Data exhaust or exhaust data refers to the trail of data left by the activities of an Internet user during his/her online activity. An enormous amount of often raw data are created. These data (which can take the form of cookies, temporary files, logfiles etc.) can help to improve the online experience. But they can also compromise privacy, as they offer a valuable insight into the user's habits. It can be used to improve tracking trends and studying data exhaust also improves the user interface and the layout design.
Unlike primary content, these data are not purposefully created by the user, who is often unaware of their very existence. A bank for example would consider as primary data information concerning the sums and parties of a transaction, whilst secondary data might include the percentage of transactions carried out at a cash machine instead of a real bank.
Most medical devices emit some form of exhaust data, such as many pacemakers, dialysis machines, and cameras used during surgery. The majority of this data is never captured, and is primarily abandoned after the surgery is completed, or the device makes it next routine check. Some issues have arisen regarding the use of the data captured by devices like pacemakers. This can lead to larger issues surrounding the use of this exhaust data.
Data exhaust? No, not exhaustion from data. Simply put, data exhaust is the data that a business collects that it doesn't currently think it can put to use. The biggest producers of data exhaust are manufacturers and sometimes retailers. Especially manufacturers of appliances, vehicles and equipment and large chain retailers. Most software or SaaS companies use almost all the data they collect as they are more likely to be keenly aware of their data and how to find value from it. For the purpose of this quick overview, I'll focus on manufacturers.
Data Exhaust from Manufacturers
Take for example, vehicle manufacturers. They receive vast amounts of data on any given day. From dealers on warranty issues, crash and fault data, dealer maintenance information and so on. Much of that data they will use to inform changes in the next model from the UX through to engine parts.
But some of the data they get isn't of much relevance to them, but it may be to their suppliers or someone else. There is always excess data that doesn't get used by the manufacturer themselves. But a lot of the data collected is relevant through the supply chain. Data that can help with quality control or product performance. To Honda, Ford or Audi this may be "data exhaust" - data they don't use or need, but the supply chain can.
Data Exhaust is a Revenue Opportunity
So an auto manufacturer such as Honda, Ford or Audi, could then sell the data down the supply chain. Not just to one supplier but to multiple suppliers. In a highly competitive industry such as automotive manufacturing, it is in the interest of manufacturing leads to promote the use of Big Data through the supply chain for product improvements, quality assurance, cost efficiencies and innovation.
This model can have significant economic benefits to the manufacturer and supply chain. Even the supply chain can in turn sell the data to their supply chain or others who may be interested. This creates a whole new value chain. So exhaust data can be invaluable to others and help defray your data centre costs.