Recommended

Teva close to API business sale
Streamlined rAAV purification enables efficient upstream analytics – access now
First new IPF treatment in US for a decade
Webinar: Radiopharmaceuticals’ time-to-result challenge
AstraZeneca/Daiichi cancer drug boost
Takeda to move away from cell therapy
GSK appoints new CEO
podcasts

EPR Podcast 25 – FAIR Data in Pharma – Giovanni Nisato, Pistoia Alliance

0
SHARES

In this podcast, Giovanni Nisato, Project Manager at the Pistoia Alliance discusses data integrity and the progress towards implementation of FAIR data principles in the pharmaceutical industry.

Podcast graphic - FAIR data

[powerpress]

 

Reserve your FREE place

 


Address the time-to-result challenge posed by short shelf-life radiopharmaceuticals.

20 November 2025 | 3:00 PM GMT | FREE Virtual Panel Discussion

This webinar showcases the Growth Direct System; an RMM (Rapid Microbial Method) that improves on traditional membrane filtration, delivering increased accuracy, a faster time to result, enhanced data integrity compliance, and more control over the manufacturing process.

Key learning points:

  • Understand the benefits of full workflow microbiology quality control testing automation in radiopharmaceutical production
  • Learn about ITM’s implementation journey and considerations when evaluating the technology
  • Find out how the advanced optics and microcolony detection capabilities of Growth Direct® technology impact time to result (TTR).

Don’t miss your chance to learn from experts in the industry – Register for FREE

 

In this podcast, Giovanni Nisato, project manager at the Pistoia Alliance discusses data integrity and the FAIR data principles. In this role, he coordinates FAIR Community of Experts, which created the FAIR Maturity Matrix.

Giovanni starts the conversation by defining data integrity and explaining why it is ‘mission critical’ for the pharmaceutical industry. 

Next, the podcast introduces FAIR (Findable, Accessible, Interoperable, Reusable) data principles and explores how their adoption have evolved over the past decade.

“This year we celebrated the 10-year anniversary of the first workshop where those FAIR data principles were reformulated. And they were then published in 2016,” Giovanni says.

Since then, the pharmaceutical industry has seen a “move towards data centricity as a way of thinking.” But adoption of the FAIR data principles is not mainstream yet. 

Adoption of FAIR data principles

Additionally, while the principles are increasingly recognised in academia and R&D, their implementation in the clinical domain is lagging behind.

While the principles are increasingly recognised in academia and R&D, their implementation in the clinical domain is lagging behind”

“I would say that the implementation of FAIR data principles is perhaps starting, but there’s a lot of room for improvement in the clinical space,” Giovanni reflects.

This is one area the Alliance has focused on, producing the FAIR4Clin guide that gives a comprehensive overview on FAIR resources in the clinical domain.

Giovanni also explains the FAIR Maturity Matrix, a framework developed to help organisations benchmark and assess their systemic level of FAIR maturity. The FAIR Maturity Matrix was co-created by members of the FAIR Community of Experts of the Pistoia Alliance. Giovanni presents seven key dimensions that make up the matrix: data, leadership, strategy, roles, processes, knowledge, and tools. He also introduces the six maturity levels of the framework.

Finally, Giovanni looks ahead, outlining two priorities that are needed to accelerate adoption of FAIR data principles in the industry.

About the speaker

Interviewee headshotGiovanni Nisato is an expert in collaborative innovation management across organisations at the international level. He has over 20 years of experience in the industrial and public-private deep tech sectors. As a Project Manager with the Pistoia Alliance, he has facilitated the FAIR Community of Experts since 2022. Its aim is to co-create and maintain pre-competitive resources to foster the implementation of FAIR data principles in the life sciences ecosystem for the benefit of pharmaceutical companies, CROs, technology providers, and ultimately, patients.

 
Share via
Share via