Ganesan Shankaranarayanan

Utility-driven assessment of data quality

Data consumers assess quality within specific business contexts or decision tasks. The same data resource may have an acceptable level of quality for some contexts but this quality may be unacceptable for other contexts. However, existing data quality metrics are mostly derived impartially, disconnected from the specific contextual characteristics. This study argues for the need to revise data quality metrics and measurement techniques to incorporate and better reflect contextual assessment. It contributes to that end by developing new metrics for assessing data quality along commonly used dimensions - completeness, validity, accuracy, and currency. The metrics are driven by data utility, a conceptual measure of the business value that is associated with the data within a specific usage context. The suggested data quality measurement framework uses utility as a scaling factor for calculating quality measurements at different levels of data hierarchy. Examples are used to demonstrate the use of utility-driven assessment in real-world data management scenarios and the broader implications for data management are discussed

The metadata enigma

Metadata promises too much value as a business management tool to dismiss its implementation and maintenance effort as the equivalent of Sisyphean torture.

Economics-Driven Data Management: An Application to the Design of Tabular Data Sets

Organizational data repositories are recognized as critical resources for supporting a large variety of decision tasks and for enhancing business capabilities. As investments in data resources increase, there is also a growing concern about the economic aspects of data resources. While the technical aspects of data management are well examined, the contribution of data management to economic performance is not. Current design and implementation methodologies for data management are driven primarily by technical and functional requirements, without considering the relevant economic factors sufficiently. To address this gap, this study proposes a framework for optimizing data management design and maintenance decisions. The framework assumes that certain design characteristics of data repositories and data manufacturing processes significantly affect the utility of the data resources and the costs associated with implementing them. Modeling these effects helps identify design alternatives that maximize net-benefit, defined as the difference between utility and cost. The framework for the economic assessment of design alternatives is demonstrated for the optimal design of a large data set

Model management decision environment: a web service prototype for spreadsheet models

In the modern day, digital enterprise data and models are widely distributed. Decision-making in such distributed environments needs secure and easy access to these resources, rapid integration of decision models, and the ability to deploy these in real time. This demands a different approach for model management--one that permits decision-makers to not only share/access but also evaluate/understand models, choose appropriate ones from a collection of models, and orchestrate the execution of the model(s) in real time. In this paper, we describe an architecture that defines a service-oriented, Web service-based approach to model management. We first present a classification of stakeholders from the perspective of model management and identify the layers of modeling knowledge required for managing models. We then define a formal representation for organizing the content knowledge using a graph-based representation. We have used spreadsheet model(s) as a vehicle for explaining and demonstrating our concepts in this paper. Finally, we describe an environment (virtual business environment, VBE) that is based on a Web services architecture that would help store, retrieve, and distribute the layers of modeling knowledge to the various categories of users identified.

Evaluating a model for cost-effective data quality management in a real-world CRM setting

Managing data resources at high quality is usually viewed as axiomatic. However, we suggest that, since the process of improving data quality should attempt to maximize economic benefits as well, high data quality is not necessarily economically-optimal. We demonstrate this argument by evaluating a microeconomic model that links the handling of data quality defects, such as outdated data and missing values, to economic outcomes: utility, cost, and net-benefit. The evaluation is set in the context of Customer Relationship Management (CRM) and uses large samples from a real-world data resource used for managing alumni relations. Within this context, our evaluation shows that all model parameters can be measured, and that all model-related assumptions are, largely, well supported. The evaluation confirms the assumption that the optimal quality level, in terms of maximizing net-benefits, is not necessarily the highest possible. Further, the evaluation process contributes some important insights for revising current data acquisition and maintenance policies.

Dual Assessment of Data Quality in Customer Databases

Quantitative assessment of data quality is critical for identifying the presence of data defects and the extent of the damage due to these defects. Quantitative assessment can help define realistic quality improvement targets, track progress, evaluate the impacts of different solutions, and prioritize improvement efforts accordingly. This study describes a methodology for quantitatively assessing both impartial and contextual data quality in large datasets. Impartial assessment measures the extent to which a dataset is defective, independent of the context in which that dataset is used. Contextual assessment, as defined in this study, measures the extent to which the presence of defects reduces a dataset’s utility, the benefits gained by using that dataset in a specific context. The dual assessment methodology is demonstrated in the context of Customer Relationship Management (CRM), using large data samples from real-world datasets. The results from comparing the two assessments offer important insights for directing quality maintenance efforts and prioritizing quality improvement solutions for this dataset. The study describes the steps and the computation involved in the dual-assessment methodology and discusses the implications for applying the methodology in other business contexts and data environments.

Enhancing Decision Making with Process Metadata: Theoretical Framework, Research Tool, and Exploratory Examination

The quality of the data used in a decision task has important implications for the decision outcome. Recent research suggests that data quality perception is context-dependent. This study examines process metadata, which describes how a particular data set was created and delivered, as a supporting aid for contextual quality assessment. We first develop a model for understanding the effects of process metadata on the decision outcome when it is provided together with intrinsic quality measurements. We then describe a research tool developed to assess the effect of process metadata. An exploratory test using this tool suggests that both data quality perceptions and the associated process metadata have beneficial effects on outcomes, when mediated by decision process efficiency. The model developed in this study and the preliminary empirical results highlight the value of embedding quality metadata within computer-supported decision environments.

Process Coordination Requirements: Implications for the Design of Knowledge Management Systems

Knowledge Management Systems (KMS) are typically built with a departmental focus, making it difficult to share and utilize knowledge across departmental boundaries. Integrating such “knowledge pockets” into a knowledge network requires resolving structural differences and coordinating both knowledge processes and software components. Here we identify a set of coordination requirements for the design of a KMS to support such knowledge networks. We first offer a classification of KM practices to define the types of KM methods and the system requirements for each. We then use coordination theory, specifically, Text-based Process Analysis, to analyze four texts (cases) that capture KM practices representing the different KM methods, to understand and identify the coordination requirements. We also propose the Enterprise Knowledge Architecture (EKA) as a standard for incorporating these requirements. The EKA supports building new KMS and analyzing existing ones by providing a common framework for designing, developing, and maintaining KMS. It thus provides a foundation for integrating knowledge pockets into knowledge networks.

A Web Services Application for the Data Quality Management in the B2B Networked Environment

Characteristics of Web services such as interoperability and platform independence make Web service a promising technique to manage data quality effectively in inter-organizational information exchanges. In this paper, we describe an application of Web services for managing data quality in the B2B information exchange that is typically characterized by large volumes of information, widely distributed data sources, and frequent information interchanges. In such environments, it is important that organizations are able to evaluate the quality of information they get from other organizations. We propose a framework for managing data quality in inter-organizational settings using the information product approach. We highlight the requirements for data quality management and the developing Web service standards to show why Web services offer a unique, yet simple platform for managing data quality in inter-organizational settings.

Modeling and navigation of large information spaces: a semantics based approach

We present techniques for modeling the semantics of large information spaces and for navigating them. This information space represents heterogeneous data stored in different formats and distributed across multiple locations on the Internet. We also describe a prototype system called SEMQUEST (SEMantics based QUEry SysTem) that employs graph-based algorithms and allows users to interactively explore, manipulate and relate data in large information spaces to their interests. It provides users with the flexibility to understand what is available in the information space, determine which parts are relevant, and query/retrieve underlying data using a visual framework. SEMQUEST also allows users to share software modules permitting them to reuse data analysis/visualization codes. We demonstrate system use with global climate change data collected by centers across the world. We believe this research serves as a foundation for future work in integrating information sources across the WWW.