Enterprise Information Integration (EII) - Revision history

User at 15:13, 7 December 2022

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User at 17:25, 11 November 2022

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User: Enterprise information integration (EII) is software that combines enterprise data and information into a unique data monitoring interface where data is expressed via uniform representation.

2018-12-24T20:25:39Z

Enterprise information integration (EII) is software that combines enterprise data and information into a unique data monitoring interface where data is expressed via uniform representation.

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 EII’s distinguishing characteristic is its ability to access and integrate current data from heterogeneous sources. How? A virtualized data federation layer delivers data to applications in real time from original sources. Various vertical segments increasingly use EII for business decisions:
 *Banking: Banks provide multiple lines of [[service]]: checking, home equity loans, brokerage services, etc. Banks want a complete view of [[customer]] activities across lines of business from multiple divisions and applications. Most banking data is in silos, making EII a natural choice for integration.
-*Securities Trading: As [[hedge]] funds trade across [[asset]] classes, traders need a complete view that incorporates up-to-the-minute data. Typically, different applications generate trading data, and different databases store it. EII allows integration from different sources, providing complete trading information across asset classes.
+*Securities Trading: As hedge funds trade across [[asset]] classes, traders need a complete view that incorporates up-to-the-minute data. Typically, different applications generate trading data, and different databases store it. EII allows integration from different sources, providing complete trading information across asset classes.
 *[[Single Customer View]]: As organizations roll out services, customer service representatives need complete views across all lines of business. For example, insurance companies are adding services besides life, automobile, and homeowners insurance. Service representatives must access all relevant data. EII technology is ideal for this application.
 *Federal Government: Certain laws and regulations make sharing information difficult. However, Homeland Security applications must integrate the data government agencies collect. Aggregating and copying data into a central repository won’t work. Agencies need to expose particular data elements, stipulate uses, and make specific data available to other agencies. EII meets this need.

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 Enterprise Information Integration (EII) applies data integration commercially. Despite the theoretical problems described above, the private sector shows more concern with the problems of data integration as a viable product. EII emphasizes neither on correctness nor tractability, but speed and simplicity. An EII [[industry]] has emerged, but many professionals believe it does not perform to its full potential. Practitioners cite the following major issues which EII must address for the industry to become mature:
-*Combining disparate data sets : Each data source is disparate and as such is not designed to support EII. Therefore, data [[virtualization]] as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data [[value]] commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases.
+*Combining disparate data sets : Each data source is disparate and as such is not designed to support EII. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data [[value]] commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases.
 *Simplicity of understanding: Answering queries with views arouses interest from a theoretical standpoint, but difficulties in understanding how to incorporate it as an "enterprise solution". Some developers believe it should be merged with EAI. Others believe it should be incorporated with ETL systems, citing customers' confusion over the differences between the two services.
 *Simplicity of deployment: Even if recognized as a solution to a problem, EII as of 2009 currently takes time to apply and offers complexities in deployment. People have proposed a variety of schema-less solutions such as "Lean [[Middleware]]", but ease-of-use and speed of employment appear inversely proportional to the generality of such systems. Others cite the need for [[standard]] data interfaces to speed and simplify the integration [[process]] in practice.