Abstract
Information provided by any applications systems in organization is vital in order to obtain a decision. Due to this factor, the quality of data provided by Data Warehouse (DW) is really important for organization to produce the best solution for their company to move forwards. DW is complex systems that have to deliver highly-aggregated, high quality data from heterogeneous sources to decision makers. It involves a lot of integration of sources system to support business operations. Problem statement: Many of DW projects are failed because of Data Quality (DQ) problems. DQ issues become a major concern over decade. Approach: This study proposes a framework for implementing DQ in DW system architecture using Metadata Analysis Technique and Base Analysis Technique. Those techniques perform comparison between target values and current values gain from the systems. A prototype using PHP is develops to support Base Analysis Techniques. Then a sample schema from Oracle database is used to study differences between applying the framework or not. The prototype is demonstrated to the selected organizations to identify whether it will help to reduce DQ problems. Questionnaires have been given to respondents. Results: The result show user interested in applying DQ processes in their organizations. Conclusion/Recommendation: The implementation of the framework suggested in real situation need to be conducted to obtain more accurate result.
Highlights
The qualities of data contain in the Enterprise Information Systems have a significant impact and crucial to the decision maker
To support the framework, it is using a prototype known as Data Quality Analysis System (DQAS) which integrated with BI/ETL opens sources tools
The result has shows by applying Data Quality (DQ) framework to Human Resource (HR) schema, it is improved the quality of data
Summary
The qualities of data contain in the Enterprise Information Systems have a significant impact and crucial to the decision maker. The value and importance of knowledge, as seen by numerous organizations today, does without a doubt play a crucial role in the current ever-challenging and aggressive business environment (Ling, 2007).The last several years has introduce many of new technologies and tool to support the business process-grid systems, ETL applications, semantic web namely as a few This technology can utilize and successes if a data resides in those of systems are qualities one. The dimensions propose in the framework of this study is emphasize on correctness (syntactic and semantic accuracy), consistency, completeness and timeliness (currency and volatility) of data. Those dimensions are important criteria to ensure the qualities of data in DW. Metadata Analysis is the process of trying to understand what the data should be (target values) by analyzing both business metadata
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