cPattern recognition, data mining, and natural language processing to simulate the work performed by humans are some of the specialized functions performed by cognitive systems. Generating and evaluating the evidence based on the hypothesis made on natural language and human communication, along with further adaption and learning from user selections and responses is the standard build of a cognitive system.
Content analytics is the process of applying business analytics and business intelligence ideas to digital content. It is the combination of text analytics and mining which has the ability to visually identify and explore trends, patterns, and other statically available facts present in different types of content across various sources. Content analytics software is largely used to give more visibility to the amount of content that is being created. Content analytics is a vital tool to gauge the content and user behavior while consuming and engaging with content. Content analytics delivers new business understanding and visibility from the content and context of textual information. Across a wide range of areas, content analytics can help address a variety of information challenges, such as improved customer satisfaction through the analysis of feedback, of automated surveys, and of brand perception.
The business intelligent architecture aimed at building and using interactive data from multiple source is called data discovery. In other words, data discovery is all about the system that scans the data available through various sources and extracts meaningful information from data pertaining to the business or report objectives. The extraction of actionable data is performed by humans or by artificial intelligence systems. The artificial intelligence systems used to extract data are called discovery software. One of the major benefits of discovery software is its enhanced analytical effectiveness for faster insights.
The major driver for the cognitive systems market is the increasing concern related to risk management in areas such as product management, marketing activities, and information flow supply chain management. Additionally, the cognitive systems market is driven by the rise in demand for data analytics from sensor networks. The content analytics market is primarily driven by the need to eliminate content-centric process inefficiencies. The prime driver for discovery software is its efficiency in handling big data. Furthermore, the shift to real-time analytics is another growth factor for the discovery software market. The Internet of Things (IoT) is a recent trend that is expected to create numerous opportunities for real-time content analytics and data visualization. The processing of dark data (the operational data that is not being used) is another trend observed in the cognitive systems and content analytics market.
The cognitive systems, content analytics, and discovery software market can be segmented on the basis of deployment and end user. By deployment type, the market comprises the on-premise and cloud segments. By end user type, it consists of the banking, financial services, and insurance (BFSI), retail & consumer goods, IT & telecom, healthcare, media & entertainment, government, travel & hospitality, and others.
Additionally, major players engaged in offering cognitive systems, content analytics, and discovery software are International Business Machines Corporation (IBM), Cognitive Systems Corporation, Microsoft Corporation, Cisco Systems Inc., Google Inc., Customer Matrix Inc., SAS Institute Inc., Lexalytics, Inc., Content ANALYTICS Inc., Tableau Software, Inc., TIBCO software Inc., QlikTech Inc., and SMARTe Inc.
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