Abstract
When it comes to search an information retrieval system within Digital Asset Management (DAM) systems, the burden is often placed on the users who are expected to solve the problem of irrelevant search results. Why? The biggest contributor to this problem is simply an overreliance on algorithms alone to deliver relevant results, when it's a human linguistic issue. Machine-understandable information, semantic, algorithms will not be advanced enough for many years, if ever, to properly understand and interpret the nuances of ‘natural language’. To deliver the most relevant results possible today you need more than algorithms, you a human understanding of language. You need someone who understands content modeling and structure, your audience's natural language, and your business vernacular in the form of metadata: taxonomies, ontologies and metadata schemas. To compound the issue of delivering relevant results further, there is an industry wide lack of knowledge and or practice of measuring the quality of search results, that is search analytics. Search analytics provides a quantifiable way to measure the relevancy of search results, as well as inform how well or poor the design and overall user experience are performing against your business objectives.
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