Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance

By Bernard Marr. 2015

298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/79640a294be414c4c054a5c39a6eee708c1aad22

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Citations in the corpus

Human resources for Big Data professions: A systematic classification of job roles and required skill sets (2017, methodology): “…Genetic algorithms, Natural Language Processing, Speech and Image recognition, Neural Networks, Predictive modeling, Regression Models, Social Network Analysis, Sentiment Analysis, Signal Processing and Data Visualization ( Chen, Chiang, & Storey, 2012, Manyika et al., 2011 ; Marr, 2015 ).”
A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, background): “Recent research works have indicated that gathering the massive IoT data alone is not sufficient [41]–[43], and it is”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “more traditional static mode to an accelerating data arena characterized by volume, velocity, variety, veracity and value (i.e. 5Vs of Big Data; Marr 2015).”
Can we trust Big Data? Applying philosophy of science to software (2016, background): “They review the range of deﬁnitions of Big Data given by leading corporations in data management (for example, International Data Corporation (IDC), IBM, and Microsoft) before settling on IDC’s 2011 deﬁnition.”
Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study (2017, background): “One more detailed definition encapsulates the expression as the 4V's (Paharia 2013; Marr 2015): Volume—massive amounts of data being generated continually in a volume never before observed, scaling to Brontobytes; Variety—distinct and unstructured formats, representing today about 80% of all…”