CAPGEMINI SE
R&D environments are typically among the first to explore new technologies or novel use cases. This means that these organizations have needs for specialist skills and services prior to the wave of industry scale up & commoditization. An example of this is the pharmaceutical industry's current exploration of quantum technologies within drug discovery.
In this blog post I'll explore various other ways in which life sciences R&D organizations differ from non-R&D environments and discuss their specific needs.
In life science R&D, data plays a huge role. However, data in R&D can differ significantly from non-R&D data. Data often brings with it complex relationships and ontologies. It may be domain and context sensitive, or involve surprising correlations and non-obvious causal relationships. Data can come directly from instruments and lab machines, can be generated using in silico methods, or can be obtained from external sources. Errors or quality issues with data may be hard for non-experts to understand.
Whether in pursuit of research, design on manufacturing, R&D environments typically operate on the forefront of modern and exploratory technologies. The methods used are typically combinations of data-driven and physics-based modelling, cutting across several scientific fields.
The needs and requirements of R&D departments rapidly change in response to new advancements in technology and research. They typically operate in a highly variable, agile, and risky environment where agile operations processes are required. Here the constant need for change and innovation bumps into risk management, governance, and associated regulatory requirements. Systems are typically more complex in these spaces, yet less mature.
We believe that domain knowledge and experience is a requirement of operations teams to ensure that solutions are stable enough to deliver value, and agile enough to evolve to meet emerging needs.
Siloed, function-based teams are slowed through individualized responsibilities, a need to balance effort among a portfolio of applications, and lack of a high-level end-to-end view. The spread of responsibilities over many teams may also lead to prolonging investigations as the interfaces between teams can cause real barriers to holistic evaluations.
In such a dynamic, complex, and fast-paced environment, an XOps team is a necessity. Teams today are typically split along functional lines. Under an XOps approach teams work cross-functionally - dedicated to solutions or products. This is the only way to effectively provide ongoing management of a solution from end to end, from the data source through to insight generation and value delivery.
On XOps teams the main principles are collaboration, automation, continuous improvement, and customer-centricity. Through these principles, an XOps team can deliver a product-based support service which is trustworthy, agile, value driven, reduces failure, & embraces a change culture.
The ability to manage large amounts of data is becoming the crucial factor for success. In high pressure teams - where being first may mean the difference between a massive payout and nothing at all - the temptation is always there to push onward and leave housekeeping tasks like organization for another day. Remember - data is your lifeblood. Nothing is more important for the long-term health of your team than the ability to understand and manage date effectively. And nothing does that better than XOps.
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Capgemini SE published this content on 20 January 2023 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 25 January 2023 15:17:06 UTC.
