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Big Data, or Just a Big Pile of Data?

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It’s best to be smart with big data and analytics.
Big-Pile

Big Pile

Big Pile-Doug Laney’s three Vs—volume, velocity, and variety—provide a useful definition of big data. However, big data is now the norm in manufacturing environments, perhaps it’s more important to think about a fourth V: value.

Almost 20 years after the phrase “big data” was coined, manufacturers have come to realize that the secret to getting the most out of big data isn’t quantity, but quality. By ensuring the collected data and analytics performed align closely with company’s objectives, businesses can improve operations and remain competitive.

Well-designed big data systems have been proven to help achieve new product development, smarter decision making, and both time and cost reductions. Intel, one of the world’s largest chip manufacturers, estimated savings of $30 million by streamlining quality assurance processes based on big data analytics.

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According to Actify.com, 33% of all data could be useful when analyzed. However, companies only analyze 0.5% of all data. By putting in place an enterprise data strategy, companies can ensure they are processing useful data and time is not being wasted on the rest. A good data strategy also ensures processes are universal across a business so that data is deftly managed, handled, and processed.

To create an enterprise data strategy, there are four key principles companies should consider. First, the strategy needs to be practical and easy to implement across the organization. It also needs to be relevant and tailored to the company’s goals, as well as evolutionary and adaptable to pace with trends. Finally, the strategy must be universally applied across the business and easy to update when necessary.

Using smart sensors, manufacturers can capture and analyze data from almost any type of machinery involved in their processes. This information can be used to monitor specific, individual parts (like motors or gaskets) to predict upcoming mechanical failures. In turn, these predictions can prevent unnecessary downtime and costs related to emergency maintenance because manufacturers are able to deal with issues before they cause problems.

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Article Credit: MD

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