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Data Integration for Lab Informatics

Effective data management and integration is an integral yet challenging component of establishing, expanding and optimizing  pharma lab informatics capabilities. Ahead of the 2019 Lab informatics summit, we sat down with 2 of our speakers to discuss the trials and business benefits associated with data integration: 

Download this article to get to know and read case studies from:

  • John Oh, Senior Manager, Application Development, Pfizer Vaccine Research, Pfizer
  • Jarrod Medeiros, Director of Informatics and IT, Casma Therapeutics

Learn more about:

  • How to get started on your own data integration journey
  • Overcoming challenges such as time constraints, legacy system complexity and project prioritization
  • Fully capitalizing on the business value data and systems integration brings to the table

Interested in learning more? Email us to request a copy of the 2019 Agenda to send directly to you to see related sessions.

Data Standardization for Lab Informatics: A 6 Step Checklist

Due to the emergence and growing sophistication of LIMs, laboratory IoT and other automated systems, R&D labs are producing more data than ever. And not just any data, but critical intelligence that supports almost all aspects of the drug development and manufacturing process. From smart R&D decision making to regulatory compliance to long-term strategic planning, robust, reliable, R&D data is the engine that drives innovation, operational excellence and growth. Ensuring that this data is of the highest quality possible and easily accessible throughout the organization is one of the most important as well as most challenging responsibilities of lab informatics leaders. 

That’s where data standardization comes in. In a nutshell, data standardization is the systematic process of consolidating data into a common format that allows for collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies. As standardization enables data to seamlessly flow from system to system, it’s the first step in achieving full, end-to-end interoperability and delivering advanced, integrated R&D insights.  

In this article we outline:

  • How data standardization drives better R&D decision making, increased efficiency and improved collaboration
  • The 6 steps for implementing data standardization
  • Data standardization resources, tools and methodologies

Interested in learning more? Email us to request a copy of the 2019 Agenda to send directly to you to see related sessions.