Exploring the Relationship between Data Quality and Business Process Management

Call for Papers: Special Issue on Exploring the Relationship between Data Quality (DQ) and Business Process Management (BPM) for Enhancing Organizational Performance

 

Editors:

Dr. Mohammad Javad Ershadi

Information Technology Department, Iranian Research Institute for Information Science & Technology (IRANDOC)

Ershadi@irandoc.ac.ir

 

Dr. Abdorrahman Haeri

Industrial Engineering Department, Iran University of Science and Technology

ahaeri@iust.ac.ir

 

Dr. Azadeh Mohebi

Information Technology Department, Iranian Research Institute for Information Science & Technology (IRANDOC)

Mohebi@irandoc.ac.ir

 

 

 

In today's fast-paced business landscape, organizations are increasingly recognizing the importance of business process management (BPM) as a critical factor in driving performance improvement initiatives. Simultaneously, data and information quality have emerged as indispensable elements for advancing organizational operations within the framework of their goals and policies. While data quality improvement has long been acknowledged as a crucial component of data management and governance, its impact on business process improvement programs remains an important area that requires thorough exploration.

Various scientific references have addressed approaches to enhance data quality. Data-driven strategies focus on directly improving the value of data. For instance, outdated data can be enhanced by updating it from another reliable database. On the other hand, process-oriented strategies involve redesigning production processes and making necessary changes to enhance data quality. While the former strategy emphasizes the essence of data and its improvement, the latter, which is gaining increasing attention, necessitates a comprehensive understanding of the business and its core processes.

Therefore, it is inevitable to consider the influence of business processes on data quality improvement projects in ongoing research. Notably, some researchers, including Sturtevant et al. (2022), Liu et al. (2020), Wahyudi, Kuk & Janssen (2018), and Tepandi et al. (2017), have explored data quality improvement in information systems using process-oriented approaches. Concurrently, other studies conducted by researchers such as Yeboah et al. (2021), Petrović (2020), and Panahi et al. (2014) have focused on the impact of data quality on improving business processes. Moreover, scholars like van Dun (2022), Betancor et al. (2021), Delgado and Calegari (2020), and Ofner, Otto, and Österle (2012) have emphasized the development of integrated models that unite data quality and business process management.

To facilitate further exploration and advancements in this field, we invite researchers and practitioners to contribute to a special issue that delves into the combined approaches of data and information quality with business process improvement programs. This special issue aims to publish applied and developmental research that focuses on the intersection of data quality and business process management.

 

Potential topics for submissions may include but are not limited to:

  • Assessment of data quality for the improvement of business processes
  • Impact of business processes on data quality management in service-oriented organizations
  • Root cause analysis tools for data quality improvement in service providers
  • Techniques and tools for detecting data quality issues in business processes
  • Case studies showcasing real-world applications of data quality improvements in business processes
  • Mutual impacts of business processes and data quality frameworks
  • Integration of data quality into business process models
  • Integrated quality management systems with data and information quality models
  • Leveraging artificial intelligence and machine learning for data quality improvement in business processes
  • The role of data governance in ensuring data quality for effective business process management
  • Data quality metrics and measurement techniques for evaluating business process performance
  • Integrating data quality checks and validations into automated business process workflows
  • Addressing data quality issues in data integration and interoperability for seamless business process execution
  • Data quality implications for regulatory compliance and risk management in business processes
  • The impact of data quality on customer experience and satisfaction in service-oriented organizations
  • Other related topics

We encourage contributors to present original research, case studies, methodologies, frameworks, and review articles that shed light on the intricate relationship between data quality and business process management. Submissions will undergo a rigorous peer-review process to ensure the highest quality of accepted papers. Accepted articles will be published as part of a comprehensive collection that aims to advance knowledge and practices in this evolving domain.

We look forward to receiving your contributions and collaborating in advancing our understanding of the relationship between data quality and business process management for organizational performance improvement.

 

Important Dates:

 

First Announcement: September 1, 2023

Second Announcement: December 1, 2023

Final Reminder: February 1, 2024

Submission Due: February 15, 2024

Reviews Due: March 30, 2024

Final Papers: April 30, 2024

 

 

References

Betancor, F., Pérez, F., Marotta, A., & Delgado, A. (2021). Business process and organizational data quality model (BPODQM) for integrated process and data mining. In Quality of Information and Communications Technology: 14th International Conference, QUATIC 2021, Algarve, Portugal, September 8–11, 2021, Proceedings 14 (pp. 431-445). Springer International Publishing.

Delgado, A., & Calegari, D. (2020, October). Towards a unified vision of business process and organizational data. In 2020 XLVI Latin American Computing Conference (CLEI) (pp. 108-117). IEEE.

Liu, Q., Feng, G., Zhao, X., & Wang, W. (2020). Minimizing the data quality problem of information systems: A process-based method. Decision Support Systems, 137, 113381.

Ofner, M. H., Otto, B., & Österle, H. (2012). Integrating a data quality perspective into business process management. Business Process Management Journal.

Panahy, P. H. S., Sidi, F., Affendey, L. S., & Jabar, M. A. (2014, December). The impact of data quality dimensions on business process improvement. In 2014 4th World Congress on Information and Communication Technologies (WICT 2014) (pp. 70-73). IEEE.

Petrović, M. (2020). Data quality in customer relationship management (CRM): Literature review. Strategic Management, 25(2), 40-47.

Sturtevant, C., DeRego, E., Metzger, S., Ayres, E., Allen, D., Burlingame, T., ... & SanClements, M. (2022). A process approach to quality management doubles NEON sensor data quality. Methods in Ecology and Evolution, 13(9), 1849-1865.

Tepandi, J., Lauk, M., Linros, J., Raspel, P., Piho, G., Pappel, I., & Draheim, D. (2017). The data quality framework for the Estonian public sector and its evaluation: establishing a systematic process-oriented viewpoint on cross-organizational data quality. Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXV, 1-26.

van Dun, C. (2022). Data-Driven Business Process Management: Advancing Process Data Quality and Process Improvement (Doctoral dissertation).

Wahyudi, A., Kuk, G., & Janssen, M. (2018). A process pattern model for tackling and improving big data quality. Information Systems Frontiers, 20, 457-469.

Yeboah, G., Porto de Albuquerque, J., Troilo, R., Tregonning, G., Perera, S., Ahmed, S. A., ... & Yusuf, R. (2021). Analysis of openstreetmap data quality at different stages of a participatory mapping process: Evidence from slums in Africa and Asia. ISPRS International Journal of Geo-Information, 10(4), 265.