Enterprise Search Implementation Services
Experience, pragmatism and the application of proven best practices are the missing ingredients in many enterprise search initiatives
Most search engine implementations fail to deliver to their full potential – not through limitations in search software – but through a lack of applied expertise and proven processes.
Every organization has its own combination of data sets, user requirements and business goals presenting:
- Unique challenges, which can only be addressed by the application of experience and expertise
- Common challenges, to which proven best practices can be applied.
THREE COMMON CHALLENGES TO ENTERPRISE SEARCH
For illustrative purposes, three typical challenges to successful enterprise search implementation are summarized below.
1. Search Quality
So the plumbing is installed, your data-sets have been connected and indexed, and the search capability fundamentally works. Yet users complain about poor results and difficulties finding the information they need.
Most of the leading search engines have the functionality to deliver great search results, but to achieve consistent results quality is it necessary to:
- Match relevancy ranking to the company's (unique) data-sets
- Take care to ensure that disparate data sets "play well together" behind a single search box
- Implement processes to ensure that search quality is maintained through time, as corporate data constantly evolves
2. Complexity Management
For large enterprise search projects, complexity poses a significant threat. Deployments are typically rolled out one data-set (or one business application) at a time, and with each new addition, complexity grows. Inter-dependencies cause complexity, and time-saving short cuts made earlier in the project close options and limit flexibility. Complexity-creep is the leading cause of large project failure.
Complexity can be managed through forward planning and the application of best practices to design and implementation.
3. Connector Maintenance
Real-world issues pose challenges to the implementation and maintenance of connectors. For most data repositories, off-the-shelf connectors require configuration. Connector performance can be problematic causing incomplete or failed indexing. This is often not the fault of the connector, but is caused by dependencies on the target repository.
Connector implementation, monitoring and maintenance can be simplified with the right approach.