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Analyst View: Software engineering leaders must understand the potential of synthetic data

Software DevelopmentAnalyst View: Software engineering leaders must understand the potential of synthetic data


Synthetic data is a class of data artificially generated through advanced methods like machine
learning that can be used when real-world data is unavailable. It offers a multitude of compelling
advantages, such as its flexibility and control, which allows engineers to model a wide range of
scenarios that might not be possible with production data.

Market awareness of synthetic data for software testing has been very low and its potential has
not yet been realized by software engineering leaders. Gartner has found that 34% of software engineering leaders have identified improving software quality as one of their top three performance objectives.

However, many software engineering leaders are inadequately equipped to achieve these objectives because their teams rely on antiquated development and testing strategies. These leaders should evaluate the feasibility of synthetic data to boost software quality and accelerate delivery.

Take Advantage of the Benefits of Synthetic Data

While market awareness of synthetic data is generally low, it is rising. Compared to large
language models, synthetic data generation is a relatively mature market. Synthetically generated data for software testing offers a number of benefits including:
Security and compliance: Synthetic data can mitigate the risk of exposing sensitive or
confidential information to comply with data privacy regulations.
Reliability: Synthetic data allows for control over specific data characteristics, such as
age, income or location, to specify customer demographics. Software engineers can
generate data that matches their product’s testing needs, and update the data as use
cases change. Once generated, datasets can be retrained for reliable and consistent
testing scenarios.
Customization: Synthetic data generation techniques and platforms provide
customization capabilities to include diverse data patterns and edge cases. Since the
data is artificially generated, test data can be made available even if a feature has no
production data, resulting in the ability to test new features and inherently enhancing the
test coverage.
Data on demand: Quality engineers can create any volume of data they need without
limitations or delays associated with real-world data acquisition. This is particularly
valuable for testing features with limited real-world data or for large-scale performance
testing.

Software engineering leaders can enhance development cycle efficiency by strategically
transitioning to synthetic data for testing. This enables teams to conduct secure, efficient and
comprehensive tests, resulting in high-quality software.

Calculate ROI for Using Synthetic Data for Software Testing

Today’s challenging economic climate is driving companies to prioritize cost-cutting initiatives,
with ROI meticulously examined before any investment is made. While the benefits of using
synthetic data are evident, it’s essential to delve into the costs organizations may encounter
during its implementation.

It is vital to determine ROI that outlines the strategic significance, expected returns and methods
for mitigating risks to generate the requisite support and secure budget for synthetic data
investment.

To accurately determine ROI, software engineering leaders should include non-financial
benefits such as improved compliance, data security, and innovation. Benchmark ROI against
other investment opportunities to determine the best allocation of capital. Reassess ROI yearly
as actual data comes in and update projections to reflect any changes.
Haritha Khandabattu is a Sr Director Analyst at Gartner where she primarily focuses on AI,
GenAI and software engineering.

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