Volume, Velocity and Variety - Defining Big Data

However, now that big data is the norm in manufacturing environments, perhaps it’s more important to think about a fourth "V".

Eu Auto

Doug Laney’s three Vs — volume, velocity and variety — provide a useful definition of big data. However, now that big data is 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 that the data collected and the analytics performed align closely with the company’s objectives, businesses can improve their operations and remain competitive.

Well optimized 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 manufacturers of processors, estimated a saving of $30 million by streamlining its quality assurance processes as a result of big data analytics.

According to Actify.com, 33 percent of all data could be useful when analyzed. However, companies only process 0.5 percent of all data. By incorporating an enterprise data strategy, companies can ensure they are processing useful data and that time is not wasted on the rest.  A good data strategy will also ensure processes are universal across a business so that data is managed, handled and processed well. 

To create an enterprise data strategy there are four key principles that companies should consider. Firstly, the strategy needs to be practical and easy to implement across the organization. It also needs to be relevant and specifically tailored to the company’s 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, 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 an issue before it causes any problems.

The gift of knowing when your equipment is likely to break down means necessary maintenance or ordering a specific part can be planned well in advance, ensuring your system runs smoothly without any surprise faults. This is an improvement on just planned maintenance alone, as it means maintenance is only performed when it is required. 

Big data, often defined by Doug Laney’s three V’s, has led to great strategic and operational improvements. However, to get the best results for your business, remember to consider the fourth V — value. Ensuring your big data is relevant and of high quality, will always be more important than the quantity.