Our world virtually revolves around data. Every day we produce 2.5 quintillion bytes of data.1 Every industry, in every corner of the globe uses some form of data analysis to glean insight and guidance for pursuing their goals and achieving success: food production to weather forecasting, retail marketing to manufacturing, transportation, finance, fuels, healthcare, and of course, pharma.
Patients’ lives literally depend upon pharma’s products
Long before the term “data mining” existed, data played a prominent role in the pharmaceutical industry’s advancement, most notably through clinical trials. But today, pharma’s data harvesting goes far beyond standard clinical trials. And that data is largely attained through, analyzed with, and stored on, the internet and devices connected to it. So are most all business processes and communications, from supply chain management to automated manufacturing systems to marketing and inventory control.
So much data surging through a pharma company’s electronic hands has exploded the threats to data integrity. Threats that must be held at bay. Patients’ lives literally depend upon pharma’s products. But “dirty” data yields poor quality, unsafe products. The best way to ensure product integrity? Ensure the integrity of the data that helped create and deliver it.
Clean Data Benefits
• Facilitates higher quality product creation
• Reduces recalls
• Amplifies trust between the company and its shareholders and patients
• Boosts company and whole-industry reputation
• Preserves compliance
• Helps avoid contract breaches
• Keeps supply chains and processes running smoothly
So important is clean data, that in 1997, the U.S FDA established regulations specifically to guard the integrity of electronic records and data with their Title 21 Code of Federal Regulations (CFR), Part 11. The European Union, followed a similar path adding Annex 11 to their Good Manufacturing Practices (GMP). Though, Annex 11 directives are suggested guidelines rather than law-binding regulations as is the CRF.
However, increasing violations in the past several years has pushed the FDA to dole out stacks of warning letters to offenders world-wide. As an attempted remedy, the FDA published Data Integrity and Compliance with CGMP, in hopes of delivering guidance in managing risk, and clarifying data integrity expectations for the pharma industry.
Conditions Leading to Dirty Data
• Weak security and access controls
• Missing or inappropriately altered audit trails
• Poorly maintained activity logs
• Failure to determine source of discrepancies
• Deletion or alteration of original data
• Failure to update data
• Lack of third-party vetting and oversight
Though some of these situations do not directly cause dirty data, then can abet actions that will, such as cyber attacks or employee tampering.
Keeping It Clean
Lax protocols and weak data management contribute to inaccurate, incomplete, or otherwise wrongfully manipulated data. Here are 14 steps towards better data integrity:
- Establish systems to track and document how, when, and by whom data is altered
- Retain restricted-access copies of original data
- Create copies of each stage of clinical trials and research
- Store all backups in separate, secure location
- Hold all data to a ‘as-needed’ access provision
- Ensure only authorized personnel have access to respective data
- Commit to frequent audits and inspections
- Regularly perform third-part audits
- Require multiple authentications methods
- Educate employees regarding data integrity best practices, cyber security, and CFR regulations
- Conduct thorough risk assessments
- Adhere to ALCOA; Data must be:
A = Attributable “to the person, system, or device generating the data.” Information obtained must properly identify the source and correctly record changes.
L = Legible and permanent. Record and store data in a method that “ensures readability” for the entire timeframe in which it must be accessed or referenced.
C = Contemporaneous. Data should be recorded live or “at the time an event is observed.”
O = Original record. True, original copies must be preserved. “Data is to be used or presented as it was created.”
A = Accurate. Data must be error-free and accuracy should be made verifiable by “repeatable calculation, algorithm, or analysis.” 1
Only clean data can produce high-quality, safer products and ensure continuity through the entire business pipeline. Pharma must extend every effort to pursue data integrity…for their reputation, products, and ultimately their patients.
For more great industry info, check out our free whitepaper:
1. Big Data in Everyday Life, innovation enterprise channels, 2018
2. Why Pharmaceutical Data Integrity is More Important Than Ever, Pharmaceutical Manufacturing, 2017
Written by Angie Longacre
As a writer for Assurance Software, Angie devotes her craft to promoting business continuity and disaster recovery awareness, and trumpeting Assurance Software’s invaluable benefits for both. When she’s not commanding the keyboard, you can find her outside for a run, searching for her next antique treasure, or lost in a good book.