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4 Tips for Harnessing the Power of Data Analytics in Life Sciences
Integrate Data Systems and Foster a Culture of Trust
Break Down Silos for Unified Access
Tear down outdated, siloed legacy architectures to enable unified data access. Consolidate disparate sources into a cohesive data foundation. As Nechama Katan of Pfizer emphasised, stepping away from distractions and reducing fragmented efforts can help connect data flows more seamlessly.
Trust and Space for Innovation
A conducive environment for innovation is one that prioritises trust and physiological safety. For data science to flourish, professionals need space for contemplation, free from undue distractions.
Prioritise Quality, Ownership, and AI-Readiness
Quality Over Quick Wins
Wolfgang Halter emphasised the importance of looking beyond quick wins and focusing on larger, more impactful projects. Such projects, backed by proper resources and planning, can unlock unprecedented potential and drive long-term ROI.
Maintain Control with In-House Development
While external platforms and tools can greatly enhance data management and analytics capabilities, it's essential for organisations to maintain control over their data and its applications. Leveraging advanced data management tools can enhance an organisation's in-house efforts, ensuring they remain in the driver's seat when it comes to their data science and R&D processes. By doing so, they can ensure the accuracy, reliability, and security of their data, while also tailoring solutions to their unique needs.
Prepare Data for AI
Standardise, clean, and structure data for seamless AI consumption. As Merck’s Wolfgang Halter explained, “It begins with building robust and reliable processes for data collection, storage, and analysis.” Ensure data is machine-readable through consistent schemas, ontologies, and governance.
Embrace Automation, Diversified Data, and Champion Data Literacy
Automate for Efficiency
Claus Andersen highlighted the untapped potential of automating tasks like data extraction from documentation and validation of legacy data. Such automation can significantly expedite processes and lead to efficiency gains in the order of magnitudes.
Diversify Clinical Trials with Data
Amir Emadzadeh shed light on an essential aspect of clinical trials — their historical lack of diversity. By leveraging diversified data sources for planning clinical trials, organisations can ensure that the trials are representative, leading to more inclusive treatments. This not only has implications for the ethical conduct of trials but also for the efficacy and applicability of the resultant treatments.
Break Down Data Silos
One of the most resounding insights from the poll was the challenge posed by data silos. Integrating fragmented data sources and ensuring seamless data flow across organisational units can unlock deeper insights and drive innovation at an accelerated pace.
Implement Agile Governance and Prioritise Talent Diversity
Agile Governance Approach
Balance oversight with flexibility through adaptable policies, iterative improvements, and democratised access. As Johnson & Johnson's Claus Andersen said, “Taking an agile approach, gathering feedback, and continuously refining data management practices are key steps.” Avoid excessive governance that stifles innovation.
Promote Data Literacy
Seek teams that combine pharmaceutical expertise with specialised software and analytics acumen. "Training clinicians to maximise the potential of data science is key to its adoption and impact," emphasised Genentech-Roche’s Amir Emadzadeh. Providing learning opportunities and embedding analytics skills can foster a more data-driven approach.
Sponsored by
We are a global player in life-science R&D informatics, providing customers with cutting-edge multi-omics data management and integration software. We develop innovative off-the-shelf and custom solutions for clients in pharma, biotech, agriscience, consumer goods, and research centers. Omics Data Manager (ODM), our flagship product, helps organisations create a FAIR catalogue of multi-omics investigations (studies, samples, omics data), with powerful tools for curating rich and standardised metadata in bulk, as well as optimised RESTful APIs for scalable cross-study, cross-omics integrative search.
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