An End-User Metadata Model on Object and Element Levels for Business Intelligence Users

An End-User Metadata Model on Object and Element Levels for Business Intelligence Users

Yuriy Verbitskiy (University of South Australia, Adelaide, Australia) and William Yeoh (University of South Australia, Mawson Lakes, Australia & Deakin University, Burwood, Australia)
Copyright: © 2016 |Pages: 9
DOI: 10.4018/IJBIR.2016070104
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The effective use of metadata can offer end users an improved understanding and greater level of assurance during the Business Intelligence (BI) report analysis process. This paper reports key findings from a case study that investigates critical end-user metadata issues in a large Australian organization. The findings led to the development of an end-user metadata model on object (report and cube) and element (term and column) levels, which can support effective BI use and potentially increase user satisfaction at the case organization. The adoption and use of BI applications by business stakeholders may be improved by incorporating the end-user metadata model.
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In recent years BI applications have been consistently ranked among the top five technology priorities in a global survey of Chief Information Officers (Gartner, 2015). Business Intelligence (BI) refers to “a broad category of technologies, applications and processes used for gathering, storing, accessing and analysing data to help its users make better decisions” (Wixom & Watson, 2010). Hence meaningful information can be delivered at the right time, at the right location and in the right form (Negash, 2004) to assist individuals, departments or even larger units to facilitate improved decision-making. Despite the importance of BI applications, in many instances they are significantly underused, partly attributable to issues of user satisfaction (Chen et al, 2000). End-user satisfaction is essential to improving the uptake of BI (Chen et al, 2000). A key influence on end-user satisfaction with BI is end-user metadata (Foshay et al, 2007). End-user metadata is needed as many end-users are not technically-oriented and require substantial support to use BI applications and fully understand the BI cube or measurement and definition of a BI term, or the meaning of a column of data originating from other systems.

Indeed, a potentially valuable solution to the problem of poor user understanding of BI data and reports is the use of end-user metadata (Wells & Hess, 2002; Foshay et al, 2007). Metadata has been afforded many definitions over the years. It has been defined as simply ‘data about the data’. Importantly, metadata plays a crucial role in an effective BI environment (Sen, 2002; Little & Gibson, 2003). Metadata serves as a mechanism that provides the context about the data and information of a BI report (Foshay et al, 2007). It addresses the how, when, why and what questions in a BI environment (Hess & Wells, 2002; Foshay et al, 2007). Gartner Research (2007) contends that metadata is one of the most important functionalities that a BI environment should deliver. Inmon et al (2008) and Wells and Hess (2002) further assert that without metadata to support BI reports, a BI application offers little value to an organization. While technical users understand the BI environment because it is one of their primary work objectives, business users need support that will help them feel confident about using the data and BI tools in general. Making available effective end-user metadata could provide such support.

Several studies discuss different types of metadata. Notably, metadata has been classified as business metadata (which relates to the data that is meaningful to business users) or technical metadata (which is used by information technology staff responsible for developing and administering a BI system) (Sen, 2002; Ballard, 2006; Shankaranarayanan & Even, 2006). In the technical domain metadata is crucial for building a data warehouse as developers need to know the data structures, source-to-target mappings, and data transformation rules during the data extraction, transformation and loading (ETL) processes (Ponniah, 2001). In the business metadata main, Foshay et al (2007) propose an end-user metadata taxonomy of four categories: definitional, data quality, lineage and navigational metadata. Despite these early studies on metadata types, to date there is little research that explores the detailed elements of metadata requirements for business users in an enterprise-scale BI environment. This paper aims to identify the key elements of business end-user metadata by conducting an exploratory interpretive case study at a large Australian organization. By synthesising key findings from the case study, the paper develops an end-user metadata model which may help BI practitioners implement metadata for business users.

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