Five ways to filter in CloudNine LAW to reduce data for review
Download the LAW Workflow: Reduce Data with Five Levels of Filtering document to learn the benefits of performing basic pre-culling filtering/searching in LAW, which include:
- Improve Relevance: Filtering ensures that only relevant documents are promoted for review.
- Compliance and Defensibility: Proper filtering may help to maintain legal and regulatory compliance.
- Enhancing Review Quality: By focusing on a smaller, more relevant data set, reviewers can conduct a more thorough and accurate review.
- Reducing Data Volume: Filtering helps to eliminate irrelevant or non-responsive data, significantly reducing the volume of documents that need to be reviewed.
- Cost Efficiency: By reducing the amount of data that needs to be processed and reviewed, filtering can lead to substantial cost savings. This includes savings on review, storage, and time.
The document describes how the five filters listed below are used in CloudNine LAW to reduce the data exported from LAW for review.
- DeDuplication
- Nist
- Email Threading
- Date Range Filtering
- Full Text Searching
Example of Data Reduction from Filters
To create this guide, we simulated a live project by compiling a data set of various electronic documents. The dataset was ingested into LAW, resulting in a total of 240,169 records. The data breakdown is:
- Edocs (loose files): 8,913
- Edoc Attachments: 675
- Emails: 146,672
- Email Attachments: 83,909
- Total: 240,169
This dataset is used for all filters in the guide. As each filters are applied, you will see the effect of the filter and the amount of data reduced. The chart above shows the results with all filters applied. The data promoted to the review platform is the orange slice, Survived Filtering.
Along with the document, there are corresponding resources that include a field template, search filters, and grid views that can be downloaded and used for your LAW cases. Adjustments may be needed to customize the resources, particularly the saved searches, for your specific data sets.