ARTICLES BY KIP WOLF
Data Integrity In A Cloud-Based World: Regulations & Best Practices
There was a time when cloud computing was thought of skeptically by the life sciences community, through both the critical observations that are typical of science-based industry and the ingrained cultural resistance to change that comes along with a heavily regulated environment. But today, cloud solutions are ubiquitous in the industry.
Implementing Data Quality By Design For Improved Data Integrity
We must adopt more planning in our data quality activities. This is data QbD in its simplest explanation, and we must include data integrity planning in those activities (e.g., considering how to meet ALCOA requirements).
People: The Most Persistent Risk To Data Integrity
HR changes continually impact overall data integrity—from a single new hire to a corporate-level change with a merger or acquisition. It's a commonly overlooked weak link in the data integrity chain.
What Your Organizational Design Says About Your Commitment To Data Integrity
Understanding how to be appropriately staffed and being prepared to explain any perception of inequity could mean the difference between success and failure of appropriate data integrity in both regulatory compliance and product support.
AI, Data Integrity, & The Life Sciences: Let’s Not Wait Until Someone Dies
Simply performing computer system validation or managing computer systems under CGMP conditions is not enough to ensure data integrity and data quality for data sets where AI/ML is intended to be applied.
To Err Is Human: Contextual Communication’s Impact On Data Integrity
Despite the best efforts of those responsible for data integrity, the potential for human error is directly and indirectly impacted by the corporate, the national/regional , and quality culture of an organization.
Why Data Integrity Is Impossible Without A Quality Culture
For successful establishment and sustainability of a quality culture, “the mindset and behavior... must start at the top and be emulated by individuals at all levels and in all functions within the company.”
Startups, Cloud Storage, & Data Integrity: Don’t Let This Happen To You!
Data integrity is of paramount importance to ensure patient health and safety and to improve shareholder value, particularly for virtual companies. Startups finding themselves in the throes of managing complex drug development programs realize they may face great risk if they do not begin with the end in mind and integrate data integrity practices early on.
Data (Integrity) Pirates: Preventing And Detecting Malicious Intent
During a recent meeting of data integrity professionals, a fundamental question was posed by a member of the group: “How can one prevent or detect malicious intent as it relates to changes to information and the impact to data integrity?”
Data Integrity, Deviations, And Shop Floor Quality
Continuous improvement in data integrity can advance a firm on the journey toward a mature culture of quality, particularly through the implementation of QA on the shop floor. Batch record review (BRR) and product disposition are often complicated by data integrity issues and poor data quality.
Where EDMS Fails: Data Integrity Pitfalls To Avoid In Metadata For Life Science Products
When creating and managing electronic documents, document metadata deserves as much attention as document content. Firms that do so can improve compliance and even gain potential competitive advantage by realizing electronic documents (with appropriate metadata) as real business assets.
Cheating In The Lab: 3 Data Integrity Pitfalls To Avoid In Laboratory Operations
At least 20 percent of all warning letters issued by the CDER Office of Manufacturing Quality in 2017 included explicit observations by inspectors of blatant data integrity violations in laboratory operations. It is incumbent upon us to enforce and reinforce basic CGMP and GDocP practices in our own laboratories and those of our suppliers
When And How To Implement Data Integrity Practices In The Product Development Lifecycle
There remains a great deal of confusion in the life sciences industry as to how to implement practices to best ensure data integrity. This confusion frequently stems from the assumption that data integrity is something to “turn on” at a particular stage of the product development life cycle.