Learn more about how quality assurance best practices can help you ensure compliant, high quality data and the success that comes with it.
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Across pharma companies, teams work together to meet regulatory commitments, possibly sharing unstructured information by email, SharePoint or other informal documents. By contrast, the quality assurance (QA) world is very structured, and very change or deviation from the standard is documented.
Following this topic, regulatory teams manage their data too, using Excel spreadsheets, noting timelines and responsibilities and, perhaps, some project management tools. Until recently, regulatory teams have continued to manage PDF documents in a similar way to paper records management. As long as changes take place at a document level, this works well.
Please keep in mind that electronic document management allows people to store, send and share large numbers of documents more efficiently. But it does nothing to contribute to end-to-end visibility of data processes - in most cases, there is no overall overview of progress.
The need for a data-first approach to compliance & data-driven standards
As some life sciences companies have begun to apply sophisticated project management tools and techniques to regulatory compliance processes, improving efficiencies when it comes to sharing documents, it does not address quality at the data level.
A shift in approach – from document to data – drives a much more granular approach to compliance. A focus on individual data rather than whole documents, soon identifies the same data in multiple documents as well as inconsistencies and omissions. A report generated for regulatory purposes may be just a single document, but it is likely to contain hundreds of thousands of data items – many with associated metadata. So, the data does not just provide a value, it is also associated with metadata describing where the value is coming from.
Accurate and detailed documentation – and the ability to retrieve it years later – is key to effective compliance records. If, five years on, there is an adverse event and an auditor comes and asks about a change that was made, the company will need to answer questions as to who made the change, when and why.
Mapping data silos
Life sciences firms are very different stage of readiness when it comes to tackling their data issues, ranging from acceptance of the need to change, right through to having a clear understanding of what a QA approach could look like for their operations and the benefits of change.
The progress companies have made in their compliance data quality initiatives depends on a number of factors, not least the resources they have to manage all the information. Depending on how data has been collected, cleansed, stored and shared, it may be possible to slice and dice quality initiatives in different ways. One approach is to combine siloes of similar data and work on those first. The impact of changing data is not always sequential, it might be parallel, sequential or a combination of the two. Mapping out data siloes and data flows is a critical first step to more effective compliance data management.
Regulatory teams have much to learn from the standardized approach that quality assurance colleagues take to managing information. The legacy, document-based view of information management is fast being replaced by a data-first approach. Up-to-date, high quality data is key to success.
Romuald has devoted his 25-year career to-date to various roles related to compliance, document management, and content management in the Life Sciences industry. He has held leadership roles both on the client side and in consulting, including delivery, sales, and project and line manager. His experiences bridge on-premise and cloud environments in Europe and the US. Romuald holds a Master’s Degree in Drug Regulatory Affairs from the University of Bonn, Germany, and a diploma in data technology from the Technical University Darmstadt, Germany.