scholarly journals Using visual-interactive properties to support data quality visual assessment on abstract and timeless data

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
João Marcelo Borovina Josko ◽  
João Eduardo Ferreira
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Visual Assessment ◽  
The State ◽  
Contextual Knowledge ◽  
Data Quality Assessment ◽  
Design Characteristics ◽  
Visualization Systems ◽  
State Of Art

Visualization systems belong to supervised tools that can make noticeable the intrinsic structures of defects on data. However, despite the significant number of these systems that assist Data Quality Assessment, few provide resources to examine these structures deeply. This situation prevents data quality appraisers from using their contextual knowledge to confirm or refute any data defect. This article explores a visualisation system’s additional features and design characteristics (named V is4DD) that uses visual-interactive properties to support data quality visual assessment on abstract and timeless data (e.g., Customer, Billing). Additionally, we conduct a full review and outline the state-of-art visualization systems related to data quality assessment and fit Vis4DD into this scenario.

Visualization properties for data quality visual assessment: An exploratory case study

2016 ◽  
Vol 16 (2) ◽  
pp. 93-112 ◽  
Author(s):  
João Marcelo Borovina Josko ◽  
João Eduardo Ferreira
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Visual Assessment ◽  
Support Design ◽  
Visualization System ◽  
Data Quality Assessment ◽  
Context Knowledge ◽  
Visualization Systems ◽  
Data Context

Data quality assessment outcomes are essential to ensure useful analytical processes results. Relevant computational approaches provide assessment support, especially to data defects that present more precise rules. However, data defects that are more dependent of data context knowledge challenge the data quality assessment since the process involves human supervision. Visualization systems belong to a class of supervised tools that can make visible data defect structures. Despite their considerable design knowledge encodings, there is little support design to visual quality assessment of data defects. Therefore, this work reports a case study that has explored which and how visualization properties facilitate visual detection of data defect. Its outcomes offer a first set of implications to design visualization system to permit data quality visual assessment.

Applying Domain Knowledge for Data Quality Assessment in Dermatology

2017 ◽  
pp. 147-156
Author(s):  
Nemanja Igić ◽  
Branko Terzić ◽  
Milan Matić ◽  
Vladimir Ivančević ◽  
Ivan Luković
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Domain Knowledge ◽  
Data Quality Assessment

scholarly journals Evaluation of a staff training programme to reimplement a comprehensive health assessment

2018 ◽  
Vol 7 (4) ◽  
pp. e000353 ◽  
Author(s):  
Luke A Turcotte ◽  
Jake Tran ◽  
Joshua Moralejo ◽  
Nancy Curtin-Telegdi ◽  
Leslie Eckel ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Information ◽  
Health Assessment ◽  
Training Programme ◽  
Care Planning ◽  
Assessment Framework ◽  
High Quality ◽  
Data Quality Assessment ◽  
Comprehensive Health

BackgroundHealth information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital’s clinical workflows.MethodsA hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme.ResultsInitial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment.ConclusionsIt is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

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