Disruption ahead: Deloitte's point of view on IBM Watson8 9. What makes Watson unique In technical terms, IBM Watson is an advanced open-domain question answering (QA) system with deep natural language processing (NLP) capabilities. At this point, the Watson Software as a Service (SaaS) platform is most effectively used to sift through massive amounts of text--documents, emails, social posts, and more--to answer questions in real time. Watson accepts questions posed by the user in natural language and provides the user with a response (or a set of responses) by generating and evaluating various hypotheses around different interpretations of the question and possible answers to it. Unlike keyword-based search engines, which simply retrieve relevant documents, Watson gleans context from the question to provide the user with precise and relevant answers, along with confidence ratings and supporting evidence. Its learning capabilities allow Watson to adapt and improve hypothesis generation and evaluation processes over time through interactions with users. Developers and other users can improve the accuracy of responses by "training" Watson. IBM is also continuing to expand Watson's capabilities to incorporate visualization, reasoning, ability to relate to users, and deeper exploration to gain a broader understanding of the information content. Watson recently launched a new platform service that has the ability to ingest and interpret still and video images, which is another significant type of unstructured data.
In 2016, many Organizations began storing, processing, and extracting value from data of all forms and sizes. Going ahead, systems that support large volumes of both structured and unstructured data will continue to rise. Circa 2017 - the market will demand platforms that help data custodians govern and secure big data while empowering end users to analyse that data. These systems will mature to operate well inside of enterprise IT systems and standards. Besides the convergence of IoT, cloud, and big data will create new opportunities for self-service analytics.
History and Evolution of Data Analytics: The concept of'Big Data' has been around for decades. Many organizations now understand that, if they capture all the data sets that streams into their businesses, they could apply analytics to get significant insights and value from the data. Even in the 1950s, decades before anyone even uttered the term "Big Data," the businesses were using analytics – especially numbers in an excel sheet which were analyzed manually to gain insights and trends. The companies used this information for future decisions. Whereas, today, the business can identify insights for immediate action as the new benefits which the big data analytics brings are efficiency and speed.
Machine learning helps in improving business operations and enhances business scalability for global enterprises. Surfing the Internet...reading online weather reports…using speech recognition, and driving a car using GPS navigation...are some of the benefits of machine learning that have already become a part of our daily lives. Today, artificial intelligence tools and machine learning algorithms have become popular in the field of business analytics; industry experts believe it deserves such attention. The term'machine learning' refers to enabling computers with the ability to draw observations from the collected data; you do not need explicit programming for this process. Machine learning helps in extracting meaningful information from raw data; it is an excellent technique of solving complex-data-rich business problems that are not resolved by the traditional approaches like human judgment or software engineering.
It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise.