Extract from Cat Casey’s article “Let’s Get Visual: Supercharging eDiscovery with Data Visualization”
They say that a picture is worth a thousand words, but in the case of electronic discovery (eDiscovery)you might want to multiply that by ten thousand. Machine Learning powered data visualization connects dots beyond what unaided human cognition can do in a fraction of the time, all without needing a human to spend time getting the ball rolling. Unlike TAR and other AI workflows in document review, many of the robust data visualizations on the market rely on unsupervised machine learning to connect patterns and uncover anomalies before a human so much as glances at a single document.
What the Heck is Data Visualization for eDiscovery?
Data visualization is simply that, a visual representation of a data set that calls out patterns, anomalies, and connections often in an interactive manner. Many of the data visualization tools are powered by unsupervised machine learning applied across an entire data set. Unlike Technology Assisted Review, these visual representations of patterns in data do not require a human to be created and they are not limited by the ability that a human mind must connect the dots in large and disparate data sets.
Flavors of Legal Visual Analytics
Immediately upon ingestion, many eDiscovery software platforms can apply artificial intelligence to uncover patterns across your data set using both metadata and the face of a document or communication. The current leader in the data visualization of data in eDiscovery matters is Brainspace and the visualizations offered include:
Communication Analysis: A visual representation of which custodians are communicating with each other and at what frequency. This visualization’ is a powerful way to prioritize which custodians are reviewed and to potentially expand or reduce the total in scope custodians on a matter based on actual communication patterns.
