An Overview of Data Quality Frameworks
Corinna Cichy,Stefan Rass +1 more
TLDR
Data quality frameworks are surveyed in a comparative way regarding the definition, assessment, and improvement of data quality with a focus on methodologies that are applicable in a wide range of business environments to aid the decision process concerning the suitability of these methods.Abstract:
Nowadays, the importance of achieving and maintaining a high standard of data quality is widely recognized by both practitioners and researchers. Based on its impact on businesses, the quality of data is commonly viewed as a valuable asset. The literature comprises various techniques for defining, assessing, and improving data quality. However, requirements for data and their quality vary between organizations. Due to this variety, choosing suitable methods that are advantageous for the data quality of an organization or in a particular context can be challenging. This paper surveys data quality frameworks in a comparative way regarding the definition, assessment, and improvement of data quality with a focus on methodologies that are applicable in a wide range of business environments. To aid the decision process concerning the suitability of these methods, we further provide a decision guide to data quality frameworks. This guidance aims to help narrow down possible choices for data quality methodologies based on a number of specified criteria.read more
Citations
More filters
Posted Content
A Survey of Data Quality Measurement and Monitoring Tools.
TL;DR: This work conducts a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools and reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.
Posted Content
Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
Shailesh Tripathi,David Muhr,Brunner Manuel,Frank Emmert-Streib,Herbert Jodlbauer,Matthias Dehmer +5 more
TL;DR: A detailed review of CRISP-DM is provided and extensions of this model are summarized into a novel framework, which is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness.
Journal ArticleDOI
Quality of Measurement Information in Decision-Making
TL;DR: In this article, a general-purpose framework aimed at capturing the elusive concept of quality of measurement information (MI), a critical issue for both researchers and practitioners when dealing with MI-enabled decision-making, is presented.
Journal ArticleDOI
Data Quality Management in the Internet of Things.
TL;DR: In this paper, a survey of data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions.
Journal ArticleDOI
A Survey of Data Quality Measurement and Monitoring Tools
TL;DR: In this paper , a survey of 667 software tools dedicated to "data quality" is presented, focusing on three functional areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring.
References
More filters
Book ChapterDOI
The Analytic Hierarchy Process
Thomas L. Saaty,Kevin P. Kearns +1 more
TL;DR: Analytic Hierarchy Process (AHP) as mentioned in this paper is a systematic procedure for representing the elements of any problem hierarchically, which organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pairwise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy.
Journal ArticleDOI
Beyond accuracy: what data quality means to data consumers
Richard Y. Wang,Diane M. Strong +1 more
TL;DR: Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs and this research provides a basis for future studies that measure data quality along the dimensions of this framework.
Book
Cost-Benefit Analysis
E.J. Mishan,Euston Quah +1 more
TL;DR: Costbenefit analysis (CBA) is the systematic and analytical process of comparing benefits and costs in evaluating the desirability of a project or program as mentioned in this paper, often of a social nature, and is fundamental to government decision making and is established as a formal technique for making informed decisions on the use of society's scarce resources.
Journal ArticleDOI
Data quality assessment
TL;DR: Concepts that can help organizations develop usable data quality metrics are described that are suitable for use in practice and not just on a whim.
Journal ArticleDOI
AIMQ: a methodology for information quality assessment
TL;DR: The methodology encompasses a model of IQ, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures, which are applied to analyze the gap between an organization and best practices.
Related Papers (5)
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era
Li Cai,Yangyong Zhu +1 more