Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product

Parssian, Amir; Sarkar, Sumit; Jacob, Varghese S.

doi:10.1287/mnsc.1040.0237

Cited by 75 publications

(59 citation statements)

References 9 publications

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“…(D. P. Ballou and Pazer 1985;D. Ballou et al 1998;Askira Gelman 2011;Parssian, Sarkar, and Jacob 2004), or use utility theory to characterize the impact of information quality (Even and Shankaranarayanan 2007;Ahituv 1980), which is difficult to apply in practice as utilities are difficult to link to key business performance indicators. In particular, the models do not consider the uncertainty that is inherent in the relationship between information quality and business outcomes.…”

Section: Introductionmentioning

confidence: 99%

A risk based model for quantifying the impact of information quality

Borek

Parlikad

Woodall

et al. 2014

Computers in Industry

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Information quality is one of the key determinants of information system success. When information quality is poor, it can cause a variety of risks in an organization. To manage resources for information quality improvement effectively, it is necessary to understand where, how, and how much information quality impacts an organization's ability to successfully deliver its objectives. So far, existing models have mostly focused on the measurement of information quality but not on the impact that information quality causes. This paper presents a model to quantify the business impact that arises through poor information quality in an organization by using a risk based approach. It hence addresses the inherent uncertainty in the relationship between information quality and organizational impact. The model can help information managers to obtain quantitative figures which can be used to build reliable and convincing business cases for information quality improvement.

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Section: Introductionmentioning

confidence: 99%

A risk based model for quantifying the impact of information quality

Borek

Parlikad

Woodall

et al. 2014

Computers in Industry

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“…The risk of poor data quality (DQ) increases as larger and more complex information resources are being collected and maintained (Madnick and Zhu, 2006;Parssian and Jacob, 2004). Because most modern companies tend to collect increasing amounts of data, good data management is becoming ever more important.…”

Section: Introductionmentioning

confidence: 99%

“…As a response, in the last two decades, the issues of DQ have received a lot of attention, both by organisations worldwide and in academic literature. Several studies are exploring DQ challenges, focusing on DQ measurement and improvement (Batini and Scannapieco, 2006;Cappiello et al, 2006;Chen and Tseng, 2010;Chengalur-smith et al, 1999;Dejaeger et al, 2010;Delone and McLean, 1992;Eppler and Wittig, 2000;Fisher and Ballou, 2003;Jarke and Vassiliou, 1997;Kahun et al, 2002;Lee et al, 2002Lee et al, , 2006Madnick and Zhu, 2006;Maydanchik, 2007;Moraga et al, 2009;Paul et al, 1996;Panse and Ritter, 2009;Parssian and Jacob, 2004;Pipino et al, 2002;Raghunathan, 1999;Rahm and Do, 2000;Redman, 1998;Shankaranarayanan and Cai, 2006;Shankaranarayanan et al, 2003;Strong et al, 1997;Tayi and Ballou, 1998;Wand and Wang, 1996;Wang, 1998;Wang et al, 1995;Wang and Strong, 1996;Ware and Gandek, 1998;Watts et al, 2009). In practice, decision makers differentiate information from data intuitively, and describe information as data that has been processed.…”

Section: Introductionmentioning

confidence: 99%

A total data quality management for credit risk: new insights and challenges

Moges

Dejaeger

Lemahieu

et al. 2012

IJIQ

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Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as 'fitness for use'. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions.

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“…Pazer (2003), Parssian, Sarkar, and Jacob (2004) and Shankaranarayanan, Ziad, and Wang (2003). More importantly, the simulation approach and mathematical models were based on the work of these academics and as such, this evaluation framework may be applied to any large-scale architecture at the design stage.…”

Section: Ballou Andmentioning

confidence: 99%

“…Taking guidance from the works of information quality practitioners such as Ballou and Pazer (2003), Parssian, Sarkar, and Jacob (2004) and Shankaranarayanan, Ziad, and Wang (2003), an evaluation approach was conceived to demonstrate the predicted effect of change produced by the eHaaS design artifact. By so doing, evaluation comprising mathematical models examined the behaviors of three system actors and their effect on patient information flows within one of the most common scenarios within the Patient Journey e.g.…”

Section: Introductionmentioning

confidence: 99%

eHealth-as-a-Service: A Service Based Design Approach for Large Scale eHealth Architecture

Black¹

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A significant challenge for the design of large scale (national) eHealth systems is the fluid nature of the Patient Journey, particularly in primary healthcare modalities.In this context, a patient's journey is predisposed to organic growth rather than intelligent design. As a result, clinical tasks requiring access to high quality information may occur as a function of known healthcare patterns or as a response to a patient's state of health. Whilst Australia's national eHealth system provides the infrastructure to accommodate the self-organizing flexibility and dynamism characteristic of complex information-based ecosystems, the system neglects certain information quality attributes. Specifically, accuracy, timeliness and completeness, which may lead to improved coordination of patient care. Based on the premise that the quality of patient information plays a significant role in the performance of health professionals, consideration must be given to these information quality dimensions as an overarching goal for eHealth architectural design activities. Framed as a problem concerning the design of large scale eHealth architecture that improves the quality of health information, this perspective presents a timely research opportunity.Within a prescriptive design science research (DSR) framework, this thesis is an example of a multi-methodological approach to create and evaluate a purposeful eHealth-as-a-Service (eHaaS) design artifact, which will improve patient information quality. This was achieved by firstly, deriving abstract meta-requirements from an ethnographic examination of care pathways to establish the technical goals of the solution space. Secondly, defining the functions, organization, and structure of an eHaaS conceptual model as an example of how service-based architectures might deliver high quality information services. Finally, by establishing the validity of the conceptual model with the development of a novel evaluation strategy to explain the predicted change produced by an eHaaS design artifact.Several original contributions emerged from the research. First and foremost, the developed eHaaS conceptual model, which encapsulates a set of design principles, service-based architectural patterns and implementation strategies represents a new class of eHealth solution. One that embodies a shift away from data-centric monolithic eHealth-as-a-Service: A Service-Based Design Approach for Large Scale eHealth Architecture iii architecture to process oriented, event-driven application services. To demonstrate the benefits of this shift, an electronic patient information system (ePIMS) was developed as an original solution for orchestrating clinical information services as personalised patient workflows. Another significant contribution of this thesis is a novel evaluation strategy for validating the utility of large scale eHealth systems at the design stage. It is innovative in its use of business process and data modelling techniques (e.g. data flow diagrams, business process modelling notatio...

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Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product

Cited by 75 publications

References 9 publications

A risk based model for quantifying the impact of information quality

A risk based model for quantifying the impact of information quality

A total data quality management for credit risk: new insights and challenges

eHealth-as-a-Service: A Service Based Design Approach for Large Scale eHealth Architecture

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