Assessing big data maturity in a large holding company: a holistic framework approach for leveraging maturity

• Ano de defesa: 2024
• Autor(a) principal: Oliveira, Felippe Eiji Tashiro de
• Orientador(a): Francisco, Eduardo de Rezende
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Palavras-chave em Inglês: Big data analytics; Big data maturity model; Big data analytics capability
• Link de acesso: https://hdl.handle.net/10438/35694

The global big data market has experienced exponential growth, reflecting the crucial role of Big Data Analytics in modern business strategies. Despite its importance, a fragmented understanding of Big Data remains, particularly regarding its implementation within organizational structures. This fragmentation is evident in the disparities between academic definitions and practical applications. This study aims to bridge the gaps in Big Data Maturity Models through a framework with a holistic view of the main organizational dimensions, aiming to answer the question, “How do the organizations leverage Big Data Analytics Capabilities?”.For this purpose, this research utilizes theories from the literature on Big Data Maturity Models in a case study with semistructured interviews at a large holding company involving 28 respondents in a period between July 2023 and February 2024. This research revealed challenges and practices within the organization that impact the effectiveness of Big Data implementation. It provides a clearer and more practical path for organizations to enhance their Big Data analytics capabilities, focusing on transitions between maturity stages and identifying critical factors influencing these progressions. The developed framework offers practical insights on effectively leveraging data maturity, promoting a more strategic use of Big Data Analytics to improve competitive performance and business agility. This could contribute by demonstrating the relationships of dimensions in an organization with action plans to leverage the use of BD, such as avoiding excess silos and circumventing the difficulties for a unified data strategy, the challenges CDOs face in demonstrating their roles amidst various verticals and how they can be more present and have more influence in leveraging data projects. The allocation of resources for data was also a discovery, given the competition with lower resource products, and the role of managers is important in this issue. Lastly, the difficulty of a unified architecture can be circumvented by more active collaboration between verticals and reference fronts for sharing best practices. These findings contribute to data maturity and a culture with practical methodologies to generate business value.