From 'big data' to 'smart data': algorithm for cross-evaluation as a novel method for large-scale survey analysis

TY - JOUR

T1 - From 'big data' to 'smart data'

T2 - algorithm for cross-evaluation as a novel method for large-scale survey analysis

AU - Kantoci, Darko

AU - Džanić, Emir

AU - Bogers, Marcel

PY - 2018

Y1 - 2018

N2 - Current research is increasingly relying on large data analysis to provide insights into trends and patterns across a variety of organisational and business contexts. Existing methods for large-scale data analysis do not fully capture some of the key challenges with data in large datasets, such as non-response rates or missing data. One method that does address these challenges is the SunCore algorithm for cross-evaluation (ACE). ACE provides a view of the whole dataset in a multidimensional mathematical space by performing consistency and cluster analysis to fill in the gaps, thereby illumining trends and patterns previously invisible within such datasets. This approach to data analysis meaningfully complements classical statistical approaches. We argue that the value of the ACE algorithm lies in turning 'big data' into 'smart data' by predicting gaps in large datasets. We illustrate the use of ACE in connection to a survey on employees' perception of the innovative ability within their company by looking at consistency and cluster analysis.

AB - Current research is increasingly relying on large data analysis to provide insights into trends and patterns across a variety of organisational and business contexts. Existing methods for large-scale data analysis do not fully capture some of the key challenges with data in large datasets, such as non-response rates or missing data. One method that does address these challenges is the SunCore algorithm for cross-evaluation (ACE). ACE provides a view of the whole dataset in a multidimensional mathematical space by performing consistency and cluster analysis to fill in the gaps, thereby illumining trends and patterns previously invisible within such datasets. This approach to data analysis meaningfully complements classical statistical approaches. We argue that the value of the ACE algorithm lies in turning 'big data' into 'smart data' by predicting gaps in large datasets. We illustrate the use of ACE in connection to a survey on employees' perception of the innovative ability within their company by looking at consistency and cluster analysis.

U2 - 10.1504/IJTIS.2018.10011688

DO - 10.1504/IJTIS.2018.10011688

M3 - Journal article

SN - 1745-0071

VL - 6

SP - 24

EP - 47

JO - International Journal of Transitions and Innovation Systems

JF - International Journal of Transitions and Innovation Systems

IS - 1

ER -