The Big data in organizations: A systematic review of impact, efficiency, and predictive capacities
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i3.1338Keywords:
Big Data, artificial intelligence, machine learning, inteligencia artificial, aprendizaje automáticoAbstract
The rapid growth of data volumes in organizational environments has accelerated the adoption of Big Data and advanced analytics as key components of digital transformation. However, alongside potential gains in efficiency, innovation, and evidence-based decision-making, significant risks and challenges arise in technological, organizational, ethical, and human-capital domains. This study aims to conduct a systematic review of recent literature on Big Data, artificial intelligence, and machine learning applied to decision-making in business and organizational contexts, to identify their main uses and applications, as well as the benefits, risks, challenges, and factors influencing effective adoption. The review followed PRISMA Statement recommendations for the search, selection, and critical appraisal of studies, applying predefined inclusion and exclusion criteria. The study concludes that responsible adoption of these technologies requires robust infrastructures, solid data governance frameworks, strategic leadership, continuous upskilling, and an organizational culture oriented toward ethics and the critical use of information.
Spanish-language metadata / Metadatos en español
Título en español:
El big data en las organizaciones: una revisión sistemática del impacto, la eficiencia y las capacidades predictivas
Resumen:
El rápido crecimiento del volumen de datos en los entornos organizacionales ha acelerado la adopción del Big Data y la analítica avanzada como componentes clave de la transformación digital. Sin embargo, junto con las posibles ventajas en materia de eficiencia, innovación y toma de decisiones basada en datos, surgen riesgos y desafíos significativos en los ámbitos tecnológico, organizacional, ético y del capital humano. El objetivo de este estudio es realizar una revisión sistemática de la bibliografía reciente sobre el big data, la inteligencia artificial y el aprendizaje automático aplicados a la toma de decisiones en contextos empresariales y organizacionales, con el fin de identificar sus principales usos y aplicaciones, así como los beneficios, riesgos, desafíos y factores que influyen en su adopción efectiva. La revisión siguió las recomendaciones de la Declaración PRISMA en cuanto a la búsqueda, selección y evaluación crítica de los estudios, aplicando criterios de inclusión y exclusión predefinidos. El estudio concluye que la adopción responsable de estas tecnologías requiere infraestructuras sólidas, marcos sólidos de gobernanza de datos, liderazgo estratégico, capacitación continua y una cultura organizacional orientada a la ética y al uso crítico de la información.
Palabras Claves:
Big Data, inteligencia artificial y aprendizaje automático
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