Data Integrity Risks

What is data integrity? The Food and Drug Administration (FDA) defines data integrity as a measurement of the completeness, consistency and accuracy of data. Data integrity relies on data being recorded exactly as the user intends, and upon retrieval, being in the same state as it was when recorded. A set of five guidelines were developed by the FDA, and later added to by the European Medicines Agency, to help improve data integrity. By following these ALCOA+ principles, companies and institutions can ensure that their data is complete, consistent and accurate. What can get in the way of maintaining data integrity? Maintaining data integrity is clearly critical for companies and institutions, but several hurdles can make this a challenging task to achieve. Some of the most common factors contributing to a reduction in data integrity include: Data integrity is critical for any company or institution that generates, manipulates or stores data. The consequences of poor data integrity in science can be severe, fueling the need for the implementation of effective and robust data practices. In this infographic, we will explore what data integrity is, why it is so important and how common data integrity risks can be avoided. A Attributable The creation or alteration of data can be linked to the person responsible. L Legible The data can be read visually and electronically. C Contemporaneous The data was created at the same time that the activity it relates to was conducted. O Original The source or primary documents relating to the activity they record are available, or certified versions of those documents are available. A Accurate The data is free of errors and any amendments or edits are documented. + Complete Data must include all experimental results. Consistent Data must maintain the sequence in which it occurred. The data must be traceable with a date and time stamp, and it should be created in a manner that is repeatable. Enduring The data should be recorded in durable media such as notebooks or in digital format with backups. Available The data should be easy to audit and inspect. Why is data integrity important? Data integrity is essential for all industries, but particularly in scientific research, where problems can cause wide-reaching impacts across policy, healthcare and education, and ultimately reduce public trust in science. “This is troubling because ensuring data integrity is an important component of industry’s responsibility to ensure the safety, efficacy, and quality of drugs, and of FDA’s ability to protect the public health.” In a guidance document, the FDA wrote: Data integrity Maintaining data integrity keeps data free from outside influence and malicious intent and ensures greater efficiency throughout the lifetime of the data. It plays an important role in current good manufacturing practice (CGMP) for drugs, and data integrityrelated CGMP violations have led to the FDA issuing several warning letters and import alerts. Policy Healthcare Education Trust CGMP Poor record management practices • Data is located in messy paper notebooks or stored in poorly organized excel sheets • Lack of backup data Cybersecurity • IT infrastructure and security is not robust and effective, leaving data vulnerable to cyber attacks Shared logins • Shared laboratory accounts breach parts 211 and 212 of the CGMP requirements, leaving data unattributable to a specific individual Company culture • Senior management don’t learn or facilitate data integrity • Lack of an open culture where mistakes can be admitted • Lack of technical knowledge and regulatory understanding How can data integrity risks be reduced? Despite the challenges, steps can be taken to avoid common data integrity pitfalls and ensure the completeness, consistency and accuracy of data. What is an audit trail? The FDA define an audit trail as “a secure, computergenerated, time-stamped electronic record that allows for reconstruction of the course of events relating to the creation, modification, or deletion of an electronic record.” Take time to fully understand policies, procedures and guidance documents. Create a comprehensive data management plan for every research project. Include information about: What data will be produced How the data will be collected, organized and stored How the data will be archived when the project is complete How contractual and legal data-handling requirements will be met Create audit trails and ensure they are up to standard. They should be reviewed internally at regular intervals and include: The change history of test results Changes to sample run sequences Changes to sample identification Changes to critical process parameters Store data as exact and complete copies of original data, and in a location and format secure from alteration, loss or erasure. Back up data to multiple sources and have disaster recovery procedures in place Document metadata (additional information that describes the content, context and origins of a dataset) Organize data effectively, use an informative file directory structure Restrict access to your data. Implement a system administrator with authorized personnel privileges. This individual should: Be responsible for all changes to computerized records or input of laboratory data into computerized records Not be responsible for the record content Ensure data is attributable to a specific individual Do not use shared logins Implement informatics tools where possible, such as a laboratory information management system (LIMS), chromatography data system (CDS) or electronic laboratory notebooks (ELNs). These tools can facilitate the collection, consolidation and auditing of data by: Time and date stamping work to generate a chronological record of your data Performing calculations that otherwise would require the use of spreadsheets Automating your instruments Increasing the ease of creating internal audit trails