"We needed to find the right technology to facilitate information discovery for compliance with FRCP regulations, and we needed to demonstrate that we're taking legal requirements seriously. NearPoint helps us do just that."
— James Prenetta, Executive Vice President and General Counsel, One Communications
Over the last several years, most organizations have relied on a reactive eDiscovery process best described as a "We'll worry about it if it ever happens to us" process. Many organizations have found out the hard way that this eDiscovery strategy is very costly and painful. Little thought was given to understanding the scope of their electronically stored information (ESI) and what that means to their ability to secure ESI under a litigation hold, review all responsive data, and turn that ESI over to the opposing counsel.
Upwards of 97% of all data generated in business is electronic. The average employee sends and receives on the average, 15 MB to 20 MB of email per day. Potentially responsive ESI can also reside in SharePoint repositories and Windows file systems. Organization's legal and IT departments need to be asking themselves if their eDiscovery process is adequate and cost effective.
Let's take a look at the breakdown of costs associated with a manual eDiscovery process for two different eDiscovery cases:
| Large eDiscovery Case | Medium eDiscovery Case | |||
| Collection/Preservation/custodian | $1,000 | Collection/Preservation/custodian | $1,000 | |
| Culling/custodian | $500 | Culling/custodian | $500 | |
| Processing per custodian | $2,500 | Processing per custodian | $2,500 | |
| Number of documents/GB | 7000 | Number of documents/GB | 7000 | |
| Number of Custodians under discovery | 50 | Number of Custodians under discovery | 11 | |
| Number of GB per custodian | 3 | Number of GB per custodian | 3 | |
| Total Number of Docs to review | 1,050,000 | Total Number of Docs to review | 231,000 | |
| Number of documents reviewable per hour | 60 | Number of documents reviewable per hour | 60 | |
| Total review hours | 17,500 | Total review hours | 3,850 | |
| Review hourly rate (blended) | $150 | Review hourly rate (blended) | $150 | |
| ESI review reduction factor | 50% | ESI review reduction factor | 50% | |
| Total cost of collection | $50,000 | Total cost of collection | $11,000 | |
| Total cost of culling | $25,000 | Total cost of culling | $5,500 | |
| Total cost of review | $1,312,500 | Total cost of review | $288,750 | |
| Total cost of processing | $125,000 | Total cost of processing | $27,500 | |
| Total cost of discovery for this case | $1,512,500 | Total cost of discovery for this case | $332,750 | |
| Total cost of discovery per custodian | $30,250 | Total cost of discovery per custodian | $30,250 | |
The table above shows the expected costs involved with a manual, reactive eDiscovery process for a large eDiscovery case with 50 custodians and a medium eDiscovery case with 11 custodians. As you can see in the large eDiscovery case, the reactive process can be expected to cost $1.5 million or approximately $30,250 per custodian. Even when there are a smaller number of custodians, as in the medium case, hundreds of thousands of dollars can be spent. The biggest costs are associated with the collection and review process − reviewing the millions of potentially responsive documents to determine if they are responsive to the case.
Proactively archiving, indexing and securing the most requested electronically stored information (ESI) data types can save time and dollars for the most costly eDiscovery processes.
| Large eDiscovery Case with Mimosa | Medium eDiscovery Case with Mimosa | |||
| Collection/Preservation/custodian | $1,000 | Collection/Preservation/custodian | $1,000 | |
| NearPoint Collection reduction factor | 80% | NearPoint Collection reduction factor | 80% | |
| Culling/custodian | $500 | Culling/custodian | $500 | |
| Culling reduction factor | 90% | Culling reduction factor | 90% | |
| Processing per custodian | $2,500 | Processing per custodian | $2,500 | |
| Number of documents/GB | 7000 | Number of documents/GB | 7000 | |
| Number of Custodians under discovery | 50 | Number of Custodians under discovery | 11 | |
| Number of GB per custodian | 3 | Number of GB per custodian | 3 | |
| Total Number of Docs to review | 1,050,000 | Total Number of Docs to review | 231,000 | |
| NearPoint review reduction factor | 80% | NearPoint review reduction factor | 80% | |
| Number of documents reviewable per hour | 60 | Number of documents reviewable per hour | 60 | |
| Total review hours before NearPoint | 17,500 | Total review hours before NearPoint | 3,850 | |
| Total review hours after NearPoint | 3,500 | Total review hours after NearPoint | 770 | |
| Review hourly rate (blended) | $150 | Review hourly rate (blended) | $150 | |
| ESI review reduction factor | 50% | ESI review reduction factor | 50% | |
| Total cost of collection | $10,000 | Total cost of collection | $2,200 | |
| Total cost of culling | $2,500 | Total cost of culling | $550 | |
| Total cost of review | $262,500 | Total cost of review | $57,750 | |
| Total cost of processing | $125,000 | Total cost of processing | $27,500 | |
| Total cost of discovery for this case | $400,000 | Total cost of discovery for this case | $88,000 | |
| Total cost of discovery per custodian | $8,000 | Total cost of discovery per custodian | $8,000 | |
| A Savings of $1,112,500 or 74% | A Savings of $244,750 or 74% | |||
The Mimosa content archive with eDiscovery option drastically cuts down the time required to collect responsive ESI like Exchange email, SharePoint records and File System data because most of the requested ESI is already in the Mimosa Content archive. The Mimosa Content archive reduces the eDiscovery culling process by filtering out duplicates and unrelated files such as system files etc. so they don't have to be reviewed.
And finally, ESI review time can be noticeably reduced because the Mimosa eDiscovery Option can search for and return results sets that more accurately reflect the eDiscovery request. NearPoint also allows for searches within searches to more quickly cull down large results sets of data.
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