GenRocket Blog

GenRocket Blog

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 our modern, data-centric world, organizations encounter numerous challenges when testing critical financial systems and applications. Managing vast amounts of data for testing these applications while ensuring data privacy, consistency and integrity can appear daunting. However, with the right tools and strategies, these challenges can become opportunities for innovation and success.

In the ever-evolving landscape of healthcare IT, the ability to process enormous volumes of information and securely exchange sensitive data within a diverse ecosystem has become an industry requirement. While healthcare payers are expected to account for the largest industry segment, many participants in the healthcare ecosystem rely on the security and accuracy of patient data exchanged between their systems.