It is essential to develop a plan and use established processes for data mapping in advance of the project. Doing so will ensure success in ETL, data integration, and business intelligence / analytics efforts Introduction Planning is arguably the most
Data mapping (source-to-target mapping) is an essential activity for all data integration, business intelligence, and analytics initiatives Introduction Data mapping is among the most important design steps in data migration, data integration, and business intelligence projects. Mapping source to target
Metadata-based extraction, transformation, and loading (ETL) can support a new approach to any organization’s data integration and development practices Introduction Traditional extraction, transformation and loading (ETL) software is a computer programming tool that enables a developer to create custom ETL
Following some proven guidelines for data integration will ensure success in data warehousing and business intelligence, analytics and related systems initiatives. Introduction Almost every Chief Information Officer (CIO) has the goal of integrating their organization’s data. In fact, the issue
Data Integration is essential for successful information management and business intelligence Introduction Without data integration, enterprise integration is extremely costly and overly complex. Many businesses are convinced that Business Intelligence the Internet, and Customer Relationship Management (CRM) are critical, strategic
Data rationalization through managed metadata and data modeling, can support semantic resolution, enabling improved analysis and knowledge transfer. Introduction Across the Internet and within internal systems, ontologies are used to improve search capabilities and make inferences for improved human or
Data Integration is an essential activity that allows an organization to combine data from disparate sources into a unified version for a variety of uses. Proper data integration starts at architecture, includes understanding requirements and the data, and ends with
Customer data integration can be improved by using generic dimensional modeling techniques Introduction If you are undertaking a Customer Data Integration (CDI) or Customer Master Data Management (MDM) project as part of a dimensional modeling endeavor, how will you tackle
