The use of data has been part of the FDA’s guidance and regulations for many years. Recent uses include Quality by Design, Process Analytical Technology, Process Validation, Data Integrity and Quality Metrics. Advances in information technology have greatly increased the opportunity to use data to develop and improve processes including the benefits of “Big Data”.
The renewed interest in data integrity raises questions regarding what is data integrity and how to assess it. Much discussion of quality metrics has ensued regarding how to respond to the FDA’s guidance and its elements. It is also critical to visualize and analyze data to solve problems and improve processes. Three critical aspects of data analysis are the context for the problem and business objectives, the visualization of the data and the analytical modeling of the data. The critical question is how to go about this in a systematic and effective manner.
In this workshop each of these data issues and questions are addressed. Emphasis is on providing practical solutions that can be implemented promptly. It is pointed out that these initiatives are inter-related and should not be treated separately. It is shown how a data-centered culture can be built to implement and sustain these initiatives over time.
Learning Objectives:
Develop awareness and understanding regarding role and importance of data and FDA Guidances regarding data-based approaches and methods
Understand the importance of data pedigree and how to assess it
Data Integrity – What is it, why is it important and how to assess it
How to identify and overcome the challenges and make effective use of quality metrics
How to use concepts, methods and tools that enable the management and execution of data-rich projects
How to build a data-based culture that enables the integration of these inter-related data challenges enabling the initiation and sustaining of these initiatives over time.
In all case guiding principles, tips and traps are presented and discussed.
Who Should Attend:
Quality Department Management and Staff
Research and Development Departments Management and Staff
Engineering Departments Management and Staff
Manufacturing Departments Management and Staff
Production Departments Management and Staff
Regulatory Affairs Departments Management and Staff
Lab Testing Personnel
Biologists and Microbiologists
Supply Chain Departments Management and Staff
Process Improvement Professionals
Product Development Personnel
Finance Departments
Everyone who collects, analyzes and presents data to solve problems and make improvement