International Journal on Science and Technology

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Breaking Silos: Architecting Cross-Functional Analytics Frameworks for Collaborative Insights

Author(s) Shafeeq Ur Rahaman
Country India
Abstract The data-driven world realizes that organizations have to break down their departmental silos to pursue a culture of collaboration and mutual insight sharing. This working paper discusses the issue of creating analytics frameworks that cut across organizational silos and enable cross-functional collaboration. Equipped with state-of-the-art technologies, including machine learning, data visualization software, and cloud-based platforms, these frameworks facilitate seamless sharing of data and analyses across departments. The article underlines strategic benefits associated with collaborative analytics ecosystems, where decision-making is strengthened, operational efficiency is ensured, and alignment within organizations will be tighter. Real-life case studies from different verticals contextualize and demonstrate the transformation possible to be had with such frameworks in pursuit of a common approach toward leveraging data. The article concludes with best practices and actionable recommendations that can drive innovation and strategic growth through the implementation of cross-functional analytics frameworks
Keywords Cross-functional collaboration, analytics framework, data silos, insight collaboration, organizational alignment, machine learning, sharing of data, strategic decision-making, data-driven innovation, cloud-based analytics
Field Computer > Data / Information
Published In Volume 14, Issue 2, April-June 2023
Published On 2023-06-06
Cite This Breaking Silos: Architecting Cross-Functional Analytics Frameworks for Collaborative Insights - Shafeeq Ur Rahaman - IJSAT Volume 14, Issue 2, April-June 2023. DOI 10.5281/zenodo.14471815
DOI https://doi.org/10.5281/zenodo.14471815
Short DOI https://doi.org/g8vk77
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