There is no question that electronic information is the lifeblood of today’s enterprises.
However, in a recent "Findability" survey conducted by FindWise in 2013, only 9% of respondents stated that it is was fairly easy to find information within their organization, while in contrast 63% said it was moderately hard or very hard to find information in their organization. Many business roles and functions, which rely on timely and accurate identification of relevant content, are increasingly being undermined and unnecessarily extended by the challenge of accessing lots of data held in disparate and far-flung sources while trying to glean intelligence from them.
Recent studies have shown that the average user query consists of 1.2 keywords (or 6-8 characters) in length. This means that on average, a search engine is trying to make relevancy determinations based upon a single word query. Another study by Jakob Nielsen has shown that a minimum of a 27 character query is necessary to retrieve relevant information within the enterprise. This “Query Gap” is one of the key challenges facing enterprise search systems.
“Minimum of 27 Characters Needed for Enterprise Relevancy”
Jakob Nielsen, Prioritizing Web Usability
Metadata is a crucial component to search and is at the heart of the “query gap”. It allows users to be much more expressive in the queries they generate, sometimes without even realizing it. Metadata is used in two ways in regards to search systems:
1Addition of metadata to a search query to increase findability
2Use of query refinement to improve navigation
- These methodologies enable more structure to be added to unstructured data, using that information to create richer queries and thereby narrowing the query gap.
Another methodology traditionally used in search systems to narrow the “query gap” is taxonomy. A taxonomy is an hierarchical representation of metadata, thereby allowing queries to be narrowed to a specific metadata tagged set of information.
The Content Classification software within our portfolio provides metadata enrichment using text analytics and semantic processing. It adds consistent, quality metadata that can be used not only in taxonomy scenarios, but also in refinement scenarios, enabling a significant increase in findability while narrowing the query gap.