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Big Data Program

Mission

Accelerate the development and adoption of data-driven innovation and standards to increase the speed and resilience of biopharmaceutical manufacturing.

Vision

Data-driven technologies optimize the productivity of biopharmaceutical manufacturing and accelerate the delivery of high-quality medicines to patients.

A woman in protective clothing monitors a large piece of equipment

What Is Big Data and How Will It Benefit the Industry?

“Big Data” encompasses advanced uses of data and data-driven technologies that could transform the manufacturing of biopharmaceuticals and better meet the needs of biomanufacturers, suppliers, regulators, and patients. Benefits to and impacts on the industry include:

  • Process Design: Develop flexible systems, operations, and facilities for product design, manufacturing, and distribution
  • Manufacturing Operations: Optimize the efficiency and sustainability of manufacturing operations
  • Quality Operations: Ensure medicine quality and consistency
  • Supply Chain and Logistics: Improve product management and resilience across the supply chain; prevent and mitigate delays in manufacturing availability
  • Tech Transfer: Share essential product and process knowledge
  • Facilities and Engineering: Harmonize equipment and materials data from disparate sources of information

Big Data Program Structure

Workstreams

The work of the program is organized around workstreams—topical focus areas—and cross-cutting key capabilities themes. 

Data Creation

Support uniform collection and exchange of Big Data to harness its value.

Key Capabilities Themes
  • Interoperable plug-and-play systems: Functionality that enables rapid equipment interchangeability and the identification of equivalent consumables
  • Advanced Sensors: Real-time assessment of CQAs and highly interactive electronic batch records to build models and predict deviations
Data Transformation

Quickly interpret data patterns and generate insights across multiple applications

Key Capabilities Themes
  • Interoperable Plug-and-Play Systems: Functionality that enables rapid equipment interchangeability and the identification of equivalent consumables
  • Advanced Sensors: Real-time assessment of CQAs and highly interactive electronic batch records to build models and predict deviations
  • Privacy Preserving Computing: Enables sharing of proprietary and sensitive data to facilitate accurate model training and in-depth data analysis and prediction
Data Storage

Facilitate the process of storing and merging data to collaborate and share information more easily

Key Capabilities Themes
  • Ontologies: Establishes a standard terminology to simplify data integration and facilitate the transfer of knowledge
  • Data Schema: Standardized file formats and tech transfer packages to enable rapid transfer of recipes, SOPs, and process data
  • Privacy Preserving Computing: Enables sharing of proprietary and sensitive data to facilitate accurate model training and in-depth data analysis and prediction 
Prediction from Data

Forecast and mitigate potential risks to maintain quality and efficiency in the biopharmaceutical manufacturing process

Key Capabilities Themes
  • Digital Twins: Equipment-specific models that aid process design through prediction and real-time control adjustments
  • Data Schema: Standardized file formats and tech transfer packages to enable rapid transfer of recipes, SOPs, and process data
  • Privacy Preserving Computing: Enables sharing of proprietary and sensitive data to facilitate accurate model training and in-depth data analysis and prediction
Control from Data

Improve quality management for pharmaceutical manufacturing and distribution

Key Capabilities Themes
  • End-to-End Real-Time Data Connectivity: Bi-directional data flow and customizable datasets between development and quality operations through the integration of equipment and data interfaces
  • Interoperable Plug-and-Play Systems: Functionality that enables rapid equipment interchangeability and the identification of equivalent consumables
  • Data Schema: Standardized file formats and tech transfer packages to enable rapid transfer of recipes, SOPs, and process data

Progress and updates

High-level program timeline

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March 2023

Convened NIIMBL Big Data Program Re-envision Workshop to develop a roadmap for the future of the Program 

2021 – 2022

Funded projects to advance standardization and contextualization, real-time control of critical quality attributes (CQAs), multivariate sensors and analytics, rich data generation, and bioprocess modeling and simulation

September 2019 - 2020:

Convened workshops to identify needs and opportunities, define priority workstreams, and plan first 2 years of the program 

Early 2019

Technical Activities Committee (TAC) prioritized Big Data as an area of focus; industry leaders and subject matter experts met at 2019 National Meeting to begin to define a program 

Program Participants

NIIMBL Program Leader

Roger Hart, Senior Fellow

Roger Hart

NIIMBL Senior Fellow

NIIMBL Scientific Program Manager

Jim Saylor, Director, Project Management Office

Jim Saylor

Director, Project Management Office

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