From 'big data' to 'smart data': algorithm for cross-evaluation as a novel method for large-scale survey analysis
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From 'big data' to 'smart data' : algorithm for cross-evaluation as a novel method for large-scale survey analysis. / Kantoci, Darko; Džanić, Emir; Bogers, Marcel.
In: International Journal of Transitions and Innovation Systems, Vol. 6, No. 1, 2018, p. 24-47.Research output: Contribution to journal › Journal article › Research › peer-review
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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
VL - 6
SP - 24
EP - 47
JO - International Journal of Transitions and Innovation Systems
JF - International Journal of Transitions and Innovation Systems
SN - 1745-0071
IS - 1
ER -
ID: 194814199