Finding the right information in a sea of structured and unstructured data sources is a real challenge. Important contextual information is often missed and there is insufficient time to spend on strategy development and decision making.
Unstructured data and document sources can cover a broad range of sources, such as assessments, industry reports, publications, project documents, website content and the list goes on.
Conventional search methods can be very time-consuming and imprecise for targeted evidence review. Above all it is a time consuming process.
Searching and combining information from many sources can be a repetitive process which can lead to search fatigue and reduced staff productivity.
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Finding the right information in a sea of structured and unstructured data sources is a real challenge. Important contextual information is often missed and there is insufficient time to spend on strategy development and decision making. Unstructured data and document sources can cover a broad range of sources, such as literature, conference abstracts, medical liaison notes, news, social media, regulatory reports and more. Conventional scientific search methods can be very time-consuming and imprecise for targeted evidence review. Searching and combining information from many sources can be a repetitive process across therapy areas or topics of interest, which can lead to search fatigue and reduced staff productivity.
NLP text mining can extract the key facts transforming data from different evidence streams into actionable intelligence for decision making. Capturing and integrating the information from a range of document sources can build a landscape of scientific, strategic and commercial knowledge, a insights platform or “hub”.