Introduction

Catalyx undertook a multi-year data integrity remediation project for a major large-molecule biotech client. The objective was to address critical challenges and implement comprehensive solutions to ensure data integrity, regulatory compliance, and modernization of the client’s infrastructure.

The project faced several significant challenges. The client’s infrastructure was aging, posing significant reliability and performance risks. Ensuring data integrity was crucial, particularly for maintaining regulatory compliance. Additionally, there was a clear need for modernization to keep pace with industry standards and technological advancements. Implementing changes without disrupting ongoing operations was critical, especially given the disjointed nature of the client’s global sites.

The Solution

To address these challenges, Catalyx implemented a comprehensive solution. We replaced 95% of the outdated equipment over the course of the project. The network infrastructure was redesigned to conform with the Purdue model, enhancing security and scalability. Data from various sites was centralized into a single repository. We adhered to ALCOA+ principles to ensure data integrity, focusing on making data Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available. The project was divided into well-coordinated phases to ensure seamless execution. We also identified and prioritized critical risks, implementing effective measures to mitigate them.

The Results:

The project yielded impressive results. We implemented a 2-tier high availability historian, significantly improving data availability and reliability. By adhering to ALCOA+ principles and centralizing data management, we reduced data integrity incidents by 90%. We achieved a near-total overhaul of the aging infrastructure within the project timeline. Additionally, by implementing Siemens SIPAT for process analytics, we enhanced process control and quality. Integrating with AWS Sitewide boosted compute resources tenfold, providing robust support for data processing and analysis.