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Synopsis: “Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems. Amid the proliferation of real-time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D. Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Telecom & IT estimates that Big Data investments will account for over $65 Billion in 2018 alone. These investments are further expected to grow at a CAGR of approximately 14% over the next three years. The “Big Data Market: 2018 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 till 2030. The forecasts are segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries. The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report. Key Findings: The report has the following key findings: - In 2018, Big Data vendors will pocket over $65 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for more than $96 Billion by the end of 2021.
- With ongoing advances in AI (Artificial Intelligence) technologies, Big Data analytics initiatives are beginning to leverage sophisticated deep learning systems with an autonomous sense of judgment – to enable a range of applications from chatbots and virtual assistants to self-driving vehicles and precision medicine.
- In order to analyze data closer to where it is collected, Big Data and advanced analytics technologies are increasingly being integrated into edge environments, including network nodes, numerous industrial machines and IoT (Internet of Things) devices.
- The vendor arena is continuing to consolidate with several prominent M&A deals such as Oracle's recent acquisition of enterprise data science platform provider DataScience.com – in a bid to beef up its capabilities in machine learning and Big Data for predictive analytics, and Google's acquisition of Big Data application platform provider Cask Data.
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