Big data: The next frontier for innovation, competition, and productivity

By J. Manyika. 2011

5,785 citations recorded by the source corpus.

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Citations in the corpus

Research Priorities for Robust and Beneficial Artificial Intelligence (2015, background): “, interact with the right to privacy? How will privacy risks interact with cybersecurity and cyberwarfare (Singer and Friedman 2014)? Our ability to take full advantage of the synergy between AI and big data will depend in part on our ability to manage and preserve privacy (Manyika et al. 2011; Agra”
Big data analytics and firm performance: Effects of dynamic capabilities (2017, background): “Some practitioners and scholars have gone so far as to suggest that BDA is the “fourth paradigm of science” (Strawn, 2012, p.34), a “new paradigm of knowledge assets” (Hagstrom, 2012, p. 2), or “the next frontier for innovation, competition, and productivity” (Manyika et al., 2011, p.1).”
Data ex Machina: Introduction to Big Data (2017, background): “The term “big data” has proliferated across society and owes its recent popularization to a report by McKinsey (Manyika et al. 2011).”
Unique in the Crowd: The privacy bounds of human mobility (2013, background): “Going forward, the importance of location data will only increase 34 and knowing the bounds of individual’s privacy will be crucial in the design of both future policies and information technologies.”
Hitching Healthcare to the Chain: An Introduction to Blockchain Technology in the Healthcare Sector (2017, background): “…estimated that more than $300 billion could be recovered per year by using health data creatively and effectively, with two-thirds of that in the form of reductions to national health care expenditure – about 8 percent of estimated healthcare spending at 2010 levels (McKinsey & Company, 2011).”
Deep learning in bioinformatics (2016, background): “In the era of ‘big data,’ transformation of large quantities of data into valuable knowledge has become increasingly important in various domains [1], and bioinformatics is no exception.”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, methodology): “According to the report from McKinsey institute [114], the effective use of Big Data has the underlying benefits to transform economies, and delivering a new wave of productive growth.”
Creating Strategic Business Value from Big Data Analytics: A Research Framework (2018, background): “In other words, what are the value creation mechanisms of BDA to have a positive impact on decisions, customers, processes, or other targets? There are different ways to create value using BDA [31].”
What is big data? A consensual definition and a review of key research topics (2015, background): “[11] and Chen [18] propose a list of Big Data Analytical Methods, that include (in alphabetical order): A/B testing, Association rule learning, Classification, Cluster analysis, Data fusion and data integration, Ensemble learning, Genetic algorithms, Machine learning, Natural Language Processing, Ne”
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors (2017, methodology): “Big Data usually contains data sets beyond the capacity of commonly used in software/tool to capture, curate, manage and process the data under a tolerable elapsed time (Manyika et al. 2011).”
Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation (2015, background): “economic variables and determine what key measurable manufacturing parameters most influence customer satisfaction [68].”
A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “There are a number of studies [43]–[45] on identifying the issues of how big data analytics creates commercial value, and where”
Uncertainty in big data analytics: survey, opportunities, and challenges (2019, background): “For instance, a detailed analysis of historical patient data could lead to the detection of destructive disease at an early stage, thereby enabling either a cure or more optimal treatment plan [11, 12].”
Big data analytics capability in supply chain agility (2019, background): “Following Manyika et al. (2011), we argue that big data is data whose volume, velocity and variety make it difficult for an organization to manage, analyze and extract valuable insights using conventional and traditional methods.”