Data management and data analytics for the process industry

Highlights

• Role of data management and data analytics for the process industry
• Data management roadmap
• Data analytics roadmap
• System validation: Part 11 compliance
• Case study: Data management and data analytics to bridge the gap between process development and manufacturing

Data management/data analytics strategy for the process industry

During the past years, lab digitalization has increased, and sensor technology has improved tremendously. The management and analysis of accumulated data from R&D, Manufacturing, and Quality Control (QC) have an immense potential to speed up time-to-market and optimize your manufacturing processes in terms of quality and economics.
However, only well-prepared and analyzed data leads to process knowledge and, finally, to process control and continuous improvement. Thus, a robust and efficient data management/data analytics strategy is one of the most valuable concepts for the process industry. The data analytics strategy defines how to:

• Conduct data management for process development, manufacturing and
• Efficiently analyze the relation between process parameters and product quality, and key performance indicators.
• Develop platform knowledge by data analytics and mathematical modeling to continuously improve manufacturing platforms

Data Management Roadmap

Step 1: Stakeholders and User Requirements Specifications

Poorly handled evaluation efforts create serious issues during every step of your implementation. Proper conceptual preparation is key since ill-defined requirements may lead to delayed completion. Get input from all stakeholders: from IT, process development, manufacturing science to quality and operations. You should carefully check all legacy systems to be integrated and which data analytics functionality is required. Systems interfacing to bioprocess data management and analytics platforms should also be examined. These systems include:

• Process Control Software
• Laboratory Information Management Systems (LIMS)
• Electronic Lab Notebooks
• Historians
• …

The right implementation strategy ensures that you are better prepared by maintaining focus on the original scope of your project and guarantees that your employees are properly trained and prepared. Designing a user requirement specification involves mining relevant data streams and classification in relevant and nonrelevant data streams regarding your company's KPIs. But don't forget that data management is not an end in itself: Carefully evaluate your data analytics requirements that you envision to reach your goals.

Step 2: Executing Implementation

Implementation can be executed as a single-step implementation or step-by-step implementation. The single-step Software Implementation can be ideal for smaller operations and businesses with up to 100 users. All users are required to migrate to the new system at once. This allows you to focus on your project scope and implementation parameters by offering simple and straightforward handling of your processes.

Migrating to a new system step-by-step allows you to implement certain key features earlier while ensuring that possible complications are isolated from working processes. While this approach is more flexible than the single-step implementation, it may take longer. You can find more on data management and analytics implementation strategies here. 

Step 3: Data Migration

The proper handling of data migration is another important aspect to consider. Evaluate data migration from all sources (legacy databases, spreadsheets, paper-based recordings, etc.). For paper-based documents, evaluate technologies such as optical character recognition (OCR) and document crawling. Migrating all available data into the new system leads to immediate user acceptance after successful data management and ensures working efficacy.

Data Analytics Roadmap

The main workflow for  process data analytics can be summarized in the following steps:

• Data alignment
  make all relevant data sources available for data analytics
• Data mining
  mine relevant information to achieve your specific analytics goals
• Data consistency testing
  test for consistency in the data set
• Identify a hypothesis
  identify correlations/models using univariate- and multivariate statistics and build a hypothesis
• Implement change or design an experiment
  dare to prove your hypothesis by implementing a change or design an experiment to prove your hypothesis

Software system validation: Part 11 Compliance

21 CFR Part 11 is the guideline of the U.S. Food and Drug Administration (FDA) regarding Electronic Records, Electronic Signatures, and Electronic Copies of Electronic Records. The scope of Part 11 is limited to records that are maintained in an electronic format instead of a paper format.

Part 11 is necessary if you would like to use data management and data analytics software in a GMP environment, e.g. a (bio-) pharmaceutical Quality department and especially for drug discovery.

Below you find the 7 main receivables for an FDA Part 11 audit

1. Validation
   All GMP relevant computerized systems have to be validated to ensure system accuracy, reliability, integrity, availability, and authenticity of required records and signatures.
2. Audit Trail
   All electronic record changes, adaptions, events, or modifications have to be monitored.
3. Access Protection
   System access must be controlled. It is mandatory to define the kind of system (open or closed system).
4. Copies of Records
   The system provides a method to copy the audit trail in a human-readable format.
5. Record Retention
   Electronic records should be stored, protected, archived, and provided by the system
6. Electronic Signature
   The system has to provide the possibility to perform electronic signing just by trained and authorized users
7. FDA Voucher
   A written voucher has to be provided to the regional FDA-office, to ensure the validity of the electronic signature

About Intravacc

Intravacc is a not-for-profit R&D organization. We develop and optimize vaccines, vaccine processes, and vaccine technologies. We aim to substantially reduce the development risks and costs of new vaccines to contribute to global health and equity in access to vaccines worldwide. We achieve our aim by developing and improving vaccine design, production processes, analytics, and technologies. In our state-of-the-art facilities, our experienced R&D institute takes your discovery up to Phase I/II clinical trials. Furthermore, we share and transfer our knowledge and technologies to public and private partners worldwide and work on collaborative R&D.