Discipline

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, as well as driving innovation more than ever before. These challenges also apply to the lab environment, which in order to contribute to the corporate goals needs to remove inefficiencies and compliance risks from lab processes and to provide a collaborative environment for innovation.

This can be achieved by removing disconnected and paper-based processes that are not only non-value added and error-prone, but also hamper the access of relevant data throughout the research, development and manufacturing lifecycle. In today’s global externalized environment 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...

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Life Sciences Quality and Compliance

Connected data-driven Quality and Business Excellence

Quality helps ensuring patient safety and efficacy of their treatments, it helps sustainability and protecting the reputation of an organization. The integrated Quality capabilities of BIOVIA ensure digital continuity providing data integrity and a “Single Source of Truth” of information. It includes capabilities for Quality Document and Content Management, for Quality Process Management and Quality intelligence. Developed for the highly regulated Life Sciences industry, the solution provides full security controls and compliance with the US Food & Drug Administration (FDA) 21 CFR Part 11. Its inspection-readiness also helps organizations to run smooth inspections and to reduce overall audit times. The modern and intuitive user interface makes user adoption easy and fast, and cloud deployment minimizes Total Cost of Ownership (TCO). The system’s scalability makes it possible to adapt the solution from small to large enterprise deployments. The solution allows organizations to...

Life Sciences Quality and Compliance
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Manufacturing Analytics

Manufacturing Analytics

Empowering Production Operations in Process Industries

Organizations need to maximize efficiency and reduce costs in their production processes. At the same time, they need to 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 with automated validation-ready workflows to provide browser-accessible outputs for teams across different departments, organizations and geographies. The  discipline supports three major areas that empower production operations, shorten time to market, and maximize profitability.

  • Process Design - Improve process design by understanding the critical process parameters
  • Process Performance - Increase process performance by monitoring variability enabling preemptive action
  • Process Improvement - Drive process improvement by understanding and...
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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 proving too costly to be 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 which support product performance. Utilizing the relatively low cost environment of in silico testing can also allow researchers to test concepts with minimum risk, potentially unlocking new avenues of ideas to explore. By tying together the virtual and real worlds with Modeling& Simulation, 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...

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. Cheminformatics, bioinformatics, proteomics, genomics and more all offer unique value, yet organizations must ensure that researchers have the tools they need to effectively analyze and share this content to maximize its impact.

Whether it is analyzing assay data or registering a novel therapeutic candidate, 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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