There is no dearth of information available on the World Wide Web; in fact the volume of information sometimes is so massive that there is a dearth of “relevant” information. Time and time again, there would have been instances where much effort had been put into finding the relevant information at the expense of creative work that could have otherwise been performed. There can be arguments on the necessity of search effort but the fact remains that it is not the search but results that matter.
This is where the concept of proactive search comes into play, in this rising field you don’t spend time to find what you need instead the proactive search will find the relevant information for you.
How is it possible?
Proactive search uses inputs from sensors, captures insights from a particular context and the user behaviour associated with it, learns them and creates answers without a user instigated enquiry. It forms a collaborative nexus of personalization, enterprise search, analytical capabilities, interactive user experience and contextualization.
Why proactive search?
In the field of business more specifically in the enterprise search effort, it is expected that the user knows what to search for and the keywords to be employed to find the right information. If the user is not aware of the right keyword the search effort becomes cumbersome. This unproductive search effort can be made more productive and precise with the enterprise search engines learning the user behaviour. This learning combined with application of analytics gives the unstructured content a structure.
Rise of the Smart assistants
Search and analytics helped by the backing of good computational power creating a situation where results are pushed rather than pulled. This becomes a good start for companies to work with smart assistants that could save you a lot of time and hassle. Such smart assistants that employ proactive search anticipate need based on the location, behaviour and contextual requirement. Smart assistants and proactive search application can automate the search process making the results relevant.
Granted these concepts would take some time to get implemented in a large scale as the algorithm involved would take time to get used to the user and his set of unique needs. But once the learning and deciphering of unstructured data takes place, these technologies can fasten the access and processing of unstructured information thus effectively decreasing the time to find the solutions to organizational queries.