Yokohama, Japan - JGC Holdings Corporation announced that JGC Corporation has commenced operation of a Data Quality Management Framework for engineering, procurement, and construction (EPC) services from April this year. The framework is designed to improve the quality and reliability of EPC-related data, enabling more efficient operations and reliable, AI driven decision making.

The framework was jointly developed with Hitachi, Ltd. with the objective of achieving continuous improvement in data quality. It adopts a hybrid PDCA*1 and OODA*2 approach, integrating the PDCA cycle--designed for long‑term review and continuous improvement--with the OODA loop, which enables rapid situational awareness and response. This hybrid model enhances the overall sophistication of data quality management processes while simultaneously improving quality in day‑to‑day operations.

*1 PDCA: A continuous improvement cycle consisting of Plan, Do, Check, and Act.
*2 OODA: A rapid decision‑making process consisting of Observe, Orient, Decide, and Act.

Figure: Conceptual illustration of a data quality improvement process combining PDCA and OODA

At JGC Corporation, data related to engineering, procurement, and construction (EPC) projects is centrally managed through its proprietary platform, DATABOX, to support data‑driven decision‑making across EPC projects. While EPC data is typically managed in separate, discipline‑specific databases, DATABOX has been designed as an integrated, cross‑functional platform that consolidates data across domains, enabling organization‑wide data integration. As a result, this integrated data environment provides a strong foundation for advanced analytics and AI‑based applications across the EPC project lifecycle.

However, as EPC projects continue to grow in scale and complexity, a more systematic approach to data quality management has become increasingly important in order to fully realize the value of such integrated data platforms.

By utilizing this framework, JGC Corporation can ensure key dimensions of data quality, including completeness, timeliness, consistency, and validity. This allows critical project information related to design, procurement, and construction to be shared seamlessly across departments, enabling executives and other decision makers to make judgments based on a consistent and trusted set of information and supporting more advanced project execution and management.

Because the effective use of AI depends heavily on the quality of input data, the continuous maintenance of high quality data is essential. Since 2023, JGC Corporation has worked jointly with Hitachi, Ltd. to develop this Data Quality Management Framework based on internationally recognized data management standards such as DMBOK*3 and ISO 8000*4. These efforts have included identifying data quality challenges, defining target quality objectives, and designing structured data quality improvement processes.

*3 DMBOK (Data Management Body of Knowledge): A globally recognized framework providing best practices and principles for data management.
*4 ISO 8000: An international standard that defines requirements for data quality.

Going forward, the JGC Group will leverage well‑governed, high‑quality data to ensure reliable project execution and contribute to solving increasingly complex global challenges.

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