Challenges in Implementing Data Quality Management

Challenges in Implementing Data Quality Management: Cases in the UAE Public Sector

Data quality is a major concern in today's world. Most companies face data quality problems in both structural and systematic contexts. Even when expensive tools have been innovated and built into the data quality, the data quality is still poor. Control Practices Common measures of data quality metrics are accuracy, integrity, reasonable dissemination, completeness, validity, uniqueness, and consistency.


Used by corporate executives, professionals, and even students to formulate efficient planning and implement feasible information and data quality control and management programs. Challenges in managing data quality. Analyze data quality management from the local and global context by providing critical inspection trends to improve data quality, tools, techniques and guidelines required to achieve the data quality goal of data management.

At the Abu Dhabi National Oil Company (ADNOC) and the Dubai Health Insurance Corporation at the Dubai Health Authority (DHA) as practical examples, the document offers a critical look at the challenges, trends, guidelines and techniques that apply to the quality management of the data .

There have been many innovations in technology in recent years, including social media, Internet of Things, cloud computing, and blockchain, among other technological innovations in information technology. Due to these technological developments, the amount of data has increased massively. observed, and the accumulation of this data has led to what is known as big data (Wang et al., 2016). However, this data may not be of use to an organization if it is not properly analyzed, so it is considered inappropriate data. as unwanted data, in this way quality data can be obtained that facilitate the correct decision-making in the company and direct the focus within the organization, so that the employees are directed towards the realization of the vision, mission and goals of the company.

(DQM) describes the series of processes that are carried out to maintain high standards of information. These processes range from obtaining the data to implementing innovative data processing techniques in data warehouses to ensure that quality data is captured (Batini et al., 2015) It is acceptable that DQM is a fundamental achievement in data analysis because quality data help to derive actionable and accurate insights from the information received.

Therefore, the thesis of this article is that when introducing data quality management techniques, companies face many challenges in acquiring the data and analyzing it in order to obtain valuable information. Some organizations have made some significant strides in addressing them.

Data quality management challenges. For example, Dubai Health Insurance Corporation introduced eClaimLink to DHA in 2012, which integrates the management of all information related to administration, medical history and transactions that is kept in this health facility and converts it into health languages ??(Ishad, 2014) The eClaimLink tool has enabled DHA to achieve its goals faster and smarter while providing healthcare facilities with a standardized data tracking system.

Funding programs (Ishad, 2014). in numerous stages of development and has proven useful in supporting 2,000+ insurance settlement providers and 60+ payers / TPAs.

This tool enabled insurance service providers to have more than 3 million members with insurance coverage in Dubai (Ishad, 2014). The main objective for DHA's establishment of eClaimLink is to improve the systems and operational policies that run insurance billing programs.

Dubai by registering around 45 active market participants. represents each of the companies.

This document examines the challenges DHA faces in implementing the eClaimLink system and possible approaches to mitigate these issues.

On the flip side, the Civil Project Division (CPD) of the Abu Dhabi National Oil Company (ADNOC) was looking for a unified collaboration solution that could manage communications, document approvals, and workflows at various stages in the execution of the design, procurement, and construction project to help manage the Delivery of an asset that requires precise data quality management, data quality and data analysis in order to process all data incoming to ADNOC.

The procurement process is handled by the existing SAP solution. The solution must be able to handle an unlimited number of projects. Once a project starts, the solution must link program execution to the project plan and strategy. CPD implements Aconex, a design software and engineering platform in the cloud, to improve workflow study (Mar?a, 2018).

The topic is as follows: In the first part the research, the topic and the problem are presented; in the second part a detailed literature review on data quality for business intelligence, local and global trends for improving data quality, guidelines and techniques in data quality management. and opportunities for value creation in the United Arab Emirates.

In the third part, the research methodology is discussed and includes the study area, data collection instruments, sample design, data collection. seven present the discussions, conclusions and recommendations.