Affiliated with:
Data integration is the collection of technical and business processes used to combine data from disparate sources into meaningful and valuable information. Data integration requires attention to enterprise architecture and its components: data architecture, application architecture, technical / infrastructure architecture; metadata management, master and reference data management, data quality management, and data governance.
Although some challenges are part of any data integration effort, the need for mastering the best practices for data integration remains essential for any organization
Effective data integration is an important, precise, detailed, and rigorous analysis process that can offer business value to any organization Effective data integration is much
Data integration can present many challenges to any organization. Understand these situations before starting a data integration effort – success depends on it! Asking ten
A variety of methods can deliver information across an enterprise, including ad hoc and on-demand reports, dashboards, and analytics capabilities. Most data warehouse environments offer
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
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
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
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
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
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
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.
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
