Data Integrity: Asset or Liability

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Data Integrity: Asset or Liability

Data Integrity: Asset or Liability

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Data Integrity



Data integrity (DI) is fundamental in any pharmaceutical quality system; this ensures that medicines are of the required quality, safety and efficacy.

Data integrity refers to the overall completeness, accuracy and consistency of data during its entire life cycle.  The data lifecycle considers the entire phases in the lifecycle, (including raw data) from initial generation and recording through processing (including analysis, transformation or migration), use, data retention, archive / retrieval and destruction.

The entire process of genuinely generating, maintaining & transforming data with completeness and accuracy is a challenging and a costly activity for any pharmaceutical company to perform.  This requires the appropriate resources in terms of time and personnel in order to comprehend and implement the principles as stated below.

Principles of Data Integrity

  • Attributable – Defining Source data and who performed an action on it.
  • Legible – Permanent recording of information and Access to easy reading anytime
  • Contemporaneous- Recording the date & time when work is performed
  • Original – Justifying if the information / data is a true copy
  • Accurate – Is the data accurate, with no errors or editing

Having stated, that the appropriate resources are required in terms of time and personnel.  Let us consider Data Integrity as an asset or a liability to any pharmaceutical company.

As an Asset, where DI has been integrated effectively into the Pharmaceutical Quality Management System (PQMS) these are the benefits a company can achieve:

  • Successful regulatory audit/ inspection in the area of DI.
  • Enhanced reputation for “Quality and Compliance” ultimately increasing  the opportunities of getting new clients  where applicable, especially for CMOs.
  • If the company is part of a supply chain, increased confidence in suppliers having confidence in the PQMS of such organisation.
  • Strong indicator that company has an effective training programme in Data Integrity.
  • Verifies the company has an effective Data Governance Procedure.
  • Indicates a positive attitude to opportunities  for quality improvement within the organisation.
  • Increased confidence in the operations of the organisations by its clients, senior management and employees.
  • The ability to attract and retain highly trained and motivated workforce.
  • Increased confidence of clinical investigators and clinical site personnel.
  • Gain trust of trial subjects and patients.


Data Integrity – Liability

Failure to implement DI in the PQM can lead to the following consequences:

  • Poor regulatory audits / inspections and in some cases warning letters such as 483 issued by the FDA.
  • Withdrawal of company’s pharmaceutical products from the market.
  • Reputational damage.
  • Loss of revenue.
  • Temporary closure of organisation.
  • Deter potential clients from doing business with the organisation.
  • Recall of products.
  • Indicates a poor understanding of quality.
  • Personnel criminal liability for individuals particularly managers or supervisors who conducted, encouraged or condoned data manipulation where this manipulation produces severe adverse consequences for patients.
  • Failure to address just one element of the data lifecycle will weaken the effectiveness of the measures implemented elsewhere in the system.



Data Integrity an asset or liability, a pharmaceutical organisation you decide the best approach to adopt in order to ensure your organisation Quality Management Systems complies with current Good manufacturing Practice guidelines and satisfies regulatory requirements.

Data Integrity and Compliance with CGMP Guidance for Industry

MHRA GMP Data Integrity guidance for the industry 2015

(MHRA GxP Data Integrity Definitions and Guidance for Industry: Draft for consultation July 2016)

GMP Drug Warning Letters Issued in Calendar Year 2015 Data Integrity Deficiencies January, 2016 FDA