Evaluating the Challenges in Integrating Business Analytics into ISO 9001-Based Quality Management Systems: A Best-Worst Method Approach

Panos T. Chountalas (), Natalia Chatzifoti () and Maria D. Karvounidi ()
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Panos T. Chountalas: University of Piraeus
Natalia Chatzifoti: University of Piraeus
Maria D. Karvounidi: University of Piraeus

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 209-225 from Springer

Abstract: Abstract This study examines the challenges organizations face when integrating Business Analytics (BA) into ISO 9001-based Quality Management Systems (QMS). Using the Best-Worst Method, input was collected from 16 experienced professionals across quality, analytics, and operations roles. Participants evaluated nine specific challenges based on their perceived impact on integration efforts. Three challenges emerged as the most critical. The first was poor data quality and weak data management practices, which raised concerns about the reliability of analytics in decision-making processes. The second most significant challenge was a lack of commitment and support from top management, which directly affects strategy, resources, and cultural alignment. Following closely was the absence of a data-driven decision-making culture, highlighting a deep-seated organizational barrier that is often underestimated. Other serious obstacles to embedding analytics into existing quality systems included the lack of a clear strategy and vision for BA integration, as well as limited access to the necessary financial, technological, and human resources. The findings suggest that successful integration requires more than just technical capability. It also depends on leadership alignment, cultural readiness, and a solid foundation of trustworthy data. Organizations aiming to modernize their quality management should treat these issues as core management priorities, rather than merely technical gaps.

Keywords: Business Analytics; ISO 9001; Quality Management System; Best-Worst Method (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23493-3_13

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