3-step guide to smarter test data management
Curiosity’s 3-step approach to test data management allows you to gain a deep understanding of your...
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Many enterprises are held back by the limitations of legacy test data tools, often without knowing it. In this guide, we compare outdated solutions with Enterprise Test Data®, highlighting how our modern approach to test data management (TDM) resolves common challenges with greater speed, scalability, and AI acceleration.
Legacy TDM tools were introduced in the 1990s and early 2000s to support manual testing by anonymising, subsetting, and copying production data to non-production environments. These tools focused on data privacy and storage efficiency, using “mask and copy” methods to reduce risk and cost.
Modern software delivery has introduced new demands, including agile methodologies, CI/CD pipelines, automated testing, and the need to support cloud-native architectures and diverse data types. The rise of Artificial Intelligence (AI) and Machine Learning (ML) enablement has further increased the need for high-quality, representative, and compliant synthetic data.
Legacy TDM tools, built for a different era, lack the flexibility and scalability required today. As a result, they often become bottlenecks in environments that demand speed, precision, and innovation. The world has changed, your test data strategy should too.
As software development has evolved, legacy test data tools have increasingly struggled to meet the demands of modern software delivery, particularly in areas such as AI/ML enablement, cloud-native environments, and real-time data provisioning.
Legacy TDM tools often lack native support for the diverse and evolving range of data sources used in modern architectures. This includes limited compatibility with NoSQL databases, cloud-native storage, and other emerging technologies. As data ecosystems become more complex, this gap creates integration challenges and restricts the ability to deliver representative test data across the full technology stack.
Today’s complex and resource-intensive data operations are a common issue for Legacy TDM tools. Organisations relying on outdated tools may face slower processing times, increased system strain, and a higher risk of failures during critical TDM tasks. It's essential for businesses to assess how well their TDM tools can handle growing data volumes and performance expectations.
Integration with modern automation frameworks and CI/CD pipelines can create significant barriers for those using legacy tools. The lack of native support for APIs and automation features means enterprises may face complex, time-consuming efforts to build custom integrations, requiring specialised skills and resources.
In agile environments, test data must be easily reusable on demand to maintain flow and avoid bottlenecks. Without this agility, organisations risk overwhelming their test data teams, who may struggle to keep up with the continuous and evolving data requests. For users of legacy TDM tools, enabling self-service capabilities often presents a significant challenge. This creates a reliance on test data engineers whenever teams need data from different environments, stalling progress and limiting the flexibility that agile teams require.
Modern development requires a range of integrated utilities to create and serve compliant test data, far beyond the methods used in the 2000s. Point solutions for masking and subsetting are no longer enough. Test data strategies must support manual requesters, automation frameworks, and CI/CD pipelines. Organisations should ensure their TDM vendor offers an integrated solution for seamless data allocation across teams and workflows.
AI initiatives require clean, unified, and compliant data streams, but many enterprises struggle with preparing and integrating data across diverse systems and formats. With pressure to deliver higher ROI with leaner teams and tighter budgets, the failure to successfully adopt AI can lead to inefficiencies, delayed test data workflows, and rising costs. Regulations like GDPR and the AI Act demand stringent governance. Without automated, robust compliance practices, organisations risk falling behind and facing potential breaches.
At Curiosity Software, we’ve redefined the global standard for test data management. Our AI-enhanced Enterprise Test Data® platform enables organisations to fully harness the power of their data, overcoming the limitations of legacy tools, navigating complex environments, and meeting regulatory demands at scale.
Enterprise Test Data supports a comprehensive range of data types, including databases, files, and messaging formats, from legacy systems to the most advanced technologies. This versatility is facilitated by a robust set of techniques designed to efficiently pull and push data across various formats. Enterprise Test Data remains fully extensible and adaptable to evolving data types and technologies, offering a future-proof platform that meets the dynamic needs of enterprise environments.
Curiosity has leveraged advanced technologies to develop a lightweight, scalable, and high-performance Enterprise Test Data platform. Cloud-native and containerised deployment ensure horizontal scalability, while performance-enhancing techniques such as load balancing, multi-threading, and parallel processing maximise efficiency. A jobs queue further enables the concurrent execution of tasks, seamlessly processing increasing data volumes and parallelised data requests.
Enterprise Test Data is designed to seamlessly integrate with environments featuring parallel teams, automation frameworks, and CI/CD pipelines. Testers and developers can easily parameterise and reuse test data jobs through intuitive self-service forms, with the added flexibility of exposing these jobs to automation frameworks and CI/CD pipelines. A variety of configurable methods enable test automation frameworks and CI/CD pipelines to trigger reusable test data jobs dynamically.
Enterprise Test Data offers a high level of flexibility with fully reusable utilities that can be parameterised at any time. Test data jobs can be combined and triggered by both manual and automated data requestors, eliminating the dependency on overburdened test data teams, and addressing new requests without creating provisioning bottlenecks. The process of exposing reusable jobs through Enterprise Test Data’s central portal is both fast and straightforward.
Enterprise Test Data provides a comprehensive suite of utilities to generate, manage, and allocate complete and compliant data on demand. These capabilities include data generation, masking, cloning and subsetting. Additionally, a comprehensive range of utilities ensures that parallel data requesters can access this data without delay, even in dynamic environments. These utilities include on-demand data virtualisation and orchestration, as well as virtual sandboxing and data allocation.
AI initiatives require clean, unified, and compliant data streams. Enterprise Test Data addresses these challenges by leveraging AI to enhance discovery, analysis, and provide actionable insights. This enables organisations to anticipate issues, optimise processes, and ensure proactive compliance across the whole enterprise. With AI-driven self-service capabilities, teams can access the right data instantly and seamlessly, reducing manual effort and wait times.
Simplify complex application landscapes and provide confidence and clarity at every step of your test data management journey with our feature complete, end-to-end Enterprise Test Data® platform.
Over 250 integrations
Metadata management
Data cataloguing
Automated discovery
Contextual tagging
Sensitive field recognition
Data profiling
Visual modelling
Full RPA Support
Data provisioning
Synthetic data generation
Data masking
Data virtualisation
Data cloning
Data quality insights
Policy enforcement
Dynamic alerts
Trend analysis
Proactive governance
Automated remediation
Data traceability
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
Build a complete picture of your application landscape, bringing thousands of data sources into a single view, fostering effective test data management strategies.
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