Big Data, or Just Data?

Back in 2001, analyst Doug Laney of Gartner, Inc. (Stamford, CT) developed the three defining properties or dimensions of big data referred to as the 3Vs (volume, variety and velocity). Volume is the amount of data, variety is the number of types of data and velocity is the speed of data processing. According to the 3Vs model, the challenges of big data management result from the expansion of all three properties, rather than just the volume alone – the sheer amount of data to be managed. Now, almost 20 years after the phrase “big data” was coined and it actually becomes the norm in manufacturing environments, a fourth V is being considered as the most critical property of all: value. Manufacturers have come to realize that the secret to getting the most out of boundless volumes of information isn’t quantity, but quality. In other words, the best results for business are gained by ensuring that big data is relevant and of high quality – and that will always be more important than the quantity.

By ensuring that the data collected and the analytics performed align closely with their business objectives, manufacturers can improve their operations and remain competitive. Well-optimized big data systems have been proven to help achieve new product development, make smarter decisions, and reduce both time and costs. Intel (Santa Clara, CA), for example, estimated that they saved $30 million in production of their processors by using big data analytics to streamline their quality assurance processes. It’s a huge opportunity: According to Actify.com, 33 percent of all data could be useful when analyzed – but companies only process 0.5 percent of all data. By incorporating some sort of enterprise data strategy, manufacturers can ensure they are processing useful information and not wasting time on the rest. A good data strategy also ensures that processes are universal across the business so that the data is managed, handled and applied effectively.

Four key principles should be considered when creating an enterprise data strategy. First, the master plan must be practical and easy to implement across the organization. It also needs to be relevant and specifically tailored to company business goals, as well as evolutionary and adaptable to keep up with current trends. Finally, the strategy must be universally applied across the business and easy to update when necessary. Using smart sensor technology, shops 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 the shop floor is able to deal with an issue before it causes any problems. This gift of knowing when equipment is likely to break down means necessary maintenance or ordering a specific part can be planned well in advance, ensuring that the system runs smoothly without any surprise faults – a big improvement on planned maintenance alone, because it means maintenance is only performed when it is required.