Data model in the context of Data modeling

A hierarchical database model is a data model in which the data is organized into a tree-like structure. The data are stored as records which is a collection of one or more fields. Each field contains a single value, and the collection of fields in a record defines its type. One type of field is the link, which connects a given record to associated records. Using links, records link to other records, and to other records, forming a tree. An example is a "customer" record that has links to that customer's "orders", which in turn link to "line_items".

The hierarchical database model mandates that each child record has only one parent, whereas each parent record can have zero or more child records. The network model extends the hierarchical by allowing multiple parents and children. In order to retrieve data from these databases, the whole tree needs to be traversed starting from the root node. Both models were well suited to data that was normally stored on tape drives, which had to move the tape from end to end in order to retrieve data.

Structured Query Language (SQL) (pronounced /ˌɛsˌkjuˈɛl/ S-Q-L; or alternatively as /ˈsiːkwəl/ "sequel") is a domain-specific language used to manage data, especially in a relational database management system (RDBMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.

Introduced in the 1970s, SQL offered two main advantages over older read–write APIs such as ISAM or VSAM. Firstly, it introduced the concept of accessing many records with one single command. Secondly, it eliminates the need to specify how to reach a record, i.e., with or without an index.

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.

Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

Unstructured data (or unstructured information) 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 (semantically tagged) in documents.

In 1998, Merrill Lynch said "unstructured data comprises the vast majority of data found in an organization, some estimates run as high as 80%." It is unclear what the source of this number is, but nonetheless it is accepted by some. Other sources have reported similar or higher percentages of unstructured data.

Data system is an organized collection of symbols and processes that may be used to operate on such symbols. Any organised collection of symbols and symbol-manipulating operations can be considered a data system. Hence, human-speech analysed at the level of phonemes can be considered a data system as can the Incan artefact of the khipu and an image stored as pixels. A data system is defined in terms of some data model and bears a resemblance to the idea of a physical symbol system.

Symbols within some data systems may be persistent or not. Hence, the sounds of human speech are non-persistent symbols because they decay rapidly in air. In contrast, pixels stored on some peripheral storage device are persistent symbols.

A name–value pair, also known as an attribute–value pair, key–value pair, or field–value pair, is a fundamental data representation in computer systems and applications. Designers often desire an open-ended data structure that allows for future extension without modifying existing code or data. In such situations, all or part of the data model may be expressed as a collection of 2-tuples in the form <attribute name, value> with each element being an attribute–value pair. Depending on the particular application and the implementation chosen by programmers, attribute names may or may not be unique.

Common examples include JSON objects such as database records where a column maps to a stored value (e.g., username → johndoe), HTTP headers like Content-Type: text/html, and configuration files with settings expressed as settingType=1.

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including:

• Data transformation or data mediation between a data source and a destination
• Identification of data relationships as part of data lineage analysis
• Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification project
• Consolidation of multiple databases into a single database and identifying redundant columns of data for consolidation or elimination

For example, a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company's data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices.

A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John").

A personal knowledge base (PKB) is an electronic tool used by an individual to express, capture, and later retrieve personal knowledge. It differs from a traditional database in that it contains subjective material particular to the owner, that others may not agree with nor care about. Importantly, a PKB consists primarily of knowledge, rather than information; in other words, it is not a collection of documents or other sources an individual has encountered, but rather an expression of the distilled knowledge the owner has extracted from those sources or from elsewhere.

A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.

In metadata, the term data element is an atomic unit of data that has precise meaning or precise semantics. Data elements usage can be discovered by inspection of software applications or application data files through a process of manual or automated Application Discovery and Understanding. Once data elements are discovered they can be registered in a metadata registry.In the areas of databases and data systems more generally a data element is a concept forming part of a data model. As an element of data representation, a collection of data elements forms a data structure.