Organizations grapple with the imperative of effective data management and integration in the era of data-driven decision-making. Customers frequently express concerns about consolidating data from multiple sources and exposing it to various applications. In the pharmaceutical and life sciences R&D sectors, the challenge of integrating and linking data generated at different stages of the research process and drug discovery value chain is particularly pronounced.
This can include data from laboratory experiments, clinical trials, or external scientific literature needed to answer research questions, support decision-making, and facilitate secondary use. Consequently, the need for an integrated view of disparate data sources and sets readily consumable by users and applications alike has become increasingly evident.
This is where a semantic layer comes into play. A semantic layer is an abstraction that bridges the gap between raw data storage and user-friendly interfaces, enabling seamless integration, organization, and information exploration. In this article, we will delve into the semantic layer (SL) concept, how it addresses these challenges, and provide an overview of the different approaches to implementing an enterprise semantic layer.
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