Abstract
Data quality is a significant concern in today’s world. The majority of organizations are being challenged by the data quality issues both in the structural and systematic context. Bad data quality is still being experienced even though expensive tools have been innovated and incorporated into the data quality control practices. Common measures of data quality metrics are accuracy, completeness, proper dissemination, integrity, validity, uniqueness, and consistency. For this reason, intense analysis and evaluation of the most applicable ways in managing data quality can serve as a roadmap that can be utilized by the company’s executive team, practitioners and even learners in formulating efficient planning as well as implementing feasible data and information quality control and management programs. This paper is based on intensive research and the use of practical examples and will seek the evaluation of the challenges faced in data quality management. The paper will also provide an analysis of data quality management from the local and global context by giving critical inspection trends to improve the data quality, the tools, techniques, and the policies that are necessary for achieving the data quality management goal. By using the Civil Project Division (CPD) in Abu Dhabi National oil company (ADNOC) and Dubai Health Insurance Corporation in Dubai Health Authority (DHA) as the real-world examples, the paper will give a critical viewpoint of the challenges, trends, policies, and techniques that apply to data quality management.
Highlights
Many innovations have occurred s in the field of technology over the past few years which include social networking, Internet of Things, cloud computing and block chain among other technological innovations in information technology
This paper is based on intensive research and the use of practical examples and will seek the evaluation of the challenges faced in data quality management
The paper will provide an analysis of data quality management from the local and global context by giving critical inspection trends to improve the data quality, the tools, techniques, and the policies that are necessary for achieving the data quality management goal
Summary
Many innovations have occurred s in the field of technology over the past few years which include social networking, Internet of Things, cloud computing and block chain among other technological innovations in information technology. Due to these technological developments, a massive increase in data has been witnessed, and the accumulation of this data has resulted in what is being termed as big data (Wang et al, 2016). Data quality management (DQM) describes the set of processes which are carried that to maintain high standards of information. It is acceptable that DQM is a basic achievement in data analysis because quality data helps in deriving actionable and exact intuitions from the obtained information
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
