Making hidden data discoverable: How to build effective drug discovery engines?
Sebastian Radestock (Elsevier, Germany)
In a complex IT environment comprising dozens if not hundreds of databases and likely as many user interfaces it becomes difficult if not impossible to find all the relevant information needed to make informed decisions. Historical data get lost, not normalized data cannot be compared and maintenance becomes a nightmare. We will discuss a new approach to address this issue by showing various examples and use cases on how in-house data and public data can be integrated in various ways to address the unique and individual needs of companies to keep the competitive edge.
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