Enabling self-service BI: A methodology and a case study for a model management warehouse

Schuff, David; Corral, Karen; Louis, Robert D. St.; Schymik, Gregory

doi:10.1007/s10796-016-9722-2

Cited by 22 publications

(11 citation statements)

References 25 publications

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“…Imhoff and White [17] refer to SSA as a facility within the Business Intelligence and Analytics (BI&A) environment. Gartner IT Glossary [53] and Weber [54] describe it as a BI&A system, and Schuff et al [55] label SSA as an ability. There is no clear definition of SSA.…”

Section: Self-service Analytics Environmentmentioning

confidence: 99%

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

Bani-Hani

Shepherd²

2021

Informatics

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The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards self-service systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industry.

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Section: Self-service Analytics Environmentmentioning

confidence: 99%

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

Bani-Hani

Shepherd²

2021

Informatics

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“…CRoss-Industry Standard Process for Data Mining (CRISP-DM) defined in Chapman et al (2000) provides the codification of a data mining process based on well defined stages. Schuff et al (2018) observe that CRISP-DM addresses business understanding and data understanding, which is the advantage of CRISP-DM. These aspects are of also particular importance for our study.…”

Section: The Data Mining Process and The Role Of Data Sourcesmentioning

confidence: 99%

Towards Increased Understanding of Open Data Use for Software Development

Grzenda

Legierski

2019

Inf Syst Front

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The way open data resources of varied type and volume are used by software applications remains only partly known. In this study, following CRoss-Industry Standard Process for Data Mining, we propose a methodology for collecting and analyzing access data describing the use of open data resources by individual software applications. The methodology includes novel categorization of the data collected at an exposition portal providing access to underlying open data portals and third-party services. Furthermore, it enables research into the use of both different open data resources and resource groups such as Big Data resources for software development. We apply the methodology to analyze the re-use of open urban data during reference software development events. The identification of open data use by individual applications is largely improved compared to baseline scenario, as shown by numerical indicators including F 1 measure. Insight into re-use of data streams and actual development time is obtained.

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“…However, recently, it has become more and more evident that the OLAP paradigm alone is no longer sufficient to keep the pace with the increasing needs of new-generation decision makers. Indeed, the enormous success of machine learning techniques has consistently shifted the interest of corporate users towards more sophisticated analytical applications (Popovic et al 2018;Schuff et al 2018). In addition, recent research envisions cross-cutting data management, analytics, and artificial intelligence in various sectors, such as applied data science (Chiusano et al 2021), behavioral research (Motiwalla et al 2019) and social impact (Gupta et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cubes with Models to Describe Multidimensional Data

et al. 2021

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The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of , one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

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Enabling self-service BI: A methodology and a case study for a model management warehouse

Cited by 22 publications

References 25 publications

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

Towards Increased Understanding of Open Data Use for Software Development

Enhancing Cubes with Models to Describe Multidimensional Data

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