Keyword searching has long been the go-to method for culling data in the early stages of eDiscovery projects but, as analytics tools and AI capabilities continue to improve, many of us are asking the question - do we need to use keyword searching any more? 

In this blog post, we will explore the pros and cons of keyword searching and what the alternatives might look like.

• The pros of keyword searching 
• The cons of keyword searching 
• Alternatives to keyword searching 

The pros of keyword searching: 

Keyword searching is a quick way of culling your dataset to handle only the documents you think are most likely relevant. This is achieved by applying a set of terms or phrases (ideally 10-15 good ones) across the dataset. The keyword-responsive documents would then make up your review population. Slight spelling variations of your search terms can be identified by applying fuzzy searching. This can help ensure that key documents are not missed due to human error, or American/English (or other) spelling differences. 

Keyword searching is a trusted and well-understood process in eDiscovery which makes it an ideal tool for those more wary of Artificial Intelligence and its use within law. The use of keywords is also especially helpful later on in the case when you have had the chance to become more familiar with your case matter. At this point, you can suggest very specific keywords with a high chance of producing relevant material.  

On top of saving you time by reducing the number of documents you have to review, keyword searching also saves you costs by culling your data in an ECA workspace which is much cheaper than in a review workspace.