NRT methodology: From big data to strategic intelligence in a VUCA and BANI world context

Abstract

Background: The current view of the world is equated to being volatile, uncertain, complex and ambiguous (VUCA), as well as brittle, anxious, non-linear and incomprehensible (BANI). Leaders are inundated with constant changes and challenges in the VUCA and BANI contexts, which directly contribute to an increasing state of paralysed dysfunction. Artificial intelligence (AI) directly contributes to the rapid growth of structured and unstructured data, worsening the VUCA and BANI contexts as organisations continue to battle to manage and make sense of data. Innovative and sustainable approaches are needed to assist with the effective management of data into Strategic Intelligence (SI).
Objectives: This study aimed to expand on the Nominal Ranking Technique (NRT) methodology, as an innovative and sustainable approach to managing and making sense of big data (BD), leading to SI for informed decision-making.
Method: Content analysis as a qualitative approach was used to analyse 225 data files. The content analysis for this study is referred to as the NRT methodology.
Results: The newly expanded NRT methodology includes six colour-coded primary categories and two colour-coded secondary categories. The primary and secondary categories contribute to the structured and systematic approach of the NRT methodology, which resulted in six SI-Relevant data files.
Conclusion: The expanded NRT methodology provides a sustainable means of converting BD into actionable SI, thereby directly supporting informed decision-making in VUCA and BANI contexts.
Contribution: The structured and systematic approach of the NRT methodology directly contributes to the effective management of BD into SI for informed decision-making.