Good Data Integrity Practices

Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable and that these characteristics of the data are maintained throughout the data life cycle. The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy) and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.

This course is detailed course which includes first two short video lectures which will give practical and simple insight about Data Integrity and Audit Trail Review.

Furthermore, there are recorded webinars which explains in details about;

Data Integrity with practical approach,

A separate dimension and that is , Data integrity Vs. Good Documentation Practices

There are separate lectures for data integrity in analytical laboratories (chemical section) and data integrity in microbiology laboratories.

Further, there is one detailed lecture on Audit Trail review based on risk based approach. Because, still many companies not implemented audit trail review based on risk based approach.

The last lecture is on Pharma 4.0 Which is a need for future pharmaceuticals.

It's a fundamental part of a pharmaceutical quality system and is important for a number of reasons, including;

Ensuring drug quality

Data integrity is essential for ensuring that medicines are safe, effective, and meet quality standards

Protecting public health

Data integrity is a tool for regulatory authorities to protect public health

Enabling informed decision-making

Data integrity makes data valuable and enables informed business decisions

Adhering to regulations

Data integrity ensures adherence to life sciences and pharmaceutical regulations

Data integrity is important at every stage of production and access, from initial recording through validation and archiving. Failure to comply with data integrity requirements can lead to:

• Un-validated results

• Post-marketing issues

• Frequent product recalls

• Fines

• Delays in product approval

• Criminal charges