Data integrity is crucial in the pharmaceutical industry to ensure patient safety and product quality. It refers to data being complete, consistent, and accurate throughout its lifecycle, from generation to storage. Issues can arise from falsification, poor documentation practices, lack of system controls, and not reviewing for errors. To minimize risks, companies should follow good documentation practices like ALCOA+, implement audit trails and quality management systems, and conduct training and system validation. Maintaining data integrity builds trust between regulators and industry.

1. DATA INTEGRITY
IN PHARMACEUTICAL INDUSTRY
A BRIEF OVERVIEW
Chaitanya S Kanade.
Quality Assurance.

2. TYPE OF DATA
• DATA
Facts, figures and statistics collected together for reference or analysis.
• RAW DATA
Raw data is defined as the original record (data) which can be described as the first capture of
information, whether recorded on paper or electronically. Information that is originally captured in a dynamic
state should remain available in that state.
• SOURCE DATA
Include all information in original records and certified copies of original records used for
reconstructing and evaluating investigation.
• ELECTRONIC RECORD
FDA regulation defined as as any combination of text, graphics, data, audio, pictorial, or other
information represented in digital form that is created, modified, maintained, archived, retrieved, or
distributed by a computer system.

3. • META DATA
These are data that describe the structure, data elements, inter-relationships and other
characteristics of data e.g. Audit trails. Metadata also permit data to be attributable to an individual
(or if automatically generated, to the original data source). Metadata form an integral part of the
original record. Without the context provided by metadata the data has no meaning.
• AUDIT TRAIL
Audit trails are metadata that are a record of GMP/GDP critical information about
creation, modification, or deletion of relevant data, which permit the reconstruction of GMP/GDP
activities. Audit trails include who, what, when, why chronologically action performed.

4. 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) (USFDA)
• ALCOA rather than ‘ALCOA +’. ALCOA being attributable, legible, contemporaneous, original, and
accurate and the ‘+’ referring to complete, consistent, enduring, and available. Data governance
measures should ensure that data is complete, consistent, enduring and available throughout the
data lifecycle. (MHRA)
• “The degree to which data are complete, consistent, accurate, trustworthy, and reliable and that
these characteristics of the data are maintained throughout the data life cycle”. (PIC/S)

5. DATA GOVERNANCE
Data governance is the sum total of arrangements which
provide assurance of data integrity. These arrangements
ensure that data, irrespective of the process, format or
technology in which it is generated, recorded, processed,
retained, retrieved and used will ensure a complete,
consistent and accurate record throughout the data lifecycle.

6. DATA LIFECYCLE
The data lifecycle refers to how data is generated, processed,
reported, checked, used for decision-making, stored and finally
discarded at the end of the retention period.

7. DATA QUALITY
ALCOA+
• ATTRIBUTABLE
• LEGIBLE
• CONTEMPORANEOUS
• ORIGINAL
• ACCURATE
• COMPLETE
• CONSISTENT
• ENDURING
• AVAILABLE

8. • ATTRIBUTABLE
It is indicate who recorded data or performed activity with sign and date. (Manually or
electronically ). record who wrote it & when.
• LEGIBLE
Data should be readable after it is recorded. Data it is recorded permanently.
• CONTEMPORANEOUS (online)
Data must record at the time it was generated .
• ORIGINAL
Data must be preserved in it’s unaltered state like raw data.
• ACCURATE
Data must correctly reflect the actual observation.

9. •COMPLETE
All data including any test, repetition or reanalysis performed. Data (records) must be complete.
• CONSISTENT
Record should be generated and date time stamps applied in the expected sequence consistently.
• ENDURING
Data should be recorded on controlled worksheets, in laboratory notebook or validated systems.
• AVAILABLE
Data needs to available and accessible for review , audit, or inspection over the lifetime of the
record.

10. THE COMMON ISSUES OF DATA INTEGRITY
• Intentional data falsification or manipulation
• Poor documentation practices that impact the reliability of the data
• Lack of control related to software, computerized systems or instruments
• Lack of a review process to ensure detectability of any data integrity gaps
• Shared identity/passwords

11. CONSEQUENCE OF DATA INTEGRITY
• Loss of trust
• Recalls
• Import alert
• Forms-483
• Waring latter
• Non compliance report
• Loss of business

12. HOW TO MINIMIZE THE RISK OF DATA INTEGRITY
• Follow good documentation practice.
• Follow ALCOA+
• Audit trail.
• Quality management system.
• Internal audit / self inspection.
• Personal training.
• Computer system validation.
• Data back up & recovery.

13. CONCLUSION
• In the pharmaceutical industry, data integrity play an important role to maintain the quality of product as
well as patient safety.
• A process of maintenance and assurance of accuracy and consistency of the data over its entire life cycle. The
integrity and trustworthiness of pharmaceutical product.
• Data integrity helps in building trust between regulatory agencies and the industry as a whole. It eliminates the
need for inspecting each and every process involved in the production and supply of drugs and other
pharmaceutical products.

14. “Quality means doing it right when nobody is looking”