A Data Quality Framework for Customer Relationship Analytics

2015 ◽  
pp. 366-378 ◽  
Author(s):  
Fei Chiang ◽  
Siddharth Sitaramachandran
Keyword(s):  
Data Quality ◽  
Customer Relationship ◽  
Quality Framework

scholarly journals Facilitating Harmonized Data Quality Assessments. A Data Quality Framework for Observational Health Research Data Collections With Software Implementations in R

2020 ◽  
Author(s):  
Carsten Schmidt ◽  
Stephan Struckmann ◽  
Cornelia Enzenbach ◽  
Achim Reineke ◽  
Jürgen Stausberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Health Research ◽  
Research Data ◽  
Formal Concept ◽  
Software Implementations ◽  
Quality Framework ◽  
Data Collections ◽  
Quality Assessments ◽  
Data Quality Indicators

Abstract Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.

Data Quality on the Internet

2009 ◽  
pp. 171-176
Author(s):  
Vincent Cho
Keyword(s):  
Data Quality ◽  
The Internet ◽  
The Past ◽  
Internet Resources ◽  
Quality Framework

This chapter will review the studies on the data quality on the Internet and will propose some suggestions to improve existing Internet resources. The layout of this chapter is as follows. First, the definitions of data quality will be visited. Next, the author would like to review the reasons of poor data quality. Framework and assessment based on the past literature will be reviewed and finally some recommendations are highlighted.

A data quality framework, method and tools for managing data quality in a health care setting: an action case study

2018 ◽  
Vol 27 (sup1) ◽  
pp. 144-154 ◽  
Author(s):  
Lu Bai ◽  
Rob Meredith ◽  
Frada Burstein
Keyword(s):  
Health Care ◽  
Data Quality ◽  
Care Setting ◽  
Health Care Setting ◽  
Quality Framework ◽  
Action Case

scholarly journals From Theory to Practice: A Data Quality Framework for Classification Tasks

Symmetry ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 248 ◽  
Author(s):  
David Corrales ◽  
Agapito Ledezma ◽  
Juan Corrales
Keyword(s):  
Data Quality ◽  
Theory To Practice ◽  
Quality Framework ◽  
Classification Tasks

scholarly journals Data Quality Issues With Physician-Rating Websites: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Pavankumar Mulgund ◽  
Raj Sharman ◽  
Priya Anand ◽  
Shashank Shekhar ◽  
Priya Karadi
Keyword(s):  
Data Quality ◽  
English Language ◽  
Quality Of Data ◽  
Contextual Data ◽  
Patient Reported ◽  
Physician Rating ◽  
Quality Framework ◽  
Quality Issues ◽  
The Impact

BACKGROUND In recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites. OBJECTIVE This study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews. METHODS We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020. RESULTS A total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues. CONCLUSIONS The results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

scholarly journals Provoking a Cultural Shift in Data Quality

BioScience ◽  
2021 ◽  
Author(s):  
Sarah E McCord ◽  
Nicholas P Webb ◽  
Justin W Van Zee ◽  
Sarah H Burnett ◽  
Erica M Christensen ◽  
...  
Keyword(s):  
Data Quality ◽  
Data Access ◽  
Cultural Practice ◽  
Quality Data ◽  
Ecosystem Change ◽  
Ecological Studies ◽  
Ecological Data ◽  
Ecological Processes ◽  
Cultural Shift ◽  
Quality Framework

Abstract Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

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