Tech Transformations: a qualitative exploration of data analytics integration in technology-driven enterprises
Alessi, Daniele (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024112931448
https://urn.fi/URN:NBN:fi:amk-2024112931448
Tiivistelmä
This qualitative study delves into the transformative landscape of technology-driven enterprises, investigating the impact of data analytics integration. By exploring personal interpretations, professional experiences, and the myriad challenges and opportunities within the tech industry, the research seeks to unravel the intricate dynamics shaping this integration.
By capturing the expertise, challenges, and decision-making processes of professionals, the thesis investigates their know-how, organizational and cultural influences, and the impact of data analytics on business outcomes. The aim is to provide a comprehensive understanding of the strategies and practices in data-driven decision-making, offering valuable insights for both practitioners and researchers in the field.
Through in-depth interviews and analysis, the study examines the nuanced interplay of individual perspectives and industry demands, shedding light on the factors that optimize the effectiveness of data analytics integration in shaping the future of tech enterprises.
The findings indicate that early integration of data analytics promotes a data-driven culture, en hancing real-time insights and operational efficiency. Experts note challenges such as data quality and resistance to change, which necessitate strong infrastructure and training. Effective implementation requires alignment with business goals, collaboration, and a blend of data with human judgment. Advanced AI and automation are poised to make analytics more accessible, spurring innovation and competitive advantage across sectors.
By capturing the expertise, challenges, and decision-making processes of professionals, the thesis investigates their know-how, organizational and cultural influences, and the impact of data analytics on business outcomes. The aim is to provide a comprehensive understanding of the strategies and practices in data-driven decision-making, offering valuable insights for both practitioners and researchers in the field.
Through in-depth interviews and analysis, the study examines the nuanced interplay of individual perspectives and industry demands, shedding light on the factors that optimize the effectiveness of data analytics integration in shaping the future of tech enterprises.
The findings indicate that early integration of data analytics promotes a data-driven culture, en hancing real-time insights and operational efficiency. Experts note challenges such as data quality and resistance to change, which necessitate strong infrastructure and training. Effective implementation requires alignment with business goals, collaboration, and a blend of data with human judgment. Advanced AI and automation are poised to make analytics more accessible, spurring innovation and competitive advantage across sectors.