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 -