Open Government Data Quality Control Enables Accurate Data Extraction
Open Government Data Quality Control Enables Accurate Data Extraction
  • By Yeon Choul-woong (
  • 승인 2016.05.02 09:51
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Open Government Data Quality Control Infographics (Source: NIA Open Government Data Support Center)

The intelligent information industry based on artificial intelligence is growing on the back of open sources, an improvement in computing power and the growing use of big data. The key to developing the intelligent information industry is to secure high-quality data. In making important decisions, extracting accurate information from vast amounts of data will make or break your business.

To improve the quality of tremendous amounts of data held by public institutions in the era of intelligent informatization, the Ministry of the Interior and National Information Society Agency (NIA) has been pushing for “Open Government Data (OGD) Quality Control Project” since 2011. In addition, OGD standards and assessment of quality assurance standards have been expanded to ensure the accumulation of high-quality data from the stage of data creation.

Unlike data held by the private sector, OGD is linked to government agencies and has complex data flows. From the perspective of data links and utilization, data quality control is divided into data diagnosis & improvement, standardization and building management systems.

First of all, data diagnosis refers to analyzing data on the basis of database codes, relationships and business rules to check out whether there are overlaps in current database values and structures, whether databases are inefficiently designed and how data modification occurs.

In consideration of the lifecycle of datasets held by each institution, quality management activities are defined in each stage (e.g. planning, building, operation and utilization). And efforts to make each institution’s quality control activities consistent with national policies are supported. In particular, errors in data uploaded into Open Data Portal ( are checked and the results of error checking will be reported to the provider of the data to ensure the opening up of high-quality data.

Data standardization refers to the process of adding consistency to data collected by different institutions for the sake of user convenience. For data standardization, 43 OGD Standard Datasets was legislated in April 2016.

Whether dataset holders are following OGD standards will be monitored to help the private sector make use of OGD and improve the quality of open data. The South Korean government plans to develop over 100 OGD standard datasets by 2017.

Furthermore, the government has put in place a system for evaluating public data quality control processes to prompt public institutions to improve their data management systems. Government inspections of each public institution’s data quality control processes will help encourage each institution to voluntarily build a credible data management system, which will eventually lead to making high-quality data available to the public in a stable, sustainable manner.

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