Enterprise Test Data Generation.pptx

ENTERPRISE TEST DATA
GENERATION
THE FUTURE OF TEST DATA MANAGEMENT &
GENERATION | GENROCKET
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 When testing new software functionality, it is important to have access to high-quality test data. This can
be challenging due to large data volumes or different sources of data with varying permissions.
 A centralized repository of all test data will reduce testing inefficiencies and storage costs. It will also
allow developers and testers to easily find and delete test data that is no longer relevant or needed.

ESSENTIAL TEST DATA CRITERIA
 Data quality is a key element of test data management. This ensures that testers get the data they need
to complete their test cases. It also helps organizations achieve their testing goals and avoid costly
errors.
 In addition to ensuring data quality, it is essential for testing teams to identify sensitive client and
employee data before they transfer it to the testing environment. This requires an in-depth analysis of
the sensitivity of the data and the testing cases that require it.
 To overcome this issue, it is important to develop a strategy for generating quality test data that is easy
to manage. This is particularly critical when data is required for negative, edge case, or combinatorial
testing.
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 Authentication is another key element of test data generation. This ensures that the
testing process complies with corporate security and compliance regulations. It can
include usernames and passwords that are checked to ensure that only authorized
users can access the system.
 Test data can also be used to verify that a system is performing correctly. This can
include comparing the data to a set of known values or a database that enables
users to compare their data to other users and applications.

SYNTHETIC TEST DATA GENERATION
 Synthetic Test Data Generation enables testing teams to replace production data with combinations and
variations that do not exist in production. These new data sets increase test coverage and reduce the
likelihood of software defects escaping into production.
 Test data generation can be a complex and time-consuming process. It requires a flexible, configurable
platform that allows testers to specify the amount and type of data they want to generate.
 In addition, it must be customizable and able to support the different requirements of different types of
testing environments. This translates into support for a variety of testing frameworks and automation
tools, as well as supporting multiple data formats.
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 The system must also provide granular control over the data it creates, enabling a wide
range of data patterns and permutations for each edge case of the tests. This enables a
higher degree of complexity than can be achieved manually, while ensuring that the
data is consistent with business rules and quality expectations.
 Synthetic test data generation can also be used to support machine learning (ML)
training. This is especially helpful for visual AI applications that need to model dynamic
humans and objects in their context.
 GenRocket is a self-service synthetic test data generator that automates the process of
creating granular, domain-specific simulated synthetic data. The platform is based on
the same algorithms used by leading AI experts to train neural networks, providing a
powerful tool for generating high-quality and high variance simulated synthetic data
for ML applications.

GDPR TEST DATA
 If you’re a software tester, you probably know that you have to be careful about using personal data
during your testing. This is especially true in light of the EU’s GDPR regulations.
 In order to be compliant with the new regulation, you’ll have to ensure that all the data that’s gathered is
protected and only used for its intended purpose. This means that, for example, you won’t be able to use
customer data or other personally identifiable information (PII) in your test cases without explicit consent
from customers.
 This is particularly risky, as many companies use production data for application testing purposes. This is
why it’s important for testing teams to understand what GDPR means and how it can impact their
processes.
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 Despite these risks, there are still plenty of options for enterprises to ensure they don’t violate the rules.
For starters, you can avoid using live customer data by incorporating synthetic test data into your
process.
 The GDPR is an incredibly complex set of rules that apply to any organization that collects or processes
personal data in the EU. This includes organizations that provide products or services to the EU or have
customers in the EU.
 While the GDPR is not a perfect piece of legislation, it does offer some guidance and frameworks for
data protection compliance. For example, it requires a privacy policy that clearly states why data is being
processed and what the data subject can do to prevent its use. In addition, it outlines specific guidelines
for how consent should be obtained before the data is collected or used.
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TEST DATA MANAGEMENT
 Enterprise test data generation is a crucial component of modern software development practices,
helping teams deliver reliable applications that will run smoothly on production deployment. To do so,
testers need to have access to realistic data that matches the nuances of real-life applications.
 However, sourcing and storing this data can be a complex task. It can also require a lot of time, which
can negatively impact the testing process.
 To overcome this challenge, organizations can implement a test data management strategy that includes
centralized test data storage, masking, and security measures. This enables them to meet compliance
and security requirements for personal identifiable information (PII) while still maintaining quality as-
close-as-real test data.
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 The data required for this purpose is scenario-based, which can make it difficult to manage. Hence, a
central repository of data that can be accessed in minutes by the team and matched with the exact tests
they need to run is critical for efficient testing.
 Additionally, the centralized test data repository can reduce the overall test cycle time by enabling faster
and more frequent testing of new scenarios and boundary conditions. This can help to lower the cost of
a testing effort and accelerate deployment, too.
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THANK YOU
 Address: 2930 East Ojai Ave Ojai, CA 93023 USA
 Email: info@genrocket.com
 Website: https://www.genrocket.com
 Phone Number: (805) 836-2879
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Enterprise Test Data Generation.pptx

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