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How to Build an Efficient Knowledge Management System Using Recent Technologies

By CIOReview | Monday, January 7, 2019

Recent technological advancements have helped the enterprises to enhance their operational efficiency, productivity. These technologies have provided companies with several cost-efficient measures to increase profitability. Technologies like artificial intelligence (AI), machine learning (ML), and many others have helped the companies to replace manual work with more agile and accurate automated processes. The technologies allow the enterprises to use their workforce in complex processes as these technological tools complete repeated processes.

Technologies like AI, Ml, NLP, and digital threading have helped the technical personals to link-up disparate data systems with one another to draw data patterns. The data patterns help to improve the knowledge management process. According to a Gartner prediction, the volume of generated data will grow by 800 percent in the next four years. With this amount of data, recent technologies can help to reduce the wastage of engineering and research and development resources by 30 percent by finishing duplicate work. These technologies can also help to increase efficiency by analyzing past data. Here is a detailed analysis of the ways by which recent technologies can help to enhance knowledge management:

Artificial Intelligence: The ability of artificial intelligence tools to process information using human analytical skills makes it a critical feature of knowledge management. AI uses data, visual information, auditory information, and written information to analyze and provide insights into the workforce of an organization.

Machine Learning: Machine learning tools allow the devices to learn and act without being explicitly programmed. Machine learning tools use algorithms to perform operations intelligently.

Natural Language Processing: Natural language processing is a part of artificial intelligence that allows a system to read and understand the written text. It is particularly relevant for engineers who have to go through an entire volume of unstructured data to find a meaningful pattern.

Digital Threading: Digital Threading uses AI, ML, and NLP techniques to create a connected flow of information about the asset. It also helps to uncover implicit links, which builds unique navigable relationships.

The AI, ML, and NLP technologies have come a long way in providing efficient services for companies across industries. Enterprises have been leveraging these technologies based on their use cases.