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
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