Are you aiming to modernise your test data strategy in 2024? Is test data holding back your software quality, privacy and delivery speed? Whether you’re migrating from a legacy platform or seeking components to complete an enterprise test data strategy, Curiosity believe that we offer the tools, techniques and expert guidance you need.
Our team have been creating test data solutions since 1995, and our enterprise test data platform is the culmination of this decades-long history. We believe that our tools and services offer you a range of differentiating capabilities. Below, we summarise just 12.
1. Partnership with test data experts:
Curiosity are pioneers in test data management and automation. Our team have crafted market-defining solutions since founding their first data start-up in 1995. We offer a rare combination of project experience, cutting-edge software, and a history of innovation, equipping us to deliver your test data needs.
While some test data vendors were founded in the last 5–10 years, our team have been creating valuable solutions for over 28 years. Other vendors might have acquired test data tools and services as part of a large portfolio. By contrast, test data is Curiosity’s focus area of expertise.
We further bring an ethos of partnering with customers, working collaboratively to solve their hardest problems in testing and development. Our expert team will offer you specialist guidance to achieve test data success, leaving neither you or our software “on the shelf”.
2. New functionality and future-proofing:
We commit time and resources to test data research and development, and have spent 28+ years developing new approaches to test data.
This ensures that our test data tools support new data types and integrations, while providing the functionality and performance required by our customers.
By contrast, some historic and non-specialist test data vendors might have reduced funding to test data R&D, for instance when a test data tool has been acquired as one tool in a broad portfolio.
3. Completeness of vision within test data:
Test Data Automation is not a point solution that offers a single utility like data masking, provisioning or generation. Instead, it offers a comprehensive set of test data activities. This includes data profiling, masking, subsetting and generation, alongside data comparisons, allocation, cloning and more:
4. Completeness of vision beyond test data:
Curiosity’s vision and expertise extends beyond test data, encompassing all of software delivery. We offer integrated technologies that identify emergent risks in software delivery, before creating user stories, automated tests, data and environments to mitigate them.
We do not treat test data in isolation from requirements and tests. Instead, we focus on creating up-to-date test data as changes are made across the whole software delivery ecosystem. This removes bottlenecks and barriers in software design, development and testing, enabling true quality at speed.
5. Built for automated and agile ways of working:
Test Data Automation aims to eradicate test data bottlenecks. It is built for environments that consist of parallel teams, automation frameworks and CI/CD pipelines.
With Test Data Automation, parallel teams and frameworks can trigger reusable data activities on demand.
Each configured test data activity is reusable on demand and can be exposed to self-service forms, automation frameworks, CI/CD pipelines, and more. The on demand processes are then triggered on-the-fly by engineers and integrated technologies. Parallel teams and tools self-serve the data they need, in the right place, at the right time.
This avoids the 20–50% of test and development time that can be lost to data-related activity.
6. Distributed skills and truly on demand data:
Test Data Automation aims to move organisations beyond a time-consuming “request and receive” approach to data provisioning. Teams do not need to submit constant data requests to a central data provisioning team. Nor do they rely on a central team to configure processes for every subtly different data request.
Instead, the work completed by test data engineers becomes reusable from a central portal. Manual and automated data requesters then parameterise and trigger the reusable data jobs on-the-fly. They receive the rich and compliant data they need in parallel and on demand, all without overburdening infrastructure and Ops teams.
Self-service forms and open interfaces in Test Data Automation allow testers, developers and CI/CD tools to self-provision exactly the data they need.
7. Coverage-driven data delivers quality:
Test Data Automation provides quality test data that satisfies the full range of test scenarios. Instead of provisioning bloated, repetitive copies of production data, it allows enterprises to create data of exactly the right volume and variety needed. This ranges from data for unit testing, all the way to high-scale stress testing.
An automated data comparison of two environments identifies missing values in each.
Multiple techniques for data and coverage analysis ensure that Test Data Automation produces comprehensive but concise test data. Meanwhile, automated data “find and makes” hunt automatically for data needed for different test scenarios and user stories, automatically generating missing combinations to provide complete data on demand.
8. A hybrid approach to compliance:
Test Data Automation recognises that organisations cannot go fully synthetic overnight, but nor can they rely on production data in testing. It therefore enables anonymisation for compliance purposes, while in time replacing masked with rich synthetic data.
This enables organisations to satisfy compliance requirements, while also unlocking the coverage and speed gains offered by generating new data from scratch. Testers, developers and integrated technologies can access all the compliant data they need on demand, developing accurately and testing rigorously to deliver quality software at speed.
9. Consistent, heterogenous data for complex tech stacks:
Test Data Automation specialises in publishing heterogeneous data consistently across multiple systems, as required in end-to-end and integration testing. As technology stacks continue to grow more complex, Test Data Automation provides integrated data, avoiding time-consuming test failures rooted in data inconsistencies.
This includes visual “Data Flow Modelling”, which designs data visually. The models assemble data generation functions and test data jobs in intuitive flowcharts, passing parameters from one publish to the next to create consistent data:
A data flow model auto-generates payment message files.
Test data processes can similarly be configured for multiple systems, passing data and parameters between processes during publishes. This leverages Test Data Automation’s extensive range of connectors, create consistent data for databases, files, messages, mainframes and more.
10. Data Flow Modelling:
In addition to creating data that links consistently across systems, Data Flow Modelling offers a range of additional benefits:
- It generates optimised data for systems with complex variables.
- It shifts test data design “left”, while linking the creation of data to the generation of user stories, test cases and automated tests.
When dealing with complex rule sets, the visual, flowchart-based data design provides an intuitive alternative to assembling functions. It allows data engineers to create data accurately for highly complex systems, modelling their logic piece-by-piece in visual diagrams.
The flow-based data generation then applies optimisation algorithms to generate the smallest set of combinations needed to “cover” the full spread of modelled combinations, or to target certain “paths” through the flow. These paths are equivalent to data combinations and test cases.
A data flow model generates a “covered” set of payment messages, along with tests for the payment system.
Using Test Modeller, the same flowcharts generate user stories, test cases and scripts. This creates traceability between test data and the software delivery lifecycle. It enables the creation of complete and accurate data, maintaining data automatically as user stories and tests evolve.
Test Data Automation has been built on modern technologies, and applies a range of techniques used to maximise performance. This includes load balancing, multi-threading, and parallel processing, as well as containerised deployment and scalable microservices architecture.
This high performance means that Test Data Automation can produce the volumes and variety of data needed by enterprises today, for instance in high-volume stress testing.
12. Fully extensible, from Mainframe to Cloud:
New data types emerge all the time, and persist alongside legacy data sources at enterprises. Test Data Automation is therefore an open and extensible technology, designed to work with the wide range of heterogeneous data types found at enterprises today.
Test Data Automation can publish data consistently across databases, APIs, files, messages, frontends, and an x3270 emulator. VIP, Curiosity’s visual workflow tool, further lets you build custom connectors quickly, assembling reusable components to publish data consistently across integrated technologies.
Ready for change?
This article has focused on just 12 facets of Curiosity’s test data automation tools and services. There are many more we could have discussed, from our AI-based test data capabilities, to wide-reaching tools for understanding data, and our intuitive web GUI.
Are you hunting for particular test data capabilities? Or perhaps you’re aiming to migrate from a legacy platform? Talk to us to learn how Curiosity can help you meet your goals for software quality, data privacy and delivery speed.
About the author: Thomas Pryce is the Communications Manager at Curiosity. He has been with Curiosity since 2018, where he enjoys researching and advising on test data, test automation, and SDLC transformations.