GenRocket Blog

Introduction

In the rapidly evolving landscape of insurance technology, Guidewire stands as a cornerstone solution, offering a comprehensive suite for managing policies, claims, and billing. As insurance companies increasingly adopt and customize Guidewire’s platform, the need for efficient, secure, and comprehensive test data automation solutions becomes paramount. GenRocket presents an advanced test data solution to address the unique challenges faced by Guidewire users in generating, automating, and securing test data throughout the software development lifecycle.

Executive Summary

A Fortune 500 company and world leader in diagnostic testing services faced significant challenges in testing their Patient Registration System (PRS). Manual data creation processes and limitations on using production data hindered their ability to conduct comprehensive performance testing in real-world conditions.

In the fast-evolving world of technology, the demand for high-quality software has never been greater. Companies are under constant pressure to release new product features that not only provide a competitive edge but also enhance the customer’s digital experience. Amid this backdrop, the need for a more secure, automated, and agile approach to Test Data Management (TDM) has become paramount. Enter Test Data Automation (TDA), a fresh new approach that promises to transform the way organizations handle test data, ensuring security, compliance, and software testing efficiency.

Part 3: How a Prominent Legal Firm is Deploying the Technology

In this third part of our series on leveraging GenAI for controlled and accurate synthetic data generation, we delve into a real-world application of GenRocket's platform, integrating the principles discussed in the previous two parts of the series. We will explore how GenRocket, combined with generative AI (GenAI), can address complex data provisioning needs, delivering robust synthetic data solutions at an enterprise scale by presenting a comprehensive use case deployed by a prominent legal firm.

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.

Explore the potential of synthetic data in helping financial services firms use AI to meet critical services objectives.