Manufacturing Sector Accounts for Nearly 1/4th of the Global Stored Data

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LONDON, Oct. 8, 2015 /PRNewswire/ -- The dawn of the Internet of Industrial Things (IoIT), fuelled by the adoption of wireless technologies and intelligent devices to improve asset connectivity, has further attributed to a proportional increase in the inflow of data. In addition to traditional data management systems, large volumes of structured and unstructured data require a scalable framework to store, integrate, and process big data. Furthermore, as end users look to derive actionable insights from their critical data, adoption of state-of-the-art analytical platforms is gaining precedence in the current market.

"Post data capturing and sorting, end users are looking to extract value from their big data and optimise their core performance metrics," notes Senior Research Analyst Rahul Vijayaraghavan. "For example, the adoption of predictive and prescriptive analytics using machine learning techniques will allow customers to identify future asset and equipment breakdowns and further help streamline their maintenance related activities."

Join Frost & Sullivan online webinar to demystify the big data ecosystem and identify key opportunities for big data in the manufacturing domain.

During this webinar we will:

• Understand the challenges and opportunities arising from the influx of big data;
• Learn about the key building blocks of a holistic big-data solution;
• Discover key applications for big data in the manufacturing domain;
• Identify future profitable streams for solution providers.

This briefing will interest all industrial suppliers, ICT companies, service providers, system integrators, and value-added resellers that have a stake in diverse manufacturing environments.

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For further information email [email protected] your full name, job title, company name, company telephone number, company email address and website, city, state and country, or register here: http://ow.ly/T9Tlw

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Julia Nikishkina
Corporate Communications – Europe
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