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Fitbit and Other Collected ESI Helps Crack Murder Investigation: New Data Sources in E-Discovery

Created on April 28, 2017

Former Content Marketing Manager at Exterro

Exterro Comprehensive Guide to Data Collection

The discussion of new data types in e-discovery is often talked about but in a speculative way; we know that it’s possible for any relevant electronically stored information (ESI) to be requested as evidence, but most often, especially in the civil court, the bulk of information is still related to email, though mobile and social information is quickly catching up.

Recently, however, we’ve seen more and more examples of new data being integral in criminal investigations (see my other blogs on the topic here, here, and here). In yet another example, on April 27th, 2017, Richard Dabate will enter his plea after being arrested for the murder of his wife, Connie Dabate, in December 2015, based on a large array of electronic data, including information from his wife’s Fitbit, that was used to put together a story much different from the one he told police.

Dabate said that an intruder broke into his house right when his wife came home from her morning run. Dabate told her to run, there was a struggle, and the intruder shot and killed his wife, then tied Dabate to a chair and tortured him. But with a lack of physical evidence to back up the story, investigators obtained search warrants for Connie’s Fitbit, both his and her cell phones, their computers, and the house alarm’s electronic log.

According to the warrant, here is the timeline detectives put together, as summarized in this article by CNN

  • At 9:01 a.m. Richard Dabate logged into Outlook from an IP address assigned to the internet at the house.
  • At 9:04 a.m., Dabate sent his supervisor an e-mail saying an alarm had gone off at his house and he's got to go back and check on it.
  • Connie's Fitbit registered movement at 9:23 a.m., the same time the garage door opened into the kitchen.
  • Connie Dabate was active on Facebook between 9:40 and 9:46 a.m., posting videos to her page with her iPhone. She was utilizing the IP address at their house.
  • While she was at home, her Fitbit recorded a distance of 1,217 feet between 9:18 a.m. and 10:05 a.m. when movement stops. (If Richard Dabate's claims were correct, detectives say the total distance it would take the victim to walk from her vehicle to the basement, where she died, would be no more than 125 feet).

With the compiled electronic evidence, along with the fact that Dabate tried to make a claim on his wife’s life insurance policy five days after the incident, the arrest was made.

But what can e-discovery teams on the corporate/civil side takeaway from cases like this? Well, namely that even if your organization isn’t running into new data types that often, that can change very quickly, and the ability (or at the very least the consideration) on how to approach these situations is key to staying defensible.

Robert Cruz, Senior Director of Information Governance at Actiance explains: “Companies are communicating on all sorts of channels beyond email at a very dramatic rate. The typical bank we're doing business with may be on eight to ten different communication networks: brokers talking to clients on Slack, on Symphony, on Fact Set, on Ice Chat, on video applications. There are now a billion users on WhatsApp. This is all happening now. So, the question becomes, "How can you know what communication channels are being used in your organization, so that you can make sure there is a plan for how that information can be controlled, researched, and produced for discovery if necessary.”

Robert continues, “It comes down to the numerous ways that organizations are conducting business every day. Beyond messaging applications, there are connected vehicles and other ways communications take place and data is collected through the Internet of Things. These additional vectors just create more complexity in determining how you need to go about identifying and collecting these content sources if it is required.”

For a more in-depth discussion of this topic, take a look at Exterro’s Comprehensive Guide to E-Discovery Data Collection.