Mage Data

Category: Blogs – SDM

  • Healthcare Reinvented: Data Security Meets Compliance

    Healthcare Reinvented: Data Security Meets Compliance

    In today’s healthcare ecosystem, data is both an operational backbone and a compliance challenge. For organizations managing vast networks of primary care centers, protecting patient data while maintaining efficiency is a constant balancing act. As the healthcare industry becomes increasingly data-driven, the need to ensure security, consistency, and compliance across systems has never been more critical.

    Primary care organizations depend on sensitive clinical and claims data sourced from multiple payers. Each source typically arrives in a different format—creating integration hurdles and privacy risks. Manual processing not only slows operations but also increases the chance of human error and non-compliance with data protection mandates such as HIPAA.

    To overcome these challenges, one leading healthcare provider partnered with Mage Data, adopting its Test Data Management (TDM) 2.0 solution. The results transformed the organization’s ability to scale securely, protect patient information, and maintain regulatory confidence while delivering high-quality care to its patients.

    The organization faced multiple, interrelated data challenges typical of large-scale primary care environments:

    • Protecting Patient Privacy: Ensuring HIPAA compliance meant that no sensitive health data could be visible in development or test environments. Traditional anonymization processes were slow and prone to inconsistency.
    • Data Consistency Across Systems: Patient identifiers such as names, IDs, and dates needed to remain accurate and consistent across applications and databases to preserve reporting integrity.
    • Operational Inefficiency: Teams spent valuable time manually processing payer files in multiple formats, introducing risk and slowing development cycles.
    • Scaling with Growth: With over 50 payer file formats and new ones continuously added, the organization struggled to maintain standardization and automation.

    These pain points created a clear need for an automated, compliant, and scalable Test Data Management framework.

    Mage Data implemented its TDM 2.0 solution to address the organization’s end-to-end data management and privacy challenges. The deployment focused on automation, privacy assurance, and operational scalability.

    1. Automated Anonymization

    Mage Data automated the anonymization of all payer files before they entered non-production environments. This ensured that developers and testers never had access to real patient data, while still being able to work with datasets that mirrored production in structure and behavior. The result was full compliance with HIPAA and other healthcare data protection requirements.

    1. NLP-Based Masking for Unstructured Text

    To mitigate the risk of identifiers embedded in free-text fields—such as medical notes or descriptions—Mage Data integrated Natural Language Processing (NLP)-based masking. This advanced capability identified and anonymized hidden personal data, ensuring that no sensitive information was exposed inadvertently.

    1. Dynamic Templates and Continuous Automation

    Mage Data introduced dynamic templates that automatically adapted to new or changing file types from different payers. These templates, combined with continuous automation through scheduled jobs, detected, masked, and routed new files into development systems—quarantining unsupported formats until validated. This approach reduced manual effort, improved accuracy, and allowed the organization to support rapid expansion without re-engineering its data pipelines.

    The adoption of Mage Data’s TDM 2.0 delivered measurable improvements across compliance, efficiency, and operational governance:

    • Regulatory Compliance Assured: The organization successfully eliminated the risk of HIPAA violations in non-production environments.
    • Faster Development Cycles: Developers gained access to compliant, production-like data in hours instead of days—accelerating release cycles and integration efforts.
    • Consistency at Scale: Mage Data ensured that identifiers such as patient names, IDs, and dates remained synchronized across systems, maintaining the accuracy of analytics and reports.
    • Operational Efficiency: Manual discovery and masking processes were replaced by automated, rule-driven workflows—freeing technical teams to focus on higher-value work.
    • Future-Ready Scalability: The solution’s adaptable framework was designed to seamlessly extend to new data formats, applications, and business units as the organization grew nationwide.

    Through this transformation, Mage Data enabled the healthcare provider to turn data protection from a compliance burden into a strategic advantage, empowering its teams to innovate faster while safeguarding patient trust.

    In conclusion, Mage Data delivers a comprehensive, multi-layered data security framework that protects sensitive information throughout its entire lifecycle. The first step begins with data classification and discovery, enabling organizations to locate and identify sensitive data across environments. This is followed by data cataloging and lineage tracking, offering a clear, traceable view of how sensitive data flows across systems. In non-production environments, Mage Data applies static data masking (SDM) to generate realistic yet de-identified datasets, ensuring safe and effective use for testing and development. In production, a Zero Trust model is enforced through dynamic data masking (DDM), database firewalls, and continuous monitoring—providing real-time access control and proactive threat detection. This layered security approach not only supports regulatory compliance with standards such as GDPR, HIPAA, and PCI-DSS but also minimizes risk while preserving data usability. By integrating these capabilities into a unified platform, Mage Data empowers organizations to safeguard their data with confidence—ensuring privacy, compliance, and long-term operational resilience.

  • Reimagining Test Data: Secure-by-Design Database Virtualization

    Reimagining Test Data: Secure-by-Design Database Virtualization

    Enterprises today are operating in an era of unprecedented data velocity and complexity. The demand for rapid software delivery, continuous testing, and seamless data availability has never been greater. At the same time, organizations face growing scrutiny from regulators, customers, and auditors to safeguard sensitive data across every environment—production, test, or development.

    This dual mandate of speed and security is reshaping enterprise data strategies. As hybrid and multi-cloud infrastructures expand, teams struggle to provision synchronized, compliant, and cost-efficient test environments fast enough to keep up with DevOps cycles. The challenge lies not only in how fast data can move, but in how securely it can be replicated, masked, and managed.

    Database virtualization was designed to solve two of the biggest challenges in Test Data Management—time and cost. Instead of creating multiple full physical copies of production databases, virtualization allows teams to provision lightweight, reusable database instances that share a common data image. This drastically reduces storage requirements and accelerates environment creation, enabling developers and QA teams to work in parallel without waiting for lengthy data refresh cycles. By abstracting data from its underlying infrastructure, database virtualization improves agility, simplifies DevOps workflows, and enhances scalability across hybrid and multi-cloud environments. In short, it brings speed and efficiency to an otherwise resource-heavy process—freeing enterprises to innovate faster.

    Database virtualization was introduced to address inefficiencies in provisioning and environment management. It promised faster test data creation by abstracting databases from their underlying infrastructure. But for many enterprises, traditional approaches have failed to evolve alongside modern data governance and privacy demands.

    Typical pain points include:

    • Storage-Heavy Architectures: Conventional virtualization still relies on partial or full data copies, consuming vast amounts of storage.
    • Slow, Manual Refresh Cycles: Database provisioning often depends on DBAs, leading to delays, inconsistent refreshes, and limited automation.
    • Fragmented Data Privacy Controls: Sensitive data frequently leaves production unprotected, exposing organizations to compliance violations.
    • Limited Integration: Many solutions don’t integrate natively with CI/CD or hybrid infrastructures, making automated delivery pipelines cumbersome.
    • Rising Infrastructure Costs: With exponential data growth, managing physical and virtual copies across clouds and data centers drives up operational expenses.

    The result is an environment that might be faster than before—but still insecure, complex, and costly. To thrive in the AI and automation era, enterprises need secure-by-design virtualization that embeds compliance and efficiency at its core.

    Modern data-driven enterprises require database virtualization that does more than accelerate. It must automate security, enforce privacy, and scale seamlessly across any infrastructure—cloud, hybrid, or on-premises.

    This is where Mage Data’s Database Virtualization (DBV) sets a new benchmark. Unlike traditional tools that treat masking and governance as secondary layers, Mage Data Database Virtualization builds them directly into the virtualization process. Every virtual database created is masked, compliant, and policy-governed by default—ensuring that sensitive information never leaves production unprotected.

    Database Virtualization lightweight, flexible architecture enables teams to provision virtual databases in minutes, without duplicating full datasets or requiring specialized hardware. It’s a unified solution that accelerates innovation while maintaining uncompromising data privacy and compliance.

    1. Instant, Secure Provisioning
      Create lightweight, refreshable copies of production databases on demand. Developers and QA teams can access ready-to-use environments instantly, reducing cycle times from days to minutes.
    2. Built-In Data Privacy and Compliance
      Policy-driven masking ensures that sensitive data remains protected during every clone or refresh. Mage Data Database Virtualization is compliance-ready with frameworks like GDPR, HIPAA, and PCI-DSS, ensuring enterprises maintain regulatory integrity across all environments.
    3. Lightweight, Flexible Architecture
      With no proprietary dependencies or hardware requirements, Database Virtualization integrates effortlessly into existing IT ecosystems. It supports on-premises, cloud, and hybrid infrastructures, enabling consistent management across environments.
    4. CI/CD and DevOps Integration
      DBV integrates natively with Jenkins, GitHub Actions, and other automation tools, empowering continuous provisioning within DevOps pipelines.
    5. Cost and Operational Efficiency
      By eliminating full physical copies, enterprises achieve up to 99% storage savings and dramatically reduce infrastructure, cooling, and licensing costs. Automated refreshes and rollbacks further cut
      manual DBA effort.
    6. Time Travel and Branching (Planned)
      Upcoming capabilities will allow enterprises to rewind databases or create parallel branches, enabling faster debugging and parallel testing workflows.

    The AI-driven enterprise depends on speed—but the right kind of speed: one that doesn’t compromise security or compliance. Mage Data Database Virtualization delivers precisely that. By uniting instant provisioning, storage efficiency, and embedded privacy, it transforms database virtualization from a performance tool into a strategic enabler of governance, innovation, and trust.

    As enterprises evolve to meet the demands of accelerating development, they must modernize their entire approach to data handling—adapting for an AI era where agility, accountability, and assurance must coexist seamlessly.

    Mage Data’s Database Virtualization stands out as the foundation for secure digital transformation—enabling enterprises to accelerate innovation while ensuring privacy and compliance by design.

  • Building Trust in AI: Strengthening Data Protection with Mage Data

    Building Trust in AI: Strengthening Data Protection with Mage Data

    Artificial Intelligence is transforming how organizations analyze, process, and leverage data. Yet, with this transformation comes a new level of responsibility. AI systems depend on vast amounts of sensitive information — personal data, intellectual property, and proprietary business assets — all of which must be handled securely and ethically.

    Across industries, organizations are facing a growing challenge: how to innovate responsibly without compromising privacy or compliance. The European Commission’s General-Purpose AI Code of Practice (GPAI Code), developed under the EU AI Act, provides a structured framework for achieving this balance. It defines clear obligations for AI model providers under Articles 53 and 55, focusing on three key pillars — Safety and Security, Copyright Compliance, and Transparency.

    However, implementing these requirements within complex data ecosystems is not simple. Traditional compliance approaches often rely on manual audits, disjointed tools, and lengthy implementation cycles. Enterprises need a scalable, automated, and auditable framework that bridges the gap between regulatory expectations and real-world data management practices.

    Mage Data Solutions provides that bridge. Its unified data protection platform enables organizations to operate compliance efficiently — automating discovery, masking, monitoring, and lifecycle governance — while maintaining data utility and accelerating AI innovation.

    The GPAI Code establishes a practical model for aligning AI system development with responsible data governance. It is centered around three pillars that define how providers must build and manage AI systems.

    1. Safety and Security
      Organizations must assess and mitigate systemic risks, secure AI model parameters through encryption, protect against insider threats, and enforce multi-factor authentication across access points.
    2. Copyright Compliance
      Data sources used in AI training must respect intellectual property rights, including automated compliance with robots.txt directives and digital rights management. Systems must prevent the generation of copyrighted content.
    3. Transparency and Documentation
      Providers must document their data governance frameworks, model training methods, and decision-making logic. This transparency ensures accountability and allows regulators and stakeholders to verify compliance.

    These pillars form the foundation of the EU’s AI governance model. For enterprises, they serve as both a compliance obligation and a blueprint for building AI systems that are ethical, explainable, and secure.

    Mage Data’s platform directly maps its data protection capabilities to the GPAI Code’s requirements, allowing organizations to implement compliance controls across the full AI lifecycle — from data ingestion to production monitoring.

    GPAI Requirement

    Mage Data Capability

    Compliance Outcome

    Safety & Security (Article 53)

    Sensitive Data Discovery

    Automatically identifies and classifies sensitive information across structured and unstructured datasets, ensuring visibility into data sources before training begins.

    Safety & Security (Article 53)

    Static Data Masking (SDM)

    Anonymizes training data using over 60 proven masking techniques, ensuring AI models are trained on de-identified yet fully functional datasets.

    Safety & Security (Article 53)

    Dynamic Data Masking (DDM)

    Enforces real-time, role-based access controls in production systems, aligning with Zero Trust security principles and protecting live data during AI operations.

    Copyright Compliance (Article 55)

    Data Lifecycle Management

    Automates data retention, archival, and deletion processes, ensuring compliance with intellectual property and “right to be forgotten” requirements.

    Transparency & Documentation (Article 55)

    Database Activity Monitoring

    Tracks every access to sensitive data, generates audit-ready logs, and produces compliance reports for regulatory or internal review.

    Transparency & Accountability

    Unified Compliance Dashboard

    Provides centralized oversight for CISOs, compliance teams, and DPOs to manage policies, monitor controls, and evidence compliance in real time.

    By aligning these modules to the AI Code’s compliance pillars, Mage Data helps enterprises demonstrate accountability, ensure privacy, and maintain operational efficiency.

    Mage Data enables enterprises to transform data protection from a compliance requirement into a strategic capability. The platform’s architecture supports high-scale, multi-environment deployments while maintaining governance consistency across systems.

    Key advantages include:

    • Accelerated Compliance: Achieve AI Act alignment faster than traditional, fragmented methods.
    • Integrated Governance: Replace multiple point solutions with a unified, policy-driven platform.
    • Reduced Risk: Automated workflows minimize human error and prevent data exposure.
    • Proven Scalability: Secures over 2.5 billion data rows and processes millions of sensitive transactions daily.
    • Regulatory Readiness: Preconfigured for GDPR, CCPA, HIPAA, PCI-DSS, and EU AI Act compliance.

    This integrated approach enables security and compliance leaders to build AI systems that are both trustworthy and operationally efficient — ensuring every stage of the data lifecycle is protected and auditable.

    Mage Data provides a clear, step-by-step plan:

    This structured approach takes the guesswork out of compliance and ensures organizations are always audit-ready

    The deadlines for AI Act compliance are approaching quickly. Delaying compliance not only increases costs but also exposes organizations to risks such as:

    • Regulatory penalties that impact global revenue.
    • Data breaches harm brand trust.
    • Missed opportunities, as competitors who comply early gain a reputation for trustworthy, responsible AI.

    By starting today, enterprises can turn compliance from a burden into a competitive advantage.

    The General-Purpose AI Code of Practice sets high standards but meeting them doesn’t have to be slow or costly. With Mage Data’s proven platform, organizations can achieve compliance in weeks, not years — all while protecting sensitive data, reducing risks, and supporting innovation.

    AI is the future. With Mage Data, enterprises can embrace it responsibly, securely, and confidently.

    Ready to get started? Contact Mage Data for a free compliance assessment and see how we can help your organization stay ahead of the curve.

  • TDM 2.0 vs. TDM 1.0: What’s Changed?

    TDM 2.0 vs. TDM 1.0: What’s Changed?

    As digital transformation continues to evolve, test data management (TDM) plays a key role in ensuring data security, compliance, and efficiency. TDM 2.0 introduces significant improvements over TDM 1.0, building on its strengths while incorporating modern, cloud-native technologies. These advancements enhance scalability, integration, and user experience, making TDM 2.0 a more agile and accessible solution. With a focus on self-service capabilities and an intuitive conversational UI, this next-generation approach streamlines test data management, delivering notable improvements in efficiency and performance. 

    Foundation & Scalability  

    Understanding the evolution from TDM 1.0 to TDM 2.0 highlights key improvements in technology and scalability. These enhancements address past limitations and align with modern business needs. 

    Modern Tech Stack vs. Legacy Constraints 
    TDM 1.0 relied on traditional systems that, while reliable, were often constrained by expensive licensing and limited scalability. TDM 2.0 shifts to a cloud-native approach, reducing costs and increasing flexibility.
    • Eliminates reliance on costly database licenses, optimizing resource allocation. 
    • Enables seamless scalability through cloud-native architecture. 
    • Improves performance by facilitating faster updates and alignment with industry standards. 

    This transition ensures that TDM 2.0 is well-equipped to support evolving digital data management needs. 

    Enterprise-Grade Scalability vs. Deployment Bottlenecks 

    Deployment in TDM 1.0 was time-consuming, making it difficult to scale or update efficiently. TDM 2.0 addresses these challenges with modern deployment practices: 

    1. Containerization – Uses Docker for efficient, isolated environments. 
    2. Kubernetes Integration – Supports seamless scaling across distributed systems. 
    3. Automated Deployments – Reduces manual effort, minimizing errors and accelerating rollouts. 

    With these improvements, organizations can deploy updates faster and manage resources more effectively. 

    Ease of Use & Automation  

    User experience is a priority in TDM 2.0, making the platform more intuitive and less dependent on IT support. 

    Conversational UI vs. Complex Navigation 

    TDM 1.0 required multiple steps for simple tasks, creating a steep learning curve. TDM 2.0 simplifies interactions with a conversational UI: 

    • Allows users to create test data and define policies with natural language commands. 
    • Reduces training time, enabling quicker adoption. 
    • Streamlines navigation, making data management more accessible. 

    This user-friendly approach improves efficiency and overall satisfaction. 

    Self-Service Friendly vs. High IT Dependency 

    TDM 2.0 reduces IT reliance by enabling self-service capabilities: 

    1. Users can manage test data independently, freeing IT teams for strategic work. 
    2. Integrated automation tools support customized workflows. 
    Developer-Ready vs. No Test Data Generation  

    A user-friendly interface allows non-technical users to perform complex tasks with ease. These features improve productivity and accelerate project timelines. 

    Data Coverage & Security  

    Comprehensive data support and strong security measures are essential in test data management. TDM 2.0 expands these capabilities significantly. 

    Modern Data Ready vs. Limited Coverage 

    TDM 1.0 had limited compatibility with modern databases. TDM 2.0 addresses this by: 

    • Supporting both on-premise and cloud-based data storage. 
    • Integrating with cloud data warehouses. 
    • Accommodating structured and unstructured data. 

    This broad compatibility allows organizations to manage data more effectively. 

    Secure Data Provisioning with EML vs. In-Place Masking Only 

    TDM 2.0 introduces EML (Extract-Mask-Load) pipelines, offering more flexible and secure data provisioning: 

    • Secure data movement across different storage systems. 
    • Policy-driven data subsetting for optimized security. 
    • Real-time file monitoring for proactive data protection. 

    These enhancements ensure stronger data security and compliance. 

    Governance & Integration  

    Effective data governance and integration are key strengths of TDM 2.0, helping organizations maintain oversight and connectivity. 

    Built-in Data Catalog vs. Limited Metadata Management 

    TDM 2.0 improves data governance by providing a built-in data catalog: 

    1. Centralizes metadata management for easier governance. 
    2. Visualizes data lineage for better transparency. 
    3. Supports integration with existing cataloging tools. 

    This centralized approach improves data oversight and compliance. 

    API-First Approach vs. Limited API Support 

    TDM 2.0 enhances integration with an API-first approach: 

    • Connects with third-party tools, including data catalogs and security solutions. 
    • Supports single sign-on (SSO) for improved security. 
    • Ensures compatibility with various tokenization tools. 

    This flexibility allows organizations to integrate TDM 2.0 seamlessly with their existing and future technologies. 

    Future-Ready Capabilities  

    Organizations need solutions that not only meet current demands but also prepare them for future challenges. TDM 2.0 incorporates key future-ready capabilities. 

    GenAI-Ready vs. No AI/ML Support 

    Unlike TDM 1.0, which lacked AI support, TDM 2.0 integrates with AI and GenAI tools: 

    • Ensures data protection in AI training datasets. 
    • Prevents unauthorized data access. 
    • Supports AI-driven environments for innovative applications. 

    These capabilities position TDM 2.0 as a forward-thinking solution. 

    Future-Ready Capabilities 

    TDM 2.0 is built to handle future demands with: 

    1. Scalability to accommodate growing data volumes. 
    2. Flexibility to adapt to new regulations and compliance requirements. 
    3. Integration capabilities for emerging technologies. 

    By anticipating future challenges, TDM 2.0 helps organizations stay agile and ready for evolving data management needs.

  • Why is Referential Integrity Important in Test Data Management?

    Why is Referential Integrity Important in Test Data Management?

    Finding the best test data management tools requires getting all the major features you need— but that doesn’t mean you can ignore the little ones, either. While maintaining referential integrity might not be the most exciting part of test data management, it can, when executed poorly, be an issue that frustrates your team and makes them less productive. Here’s what businesses need to do to ensure their testing process is as frictionless and efficient as possible.

    What is Referential Integrity?

    Before exploring how referential integrity errors can mislead the testing process, we must first explore what it is. While there are a few different options for storing data at scale, the most common method is the relational database. Relational databases are composed of tables, and tables are made up of rows and columns. Rows, or records, represent individual pieces of information, and each column contains an attribute of the thing. So, a “customer” table, for example, would have a row for each customer and would have columns like “first name,” “last name,” “address,” “phone number,” and so on. Every row in a table also contains a unique identifier called a “key.” Typically, the first row is assigned the key “1”, the second, “2,” and so on.

    The key is important when connecting data between tables. For example, you might have a second table that stores information about purchases. Each row would be an individual transaction, and the columns would be things like the total price, the date, the location at which the purchase was made, and so on. The power of relational databases is that entries in tables can reference other tables based on keys. This approach helps eliminate ambiguity. There might be multiple “John Smiths” in your customer table, but only one will have the unique key “1,” so we can tie transactions to that customer by using their unique key rather than something that there might be multiple of, like a name. Therefore, referential integrity refers to the accuracy and consistency of the relationship between tables.

    How Does Referential Integrity Affect Test Data?

    Imagine a scenario in which a customer, “John Doe,” exercised his right under GDPR or CCPA to have his personal data deleted. As a result of this request, his record in the customer table would be deleted, though the transactions would likely remain, as they aren’t personal data. Now, your developers could be working on a new application that processes transactional data and pulls up user information when someone selects a certain transaction. If John’s transactions were included in the test data used, the test would result in an error whenever one of those transactions came up, as the reference included in those transactions has been deleted.

    The developers’ first reaction wouldn’t necessarily be to look at the underlying data, but to instead assume that there was some sort of bug in the code they had been working on. So, they might write new code, test it, see the error again, and start over a few times before realizing that the underlying data is flawed.

    While that may just sound like a waste of a few hours, this is an extremely basic example. More complex applications could be connecting data through dozens of tables, and the code might be far longer and more complicated…so it can take days for teams to recognize that there isn’t a problem with the code itself but with the data they’re using for testing. Companies need a system that can help them deal with referential integrity issues when creating test data sets, no matter what approach to generating test data they use.

    Referential Integrity in Subsetting

    One approach to generating test data is subsetting. Because your production databases can be very, very large, subsetting creates a copy of some of the database which is more manageable in testing. When it comes to referential integrity, subsetting faces the same issues that using a live production environment does: Someone still needs to scrub through the data and either delete records with missing references or create new dummy records to replace missing ones. This can be a time-consuming and error-prone process.

    Referential Integrity in Anonymized/Pseudonymized datasets

    Anonymization and pseudonymization are two more closely related approaches to test data generation. Pseudonymization takes personally identifiable information and changes it so that it cannot be linked to a real person without combining it with other information stored elsewhere. Anonymization also replaces PII data but does it in a way that is irreversible

    These procedures make the data safer for testing purposes, but the generation process could lead to referential integrity issues. For example, the anonymization process may obscure the relationships between tables, creating reference issues if the program doing the anonymization isn’t equipped to handle the issue across the database as a whole.

    How Mage Data Helps with Test Data Management?

    The key to success with referential integrity in test data management is taking a holistic approach to your data. Mage Data helps companies with every aspect of their data, from data privacy and security to data subject access rights automation, to test data management. This comprehensive approach ensures that businesses can spend less time dealing with frustrating issues like broken references and more time on the tasks that make a real difference. To learn more about Mage’s test data management solution, schedule a demo today.

     

  • The ROI of Test Data Management Tool

    The ROI of Test Data Management Tool

    As software teams increasingly take a “shift left” approach to software testing, the need to reduce testing cycle times and improve the rigor of tests is growing in lock-step. This creates a conundrum: Testing coverage and completeness is deeply dependent on the quality of the test dataset used—but provisioning quality test data has to take less time, not more.

    This is where Test Data Management (TDM) tools come into play, giving DevOps teams the resources to provision exactly what they need to test early and often. But, as with anything else, quality TDM tool has a cost associated with it. How can decision makers measure the return on investment (ROI) for such tool?

    To be clear, the issue is not how to do an ROI calculation; there is a well-defined formula for that. The challenge comes with knowing what to measure, and how to translate the functions of TDM tool into concrete cost savings. To get started, it helps to consider the downsides to traditional testing that make TDM attractive, proceeding from there to categorize the areas where TDM tool creates efficiencies as well as new opportunities.

    Traditional Software Testing without TDM—Slow, Ineffective, and Insecure

    The traditional method for generating test data is a largely manual process. A production database would be cloned for the purpose, and then an individual or team would be tasked with creating data subsets and performing other needed functions. This method is inefficient for several reasons:

    • Storage costs. Cloning an entire production database increases storage costs. Although the cost of storage is rather low today, production databases can be large; storing an entire copy is an unnecessary cost.
    • Cloning a database and manually preparing a subset can be a labor-intensive process. According to one survey of DevOps professionals, an average of 3.5 days and 3.8 people were needed to fulfill a request for test data that used production environment data; for 20% of the respondents, the timeframe was over a week.
    • Completeness/edge cases. Missing or misleading edge cases can skew the results of testing. A proper test data subset will need to include important edge cases, but not so many that they overwhelm test results.
    • Referential integrity. When creating a subset, that subset must be representative of the entire dataset. The data model underlying the test data must accurately define the relationships among key pieces of data. Primary keys must be properly linked, and data relationships should be based on well-defined business rules.
    • Ensuring data privacy and compliance. With the increasing number of data security and privacy laws worldwide, it’s important to ensure that your test data generation methods comply with relevant legislation.

    The goal in procuring a TDM tool is to overcome these challenges by automating large parts of the test data procurement process. Thus, the return on such an investment depends on the tool’s ability to guarantee speed, completeness, and referential integrity without consuming too many additional resources or creating compliance issues.

    Efficiency Returns—Driving Down Costs Associated with Testing

    When discussing saved costs, there are two main areas to consider: Internal costs and external ones. Internal costs reflect inefficiencies in process or resource allocation. External costs reflect missed opportunities or problems that arise when bringing a product to market. TDM can help organizations realize a return with both.

    Internal Costs and Test Data Procurement Efficiency

    There is no doubt that testing can happen faster, and sooner, when adequate data is provided more quickly with an automated process. Some industry experts report that, for most organizations, somewhere between 40% and 70% of all test data creation and provisioning can be automated.

    Part of an automated workflow should involve either subsetting the data, or virtualizing it. These steps alleviate the need to store complete copies of production databases, driving down storage costs. Even for a medium-sized organization, this can mean terabytes of saved storage space, with 80% to 90% reductions in storage space being reported by some companies.

    As for overall efficiency, team leaders say their developers are 20% to 25% more efficient when they have access to proper test data management tools.

    External Costs and Competitiveness in the Market

    Most organizations see TDM tools as a way to make testing more efficient, but just as important are the opportunity costs that accrue from slower and more error-prone manual testing. For example, the mean time to the detection of defects (MTTD) will be lower when test data is properly managed, which means software can be improved more quickly, preventing further bugs and client churn. The number of unnoticed defects is likely to decline as well. Catching an error early in development incurs only about one-tenth of the cost of fixing an error in production.

    Time-to-market (TTM) is also a factor here. Traditionally, software projects might have a TTM from six months to several years—but that timeframe is rapidly shrinking. If provisioning of test data takes a week’s worth of time, and there are several testing cycles needed, the delay in TTM due only to data provisioning can be a full month or more. That is not only a month’s worth of lost revenue, but adequate space for a competitor to become more established.

    The Balance

    To review, the cost of any TDM tool and its implementation needs to be balanced against:

    • The cost of storage space for test data
    • The cost of personnel needs (3.8 employees, on average, over 3.5 days)
    • The benefit of an increase in efficiency of your development teams
    • Overall cost of a bug when found in production rather than in testing
    • Lost opportunity due to a slower time-to-market

    TDM Tools Achieve Positive ROI When They Solve These Challenges

    Admittedly, every organization will look different when these factors are assessed. So, while there are general considerations when it comes to the ROI of TDM tools, specific examples will vary wildly. We encourage readers to derive their own estimates for the above numbers.

    That said, the real question is not whether TDM tools provide an ROI. The question is which TDM tools are most likely to do so. Currently available tools differ in terms of their feature sets and ease of use. The better the tool, the higher the ROI will be.

    A tool will achieve positive ROI insofar as it can solve these challenges:

    • Ensuring referential integrity. This can be achieved through proper subsetting and pseudonymization capabilities. The proper number and kind of edge cases should be present, too.
    • Automated provisioning with appropriate security. This means being able to rapidly provision test data across the organization while also staying compliant with all major security and privacy regulations.
    • Scalability and flexibility. The more databases an organization has, the more it will need a tool that can work seamlessly across multiple data platforms. A good tool should have flexible deployment mechanisms to make scalability easy.

    These are specifically the challenges our engineers had in mind when developing Mage’s Data TDM capabilities. Our TDM solution achieves that balance, providing an ROI by helping DevOps teams test more quickly and get to market faster. For more specific numbers and case studies, you can schedule a demo and speak with our team.

  • Static vs Dynamic Masking: What to Choose?

    Static vs Dynamic Masking: What to Choose?

    Although both static data masking (SDM) and dynamic data masking (DDM) have been around for half a decade, there is still some general confusion as to how these tools differ. The problem is not that the technical differences are not well understood—they are. The deeper issue is that it is not always clear what kinds of situations call for a static data masking solution, and which call for a dynamic masking solution. It does not help that the companies selling these solutions tend to re-use the same tired examples every time they write about the topic.

    Although both approaches do more or less the same thing—they replace sensitive data with comparable but “fake” information—the details of where and how they do this differ, and that has some pretty big ramifications for how they should be used.

    Any organization that needs to protect sensitive data would do well to recognize when one or the other is needed. In larger organizations, both kinds of data masking may be in play.

    What is Static Data Masking?

    Static data masking (SDM) involves changing information in the source database—that is, changing information while “at rest.” Once the information is changed, the database is then used, or copied and used, in the various applications in which it is needed.

    SDM is often used to create realistic test data in application development. Instead of creating data out of whole cloth, the development team can create datasets that are realistic because they are literally generated from real production data—while still preserving the privacy of their users.

    SDM is also used when sensitive data needs to be shared with third parties, especially if that third party is located in a different country. By masking the data, relationships can be preserved while still protecting any sensitive or personal information.

    The beauty of SDM is that it is straightforward and complete. All of the data in question is replaced, so there is no way a person or application could somehow access the true data accidentally—nor could a malicious actor compromise the database. The data is protected “across the board,” without the need to configure access on a user-by-user or role-by-role basis.

    Example of a Use Case for Static Data Masking: A financial institution wants a third party to run an analysis on some of their data. The financial institution wants to protect sensitive information and financial information of its clients, and must also comply with laws about data crossing national boundaries. They mask the data in their database before giving the analytics firm access, ensuring that no sensitive data can possibly be accessed or copied.

    What is Dynamic Data Masking?

    Dynamic data masking (DDM) involves masking data on-demand at the point of use. The original data remains unchanged, but sensitive information is altered and masked on-the-fly. This allows for more fine-grained access control.

    Whereas SDM creates a copy of a database with masked data which teams then can access, DDM preserves access to the original database but modifies what a particular person can see. This means that the masked data a person sees with DDM is as close to real-time data as one could hope for, making it ideal for situations where someone needs to access fresh data but in a limited way.

    Example of a Use Case for Dynamic Data Masking: A large company might keep a large employee database that includes not only names and addresses, but Social Security numbers, direct deposit information, and more. An HR professional running payroll might need to access addresses and direct deposit information, but other HR professionals probably do not. What any given HR employee could see in the system would depend on a specific set of rules that masked data according to user or role.

    Because DDM allows organizations to enforce role-based access control, it is sometimes used for older applications that don’t have these kinds of controls built in. Again, think of older legacy HR databases, or customer service systems that might store credit card information.

    Static vs. Dynamic Masking: Main Differences

    Here is a summary, then, of some of the main differences between static data masking and dynamic data masking:

    Static Data Masking (SDM) Dynamic Data Masking (DDM)
    Deployed on Non-Production Deployed in Production
    Original data is overwritten Original data is preserved
    All users have access to the same masked data Authorized users have access to original data

    Key Questions to Ask When Deciding on a Data Masking Solution

    Many vendors tip their hand when discussing data masking solutions; it becomes obvious that they favor one or the other. Unsurprisingly, the one they favor is the particular kind that they sell.

    Here at Mage, we have data masking solutions of both types, static and dynamic. Our goal is to find the solution that best fits your use cases. Many times, it turns out that a large organization needs both—they simply need them for different purposes. It pays to engage a vendor that understands the small differences and is adept at implementing both kinds of solution.

    For example, here are some of the questions we might have a new client consider when trying to decide between SDM and DDM for a particular use case:

    • Do you require the data being masked to reflect up-to-the-minute changes? Or can you work with batched data?
    • Are you looking to implement role-based access? Or do you feel more comfortable with a more complete masking of the data in question?
    • How much of a concern is the protection of your production environment?
    • What privacy laws or regulations are in play? For example, do you need to consider HIPAA laws for protected health information (PHI)? Or regulations like the Gramm-Leach-Bliley Act (GLBA) and Sarbanes-Oxley Act (SOX) because you handle personal financial information (PFI)?
    • How are you currently identifying the data that needs to be masked? Is sensitive data discovery needed in addition to any masking tools?

    There are other considerations that go into selecting a data masking tool as well, but these questions will help guide further research into which particular type of masking your organization might need.

    And again, if it turns out you have a need for both, it is worth contacting us to discuss your needs and set up a demo. You can also see one of our masking tools in action.

     

  • What is Data Provisioning in Test Data Management?

    What is Data Provisioning in Test Data Management?

    If your company has taken the time to master test data generation—including steps to ensure that your test data is free from personally identifiable information, is suitable for different tests, and is representative of your data as a whole—data provisioning might feel like an unimportant step. But like a runner who trips a few feet before the finish line, companies who struggle with data provisioning will face delays and other issues at one of the last steps in the Test Data Management process, wasting much of their hard work. The good news is that getting data provisioning right is a straightforward process, though it will require businesses to have a strong inventory of their data management needs.

    What is Data Provisioning?

    Data provisioning is taking prepared datasets and delivering them to the teams responsible for software testing. That process might sound deceptively simple at first, but data provisioning faces similar challenges to last-mile logistics in package delivery. Moving packages in bulk from San Francisco to Dallas on time and at a low cost is relatively easy. It’s much more challenging to achieve a low price and on-time delivery when taking those same packages and delivering them to thousands of homes across the DFW metro area.

    In the same way, creating one or more high-quality datasets that help testers identify issues before launch is not that complicated, relatively speaking. But doing it when multiple teams may be testing different parts of an app, or even testing across multiple apps, can be a big lift. And if your company is using an agile software development approach , there could be dozens of different teams doing sprints, potentially starting and stopping at different times, each with its own unique testing needs. Those teams may start on an entirely new project in as little as two weeks, which means those managing your test data could receive dozens of requests a month for very different datasets.

    Why Does Data Provisioning Matter?

    Failing to deliver test data on time can have severe consequences. For example, a lack of test data could mean that the launch of a critical new feature is delayed, despite being essentially complete. Data that’s even a day or two late could lead to developers being pulled off their new sprints to resolve bugs revealed in testing. When that happens, other teams are potentially disrupted as personnel are moved around to keep things on track, or else the issue can potentially lead to cascading delays.

    In other scenarios, the consequences could be smaller. The test data could exist, but not be stored in a way that testers can easily access. That could mean that your test data managers are spending time in a “customer service” role, where they have to spend time ensuring testers have what they need. If the friction of this process grows too large, testers might start reusing old datasets to save time, which can lead to bugs and other issues going undetected. The data provisioning challenge for businesses is ensuring that testers always have what they need, when needed, to ensure that testing catches bugs before they go live and becomes much more expensive to fix.

    Strategies for Effective Data Provisioning

    Does that mean that an IT-style approach is right for data provisioning? For the typical IT department, as long as there is enough capacity to support all needs on the busiest days, there won’t be any significant IT problems. However, data provisioning is significantly different from IT needs. IT needs are unpredictable, with some days having heavy demands and others producing very few requests. Data provisioning needs are tied to the development process and are nearly 100 percent predictable. Because of its predictability, companies can be efficient in resource usage for data provisioning, aiming for a “just-in-time” style process rather than maintaining excess or insufficient capacity.

    Self Service

    Of course, achieving a just-in-time process is easier said than done. One of the most effective steps companies can take to streamline their data provisioning process is to adopt a self-service portal. While it will vary from company to company, a significant portion of test data generally needs to be reused in multiple tests. This could be for features in continuous development or applications where the data structure remains unchanged, even as the front end undergoes transformations. Enabling developers and testers to grab commonly needed datasets on their own through a portal frees up your data managers to spend more time on the strategic decision-making needed to create great “custom” datasets for more challenging use cases.

    Automation

    Test data sets, whether in a self-service portal or used on a longer project, need to be regularly refreshed to ensure the data they contain is up-to-date and reflective of the business. Maintaining these portals can be a very time-consuming task for your data managers. Automating the process so that this data can be regularly refreshed, rather through a request in a self-service portal or by regular updates on the backend based on rules set by the test data managers, can help ensure that data is always available and up to date.

    How Mage Data Helps with Data Provisioning

    The reality of data provisioning is that your process may not look anything like anyone else’s, and that’s a good thing, as it means that you’ve customized it to your specific needs. However, getting to that point by building your own tools could be a long and expensive process. At the same time, off-the-shelf solutions may not meet all your needs. With Mage Data, companies can have the best of both worlds. With its suite of powerful tools, Mage Data gives companies just about everything they need for data provisioning and Test Data Management as a whole right out of the box. However, everything is customizable to a company’s specific needs, allowing you to obtain the benefits of customized software without the price tag. To learn more about what Mage Data can do for you, contact us today to schedule a free trial.

  • How to Create a Secure Test Data Management Strategy

    How to Create a Secure Test Data Management Strategy

    Proper Test Data Management helps businesses create better products that perform more reliably on deployment. But creating test data, in the right amount and with the right kinds of relationships, can be a much-more-challenging process than one would think. Getting the most out of test data requires more than simply having a tool for generating or subsetting data; it requires having a clear Test Data Management strategy.

    Test Data Management might not be the first area that comes to mind when thinking about corporate strategy. But testing generally holds just as much potential as any other area to damage your business if handled incorrectly—or to propel you to further success if handled well.

    An upshot of this is that it can help you find the best Test Data Management tools as well. After all, if the creator of a tool understands what is involved in a Test Data Management strategy, you can rest assured their tools will actually be designed to make those strategic goals a reality.

    Here, then, are the elements for a successful and secure Test Data Management strategy.

    The Core Elements of Test Data Management Strategies

    Creating a secure Test Data Management strategy starts with having a plan that makes your goals explicit, as well as the steps for getting there. After all, it doesn’t matter how secure your strategy is, if you don’t achieve the outcomes you’re looking for. All effective Test Data Management strategies rely on the following four pillars.

    Understanding Your Data

    First, it’s essential that you understand your data. Good testing data is typically composed of data points of radically different types sourced from a different database. Understanding what that data is and where it comes from is necessary to determine if it will produce a test result that reflects what your live service offers. Companies must also consider the specific test they’re running and alter the data they choose to produce the most accurate results possible.

    De-Identifying Data

    Second, producing realistic test results requires using realistic data. However, companies that are cavalier with their use of customer data in their tests put themselves at greater risk of leaks and breaches and may also run afoul of data privacy laws.

    There are many different methods for de-identifying data. Masking permanently replaces live data with dummy data with a similar structure. Tokenization replaces live data with values of a similar structure that appear real and can be reversed later. Encryption uses an algorithm to scramble information so it can’t be read without a decryption key. Whichever approach you use, ensure your personally identifiable information is protected and used per your privacy policy.

    Aggregating and Subsetting Data

    Third, your company may hold hundreds or billions of data points. Trying to use all of them for testing would be extremely inefficient. Subsetting, or creating a sample of your data that reflects the whole, is one proven method for efficient testing. Generally, data must also be aggregated from multiple different sources to provide all the types of data that your tests require.

    Refreshing and Automating Test Data in Real Time

    Finally, your company is not static. It changes and grows, and as it does, the data you hold can shift dramatically. If your test data is static, it will quickly become a poor representation of your company’s live environment and cause tests to miss critical errors. Consequently, test data must be regularly refreshed to ensure it reflects your company in the present moment. The best way to accomplish that task is to leverage automation to refresh your data regularly.

    What Makes a Test Data Management Strategy Secure?

    The reality of using test data is that, if improperly handled, it multiplies the preexisting security issues that your company already has. For example, if you take one insecure dataset and create five testing datasets, you end up with at least six times as much risk.

    When your data isn’t secure to begin with, securing your test data won’t make a meaningful impact on your overall security posture. At the same time, creating test data comes with its own risks. Data will be stored in new locations, accessed by more people than usual, and used in ways that it might not be during the normal course of business. That means you need to pay special attention to your test data to keep it secure.

    The following framework provides a way to think through the new risks that test data create.

    Who?

    First is the who. In addition to the people assembling the test datasets, other people (such as back- and front-end developers, or data analysts) will come in contact with the test data. While it’s tempting to provide all of them with the same data, the reality is that the data they need to do their job will vary from role to role. Your experienced lead developer will need a higher level of insight for troubleshooting than a junior developer on their first day on the job. To maximize your security around this data, you need a tool that can help you make these kinds of nuanced decisions about access.

    What?

    Knowing what data you’re using matters. With an ever-growing number of data privacy laws around the world, businesses must be able to detail how they’re using data in their operations. Using data that’s not covered by your privacy policy or using data in a manner that isn’t covered could result in serious regulatory action, possibly in multiple countries at once. Companies increasingly need to be able to prove they’re in compliance, which is most easily accomplished with robust audit logging.

    Where?

    An increasing number of countries are penalizing companies for offshoring data, especially if it isn’t declared in the enterprise’s privacy policy. Even with that in mind, running your data analysis in other countries may still make financial sense. In that situation, companies should evaluate whether masked or entirely synthetic datasets would suffice to reduce the risk of regulatory action or leaks that come with moving data across borders.

    How?

    The growing complexity of securing your Test Data Management process means that it’s no longer possible for humans to oversee every part of the process. A good policy starts with your human workers setting the rules, but then a technological solution is needed to handle the process at the scale required for modern business applications.

    Overall, Test Data Management strategies will vary from company to company. However, by following the principles in this article, companies can develop an approach that meets their testing needs while ensuring that data is kept secure.

    How Mage Data Can Help with Test Data Management

    While it would be dramatic to suggest that a poor Test Data Management strategy could doom a business, it’s not an exaggeration to suggest that a poor strategy drives up costs in a measurable way. Poor testing can easily lead to a buggier product that takes more time and costs more money to fix. And a worse product could lose customers, even as the expanded fixes hurt your bottom line. The good news is that companies don’t have to develop their Test Data Management strategy on their own. Mage’s Data suite of Test Data Management tools provides everything businesses need to build their test data pipeline while having the customization they need to make it their own. Schedule a demo today to see Mage Data in action.

  • Best Practices for Test Data Management in Agile

    Best Practices for Test Data Management in Agile

    Agile is a growing part of nearly every business’ software development process. Agile can better align teams with the most pressing customer issues, speed up development, and cut costs. However, like just-in-time manufacturing, Agile’s unique approach to development means that a delay in any part of the process can lead to a screeching halt across all of it. Testing software solutions, as the last step before deployment, is critical to ensuring that companies ship working software, as well as catching and resolving edge cases and bugs before the code goes live (and becomes far more expensive to fix). If Test Data Management is handled poorly in an Agile environment, the entire process is at risk of breaking down.

    Why Test Data Management is a Bigger Challenge in Agile

    As companies produce and consume more and more data, managing your test data is an increasing challenge. The key to success in leveraging test data is the realization that, the more your test data represents your live data, the better it will be at helping you uncover bugs and edge cases before deployment. While using your live production data in your tests would resolve this issue, that approach has serious data privacy and security concerns (and may not be legal in some jurisdictions). At the same time, the larger your dataset, the slower your tests.

    In a traditional waterfall approach to development, a “subset, mask, and copy” approach generally ensures that data is representative of your live data, small enough for efficient testing, and meets all data privacy requirements. With the testing cycle lasting weeks or months and known well in advance, it’s relatively easy to schedule data refreshes as needed to keep test data fresh.

    Agile sprints tend to be much shorter than the traditional waterfall process, so the prep time for test data is dramatically shortened. A traditional subset, mask, copy approach could severely impede operations by forcing a team to wait on test data to start development. Even worse, it could create a backlog of completed but untested features waiting for deployment, which would require companies to keep teams from starting new stories or pull people off a project to fix bugs after testing is completed. Both hurt efficiency and prevent companies from fully implementing an Agile development process.

    Best Practices for Effective Test Data Management in Agile

    Unfortunately, there are no shortcuts to Test Data Management in an agile system. You have to do everything you would have done in a traditional approach, but significantly speed up the process to ensure it’s never the bottleneck. Implementing this system can require a change in institutional thinking. Success in this area means finding new ways to integrate your testers and data managers into the development process and providing them with the tools they need to succeed in an Agile environment.

    1. Integrate Data Managers into the Planning Process

    No matter how efficient your test data managers are, creating the right dataset for testing for a particular customer story takes at least some time. Waiting until after the planning phase is over to inform your data team of the needed data will lead to delays just from the time needed to create a dataset. However, if more esoteric data is required, the delay could be much longer than typical. By integrating your data team into the planning phase, they can leverage their expertise to help identify potential areas of concern before the start of the development phase. They can also begin working on the test datasets before the start of development, potentially providing everything needed for development and testing on the first day of development.

    2. Adopt Continuous Data Refreshing

    At most companies, data managers support multiple teams. With different customer stories requiring different amounts of time to complete, the data team must be flexible and efficient to meet sometimes unpredictable deadlines. However, that doesn’t excuse them from ensuring that data is up to date, that it’s free of personally identifiable information, or that it’s subset correctly for the test.

    The good news is that significant portions of this process can be automated with the right tools. Modern tools can identify PII in a dataset, enabling rapid, automated transformation of an insecure database into a secure one for testing. Plus, synthetic generation tools can help companies rapidly create great datasets for testing that include no reversible PII while maintaining important referential integrity. With these processes in place, testing teams will be better equipped to handle the pace of Agile while also spending more time on high-value planning operations rather than low-level data manipulation.

    3. Create a Self-Service Portal

    One thing that’s guaranteed to slow Agile teams down is a formal request process to access test data. While tracking who is accessing what data is important, access requests and tracking can largely be automated with today’s tools. This idea can be taken one step further by creating a self-service portal that includes basic datasets for common development scenarios. A self-service portal ensures that smaller teams or side projects can run meaningful tests without tying up your data manager’s resources. Just like with your primary testing datasets, these must be kept reasonably up to date, but automation can significantly help reduce this burden.

    How Mage Data Helps with Agile Test Data Management

    Agile is a process that can greatly speed up development and transform the delivery of new features to your customers. However, teams need more training and tooling to execute it effectively. Not all Test Data Management solutions are up to handling an Agile approach to development. But, Mage’s Data Test Data Management solution is, providing just about everything a company could want right out of the box, while providing flexible customization options to enable companies to build the test data pipeline that works best for their needs. Contact Mage Data today for a free demo and learn more about how Mage Data can help streamline your Agile Test Data Management process.