For pharmaceutical and life sciences firms, a case-by-case approach to information and content management has been prevalent for too long. It is inefficient and results in different parts of a business collating overlapping data, rendering the prospect of consolidating information more or less impossible. This is especially true of product and regulatory information in life sciences.
This case-by-case approach to information and content management poses a challenge to progress – especially when it comes to companies’ ambitions for innovation and process automation. In life sciences, as in most other industries, it is now a commonly-stated strategic aim for organisations to become more ‘data-driven’: able to react at speed and to predict, plan and pre-empt future scenarios using sophisticated intelligence gleaned from everyday data. That could be signals about potential issues with new products, alerts to emerging gaps in the market, or insight into what constitutes a successful regulatory submission – and the ability to skip straight to a more robust initial application, auto-filled with high-quality, pre-approved content.
The pursuit of this data-driven approach is driving life sciences and pharma firms to rethink the way they organise and manage routine information, and combine this with broader intelligence to create something much more useful and powerful than the sum of its parts.
Getting to this new, more dynamic, data-driven state begins with new thinking about the way information is captured and stored. If information is locked inside static documents, or proprietary, single-use database entries specific to a particular function, its value will be limited.
Yet this is a common restriction. Re-using information in other parts of the organisation may involve manual data re-entry into other systems, or complex and expensive systems integration. Unless data-sharing capabilities were envisioned from the outset, organisations risk complexity, cost and data integrity as they try to fashion something empowering and inclusive from systems which, by and large, were designed to stand alone.
Attempts to achieve more holistic regulatory information management (RIM) have highlighted the constraints and challenges caused by the traditional piecemeal approach to managing data.
Historically, the different elements of product information and regulatory intelligence have existed in pockets across the business, making it very hard for responsible teams to get a clear and accurate view of the current, correct status of anything at any given time. This is in contrast to the big data analytics world, where information is combined to create meaningful insights at speed, however large and diverse the original sources.
