Key Technologies for Enterprise Search Solutions
Still today, the majority of enterprise search systems fail to deliver to their potential. Surveys of enterprise search users illustrate a consistent theme of user dissatisfaction. The many advances in core indexing technology during the past ten years have failed to significantly improve the situation.
Why is this?
From the experience of hundreds of enterprise search implementation projects, we have observed that the failure of enterprise search systems to provide a compelling ROI frequently stems from a few key areas. This article outlines these important functional areas, and references resources where you can learn more.
At Search Technologies, we have developed a range of search platform-independent technology assets to directly address these frequently occurring challenges.
PRIMARY ENTERPRISE SEARCH SOLUTION CHALLENGES
- Lack of Access to Content: If it is not indexed, then it cannot be found. Gaining timely and complete access to content-sets can be tough, especially where document-level security, remote locations, or very large content sets are involved.
- Lack of Search Relevancy is the most common user complaint. A great algorithm alone is not sufficient to produce great relevancy. Content cleansing, metadata capture and automated creation, the normalization of both content and metadata, and application-specific user query enhancement, are all important to the achievement of search relevancy.
These issues distil to five key technology areas. Take each of these seriously, and your enterprise search project will almost certainly be successful:
- Data Connectivity: You need a strategy and not just a few plug-in software components.
- Content Cleansing and Normalization: An often neglected aspect of search systems, yet important to search quality, and user satisfaction.
- Metadata: The capture, creation, and mapping of metadata to support advanced user interface functions. Almost all enterprise search features rely on metadata. Within most enterprises, metadata is probably of inconsistent quality. A strategy is needed for the application of appropriate text analytics techniques for the automated enhancement of metadata.
- Integration with Big Data technologies, and support for emerging enterprise architectures. This will help to tame data growth.
- Query Improvement enables today's sophisticated search engines to deliver great results in reply to simplistic user queries, regardless of how large the content set has become.
Through addressing each of these issues within your project, enterprise search excellence is achievable. The resources below provide further information about these important aspects of enterprise search system building.
Our Enterprise Search White Papers cover all four aspects from a search product-neutral perspective.
Aspire Data Connectors provide a highly customizable, search engine independent approach to gathering content.
The Aspire Content Processing framework enables the cleansing and normalization of content, prior to indexing, and also provides metadata enrichment functionality.
The Query Processing Language (QPL) enables customized query enhancement systems to be easily built and maintained.
All of these technologies are search engine independent. Technical briefings for Aspire and QPL are also available online.