fme AG
fme SRL
In life sciences, such considerations are often underestimated. Organizations are so ready to untether themselves from the complexity and constraints of old legacy systems that they can become distracted from other factors which need to be considered to get the most out of their new investment.
Priority goals may include transforming the way they manage and work with regulatory information management, to drive greater efficiency, accuracy, visibility as well as compliance with the evolving demands of regulators. But the scope of even the most dynamic new platform or system will be dependent on, and limited by, the business data available. If that data has material gaps in it, contains significant duplication and/or errors, or is not aligned with the fields and formats required for target/future use cases in conjunction with the data governance strategy, even the best-planned project will not deliver effectively.
Start by considering what’s possible
As life sciences organizations form their digital transformation strategies and reset their goals, it’s important that they understand the potential opportunity to streamline and improve associated processes – and the way that these new or reframed processes will harness data to deliver step changes in execution and output.
One opportunity, for example, could be to transform the way companies manage their product portfolios – via a more dynamic, finer-grained definition of that portfolio and an end-to-end view of its change management, registration/licensing and commercial status in every market globally.
Another is to harness regulatory information (RIM data) to streamline the way a whole host of other functions plan and operate. There’s a lot of interest now in flowing this core data more fluidly into processes beyond Regulatory Affairs – such as Clinical, Manufacturing, Quality, Safety, and Pharmacovigilance. Rather than each function deploying and managing its own applications and data set to serve a single purpose, as has been largely the case up to now, the growing trend is to take a cross-functional platform approach to data, change, and knowledge management. This means that each team can draw on the same definitive and live information set to fulfil their business need.
All of this is much more efficient, as well as less error prone – because similar or overlapping data is not being input many different times, in slightly different ways. This, in turn, will expose companies to much lower risk as regulators like EMA start to require simultaneous data-and-document based submissions for marketing authorizations and variations/updates, which inevitably will see them implement formal cross-checks to ensure information is properly synchronized and consistent.
There are no shortcuts to rich, reliable data
The process transformation opportunities linked to all the above are considerable, and they are exciting. However, they rely on the respective teams understanding and harnessing that potential through advanced, proactive planning. By agreeing, collectively, on the scope for greater efficiency, and on the strategic advantages that are made possible through access to more holistic intelligence and insights, teams can start to move together toward a plan that will benefit everyone.
Practically, this will require an investment of time and thought, considering the state and location of current data, and what will need to happen to it to ensure that it is of sufficient quality, completeness and multi-purpose reusability to support improved processes in the future state. Unquestionably, this will also require a considerable amount of targeted work to ensure existing data is aligned and of high quality; that it uses agreed vocabularies; and consistently adheres to standardized/regulated formatting, data governance, and naming conventions.
Source expert help as needed
All of this may sound like a lot of “heavy lifting”, but it is exactly the kind of activity our experts can advise on. We can start by helping life sciences companies put together a strategy based on how data will ideally be used in the future state, and what needs to happen to it to prepare it for migration.
Working alongside the various business subject-matter experts (e. g. the people closest to the product portfolios and the processes involved in managing these), we’ll help scope the work involved and the resources that will be required. We can also help to determine the historical, current, and future role of respective data, so that only active data is prioritized for preparation for migration to the new system or platform (in terms of refactoring/clean-up/enrichment).
Forewarned is forearmed, as they say. Although preparing data so that it’s migration-ready may sound like an onerous undertaking, it is far better to know this and be able to do something about it ahead of time, than to be caught out once a critical technology project is already well advanced – by which time fundamental data transformation considerations may be too late.
To sound out our experts about data preparations needed for an upcoming new systems project, please get in touch, I’m happy to support you.