Data Governance and Data Integrity (DI)

Introduction:

Data governance and data integrity (DI) are important elements in ensuring the reliability of data and information obtained in the production and control of pharmaceutical products. The data and information should be complete as well as being attributable, legible, contemporaneous, original and accurate, commonly referred to as meeting “ALCOA” principles.

In recent years, the number of observations made regarding the integrity of data, documentation and record management practices during inspections of good manufacturing practice (GMP), good clinical practice (GCP) and good laboratory practice (GLP) has been increasing.

Possible causes for this may include

(i)                  too much reliance on human practices;

(ii)                 the use of computerized systems that are not appropriately managed and validated;

(iii)               failure to adequately review and manage original data and records.

What is Data Integrity?

data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate (ALCOA).

Principles of data integrity and good documentation practices:

Ø  There should be a written DI policy

Ø  Senior management is responsible for the establishment and implementation of an effective quality system and a data governance system. This applies to paper and electronic generated data.

Ø  Data should be Attributable, Legible, Contemporaneous, Original, and Accurate (ALCOA) and be Complete, Consistent, Enduring, and Available (+). This is generally referred to as ALCOA+.

Ø  The quality system, including documentation such as procedures and formats for recording data, should be appropriately designed and implemented to provide assurance that records and data meet the principles contained in this guideline.

Ø  Data governance should address data ownership and accountability throughout the life cycle and consider the design, operation and monitoring of processes/systems to comply with the principles of DI, including control over intentional and unintentional changes to data.

Data governance systems should include:

ü  training in the importance of DI principles;

ü  the creation of an appropriate working environment;

ü  active encouragement of the reporting of errors, omissions and undesirable results.

 

References:

https://www.who.int/medicines/areas/quality_safety/quality_assurance/QAS19_819_data_integrity.pdf

https://www.fda.gov/files/drugs/published/Data-Integrity-and-Compliance-With-Current-Good-Manufacturing-Practice-Guidance-for-Industry.pdf