Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run

A Pattern-based Approach to Quantitative Enterprise Architecture Analysis

Enterprise Architecture (EA) management involves tasks that substantially contribute to the operations of an enterprise, and to its sustainable market presence. One important aspect of this is the availability of services to customers. However, the increasing interconnectedness of systems with other systems and with business processes makes it difficult to get a clear view on change impacts and dependency structures. While management level decision makers need this information to make sound decisions, EA models often do not include quality attributes (such as availability), and very rarely provide quantitative means to assess them. We address these shortcomings by augmenting an information model for EA modeling with concepts from Probabilistic Relational Models, thus enabling quantitative analysis. A sample business case is evaluated as an example of the technique, showing how decision makers can benefit from information on availability impacts on enterprise business services

Quantifying IT Impacts on Organizational Structure and Business Value with Extended Influence Diagrams

Abstract. This paper presents a framework for analysis of how IT systems add business value by causally affecting the structure of organizations. The well established theory of organizational behavior developed by Mintzberg combined with more recent research on business value of IT is used to develop a quantitative theoretical framework showing which business values are affected by IT in relation to the organizational structure. This framework, which is based upon a qualitative equivalent developed in an earlier paper, describes relationships in an Extended Influence Diagram for quantified conditional probability tables and open up for an empirical appliance. Hence obtained data can be mathematically expressed for more sound assessments. The intention is to create a fully functioning tool for analyses of what kind of IT system should be used by an organization with a given structure to maximize its business value