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Corporate taxonomy is the hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets and other information. Taxonomies can cover virtually any type of physical or conceptual entities (products, processes, knowledge fields, human groups, etc.) at any level of granularity.
Granularity, the condition of existing in granules or grains, refers to the extent to which a material or system is composed of distinguishable pieces. It can either refer to the extent to which a larger entity is subdivided, or the extent to which groups of smaller indistinguishable entities have joined together to become larger distinguishable entities.
Corporate taxonomies are increasingly used in information systems (particularly content management and knowledge management systems), as a way to promote discoverability and allow instant access to the right information within exponentially growing volumes of data in learning organizations. [1] Relatively simple systems based on semantic networks and taxonomies proved to be a serious competitor to heavy data mining systems and behavior analysis software in contextual filtering applications used for routing customer requests, "pushing" content on a Web site or delivering product advertising in a targeted and pertinent way.
Content management (CM) is a set of processes and technologies that supports the collection, managing, and publishing of information in any form or medium. When stored and accessed via computers, this information may be more specifically referred to as digital content, or simply as content.
Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organisation. It refers to a multidisciplinary approach to achieving organisational objectives by making the best use of knowledge.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
A powerful approach to map and retrieve unstructured data, taxonomies allow efficient solutions in the management of corporate knowledge, in particular in complex organizational models for workflows, human resources or customer relations.
Unstructured data is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated in documents.
Human resources are the people who make up the workforce of an organization, business sector, or economy. "Human capital" is sometimes used synonymously with "human resources", although human capital typically refers to a narrower effect. Likewise, other terms sometimes used include manpower, talent, labour, personnel, or simply people.
As an extension of traditional thesauri and classifications used in a company, a corporate taxonomy is usually the fruit of a large harmonization effort involving most departments of the organization. It is often developed, deployed and fine tuned over the years, while setting up knowledge management systems, in order to assure the survival and good use of valuable corporate know-how.
Enterprises have varying interest in the usage of taxonomies, from the usual enterprise information searches to the direct business benefits of taxonomies benefiting quicker and more accurate searches for the merchandise or the services of e-commerce or e-library sites. Such organisations may need to build large and complex vocabularies and deal with information assets that are largely in the public domain. Consequently, they are looking to shortcut their metadata schema development and avoid reinventing the wheel. Such shortcuts include the licensing of ready-built taxonomies and vocabularies with which to enhance their search results quickly.
