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Test data management is the technique of providing modern teams with restricted data access throughout the Software Development Lifecycle (SDLC). By giving fast access to fresh, relevant data downstream for code development, automated tests, debugging, and validation, modern Test Data Management solutions help organizations increase application development speed, code quality, data compliance, and sustainability initiatives.

To support agile development and automated testing, test data management entails synchronizing numerous data sources from production, versioning copies, sensitive data discovery, compliance masking data, and multicolor dissemination of test data.

This article will examine how Linux admins and organizations can securely manage confidential data through proper and secure test data management.

Managing confidential data

As part of test data management operations, a test data management solution assists CIO and CISO teams in administering security controls like as data masking, authorization, authentication, fine-grained data access management, and audit logs in downstream environments. This enables organizations to swiftly comply with compliance and data privacy standards when delivering test data while also minimizing data friction for AppDev and software test teams.

What Is The Current State of Test Data Management Tools?

Data from tests is required.

For software testing early in the SDLC, modern DevOps teams require high-quality test data based on real-world production data sources. This enables development teams to bring high-quality applications to market at a faster and more competitive rate.

Information for DevOps

Despite the fact that many organizations have implemented agile software development and DevOps approaches, there has been a lack of investment in test data management technologies, which has hampered innovation.

Boost DevOps Initiatives

Modern DevOps teams are concerned with increasing system availability, decreasing time-to-market, and minimizing costs. By dramatically enhancing compliant data access across the SDLC, test data management enables organizations to accelerate important initiatives such as DevOps and cloud. Software development speed, code quality, data compliance, and sustainability initiatives all benefit from test data management.

Common Test Data Issues

Application development teams want quick, dependable test data but are limited by the speed, quality, security, and cost of transporting data to environments during the software development lifecycle (SDLC). The most typical issues that organizations encounter when it comes to managing test data are listed below.

Provisioning test environments is a time-consuming, manual, and high-touch operation.

Most IT organizations use a request-fulfillment approach, which means that developers' and testers' requests are queued behind others. Because creating test data requires substantial time and effort, provisioning new data for an environment might take days if not weeks.

The time it takes to turn around a new environment is frequently exactly proportional to the number of people involved in the process. In most cases, four or more administrators are engaged in setting up and provisioning data for a non-production environment. This procedure not only strains operations staff but also causes time drains throughout test cycles, reducing the speed of application delivery.

High-quality data is lacking in development teams.

Development teams frequently lack access to purpose-fit test results. A developer, for example, may want a data set at a given moment in time, depending on the release version being tested. However, due to the intricacy of refreshing an environment, one is frequently compelled to operate with a stale copy of data. This can lead to lost productivity as a result of time spent resolving data-related issues, as well as an increase in the likelihood of data-related faults leaking into production.

Data masking complicates release cycles.

Data masking is necessary for many applications, such as those that process credit card numbers, patient records, or other sensitive information, to ensure regulatory compliance and safeguard against data breaches. According to the Ponemon Institute, the average cost of a data breach (including cleanup, customer churn, and other losses) is $3.92 million. Masking sensitive data, on the other hand, frequently adds operational overhead; an end-to-end masking procedure may take an entire week due to the difficulty of ensuring referential integrity across various tables and databases.

Storage prices are constantly rising.

This causes IT organizations to make several redundant copies of test data, resulting in inefficient storage use. Operations teams must manage test data availability across many teams, apps, and release versions in order to meet concurrent needs within the constraints of storage capacity. As a result, development teams frequently compete for restricted, shared environments, causing essential application projects to be serialized.

Common Test Data Types

Business CybersecurityIn the SDLC, there are four popular methods for creating test data for application development and testing teams.

  • Data on Production: Real-world data from production systems provides the most comprehensive test coverage, but it can generate friction in the absence of contemporary DevOps TDM tooling because of security controls around sensitive data.
  • Subsets of Data: Subsets of test data can enhance static test performance while saving money on computation, storage, and software licensing. Subsets, on the other hand, do not provide adequate test coverage for system integration testing. Because it is still a direct duplicate of production values, subsets inherently omit test cases and contain sensitive data.
  • Masked Data: Production data obfuscation by masking techniques enables teams to exploit current data in a compliant manner in order to swiftly offer test data that fulfills regulatory criteria such as PCI, HIPAA, and GDPR. Masking removes all data from production, uses algorithms to identify sensitive data, obfuscates PII and sensitive fields, and retains just relevant data for testing. This allows for the provisioning of realistic values in test data without generating hazardous levels of risk.
  • Synthetic Data Generation: Synthetic data Generation has no personally identifiable information or sensitive information by definition. As a result, synthetic data generation is an intriguing option for the early development of new features or model exploration of test data sets. Synthetic data generation often entails mathematically generating values or picking list items to meet a statistical distribution using algorithms. While synthetic data can aid in developing first-unit tests, it cannot substitute comprehensive data sets required throughout the testing process. Realistic production data includes valuable test cases that are required to validate the program early and frequently in order to shift left issues in the SDLC.

Test Data Management Best Practices

A holistic strategy should aim to improve test data management in the following areas:

  • Data delivery: shortening the time it takes to deliver test data to a development or testing team.

  • Data quality: satisfying high-fidelity test data criteria

  • Data security: reducing security risks without sacrificing speed

  • Infrastructure expenses: decreasing the costs of testing data storage and archiving.

  • Data Transmission: Copying real data from production environments for development or testing is a time-consuming, labor-intensive procedure that generally lags demand. Modern organizations require optimized, repeatable data delivery methods that include the following:

Automation: In most cases, modern DevOps toolchains contain technology for automating build processes, infrastructure delivery, and testing. Organizations, on the other hand, frequently lack equivalent technologies for producing test data at the same level of automation. A streamlined method to test data management reduces manual operations such as target database initialization, configuration stages, and validation checks, resulting in a low-touch approach for new ephemeral data settings.

Integration of toolsets: A modern approach to test data management should integrate technologies for data versioning, data masking, data subletting, and synthetic data synthesis. To truly enable automated declarative workflows for both infrastructure and data, technologies must have open APIs or direct interfaces.

Self-service: Rather than relying on IT ticketing systems, a modern approach to test data management harnesses automation to allow users to furnish test data on demand. Not only should self-service features include test data distribution, but also versioning, bookmarking, and sharing. Individuals should be their own test data manager, utilizing capabilities like bookmarking, refreshing, rewinding, archiving, and sharing without relying on Data Administrators or contacting IT Operations teams.

Data Accuracy

IT Operations teams must balance needs on three essential dimensions when creating test data, such as masked production data or synthetic datasets.

TEST Data Expiration Date

Operations teams are frequently unable to meet ticketed demand because of the time and effort necessary to prepare test data. As a result, data in non-production environments frequently grows stale, affecting test quality and resulting in costly, late-stage failures. A TDM approach should seek to decrease the time it takes to refresh an environment, allowing access to the most recent test data.

TEST Data Dimensions

In order to reduce storage footprints, developers may explore employing data subsets in order to enhance agility. However, subsets cannot meet all functional testing needs, resulting in missing test cases and transferring issues around the SDLC, raising overall project expenses.

A modern TDM system should strive to reduce the number of unmonitored copies of test data across environments, allow for the sharing of common data blocks across similar copies (saving on storage), and reduce manual processes with improved workflow automation to reduce operational expenses.

Data Security

Masking tools have arisen as a dependable and practical means of shielding actual data from production by replacing sensitive data fields indefinitely with fictional but plausible data values. Masking ensures regulatory compliance in test settings by totally eliminating the danger of data breaches. Organizations should consider the following requirements to make masking possible and effective:

Full solution

Many organizations fail to appropriately mask test data because they lack a comprehensive solution that includes out-of-the-box capability for discovering sensitive data and auditing the trail of masked data. Furthermore, a successful approach should consistently hide testing data while retaining referential integrity across many heterogeneous sources.

There is no requirement for development knowledge.

Lightweight masking tools that may be set up without scripting or specialized development experience should be sought after by organizations. Tools with rapid, predetermined masking algorithms, for example, can drastically minimize the complexity and resource requirements that prevent masking from being used consistently.

Masking and distribution are combined.

Because of the difficulties in transmitting data downstream, only roughly one out of every four organizations uses masking techniques. Masking operations should be strongly connected with data delivery to overcome this.

Organizations will benefit from a method that allows them to disguise data in a safe zone before quickly distributing compliant data to non-production environments such as remote data centers or public clouds.

Costs of Infrastructure

Physical Cyber Sec 2TDM teams must develop a toolset that maximizes the efficient use of infrastructure resources in light of the fast proliferation of test data. A TDM toolbox should, in particular, meet the following requirements:

  • Data aggregation: Organisations frequently keep non-production environments where 90% of the data is redundant. A TDM strategy should strive to consolidate storage and reduce costs by exchanging common data across environments, including those used for development, reporting, production support, and other use cases.
  • Archiving of data: A TDM method should make it possible to manage test data libraries by optimizing storage and enabling quick retrieval. Data libraries should be automatically version-controlled in the same manner that code versioning tools like Git exist.
  • Reduced Contention: Due to contention in shared software testing environments during working hours, most IT organizations serialize data access. Environments are frequently underutilized during the testing process since systems are left running when not in use due to the time required to load a fresh environment with configurations and test data. A modern TDM strategy should allow for the ephemeral usage of instantaneously available data from any point in time.
  • Environments for Ephemeral Data: Using their test data management tools, users should be able to bookmark data, tear down infrastructure environments, and reinstall a new data environment supplied by a bookmark in minutes. This removes shared resource contention during peak times, allows for resource freeing during off-peak hours, and allows for parallelizing discrete data sandbox environments.

An optimized TDM strategy can remove congestion while increasing resource utilization by up to 50%.

The Modern Method of Test Data Management

Organizations may improve how teams handle and consume suitable test data by implementing a contemporary DevOps TDM approach. IT operations can hide and transmit data 100 times faster while taking up ten times the space. What is the end result? More projects can be done in less time with fewer resources.

Release cycles and time-to-market are being shortened: It takes 3.5 days to refresh an environment versus 10 minutes using self-service.

Higher quality and lower costs: 15% vs. 0% data-related faults.

Data privacy and regulatory compliance were ensured: data was safeguarded in non-production environments.

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Stay safe out there, Linux security enthusiasts!