Designing a Prototype for Analytical Model Selection and Execution to Support Self-Service BI

This paper presents a prototype of a modeling tool specifically designed for business analysts with little modeling experience. The proposed tool has an interactive user interface for a dimensional data store that contains a library of analytical models that business analysts can evaluate and use to create models they can run on their own data sets. Using a design science approach, we review the relevant literature in self-efficacy and feedforward to provide a kernel theory that informs the design criteria met by our proof of concept prototype. Specifically, we demonstrate the prototype’s user interface with a prediction problem faced by the United States Department of Labor

Word Ambiguity and Search: Implications for Enterprise Performance Management

The proliferation of unstructured data is a growing threat to effective enterprise performance management. Enterprise search is a tool to help organizations more effectively manage this document-based information. The success of full-text enterprise search is limited by ambiguity in word meanings, which can result in many documents returned which are not relevant to the searcher. While early work by Zipf provided a first attempt at quantifying the impact of this issue on search, little work has been done to demonstrate the applicability of Zipf’s work to contemporary document collections. In this paper we examine whether the frequency-meaning relationship discovered by Zipf holds for contemporary document collections, and whether it consistently holds across different subject domains. We then discuss the implications of our results for the development and use of user-centered KPIs designed to measure the enterprise wide effectiveness of search activities

Enabling Self-Service BI through a Dimensional Model Management Warehouse

The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI, but prior attempts at developing modeling languages for nontechnical audiences have gone largely unadopted. This paper seeks to address this unmet need, bringing model-building to a mainstream business audience by introducing a structured methodology for model formulation specifically designed for practitioners. We also describe the design for a dimensional Model Management Warehouse that supports our methodology and demonstrate its viability using an illustrative example. The paper concludes by outlining several areas for future research

Enabling Self-Service BI: A Methodology and a Case Study for a Model Management Warehouse

The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the missing link in self-service BI, but prior attempts at developing modeling languages for non-technical audiences have not been widely implemented. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor. The paper concludes by outlining several areas for future research