Management system, thus, is fundamental to the success of your data strategy & overall testing strategy.

At Cigniti, we have implemented robust test data management practices for complex enterprise platforms. With over 200 experts in the Data Governance practice, we have supported organizations in the implementation of solutions for centralized data governance, automated data management, data analysis & identification, and high availability of test data. In addition, the custom accelerators developed by us aid in synthetic test data generation, test data optimization and test harnesses for a wide variety of platforms. The Data Governance experts comprising of Certified DBA’s, SQL & Data Modeling bring in rich experience in tools such as IBM Optim, GridTools, DataMaker, QuerySurge, etc.

Cigniti’s extensive data consulting expertise helps you improve test effectiveness by using accurate test data, institutionalizing test data management process, implementing test data creation & simulation, data masking, data quality management, data subset, data archiving, automating test data management systems, & protecting data sensitivity.

TDM makes available voluminous, on-demand, fit-for-purpose data that further helps in the management of test data. An intelligent data sub-setting system leads to an optimized infrastructure, storage costs, and total cost of ownership (TCO), and in implementing an effective risk mitigation system. TDM also acts as a centralized tool for data request and distribution, which leads to quicker data provisioning. With an automated test data process, and cloned data subsets resulting in a quicker test data set up, the test cycle time is also reduced. A few benefits of having an established test data management system are:

• Improved:
  • Testing effectiveness with accurate data
  • Test coverage & test data quality assurance
  • Data privacy through data masking techniques
  • Risk mitigation through reduced data exposure
  • Compliance with regulatory requirements & guidelines such as Basel, PCI, SOX, SEPA, and Solvency
• Coordinated & easy-to-manage test data management process
• Intelligent data sub-setting
• Centralized tool for data request & distribution
• Effective risk mitigation

Cigniti’s TDM services are designed to meet and satisfy all test data demands of enterprises and Independent Software Vendors (ISVs) for complex IT deployments. The key services offered by Cigniti Data Governance practice include:

• Data profiling & analysis: Identifying & modeling of complex data simulations and business process coverage
• Data governance: Processes & Guidelines for policy, controls, provisioning & techniques
• Data provisioning: Data refresh, masking, subsetting, obfuscation, & retrieval procedures
• Data generation: Automated set up/tear down of synthetic data using off the shelf tools and Cigniti Test Data Generator
• Data environment management: Deployment & Management of data sources
• Data privacy: Employment of techniques such as NULL’ing, Substitution, Shuffling, Number Variance, Gibberish & Encryption/Decryption

• NULL’ing Out: Replaces an entire column of data with NULL values
• Masking Data: Replaces existing characters with designated characters or numbers (such as an X), thus disguising data while retaining formatting
• Substitution: Randomly replaces real production data with realistic-looking fake data and preserves formatting
• Shuffling records: Shuffling is similar to substitution except that the substitution data is derived from the column itself
• Number Variance: The algorithm involves modifying each number value in a column by some random percentage of its real value
• Gibberish Generation: Usually fields are just substituted with a random quantity of equivalently sized gibberish or random words. [letters, memos and notes]
• Encryption/ Decryption: Encrypts data into special characters and destroys formatting so that the database is unreadable. Decryption keys revert data back into its readable form