Data Quality Project
Purpose
This project seeks to advance deeper knowledge of how data quality can be measured, analyzed, improved, and secured. In addition, it focuses on strengthening awareness among executives and specialists of the critical importance of high-quality data, while developing insights into how data quality initiatives can be effectively organized, managed, and led to achieve sustainable impact.
Why this Project
Data and information play an important role in nearly everything that happens today. Without access to accurate data, it is impossible to make sound decisions. As digitalization accelerates and organizations become increasingly data-driven, the importance of high-quality data has grown rapidly.
​
The current development of Artificial Intelligence (AI) further raises requirements for data. AI systems require vast amounts of reliable, high-quality data. Training models on poor data not only results in underperforming systems but can also create serious risks and threats. Equally critical is ensuring that emerging autonomous systems operate with correct data, since the human role as a filter between data and decision-making is diminishing, meaning that data errors can directly translate into faulty outcomes.
​
Regulatory requirements for compliance, reporting, and follow-up also place new demands on data quality. Without accurate data, it becomes impossible to meet such requirements. In many cases, deficiencies lead to fines, penalties, or even legal consequences, creating significant risks for organizations.
​
Beyond the corporate sphere, good data quality is vital for societal development and democratic systems. Policymakers and citizens alike depend on accurate and relevant information to make rational decisions. Achieving sustainable development also relies heavily on robust data, particularly concerning the 169 targets and 244 indicators defined under the United Nations’ 17 Sustainable Development Goals.
​
Finally, data quality directly affects business valuation and financial markets. Today’s stock exchanges place substantial emphasis on intangible assets such as data and information. If such data proves to be inaccurate, it can trigger rapid and dramatic shifts in market value.
Background and the Road Ahead
Awareness of the importance of correct, relevant, and high-quality data is not new. The data quality pioneer Tom Redman already highlighted the challenges in the 1990s and recognized that many solutions could be learned from the field of quality management. Yet, despite the dramatic increase in the value and use of data over time, understanding and interest in data quality have not grown at the same pace. As a result, the world is rapidly moving toward a data quality crisis.
​
Today, organizations often try to address poor data by cleaning low-quality datasets to make them more usable. However, this reactive approach is insufficient to meet the growing demands for reliable data. Data cleaning also causes delays, wastes energy, and risks leaving critical errors undetected. A more proactive approach is required to achieve sustainable success in data management.
​
Recognizing this urgency, IAQ has identified data quality as a top priority for the future. Two IAQ teams are currently dedicated to this area, and it is also one of the two prioritized domains within the IAQ Doctoral Academy. In 2023–2024, IAQ initiated a pre-study to gain a comprehensive overview of the field, involving experts and senior leaders from companies such as Scania, ABB, and Toyota. The results of this study are publicly available for download.
​
In the near term, this project will deepen knowledge and understanding of the effects and consequences of data quality, as well as current practices within companies and organizations. This will be achieved through extensive international surveys and case studies. In the longer term, the project will focus on developing effective methods, practices, and tools for managing data quality, while also advancing modern approaches to organizing and leading this work.
Project Team
IAQ has established two Think Tank teams dedicated to data quality. One team operates within the Quality in Digitalization and AI Think Tank (QiDATT), and the other within the Quality in Statistical Engineering Think Tank (QiSETT). These two groups work both independently and in close collaboration as part of IAQ’s Data Quality Project.
​
Project Coordinator
Lars Sörqvist, IAQ Chair
​
Team – QiDATT
Lars Sörqvist (Sweden), Jiju Antony (UK), Pedro Saraiva (Portugal), Nicole Radziwill (USA), Blanton Godfrey (USA), J. Ravikant (India), Marco Reis (Portugal), Emil Sörqvist (Sweden), Tao Xu (China), Pedro Alexandre Marques (Portugal), Ola Ringström (Sweden), Mats Thuresson (Sweden), Jacob Hallencreutz (Sweden)
​
Team – QiSETT
Roger Hoerl (USA), Tom Redman (USA), Ron Snee (USA), Dennis Lin (USA), Beth Cudney (USA), Emil Sörqvist (Sweden)
Deliverables
This project aims to publish articles in both peer-reviewed journals and professional magazines, as well as reports and books. Findings and results will further be disseminated through conference presentations, seminars, digital video content, and active engagement on social media
Publications
Sorqvist E, Sorqvist L, 2024, Critical Areas for Future Research in Data Quality Management, IAQ White Paper
​
Sorqvist E, 2024, Data Quality Management - Achieving Success and Excellence in the Digital Age, Royal Institute of Technology, Stockholm, IAQ Pre-study on Data Quality
​
​Sorqvist E, Sorqvist L, 2023, Data quality is a foundation for digital transformation, EOQ Congress, Porto