Hitachi to Support JGC Global in Advancing Data Management for the AI Era
Hitachi will begin supporting JGC Global's operationalization of a Data Quality Management (DQM) framework starting April 2026. This initiative aims to enhance data management in preparation for the AI era, recognizing that high-quality data is crucial for improving the accuracy of AI-generated responses.
📋 Article Processing Timeline
- 📰 Published: April 8, 2026 at 23:00
- 🔍 Collected: April 8, 2026 at 14:30
- 🤖 AI Analyzed: April 20, 2026 at 14:37 (288h 7m after Collected)
Hitachi, Ltd. (hereinafter referred to as Hitachi) has commenced an initiative in April 2026 to support JGC Global Corporation (hereinafter referred to as JGC Global) in operationalizing a Data Quality Management (DQM) framework to advance their data management for the AI era.
The accuracy of AI responses heavily depends on the quality of input data, making the advancement of data management more important than ever for promoting AI utilization across an entire organization. In particular, establishing DQM, which continuously maintains and improves data completeness, timeliness, and reliability, is indispensable as a foundation for AI application.
To date, both companies have jointly examined improvement policies for DQM, based on international data management standard frameworks such as DMBOK*1 and ISO 8000*2. This included identifying current issues at JGC Global, defining future goals, and designing an improvement process that combines PDCA*3 and OODA*4. Based on this policy, they aim to establish and entrench a DQM system that can continuously and flexibly manage and improve data quality, thereby promoting AI utilization and maximizing the value of data.
*1 DMBOK: Data Management Body of Knowledge
*2 ISO 8000: International standard for data quality
*3 PDCA: A process for continuous business improvement and efficiency through repeating Plan, Do, Check, and Action.
*4 OODA: A process for rapid decision-making and action in response to changing situations by quickly cycling through Observe, Orient, Decide, and Act.
While the use of AI is rapidly expanding to solve issues like labor shortages and skill succession, many companies are facing challenges in improving response accuracy, such as not achieving expected precision or generating factually incorrect answers. Many of these issues stem from inadequate DQM, including inconsistencies in data units and formats, and variations in quality.
The fundamental concepts for continuously improving data quality are defined in DMBOK and ISO 8000. However, the implementation of DQM in the field has been slow due to difficulties in achieving cross-organizational consensus and a shortage of specialized personnel. On the other hand, continuous improvement of data quality is essential for maximizing the value of data, making the creation of a practical, on-the-ground system an urgent task.
The JGC Group is working on significant efficiency improvements and business transformation through the use of digital technologies such as AI and IoT in each phase of project execution in its EPC*5 business. Leveraging its specialized knowledge in data management cultivated in diverse industries such as manufacturing, finance, and social infrastructure, Hitachi will provide hands-on support for JGC Global's efforts to improve data quality management based on this strategy.
Specifically, Hitachi will comprehensively support the creation of the necessary DQM mechanisms, from setting data quality standards and establishing monitoring methods to designing the overall operation of the data management infrastructure and building data governance. In creating these mechanisms, a hybrid 'PDCA x OODA' improvement process will be adopted, combining the planned and steady execution of PDCA with the rapid situational awareness and response of OODA.
The accuracy of AI responses heavily depends on the quality of input data, making the advancement of data management more important than ever for promoting AI utilization across an entire organization. In particular, establishing DQM, which continuously maintains and improves data completeness, timeliness, and reliability, is indispensable as a foundation for AI application.
To date, both companies have jointly examined improvement policies for DQM, based on international data management standard frameworks such as DMBOK*1 and ISO 8000*2. This included identifying current issues at JGC Global, defining future goals, and designing an improvement process that combines PDCA*3 and OODA*4. Based on this policy, they aim to establish and entrench a DQM system that can continuously and flexibly manage and improve data quality, thereby promoting AI utilization and maximizing the value of data.
*1 DMBOK: Data Management Body of Knowledge
*2 ISO 8000: International standard for data quality
*3 PDCA: A process for continuous business improvement and efficiency through repeating Plan, Do, Check, and Action.
*4 OODA: A process for rapid decision-making and action in response to changing situations by quickly cycling through Observe, Orient, Decide, and Act.
While the use of AI is rapidly expanding to solve issues like labor shortages and skill succession, many companies are facing challenges in improving response accuracy, such as not achieving expected precision or generating factually incorrect answers. Many of these issues stem from inadequate DQM, including inconsistencies in data units and formats, and variations in quality.
The fundamental concepts for continuously improving data quality are defined in DMBOK and ISO 8000. However, the implementation of DQM in the field has been slow due to difficulties in achieving cross-organizational consensus and a shortage of specialized personnel. On the other hand, continuous improvement of data quality is essential for maximizing the value of data, making the creation of a practical, on-the-ground system an urgent task.
The JGC Group is working on significant efficiency improvements and business transformation through the use of digital technologies such as AI and IoT in each phase of project execution in its EPC*5 business. Leveraging its specialized knowledge in data management cultivated in diverse industries such as manufacturing, finance, and social infrastructure, Hitachi will provide hands-on support for JGC Global's efforts to improve data quality management based on this strategy.
Specifically, Hitachi will comprehensively support the creation of the necessary DQM mechanisms, from setting data quality standards and establishing monitoring methods to designing the overall operation of the data management infrastructure and building data governance. In creating these mechanisms, a hybrid 'PDCA x OODA' improvement process will be adopted, combining the planned and steady execution of PDCA with the rapid situational awareness and response of OODA.