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Since the advent of the computer, terminology management can be carried out by almost anyone who has learnt to use a computer. Terminology management has proved to be an efficient tool in international communications in industry, education and international organisations. Software packages are readily available and international corporations often have their own terminology database. Following these developments, translators and terminologists are confronted with a specialised form of information management involving compilation and standardisation of vocabulary, storage, retrieval and updating.A Practical Course in Terminology Processing provides the key to methods of terminology management for the English language, for general and specific purposes. This unique course has been developed on the basis of years of teaching experience and research at the University of Manchester Institute of Science and Technology (UMIST, UK) and is particularly suitable for translation courses, freelance translators, technical writers, as well as for non-linguists who are confronted with terminology processing as part of their profession. The 1996 reprint of the paperback edition includes an index.
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.