As the world's oldest and largest open-source intelligence agency, Jane's has spent the last 120 years collecting, classifying and analyzing information. We support national security analysts who are trying to understand the emerging threats to a country's national interest and how best to respond to them. Those analyst teams, our customers, could spend many weeks collecting the information, understanding it, verifying it, and then reporting on it. Or they can work with us to get highly accurate, relevant information from our open and classified sources. Then they can quickly get down to the business of reporting and providing decision-support to their agencies and governments.

Machine learning itself is not new. The concepts have been around for decades, and many companies have been building ML models and doing predictive analytics for a while. So what exactly is IBM doing in this space? I would like to say, "IBM takes an enterprise approach to ML," but that sounds too vague. This post details IBM's commitment to machine learning and how our approach goes beyond hype and hand-waving to offer the platforms, tools, and processes our enterprise customers need. Let's look at each one.

I'm sure you've experienced the feeling of having made a decision -- and then discovering information that might result in a different outcome and saying to yourself -- "If I knew then what I know now ….." Buyer's remorse, post cognitive dissonance, hindsight, wiser after the event -- all expressions of regret in some form or another. With so much data created every second, we can never have every bit of information available at our fingertips so we make decisions based on what we think we know and have at the time. In theory, the more information we have (provided it is accurate) the better the decision and outcome, since information is tied to risk, revenue, growth -- and their associated antonyms. But, total information awareness is more an aspiration than a reality as we can never know everything and its context. You might ask "Where and how do I start discovering what information is out there? And how do I expand that information into the different forms of knowledge so that I can encompass more and more data moving forward?"

The search function is a very powerful tool; assuming you have concrete keywords or concepts to find in your data. And that does not even take into account the size of the information you might be searching. Business professionals are often working with massive amounts of information. In that circumstance, how would you compile all the keywords you want to search on? And that just accounts for what you know you're looking for.

IBM Watson is a platform that represents a new era of computing based on its ability to interact in natural language, process vast amounts of disparate forms of big data and learn from each interaction. It is able to sift through and understand massive amounts of big data at unprecedented speeds to assist professionals in understanding data quickly and easily, while increasing knowledge and gaining value over time. IBM Watson is not only solution in this space, Digital Reasoning and several other players also have compelling offerings. IBM Watson just happens to have the most name recognition. Watson is a cognitive system that sifts through massive libraries of data to discover insights that can help its users answer simple to the most complex of questions.

The company believes it will increase productivity by 30% and see a return on its investment in less than two years. It expects to save about 140 million yen (US$1.19 million) a year after the 200 million yen AI system is installed this month. Maintaining it will cost about 15m yen a year, said a Guardian report. The IBM's Watson Explorer is said to possess "cognitive technology that can think like a human", enabling it to "analyse and interpret all of your data, including unstructured text, images, audio and video", according to IBM. Its technology will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, said the Mainichi Shimbun. While the use of AI is expected to drastically reduce the time needed to calculate Fukoku Mutual's payouts – which reportedly amounted to 132,000 during the current FYI– the sums will not be paid until they have been approved by a member of staff, the Mainichi Shimbun noted.

As enterprise enters further into the digital age, data has become the strategic asset that knowledge workers, small or large, rely on to guide their decisions. However, managing such large volumes of data has exposed some unprecedented challenges for the enterprises. Enterprises have learned that the data that they hold, comes in a variety of formats, resides in different and distributed systems and is specific to the organization and its domain. Setting these challenges as the backdrop, IBM's Watson division has built solutions that not only allow for data connectivity but also the analysis of unstructured data and its customization to an enterprise domain. IBM Watson Explorer is Watson's flagship product for text analytics and discovery.

As enterprise enters further into the digital age, data has become the strategic asset that knowledge workers, small or large, rely on to guide their decisions. However, managing such large volumes of data has exposed some unprecedented challenges for the enterprises. Enterprises have learned that the data that they hold, comes in a variety of formats, resides in different and distributed systems and is specific to the organization and its domain. Setting these challenges as the backdrop, IBM's Watson division has built solutions that not only allow for data connectivity but also the analysis of unstructured data and its customization to an enterprise domain. IBM Watson Explorer is Watson's flagship product for text analytics and discovery.

Next-generation cognitive computing is rapidly changing how we live and work. Thousands of brands like 1-800-Flowers and Sesame Street are already using cognitive solutions like IBM Watson to redefine how they improve performance, customer service and revenue. Today's business challenges have never been more complex, and the critical insights that can help address these challenges are often buried in an avalanche of data. With cognitive computing, we are now able to unlock the value in ALL the data -- from internal, external and even publicly available sources -- available to a business. Much of this data was previously inaccessible as it existed in was unstructured (documents, emails, social media posts and images etc.), or was dispersed among any many systems and silos.