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How analytics is revolutionizing search

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The future of search is to not search at all. This may sound contrarian, but we are on the threshold of search technology that will eliminate the need to explicitly ask for information. Search has come a long way from its initial focus on relevance, to incorporating a social perspective, to heading towards a future of personalization where the Internet will be essentially customized to each user.

How did this evolution happen? Why and how did search come into existence? In the late 20th century, the introduction of personal computing by IBM and the invention of the Internet resulted in the creation of the disorganized and unregulated World Wide Web. In 1994, Yahoo! attempted to organize the Internet by creating the first online directory of websites. As the Internet grew, searching for relevant information became a nightmare. In 1998, Larry Page, co-founder of Google, had an idea that revolutionized search on the Internet. Drawing inspiration from citations in academic journals he developed the PageRank algorithm to rank search results by using links on millions of websites to measure the relevance of web pages.

Semantic search was the next big thing, aimed at understanding the searcher's intent and facilitating serendipitous content discovery. Kosmix built a topic exploration engine that could operate like a human mind and return pages that were part of the topic of the user's query. Even though this system was able to return more relevant results, it was unable to interpret a query posed in natural language. In 2007, IBM built a super computer Watson, capable of answering questions posed in natural language. This was one of the first breakthroughs in cognitive computing where a machine demonstrated the capability of thinking like a human. Since then, leading search providers have incorporated a semantic flavor into their own search engines and are able to return answers in addition to providing a series of links.

An interesting twist in search technology was triggered by the proliferation of recommendation systems built by companies like Amazon and Netflix. These systems were capable of tracking user activities and preferences and providing customized recommendations. As recommendations from a trusted friend serve to be more valuable, the incorporation of social network into recommendation systems and search technology has set off a new wave of change. Facebook is collecting large volume and high quality data on user preferences and interactions, and in early 2013 the company launched Graph search, a social search engine that enhances the search experience by adding a new dimension of personalization.

The era of mobile computing is fueling search technology to grow at an even more rapid pace. Smartphone applications are able to track users and collect abundant information implicitly and explicitly about them. Voice search applications like Google Now and Siri have demonstrated how search can integrate with voice recognition, natural language processing and the user's social context to make search more relevant and accessible. Applications are being built that can detect a user's mood and can passively listen to phone conversations and pull out relevant information skipping the need to ask for it.

Lately, Google's augmented reality glasses project is generating a lot of excitement about the potential of wearable technology and its impact on our lives.  There is mixed opinion about its widespread adoption, but it's almost certain that it is going to revolutionize mobile computing and change the way we interact with search technology. As technology evolves the interface that enables search will recede into the background and the collection of data on every user's interest, preference, behavior and activity will become a reality. As user interaction with digital technology increases, data will grow exponentially and user expectations from technology will increase, resulting in traditional search engines failing to deliver direct answers for highly complex and specific questions. Recent research by Microsoft and MIT has shown that adding an element of crowdsourcing information can improve search results and expand the range of direct answers. Companies are already building applications that can provide real-time and up-to-date information by incorporating crowdsourcing information for specific questions like the vibe at a restaurant.

The search technology of tomorrow will be built on a truly intelligent system that can think, understand and make decisions on its own. While initial attempts at analytics included brute force techniques of feeding enormous data and rules to systems, improvements in machine learning algorithms (that learn on their own, simulating the working of the human brain) have revolutionized search significantly. As the ability to capture and process large volumes of data increases, the disparity between offline and online behavior of users will be bridged. A user's entire online profile from friends to activities to preferences to shopping patterns and hobbies stored across platforms will be integrated to gain insights into their needs and wants. We are on the threshold of a personalized engine that can deliver the right content at the right time. Some may argue that a high degree of personalization will limit information and restrict serendipity, but the search engine of the future has the potential to facilitate a higher degree of awareness and a greater understanding of oneself.


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