Compliance data management: borrowing best practice from quality assurance

Regulatory teams have much to learn from the standardised 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, argues Romuald Braun, VP of Strategy for Life Sciences, AMPLEXOR.

Across pharma businesses, teams work together to meet Regulatory commitments, perhaps sharing unstructured information by email, SharePoint or informal document sharing. By contrast, the quality assurance (QA) world is very structured. Every change or deviation from the standard is documented.

Of course, Regulatory teams manage their data too. Commonly, they will have 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. 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.

Shifting from document to data

To address these issues, some Life Sciences companies have begun to apply sophisticated project management tools and techniques to Regulatory compliance processes. While this might improve 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.

Realisation of the need for a data-first approach to compliance is dawning slowly. Manufacturers, for example, are starting to look around and ask how competitors manage their data control, because every element changed in a manufacturing process must follow a procedure that is accurately recorded and cross-referenced across relevant data repositories. This should include impact and risk assessments whenever there are changes or variations to information or requirements.

Data-driven standards

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.

With the advent of IDMP and other data-driven initiatives, Life Sciences companies are increasingly looking at managing all data in a more structured, more controlled and more standardised way. Yet, acceptance of quality tools to support data management processes in a Regulatory environment is in its infancy. Professionals in the Regulatory sphere might struggle initially with the idea of applying the vernacular of a QA approach to compliance, but some convergence looks inevitable. In future, the disciplines of compliance and quality will share many techniques and methodologies.

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.

The challenge of change control is becoming increasingly complex. Fear of this complexity is a barrier for many Life Sciences businesses that are considering tackling their data quality issues and getting a grip on compliance. The transition may not be pain-free. Inefficiencies will come to light. Change requests may increase as legacy issues are addressed. But transformation to quality-led, data-first compliance data management is not optional.

Creating opportunity

An integrated approach where all data is centralised as common master data will drive quantifiable benefits for Life Science organisations. Compliance is traditionally viewed as a business cost. But once Life Sciences companies gain control over their data, costs will decrease and the data will increasingly represent an opportunity. As well as removing the risk and cost of breaches of compliance, effective management of data quality can do so much more than ensure compliance. A foundation of quality data supports faster time to market and more responsive product development and customer service.

About the author

Romuald Braun is VP of Strategy for Life Sciences at AMPLEXOR. He holds a Master’s degree in Drug Regulatory Affairs, an Engineers’ diploma in Data Technology, and has spent the last 26 years working in compliance, document management and content management related roles in this industry - in client-based as well as consulting and project management roles.