Data integration is the process of combining data residing at different sources and providing the user with a unified view of the data.
Access – Data comes from many sources, including legacy application and systems, databases, modern applications, various XML messages and numerous types of documents (spreadsheets, project plans, text documents, etc). Identifying and accessing these sources is the first step to data integration.
Discovery - This involves bringing all data sources out into the open, and documenting the uses and structures of poorly understood or described sources. This is also the point at which data semantics (patterns or rules that emerge from its structure and use) and quality issue should be noted and flagged for further action.
Cleansing – Data is cleaned up for accuracy and integrity. Clean-up can involve detecting and correcting errors, supplying missing elements and value, enforcing data standards, validating data and purging duplicate entries.
Integration - This step involves consolidating data across all systems and applications, accessing their fragmented data, creating an accurate and consistent view of their information assets, and leveraging those assets to drive business decisions and operations. This often means resolving inconsistent utilization and definition for identical terms across different contexts.
Delivery – Correct, relevant data is made available in proper form, in a timely manner, to all users and applications that need such access. This might mean responding to queries that result in single records or small answer sets to delivering entire data sets for trend analysis or enterprise-wide reporting. This step also addresses needs for data security, availability, privacy and compliance requirements related to access and use.
Development and Management - This is where XML-based toolsets enable those who manage data; business analysts, architects, developers and managers to work together in creating a comprehensive set of data integration rules, processes, practices and procedures, thereby capturing and implementing all the substantive work done in the five preceding steps. This step also tackles issues related to performance, scalability and reliability needs for key enterprise applications and services.
Auditing, Monitoring and Reporting – Once its semantics and uses have been captured, omissions remedied, errors corrected, and quality examined and assured, ongoing observation and analysis is required to keep the data clean, correct, reliable and available. This part of the process makes it possible to flag potential issues as they occur and to cycle them back through this lifecycle to make sure they resolved. Auditing also helps to make sure that data remains visible, under control, and able to guide future changes and enhancements.
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The Data Integration Lifecycle