Critical Success Factors of Enterprise Data Analytics and Visualization Ecosystem: An Interview Study

Critical Success Factors of Enterprise Data Analytics and Visualization Ecosystem: An Interview Study

Mohammad Daradkeh (Department of Management Information Systems, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan)
Copyright: © 2019 |Pages: 22
DOI: 10.4018/IJITPM.2019070103
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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.
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The current business ecosystem is characterized by the rapidly increasing scale, complexity, and rate of change of business data. Enterprises generate and accumulate an immense quantity of data, which has become a key asset for leveraging business analytics and sustaining a competitive advantage (Sharda, Delen, & Turban, 2017). These enterprises rely heavily on business analytics to optimize operations, improve production, inform sales and decisions, prevent threats and fraud, and strengthen customer engagement (Basole, 2014; Daradkeh, 2017a). However, the growing silos of data that enterprises continue to amass impose significant challenges on business analysts and decision-makers to comprehend and make strategic and operational decisions. As most of today’s enterprises are transforming into analytics-driven enterprises, this also requires that business users across the enterprise become comfortable with, and sophisticated in, using data and analytics for decision-making (Daradkeh, 2017b). The growing demand for analytics-driven decisions, combined with the huge proliferation of big data, provides a strong motivation for enterprises to implement data analytics and visualization (DAV) ecosystem to ascertain valued knowledge and actionable insights from corporate data to support decision-making (Daradkeh, 2015).

The DAV ecosystem is an emerging capability for a wide range of business users, including business analysts, data scientists, information officers, and corporate decision-makers (Basole, 2014). It enables the discovery of new business insights, identification of crucial patterns, analysis of trends, and identification of outliers. It also makes business data more accessible and provides an effective means for improved communication and information sharing (Sedlmair, Michael, Isenberg, Baur, & Butz, 2010). Today’s business analytics software, especially in the area of self-service business analytics, is no longer accepted by business users without interactive and intuitive user interfaces that offer them with different visualization options (Daradkeh, & Al-Dwairi, 2017; Kohlhammer, Proff, & Wiener, 2016). An interactive DAV ecosystem is particularly valuable because it moves beyond traditional static business reports and performance indicators to mapping, exploration, discovery, and sensemaking of complex business ecosystems (Basole, Huhtamäki, Still, & Russell, 2016). Consequently, enterprises using DAV ecosystem can comprehend hidden meanings in data, predict and solve business problems in ways that increase enterprise knowledge, implement new business model, optimize decision-making processes, and establish and achieve business goals effectively (Basole et al., 2016).

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