Source: Global Market Insights, Inc

Cloud Natural Language Processing Market to hit $6bn by 2024: Global Market Insights, Inc.

Cloud Natural Language Processing (NLP) Market Size By Product (Rule Based, Statistical, Hybrid), By Deployment Model (Public, Private, Hybrid), By Technology (Recognition, Analytics, Operational), By Application (Information Extraction, Machine Translation, Processing & Visualization, Question Answering), By End-use (BFSI, IT & Telecommunication, Defense, Government Organization, Retail & E-Commerce, Healthcare, Energy & Utility), Industry Analysis Report, Regional Outlook (U.S., Europe, UK, Germany, France, Italy, Spain, Australia, China, India, Japan, Brazil, Mexico, UAE, Israel), Growth Potential, Competitive Market Share & Forecast, 2017 - 2024

Sellbyville, Delaware, Nov. 23, 2017 (GLOBE NEWSWIRE) --

Cloud NLP Market is expected to surpass USD 6 billion by 2024; according to a new research report by Global Market Insights, Inc. Exponential growth of the digital data is one of the major factors that is driving the growth of the cloud natural language processing market. Data/information has emerged as one of the most important assets of the organizations. Companies are collecting, analyzing, and reporting vast volume of data for extracting meaningful insights to get competitive edge.

Growing adoption of the big data technologies such as Hadoop and IoT among organizations along with popularity of cloud deployment is one of the major factors that are fueling the growth of the digital data. Digital data is estimated to grow at a rate of 40% for next 10 years and by the year 2020 data generation is expected to reach 44 zettabytes per year. As the data increases, it leads to the requirement of an effective analytics solution to process the information, therefore, driving the cloud NLP market growth.

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Healthcare applications are estimated to exhibit higher growth rate than other industry verticals. Healthcare organizations are widely adopting NLP solutions for analyzing clinical transcriptions, managing medical notes, and documentation purposes. Furthermore, these solutions also assist in reducing medication errors and adverse drug events by managing Health Information Management (HIM) systems. In addition, implementation of ICD-10 regulation and adoption of auto-coding technology in healthcare is further estimated to fuel the demand in the healthcare sector.

Media & advertisement and the retail sector is also one of the major end-users of the cloud NLP market solutions. They are leveraging NLP capabilities along with social media analytics modules and CRM solutions for lead generation and brand recognition. Furthermore, increasing interest of the market players towards these industry verticals is also backing the growth of the market. For instance, IBM, HP, SAS and EasyAsk are providing NLP tools such as sentiment analysis and text analysis to these industry sectors.

Browse key industry insights spread across 230 pages with 348 market data tables & 42 figures & charts from this 2017 report Cloud Natural Language Processing (NLP) Market in detail along with the table of contents at:      

https://www.gminsights.com/industry-analysis/cloud-natural-language-processing-nlp-market

Asia Pacific is estimated to be the fastest growing region in the global cloud natural language processing market owing to the increasing adoption of the smart devices. Moreover, increasing investment in AI by Chinese players such as Baidu and Alibaba are also contributing significantly towards the revenue growth.

Some of the vendors offering NLP solutions are Google, Microsoft, IBM, HP, AWS, Baidu, Dolbey Systems, Facebook, Netbase Solutions, Fuji Xerox, Lexalytics, SAS, and Verint Systems. Vendors are trying to capture the market with customized product portfolio, that will help them in gaining more market share. Also, they are collaborating with cloud providers to offer products and services especially, to SMEs. The market is quite attractive as majority of the players are ready to invest in technologies such as AI, deep learning, and machine learning.

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