Today, many organisations rely on rudimental tools and techniques for creating and managing their test data. These outdated techniques not only hinder development speed and overall agility; they also undermine quality and the pace of change across testing and development.
Having a range of test data management (TDM) tools and capabilities isn’t enough. A good test data strategy must also address the scope of capabilities and complexity that the tooling adds to an organisation and the software delivery lifecycle (SDLC). To optimise their test data capabilities, organisations must review all aspects of their test data strategy, alongside the tools they use.
In this series of blogs on test data strategy success, we’ve covered a range of test data management topics, including data regulation, methodology, technical debt and data delivery. In this final blog, we’re covering the tooling required for delivering a complete test data strategy.
Read other parts of the test data strategy success series here:
- Test Data Strategy Success: Data Regulation
- Test Data Strategy Success: Technology and Methodology
- Test Data Strategy Success: Tech Debt & Data Delivery
Learn how you can transform the relationship that your teams and frameworks share with data by reading our Test Data-as-a-Service solution brief!
Legacy Test Data Tools
If organisations persist with tools that only deliver test data masking and provisioning, their test data problems will persist. For organisations who stick with manual TDM techniques, the situation is even worse.
Solving test data problems is not only about having the right tools but also about understanding the problems that need to be addressed. Misunderstanding these core test data problems leads organisations to adopting toolsets that don’t offer the capabilities they need.
A successful test data strategy must reduce tool complexity while also helping teams understand their data and how it flows through their system. Consider your test data strategy and the tools you use, do they help you answer these questions:
- Do you understand where PII (Personal Identifiable Information) exists, and which data needs anonymizing?
- What’s the size and shape of your data and the pace and efficiency of usage?
- Do you know where technical debt exists within your system?
- Do you have the requisite tooling that helps you understand and resolve the problems you are facing when it comes to test data?
If you can’t answer these questions, then you must consider a new approach for your test data strategy, one that utilizes modern tooling and techniques.
Learn more by watching the “Tooling to Meet The Strategy” episode of Test Data at The Enterprise by Rich Jordan:
The Tool for Success: Curiosity’s Test Data Automation
The right tool and techniques can help you form a complete understanding of your data and how it flows through your system. This is one of the core uses of Curiosity’s Test Data Automation.
Test Data Automation integrates key test data technologies into a single platform, making them available on-demand. These technologies include Synthetic Test Data Generation, Test Data Masking, Data Subsetting, Data Virtualisation, Test Data Cloning and more.
So what are some of the test data challenges you are facing that Test Data Automation can help with?
There are a range of test data use cases and solutions to consider when adhering to data regulations. For example, If you are looking to understand where PII data exists in your systems, you should look at data profiling capabilities. If you are looking to anonymize data and remove PII, you can consider data masking capabilities. Or, you might consider profiling to understand the size and shape of your data so that you can then synthetically generate it.
Curiosity’s Test Data Automation provides a quick and simple approach to masking complex data, while weaving in rich synthetic test data to combine compliant testing with quality testing. While data masking is the minimum requirements for compliance, synthetic test data is a safer approach to removing risky live data from test environments, as it creates wholly fictitious data.
This synthetic data also comes with a complete data pattern analysis utility, so that you can create synthetic data that matches production. Furthermore, all of this is automated, so you can provide complete and compliant data on-the-fly.
Compliance with data regulations can help you build a better understanding of how sensitive data is being dealt with at your organisation, and therefore help you deliver a far better and more effective test data strategy.
DevOps and Agile Methodologies
As organisations continue to adopt DevOps methodologies across the SDLC, test data management continues to be left behind. Therefore, understanding your delivery method and the needs of the test team becomes key to creating an effective test data capability within your organisation.
Organisations looking to align test data to their DevOps pipelines must consider adopting an automated test data strategy. This requires data virtualisation capabilities in order to fit in seamlessly with their ephemeral environment strategy. Curiosity’s Test Data Automation provides an automated approach to test data and is equipped for modern DevOps environments and hybrid architectures.
Test Data Automation integrates seamlessly with database orchestration engines and schedulers, while offering on-demand virtualisation of databases. Testers, automation frameworks and CI/CD pipelines can therefore deploy and access the data they need in parallel and on demand, filling their environments with rich and compliant data on-the-fly:
Test Data Automation provides fit-for-purpose test data on-demand.
They can furthermore trigger Test Data Automation’s extensive test data utilities on demand, receiving the right data, in the right place:
Test Data Automation’s integrated activities are available “just in time”, providing self-service data.
Effective implementations of test data strategies bring harmonisation between agile teams, frameworks and test assets. A successful test data tool must therefore also be agile, in order to deliver data to your systems and teams at speed. Test Data Automation is that tool.
Today, business-critical databases are growing increasingly historical, poorly documented, and poorly understood. Developers lack understanding of complex data and it’s relationships, and therefore technical debt is accrued. Organisations looking to address technical debt must better understand their databases and systems.
One key way that your organisation can start paying back technical debt is by carrying out data analysis and profiling. At Curiosity, we provide extensive capabilities in both. For instance, Test Data Automation’s database compare and data pattern analysis utilities help you maintain understanding of changing systems and data structures.
Data compare and analysis from Test Data Automation provides deeper understanding of complex systems and automatically identifies what data has been added to, removed from or edited in a database. Snapshot comparisons and high watermark comparisons can help you pay off technical debt associated with legacy and back-end systems by uncovering relationships in complex data and showing which data will be updated by new functionality.
This increased understanding provides teams the confidence needed to modernize, migrate, and integrate their pipelines with new tools and functionality, reducing technical debt. This is key to implementing a successful test data strategy at your organisation.
Check out Curiosity’s Data Comparisons in 2 Minutes overview video to learn more:
Organisations often waste a lot of time waiting for data provisioning to provide the necessary data required for parallel testing and development. If you are looking for pace and efficiency, you should use data subsetting utilities to shrink your test data volumes in order to provide a rapid, parallelised and “rightsized” approach to data provisioning.
Data subsetting from Test Data Automation provides the right volumes of test data on demand, improving provisioning time and shortening release cycles. Additionally, further time is saved during testing, as the concise data sets require less time and fewer resources to run, while testing with the smallest possible data set supports legislative compliance.
Testers, developers and automation frameworks can therefore self-service the smallest set of data needed and reduce the size of non-production data sets while retaining data variations and relationships needed for rigorous testing. Furthermore, each subsetting job is reusable, making it easier and faster to provision “rightsized” sets of data every time.
The subsets can additionally be virtualised, masked or completely synthetic as all Test Data Automation utilities can work in tandem to not only improve the speed of data delivery, but also help you deliver a successful test data strategy.
Check out Curiosity’s Data Subsetting in 2 Minutes overview video to learn more:
Data Quality and Coverage
All of the techniques we’ve covered so far would be undermined without quality data to support them. Ensuring that the data your teams are self-provisioning is high-quality is paramount to delivering quality software at speed.
Curiosity’s Test Data Automation has a range of utilities for improving data quality and coverage, including data analysis and comparisons, on-the-fly test data find and makes, synthetic data generation, and data cloning.
By utilising data analysis and comparisons you can automatically identify gaps in data density and variety, which is crucial for gaining a comprehensive understanding of your data. Utilising data find and makes can then help you generate the missing data combinations needed on demand, improving coverage and in turn quality.
With o n-the-fly test data find and makes, parallel teams and frameworks can create the data they need on demand. “Finds” search for data based on particular scenarios, while “Makes” utilise integrated synthetic test data generation to create missing data. This enables your team to create the diverse data combinations, which are often missing in manually created or production data sets.
On-the-fly “find and makes” ensure that every tester, developer and automated test comes equipped with the data they need.
Test coverage can also be further improved through the use of data cloning. With Test Data Automation’s data cloning utility, you can generate multiple instances of data combinations, guaranteeing seamless parallel execution of all your tests without any failures.
Cloning is particularly useful for automated testing scenarios that quickly exhaust their data, as it ensures new data is always readily available. This significantly enhances in-sprint test coverage as each test within a suite has the data required to execute.
Combining these Test Data Automation utilities will ensure that your organisation can create and allocate all the required data needed for rigorous in-sprint testing, boosting quality and coverage, and ensuring you deliver a successful test data strategy.
Delivering a Successful Test Data Strategy
Delivering a successful test data strategy is challenging for organisations of all sizes. However, with the right tools and a clear understanding of your data, not only can you automate your test data processes, but you can also bring your test data management up-to-date with developments in compliance, delivery practices, and DevOps toolchains.
Curiosity’s Test Data Automation can help you do exactly that by combining a range of TDM utilities within a modern test data strategy. Don’t let test data hold you back: consider the topics we’ve discussed in this blog series and how they can help you build a faster delivery pipeline at your organisation.
If you’re looking for more support or information on Test Data Automation then speak to a curiosity expect to find out how our tools and people can help you understand and resolve your test data problems.
About the author: Mantas Dvareckas, is a Digital Marketing Specialist at Curiosity Software. He holds an MSc in Digital Marketing Management, and is passionate about all things technology, gaming and Formula 1.