Identify, Qualify and Prioritize Data Sources

This is the sixth installment in a blog series on Fact Crashing™, the acceleration of the consideration of ACTION data (Ambient, Contextual, Transactional, IoT, Operational, Navigational) to the benefit of resolving disputes.

There are 9 Principles of Fact Crashing™. So far, I’ve covered: 

Principle 1: Data is Evidence and is Discoverable.

Principle 2: Data Should be Addressed Early

Principle 2: Deep Dive

Principle 3: Frame Case Issues as Data-Centric Inquiries

Let us continue.

At the end of this exercise, your list will be shorter. You’ve separated the possible from the impossible. But you will likely still have a long list of data sources.

Once you start thinking about your case issues as measurable inquiries, it’s time to identify the potential sources of data for answering those inquiries. This is a two-step process.

Part 1: Identify the Sources

First, you identify potential data sources. Here it is helpful to be expansive as possible. The more candidates, the better.

When I teach law school classes on data systems for lawyers, there’s an exercise we run. We set up a hypothetical scenario, a two-car fender-bender. I then ask the students to identify data sources related to either the condition of the vehicles or the drivers’ health. These are assessed before, during, or after the accident.

We always try to find at least one source for each student in the class. We have never failed to hit this mark. The list includes traffic cams, neighborhood WiFi, Smartphone Accelerometers, OnStar systems, computer carburetors, gym memberships, wearables, intersection induction coils, and even NSA satellites.

NSA Satellites? The question at this point is not whether or not you might actually get the data. That’s for the next step. The only issue is to identify the options.

So go crazy. Think of any potential sources like:

• Corporate systems
• Personal data
• Municipal data
• Utilities
• Government systems
• Facility systems
• Transportation
• Wearables
• Cell carrier logs
• Social media

By no means is this list complete. But it gives you an idea of the diverse systems that can contain potential sources of data.

Part 2: Qualify the Sources

If you did part one right, you would have a long list of data sources. Some of those are going to seem obvious. Some will seem challenging. Some will seem unlikely or impossible to acquire or work with.

When we qualify sources, there are three options. We can exclude them outright, identify them as too difficult to acquire, or label them as too challenging to deal with.

At the end of this exercise, your list will be shorter. You’ve separated the possible from the impossible. But you will likely still have a long list of data sources.

For the remaining sources, like adding ACTION data to VERBAL data, you will want to add measurable characteristics to your data. Your list may be different from my list, but some things I’d like to know:

• Size
• Source
• Purpose
• Availability
• Accessibility
• Incremental Cost
• Incremental Benefit

Once I’ve set up this list, I can then start to prioritize my data sources…

Part 3: Prioritize the Sources

Once you have identified and qualified potential data sources, then prioritize them. Compare the relative advantages of each data source to the relative disadvantages. How well will it address your issues? Can you acquire the data? Will it have the level of detail you need? How long will it take to get it? How much will it cost? Will you be able to admit the results into evidence?

At the end of the day, it may only take one data source to resolve your issues. In some cases, we’ve dealt with more than 40 systems. It can vary. But once you have your list prioritized, you are ready to start reading some benefits from Fact Crashing™.

In a typical wage and hour case, we deal with:

• HR records
• Time and attendance
• Payroll
• Business-related application data
• Cellphone carrier
• Text messaging
• Emails (Corporate and Personal)
• Invoicing
• Dispatch / Scheduling

Collectively, these can be sufficient to deal with a wide variety of on-the-clock / off-the-clock, misclassification, overtime, meal, and rest break issues. Again, your mileage may vary.

Note what we are not doing, reading the emails or text messages. At least not initially. Sometimes they are reviewed, but typically only to classify them as business or personal and add some ACTION data. Otherwise, everything is analyzed based on who, what, when, where, how long. And this list of systems is where we often end up. It’s as if we have been implicitly Fact Crashing™. Now it’s time to convert that implicit process into a scalable, repeatable, improvable explicit process.

iDS provides consultative data solutions to corporations and law firms around the world, giving them a decisive advantage  – both in and out of the courtroom. Our subject matter experts and data  strategists specialize in finding solutions to complex data problems – ensuring data can be leveraged as an asset and not a liability.