A tasty intro to data integrity
Perhaps you’ve had a dish from a beloved relative—a lasagna, a cake, a stew. Just the smell transports you back to their kitchen. So, when it comes time for you to take on the tradition, you want an exact copy. You buy the same ingredients, follow the same recipe, maybe even listen to the same music. You do this because you want control over the outcome. More specifically, you want identical outcomes, and you want to ensure lossless information transfer from that hand-scribbled recipe card to your kitchen. In essence, you want data integrity…for cuisine.
Now let’s leave the kitchen for the labs of biopharma.
Why does data integrity matter to biopharma?
Scientific data is the most valuable asset for any biopharmaceutical company. Its integrity impacts the entire value chain—from discovery to commercialization—in multiple ways:
Decision-making
Organizations rely heavily on data to make informed decisions. Reliable and high-quality data enables companies to evaluate outcomes correctly and plan accordingly. Inaccurate or missing data compromises these critical functions and ultimately slows time-to-market for therapeutics.
Regulatory compliance
Data integrity is essential to meet the many regulatory obligations of biopharmaceutical companies. Implementing robust data practices mitigates legal, safety, and business risks, including potential penalties from regulatory bodies like the United States Food & Drug Administration (FDA) or European Medicines Agency (EMA), as well as the reputational harm caused by data breaches or non-compliance.
Operational efficiency
By implementing robust data integrity and documentation, biopharma companies can reduce errors, data rework, or troubleshooting time. This then lowers costs and allows the organization to focus on core business activities.
Data-driven innovation
Data that is accurate, complete, and consistent forms the foundation for advanced analytics, artificial intelligence (AI), and machine learning (ML) applications. These technologies rely on high-quality data to derive valuable insights, improve research and development processes, accelerate drug discovery, and optimize scientific outcomes.
Product efficacy and quality
The regulated environments in biopharma depend on scientific data—and lots of it—to assess the efficacy, quality, and safety of their therapeutics. Data integrity is critical in drug characterization, batch release, stability testing, and technology transfer.
High-fidelity data through ALCOA++
Given the value, volume, and variety of scientific data, biopharma organizations need a robust and comprehensive approach to maintaining data integrity. That’s where ALCOA++ comes in. It’s a set of principles and guidelines used in the life sciences and other regulated spaces. The acronym, which has expanded over the years (hence the pluses), represents ten key tenets for ensuring the integrity of data throughout its life cycle:1-3
- Attributable: Data should be attributable to a source (human or program) that created, modified, or reviewed it. Actions like data entry, changes, approvals, and movements should be credited. With accountability, potential errors and discrepancies can be quickly corrected.
- Legible: Data must be easily readable and understandable throughout its lifecycle. This includes maintaining consistent formatting in electronic records and avoiding abbreviations or other jargon that may introduce ambiguity. Legibility ensures accurate interpretation.
- Contemporaneous: Data should be recorded in a timely manner, as soon as possible after the event or observation occurred. This recording helps minimize the risk of information loss or distortion. It also supports accurate and reliable documentation for critical events.
- Original: Data should be captured without alterations, manipulations, or unauthorized edits. Steps should be taken to avoid any unauthorized modifications that could compromise data accuracy or reliability. Original data provides a reliable, reusable source of information for analysis, audits, and regulatory compliance.
- Accurate: Data must be complete and free from errors or omissions. It should reflect the true values, observations, or results obtained during data collection or processing. Accuracy ensures that the data can be trusted for downstream decisions.
The first “plus” of ALCOA++ covers the next four principles.
- Complete: All relevant data is captured, including any necessary metadata.
- Consistent: Data recording practices are uniform and standardized across different systems, instruments, or operators.
- Enduring: Data is preserved over time with retention requirements.
- Available: Data can be retrieved when needed.
The second plus adds one more.
- Traceable: Data can be tracked throughout its entire life cycle.
Together, the ALCOA++ tenets establish a strong framework to ensure data is trustworthy and reliable.
Putting ALCOA++ into practice with TetraScience
While biopharma organizations can't rely on technology alone to achieve ALCOA++, having the right data architecture can go a long way. The Tetra Scientific Data Cloud™ is designed with data integrity in mind. Its features support all ALCOA++ principles throughout the entire scientific data journey.
Download our fact sheet on data integrity to learn more.
References
- U.S. Food and Drug Administration, Data Integrity and Compliance With Drug CGMP: Questions and Answers; Guidance for Industry, FDA-2018-D-3984, 2018, https://www.fda.gov/media/119267/download.
- Pharmaceutical Inspection Convention/Pharmaceutical Inspection Cooperation Scheme, Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments Draft (PI-041), 2021, https://picscheme.org/docview/4234.
- European Medicines Agency, EMA Draft Guideline on Computerized Systems and Electronic Data in Clinical Trials, 2021, https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-electronic-data-clinical-trials_en.pdf.