Why Search Is the Wrong Answer To the Wrong Problem
Search is the enterprise application that suffers the most from reinvention of the wheel. While you are researching a business opportunity, competitive development, or market event using a search engine, other people in your company are probably repeating the same search. They may be in an office near you, on another floor of the building, across the corporate campus, or in a business unit on another continent. There is no greater loss productivity from any enterprise application than in the search process when many times each day employees in different locations, divisions, and product groups of the same company, unbeknownst to each other, search their enterprise repositories and licensed market intelligence information for the same (or closely related) pieces of information. The search engine industry has even institutionalized this problem, making it a “feature not a bug,” by popularizing notions such as “most popular queries.” Think about it, most popular queries? How again is it good that same query is repeated over and over?
Not only are huge amounts of professional labor wasted, the results and success of these duplicated searches vary widely based on the different levels of skill and domain expertise of the individuals conducting them. That means some people (the less proficient searchers and those with less time to spend on the search process) will end up making less well-informed decisions, because they didn’t find the most relevant information, the material with the best commentary, analysis, and perspective when they did their research.
The good news is search applications are evolving from “find me a list of documents to read” to “find me a person that knows the answer.” It turns out that a particular capability that strategic research applications can borrow from the Web 2.0 world can form a basis for turning the search engine into a collaborative environment. That capability is bookmarking and tagging.
For instance, you are researching a particular topic and find a report especially insightful or useful. You want to be able to recall this document later. Of course, all search applications suffer from the problem of the document you loved was hit number 3 today, but four months from now when you want to find it again it is hit number 367 because so much new content has been added. And you have to remember how you found it in the first place…what were those query terms, was the document from this source or that one, was its title this or that? The absolute solution to this problem is to bookmark the document and tag it with a useful word or two to help you find it later; tags that indicate why you liked it in the first place. Maybe you also put a note on the document explaining even more, a note that becomes part of the document record and travels with it. To find the document again later, you simply click on the relevant word in a tag cloud of all of your bookmarked documents and presto! there it is.
Users bookmark and tag documents to make their own life easier, not for altruistic reasons. They are not taking their precious time “rating” documents for unknown future users who may or may not benefit from the ratings. (You don’t rate for yourself, which explains why document rating applications never work in the enterprise.) But who cares what the motivations are – they’ve done it. A strategic research application can observe each user’s tagging behavior and the expose it in other places to other users that will benefit from seeing the tags and notes when the context is right. Such a strategic research application becomes a vehicle for discovering not only documents that have already been deemed especially useful or relevant in the context of a particular research inquiry, but also for identifying the individuals who were there first and most often. So the search application can lead subsequent researchers to a domain expert and natural collaboration partner without that person doing anything other than what he or she already does for his or her own purposes. This has the effect of greatly levering that domain expert’s impact on the business.
Users of such a “collaborative” enterprise search application get to know over time those individuals in their extended organization has research interests similar to their own, and can subscribe to the bookmarking behavior of those individuals, following them a la Twitter. Users can follow the path of breadcrumbs to the internal experts and go directly to them with the hardest, most important research problems. What’s more, each user can affect the relevance ranking of documents identified through a particular search query through their tagging activity. There is no mathematical algorithm that a search engine company will ever come up with for relevance ranking that can match the intelligence of a domain expert deeming a document worthy by tagging it and by providing additional texture through the notes and commentary they add.
It’s time to advance to enterprise search applications that lead people to the domain experts on a topic, as well as to documents that contain information about it. Better than asking a search engine a question is asking a person that knows the answer.