Improved dynamism is now a strategic priority in life sciences. The Covid-19 response has seen a radical transformation of the drug development and approval process – reducing cycles that once spanned 10 years to a matter of months. Going forward, pressure on major authorities such as the FDA will only increase as all stakeholders look to maintain the current pace of innovation.
In this context, and to improve their own agility and responsiveness, Research and Development (R&D) organizations have now set out two areas of renewed interest for strategic focus and investment. One is biobanking, as the demands around clinical trial sample storage soar. The other is the need to strengthen data assets, as the ability to apply these confidently and swiftly across all kinds of regulatory processes becomes crucial to speed to market.
The data imperative – which surrounds the integrity, quality and traceability of regulated product data – concerns all pharma organizations. The driver for change could be a regulatory information management (RIM) consolidation project following a merger; it might be an initiative to standardize on IDMP fields and vocabularies; or an attempt to bring new traceability to medical device or cosmetics manufacture. But, too often, companies set the wheels in motion and start to implement a new business solution, before they consider the work that might be necessary to vet and prepare data so that it can be migrated reliably into the target IT system and/or new data model.
In some cases, project owners assume that any matters relating to data assessment, preparation and migration will be taken care of by the new system vendor, and that this would be addressed as part of their proposal. It’s only when the analysis phase of the project begins – that the realization dawns that the incoming data is messy/conflicting/incomplete – that they begin to understand that they have underestimated, and skimped on, this critical cornerstone of a successful delivery.
Instead of expecting software vendors (which typically lack the depth of data experience) to provide for data assessment, cleaning and enhancement as vital preparation work ahead of any data migration, the only real way to make a proper job of it is to itemize it separately – in other words, break out this work with a separate request for information (RFI), or request for proposal (RFP).
By separating out data-specific activity, companies will also save themselves from any bill shocks as vendors are forced to bring in specialist partners to rescue a project – at short notice, and with their own mark-up on the extra costs. If the parameters are known much earlier on in the project cycle, the data preparation and migration work can be more accurately planned for and integrated more seamlessly into the overall deployment – with much less risk of the project over running or exceeding its budget.
It’s one thing to prioritize cost when sizing up vendors for a new system project, but if this introduces new risk, because the required specialist skills and resources have not been allowed for, it is a false economy. Certainly, the work could end up costing a lot more and taking a lot longer if critical data preparations turn into last-minute firefighting. Far better to have the right skills cued up from the outset, with a clear remit which includes responsibility for servers, security and more during the data preparation and migration phase.
In 2022, a whole range of digital transformation drivers including IDMP compliance preparations and improved traceability will see new system implementation projects and associated data migration initiatives increases. To maximize success, it’s definitively advisable to separate out your data requirement and prioritize this from the outset so that any system project builds on solid foundations.