Big Data Analytics Driven Enterprise Asset Management For Asset Intensive Industries

6539 WordsJun 29, 201527 Pages
¬Big Data Analytics Driven Enterprise Asset Management for Asset Intensive Industries. Abstract “Information is the oil of the 21st century, and analytics is the combustion engine.” was introduced by Peter Sondergaard during Gartner Symposium/ITxpo 2011. In fact, data is like oil! It has value, but it needs to be extracted and refined to get the true value from it. In today’s business climate, organisations across various sectors are realising the importance of collecting data from different business processes across their enterprise. This increase in data gathering and integration is fuelled and driven by advanced technologies for collecting data from various data sources, storing the data using standardised approaches and most importantly advances in Artificial intelligence (AI) and Big Data analytics to extract value from data. Enterprise Asset Management (EAM) is a strategic approach for organisations that heavily rely on physical assets to generate revenue, it’s a data driven process that collects and uses data about assets to achieve optimal allocation of resources for the management, operation, maintenance, and preservation of asset infrastructure. Oil and Gas industry is an asset intensive industry where asset management is one of the main business drivers, asset manager’s needs to make critical decisions in real time in order to make sure their assets are running in the most optimal way. The focus of this research is a marriage between Big Data analytics and EAM in

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