Logo image
Back
Exploring Data Workers’ Behaviours in Data Quality Discovery
Conference paper   Open access   Peer reviewed

Exploring Data Workers’ Behaviours in Data Quality Discovery

Tianwa Chen, Gianluca Demartini, Marta Indulska and Shazia Sadiq
ACIS 2023 Proceedings, pp.1-17
Australasian Conference on Information Systems (ACIS), 34th (Wellington, New Zealand, 05-Dec-2023–08-Dec-2023)
Australasian Association for Information Systems (AAIS)
2023
pdf
Exploring Data Workers_ Behaviours in Data Quality Discovery2.21 MBDownloadView
Published Version Open Access CC BY-NC V3.0

Abstract

data quality discovery sensemaking behaviours eye tracking
The rise of data re-purposing has resulted in an unprecedented opportunity to create new value from existing data assets. Unlike previous approaches that relied on well-understood data quality requirements, this open-ended setting demands a discovery-oriented approach. Despite a growing market of tools that assist with data exploration and profiling, data quality discovery continues to drain the time of data workers due to its cognitively demanding and contextual nature. Accordingly, we study data quality discovery through purpose-built experimental platforms that mimic typical data exploration platforms, and explore implicit sensemaking behaviours of data workers when required to write code or use available functions, by exploiting eye-tracking and log data. Our analysis of visual scanning and attention patterns reveals various sensemaking loops amongst data workers with different performance levels in different settings. Our findings provide insights for researchers and tool designers to consider how to support different data workers in data quality discovery.

Details

Metrics

1 Record Views
Logo image