For R&D organizations focused on materials science, information management involves a lot more than crunching the latest sales figures or integrating statistics from inventory and accounting systems. Whether working to develop affordable fuel cells, lighter airplane parts or scratch-resistant paint, materials and CPG companies must routinely deal with extremely complex data spanning multiple scientific disciplines, including chemistry, toxicology, biology, physics and more. And although they invest millions of dollars in research efforts and go to great lengths to capture the results, many are failing to fully capitalize on the value of their organizational knowledge.
The issue is that traditional databases and legacy systems isolated within specific departments or research specialties lack the depth and breadth to keep up with the information management requirements of the modern scientific enterprise. These tools help users keep track of what happened, but not necessarily why it happened. And above all, they can’t make reasonable predictions about what the future holds. “Predictive analytics” such as molecular modeling or response surface methodology offer ways for materials companies to more effectively leverage all the many sources of information available to them, reduce the need for expensive lab experiments and ultimately speed product development efforts. Here are three key ways that research organizations can utilize analytics to extract greater value from their data and engage in more predictive science.
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