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.
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.
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.
Big data is proving its value to organizations of all types and sizes, and in a wide range of industries. Enterprises that make advanced use of big data are realizing tangible benefits, from improved efficiency in operations and increased visibility into rapidly changing environments to the optimization of products and services for customers. The result is that as organizations find uses for these large stores of data, big data technology, practices and approaches are evolving. New techniques for collecting, processing, managing and analyzing the gamut of data across an organization continue to emerge. Dealing with big data is more than just dealing with large volumes of stored information.
Advances in technology are on the unprecedented rise. These developments are increasingly changing the way businesses are done, paving new paths to digital innovation. Business intelligence is one such effective technology for today's data-driven organizations. It converts raw data into actionable information. Business intelligence interprets data and understands trends, assisting enterprises to make data-driven decisions. While the adoption of this technology is surging rapidly, the buzzwords defining different BI software techniques are multiplying every year.