Data Loss Risk: A Multivariate Statistical Methodology Proposal
Given that an adequate prioritization of data losses (DL) events is crucial for risk management in institutions of any nature, the present paper proposes a methodology aimed at hierarchizing the events associated with this type of risk. This proposal incorporates three specifications : parametric independence, objectivity and applicability. To illustrate , a framework was applied to records of DatalossDB, a US risk database. An hierarchy model based on Conjoint Analysis (CA) was developed by associating DL with industry sector, incident source and incident type variables. The flexibility of CA derives from its ability to use metric or non-metric variables, as well as from the lack of rigid rules regarding the relation between the combination of attributes and the preferences. The procedure determined the importance of the attributes involved and allowed the prioritization of risk events, which will certainly be useful in guiding the actions towards minimizing the problem.
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