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

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.