Before we choke on our “digital exhaust,” Hunter McMahon of iDS suggests we take a deep breath and examine the unnecessary risks we may be taking.

CCBJ: What is digital exhaust and how does it affect corporate environments?

McMahon: We’ve all heard the predictions that we’d have hovercrafts or personal helicopters by now. While those things exist, we have yet to see them adopted into consumer transportation. What we have seen, however, is a surge in the use of transportation technology for consumers and businesses. This includes computer diagnostics on vehicles, apps that can turn on a car or automatically set a user’s preferences, as well as a variety of computer devices that help corporations track activities and locations, and much more. Some of this technology may be integrated into a vehicle or an attached device, while others are available through a mobile phone app. All of the information that users create by their digital interactions is what I refer to as digital exhaust.

In many ways the same systems that create digital exhaust are also streamlining how corporations operate. For example, regulatory reporting requirements can now be done by uploading data. Also, a corporate operations center can view the status and location of all their resources. I’m sure Uber can see all available drivers, current riders and much more. These data points and a real-time feed of information allow for optimization of routing and distribution of resources.

What are the privacy implications?

This is a double-edged sword. We now have a great deal of information on companies, employees and consumers, which allows for a very rich analysis from a variety of angles, like division performance, employees’ day-to-day activities or consumer habits, for example. But many of these systems have not been adopted with that in mind, so they may not have the necessary safeguards – encryption, retention policies, etc. – in place, and companies can often find themselves exposed to unnecessary risk.

What about international companies that have various transportation channels?

The list of concerns associated with international data has grown significantly, especially since GDPR went into effect. What is acceptable to track, retain and analyze in one country may not be in another. Where is the data being created? About whom? Where is it retained? Those are just a few initial questions you should be asking.

What’s the realistic use of this data?

We are finding that while businesses may have some data, it’s rarely in ideal form or with the requisite structure and details. We must keep in mind that what may be the most effective for business operations is not always the most effective for subsequent data analytics. For example, the convenience of free-form text fields allow for a variety of use cases; however, it limits what kind of analyses can be done on those fields.

We have learned various methods of integrating data from multiple sources that can fill in gaps, confirm or enhance data, which is a crucial capability. Specifically, layering a multitude of data streams together to understand a scenario is almost always required. You may not be able to tell from a vehicle’s data who was driving, but when combined with mobile and financial digital exhaust, you can deduce that a specific individual was driving.

Where do things go from here?

It’s simple: Data will continue to grow, not just in volume but also in variety and velocity. We are using technology to solve problems and with each solution there’s more data.  Understanding how you can leverage data without creating unreasonable – that is, unprofitable – risk is key. Don’t retain data with the hope that it will be valuable one day. Have a plan and consider implementing data anonymization techniques.

What are some key takeaways about this kind of data?

First, remember that data doesn’t lie. It has perfect recall, even when witnesses do not.  But you need to understand the different characteristics of data, like system-generated information versus user input or controlled input.

Layer your data, just like your mom told you growing up to always wear layers when it’s cold outside. When you have multiple layers of data, you can often account for individual anomalies in a given data set with a proper understanding and correlated offset. You need to consider a variety of data sources (layers) that together offer full coverage. If you wear three jackets but forget gloves, your hands will still be cold.

Any final thoughts you’d like to offer?

Know the data better than your competition. Whether it is a business competition or an opposing party in litigation, it is a decided advantage.

Trust but verify. Data analysis can be very elegant or powerful, but also easy to misunderstand and misinterpret.

Don’t jump to conclusions. Because of digital exhaust, no one is data poor anymore, but make sure you use data to help ask the right questions.