3 common pain points and how to avoid them. As I explored recently, digital transformation can surface a series of perplexing data challenges for most organizations, but particularly those operating in life sciences. Such has been the inertia in the sector previously around system modernization that, when a project is finally approved and chartered, stakeholders often rush toward the benefits without looking back. This can result in disappointment and frustration when the ‘as is’ data migrated to the new set-up turns out to be in a poor state and largely unusable.

In this blog, I want to focus on 3 particular takeaways from such experiences which companies can extrapolate from to avoid making the same mistakes.

1.     Technology is just one piece of the puzzle in a successful system migration

The successful execution of any project involves equal focus on people, process, and technology, so that everything is aligned to deliver the expected outcomes. Certainly, it’s as important to line up sufficient resources to plan and manage the transition, and to build engagement and momentum among all stakeholders and users, as well as any new skills and resources they might need.

But another element that’s often neglected is the data the new system will draw on to deliver the expected process streamlining, and improved visibility. However fast and feature-rich the new technology platform, if it’s dependent on old and inadequate data quality it won’t be able to fulfill its promise. If the new system can’t fulfil future-state governance or meet new standards for regulatory compliance, then the ‘retro-fitting’ burden could be immense as teams try to massage and improve the data after the fact. Unplanned data enrichment is hugely time-consuming.

The lesson here is about scoping system projects thoroughly and planning them early enough so that every dimension is well catered for in plenty of time.

If delivery is scheduled close to a deadline for adhering to new health authority submissions standards, companies will want to be sure that the data coming across is in good shape and fit for purpose to allow that deadline to be confidently met. Filling CMC Module III in eCTD submissions is already a hot spot, highlighting where existing system data typically falls short. Companies can learn from this by doing their due diligence and performing a detailed data assessment up front, to help them mitigate these kinds of risks.

2.     Time & resources are critical success factors

Unless a project has a sufficient runway leading up to it, pressures will mount, and good intentions are likely to give way to compromise and the cutting of corners. Even if they plan to bring in external help, the key project stakeholders will need to set aside their own people’s time to do the vital groundwork. That’s in understanding old and new data governance parameters, so that any data preparations and actual data migration is in line with current and emerging requirements. Without those parameters, and a clear picture of the ‘as is’ status, project teams risk investing their time and budgets in the wrong places and setting themselves up for a big clean-up operation later on post migration.

So, even before performing a data quality assessment, it’s a good idea to seek a bit of preliminary strategy advice from a trusted expert – almost as a Phase 0 – to understand the bigger picture and how everything needs to align to deliver against it.

This isn’t about engaging specialists prematurely, but rather about making sure that any investment that follows (and any external help brought in) is well targeted, so delivering maximum value.

3.     It’s important to allow and plan for failure

Despite the best intentions, projects can go awry due to the many moving parts that make up the whole. So, it’s important to factor ‘the unexpected’ into all planning.

This includes allowing for a certain number of iterations based on the finding of data quality assessments, to get the data to fit the required data governance standards going forward. If the data coming across is in a disastrous state, a planned migration phase duration could quickly turn into a material schedule delay. Underestimating the work involved is very common. I have seen this in many client projects. For example, if the ‘happy path’, where everything goes to plan, was expected to take 10-12 months, the real-life route situation took 18 months– so to de-risk the project, allow for contingency. If in doubt, take the forecast number of days and double it.

All of the preparatory work recommended above should help contain delays and protect against timescale or budget shocks, but it’s better to plan properly so that the journey goes as smoothly as it can. Although, ultimately, the client company is responsible for the quality and integrity of its own data, technology vendors and service providers will need to plan their involvement too and ensure they have the right skilled resources available at the right time.

Our experts can provide guidance on all of the above. Ultimately, this is about setting the right expectations and a realistic schedule, and resourcing projects so that they optimize the time to value. A little foresight can go a long way.

To discuss your own data migration journey, please fill out this contact form and we’ll put you in touch with our experts.

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.

Get in touch with us

Yes, the initial plans have altered to buy everyone a bit more time, but the broader plan is still on track – to make product data submissions at least as important as electronic documents (and ultimately the priority/default) in all communications with the regional Regulator.

In other words, whatever tweaks companies and software vendors make to their roadmap and immediate capabilities over the next few months, these should not detract from – nor dilute – the overarching plan to get regulated product data in complete, consistent and up-to-date order.

That’s so that when it is time to migrate to – and go live – with an optimized new regulatory information/submissions management platform or system, there is comprehensive, compliant, high-quality data ready to run across it.

And of course, the foundational work done now will stand companies in good stead for when other regions across the world implement their own take on IDMP – given that dynamic data exchange is where all of this will go, globally, in due course.

What’s changed, and how does it affect you?

To recap what’s changed in the interim, EMA’s Digital Application Dataset Integration (DADI) interface – a project that has been evolving alongside IDMP and SPOR (see https://esubmission.ema.europa.eu/ for more information) – will in the short term, from April 2023, serve as the means for entering electronic application forms through interactive web forms.

This will enable companies to pull data from the PMS (that has been migrated from XEVMPD and other EMA databases) and to get out the human-readable PDF format and machine-readable XML format. Both formats will be submitted along with the eCTD; this part of the process will not change.

This latest development is an important step toward the IDMP goal of reusing data from PMS, and the first step toward the IDMP standardization of data. EMA will support this approach for variation forms only at this point, extending it to initial applications and renewals later.

Ultimately, EMA’s plan is for standardized medicinal product data currently held in the xEVMPD database to be enriched for the PMS, where fuller, more granular, IDMP-compliant medicinal product detail will be kept and updated over time.

There are some practical challenges still to be worked out, such as how IDMP detail that is currently missing will be added to PMS, and how internal company RIM systems and EMA’s PMS database will get to a point of being able to exchange data more seamlessly without requiring manual data re-entry. But for now, this is a chance for companies to update and correct their data with EMA’s dictionary through the familiar XEVMPD process. (Ultimately, FHIR – the global industry standard for passing healthcare data between systems – will support more dynamic data exchange/sync’ing between companies’ RIM systems and EMA’s PMS.)

Rather than play for time, here are 4 opportunities that the interim DADI move makes possible, as well as 5 next steps that companies should take to stay on track with their data preparations:

4 benefits to exploit

1. The use of the DADI interface for getting data from the EMA PMS allows life sciences companies and software providers to take a breath as they prepare for full-scale IDMP implementation and compliance. FHIR-based submissions via API have been pushed back for now (this will still happen, just not within the next year).
2. The industry is now less dependent on immediate technology changes. There is no need for their RIM systems to support DADI, as at this point data won’t flow directly between RIM records and EMA’s PMS.
3. The EMA’s roadmap allows for implementation to happen in manageable chunks. EMA’s ‘DADI first’ approach allows for Product (PMS) data re-use, and accounts for the largest proportion of regulatory submissions.
4. This is a chance to reset or adapt IDMP/regulatory data strategies, catch up, and prepare to deliver maximum benefits and efficiencies from the preparations (e. g. by doing sufficient groundwork to enable a confident system migration, when the time comes).

5 things to do next, for pharma companies

1. Set or re-set your strategy and position around regulatory, structured data.
2. Collect and assess product data and prepare this for compliance (scoping and getting stuck into any data enrichment now) – so that it addresses the granularity of IDMP requirements and maps to EMA’s dictionaries/vocabularies.
3. Prepare to support xEVMPD e-submissions based on the new data model and all of the levels of detail that are expected, to be ready for the future and to enable a rapid transition to IDMP.
4. Improve your ability to respond and adapt quickly to further changes to regulatory requirements. EMA’s switch to using DADI to submit data to the PMS highlights just how swiftly the roadmap can change, and why an Agile approach to project management is so important.
5. Start to migrate your content into the new target system as soon as possible. If you have started with the collection of data in Excel files locally, this data could become outdated if not maintained. Don’t leave thoughts of migration until the last minute. Plan for this now, as part of your overall scoping work.To maximize your IDMP system migration, or discuss your best route to IDMP data preparation as you plan for this, please fill out the contact form below and we’ll put you in touch with our experts.
   Get in touch with us