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Effective Test Data Management While Ensuring Sensitive Data Protection

Updated: Jun 27


Unlocking Data Access: Accelerating Development and Complying with Data Privacy Regulations


For today's DevOps teams, effective test data management (TDM) is critical for ensuring software quality, security, and compliance. As organizations grapple with complex data landscapes, they seek innovative solutions to streamline TDM processes.


btpicon offers a platform that produces highly realistic, de-identified replicas of pre-existing data for software development, testing and analytics purposes. This allows developers to work with compliant data that functions exactly the same as the real thing.


The Challenges of Test Data Management


Numerous companies handling sensitive data in industries like healthcare, financial services, insurance, and retail must comply with data protection regulations such as PDPA, GDPR, and HIPAA. Commonly, they generate dummy datasets, constructed manually, or employ subpar tools for masking or altering sensitive data.


Unfortunately, these approaches frequently lead to poor-quality and unrealistic data.

As software complexity continues to increase, and as traditional methods to make data private don’t evolve, past methods to ensure compliance are causing broad dysfunction in engineering teams. Existing technology has hardly changed for over a decade.

All companies must adhere to compliance requirements. These are what our experts see companies already doing in efforts to be compliant:


  • Manually Generating Data: Developers often spend significant time manually creating data or using scripts for random data generation, leading to unrealistic datasets that lack real-life complexity and messiness.

  • Traditional Data Masking: Techniques like anonymization and masking, through character scrambling or substitution, aim to protect sensitive information but can compromise data integrity and realism, making the data less useful for testing. Companies will often pull from production a few times a year, and then spend time masking it manually or with a tool.

  • Data Synthesis and Sampling: Creating synthetic data based on rules or sampling from production data attempts to mimic real datasets but often falls short in capturing the nuanced relationships and variability, requiring additional efforts to introduce realistic noise or enhance data signals.

  • Copying Production Data and External Datasets: Directly copying production data for testing poses security and compliance risks, while relying on external or third-party datasets may not accurately reflect the specific needs and complexities of the company’s data environment.


To summarize what pain this causes:


  • Delays in data provisioning, wasting valuable development time

  • Poor quality datasets leading to unreliable test results

  • Lack of consistency across multiple databases, making data incoherent at scale

  • Inadequate data masking tools compromising data usefulness

  • Increased risk of software failures or crashes due to unrealistic data

  • Difficulty maintaining compliance with data protection regulations


How Do We Help?


btpicon delivers a software and services solution that provisions data so realistic that it feels like working with production data. Existing complexity is retained. By modeling our data on real-world information and removing all personally identifiable information (PII), we ensure compliance without sacrificing usability. This enables developers to work more efficiently, deploy projects faster, and avoid complications related to data preparation.


Core Features:


  • Database Agnostic: Our solution works natively across multiple types of databases, whilst constantly adding new ones. This includes NoSQL, Data Warehouses, Files, and more.

  • Cross-database consistency: Consistent de-identification across an entire enterprise and all of the data pipeline complexity that entails

  • Relationship linking: Captures subtle nuances across a dataset, closely modeling de-identified data after the original.

  • Sub-setting: Creates referentially intact, realistic subsets of de-identified data at fractional sizes. Works on Application Databases, including NoSQL like MongoDB.

  • Privacy reporting: Identifies sensitive information, recommending de-identification methods, and providing full audit reporting.

  • Data Provisioning: Container Artifacts: It allows data (including subsets) to be output into container artifacts, enabling engineers to stand up data as quickly as they can download it.

  • Data Provisioning: Ephemeral Environments: It can also be used to effortlessly spin up (and down) databases pre-hydrated with de-identified data (including just subsets if preferred). Engineers instantly have access to the test data they need as self-service.


What Does this Enable?


Our solution significantly benefits the application development and testing process by providing realistic and compliant test data for various scenarios. Here are some key areas where application development, testing and analytics teams find immediate benefit:


  • Rapid Data Provisioning: Accelerating setup for development environments with on-demand data access, reducing wait times for data readiness.

  • Intelligent Data Masking: Applying context-aware masking to protect customer privacy during testing, without compromising data utility.

  • Efficient Data Sharing: Enabling seamless collaboration between cross-functional teams by providing consistent, secure access to shared data sets.

  • Continuous Data Refresh: Keeping test environments aligned with production changes, ensuring tests are run against up-to-date and relevant data.

  • Resource Efficiency: Streamlining compliance and reducing overheads through automated data handling processes, minimizing manual interventions and costs.


What Are Sample Use Cases?


  1. Healthcare: Hospitals, medical device manufacturers, pharmaceutical companies, and healthcare providers must comply with regulations such as HIPAA to protect patient data.

  2. Financial Services: Banks, credit unions, investment firms, and insurance companies are subject to various regulations, including GDPR, to protect customers' financial information.

  3. Insurance: Health, life, and property insurance providers need to protect sensitive policyholder data, often following industry-specific regulations.

  4. Retail: Online and brick-and-mortar retailers dealing with customer data, payment information, and loyalty programs must comply with data protection regulations.

  5. Education: Educational institutions, including schools, universities, and online learning platforms, are responsible for safeguarding student data and complying with applicable regulations.

  6. Telecommunications: Telecom providers handling customer data, call records, and other sensitive information must adhere to data protection standards.

  7. Legal Services: Law firms and legal service providers handling sensitive client information must maintain strict data confidentiality and comply with regulations.

  8. Human Resources: HR software and service providers managing employee data, payroll, and benefits must adhere to data protection standards to ensure compliance.


Next Steps


Talk to our team about your test data management use cases and together we can explore best-fit approaches to meet your business requirements. We provide Free TDM Assessments to demonstrate your potential Return on Investment of a test data management project, as well as the delivery of automation software, services and support to your teams.




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