Narrow Results

Today, businesses have to respond with flexibility and speed to ever-changing customer demand and market opportunities. Service-Oriented Architecture (SOA) is the best methodology for developing new services and integrating them with adaptability — the ability to respond to changing and new requirements. In this paper, we propose a framework for ensuring data quality between composite services, which solves semantic data transformation problems during service composition and detects data errors during service execution at the same time. We also minimize the human intervention by learning data constraints as a basis of data transformation and error detection. We developed a data quality assurance service based on SOA, which makes it possible to improve the quality of services and to manage data effectively for a variety of SOA-based applications. As an empirical study, we applied the service to detect data errors between CRM and ERP services and showed that the data error rate could be reduced by more than 30%. We also showed automation rate for setting detection rule is over 41% by learning data constraints from multiple registered services in the field of business.

To face the challenges posed by new techno-savvy market players, the Public Sector Banks (PSB) and the old private banks in India, have introduced Core Banking Solutions (CBS) to replace disparate branch automation systems. CBS provides centralized online banking operational database which can be exploited for building Decision Support System (DSS) in key areas. While promptness of data is ensured, other data quality needs are to be appraised before implementing any such DSS. Hence an assessment of data quality in two key areas – Customer Relationship Management and Borrower Behaviour was carried out for a sample bank for data profiling, inter-field consistency, attribute value dependent constraints, domain constraints. The study has identified critical areas for data quality improvement both for legacy data that has been migrated and new data being captured by the CBS. Measures for data cleaning and implementation of additional constraints at the database or application level are proposed for improvement of data quality for implementing these DSS.