Editing Data modeling (section)

== Overview ==
Data modeling is a [[Software development process|process]]  used to define and analyze data [[requirement]]s needed to support the [[business process]]es within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system.<ref name = "RS001">Simison, Graeme. C. & Witt, Graham. C. (2005). ''Data Modeling Essentials''. 3rd Edition. [[Morgan Kaufmann Publishers]]. {{ISBN|0-12-644551-6}}</ref>  The data requirements are initially recorded as a [[Conceptual schema|conceptual data model]] which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The [[Conceptual modeling|conceptual model]] is then translated into a [[logical data model]], which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a [[physical data model]] that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.<ref>[http://knowledge.fhwa.dot.gov/tam/aashto.nsf/All+Documents/4825476B2B5C687285256B1F00544258/$FILE/DIGloss.pdf Data Integration Glossary]  {{webarchive|url=https://web.archive.org/web/20090320001015/http://knowledge.fhwa.dot.gov/tam/aashto.nsf/All+Documents/4825476B2B5C687285256B1F00544258/%24FILE/DIGloss.pdf |date=March 20, 2009 }}, U.S. Department of Transportation, August 2001.</ref>

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:

* to assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand and use an agreed-upon semi-formal model that encompasses the concepts of the organization and how they relate to one another
* to manage data as a resource
* to integrate information systems
* to design databases/[[data warehouse]]s (aka data repositories)

Data modelling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead, a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. [[Jeffrey L. Whitten|Whitten]] et al. (2004) determined two types of data modelling:<ref name="WBD04"/> 
* Strategic data modelling: This is part of the creation of an information systems strategy, which defines an overall vision and architecture for information systems. [[Information technology engineering]] is a methodology that embraces this approach.
* Data modelling during systems analysis: In [[systems analysis]] logical data models are created as part of the development of new databases.

Data modelling is also used as a technique for detailing business [[requirement]]s for specific [[database]]s. It is sometimes called ''database modelling'' because a [[data model]] is eventually implemented in a database.<ref name="WBD04">[[Whitten, Jeffrey L.]]; [[Lonnie D. Bentley]], [[Kevin C. Dittman]]. (2005). ''Systems Analysis and Design Methods''. 6th edition. {{ISBN|0-256-19906-X}}.</ref>