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EII works by providing individuals or applications a single, virtual view across multiple data sources. A view represents a business entity - a customer, a sales pipeline, or the performance of a manufacturer's production floor - annotated with a metadata-based description. Applications access a view as if its data were physically located in a single database, even though individual data may reside in different source systems. When an application accesses a view, the EII platform transparently handles connectivity with back-end databases and applications, along with related functions, such as security, data integrity, and query optimization.

Applications address queries to the EII layer, which then communicates with the underlying data sources to gather and return the answers. (See Figure Below) This shields applications from the details such as location and format of the information, protocols and query languages supported by the information sources, and the programming interfaces supported by the database servers. It also allows the applications to process data independent of changes to the underlying data management infrastructure.
 
 
Figure 1 - Enterprise Information Integration (EII) allows applications to use information from a variety of sources. The EII server evaluates requests for information, queries the individual data sources, and delivers customized output to the requesting application.

EII can work in conjunction with existing integration technologies like ETL (extract, transform, load) and EAI (enterprise application integration), but some people blur the distinctions among these technologies. Unlike ETL, EII leaves source data in place and retrieves what it needs, on demand. Compared with EAI, EII is clearly for combining information assets, not for scheduling data flows between applications. Table 1 below highlights the differences.

EII
Enterprise Information Integration		 ETL
Extract, Transform, Load		 EAI
Enterprise Application Integration
Primary focus on supplying applications with current, integrated information Primary focus on feeding decision support applications Primary focus on making applications communicate with each other
Views over existing data sources distributed across the enterprise Data accumulated in a central store Point-to-point integration of applications
No data migration Large scale data migration Message passing between applications
Access to various data formats:
•  Structured (Oracle, DB2, SQL Server, Sybase, MySQL)
•  Semi-Structured (emails, spreadsheets, XML)
•  Unstructured (Office documents, content management systems)
Data integration often addresses only structured information. Integrated information is stored in a data warehouse. Message brokers can convert between several different data formats understood by applications.
Accesses most current views of information from operational systems Intended for historical views, snapshots of data taken in batch mode. N/A
Applications can update some types of information sources. Cannot update any information sources. Data flow is uni-directional. Applications can affect updates in other applications by passing appropriate messages.



EII Applications
With EII defined and contrasted against other data integration approaches, this section will now discuss some criteria that will help identify when a situation exists that could benefit from an EII approach.

EII solves a set of business problems that share several common characteristics. IT managers should consider EII if their application needs to accomplish three of more of the tasks from the following list.
  • Access data distributed across multiple sources (relational databases, enterprise applications, data warehouses, documents, XML)
  • Combine data in different formats (relational databases, flat files, Word or Excel documents, XML)
  • Integrate corporate data with information from outside the firewall
  • Merge static data with messages, web services, or other data streams
  • Perform queries that include archived data with live information
  • Mix information from a data warehouse with current information
  • Analyze information on-the-fly to drive an application
  • Report to a variety of formats using widely distributed information
  • Implement a path to a service-oriented architecture with a minimal impact to existing IT infrastructure
Several broad classes of applications illustrate how EII solves a variety of data integration problems across a range of industries.

Application Use
Customer-Centric Analysis Combine information from multiple systems into a unified view of key customers
Financial Dashboards Combine multiple financial and operational systems (across regions, product lines, and accounts) into one view
Risk Management Extract needed information across multiple systems, trading desks, and/or product lines
Derivatives Trade Management Integrate and manage trade information across complex products
Market Data Management Create a unified view of market data from multiple internal and external sources
Financial Reporting Combine financial data from multiple systems into standardized report with audit trail



Key Capabilities
Enterprise customers that are considering an EII solution must first examine whether the product will meet their main objectives: integrating data from disparate sources, adding value to the information, and directing it to an application that will allow a user to make an informed business decision.

Some key issues to consider:
  • Data source breadth - connecting to all the relevant sources of information that drive operational decisions
  • Automation - making developers and administrators more productive
  • Intelligence - adding value to information to make it more usable
  • Performance - providing rapid response to requests for integrated information
  • Scalability - allowing adoption of emerging IT architectures such as SOA
  • Open standards and deployment - fiting into an existing an IT infrastructure
Data Source Breadth
EII systems must connect applications to the underlying data sources, regardless of their location or format. Typical enterprise data sources include relational data (Oracle, DB2, SQL Server, MySQL), structured and unstructured documents (Word, Excel, PDF, CSV), application data (SAP, Siebel), data warehouses, Web pages, Web Services, as well as message queues.

Automation
System administrators and application developers have enough to do without having to manage another data services layer. Any new infrastructure component must therefore include automated tools to simplify implementation. Some examples of functions that streamline deployment include wizards to guide novice users, auto-discovery of data sources and metadata, and scriptable interfaces for expert users.

Intelligence
For an EII implementation to truly add value, it must go beyond basic information integration (although it must perform that function flawlessly) and intelligently process the data that passes through it. Intelligence can come in the form of rules processing that screens incoming data according to business criteria, advanced indexing functions that can perform calculations and aggregate statistics on data streams, and data lineage tracking that automatically annotates each combined data element with its source, last update, and list of people who changed the information. Intelligent processing using EII can deliver a powerful tool for compliance with Sarbanes-Oxley, Basel II, and other regulations.

Performance
Another important selection criterion for EII is performance. The product must support the applications to which it delivers data with a timely response. Therefore, the overhead that the EII layer adds needs to be minimal. And, it should not affect the production system from which it extracts the original information. EII performance functions should include cost-based query optimization, leveraging relational database systems for complex joins, and caching to reduce load on production systems for repetitive queries.

Scalability
EII products must scale seamlessly to accommodate more users, larger queries, and more data sources. An EII solution should also be able to adapt to changes in IT infrastructure. Scalability concerns include accommodating all current and planned data sources, including service-oriented architectures, which rely heavily on XML.

Open Standards and Deployment
Since EII relies on disparate data sources, including some outside the enterprise boundaries, the more open the solution, the easier to deploy. Open standards allow EII products to connect to databases (via JDBC or ODBC), run on different application servers (Apache, WebSphere, WebLogic, and JBoss, for example), and connect to the applications they serve (via .NET, J2EE, or Web Services APIs). A vendor-neutral approach that does not lock a company into any proprietary data formats, query languages, or platforms simplifies deployment in a heterogeneous IT environment and provides companies the option to change vendors, if necessary.
 

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