An Approach To Enterprise Data Rationalization Enalber

Enterprise data is now becoming a important resource for business operation and corporation competitiveness development. As a result of changes in business environment, enterprise data requirement evolvement is a long term, ever-lasting progress. Besides, the existence of diverse data requirement between different department, business process and location leads to a distributed data storage and usage. And the fact that corporate information system is largely planned and implemented by phase also brings heterogeneity to data systems. These three factors makes enterprise data environment more and more sophisticated.A Bad-Organized data environment brings out many problems, including decreasing proficiency of business process, reducing the incredibility of information systems and adding cost and risk in data warehousing and data mining projects.Current approaches can be classified into two categories: data quality and data integration. Data quality approaches focus on quality dimension definition, measurement and improvement, with less method discussing on "Fitness for Use" of enterprise data;and approaches proposed in data integration literatures aim at solutions for data integrity both semantically and syntactically in heterogonous autonomic and distributed data. These approaches solve inconsistency and quality problem to some degree but often in static and partial way.This thesis proposed a composite approach Enterprise Data Rationalization Approach to fulfill enterprise data requirement and improve both data integrity and quality with dynamic and overall method. Enterprise data rationalization approach separates enterprise core data from operational data, adopting flexible data modeling techniques and central architecture for core data, and implement data enrichment workflow management together meta data repository and service to ensure core data's quality.There are three data rationalization enables in the proposed approach: 1. Core data separation, central data repository and flexibledata modeling tehiniques altogether to meet themulti-dimensional and changing data requirement,2. Data enrichment workflow management system to ensure the quality and traceability of data process and provide workflow definition customization to align with process reengineering.3. Meta data repository and services to store and management business and technical knowledge and provide quality assurance.This thesis also gives an example implemented by a multinational company based on proposed approach, illustrating how to combine these three enablers to reengineer and rationalize corporation data environment.