The Ultimate Guide To Connected Quality Data

By James Jardin

Increasingly more life sciences organizations are moving away from quality management methodologies that focus on a large “data object” like a document and toward a model that enables more granular access to all the data within the document and the ability to connect and analyze that data across product lifecycles to predict issues and prescribe preventative actions.

The primary driver behind life sciences’ urgency to adopt more data-centric approaches to quality and compliance is best understood through the lens of unstructured data, which accounts for more than 80% of data in the life science development, production and commercialization life cycle. Think about locked PDFs, scanned documents, uploaded images and so forth — all elements that can be ‘managed’ within today’s QMS software solutions, but all elements that contain massive amounts of granular data and insights that are currently difficult to extract and hard to correlate and analyze in real time.

This shift is not about replacing documents and processes. It’s an additive evolution that is moving toward a seamless connectivity of all quality and compliance data across the entire product life cycle. The digital documentation layer of quality and compliance processes is still critical and will never go away, but advanced technologies are helping life sciences organizations unlock the data side of the equation.