Max Kelleher- Chief Operating Officer, Generis; Remco Munnik- Director, Iperion, a Deloitte business.
As pharma companies work on creating a continuous, reliable, harmonized data flow across their operations, the focus is turning to ‘in flight’ data control. This is about ensuring that information captured and used in one part of an extended process can be matched and reconciled with related information elsewhere. Max Kelleher, Chief Operating Officer at Generis and Remco Munnik, a Director at Iperion, a Deloitte business, provide actionable advice on how companies can control and harness the flow of data between functional silos to deliver measurable benefits.
Leveraging live company master data more effectively and strategically, thereby creating a flow of broader data and insights between functions, will enhance a range of different use cases. Much of this ‘in-flight’ data is incidental information captured as part of a task, yet its value in providing oversight, traceability and impact assessment to senior management could be considerable – if only companies could find a way to harness and control it more systematically.
The handover of data between point software solutions – such as regulatory systems (RIMS), clinical trial management (CTMS), pharmacovigilance (PV) – is where gaps and discrepancies in information between systems occur, leading to operational blind spots and strategic oversights at best, or regulatory incompliance at worst. This makes hard work of change management, and could mean that product development information, and patient safety events, aren’t fully traceable.
Overcoming the silos, interconnecting the data, and keeping those connections dynamic and smart, is the next big opportunity – and provide the key to using everyday operational data to drive business improvements.
But how?
The answer lies in understanding where key data is generated, and how the supply and demand of that data looks across the ‘chain of custody’, as that data is re-used in different ways. Then a plan can be devised for improving the connection and flow of more unified data (one enhanced source, rather than inconsistent duplicates) across departmental divides.
For young biotech companies starting from scratch, there is a clear opportunity to establish clean, consistent and definitive data from the outset, whereas for larger and more established companies the best options may be around intelligently mapping existing data sources and data flow. Then interconnections and interdependencies can be identified and managed more effectively, until such time as data remediation and end-to-end standardization can be achieved (e.g., to bring data fields and formats into line).
It’s in this context that leveraging Ontologies is attracting interest as an option, for instance – allowing inconsistently-formatted data to coexist, while recognizing that the items referenced are the same, and linked. This is a useful first step in the move to treat all data as one joined-up resource, so that it can drive new actionable insights, decisions and processes. A more thorough overhaul of data can then happen more gradually over time.
With all of this in mind, here are some considerations and tips for tackling internal data transformation.
The Road to Data-Based Operational Agility
Unless the company is a young biotech with a largely greenfield tech set-up, Life Sciences companies will be approaching the road to data based operational agility with a considerable amount of baggage.
Large legacy systems, vast volumes of data, and the variable quality and availability of that data, will make it hard to know where to start in transforming its contribution and value. Rather than try to tackle everything everywhere all at once, the prudent choice involves identifying some tangible gains from higher-quality, interconnected data which, once cleaned and combined, will tell a fuller story. That might be linking supply chain data to Regulatory data, to enable serialization, (semi)automated batch release, and mitigation of shortage reporting, for instance. Or perhaps the aim is to shave a week off clinical development timescales, or complete eCTD applications or submit variations more speedily. All depending on the priority and size of the company.
Map Data Along a Process
Mapping what data exists, and where, is the best place to start with all of this. It is only through visualizing the current spread of information assets and associated use cases that companies will appreciate the potential for greater uniformity and fluidity of data use between the different departments.
This will help the company establish key processes to transform, for quick yet potentially far-reaching wins for the business. An effective map will chart where given data is used along a process including creation, modification, and re-use by different teams and systems.
Where there is an existing process optimization or digital transformation team in place, or consultants that are advising on associated initiatives, these professionals would be the ideal drivers of a cross-functional data map – in partnership with key functions such as Regulatory Affairs, Quality, and so on. Companies that have already appointed Chief Data Officers, or equivalents, will have a head start as these roles typically take more of a view of the commercial value of data, where Regulatory Affairs might not be the direct creator of the data, but more the guardian - as the spider in the web - providing a more detailed perspective of data’s links and touch points.
How to Maintain High Quality Data
A lot has been said and written already about the importance of improving and maintaining high data quality, as its day-to-day value in supporting real-time business processes increases.
While some arguments favor a strong sense of data ownership within specific functions with the most involvement with the given data, it can be more powerful to encourage everyone across the company to buy into the value of consistent data so that all functions and teams play their own part in keeping data clean, compliant, comprehensive and current.
Effective strategies here involve strong, broad communication of the associated benefits of robust data, and incentives (recognition and reward) for those who actively play their part. Instead of data ‘ownership’, think in terms of a ‘chain of data custody’ spanning multiple groups of data processors and guardians over time.
Once companies can more readily visualize their current data position and the full scale of the task ahead of them - to make their data work harder for the organization -it’s time to decide the most prudent way forward.
In the case of large pharma companies with extensive product portfolios and vast system and data legacies, comprehensive data remapping and/or investing in master data management is likely to be an overwhelming undertaking that could take many years.
Driving a Culture Shift
Regulators, through their adoption of data standards, are championing global identifiers for medicinal products and their active substances. Life Sciences companies that are inventing and developing these products and substances would benefit greatly from adopting data standards consistently from early development, and throughout their marketing authorization/registration information and variations submissions.
Life Sciences companies have a significant opportunity to enhance their operations by implementing data standards internally, facilitating the smooth flow of consistently formatted data across their processes while eliminating discrepancies and overlap. By recording data uniformly and sharing it reliably between functions throughout extended processes, companies can effectively and efficiently leverage their data to enhance productivity, process agility, and drive innovation.
There needs to be a sense of shared purpose– for example the role of improved process efficiency in staying agile, enabled by the ability to speak the same language internally about products right across the organization. As long as individual departments all mean something different with their definitions, there is the potential for risk and delayed innovation.
Life Sciences is traditionally a very conservative sector so there is a challenge ahead in achieving an agile culture that underpins data transformation. Companies must be able to trust data yet allow for mistakes, be prepared to try something, fail at it, learn from that and move forward. With the right systems and culture working together, business process enhancement will be rapid and effective.
Max Kelleher is Chief Operating Officer at Generis and formerly the company’s Head of European Operations. He is passionate about providing a viable, pragmatic path for modernizing enterprise information management in regulated industries. His close work with both pharma companies and specialist solution partners has afforded him deep insight into the critical modern-day challenges that traditional approaches to business processes and information use in complex industries like Life Sciences do not fulfill. max.kelleher@generiscorp. com https://www.generiscorp.com/
Remco Munnik is a Director at Iperion, a Deloitte business, and a respected subject matter expert in RIM, eCTD, xEVMPD and ISO IDMP. He is Chair of Medicines for Europe Telematics group; and President of the IRISS Forum, a global, open, multidisciplinary, non-profit networking organization for life science professionals by life science professionals. Iperion, a Deloitte business is a globally operating life sciences consultancy firm which is paving the way to digital healthcare, by supporting standardization and ensuring the right technology, systems and processes are in place to enable insightful business decision-making and innovation. [email protected] https://www2.deloitte.com/nl/nl/pages/life-sciences-en-gezondheidszorg/ topics/iperion.html
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