Intelligent Search Goes Beyond the Web
Search is a crucial component of the modern workplace. The ability to find information quickly and efficiently contributes not only to business success but also to worker satisfaction. It’s frustrating to waste time looking for a piece of information, or a data point, when you could be completing a task. When you get ready to leave the office, it’s much more satisfying to look back on a productive day where you actually finished a project instead of contemplating a day where you found yourself constantly impeded by the lack of necessary information and your productivity diminished by fruitless searching.
Search has become ingrained as part of everyday life, thanks to mobile phones. As a child, I must have been a trial to my parents, with my nose always buried in a book. I was that annoying kid who just couldn’t let conjectural statements go unanswered, uncorrected, or unverified. My trigger points were people saying things like, “I think that happened around 1864.” Or, “I’m pretty sure the capital of Italy is Milan.” Or, “I can’t remember the names of the industrialists who were called ‘robber barons’ after the California gold rush happened, but I think Stanford was one of them.”
In the pre-web, pre-mobile phone world of my youth, my recourse was the set of encyclopedias my parents had been cajoled into buying. I would drag out the large, heavy books so I could clarify that the date of the event was 1866, that Rome is the capital of Italy (but that wasn’t always so), and the robber barons were Leland Stanford, Collis Potter Huntington, Mark Hopkins, and Charles Crocker.
Today, of course, there’s no need to pull a volume of an encyclopedia off a shelf or even leave the room to find answers. In a much more subtle fashion, you can simply and unobtrusively look down at your phone to search for answers to factual questions. Google and Wikipedia have redefined what it means to search. But have they made search any more intelligent? They certainly satisfy the itch to correct people on event dates, geography, and historical characters. When it comes to the workplace, however, search encompasses a great deal more than fact checking, and intelligent search goes well beyond the web.
We’ve moved from an era when the world’s knowledge could be confined to a set of encyclopedias (which I think is what my parents believed when they bought those books) to one in which an abundance of information in digital form is readily available. Search has gone mainstream. People use the word “search” when they want to locate a retail store or book a hotel. That simplistic notion of search does not carry over particularly well to finding information essential to doing your job.
Teasing Out the Meaning of the Search
Part of moving from a simple Google search to a more sophisticated model involves language. Take furniturec for example. That thing you’re sitting on, is it a couch, a sofa, a love seat, a davenport, or a chaise lounge? As Expert System’s Daniel Mayer points out, web search engines are “flying blind.” They don’t know the worldview of the searcher. What we need, he thinks, is a “meaning engine,” a semantic enrichment platform designed specifically for your organization. If you manufacture sofas—and you call them sofas—someone searching for any of its synonyms should be able to find your product no matter what term they enter in a search box. Semantics matter, but it’s not the only factor for a successful search experience. Mayer recommends better metadata and better data.
Isabell Berry, from Adlib, concurs. She sees the underlying content as a potential roadblock to findability. Standardizing content in one format—her example is high-definition PDFs—creates better visibility and fewer irrelevant search results. You may be able to avoid overly complex algorithmically based search engines by improving content processing, eliminating duplication, and using a single taxonomy.
Almost anyone looking at search within the enterprise stresses findability. If you’re looking for the company’s holiday schedule, you don’t want the one from 3 years ago, you want the most recent one. Similarly, if you’re building a website for external use, you want potential customers to find what you’re selling. You want to back up your sales efforts with excellent customer service. This is another opportunity for intelligent search to shine, since customers increasingly prefer to help themselves without using an intermediary. They like self-service, but only if it answers their questions.
Semantics plays a role in customer service, writes Inbenta’s Jordi Torras, explaining that its analysis of the contextual meaning of words enhances the quality of answers. One example is FAQs. Customers might enter in “How much will it cost me…” while your FAQ phrases this as “What is the price…” To be findable, your customer’s search query must translate to your words. Torras defines findability as “Semantic Coincidence” and recommends “lexical functions” that map many kinds of semantic relationships. An advocate of natural language searching,
Torras wants people to ask real questions, not rely on keywords.
Pascal Soucy thinks the definition of intelligent search goes beyond findability. A search engine should know what you need and what your colleagues found valuable, and supply it to you when you need it. For Coveo, “the power and sophistication of machine learning technology is the driving force behind intelligent search.” Intelligence springs from usage and analytics data, along with a multitude of other factors, the components of which are hosted and managed by companies such as Coveo.
Regardless of how you define intelligent search, it’s clear that enterprise search requirements go well beyond what Google or Wikipedia can provide—or an old print encyclopedia, for that matter. Different approaches to intelligent search provide much to think about when implementing, redesigning, and rethinking enterprise search. Intelligent search goes well beyond what searching the web looks like.