People vs. Machine: the State of Enterprise Search PT 1

We at iQuest Analytics believe that the greatest pattern matcher is the human being. It is NOT the role of the machine to do that work for the searcher. It is NOT appropriate to impose bias into the enterprise search equation. It is simply foolish to believe that machine AI can approximate to the skill, accuracy and speed of a human being to find meaningful patterns in data or the natural environment. Infants, during the earliest period of bonding, rapidly identify patterns of behavior of the caregiver- a necessary precondition of early survival. 

We believe that iQuest can provide a system of search that will allow patterns in the data, based on grammatical network relationships, to assist the skilled knowledge worker in creating meaning and context.

Current Enterprise Search applications, models and architecture fall short of the promised goal of allowing users, from the least skilled to the most skilled, to find, in context, what is needed for the search to be successful. Some organizations I am familiar with have 100's of repositories, multiple context management, collaboration and MDM systems and generally lacking Enterprise Federated Search applications.

It is a fact that tens of  millions of dollars are spent on paying skilled knowledge workers to "manage" the meta layer and resultant onthologies and taxonomies. It would appear that such valuable resources are better served applying their expense skills in the service of moving the enterprise forward.

It is a fact that most if not all enterprise-wide search and discovery systems require continuos top-down management of the data, presuming that there is enterprise-wide awareness of where that data lives.

I am of the belief that given the right architecture and search philosophy, enterprise search based on a combination of NLP, grammatical network analysis and sound semantic modeling, all data, structured and not, can be accessible to the searcher without the initial requirement of the heavy cost of top down translation, normalization and ordering of the data.