Work Flow

  • Information Management


    • Identification


      • Preservation


      • Review


        • Production


          • Presentation


      • Collection

The Process

Volume

DSU Discovery is capable of efficiently processing massive volumes of data. As this data meanders through our collection and processing work flow, the volume drastically decreases. We achieve this data reduction through preliminary culling processes such as applying date and file type filters, family level document deduplication and key word searches. After these basic criteria have reduced the dataset, we then leverage additional technology including near dupe identification and analysis, semantic clusters, quick entity, email thread grouping and predictive coding.

 

 

Relevance

As the massive volumes of unstructured data are processed and culled, a higher level of relevance is achieved. At DSU Discovery, we understand that the most expensive aspect of any litigation is the attorney review hours spent reviewing very large data sets to pinpoint key documents. We take a collaborative approach to ensure that our data reduction methods are on target with the focus of the case. Our ultimate goal is to streamline the initial data set, prioritizing the documents that are most substantive to the case so that they can be reviewed in the most cost-effective way possible.

Process Step