Papers by Ganesan Shankaranarayanan
Blockchain adoption in the supply chain – a game theoretic perspective: the case of the diamond industry
Journal of Information Technology Case and Application Research
Journal of International Technology and Information Management
When there is a disparity in the value of different data records and fields, there is a need for ... more When there is a disparity in the value of different data records and fields, there is a need for an optimization of data resources. Not all data necessarily contribute the same value. It depends on the usage of the data, as well as a variety of other factors. This paper presents models for optimizing data management in the presence of a disparity between the values contributed by different data. We expound on what disparity of data value represents and illustrate models to derive a numerical measure of such disparity. We then use real-world data from a large data resource used to manage alumni relations, and demonstrate our optimization methods and results. We then discuss the tradeoffs involved between value and cost, and the implications for data management, both in this real-world context and in general.
Is bigger better? A study of the effect of group size on collective intelligence in online groups
Decision Support Systems
Poor quality of data in a warehouse adversely impacts the usability of the warehouse and managing... more Poor quality of data in a warehouse adversely impacts the usability of the warehouse and managing data quality in a warehouse is very important. In this article we describe a framework for managing data quality in a data warehouse. This framework is of interest to both academics and practitioners as it offers an intuitive approach for not just managing data quality in a data warehouse but also implementing total data quality management. The framework is based on the information product approach. Using this approach it integrates existing metadata in a warehouse with quality-related metadata and proposes a visual representation for communicating data quality to the decision-makers. It allows decision-makers to gauge data quality in context-dependent manner. The representation also helps implement capabilities that are integral components of total data quality management.
Abstract Supporting data quality management in decision-making
In the complex decision-environments that characterize e-business settings, it is important to pe... more In the complex decision-environments that characterize e-business settings, it is important to permit decision-makers to proactively manage data quality. In this paper we propose a decision-support framework that permits decision-makers to gauge quality both in an objective (context-independent) and in a context-dependent manner. The framework is based on the information product approach and uses the Information Product Map (IPMAP). We illustrate its application in evaluating data quality using completeness—a data quality dimension that is acknowledged as important. A decision-support tool (IPView) for managing data quality that incorporates the proposed framework is also described. D 2005 Elsevier B.V. All rights reserved.
In the complex decision-environments that characterize e-business settings, it is important to pe... more In the complex decision-environments that characterize e-business settings, it is important to permit decision-makers to proactively manage data quality. In this paper we propose a decision-support framework that permits decision-makers to gauge quality both in an objective (context-independent) and in a context-dependent manner. The framework is based on the information product approach and uses the Information Product Map (IPMAP). We illustrate its application in evaluating data quality using completeness-a data quality dimension that is acknowledged as important. A decision-support tool (IPView) for managing data quality that incorporates the proposed framework is also described. D
Data quality (DQ) metadata is the set of quality measurements associated with the data. Literatur... more Data quality (DQ) metadata is the set of quality measurements associated with the data. Literature has demonstrated that the provision of DQ metadata can improve decision performance. However, it also showed that DQ metadata can overload decision-makers and consequently have a negative impact on decision performance. In this paper, we describe a prototype system, SPIDEQ, for visualizing DQ metadata. We believe that the visualization will shift the data overload from cognitive to perceptual and thus improve the decision capacity of the decision maker. Decision performance will improve even when DQ metadata is provided, a hypothesis that is not addressed in this paper. SPIDEQ is a prototype system that supports the provision of DQ metadata during the decision making process. The results of this study offer insights for the design of decision support systems and the provision of DQ metadata.
Framing data quality research: A semantic analysis approach
Architecture for Dynamic Schema Evolution in Heterogeneous Database Environments : A Prototype System and Its Evaluation Working paper # 2003-10
Dynamic schema evolution is the process of evolving a database schema by incorporating changes in... more Dynamic schema evolution is the process of evolving a database schema by incorporating changes in a timely manner, without loss of existing data, and without significantly affecting the day-to-day operations of the database. Systems that manage schema evolution are described in the literature, but address schema evolution single, stand-alone, object-oriented databases. Organizations typically use an integrated set of multiple different databases for satisfying their complex data needs. Managing schema evolution in such heterogeneous data environments (HDE) has not been dealt with. A logical architecture for managing dynamic schema evolution in a HDE is proposed in this paper. The architecture incorporates a graph-theoretic framework that is based on a set of requirements identified for dynamic schema evolution in a HDE. Its implementation in a prototype software system (SEMAD) is described. Implications for automating dynamic schema evolution are examined using SEMAD. An exploratory...
From Content to Context
Journal of Data and Information Quality, 2017
Research in data and information quality has made significant strides over the last 20 years. It ... more Research in data and information quality has made significant strides over the last 20 years. It has become a unified body of knowledge incorporating techniques, methods, and applications from a variety of disciplines including information systems, computer science, operations management, organizational behavior, psychology, and statistics. With organizations viewing “Big Data”, social media data, data-driven decision-making, and analytics as critical, data quality has never been more important. We believe that data quality research is reaching the threshold of significant growth and a metamorphosis from focusing on measuring and assessing data quality—content—toward a focus on usage and context. At this stage, it is vital to understand the identity of this research area in order to recognize its current state and to effectively identify an increasing number of research opportunities within. Using Latent Semantic Analysis (LSA) to analyze the abstracts of 972 peer-reviewed journal a...
Handbook of Research on Modern Systems Analysis and Design Technologies and Applications
This chapter introduces a novel perspective for designing and maintaining data resources. Data an... more This chapter introduces a novel perspective for designing and maintaining data resources. Data and the information systems that manage it, are critical organizational resources. Today the design and the maintenance of data management environments are driven primarily by technical and functional requirements. We suggest that economic considerations, such as the utility gained by the use of data resources and the costs involved in implementing and maintaining them, may significantly affect data management decisions. We propose an analytical framework for analyzing utility-cost tradeoffs and optimizing design. Its application is demonstrated for analyzing certain design decisions in a data warehouse environment. The analysis considers variability and inequality in the utility of data resources, and possible uncertainties with usage and implementation.
An Information Product Approach
MANAGING ACCURACY OF PROJECT DATA IN ADistributed PROJECT SETTING
Case-Based Research in In
Organizations (principals) manage projects by outsourcing tasks to partners. Coordinating and man... more Organizations (principals) manage projects by outsourcing tasks to partners. Coordinating and managing such projects requires sharing project-data, status data on the work-in-progress residing with the partners and estimates of completion time. Project data is rarely accurate due to errors in estimation, errors in aggregating data across partners and projects, and gaming by the partners. While managers are aware of the inaccuracies, they are forced to make decisions regarding outsourcing the tasks (how much, to whom, and when). In this paper, we develop a control theoretic model that analyzes utilization of capacity of both the principal and partners. This model also permits corruption of project-data regarding progress status. We use this model to compute the costs of using perfect project-data versus inaccurate project-data and show that these costs can be significant. We propose a control policy, using filters, to correct inaccurate project-data and generate an estimate of true p...
A Comparative Examination of AR and Video in Delivering Assembly Instructions
Internet of Things, Infrastructures and Mobile Applications, 2020
Quality of Social Media Data and Implications of Social Media for Data Quality
Enhancing decision-making with data quality metadata
Journal of Systems and Information Technology, 2021
Purpose Data quality metadata (DQM) is a set of quality measurements associated with the data. Pr... more Purpose Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM. Design/methodology/approach The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting. Findings The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM. Research limitations/implications A drawback of this study is the sample ...
Utility Cost Perspectives in Data Quality Management
Journal of Computer Information Systems, 2009
The growing costs of managing data demand a closer examination of associated cost-benefit tradeof... more The growing costs of managing data demand a closer examination of associated cost-benefit tradeoffs. As a step towards developing an economic perspective of data management, specifically data quality management, this study describes a value-driven model of data products and the processes that produce them. The contribution to benefit (utility) is associated with the use of data products and costs attributed to the different data processing stages. Utility/cost tradeoffs are thus linked to design and administrative decisions at the different processing stages. By modeling and quantifying the economic impact of these decisions, this study shows how economically superior data quality management policies may be developed. To illustrate it, the study uses the model to develop a data quality management policy for online error correction. The results indicate that decisions that consider economic tradeoffs can be very different compared with decisions that are driven by technical and funct...
Approaches and Methodologies
Increasingly, academic research in Information Technologies and Systems (ITS) is emphasizing the ... more Increasingly, academic research in Information Technologies and Systems (ITS) is emphasizing the application of research models and theories to practice. In this chapter, the authors posit that case-based research has a significant role to play in the future of research in ITS because of its ability to generate knowledge from practice and to study a problem in context. Understanding context—social, organizational, political, and cultural—is mandatory to learning and effectively adopting best practices. The authors describe some examples of case-based research to highlight this point of view. They further identify key topics and themes based on examining the abstracts from prominent case-based research over the past decade, analyzing their trends, and hypothesizing what the role of case-based research will be in the coming decades.
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Papers by Ganesan Shankaranarayanan