The costs of poor data quality

Haug, Anders; Zachariassen, Frederik; Liempd, Dennis van

doi:10.3926/jiem.2011.v4n2.p168-193

Cited by 113 publications

(72 citation statements)

References 34 publications

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“…If an organisation is not able to evaluate the quality of its data how can it determine its value in relation to the corporate decision making process? A number of studies have attempted to develop forms of cost classification models 3,4,13,14,15,16,17 and whilst these have developed focussed taxonomies on the related major elements, they may be perceived to be somewhat generic.…”

Section: Introductionmentioning

confidence: 99%

‘Accounting’ for Data Quality in Enterprise Systems

O’Brien

2015

Procedia Computer Science

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Organisations are facing ever more diverse challenges in managing their enterprise systems as emerging technologies bring both added complexities as well as opportunities to the way they conduct their business. Underpinning this ever-increasing volatility is the importance of having quality data to provide information to make those important enterprise-wide decisions. Numerous studies suggest that many organisations are not paying enough attention to their data and that a major cause of this is their failure to measure its quality and value and/or evaluate the costs of having poor data. This study proposes an integrated framework that organisations can adopt as part of their financial and management control processes to provide a mechanism for quantifying data problems, costing potential solutions and monitoring the on-going costs and benefits, to assist them in improving and then sustaining the quality of their data.

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Section: Introductionmentioning

confidence: 99%

‘Accounting’ for Data Quality in Enterprise Systems

O’Brien

2015

Procedia Computer Science

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“…This should have a high impact on the way applications are built, as developers are greatly interested in programming systems that are reliable, even in the presence of poor quality data. The problem is that although the analysis and improvement of data quality have gathered plenty of attention (e.g., to carry out data cleaning operations) from practitioners and researchers [6,[24][25][26][27][28], and despite the well-known impact of poor quality data in critical data-centric systems [29], understanding how well an application is prepared to handle the inevitable appearance of poor data has been largely overlooked. For this purpose, the identification of representative data quality problems and how they should be integrated in software verification activities (e.g., software testing) is essential.…”

Section: Background and Related Workmentioning

confidence: 99%

Testing data-centric services using poor quality data: from relational to NoSQL document databases

et al. 2017

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Businesses are nowadays deploying their services online, reaching out to clients all around the world. Many times deployed as web applications or web services, these business-critical systems typically perform large amounts of database operations; thus, they are dependent on the quality of the data to provide correct service to clients. Research and practice have shown that the quality of the data in an enterprise system gradually decreases overtime, bringing in diverse reliability issues to the applications that are using the data to provide services. These issues range from simple incorrect operations to aborted operations or severe system failures. In this paper, we present an approach to test data-centric services in presence of poor quality data. The approach has been designed to consider relational and NoSQL database nodes used by the system under test and is based on the injection of poor quality data on the database-application interface. The results indicate the effectiveness of the approach in discovering issues, not only at the application-level, but also in the middleware being used, contributing to the development of more reliable services.

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“…Haug et al [6] focuses on how to identify the optimal data quality level. Wang et al [7] has cited a number …”

Section: Attributes Of Data Qualitymentioning

confidence: 99%

Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence

Debbarma¹,

Nath²,

Das³

2013

IJCA

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A Data Warehouse is an integral part of those enterprises which want to have a clear business insights from customer and operational data. It includes collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions. It is expected to present the right information in the right place at the right time with the right cost in order to support the right decision. Over the years ,the practice of Data warehousing proved that the traditional online Transaction Processing (OLTP) systems are not fully appropriate for decision support. From the survey and evaluation of the literature related to Data Warehouse and with consultation and feedback of the data warehouse practitioners working in renowned IT giants ,it has been observed that the fundamental problems arise in populating a warehouse with quality data. . This paper mainly focuses on the study of the issues that hinder the data quality and performance of the Data warehouse and some of the means that may be opted to realize a better performance with respect to accuracy and quality to meet the challenging and dynamic needs of the corporate world.

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The costs of poor data quality

Cited by 113 publications

References 34 publications

‘Accounting’ for Data Quality in Enterprise Systems

‘Accounting’ for Data Quality in Enterprise Systems

Testing data-centric services using poor quality data: from relational to NoSQL document databases

Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence

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