The document outlines the significance of test data management (TDM) in enhancing software quality assurance through better analysis, development, testing, and maintenance of test data. It highlights the lifecycle of TDM, including profiling, planning, provisioning, and population, while addressing challenges such as data privacy, compliance, and synchronization among teams. The content also suggests that effective TDM can lead to improved data quality, reduced costs, and faster time-to-market.

1. Test Data Management
MANAGED SERVICE FOR SOFTWARE QUALITY ASSURANCE

2. Introduction
• Management &
Streamlining Of Test Data
Important
• Test Data Management
• Analysis
• Development
• Testing
• Maintenance

3. The Importance of
Managing Test Data
• Relevant Test Data Improves
• Reliability & Quality Of Software Quality Testing
• Frequent Changes Require A Re-look
• Constant Refreshes
• Versions

4. TDM – The Growing Importance
Generates test Data Setup for new apps
Synchronizes Test Data Across Apps
Brings Agility to Test Data Management
•Revises Test Data for Ugrades
•Maintains & Supports all Data Needs
Streamlines Feedback Management to Make Data Better

5. Challenges for Test Data
Management
• Improper Analysis
• Frequent Changes
• Unexpected Test Data Requirements
• Ensuring Data Privacy & Confidentiality
• Adhering To Regulatory Compliances
• Keeping Version Control Mechanisms
• Provisioning Back-ups
• Preventing Test Data Redundancy
• Synchronization Between Teams
• Application Development
• Testing & QA
• Infrastructure
• Database Facilitators

6. The TDM Lifecycle
Test Data Profiling
• Comprehend Data Sources
• Understand Requirements of
Test Data
Test Data Planning
• Devise Test Data Strategy
• Identify Tools for Test Data
Management
Test Data Provisioning
• Create Sub data sets
• Mask Test Data
Test Data Population
 Populate Test Data
 Continuous Refresh for Test
Data
 Devise Backup Schedules

7. Test Data Profiling
• Enterprise Business
• Mapped To Data
Structures
• Test Data
Requirements Be
Identified Based On
These
• Test Data Profiling
• Ensures A Sync

8. Test Data Planning
• Understand The Intricacies Of The Production
Scenario
• Evaluate Test Cases

9. Test Data Provisioning

10. Sub-setting Data
from multiple
sources
• Identifies Realistic Data
• Encompassing As Many
Scenarios &
Combinations
• Small And Allow Quick
Testing
• Enforces Boundary
Conditions & Error
Possibilities

11. Keeping the Data private
• Data Masking
• Hides Actual Data
• Brings Anonymity

12. Test Data Population

13. Automated Test Data Generation &
Comparison
 Keeping Check On
 Anomalies & Inconsistencies Of Test Data
 Increases The Speed & Efficiencies

14. Periodic Refresh of
Test Data
• Monitoring Test Data
• Get Away With Any
Divergences
• Ensure Optimal Testing
Environments

15. Rules of Thumb
Manage the Test Data Environment
 As Similar As Possible To The End
User Environment
 Address Practical Needs
 Explore Practical Platforms
 Virtualization Or Cloud
Environment
 Easily Configurable

16. Yes or No for Test Data Management
NO
•Inadequate testing means a low quality solution
•More time-to-market
•Rising costs because of random test data with no
strategies
•Non-compliance with regulatory norms for data privacy
Yes
•Reduced cost in test processes
•Improved data quality resulting in better systems
•Timely data delivery, with faster test executions reducing
the time-to-market
•Methodical adherence to regulatory norms on data
confidentiality

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