Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research

By Ritu Agarwal, Vasant Dhar. Information systems research, 2014

657 citations recorded by the source corpus.

Subfields: Epidemiology, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/87a7e55b4c3116751edb4b0f74e0484eaf7a853d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “Most studies are based on anecdotal evidence, particularly in relation to the impact of a ﬁrm’s BDA capability on performance (Agarwal and Dhar 2014; Akter et al. 2016a).”
Big data and disaster management: a systematic review and agenda for future research (2017, background): “Therefore, the biggest challenge is to develop a data integration protocol that ensures proper data gathering, modelling and notification systems (Agarwal and Dhar, 2014; Miller, 2013).”
Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “One of the most exciting opportunities pertains to predicting/analyzing micro-level outcomes (Agarwal & Dhar, 2014).”
Critical analysis of Big Data challenges and analytical methods (2017, background): “Akerkar (2014) and Zicari (2014) refer veracity to as coping with the biases, doubts, imprecision, fabrications, messiness and misplaced evidence in the data.”
An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective (2018, background): “…, data analytics , business analytics and supply chain analytics to describe capabilities that enable ﬁrms to collect and analyze data to gain critical insights (Agarwal and Dhar 2014, Davenport 2006, Fisher et al. 2012, Hsinchun et al. 2012, Nichols 2013, Sinha and Wege-ner 2013, Souza 2014).”
Modelling quality dynamics, business value and firm performance in a big data analytics environment (2017, background): “represents a challenging and controversial mission in terms of systems and information quality and their impact on business value and firm performance (Agarwal and Dhar, 2014; Goes, 2014; Lavalle et al., 2011; McAfee and Brynjolfsson, 2012c; Verbraken et al., 2012).”
Resource management in big data initiatives: Processes and dynamic capabilities (2017, background): “Agarwal and Dhar (2014) observe that while big data is premised on open access to data so that prior knowledge can be tested and falsi ﬁ ed (Popper, 1963), academics base their work on data that they do not release for others to challenge or con ﬁ rm those ﬁ ndings.”
The impact of big data on world-class sustainable manufacturing (2015, result): “Our study is a response to the call by BD scholars (Agarwal and Dhar, 2014; Dutta and Basu, 2015) for more studies on the opportunities enabled by BD.”
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (2019, neutral): “we are in principle already there” [2]”
Big Data Analytics in Operations Management (2018, background): “…discussion papers related to big data analytics include an editorial discussion on research issues around big data in information systems research (Agarwal and Dhar 2014), a systematic review on the business value of big data (Wamba et al. 2015), a review on the role played by cloud computing in…”
The impact of big data analytics on firms’ high value business performance (2016, background): “With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at huge scale, novel insights can be obtained from the highly detailed, contextualized, and rich contents of relevance to any firm (Agarwal and Dhar 2014; Chen et al. 2012).”