Data-Warehouse as a Dynamic Capability: Utility/Cost Foundations and Implications for Economically-Driven Design

IS design today is driven primarily by technical and functional requirements, and the economic implications for design are not yet well understood. This study argues that system design and architecture must reflect assessments of economic trade-offs besides satisfying technical/functional requirements. Modeling the economic performance structure behind IS design can highlight these trade-offs and help economically assess design alternatives. This study examines economics-driven design in the context of the Data Warehouse (DW). The DW environment is treated as a dynamic capability, providing the capacity for managing data resources and turning them into useful information products. These products contribute value when used for exploitative and/or explorative business processes. Recognizing possible uncertainties in usage, DW capacities are evaluated as real-option investments toward the development of a framework for modeling cost-utility effects of DW design decisions. This framework is used to evaluate important design scenarios along the layers of a DW stack architecture and optimize design outcomes accordingly

Managing Metadata in Data Warehouses: Pitfalls and Possibilities

This paper motivates a comprehensive academic study of metadata and the roles that metadata plays in organizational information systems. While the benefits of metadata and challenges in implementing metadata solutions are widely addressed in practitioner publications, explicit discussion of metadata in academic literature is rare. Metadata, when discussed, is perceived primarily as a technology solution. Integrated management of metadata and its business value are not well addressed. This paper discusses both the benefits offered by and the challenges associated with integrating metadata. It also describes solutions for addressing some of these challenges. The inherent complexity of an integrated metadata repository is demonstrated by reviewing the metadata functionality required in a data warehouse: a decision support environment where its importance is acknowledged. Comparing this required functionality with metadata management functionalities offered by data warehousing software products identifies crucial gaps. Based on these analyses, topics for further research on metadata are proposed

Data and Information Quality: Research Themes and Evolving Patterns

Research in data and information quality has made significant strides in the last decade and has created an expansive body of knowledge. Given the multiple different research perspectives and research methodologies adopted, it is important for us to understand the research topics and themes that have evolved and currently define this body of research. Here, we present the results of a preliminary study that aims to provide a better understanding of this research area by identifying the core topics and themes. We analyze abstracts of 850 journal and conference articles published over the past 15 years in data and information quality. From the analysis, we identify 5 core topics and 20 core themes of data quality research. The results from this research can significantly improve our understanding of the body of literature in data and information quality

Visualizing Data Quality Metadata for Decision Support: A Prototype and Evaluation

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 negatively affect decision performance. In this paper, we examine a technique for reducing this overload by using visualization. Recognizing that visualization shifts the burden of absorbing DQ metadata to the perceptual capacity of the decision maker, we argue that it will reduce cognitive load. We theorize that decision-makers can hence absorb DQ metadata more easily. Decision performance will improve even when DQ metadata is provided. We describe the visual interface for visualizing data and DQ metadata and describe an experiment to test the impacts of visualizing DQ metadata on decision outcome. The results of this study offer insights for the design of decision support systems and the provision of DQ metadata

Optimizing Data Management with Disparities in Data Value

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

Tradeoffs in Managing the Quality of Marketing Data

A large majority of work in database marketing deals with what to do with data when it is available. This paper focuses on an aspect of data that has been infrequently examined in the database marketing literature – managing quality of data resources from a profit perspective. The notion that “more is better” often prevails in quality management decisions, with very little consideration, if any, of cost. This paper suggests that such decisions should be driven by consideration of cost-benefit tradeoffs and profit maximization. It specifically addresses data-quality decisions which are relevant in the database marketing area: the time-span covered by and targeted quality levels within datasets. These decisions are routinely made based on satisfying technical and functional requirements. We propose a model that quantifies the benefits and costs associated with these decisions and helps maximize profit. The paper describes the model development, discusses its implications for data-quality management decisions, and highlights its contributions with illustrative examples

Sub-leading contributions to the black hole entropy in the brick wall approach

[Abridged] We compute the canonical entropy of a quantum scalar field around static and spherically symmetric black holes through the brick wall approach at the higher orders (in fact, up to the sixth order in \hbar) in the WKB approximation. We explicitly show that the brick wall model generally predicts corrections to the Bekenstein-Hawking entropy in all spacetime dimensions. In four dimensions, we find that the corrections to the Bekenstein-Hawking entropy are of the form (A^n \log A), while, in six dimensions, the corrections behave as (A^m + A^n \log A), where A denotes the area of the black hole event horizon, and (m, n) < 1. We compare our results with the corrections to the Bekenstein-Hawking entropy that have been obtained through the other approaches in the literature, and discuss the implications.Comment: 21 pages, Revtex 4; Final verson - 22 pages, References added, Accepted in Phys. Rev.