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Test Data Automation

Part 2: Scalability Requirements for Managing the Full Data Provisioning Life Cycle

In Part 1 of this series, we focused on how to leverage generative AI (GenAI) tools for provisioning synthetic data to ensure data quality in a complex enterprise environment. It described the limitations and risk factors presented by GenAI tools and their Large Language Models (LLMs). In Part 2, the essential factors for provisioning data on a global scale are examined along with strategies for leveraging GenAI using a single data platform at enterprise scale.

Part 1: The Impact of GenAI on Data Quality in Complex Data Environments

Overview: GenAI Enterprise Adoption and Risk Factors

According to a recent study by PagerDuty, 98% of fortune 1000 companies are experimenting with GenAI. At the same time, most are taking a cautious approach as they establish appropriate use cases, guidelines, and quality standards to govern its deployment. There are many risks associated with GenAI and they are giving many executive leaders cause for concern.

Provisioning test data for workflow testing in software is fraught with difficulties due to several inherent challenges. The traditional method of copying and masking production data for workflow testing can be problematic because developers and testers have little or no control over the data variations contained in the test dataset. It’s impossible to validate business rules and boundary conditions without some level of control over data variety. This often leads to manual data creation to augment production data and adds time to the provisioning process.

In today's software-driven world, efficient testing is critical. Traditional Test Data Management (TDM) practices, while familiar, come with limitations that hinder testing effectiveness and cost efficiency. Enter GenRocket, a groundbreaking solution that not only addresses these limitations but also delivers exceptional value and unmatched Return on Investment (ROI). In this business case, we'll delve into the compelling reasons why GenRocket is the ultimate choice for businesses seeking to optimize their testing processes.

The traditional paradigm for provisioning data for software testing is evolving. What the industry currently refers to as Test Data Management (TDM) is changing with the times. Everything associated with the software release pipeline is being automated and integrated, except, that is, for the traditional and monolithic TDM model. With the help of synthetic data and Test Data Automation (TDA), software development and testing teams can unlock new levels of quality and efficiency.

Some analysts believe that the market for machine learning may surpass $9 Billon USD this year. The surge in growth in the ML market is exponential as new avenues for software development and applications emerge. With this surge in new applications comes the need for massive volumes of data to train ML models to perform at a high level of accuracy and consistency. Here, we present an overview of the role synthetic data can play in training machine learning algorithms. And we’ll identify the best applications for GenRocket’s Synthetic Test Data Automation platform in this rapidly growing industry.

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