Organizations are on an ongoing journey to improve findability and increase search performance. The task is difficult, but artificial intelligence approaches, including advanced machine learning and text analytics, are helping these businesses improve performance and increase user satisfaction.
Why is getting search right so hard? The primary obstacles are:
- Poor content architecture: Inconsistent taxonomies, metadata that does not reflect the needs of a user or support a process.
- Insufficient tagging: Even when content has the right structure, it is frequently missing tags or tagged incorrectly.
- Volume of information: Overwhelming amount of unstructured information produced across departments throughout the enterprise.
- Ambiguous nature of language: The same terms are used to describe different things and different terms are used to describe the same things.
- Inability to understand the searcher’s context: Their background, level of expertise, the nature of their problem or objective.
- Disconnected systems: Information needed by a function or department is frequently locked up in multiple systems, sometimes with limited access.
- Large monolithic documents: Many times, a needed piece of information is in a large document where the answer can be found only by scanning or reading the entire document.
- Insufficient or non-existent resources devoted to improving search: Too many organizations still fail to devote the necessary people and time to the task.
While...
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