Textual data marches on &#8211; Anticipating and Preventing Lawsuits
In the beginning relational data bases attempted to address the challenges of textual data with “blobs”. Then there was the technique known as “tagging”. After tagging came NLP – Natural Language Processing. With each new approach in the attempt to manage narrative text came new opportunities that attempted to address the challenges of making corporate decisions based on text. The attempt to address the challenges of making decisions based on text soon appeared in looking at the sentiment of customers using NLP. Indeed, NLP addressed some of the challenges of text and decision making. With NLP, at least people could start to include the sentiment of the customer in the corporate decision making process. But there were some limitations to NLP processing of customer sentiment. Most sentiment analysis was based on the reading and interpretation of social media data. And while social media data is good for expressing some limited feelings, when it comes to longer, fuller, more sophisticated expressions of feelings, analysis of social media leaves a lot to be desired. And too, the techniques used in NLP processing are somewhat limited and artificial, in any case. However you want to look at it, there is only so much value in using NLP processing for sentiment analysis of social media. ANTICIPATING LAW SUITS There are other much more productive venues to use analytical processing of text as a basis of making decisions based on textual data in the corporation. One of those places is in the anticipation of and prevention of the filing of law suits. When the corporation opens its mail and finds a law suit that has been filed, there is an automatic and immediate negative effect on the bottom line of the corporation, regardless of whether the corporation wins or loses the law suit. In addition there is the negative impact on the corporation’s image when the lawsuit is filed. It just is not good for the corporate image when word gets out that a lawsuit (or a series of related law suits) has been filed, even if the lawsuit is ultimately successfully defended It is estimated that the average settlement of EACH lawsuit that has been filed costs the corporation approximately $300,000. So there is tremendous justification for the corporation to minimize the lawsuits that cross its transom. With this background information in mind. how is this for an idea? Anticipate that a lawsuit is in the works and resolve the problem before the problem turns into a lawsuit. Create an “early warning system” that a law suit is brewing and address the issues and the parties involved BEFORE the lawsuit is filed. Just think how much money that can save the corporation! TWO TECHNOLOGICAL ADVANCES There are two new technological breakthroughs that enable an early warning system for the anticipation of a lawsuit. The first of those technology advances is the advent of technology known as “textual disambiguation”. Textual disambiguation has some similarities to NLP. But textual disambiguation also has some novel features as well. For example, textual disambiguation relies heavily on the identification and usage of the context of text, not just the text itself. Not only is text found and addressed by textual disambiguation but the context of that text is also found and is an equal partner to the text itself. And there are other important differences between NLP and textual disambiguation as well. The second technological advancement that makes an early warning system for the anticipation of a lawsuit possible is that of “Big Data”. With the technology surrounding Big Data it is now possible to store and process HUGE amounts of data. In years past there was always a limit on how much data could be stored and processed in a system. The limit was both a technological one and an economic one. At the back of every system manager’s mind was the notion that the system must not consume too much data. But with the advent of Big Data that limitation no longer exists. In today’s world it is technologically and economically feasible to build systems of enormous size. Because of these two technological advances it is now possible to construct an early warning system for corporate litigation. Now the organization can read, organize and analyze all sorts of textual information in search of the potential law suit. LAW SUITS So what kinds of law suits can be identified and anticipated? There are MANY kinds of law suits that can be detected. Two types of law suits that are obvious and apparent are employment discrimination lawsuits and product liability law suits (among many others.) The discrimination lawsuits include all sorts of discriminations – sexual, age, religious, racial, sexual preference, and so forth. And where is the basic early warning information for these types of law suits found? The answer is – in lots of places. Typical places include emails, call center conversations, help desk conversations, warranty claims, insurance claims, and so forth. But wherever it is found, inevitably the information is in the form of text. Heretofore these sources of information have not been able to be effectively analyzed. But with the advances that have been described, it is now possible to do analysis that leads to an early detection of the causes for potential law suits. It is noted that there are MANY kinds of law suits, just the ones that have been mentioned. THE BUSINESS VALUE OF ANTICIPATING LAW SUITS And with an effective early warning system comes the potential of payback measuring in the MILLIONS and MILLIONS of dollars. And the measurement of the effect of protection and enhancement of the corporate image goes beyond measurement in dollars. The business value of lawsuit anticipation and prevention eclipses the payback of sentiment analysis of social media. TO FIND OUT MORE For more information about such an early warning system for anticipating law suits and how such a system might be built, refer to the newly published book PREVENTING LITIGATION: An Early Warning System to get Big Value from Big Data, BEP PRESS. Or for more information about textual disambiguation implementation and the contextualization of text into a standard data base management system refer to www.forestrimtech.com.
In the beginning relational data bases attempted to address the challenges of textual data with “blobs”. Then there was the technique known as “tagging”. After tagging came NLP – Natural Language Processing. With each new approach in the attempt to manage narrative text came new opportunities that attempted to address the challenges of making corporate […]
In the beginning relational data bases attempted to address the challenges of textual data with “blobs”. Then there was the technique known as “tagging”.
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["big data", "Go", "AWS", "sentiment analysis", "AI", "RAG", "NLP", "Aim", "CLIP", "R"]
["AI", "NLP", "Aim", "RAG", "sentiment analysis", "AWS", "R", "Go", "big data", "CLIP"]
William H. Inmon (born 1945) is an American computer scientist, recognized by many as the father of the data warehouse. Bill Inmon wrote the first book, held the first conference (with Arnie Barnett), wrote the first column in a magazine and was the first to offer classes in data warehousing. Bill Inmon created the accepted definition of what a data warehouse is - a subject oriented, nonvolatile, integrated, time variant collection of data in support of management's decisions.