YOUR GO-TO INTELLIGENCE RESOURCE FOR THE SCHOLARLY PUBLISHING INDUSTRY

Most of us working on big data and big content are familiar with the challenges of locating important information residing within various systems and repositories. What if we could use artificial intelligence (AI) to overcome these challenges? For instance, can we use AI to search and recycle useful information, and even get rid of redundant content? According to David Jones, VP of product marketing at Nuxeo, we can do all of the three with AI. He also forecasts that these are set to be three of the hottest trends around AI in Information Management in 2019.

Imagine this scenario: you have a legacy enterprise content management (ECM) repository containing customer documents. Despite the best intentions, these contracts are not as well managed as they should be, and the only relevant metadata attributes associated with these documents are customer reference numbers. By passing the content through an AI enrichment engine, additional metadata attributes can be appended to each one of the files currently stored. This immediately adds more context, intelligence, and insight into your information management ecosystem.

Here is another way AI can help with information management. An AI-driven engine can simplify the process of classifying content stored within legacy systems. This classification is important because it can help us apply the relevant retention policies to it. How does AI help in this instance? Simple AI tools can identify the difference between a contract and a resume. However, advanced engines can be used to expand this principle to build AI models based on content specific to an organization. For example, a specifically trained AI model can be used to determine the difference between a personal life insurance document and a life annuity document. In fact, it will provide detailed information that will make it easier to identify redundant, obsolete or trivial (ROT) documents.

After clearing out the ROT documents, the task of identifying relevant content to apply retention policies becomes simpler. Subsequently, AI can be used on the remaining content to identify the type of content in more detail. Additionally, it can be used to match the content to the retention rules, and then make recommendations to the relevant employees. This makes the whole process of identifying, declaring and managing records incredibly straightforward and scalable. Furthermore, AI makes the process cost-effective by reducing the storage requirements by reducing redundant documents.