A step by step approach to intelligent data-centricity

Most life sciences Regulatory teams still think and work in terms of documents. Yet it is data, rather than pre-prepared dossiers, that is moving into focus and many companies do not have the necessary skills base to handle this.

Stakeholders across the life sciences and healthcare ecosystem are increasingly realising that a data-first approach to collecting, managing and communicating product information will be the most efficient and reliable way to maintain consistent, definitive, current and high-quality record of a product entering or on the market. One that can be interpreted and use in a wide range of use cases, by the broadest possible range of people (from regulators to clinicians, pharmacists and ultimately patients).

Given that this data-centric approach will be the new reality before long, the question for existing product information managers/Regulatory teams is whether their skill sets now need to be refreshed to reflect the target new ways of working (first, data and document sets needing to be carefully aligned, then a direct flow of good data to the regulators).

Progress report

So where are companies with all of this today? With the exception of very large pharma organisations with the budget and people resources to have already started exploring the wider possibilities, most companies still lack awareness both of the wider potential and of the work ahead of them in building the right capabilities.

At one level, this is about how they manage product information so that (a) it fulfils the demands of new IDMP structured data requirements, and (b) becomes sufficiently reliable to form a foundation for not only product registrations and their maintenance, but all sorts of other processes too.

On another level, the opportunity extends to leveraging reporting and analytics to smart effect – first to help users fill gaps and increase the quality of the data; then with a more strategic emphasis, even using AI-assisted tools to investigate scope for process improvement.

It can be tempting to imagine that IT is going solve all of this, and that by default users will be swept along on the journey. Yet failure to adapt internal Regulatory capabilities, and to cultivate new data skills, is likely to severely compromise Regulatory Affairs’ data-based progress.

Of course, having efficient and user-friendly solutions that have been built not just with additional data fields to satisfy IDMP - but also with an appreciation for what new data-centric process management models will mean for Life Sciences Regulatory and other teams (and for the pharma industry in general) - will be important.

Realising the potential of a data-driven Regulatory future

As teams look to use ‘live’ data to build reports, they will need help understanding how to make the most of analytics and of pre-built dashboards, too. And before too long, teams will need to be able to transition towards more advanced analytics.

This will take them deeper into the realm of data science, as they start to harness AI-enabled tools allowing entire data-based processes to benefit from a new, streamlined approach. Yet it is here that existing teams are most likely to find that they lack the appropriate skills..

Regulatory teams will need to develop domain, tool and data discovery knowledge. A good starting point for new skills adoption is to carry out a pilot initiative that targets either Regulatory’s biggest pain points or the most complete source of existing data. This is likely to mean building software and Regulatory domain knowledge, later growing data science capabilities through a combination of collaborative team-building and targeted training and skills transfer.

Building the right balance of skills is the key challenge if companies are to realise the potential of a data-driven Regulatory future. Identifying and plugging skills gaps must be an urgent priority.