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How to Implement an Effective Big Data Strategy

by Richard F. Shakour, Merck & Co., Mark DiMartino, Amgen, Chris Garvin, Amgen, Wilfred Mascarenhas, Eli Lilly &Co., | Nov 04, 2019

An Update on the Governance and Controls Technical Report

Big data from pharmaceutical manufacturing is being collected at an accelerated rate, beyond even the human capability to process it using traditional mechanisms (volume, variety, veracity and velocity). Process automation and data capture capabilities are pervasive, so we are generating and capturing more data than ever. There is a shift from governing and protecting data as it is being generated to governing the generation of data driven insights. In particular, there is a move to using that data to:

  • Accelerate development of new therapies
  • Provide insights into emerging risks
  • Offer affordable medicine to our patients
  • Increase internal and external diagnostics and collaborations

The Governance and Controls subteam of PDA’s Manufacturing Intelligence Task Force has been looking into these challenges, specifically, the need for governance and active controls of data and information. The team consists of representatives from business, IT and quality spanning multiple biopharmaceutical companies.

Big data challenges require rethinking conventional processes, roles and standards that ensure the effective and efficient use of information and technology to drive value. Additionally, there is a need to incorporate the predictive/prescriptive analytical capabilities emerging from the broader technology landscape in a regulated environment. The Governance and Controls team will be looking into the challenges illustrated in Figure 1.

Figure 1 Challenges and Opportunities in Capturing and Using Data Effectively

The Governance and Controls team will be publishing a PDA technical report intended to provide practical guidance and best practices needed throughout the data lifecycle. The intention is to propose processes and technologies that can generate and enable manufacturing data insights through purposeful implementation of processes, roles and standards. Furthermore, it will provide best practices in enabling organizations to obtain more reliable data. Figure 2 illustrates some of the components the team will be assessing in establishing effective data governance and controls within the data lifecycle.

Figure 2 End-to-End Data Governance and Controls (People, Process and Data)

Many companies design their big data strategies with minimal thought to data governance and appropriate controls. Typically, this enables organizations to accelerate their journey in collecting data, however, this shortsightedness leads to roadblocks when it comes to effectively analyzing and visualizing data.

Data governance and controls must have the flexibility to expand or contract as needed but must also be robust enough to ensure consistency and sustainability. The intent is to provide guidance for global use which applies to both new and existing (i.e., legacy) manufacturing big data/analytical models.