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

Using a Markov-Chain Model for Assessing Accuracy Degradation and Developing Data Maintenance Policies

Accuracy reflects the extent of correctness of data. It is often evaluated by comparing the values recorded to a baseline perceived as correct. Even when data values are accurate at the time of recording – their accuracy may degrade over time, as certain properties of real-world entities may change, while the data values that reflect them are not being updated. This study uses the Markov-Chain model to develop an analytical framework that describes accuracy degradation over time – this by assessing the likelihood of certain data attributes to transition between states within a given time period. Evaluation of the framework with real-world data shows its potential contribution for key data-quality management tasks, such as the prediction of accuracy degradation, and the development of data auditing and maintenance policies

A Framework for Economics-Driven Assessment of Data Quality Decisions

Economic perspectives have raised growing attention in recent data quality (DQ) literature, as studies have associated DQ decisions with major cost-benefit tradeoffs. Despite the growing interest, DQ research has not yet developed a robust, agreedupon view for assessing and studying the link between DQ and economic outcome. As a contribution, this study proposes a framework, which links costs to the decisions made in managing the information process and improving the DQ, and benefits to the use of information-product outcomes by data consumers. Considering past research contributions, we develop this framework further into a high-level optimization model that permits quantitative assessment of cost-benefit tradeoffs, towards economically-optimal DQ decisions. We demonstrate a possible use of the proposed framework and the derived model, and highlight their potential contribution to an economics-driven view of DQ issues in both research and practice

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

USING THE MUTUAL INFORMATION METRIC TO IMPROVE ACCESSIBILITY AND UNDERSTANDABILITY IN BUSINESS INTELLIGENCE TOOLS

The rapidly-growing organizational data resources introduce a growing difficulty to locate and understand the relevant data subsets within large datasets – what can be seen as a severe information quality issue in today\u27s decision-support environments. The study proposes a quantitative methodology, based on the mutual-information metric, for assessing the relative importance of different data subsets within a large dataset. Such assessments can grant the end-user with faster access to relevant subsets within a large dataset, the ability to better understandits contents, and gain deeper insights from analyzing it – e.g., when such a dataset is being used for Business Intelligence (BI) applications. This manuscript provides the background and the motivation for integrating the proposed assessments of relative importance. It then defines the calculations behind the mutualinformation metric, and demonstrates their applications using illustrative examples

Enhancing Business Intelligence Applications with Value-Driven Feedback and Recommendation

Business intelligence (BI) systems support activities such as data analysis, managerial decision making, and businessperformance measurement. Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our working hypothesis is that the integration of FRM will improve the usability of BI tools and increase the benefits that end-users and organizations can gain from data resources. Our first research stage focuses on FRM based on assessment of previous usage and the associated value gain. We describe the development of such FRM, and the design of an experiment that will test the usability and the benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel methodology for tracking and communicating the usage of data, linked to a quantitative assessment of the value gained. We describe a high-level architecture for supporting the collection, storage, and presentation of this new metadata form, and a quantitative method for assessing it

Making money with clouds: Revenue optimization through automated policy decisions

Business intelligence (BI) systems and tools are broadly adopted in organizations today, supporting activities such as data analysis, managerial decision making, and business-performance measurement. Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our working hypothesis is that the integration of FRM will improve the usability of BI tools and increase the benefits that end-users and organizations can gain from data resources. Our first research stage focuses on FRM based on assessment of previous usage and the associated value gain. We describe the development of such FRM, and the design of an experiment that will test the usability and the benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel methodology for tracking and communicating the usage of data, linked to a quantitative assessment of the value gained. We describe a high-level architecture for supporting the collection, storage, and presentation of this new metadata form, and a quantitative method for assessing it

The Impact of BI-Supported Performance Measurement System on a Public Police Force

Performance Measurement Systems (PMS) may assist implementing organizational strategy and act as an effective tool for control and surveillance. The PMS explored in this study was implemented by the public police forces, using advanced Business Intelligence (BI) technologies. Using that system, police commanders can analyze performance scores of their own units and get feedback on their success. The study examines the system\u27s impact, through analysis of the metric results over a 5-year time period. The results indeed show a positive impact, as with the vast majority of the metrics examined, the performance indeed improved. Further, the results confirmed the preliminary assumptions that the relative weight of each metric will moderate the improvement, and that metrics that reflect activity will behave differently than those that reflect outcomes. The study discusses the implications of these results, and proposed directions for future research