Data integrity in life sciences

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Data integrity in life sciences

Regulatory bodies now have high expectations with regard to data quality and integrity owing to the life sciences industry’s growth, globalisation and adoption of advanced technology, such as highly automated systems and storage of data in ‘The Cloud’. Good data practices will enrich the quality of data, allowing life sciences companies to make strategic decisions backed by analytics and data-driven insights.

What does data integrity mean?

According to the guidelines published by the regulatory bodies, data integrity is defined as the extent to which all data are complete, consistent, and accurate throughout the data lifecycle. The requirements for data include that they are attributable, legible, contemporaneous, original and accurate (ALCOA). Implicit in the requirements for ALCOA are that data should be complete, consistent, enduring, and available (usually referred to as ALCOA+). 

Why is there an increasing focus on data integrity compliance by regulators?

In recent years, there has been an increase in the number of violations in data manipulation and other data issues in pharmaceutical manufacturing facilities, particularly ones based in Asia during GMP inspections by the regulators. Due to such frequent violations of basic data integrity practices, regulators globally are focusing on enforcing principles and practices to ensure product quality and patient safety.

What are the consequences of data integrity failing?

Current guidance indicates that failures in data integrity can result in the following regulatory and non-regulatory consequences:

  • Frequent inspections or suspension of product approvals
  • Import bans, forced recalls, plant shutdowns, debarment
  • Criminal enforcement
  • Loss of reputation and public trust
  • Lack of strategic data insights
Approach to data integrity compliance

When implementing a programme to identify, develop, review and improve data integrity across an organisation, a focus on standardisation and procedures, risk assessment, technology and systems, and data governance will be essential. These steps should be part of a larger cultural shift driven by engagement and education.

  • Education and culture
  • Standardisation and procedures
  • Risk assessment
  • Technology and systems
  • Data governance
Conclusion

The growing issues of data integrity across life science companies means that organisations need to be able to adapt rapidly to prevent violations and regulatory consequences. Companies that fail to develop and implement suitable standards risk falling behind global regulatory requirements and may face consequences ranging from recalls and plant shutdowns to criminal charges, in addition to losing the competitive advantage of valuable data insights.

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