Database Development

Database Development

INTRODUCTION

Data resources of organization can be optimized and useful by high quality data. High quality data helps in increasing the benefits of business that has been achieved by using them. High quality data helps in providing improved and enhanced decision making, improved faith in information systems and effective and efficient business operations. Collection of enormous amount of data can be achieved by advancing and improving information systems within the organization. Advanced information systems become useful manage and build complicated data resources (Even & Shankaranarayanan, 2009). In order to develop analytics, to acquire business intelligence and to improve business processes, organizations use these data resources which help in gaining competitive advantage. The complications or complexity and size are responsible for making data resources susceptible to data defects which reduce the quality of data. It becomes expensive to improve quality of data and to detect defects. When there is high quality level then the cost usually cancels out the benefits. Providing the economic trade-offs in sustaining and achieving high quality of data, there are new economic perceptions for management of quality of data (Even & Shankaranarayanan, 2009).

Data Quality Improvement

Data of high quality is important for information system's successful integration within the organization. Missing, outdated, inaccurate and invalid values can be the defects that will reduce the performance of datasets. Low quality of data impedes decision making, reduces customer satisfaction, causes mistrust towards information systems, declines the business performance and increases costs (Even & Shankaranarayanan, 2009).

Techniques of data quality management are used for preventing, reducing and assessing the occurrence of errors. These techniques are given below:

Detection and Correction of Errors

Data is compared with the correct baseline in order to detect the errors. Correct baselines can include predefined calculations/rules, real world entities, a validated dataset and a value domain. Examining the ...