Prof. Dr. Jibril bin Hassan Al-Arishi *

Data quality is not a small or local problem, but rather a global problem. There are different views on the multiple dimensions of data quality. They can include factors such as completeness, coverage, conformity, consistency, accuracy, non-duplication, and updating. Data quality is evaluated based on the viewpoints of its users, their needs, and their priorities, which are matters that differ according to them. Experts describe data quality with a list of criteria that organizations may succeed in meeting some of while failing to meet others. Most countries in the world follow multi-dimensional methodologies to raise data quality, which can be summarized in three dimensions: legislative, regulatory, and technical. We present below a vision for a methodology to raise the quality of vernment data in the Kingdom from the perspective of these three dimensions.

1 - Legislation:

Many countries in the world have enacted legislation aimed at maximizing quality and increasing the benefit and transparency of vernment data and thus increasing its reliability. The following are the features of the system that should be enacted in the Kingdom - following the example of many countries in the world - to ensure the quality of information in vernment departments. Each vernment department at the state level does the following:

• Issuing guidelines that ensure and enhance the quality, objectivity, benefit, and safety of information (including statistical information) published by the department or sent to other vernment departments, no later than one year after the date of issuance of the system.

• Creating an independent function specialized in data quality at the department level.

• Determining the party responsible for each type of open data published by the department.

• Taking appropriate steps to treat information quality as one of the performance indicators that the vernment department is committed to before the higher authority.

• Defining standard definitions and names that the vernment department commits to in the data it publishes or sends to other vernment departments.

• Adopting quality standards appropriate for the cateries of information published by the department or sent to other vernment departments.

• Merging the data entry process into the department's operations, so that it is an inherent part of those operations, with the aim of removing manual entry steps, and thus increasing its accuracy.

• Merging information quality standards into work procedures within the department, so that information circulated in the department, or sent to other departments, is of high quality and reliability.

• Creating administrative mechanisms that allow beneficiary vernment departments or ordinary citizens benefiting from information to take necessary measures to correct information held and distributed by the vernment department.

• Providing a periodic report to the higher president of the department manager including: (1), number and nature of complaints received regarding info accuracy, (2) how they were handled.

2 - Organization

The organizational dimension includes the most important suggested practices that should be resorted to in vernment departments for the purpose of increasing the quality of the data they have. We clarify them below:

1-2 Merging the data update process into the work cycle

One of the most important known sources of data inaccuracy is that it is entered or updated in computer systems in parallel with oning work, i.e., work proceeds in its traditional form, then employees feed the computer systems with data resulting from the work cycle after the work ends. These practices are one of the biggest sources of error in vernment institutions. However, if the process of entering and updating data is merged with the work cycle, this means mandatory updating of databases during that cycle, so that the work cycle stops if the update is not done. This leads to the elimination of causes of data inaccuracy arising from human errors in copying or neglect in entering updates as soon as they occur or otherwise. In addition, reports that are published, or sent to other departments, are issued automatically containing data that reflects the actual reality in the vernment department.

2-2 Standardization

Standard definitions and names play an important role in ensuring data quality and making it more interoperable. Therefore, a system should be enacted that imposes defining standard definitions and names that vernment departments commit to in the data they publish or send to other vernment departments. The al is to create standard definitions for names and technical terms or those specific to business leading to achieving compatibility between data of different vernment departments allowing for integration, shared use, or circulation in a consistent manner. These standards must be updated constantly to ensure sustainability.

3-2 Community Participation (Crowdsourcing)

Crowdsourcing is used as a strategy to improve data by inviting its users to help correct errors in it or add new information to it. For this strategy to be effective, it must involve more than just publishing data then waiting for its users to help improve it, but rather there should be incentives that encourage participation and interaction. ogle's Mapmaker program is a leading example of using crowdsourcing to improve data quality. The system allows ordinary users to share info about places they know and identify errors. This system allows ordinary users to gain experience, rewards activists, and results in accurate data.

4-2 Open Data

Publishing data to the public and then seeking feedback from beneficiaries is also a form of crowdsourcing which is necessary to ensure that data is useful and correct. Some see that open data leads to the emergence of an army of high-efficiency data reviewers. Issuing data publicly has a positive effect on its quality. Just preparing data for publication can reveal problems related to work procedures that would not have appeared without the intention to publish it. Also, open data policy can lead to pressure on the vernment, especially if it includes providing citizens with info about their entitlements. This makes them able to compare between services the vernment claims to do and actual quality. No matter what systems man puts for state management, monitoring, or accounting, the citizen's eye remains a powerful tool in monitoring. Moreover, officials' knowledge that citizens follow their work and provide feedback contributes to data quality and motivates those working in the cycle that produces it. Open data is a tool for activating "vernance," including concepts of transparency and monitoring, representing one of the main aspirations of Saudi Vision 2030.

3 - Technology

Providing direct access to the databases of sub-departments by the employees of the higher level (the Ministry) is one of the radical trends to face many data accuracy problems. Viewing the data of vernment departments by ministries directly without referring to these departments forces them to raise the quality of their data and at the same time eliminates the problems of transferring data and reports. Technology provides many solutions, such as periodically copying data from sub-departments to the Ministry's database automatically at pre-determined times. This ensures the Ministry has updated copies of databases all the time. Being able to extract reports without referring to sub-departments makes those departments keen on high quality.