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Untersuchung der Wirkung von Data Storytelling auf das Datenverständnis von Dashboard-Nutzer:innen
Abstract
Mit dem zunehmenden Einsatz von Big Data und Unternehmensanalysen hat Data Storytelling als wirksames Mittel zur Vermittlung von analytischen Erkenntnissen an das Publikum an Popularität gewonnen, um die Entscheidungsfindung zu unterstützen und die Unternehmensleistung zu verbessern. Allerdings gibt es nur wenige empirische Belege für die Auswirkungen von Data Storytelling auf das Datenverständnis. Diese Studie validiert das Konzept des Data Storytelling als Konstrukt im Hinblick auf seine Wirkung auf das Datenverständnis der Nutzer. Basierend auf einer empirischen Datenanalyse zeigen die Ergebnisse dieser Studie, dass Data Storytelling-Kompetenz positiv mit der Unternehmensleistung assoziiert ist, was teilweise durch die Entscheidungsqualität vermittelt wird. Diese Ergebnisse bieten eine theoretische Grundlage für die weitere Untersuchung potenzieller Antezedenzien und Konsequenzen von Data Storytelling.
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Self-service Business Intelligence (SSBI) allows non-technical users to use Business Intelligence in a self-reliant manner without the support of technical users. Many organizations struggle to utilize the potential of SSBI and experience implementation challenges. This study aims to explore user-related SSBI challenges by conducting 30 qualitative interviews with 2 SSBI implementation projects. Analysis revealed challenges that can help practitioners to avoid unnecessary obstacles when implementing and using SSBI, and guide researchers in simplifying the implementation process.
Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.
The increasing popularity of self-service analytics (SSA) is empowering business users to analyze data and generate actionable insights autonomously. While there are many benefits to SSA tools, there is a scarcity of research on the factors influencing their adoption in business organizations. This article presents an extended technology acceptance model (TAM) that incorporates the task-technology fit (TTF), compatibility, and user empowerment as critical antecedents of users' intention to adopt SSA tools for reporting and analytics tasks. To test the proposed model, data were collected through a questionnaire survey of 211 business users working in different industries in Jordan. The collected data were analysed using structural equation modeling (SEM). The results of this study demonstrate that the task-technology fit, compatibility, and user empowerment are significant predictors of users' perceptions of usefulness and ease of use of SSA tools. Both of perceived usefulness and perceived ease of use have a positive effect on users' intention to adopt SSA tools. Collectively, all these factors account for 51.6 percent of the variance in the behavioral intention. The findings of this study provide several key implications for research and practice, and thus should contribute to the design and adoption of more user-accepted SSA tools and applications.
With the huge proliferation of Big Data, combined with the increasing demand for analytics-driven decision-making, the data analytics and visualization (DAV) ecosystem is increasingly becoming a trending practice that many enterprises are adopting to gain actionable insights from corporate data for effective decision-making. Although DAV platforms have tremendous benefits, extant research has paid insufficient attention to the investigation of the critical success factors (CSFs) underpinning their successful implementation in enterprises. In order to bridge this knowledge gap, this study presents an integrative framework synthesizing a set of CSFs for implementing DAV platforms in enterprises. A qualitative research methodology, comprising semi-structured interviews with IT and business analysts, was conducted to collect and analyze the interview data. Analysis of results revealed that the CSFs of DAV implementation exist in various dimensions composed of organizational, technological, process, and people perspectives. This study provides several theoretical and practical implications.
Due to the rapidly increasing popularity of business analytics (BA), investigation of the antecedents/determinants of the adoption of BA and the subsequent impact of the same to the firm performance has become an important research topic. Drawing on the fundamentals of the resource-based view (RBV), this study proposes a model that examines the effects of the BA adoption on business process performance (BPER) and the mediating
role that BPER plays in the relationship between the adoption of BA and firm performance (FP). Based on the data collected from 204 medium- to high-level business executives in various industries, the results of this empirical study indicate that the adoption of BA positively influences BPER. There is also positive relationship between BPER and FP. Finally, the results show that BPER fully mediates the relationship between BA adoption and FP.
Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically , which improves the interactivity and in-terpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models , and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on ROTOWIRE datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.
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The availability of data in massive collections in recent past not only has enabled data-driven decision-making, but also has created new questions that cannot be addressed effectively with the traditional statistical analysis methods. The traditional scientific research not only has prevented business scholars from working on emerging problems with big and rich data-sets, but also has resulted in irrelevant theory and questionable conclusions; mostly because the traditional method has mainly focused on modeling and analysis/explanation than on the real/practical problem and the data. We believe the lack of due attention to the analytics paradigm can to some extent be attributed to the business scholars' unfamiliarity with the analytics methods/methodologies and the type of questions it can answer. Therefore, our purpose in this paper is to illustrate how analytics, as a complement, rather than a successor, to the traditional research paradigm, can be used to address interesting emerging business research questions.
Many organisations consider business analytics to be a key organisational capability. To date, there is little evidence on how organisations have included analytics at the heart of their strategy processes. This paper addresses this issue by exploring the activities within a strategy process and considering the potential role that business analytics might play in providing support to such processes. We perform a search in multidisciplinary databases for evidence of the use of business analytics within strategy processes and we reflect on its use in two case studies performing strategic analysis within the pharmaceutical industry. The findings indicate business analytics is still an emerging field without a structured approach. Business analytics can provide important data-driven insights into strategy processes; we therefore recommend its further integration with other traditional OR and strategy tools in order to support strategic decision-makers.
This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary test of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze-insight-decision-action process through which use of an organization's business-analytic capabilities create business value. The variance model proposes that the five factors in Davenport et al.'s (2010) DELTA model of BA success factors, six from Watson and Wixom (2007), and three from Seddon et al.'s (2010) model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary test of the model was conducted using data from 100 customer-success stories from vendors such as IBM, SAP, and Teradata. Our conclusion is that the model is likely to be a useful basis for future research.
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