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  • Disciplines

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    Data Science

    Driving Innovation with Knowledge-driven Decisions

    Businesses today are swamped with data. Valuable insights are hidden among different data silos, leading to inefficiencies across the entire organization. While data scientists can help tame the flood of data, qualified individuals are in short supply. As a result, the few on staff are left to deal with piles of ad hoc analyses and manual, labor-intensive projects that yield little value to the organization.

    Organizations therefore need a scalable framework to create, validate, and consume data science workflows. From accessing and aggregating data to sophisticated analytics, modeling and reporting, automating these processes allows novice users to get the most of their data while freeing up expert users to focus on more value-added tasks. Utilizing a common framework also ensures best practices are captured and shared enterprise-wide. Democratizing data science helps teams do more with less and unlock the innovations that today’s businesses need to survive and thrive.

    Data Science
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    Laboratory Informatics

    Laboratory Informatics

    Accelerating Innovation and Decision-Making

    Science-based organizations need to optimize operations by improving efficiency while maximizing quality and adhering to regulations, while driving innovation. These challenges also apply to the lab environment, which needs to remove inefficiencies and compliance risks from lab processes and to provide a collaborative environment for innovation.

    The solution is to remove disconnected and paper-based processes that are error-prone and hamper access of relevant data throughout the research, development and manufacturing lifecycle.  It is imperative to make decisions as early as possible in the lifecycle, in order to drive innovation and to optimize processes and products. Digital Laboratory Informatics capabilities allow for streamlined and more efficient lab workflows, harmonization and standardization and a fully integrated and automated easy-to-deploy process. 

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    Life Sciences Quality and Compliance

    Connected data-driven Quality and Business Excellence

    Quality helps ensuring patient safety, treatment efficacy, sustainability and protection of brand reputation. Dassault Systèmes helps achieving Quality and Business Excellence with a new a comprehensive data-centric approach to Quality, ensuring digital continuity, data integrity and a “Single Source of Truth” of information. The integrated capabilities include Quality Document and Content Management with automated tasks, electronic signatures, standardized controlled processes and audit trails, Quality Process Management (like CAPA investigations or root-cause analysis) with immediate access to data and documents with hyperlinks and Quality Intelligence using machine learning and federated search. Developed for the highly regulated Life Sciences industry this cloud-based solution provides full regulatory compliance, has a modern and intuitive user interface and is easily scalable from only a few to 100.000 users.

    Life Sciences Quality and Compliance
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    Manufacturing Analytics

    Manufacturing Analytics

    Empowering Production Operations in Process Industries

    Organizations need to maximize efficiency, reduce costs and control product quality, variability and yield. BIOVIA provides process development, quality, and manufacturing users with self-service, on-demand access to process and quality data from disparate databases and paper records. It automatically aggregates and contextualizes the data and enables ad-hoc statistical investigations. Teams across different departments, organizations and geographies can collaborate and gain actionable insights. The discipline supports three major areas that empower production operations, shorten time to market, and maximize profitability. It helps improve process design by understanding the critical process parameters, increase process performance by monitoring variability enabling preemptive action and drive process improvement by understanding and control process and product variability.

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    Modeling and Simulation

    Exploring the Virtual World to Understand the Real

    Declining R&D productivity is forcing organizations to think outside the box to keep up with increasing consumer demands. Relying on physical experimentation alone is not economically sustainable in such a climate. Researchers need to facilitate a deeper understanding of both how and why their products work to better tie them to project and business goals.

    Modeling & Simulation provides a snapshot of the fundamental atomic interactions supporting product performance. In silico testing allows researchers to test concepts with minimum risk and lower costs, unlocking new avenues of ideas to explore. By tying the virtual and real worlds together, researchers can better guide their projects with virtual tests guiding physical ones and vice-versa. As a result, teams are able to create better performing, safer and cost-effective products, leading to improved patient outcomes.

    Modeling and Simulation
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    Research Informatics

    Research Informatics

    Maximize the Value of Your Scientific Intellectual Property

    Scientific discovery arises from the collaboration of diverse teams. The types of content they utilize can be equally as diverse, across disciplines such as cheminformatics, bioinformatics, proteomics, genomics and more. Organizations must ensure that researchers have the tools they need to effectively analyze and share this content to maximize its impact.

    Leveraging a common framework for managing scientific content helps facilitate an environment of collaboration across internal and external R&D networks. Researchers can easily aggregate, process and analyze data while rapidly sharing and discussing results. Scientifically-aware tools also help guarantee that researchers have the capabilities they need to explore their data more deeply. Together, such an environment facilitates innovation and helps researchers guide their work via data-driven decisions.

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  • 统筹乡村振兴和生态保护 推动乡村绿色发展 2019-10-05
  • 无法开盘,等待摇号细则!6月起西安限购区域内可买房源大大降低 ——凤凰网房产西安 2019-10-05
  • 亚冠小结:恒大权健暴露隐患 上港申花一喜一忧 2019-09-08
  • 白岩松点评“小凤雅事件”:小家庭扛不住 大家庭共同扛 2019-08-15
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