Pharmaceutical Outsourcing Q3 2023 - 12

CONTRACT RESEARCH
In-Flight Data Control:
Key to Efficient Life Sciences
Business Processes
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 'inflight'
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 blindspots
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 databased
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
Pharmaceutical Outsourcing | 12 | July/August/September 2023
Pharmaceutical Outsourcing Q3 2023

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