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The foundation for standardizing data across a wide range of raw input data lies in the data model. A solution for industrial data operations can enable users to develop models that standardize and contextualize industrial data and serve as a data hub. Explore six commonly asked questions people have when considering a data-modeling solution.
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To solve data architecture challenges and address the need for data contextualization and standardization, a new category of software solutions is emerging that may be the key to digital transformation for life sciences companies. Learn more about this category of DataOps and how it aids manufacturing environments when tailored for industrial data.
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To ensure a successful data modeling project, manufacturers must consider many factors. Here, we share best practices that serve as a guideline for commonly encountered challenges and how life sciences manufacturers can overcome them to achieve success in their data modeling projects by leveraging an Industrial DataOps solution.
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The ISA-95 specification defines hierarchal models for systems, detailed information models, and a data flow model for manufacturing operations management. Explore these core elements and how the ISA-95 specification can be applied within an integration solution designed for industrial data modeling, delivery, and governance.
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Life science manufacturers are striving to increase their agility and utilize data more effectively to drive business growth. Gain valuable insights from digitalization leaders on how some life sciences manufacturers are successfully scaling their Pharma 4.0 initiatives beyond the pilot stage and paving the way forward.
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Process development laboratories face unique challenges due to highly varied equipment and often poorly supported data connectors. Delve into the solution that enabled Catalent to create a data model for its bioreactors that resulted in easily contextualized data, time savings in manual tasks, and the ability to share real-time insights with customers.
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