However, this is to misunderstand what Watson actually is. An assumption prevails that Watson is a supercomputer so expensive and exclusive that it is out of the reach of even today’s largest organisations. IBM, however, is already commercializing the Watson technology, initially in healthcare, while key elements of the technology behind Watson are available and already in use in businesses today. 

 

What Watson achieved in winning Jeopardy! is really quite simple. It successfully leveraged existing Natural Language Processing (NLP) technology with new invention to create DeepQA automatic question answering technology. NLP enables Watson to understand the questions being asked of it, while DeepQA provides accurate and confidence-based answers to questions. 

 

Just like a human brain, Watson understands the idea of context – depending on the way the question is framed and the words contained with it, Watson is able to understand what is actually being asked of it, and can then search available information sources for the best possible answer. One way Watson surpasses the human brain is in its ability to rapidly analyse a gigantic volume of information to produce this outcome.

 

So what’s the value for business? To understand this, it’s worth considering the simple statistic that more than 80% of all information stored by the average company is in an unstructured, text-based format – Word documents, emails, presentations etc. Traditionally, this information has not been easy for a machine to understand and process. Moreover, it’s not a form that humans can extract relevant data from quickly either.

 

And it doesn’t end there. New ways of working together such as collaborative wikis, communities and RSS are generating even more text based content expressed in natural language. Social business is not just inside the firewall though. Blogs, wikis and social network conversations are giving consumers and businesses a voice and power they’ve never have before, again based in text and expressed in natural language. This is a big deal.  

 

There is an amazing amount of information and insight captured in these conversations and exchanges. Companies can learn from their customers about product quality, customer experience, price, value, service and more, while internal conversations may contain valuable nuggets relating to strategy, projects, issues, risks and business outcomes.

 

But the problem is that the old data analysis tools aren’t fit for purpose in sifting the gold from all this information - don’t expect today’s business intelligence software suites to understand language, threaded conversations or the nuances of context. In simple terms, today’s computers are simply bigger and faster mathematical extensions of their predecessors (calculators, punch cards, tabulating machines). When you analyse data using these tools, a “5” is always a “5”.  You don’t have to understand what a 5 is or figure out what it means. You just have to calculate it against other numeric indicators and metrics.  

 

Unfortunately, that’s not how verbal and written conversations work – natural language exchanges don’t follow simple, logical rules. Take, for instance, the word “premiere” - noun, verb or adjective?  It could be the title of a person, the raising of the curtain on a theatre play, or description of something that is foremost in its field. Natural language is full of ambiguities like this - it is nuanced and filled with contextual references. Subtle meanings, irony, riddles, acronyms, idioms, abbreviations and other language complexities all present unique computing challenges not found with structured data.

 

How Watson works

In short, Watson is the first computer to overcome these challenges. Unlike BI and enterprise search technologies, it doesn’t just find and flag relevant information that human beings still have to interpret and analyse. Rather, it comes up with exact, nuanced answers to questions, doing the majority of the analytic spadework itself. As such, it can deliver significant time and efficiency savings within the enterprise.

 

By advancing NLP with DeepQA automatic question answering technology, Watson represents the future of content and data management, analytics, and systems design. It leverages core content analysis, along with a number of other advanced technologies, to arrive at a single, precise answer within a very short period of time. The business applications for this technology are virtually limitless, with innovators in areas such as clinical healthcare, customer care, academic research and government intelligence already benefiting from the technology behind Watson.

 

For example, healthcare provider BJC Healthcare had a vast amount of information about disease progression, treatment effectiveness and outcomes, plus demographic data about patients, ‘trapped’ in clinical notes and diagnostic reports. Previously, there was only one way to analyse this information – by getting humans to read and structure it so that further analysis could be carried out by computers. Using IBM Content Analytics, BJC can now examine all the data as is and at once. It can ask questions such as “Does the patient smoke? If so, how long? If not, how long since giving up?” then it can combine this information with other factors to identify trends and patterns that can improve future healthcare. All this from unstructured clinical records- not a perfectly organized database. IBM Content Analytics uses the same NLP technology as Watson.

 

In the area of customer satisfaction and marketing, identifying the latest trends around what makes consumers tick is vital to success. The Hertz Corporation and Mindshare Technologies - a leading provider of enterprise feedback solutions - are using IBM Content Analytics to examine a mass of customer data, including web surveys, text messages and emails. This has provided deep insight into car and equipment rental performance levels and enabled Hertz to identify and carry out vital adjustments to its customer service and satisfaction strategy. 

 

In the area of business development, North Carolina State University sought a solution to efficiently analyse vast quantities of data to better identify companies that could help bring new ideas to market.  The objective was a solution designed to parse the content of thousands of unstructured information sources, perform data and text analytics and produce a focused set of useful results. Using IBM Content Analytics, NC State has reduced the time needed to find target companies from months to days.  The result is the identification of new commercialisation opportunities, with tests yielding a 300 percent increase in the number of candidates.  By obtaining insight into their extensive content sources, NC State’s Office of Technology Transfer was able to find more effective ways to license technologies created through research conducted at the university. 

 

These are just a few applications of content analytics, but the potential is virtually endless. Many businesses have a huge quantity of valuable information stored in unstructured formats that previously could not be properly analysed and acted upon – Watson has changed this. In the coming decade, the management of data and the ability to extract the right insights from that data will a crucial driver of competitive advantage. It’s therefore vital to start thinking about what data your business has left unexploited, and what advantages it could bring you.

 

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