Submit a Manuscript to the Journal
Quality Engineering
For a Special Issue on
Data Quality
Manuscript deadline
Special Issue Editor(s)
Roger Hoerl,
Union College, New York, USA
hoerlr@union.edu
Data Quality
Data have wound their way into every aspect of private-sector organizations, government agencies, academia, healthcare, and even our personal lives. Further, data is the essential “raw material” for producing large language models and artificial intelligence (AI) systems.
Unfortunately, even as the use of data became more widespread, efforts to ensure it is of sufficiently high quality have lagged behind. Ten years ago, the cost of bad data to the US economy was estimated to be over $3 trillion per year. Today this cost is likely even higher.
Much of the existing literature focuses on data accuracy. Rarely are the original root causes of poor data identified and eliminated. A more critical question in applications, especially AI, is whether we have the right data to perform the required tasks or solve the problems at hand. As pointed out by John Tukey, “The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.” Unfortunately, data collected for one purpose are rarely the best data for a different purpose.
It is with this background that Quality Engineering announces this call for papers for a Special Issue on Data Quality.
Submission Instructions
The special edition will be published in 2027. Potential topics include the following:
· How to best measure data quality, in terms of both the “data are right” and “right data” questions
· Development of novel methods to find and address root causes of poor data quality
· Identifying the potential implications of what David Hand called “dark data”, that is, data that we should have but don’t
· How to think about data quality relative to unstructured data, such as video, email messages, manuals, and so on
· Case studies demonstrating the perils of poor data quality, and success stories illustrating how to improve data quality
· Discussion of “retrieval augmented generation” (RAG), and other methods to utilize the “right data” to enhance AI model performance
· How to apply quality management systems, such as ISO-8000, or the Generic Statistical Business Process Model (GSBPM) used for official statistics, to enhance data quality
· Discussions of data governance, stewardship, and organizational responsibility for data quality
· Literature reviews on data quality
· The consequences of fabricated data and data that are manipulated inappropriately
Submissions intended for the special edition must be submitted by January 31st, 2027.
To submit a paper for the special issue:
- Please submit through the Quality Engineering submission site.
- When prompted with the question "Are you submitting your paper for a specific special issue or article collection?", select "Yes".
- A dropdown menu will appear; select "Special issue on data quality” from the drop-down menu.
For more information, please contact the Guest Editor: Roger Hoerl (hoerlr@union.edu), Guest Editor of the Special Issue on Data Quality