Competing on analytics.

By T. Davenport. Harvard Business Review, 2006

1,294 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/59da6e8605df8b528fae1de75ea0ec66cd370c87

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Citations in the corpus

A unified foundation for business analytics (2014, background): “The framework devised later is offered as a unifying core for understanding the full scope of BA.”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, background): “In this changing business environment, business leaders prefer to take decisions bearing in mind the datadriven insights rather than relying on their intuitions (Davenport, 2006).”
Dynamic capabilities through continuous improvement infrastructure (2009, background): “Managing information so that it is available laterally across the enterprise and to senior management in a timely manner can turn an organization into an ‘‘analytic competitor’’ and enhance the effectiveness of its process improvement efforts (Davenport, 2006).”
Business process management (BPM) standards: a survey (2009, background): “From a business viewpoint, business analytics (Davenport and Harris, 2006) underscores the need for the diagnosis stage of the BPM life cycle.”
Business Intelligence and Analytics: From Big Data to Big Impact (2012, background): “In the late 2000s, business analytics was introduced to represent the key analytical component in BI (Davenport 2006).”
Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “As business processes become major differentiators for organizations in many industries, organizations are increasingly using analytics to “wring every last drop” of value from those processes (Davenport, 2006).”
Business intelligence & analytics in management accounting research: Status and future focus (2018, methodology): “BI&A is de ﬁ ned here as a technology and a process for analysing data and presenting actionable information to help organizational decision makers make better decisions (Chaudhuri et al., 2011; Howson, 2007; Davenport, 2006, 2010, 2013, 2014; Sharda et al., 2014).”
Big data analytics and firm performance: Effects of dynamic capabilities (2017, result): “Consistent with several earlier studies (Davenport, 2006; Davenport & Harris, 2007; Goes, 2014;Mcafee & Brynjolfsson, 2012b), in this study, we view BDAC as an important organizational capability leading to sustainable competitive advantage in the big data environment.”
Big data analytics capability in supply chain agility (2019, unknown): “Moreover, our results strengthen the notion that managers who can exploit the innovative technology in attempts to build capability at supply chain level can expect their organizations to gain competitive edge over their competitors (Davenport, 2006; Akter et al. 2016; Srinivasan and Swink, 2017).”
Impact of Industry 4.0 on supply chain performance (2020, background): “Similarly, Davenport (2006), Hazen et al. (2014) and Gunasekaran et al. (2017) argue that big data analytics can enable simultaneous and systematic data collection and analysis, optimising supply chain performance and leading to a competitive advantage.”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “In the context of big data, it is important to identify the different types of resources, since the level of their infusion in various business functions can be a source of competitive differentiation (Davenport 2006).”
Forecasting cancellation rates for services booking revenue management using data mining (2010, background): “Davenport (2006) cites the”
A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “Davenport [41] listed a number of real and anecdotal examples of how organizations design strategies for using collected datasets and mine value from those datasets.”
Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, background): “However, such applications need to be implemented throughout the supply chain in order to reap full benefits [8].”