"A method of representing a database using a logical and graphical view. Data modeling can be performed using something as simple as pencil and paper or as involved as sophisticated software. The purpose of data modeling is to bridge the gap between the actual business process and the physical database implementation. The output of data modeling is usually a graphical representation of the data structures." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)
"A process of defining the entities, attributes, and relationships between the entities in preparation for creating the physical database." (Bob Bryla, "Oracle Database Foundations", 2004)
"The activity wherein subject areas of data and relationships between them are depicted in a diagram." (Margaret Y Chu, "Blissful Data ", 2004)
"A structured approach used to identify major components of an information system’s specifications. Data modeling enables you to promote data as a corporate asset to share across the enterprise, provide business professionals with a graphical display of their business rules and requirements, bridge the gap between business experts and technical experts, establish consensus/agreement, and build a stable data foundation." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)
[Evolutionary data modeling:] "A process in which you model the data aspects of a system iteratively and incrementally, to ensure that the database schema evolves in step with the application code." (Pramod J Sadalage & Scott W Ambler, "Refactoring Databases: Evolutionary Database Design", 2006)
[evolutionary data modeling]: "Methodologies to iteratively and incrementally model database systems so that schema and applications evolve in a parallel way." (Vincenzo Deufemia et al, "Evolutionary Database: State of the Art and Issues", 2009)
[E-R Data Modeling:] "A popular data modeling technique used for representing business entities and the relationships among them." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)
"1.An analysis and design method, building data models to a) define and analyze data requirements, b) design logical and physical data structures that support these requirements, and c) define business and technical meta-data. 2.The act of creating a data model." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
[enterprise data modeling:] "The development of a common consistent view and understanding of data entities and attributes, and their relationships across the enterprise." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Data modeling is the ability and process of specifying and constructing complex data structures that represent specific semantics. In SQL, this can be performed with the ANSI-92 LEFT outer join operation that can inherently define and process complex data structures." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)
"A model that is used to either logically or physically organize the data elements in a database, including the definition of the data elements and of the relationships among the data elements for a specific industry, such as banking." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)
"Considers data independently of the way the data are processed and of the components that process the data. A process used to define and analyze data requirements needed to support the business processes." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)
"The process of architecting data objects and structures as they relate to a business or other context." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)
"The process of identifying and representing the definition, usage, and/or storage of data." (George Tillmann, "Usage-Driven Database Design: From Logical Data Modeling through Physical Schmea Definition", 2017)
"With dimensional data modeling or denormalization, data is collapsed, combined, or grouped together. Within dimensional data modeling, the concepts of facts (measures) and dimensions (context) are used. If dimensions are collapsed into single structures, the data model is also often called a star schema. If the dimensions are not collapsed, the data model is called snowflake. The dimensional models are typically seen within data warehouse systems." (Piethein Strengholt, "Data Management at Scale", 2020)
"A method that is used to define and analyze the data requirements that are needed in order to support the business functions of an enterprise. These data requirements are recorded as a conceptual data model with associated data definitions. Data modeling defines the relationships between data elements and structures." (Genesys)
"The analysis of data objects using data modelling techniques to create insights from the data." (Analytics Insight)
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