How Big Does Big Data Need to Be?
Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review
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Enterprise Big Data Engineering, Analytics, and Management. ed. / Martin Atzmueller; Samia Oussena; Thomas Roth-Berghofer. Hershey: Business Science Reference, 2016. p. 1-12.
Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review
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TY - CHAP
T1 - How Big Does Big Data Need to Be?
AU - Stange, Martin
AU - Funk, Burkhardt
PY - 2016/6
Y1 - 2016/6
N2 - Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter,a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.
AB - Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter,a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.
KW - Business informatics
KW - Big Data
KW - Predictive Analytics
KW - Learning Curve
UR - http://www.igi-global.com/chapter/how-big-does-big-data-need-to-be/154550
U2 - 10.4018/978-1-5225-0293-7.ch001
DO - 10.4018/978-1-5225-0293-7.ch001
M3 - Contributions to collected editions/anthologies
SN - 9781522502937
SP - 1
EP - 12
BT - Enterprise Big Data Engineering, Analytics, and Management
A2 - Atzmueller, Martin
A2 - Oussena, Samia
A2 - Roth-Berghofer, Thomas
PB - Business Science Reference
CY - Hershey
ER -