It is essential to develop a plan for data mapping in advance of the project to ensure success in ETL, data integration, and business intelligence / analytics efforts Introduction Planning is arguably the most important stage of the entire data mapping project. Documentation needed for a project’s sources should include data maps and data dictionaries to deliver a complete definition
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 data greatly influences project success – perhaps more than any other task. The outcome of the mapping process is a
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 code. Custom coding is a management challenge with expensive overhead and complex coordination between the business, application, and data teams.
Samuel B. Holcman An enterprise architecture (EA) is a series of conceptual blueprints that defines the structure and operation of an organization to enable strategic progress. Enterprise Architecture (EA) explicitly describes an organization through a set of independent, non-redundant artifacts, defines how these artifacts relate with each other, and develops a set of prioritized, aligned initiatives and roadmaps to understand
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 trusted valid results. Introduction Data integration is the collection of technical and business processes used to combine data from disparate
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 computer reasoning. By relating terms in ontology, the user doesn’t need to know the exact term actually stored in the
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 the problem of how to store customer addresses? Using customer addresses for direct marketing, analysis of household penetration, customer retention
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 of data integration has risen all the way to the Chief Financial Officer (CFO) and Chief Executive Officer (CEO) level
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 areas of investment. However, most organizations are struggling to deploy the enabling technologies. What are the constraints that prevent businesses
