Gathering data is something all manufacturing facilities do, but making the most of this information and transforming it into actionable insights is what gives data its purpose. John Young explains how manufacturers can make the most out of big data to achieve business value.

Roughly 80% of the mass that makes up our universe is material that scientists cannot directly observe — known as dark matter. While we are unable to see dark matter, physicists are confident it exists because of its gravitational effects. For many organisations, dark data is of a similar character. But what use is unseen, unused data?

Gartner defines dark data as “the information assets organisations collect, process and store during regular business activities, but generally fail to use for other purposes”. Just like dark matter in space, dark data comprises most businesses’ universes of information assets. Also like dark matter, many know little of its existence. So, what qualifies as useful data, and how can manufacturers make sure they’re getting the most out of it?


Doug Laney, a Gartner analyst, defined big data using three words — volume, velocity and variety. Big data is, unsurprisingly, large in volume. It relies on massive datasets in volumes as large as petabytes and zetabytes. While such a scale may seem unmanageable, these large datasets aren’t as difficult to collect as you may imagine.

Technology is increasing the size of datasets that every device and system generates — and it’s growing at an exponential rate. Manufacturing facilities are being overloaded with data, with every machine on the shop floor generating information that has the potential to create business value.

The increase in smart technology, including the use of sensors, means that a manufacturing plant is capable of capturing data from almost any type of machinery. Variables such as temperature, vibration and changes in operations can be used to monitor individual parts, such as motors or gaskets, to predict equipment failure.

Using data analytics to predict when a piece of equipment is likely to break down means that any maintenance or re-ordering of a part can be planned well in advance, minimising costly downtime.


Velocity refers to the speed at which data is being generated and the time it takes for this data to become ready for use.

The faster the data is analysed, the quicker it can be transformed into actions. But with today’s data deluge, keeping pace with this speed can be difficult.

Let’s go back to thinking about motors. An accelerometer is responsible for collecting vibration data by generating a voltage signal that corresponds to the amount and frequency of vibration the motor is producing. However, improved sensor technology now also allows for non-contact, high-speed laser sensors that can detect issues accelerometers cannot. With laser sensors able to identify everything an accelerometer can, in addition to characteristics such as joint domain and modal analysis, condition monitoring gets a lot more complex.

Ultimately, a big data infrastructure that is capable of processing all this data, and fast, is crucial.


Variety refers to the diversity of big data, both structured and unstructured. Equipment condition, consumer habits, inventory and product lifecycle management are just some of the touchpoints in a manufacturing facility that build up a complex web of data.

Managing this information requires several different systems. These systems cannot remain in siloes — they must be integrated for an all-encompassing view of the facility. For example, condition monitoring data could identify when an industrial part is showing signs of failure, then automatically cross-reference this data to see if the part is in stock. If a replacement is unavailable, repurchasing from a trusted automation parts supplier can be completed using an enterprise resource system (ERP).


The growing volume, velocity and variety of data are all rendered useless without one other ‘V’ — value. Gathering masses of diverse data is useful, but it must be analysed and transformed into actionable insights for it to be of any worth to the business.

The speed at which data is being produced, and the sheer number of variables that can be tracked, can lead to masses of data that are left unused and unseen. However, making the most of big data is a case of quality over quantity. By taking a wider look at a facility’s business segments and evaluating how data can link them together, big data can help create enormous business value.

John Young is Sales Director – APAC at automation parts supplier EU Automation.