Journal Article•
Big data: the management revolution.
TL;DR: Big data, the authors write, is far more powerful than the analytics of the past, and executives can measure and therefore manage more precisely than ever before, and make better predictions and smarter decisions.
Abstract: Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.
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TL;DR: Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.
Abstract: In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?
2,062 citations
TL;DR: In this article, a taxonomy of pertinent application domains, namely, natural resources and energy, transport and mobility, buildings, living, government, and economy and people, is presented.
1,620 citations
Cites background from "Big data: the management revolution..."
...In other words, hard domains are the city settings in which the vision of a city that senses and acts can be the most applicable, thanks to the use of sensors, wireless technologies and software solutions to handle ‘‘big data’’ (McKinsey Global Institute, 2011; McAfee and Brynjolfsson, 2012)....
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TL;DR: This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity.
1,449 citations
Cites background from "Big data: the management revolution..."
...This includes sales prediction, user relationship mining and clustering, recommendation systems, opinion mining, etc. [6-10]....
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TL;DR: Information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad.
Abstract: ZusammenfassungMit “Big Data” werden Technologien beschrieben, die nicht weniger als die Erfüllung eines der Kernziele der Wirtschaftsinformatik versprechen: die richtigen Informationen dem richtigen Adressaten zur richtigen Zeit in der richtigen Menge am richtigen Ort und in der erforderlichen Qualität bereitzustellen. Für die Wirtschaftsinformatik als anwendungsorientierte Wissenschaftsdisziplin entstehen durch solche technologischen Entwicklungen Chancen und Risiken. Risiken entstehen vor allem dadurch, dass möglicherweise erhebliche Ressourcen auf die Erklärung und Gestaltung von Modeerscheinungen verwendet werden. Chancen entstehen dadurch, dass die entsprechenden Ressourcen zu substanziellen Erkenntnisgewinnen führen, die dem wissenschaftlichen Fortschritt der Disziplin wie auch ihrer praktischen Relevanz dienen.Aus Sicht der Autoren ist die Wirtschaftsinformatik ideal positioniert, um Big Data kritisch zu begleiten und Erkenntnisse für die Erklärung und Gestaltung innovativer Informationssysteme in Wirtschaft und Verwaltung zu nutzen – unabhängig davon, ob Big Data nun tatsächlich eine disruptive Technologie oder doch nur eine flüchtige Modeerscheinung ist. Die weitere Entwicklung und Adoption von Big Data wird letztendlich zeigen, ob es sich um eine Modeerscheinung oder um substanziellen Fortschritt handelt. Die aufgezeigten Thesen zeigen darüber hinaus auch, wie künftige technologische Entwicklungen für den Fortschritt der Disziplin Wirtschaftsinformatik genutzt werden können. Technologischer Fortschritt sollte für eine kumulative Ergänzung bestehender Modelle, Werkzeuge und Methoden genutzt werden. Dagegen sind wissenschaftliche Revolutionen unabhängig vom technologischen Fortschritt.Abstract“Big data” describes technologies that promise to fulfill a fundamental tenet of research in information systems, which is to provide the right information to the right receiver in the right volume and quality at the right time. For information systems research as an application-oriented research discipline, opportunities and risks arise from using big data. Risks arise primarily from the considerable number of resources used for the explanation and design of fads. Opportunities arise because these resources lead to substantial knowledge gains, which support scientific progress within the discipline and are of relevance to practice as well.From the authors’ perspective, information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad. The continuing development and adoption of big data will ultimately provide clarity on whether big data is a fad or if it represents substantial progress in information systems research. Three theses also show how future technological developments can be used to advance the discipline of information systems. Technological progress should be used for a cumulative supplement of existing models, tools, and methods. By contrast, scientific revolutions are independent of technological progress.
1,288 citations
TL;DR: In this article, the authors engaged in an international and interdisciplinary research effort to identify research priorities that have the potential to advance the service field and benefit customers, organizations, and society.
Abstract: The context in which service is delivered and experienced has, in many respects, fundamentally changed. For instance, advances in technology, especially information technology, are leading to a proliferation of revolutionary services and changing how customers serve themselves before, during, and after purchase. To understand this changing landscape, the authors engaged in an international and interdisciplinary research effort to identify research priorities that have the potential to advance the service field and benefit customers, organizations, and society. The priority-setting process was informed by roundtable discussions with researchers affiliated with service research centers and networks located around the world and resulted in the following 12 service research priorities:
• stimulating service innovation,
• facilitating servitization, service infusion, and solutions,
• understanding organization and employee issues relevant to successful service,
• developing service networks and systems,
• leveraging service design,
• using big data to advance service,
• understanding value creation,
• enhancing the service experience,
• improving well-being through transformative service,
• measuring and optimizing service performance and impact,
• understanding service in a global context, and
• leveraging technology to advance service.
For each priority, the authors identified important specific service topics and related research questions. Then, through an online survey, service researchers assessed the subtopics’ perceived importance and the service field’s extant knowledge about them. Although all the priorities and related topics were deemed important, the results show that topics related to transformative service and measuring and optimizing service performance are particularly important for advancing the service field along with big data, which had the largest gap between importance and current knowledge of the field. The authors present key challenges that should be addressed to move the field forward and conclude with a discussion of the need for additional interdisciplinary research.
1,168 citations