Global Enterprise Search Industry Trends

Market Trends The growing need to efficiently supervise large volumes of data in an organization in order to improve the operational efficiency is propelling the adoption of enterprise search solutions  The global enterprise search market generated revenues of more than $1.47 billion in 2012 and is expected to reach USD 8.90 billion by 2024, according to a new study by Grand View Research, Inc. Technological advancements and the emergence of cloud computing and Software as a Service (SaaS) are driving the adoption of hosted enterprise search solutions in the small and medium enterprises The trend is likely to continue in the near future as well. However, the availability of the open source free enterprise search solution may pose a challenge to companies operating in the market While big names like Google, HP, SharePoint and IBM are leading the current market, but new medium size companies like Attivio, Coveo, Exalead, LucidWorks, Sinequa and may more have started gaining more traction with their less expensive search and analytics solutions Organization now have started investing in Search and Analytics and the percentage has gone from 25 percent (few years back) to 60 percent now

A Drift in a way we see it Search Engine Artificially Intelligent Knowledge Engine Current methods of evaluating search accuracy are usually based on golden query sets, key documents, zero result searches, or top results Search Result Personalization- Right now the personalization is based on the user profile from within the enterprise or from external networking sites like Linked-in, FB etc. Most of the legacy search engines rely on matching words, one to one, with the exception of stop-words and some linguistic base form reduction We search for something and make decisions to perform actions based on the results we get back A more sophisticated and automated approach is using log file information from the search engine (search queries) and web servers (clicks) to create a holistic view on search accuracy instead of focusing on limited amounts of information Search Result Personalization- Search result quality may also be improved by tailoring the search results to individual users or user groups. With log analytics, users may be clustered based on their search behavior, documents downloaded, user data (profiles), or content contributions With increasing computing power, it is now possible to effectively extract entities (statistical phrases or acronyms), compute semantic co-occurrence (semantically related terms occurring together), and perform semantic weighting (based on the document corpus, the user’s history, or both) About to happen with search- the search technology will be performing the next step. In the same way, as information systems have improved the quality of knowledge work, the next generation of information systems will start to do the low-level knowledge work, allowing us to focus on higher-level tasks

A Drift in a way it is Designed Old Search System New Search System

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