The inability to identify the value in unstructured content is the primary challenge in any application that requires the use of metadata. Search cannot find and deliver relevant information in the right context, at the right time without good quality metadata.
An information governance approach that creates the infrastructure framework to encompass automated intelligent metadata generation, auto-classification, and the use of goal and mission-aligned taxonomies is required. From this framework, intelligent metadata enabled solutions can be rapidly developed and implemented. Only then can organizations leverage their knowledge assets to support search, litigation, e-discovery, text mining, sentiment analysis and open source intelligence.
Manual tagging is still the primary approach used to identify the description of content, and often lacks any alignment with enterprise business goals. This subjectivity and ambiguity is applied to search, resulting in inaccuracy and the inability to find relevant information across the enterprise.
Metadata used by search engines may be comprised of end user tags, pre-defined tags, or generated using system defined metadata, keyword and proximity matching, extensive rule building, end-user ratings, or artificial intelligence. Typically, search engines provide no way to rapidly adapt to meet organizational needs or account for an organization’s unique nomenclature.