We Take Search for Granted
Remember the days when you wanted to look for a document using the file manager on your desktop and you’d forgotten exactly where it was stored? It would take forever and sometimes freeze the operating system, and then you still didn’t find the file you were looking for, so you went to each folder on the C-drive and looked through everything manually. Ah, the good old days!
Today, we don’t really have those same issues. We can search files on our devices in an instant. Photos are automatically categorized by date, location, and even by the people or objects in the picture. And when we go to our email, we can type in a single word or phrase, and the search spans the to, from, and subject fields, as well as the text within the email. We’ve come a long way.
But when it comes to searching through the voluminous message data created in collaboration applications like Slack and MS Teams, search capabilities still face quite a few challenges. That’s where embedded Artificial Intelligence comes into play.
Artificial Intelligence to the Rescue
Collaboration platforms, like Slack and MS Teams, have been the source of massive volumes of highly complex collaboration data since their mass adoption in the wake of the COVID-19 pandemic. And according to recent statistics gathered from users, these numbers continue to go up. These large datasets have created unprecedented challenges for large enterprises from simply preserving the information to controlling data volumes, managing complexity, and, most notably, getting rapid insights for investigations and informed case strategy, all while reducing cost and risk.
Dave Ruel, VP of Product at Hanzo said, “It's not uncommon for large organizations to have thousands of channels and millions of messages in their collaboration data environments. This is why embedding AI and adding categorization libraries into collection tools is necessary to rapidly analyze massive repositories of data, documents, and other artifacts. By doing this, large enterprises can more easily discover, classify, and protect sensitive data stored within cloud applications like Microsoft Teams, Slack, Google Workspace, and others."
Adding this data intelligence layer to ediscovery tools helps users visualize data, offering large enterprises the scalability necessary to efficiently manage early case assessment (ECA) of collaboration data. The AI-powered data enhancement automates the detection of PII, toxicity, unwanted behaviors, and more. End-users can rapidly filter using facets to pinpoint sensitive information and discover specific classifications during ECA for rapid insights into the data before exporting to outside counsel for review. By providing quick and accurate message and file intelligence, in-house legal teams have the information necessary to support internal investigations or ediscovery matters, giving them the ability to cull large datasets, build case strategies, and move toward a speedy and cost-effective resolution to the matters at hand.
"Enterprises across many industries are accelerating the adoption of collaboration applications. Yet, many don't have solutions to govern this data, discover insights from it, and reliably preserve it to meet their legal and regulatory requirements," said, Ruel. "Smarter collection solutions and proven, scalable AI enhancements give large enterprises the ability to manage the ever-growing risk associated with short-message business communications."
To learn more about
- New Trends for Collaboration Data and the Enterprise Ediscovery Process
- Why Built-In Ediscovery Tools Fall Short with Collaboration Data
- What Organizations Are Worried About When It Comes To Managing Collaboration Data During Ediscovery
Download THE HANZO GUIDE TO Enterprise Collaboration Data Challenges
