Most common data integrity violations in life science manufacturing and how to avoid them

Data integrity is defined as the extent to which all data are complete, consistent, and accurate throughout the data lifecycle. It includes all original records and true copies, including source (raw) data, metadata and all subsequent transformations and reports of these data. The requirements for data include that they are attributable, legible, contemporaneous, original and accurate (ALCOA).

Data integrity is not a new topic – it has been around for more than two decades, with FDA warning letters citing lack of control with computerized systems already in early 2000s. But why is it suddenly becoming such a hot focus area for the regulators?

Technological progress has enabled introduction of numerous IT systems and solutions to support manufacturing processes. On one hand, this means that increasing amounts of data are being generated, which can be used for process insights as well as quality and efficiency purposes. On the other, expectations from the regulators regarding monitoring and reporting tools and procedures have also increased. Thus, a positive loop was created of higher expectations and additional (required) progress in manufacturing IT solutions for data gathering, aggregation and processing.


Advisory company Deloitte analyzed FDA warning letters and identified that majority of them contain elements of data integrity. Moreover, the trend has been worsening in recent years. Four most common data-related infractions were:

  1. Data not fully and accurately documented.
  2. Critical deviations not investigated.
  3. Lack of adequate access controls. 
  4. Data not recorded contemporaneously.

Moreover, a lot of data is siloed in production systems and machines, without any connection to manufacturing IT systems, and is thus dispersed and unmanageable in “Data Integrity” principles. Furthermore, such data is inaccessible for reporting and analytics purposes. It gets even worse – as per market research company Vanson Bourne, as a result of disconnected data, workers are on average also spending more time searching for, acquiring, entering, or moving data (8 hours per week) than they do making decisions on that data (7 hours per week)!

That is why more and more life science companies are actively working on breaking down the data-isolating silos in their manufacturing not only to solve Data Integrity and regulatory compliance issues, but also to exploit the gathered data to the fullest extent for process insights, reporting and analytics.

Solution lies in a central manufacturing data management platform

To connect all the manufacturing sources of GMP-relevant data, from existing data historians to dispersed data storages on the machines, life science companies are opting more and more for the introduction of a central manufacturing data management platform. This way, they are enabling proper “Data Integrity friendly” data management as well as access to real-time (contextualized) production data for reporting and analytics.

Contemporary central data management platform must provide the following functionalities:

  • Connectivity to various data storage historians and databases as well as ability to provide automatic data acquisition from manufacturing devices / systems
  • Central data management and contextualization
  • Ability to enter manual inputs (where automatic acquisition is not possible)
  • High user security and full audit trail
  • Seamless exposure of data to reporting and analytics systems (including 3rd party)

With such a platform, all ALCOA requirements can be met and full data integrity achieved.

MePIS PDM – State-of-the-art platform for production data management, reports and process analytics

MePIS PDM is an advanced process data management platform that enables you to gather, organize and manage production data. It is the cornerstone for the creation of production reports, comprehensive production analyses and data integrity in life science manufacturing.

On the one hand, MePIS PDM enables you to create tailored production reports with custom report approval workflows and also has an embedded audit trail, contributing to higher levels of GMP and regulatory compliance.

On the other hand, it provides a basis for production transparency and process analytics. MePIS PDM ensures a real-time overview of equipment status and quicker reaction times in production. It also allows you to compare time-series data with events and compare batches to identify the “golden batch”.

MePIS PDM can seamlessly connect to production systems and machines on the one hand, and production IT systems (MES) on the other.

Key MePIS PDM functionalities:
Centralized data management – Retrieval of process data – time series, raw report data, alarms and events, files and meta data
– Data contextualization
– Centralized data storage (GE Historian/OSI PI + SQL)
– Data exports for reporting, analytics and other purposes (also 3rd party applications)
Production reports– Raw report data automatically uploaded to central database or generated from raw historian values
– Multiple data sources
– Production report versioning & storage
– Production report approval workflows
– Role-based status, security and access
– Comprehensive report builder/designer
– Visualization of report data
Comprehensive data analysis– Batch analysis and comparison (“golden batch”)
– Audit trail analysis
– Comprehensive charting
– Time-series and discrete events on one chart
– Real time overview of device status
– Export of analyses results for further investigation
Achieve effective reporting, process visibility and stressless audits

MePIS PDM users report the following benefits:

  • Improved data integrity, GMP and regulatory compliance
  • Seamless and fast report preparation, tailored to production needs
  • Improved transparency of production processes and faster response to events in production
  • Fewer deviations and operator mistakes
  • Analysis of time-series data and events, ability to compare batches and identify the “golden batch”
  • Systematic development of process and quality improvements
  • Fast data export and access for further analysis and data mining

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