GET A BETTER UNDERSTANDING OF THE PEOPLE IN YOUR CASE: NAME NORMALISATION
In an ideal world, it would be easy to find and use all the information about every person in your case. But there's no single identity in the digital world. Most people have multiple email addresses - both personal and professional - and beyond that, those email addresses show up in many different formats across email clients and devices.
Let's consider a fictional character, John Doe. Below are the many ways he might be referred to in email.
- [email protected]
- [email protected]
- O=relativity/OU=Executive/CN=Recipients/CN=5a186ad30a3bd4aaeb44258a723a95054- John+20cDoe
- John Doe
- Jonathan R. Doe
- [email protected]
- [email protected]
Finding and managing the numerous aliases for someone like John is a time-consuming and sometimes impossible challenge. If you wanted to search for all the communications from John, the search would be messy, and you might not have full confidence that you've captured all his aliases.
Name normalization, now available in RelativityOne, solves the problem. Not only does it automatically comb through the system to condense all of John's aliases into one entity, you can also customize and extend the entity throughout your project.
Add additional attributes, like company, role, department, expertise, and any other details that will help you better understand who John is. Every detail you add enriches your data, giving you a clearer picture of the people and patterns in your case.
With the ability to instantly recognize every variation of a person's email address, you can accelerate your investigation and start your case with a more complete understanding of who's involved.
Productions are made easier too when you don't have to cross reference and compile separate personal and professional email addresses to ensure you have all the evidence tied to an individual. Previously cumbersome tasks like searching or creating privilege logs become a snap.
And the best part is these entities can be carried over from case to case, so you can avoid recreating the insight you developed during a case from scratch.