Proposed extended analytic hierarchical process for selecting data science methodologies

Decision making can present a considerable amount of complexity in competitive environments; where methods that support possess great relevance. The article presents an extension the Hierarchic Analytical Process; complemented with Personal Construct Theory, which purpose is to reduce ambiguity when defining and establishing values for criteria determined problem. In recent years, scope decision based on data has considerably raised, why Data Science as scientific field rising popularity; one main activities scientists selecting adequate methodology guide project this traits. steps defined proposed model task, from prioritizing degrees compliance, grouping them by levels, completing hierarchical structure problem, performing correct comparisons through different levels ascendant manner, finally obtaining definitive priorities each validation case sorting their adequacy percentages. Both disparate cases, referred industrial/commercial other academic field, were effective corroborate extent usefulness model; both cases MoProPEI obtained best results.