Concept
Master data management
Related concepts (13)
Data virtualization
Data virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, and can provide a single customer view (or single view of any other entity) of the overall data. Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data.
Data governance
Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate data governance. On the macro level, data governance refers to the governing of cross-border data flows by countries, and hence is more precisely called international data governance. This new field consists of "norms, principles and rules governing various types of data.
Single source of truth
In information science and information technology, single source of truth (SSOT) architecture, or single point of truth (SPOT) architecture, for information systems is the practice of structuring information models and associated data schemas such that every data element is mastered (or edited) in only one place, providing data normalization to a canonical form (for example, in database normalization or content transclusion).
Master data
Master data represents "data about the business entities that provide context for business transactions". The most commonly found categories of master data are parties (individuals and organisations, and their roles, such as customers, suppliers, employees), products, financial structures (such as ledgers and cost centres) and locational concepts. Master data should be distinguished from reference data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities.
Data profiling
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a ) and collecting statistics or informative summaries about that data.
Record linkage
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Record linkage is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
Data transformation (computing)
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps.
Data management
Data management comprises all disciplines related to handling data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems.
Data quality
Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose.
Extract, transform, load
In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed (cleaned, sanitized, scrubbed) and loaded into an output data container. The data can be collated from one or more sources and it can also be output to one or more destinations. ETL processing is typically executed using software applications but it can also be done manually by system operators. ETL software typically automates the entire process and can be run manually or on reoccurring schedules either as single jobs or aggregated into a batch of jobs.