What is ‘Data Exhaust’
Data exhaust is the trail of information left behind as a result of a person’s digital activity. Data exhaust can be mined to create a fairly accurate portrait of a person’s daily life given that so much of our lives has a digital component.
Activity monitors and smartphones have a data exhaust that can piece together daily movements; working hours can be tracked through office applications; and social media can often fill in remaining gaps. Data exhaust as a concept is particularly interesting for marketers and researchers as it is hard data rather than self reported preferences that are prey to reporting bias. Data exhaust is big data in that it can be mined, but the data sets making up data exhaust are much larger than data sets and sources big data works with.
BREAKING DOWN ‘Data Exhaust’
Digital activity may understate the volume of data that makes up data exhaust. Data is generated from every transaction, every device, and every click of a button within a system. Sometimes this data is being collected and shared back with the person that generates it, as with the results from a step tracker. However, it is equally likely that the data is being unthinkingly generated and may not be actively mined for any purpose. When it is collected with a purpose in mind, it can be combined with other sources and mined with big data techniques. There is too much data exhaust, however, for it all to be relevant enough to be used for purpose-driven big data analysis. Although our computers and techniques are improving, there is still a processing cost to be paid that keeps data intake from including every possible data set.
Data Exhaust in the Workplace
Even just looking at a widely used office application like email, there is data exhaust such as average response time, number of bounced emails, times the program is accessed and so on. This data exhaust could be captured and combined with the data coming out of other workplace applications, access card swipes, vending machine purchases, employee social media updates and so on to paint a picture of the productivity of the office over time.
From this mass of data, insights could be teased out that offer any number of tweaks to increase that productivity. For example, what seating configuration has a positive impact on average email response time? At what temperature are people most efficient? Around what times in the day do task completion times start to slow? If this line of reasoning has you imagining a nightmare scenario where you are being constantly evaluated and experimented on, ending with you sitting in a cold corner of the office with a locked down computer watching a timer countdown in your inbox, you are not alone.
Your Personal Data Exhaust
More and more people are attempting to take ownership of their data exhaust. In this context, the data exhaust can become big data that serves you. Want to be more efficient at work? Start collecting more data on when your peak periods of productivity are and work back to things like what you ate before this period, how you slept, and even what playlist was on during your peak performance. Of course, you don’t need to stop at work.
The data exhaust that is so attractive to marketers is even more meaningful in your hands. How much time do you spend online? What is the composition of a regular day? How often do you eat out alone? How well do you stick to your budget? All of these questions correspond with established research on health, happiness and satisfaction. If you can capture data exhaust, you can start benchmarking your performance and making tweaks for a better life.
We create data exhaust almost constantly in our digitally connected world. When a marketer or researcher captures it, they can use big data techniques to better understand you and people like you. If you start capturing your data exhaust, it becomes personalized data that tells you about who you are now and can help you make those small changes that will improve things for you in the future.
Risks Associated With Data Exhaust
As with anything related to data retention, there are risks that come with data exhaust. Sometimes knowing too much about consumers and their behavior might be detrimental to a company. For example, an insurance company may raise its rates for some customers if they can track where customers drive through or regularly park. In addition, some of the data retained may not be useful, and hanging on to it could become costly and unnecessary.