“Early Case Assessment” (ECA)
According to various sources, more than 90% of all legal cases are being settled before going to lengthy trial. While this is potentially a good news scenario, it doesn’t necessarily mitigate the amount of work that goes into preparing a defense, whether to settle or for trial. Either way you must put yourself into a legally defensible position and that requires data analysis. Much of the organizations costs are incurred during this process and so today, we address the issue of reducing your risk with specific software: Sherpa’s “Early Case Assessment” (ECA) software.
Litigation requires an assessment of risk. eDiscovery and Early Case Assessment can help with this process. Early Case Assessment or ECA was born of the lengthy process whereby trail lawyers would collect, survey, analyze and synthesize data from multiple sources to determine key points. Asking questions like whether a case has merit and how much will it cost the company would help place it into position to fight or settle the particular claim. ECA software essentially borrows on these procedures – creating a data management process that allows in-house council and the management team to assemble and analyze data before either outside council is sought, or a trial takes place. Why is this important? Because it will save the organization money!
Early Case Assessment software organizes and sorts eDiscovery data into a variety of visual reports within any number of categories such as: by date, by attachment type, by who the sender is and many other categories. Visual reports are easier to review and paint a true picture for outside council (when the time comes) to help in determining whether a case can be settled without trial. It’s a win-win situation because your data has already been organized should you need to face trial but such early preparedness might help you to avoid trial in the first place. Either way, your ability to settle a claim prior to, or to defend during with far less potential cost, is assisted through the direct application of a software tool like ECA. Check out Sherpa Software’s Early Case Assessment platform for yourself and see how it can help you reduce the risk and identify any potential trouble too!
Machines that can think?
Since the onset of computer technology, arguably as early as the 1950’s when Arthur Samuel developed a checkers playing program for IBM, there has been significant debate about the benefits, logistics and indeed even the morality of creating computers that can think for themselves. “Machine learning,” according to Samuel, “is a field of study that gives computers the ability to learn without being explicitly programmed.” (1) Today, we understand machine learning to take place as either supervised (structure applied to the data in the form of learning algorithms) or unsupervised (no predefined attribute structure.) These definitions are central to our current discussion on the issue of machine learning and information governance (IG).
The CAAT “machine learning” engine, integrated with Altitude IG, will create opportunities for a software platform that can automatically group and classify an organizations electronically stored information (ESI) and at a previously unheard of rate of accuracy. Created by the Content Analyst Company the key differentiator with this technology is that it does not need to rely on a manually created list of keywords, terms or phrases but rather, can learn, infer and apply knowledge as it goes along. For example: applying the IG process to determining defensible deletion of emails might typically involve the creation of a long list of associated search words like “unsubscribe” in order to ferret out the documentation. With machine learning, as applied to the Altitude IG platform, the software will take the example of the word “unsubscribe” but then apply a “find more like this” approach, determining for itself whether similar words like “opt out” and “manage your subscriptions” should also fall within the scope of the defensible deletion IG process. Machine learning understands that each of these phrases may be applicable to your search and instead of having to create a complex series of rules to filter and sort, CAAT learns to semantically sort content into specific categories. As more and more documents are added, it learns new ways to filter them thus eliminating the need for an IT professional to constantly maintain complex filtering rules.
What this means for an organization in terms of bottom line is pretty simple. For the investment into the CAAT/Sherpa Software, the return is quicker sorting of documentation, less employee time commitment to the task of IG (without losing any efficiencies) and smoother integration of various software tools – saving both time and money in the long run. That just makes sense, whether man or machine thought of it!
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“Early Case Assessment” (ECA)