Editing Data warehouse (section)

==History==
The concept of data warehousing dates back to the late 1980s<ref>{{cite web |url=http://www.computerworld.com/databasetopics/data/story/0,10801,70102,00.html |title=The Story So Far |date=2002-04-15 |access-date=2008-09-21 |url-status=dead |archive-url=https://web.archive.org/web/20080708182105/http://www.computerworld.com/databasetopics/data/story/0%2C10801%2C70102%2C00.html |archive-date=2008-07-08 }}</ref> when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to [[decision support system|decision support environments]]. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations, it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as [[legacy system]]s), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "[[data mart]]s" that was tailored for ready access by users.

Additionally, with the publication of The IRM Imperative (Wiley & Sons, 1991) by James M. Kerr, the idea of managing and putting a dollar value on an organization's data resources and then reporting that value as an asset on a balance sheet became popular. In the book, Kerr described a way to populate subject-area databases from data derived from transaction-driven systems to create a storage area where summary data could be further leveraged to inform executive decision-making. This concept served to promote further thinking of how a data warehouse could be developed and managed in a practical way within any enterprise.

Key developments in early years of data warehousing:

* 1960s – [[General Mills]] and [[Dartmouth College]], in a joint research project, develop the terms ''dimensions'' and ''facts''.<ref name="kimball16">Kimball 2013, pg. 15</ref>
* 1970s – [[ACNielsen]] and IRI provide dimensional data marts for retail sales.<ref name="kimball16" />
* 1970s – [[Bill Inmon]] begins to define and discuss the term Data Warehouse.<ref>{{Cite web|title=The audit of the Data Warehouse Framework|url=http://ceur-ws.org/Vol-19/paper14.pdf |archive-url=https://web.archive.org/web/20120512064024/http://ceur-ws.org/Vol-19/paper14.pdf |archive-date=2012-05-12 |url-status=live}}</ref><ref>{{Cite web |last=Kempe |first=Shannon |date=2012-08-23 |title=A Short History of Data Warehousing |url=https://www.dataversity.net/a-short-history-of-data-warehousing/ |access-date=2024-05-10 |website=DATAVERSITY |language=en-US}}</ref><ref>{{Cite web |title=Data Warehouse – What It Is & Why It Matters |url=https://www.sas.com/en_gb/insights/data-management/data-warehouse.html |access-date=2024-05-10 |website=www.sas.com |language=en-GB}}</ref>
* 1975 – [[Sperry Univac]] introduces [[MAPPER]] (MAintain, Prepare, and Produce Executive Reports), a database management and reporting system that includes the world's first [[Fourth-generation programming language|4GL]]. It is the first platform designed for building Information Centers (a forerunner of contemporary data warehouse technology).
* 1983 – [[Teradata]] introduces the [[DBC 1012|DBC/1012]] database computer specifically designed for decision support.<ref>{{Cite news |title= Will Teradata revive a market? |author= Paul Gillin |pages= 43, 48 |work= Computer World |date= February 20, 1984 |url= https://books.google.com/books?id=5pw6ePUC8YYC&pg=PA48 |access-date= 2017-03-13 }}</ref>
* 1984 – [[Metaphor Computer Systems]], founded by [[David Liddle]] and Don Massaro, releases a hardware/software package and GUI for business users to create a database management and analytic system.
* 1988 – Barry Devlin and Paul Murphy publish the article "An architecture for a business and information system" where they introduce the term "business data warehouse".<ref>{{cite journal|title=An architecture for a business and information system|journal=IBM Systems Journal | doi=10.1147/sj.271.0060|volume=27|pages=60–80|year=1988|last1=Devlin|first1=B. A.|last2=Murphy|first2=P. T.}}</ref>
* 1990 – Red Brick Systems, founded by [[Ralph Kimball]], introduces Red Brick Warehouse, a database management system specifically for data warehousing.
* 1991 – James M. Kerr authors The IRM Imperative, which suggests data resources could be reported as an asset on a balance sheet, furthering commercial interest in the establishment of data warehouses.
* 1991 – Prism Solutions, founded by [[Bill Inmon]], introduces Prism Warehouse Manager, software for developing a data warehouse.
* 1992 – [[Bill Inmon]] publishes the book ''Building the Data Warehouse''.<ref>{{cite book|last=Inmon|first=Bill|title=Building the Data Warehouse|year=1992|publisher=Wiley|isbn=0-471-56960-7|url=https://archive.org/details/buildingdataware00inmo_1}}</ref>
* 1995 – The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.
* 1996 – [[Ralph Kimball]] publishes the book ''The Data Warehouse Toolkit''.<ref name=":0">{{cite book|title=The Data Warehouse Toolkit|last=Kimball|first=Ralph|publisher=Wiley|year=2011|isbn=978-0-470-14977-5|page=237}}</ref>
* 1998 –  Focal modeling is implemented as an ensemble (hybrid) data warehouse modeling approach, with Patrik Lager as one of the main drivers.<ref>[https://topofminds.se/wp/wp-content/uploads/Focal-Introduction-to-Focal-implementation.pdf Introduction to the focal framework]</ref><ref>[https://www.youtube.com/watch?v=C2y92n0sPok Data Modeling Meetup Munich: An Introduction to Focal with Patrik Lager - YouTube]</ref>
* 2000 – [[Dan Linstedt]] releases in the public domain the [[Data vault modeling]], conceived in 1990 as an alternative to Inmon and Kimball to provide long-term historical storage of data coming in from multiple operational systems, with emphasis on tracing, auditing and resilience to change of the source data model.
* 2008 – [[Bill Inmon]], along with Derek Strauss and Genia Neushloss, publishes "DW 2.0: The Architecture for the Next Generation of Data Warehousing", explaining his top-down approach to data warehousing and coining the term, data-warehousing 2.0.
* 2008 – [[Anchor modeling]] was formalized in a paper presented at the International Conference on Conceptual Modeling, and won the best paper award<ref>{{Cite book |last1=Regardt |first1=Olle |last2=Rönnbäck |first2=Lars |last3=Bergholtz |first3=Maria |last4=Johannesson |first4=Paul |last5=Wohed |first5=Petia |chapter=Anchor Modeling |title=Conceptual Modeling - ER 2009 |series=ER '09 | year=2009 |volume=5829 |isbn=978-3-642-04839-5 |location=Gramado, Brazil |pages=234–250 |publisher=Springer-Verlag|doi=10.1007/978-3-642-04840-1_19 |bibcode=2009LNCS.5829..234R }}</ref>
* 2012 – [[Bill Inmon]] develops and makes public technology known as "textual disambiguation". Textual disambiguation applies context to raw text and reformats the raw text and context into a standard data base format. Once raw text is passed through textual disambiguation, it can easily and efficiently be accessed and analyzed by standard business intelligence technology. Textual disambiguation is accomplished through the execution of textual ETL. Textual disambiguation is useful wherever raw text is found, such as in documents, Hadoop, email, and so forth.
* 2013 – Data vault 2.0 was released,<ref>[[#dvos2|A short intro to #datavault 2.0]]</ref><ref>[[#dvspec2|Data Vault 2.0 Being Announced]]</ref> having some minor changes to the modeling method, as well as integration with best practices from other methodologies, architectures and implementations including agile and CMMI principles