Hadoop tools predict mechanical breakdown
The manufacturing, utility and oil industries can especially benefit from big data applications. The huge numbers of datasets generated by these sectors can be gathered and processed more efficiently now, allowing data analysts to extract valuable information regarding many operations.
For instance, the advent of smart meters has allowed utility companies to gather information on consumer energy consumption. According to a survey conducted by Oracle about the utility industry, companies plan to leverage big data tools to predict electricity demands and minimize the scope of power outages. Enterprise CIO Forum contributor Jamal Khwaja argued that the information gleaned from smart meters could be used to determine exactly how much wasteful energy consumption practices – such as using incandescent light bulbs or leaving appliances running through the night – are costing homeowners on their electric bill.
One promising development in data analytics has been the recent announcement of Hadoop-powered software that could predict the likelihood of failure in expensive machinery, according to IT Jungle. Utility, manufacturing and oil companies rely on the continued functionality of their equipment. Hardware failures can result in significant financial losses as well as damage a company's brand image. Equipment breakdown can lead to power outages, product delays and environmental disasters.
Build upon a Hadoop architecture, the software gathers data from a variety of sources, including equipment sensors, maintenance logs, telematics and environmental monitoring systems. Using that information, the system uses predictive analytics tools to determine the likelihood of mechanical failure. Engineers and mechanics can then take necessary steps to fix the machinery and prevent catastrophic hardware failure down the line.
Using Hadoop big data tools, companies in the manufacturing, oil and utility industries can enhance their operations by finding new insights into their business operations and marketplaces, increasing their efficiency and reducing expenses.
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