The concept of Garbage In, Garbage Out (GIGO) is almost as old as computing itself. Its origins have been traced back to the 1950s and basically means that if you start with bad information, you get faulty results. It’s a pretty simple concept that remains at the core of computing.
How many times have you switched your mobile phone service provider when the service or support was poor? How hard did that service provider work to keep you? It’s likely they didn’t try very hard. They have many customers, so losing one isn’t that big of a deal. But for companies that provide complex products like those in manufacturing, aerospace or oil and gas, a high-quality customer support program is critical. The question is, what does a quality customer support program look like?
As we’ve explained in many blogs and 5-minute guides, a cognitive search platform should combine AI technologies such as natural language processing, machine learning, and knowledge graphing to deliver a contextualized search and discovery experience without compromising security. Those technologies can turn ordinary search into something much more powerful and transformative for any organization.
Today’s business users don’t search for documents that may have the information they seek buried within, instead they ask their systems for answers. This shift in attitude is a key driver behind the move to cognitive search.
Hurricane Harvey hit Houston hard. Harder than many expected, including a number of the oil and gas companies located in the area. Some evacuated early and had no idea when they would reopen for business.
Attivio 5.5—the latest release of our market leading Cognitive Search and Insight Platform—has a lot going for it. If you just want a quick summary of all its new features, the launch press release is a good place to start.
But I’m going to focus on one—the platform’s use of machine learning to improve relevancy. After all, relevancy is the heart of cognitive search.
Some days, it seems that new machine learningapplications are popping up everywhere you look in the news. In this author’s opinion, the search market seems to have anointed machine learning as the new hotness. This is a fascinating realization, because anyone who has spent more than a couple years deploying search in the enterprise knows that machine learning has been used and applied in exciting and unique ways for years and years.
Sir Arthur Conan Doyle’s 1886 fictional “consulting detective,” Sherlock Holmes, was a great mind renowned for his highly advanced powers of observation and reasoning. He was often assisted by Dr Watson, who was unfailingly loyal, if noticeably less bright. At the end of each thrilling tale starring the duo, the anxious reader would always be delighted to hear Sherlock announce that he had solved the latest mind-bending riddle, inevitably characterizing the solution to his trusty helper as, “Elementary, my dear Watson!”
Search engines index millions of pieces of information, structured and unstructured. But simply indexing information isn’t enough to give a user the results they need when they perform a search.
The Need for Relevancy
The goal of relevancy tuning is to help a user get the best results for a given query they are trying to run. Relevance is telling the search engine how to best sort the information in its index to ensure search results match search queries as closely as possible. It’s the process of bringing the most relevant information to the top of the result list.