Given how deeply search is ingrained in our lives, there's a good argument to teach search syntax instead of calculus. But just because you can find what you need on Google doesn't mean it's as easy in eDiscovery. The key differences come down to intent, the role of analytics, and the presentation of results - all discussed here.
As with many complex tasks, a refresher on the fundamentals is often in order. Let's dig deeper into a few of those, as well as some of the underlying search technology used in eDiscovery.
In daily life, you often run searches that use algorithms and machine learning to get to the best answer. "What is the best action movie this year" isn't so much a keyword search as it is a robust question posed to advanced analytics engines. The results are based on the training of those systems from other people who asked a similar or the same question and the results they clicked on afterward.
In eDiscovery, there is no such training – you're running the search for the first time with a particular data set, and there's no information for the computer to have learned from other users. There are some moves to apply learning from other cases, which would bring eDiscovery closer to the ubiquitous search you experience on the Internet.
Results presentation is another notable difference between everyday search and eDiscovery. Users expect to find what they need within the first dozen results when using a search engine. In eDiscovery, users need to determine whether they are searching for one specific thing or all the things that lead to an answer. In the former, even if you run a very tailored search, it is often necessary to review the initial results and modify the search parameters to achieve the best result. An issue can arise when performing a search of agreed-upon terms because modifying or clarifying is so often necessary.
There are four basic types of search strategy: filter, keyword search, concept search, and email threading. Filtering can be done by date, document type, and other metadata fields if the data set includes them. Keyword searches are probably the most familiar, and they use AND, OR, and NOT to create a more specific set of parameters. Advanced search syntax includes the use of proximity operators (within, near) and wildcard characters (such as *). Concept searching uses analytics to group documents with similar themes. Email threading groups all emails from a conversation together to make them easier to review in context.
Search technology is construction to find just the best results, but not all of them. It would take forever if you had to go to the end of the Internet to find out where your name occurred most often. That's why Google and other search technology have spent considerable time and energy prioritizing results.
In eDiscovery, you are often faced with finding all of a particular term in order to review identified documents and ultimately produce some of them. So, that means you actually do have to hunt down every instance and see if it's the one you're looking to find. This is where a knowledge of the aforementioned search syntax fundamentals comes into play; you can create a very specific, very complex search statement to find exactly what you need.
Search is one of the most powerful drivers behind a successful eDiscovery project. Although it may seem simple based on the way most of us interact with search on a daily basis, there are differences in eDiscovery search that can cause a less efficient review. Knowing the importance of search syntax, the underlying assumptions made about search, and the design behind search technology can all mean a much more thorough and timely eDiscovery process.