Why eDiscovery Search Needs To Be Transparent
e Discoverycovers a vast spectrum of information, and its primary objective of search can be intimidating and overwhelming if pursued on too general a basis. Strategies to increase search possibilities and produce quality results can render tremendous outcomes for this complex task.
Every piece of evidence is vital to the eDiscoveryprocess, and no item can be spared if the search procedure is to be maximized. Thus, with volumes of information to cover, search becomes a formidable hurdle rather than convenient assistance. The typical eDiscovery process is severely lacking in three areas:
Over-inclusive and under-inclusive results – Several technologies, such as keyword search, wildcard, stemming, concept and fuzzy search, have been developed to facilitate search functions. However, all of them either provide insufficient or excessive results which make the whole process totally ineffective.
Testing and refining searches increases cost – Searches become time consuming and expensive when only one can be conducted at a time. And, ironically, this is contrary to the goal of making it a cost-effective tool.
Manual documents – The entire eletronic discovery process has to be documented for the benefit of the courts. The manual documentation of search refinement is erroneous and insufficient.
Since eDiscovery demands the review of every single document, transparency in the process can address the problems of over-inclusiveness and under-inclusiveness. A transparent search with a greater visibility scope can help investigators understand the mechanism of obtaining results and eliminating issues, and make it one of the top eDiscoverymethodologies. The transparent search option should have some important characteristics:
Transparent query expansion – By implementing the query expansion process, search tools can conduct an expansive query and transform it into a new form. This is generally done with wildcard, stemming, concept and fuzzy search methodologies. So there is a large array of expanded keywords that investigators can view, and discard those terms which have no relevance or have been falsely expanded. This can help control the complexities of the process and thereby help cull data more efficiently.
Handling multiple queries – When multiple keywords are submitted, the tools should be able to provide the results of each individual query as well as a combination of all of them. If the user is provided with 100 search results, it should also be clear which are worthwhile. This helps bring an organized mechanism into action for search testing, sampling and refinement.
Rapid sampling – Rapid sampling tests conducted on the results highlight the quality of the transparent search tool. Along with this, it should also be capable of taking samples of documents that do not match the queries to help identify and confirm that documents containing relevant content are not missed.
Automated documentation – The transparent search needs to document the entire process, including things like keywords that have been excluded, multiple as well as individual queries, etc. These can be produced before the court if the investigating methodology should come into question.
There is no possibility of critical errors with a transparent search methodology. This approach provides stronger defensibility in legal eDiscovery, which can reduce time and costs by eliminating all deceptive perspectives and accentuating the culling process effectively.