Maximize Your Data Impact
The article discusses how R&D leaders can maximize the impact of their data by adopting AI/ML technologies, establishing robust data infrastructures, and leveraging automation and external partnerships, as highlighted by insights from Dotmatics' collaboration with LINUS and their State of Science survey revealing scientists' strategic shifts toward automation and partnerships amid scientific and economic challenges.
Choosing the Right Technologies and Partners
Data is more important than ever in today’s challenging R&D environment, but as most lab directors, research directors, and senior strategy leaders will attest, strategic use of R&D data can be as challenging as it is promising. For example, artificial intelligence and machine learning (AI/ML) present opportunities for organizations to leverage decades of proprietary R&D data to guide and accelerate new initiatives. However, success with AI/ML depends on addressing challenges such as identifying realistic use cases and preparing the multimodal data needed to train models. Establishing the necessary data infrastructure to capture and process vast volumes of diverse data is essential. This often requires sourcing new technologies and trusted partners to help establish strong data processes from data collection and automation through to querying, modeling, and analysis.
Dotmatics recently collaborated with the strategy and innovation consultancy group LINUS to discuss solutions that help researchers maximize the impact of their data, explore how R&D organizations are adjusting to scientific and economic pressures, and examine obstacles to success.
The LINUS State of Science Survey
In its semi-annual State of Science survey, LINUS questioned nearly four hundred scientists across multiple geographies, organization types, application areas, and levels of responsibility. Results show that scientists are adapting to current scientific and economic pressures by turning to:
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Automation: Teams are using automation to meet productivity goals despite budget cuts and layoffs, and as an initial step toward supporting reproducibility, guiding innovation, and setting a strong data foundation for AI/ML. Survey respondents indicated that the number one technology they plan to purchase in 2024 is automated systems, which they see as a stepping stone toward AI/ML.
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Partnerships: Teams are looking externally to access additional resources and data needed to strategically innovate, adopt technologies like AI/ML, and further optimize productivity. Academia and biopharma/pharma are enhancing their research by turning to external collaborations for challenges such as ensuring reproducibility or performing complex analyses in translational research, bioinformatics, and clinical research. Survey results show that 43% of respondents will be prioritizing new collaborations and partnerships in 2024.
Maximize Impact by Prioritizing Data Management
Maximizing the impact of data—through strategic collaboration or technologies like AI/ML—is only possible when strong fundamental data-management capabilities are in place. Key obstacles include:
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Data volume: Automated lab technologies and workflows create huge volumes of data that need to be collated, centralized, and modeled for usability. Both academic and industry respondents indicated that their life science research in 2024 will involve processing more samples and collecting higher volumes of data.
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Data diversity: The variety of specialty systems used by internal and external cross-functional researchers creates diverse multimodal data that needs to be properly collected, integrated, standardized, and correlated.
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Data accessibility: Without self-service, permission-controlled tools to query across all available data and curate richly contextualized datasets, an organization’s data may be FAIR (findable, accessible, interoperable, and reusable) in theory, but not in practice.
Many teams face these obstacles due to the gradual evolution of technology. Scientists have always adopted new technologies and data-collection solutions to improve research. For example, 46% of LINUS survey respondents cited “adopting new techniques to acquire new types of data” as their top scientific-work priority in 2024. However, new R&D technologies are typically rolled out independently over time, resulting in a convoluted mix of platforms, automation, and data-collection systems. This makes it difficult for scientists to extract true intelligence from their R&D data. To address this, organizations are seeking solutions that unite disparate technologies and interconnect their systems of record, enabling scientists to analyze data in context and apply it more meaningfully. With an integrated data fabric, researchers can more easily contextualize their data, collaborate, and gain insights to advance their work.
R&D teams struggling with convoluted systems that keep data siloed and uncontextualized are aiming to migrate toward platform-based solutions that can keep pace with growing data volumes and unite disparate technologies and datasets to deliver better insights.
Dotmatics Luma R&D Data Management Platform
Dotmatics has developed the Luma scientific data management platform to help R&D teams maximize their data impact. This composable R&D platform helps organizations optimize data management, scientific workflows, material management, and instrument integration. It simplifies the collection and processing of multimodal R&D data from both internal researchers and external partners using diverse technologies. With Luma, R&D teams can unite, contextualize, and find their data; leverage it within advanced scientific workflows, specialty software applications, and AI/ML models; and ultimately gain actionable insights and maximize the impact of their data.
On-Demand Webinar: Maximize Your Data Impact in 2024
An on-demand webinar, "Maximize Your Data Impact in 2024: Key Trends and Strategic Insights for Scientific R&D," features a panel of experts who:
- Dive deeper into the results of the LINUS 2024 State of Science survey.
- Explore how scientists from biopharma, pharma, and academia are enhancing research innovation and productivity despite economic and scientific challenges.
- Review key considerations for R&D teams aiming to:
- Automate research and data workflows
- Integrate diverse systems and data types
- Expand data access and insights
- Adopt AI and MI
- Support evolving research needs with a composable multimodal R&D platform
Panelists include:
- Alister Campbell, VP Science & Technology at Dotmatics
- Natalie LaFranzo, VP of Strategy at LINUS Group
- Erin Legwold, Associate Strategist at LINUS Group
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