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Author: search.filters.author.Alamsyah, NoorHas files: No

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    Leveraging Data Analytics Across Digital Product Development Stages: A Systematic Review and Conceptual Framework
    (2026-06-01)
    Alamsyah, Noor
    ;
    Thanasopon, Bundit
    ;
    Jamsri, Pornsuree
    Background: Data analytics (DA) is a field that has expanded greatly and is an important tool for digital product development, and has captured researcher and practitioner interest. Nevertheless, from an Information Systems and Business Intelligence (IS/BI) view, there is still a lack of knowledge regarding the role that data analytics plays in the digital product development lifecycle for decision making. The volume and the complexity of digital product innovation and analytics continues to increase, thereby further enhancing the need for existing knowledge to be consolidated in this area. Objective: This research systematically reviewed the latest academic research in the field of data analytics in digital product development and explain the specific uses of data analytics in the different stages of digital product development for supporting decision making and innovation activities. Methods: This study followed the systematic literature review method through ScienceDirect, IEEE Xplore and Emerald databases. Upon initial search, 1,554 articles were found; 33 relevant articles were identified after a structured screening and eligibility assessment of the articles in line with Kitchenham's protocol. Results: The results reveal the differentiated use of data analytics in the various stages, namely opportunity identification through text mining, feasibility assessment through predictive modelling, prototyping through digital twins and generative design and market responsiveness through predictive analytics and recommender systems. Even with analytical processes and concepts in place, companies often face challenges due to data integration issues, analytical skill, and organizational preparedness. The data quality issues, algorithmic bias, ethical considerations, or lack of algorithm transparency all contribute to these limitations, hindering the full potential of data analytics in digital product development. Conclusion: To realize more effective and aligned outcomes of innovation, it is important to understand how data analytics can assist in decision making throughout the digital product development lifecycle. This research is valuable for researchers and practitioners as it provides a structured conceptual framework for association of analytics initiatives with digital product development goals. There is potential for this work to be extended in future studies, involving the creation and validation of scalable analytics frameworks in various organisational contexts.
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    Data Analytics Maturity Model for Digital Product Innovation in Firm: An Overview
    (2025-01-01)
    Alamsyah, Noor
    ;
    Thanasopon, Bundit
    ;
    Jamsri, Pornsuree
    This paper aims to report the results of the review, map the characteristics, and conduct a comparative analysis of the selected data analytics maturity models that are commonly implemented in firms engaged in digital product innovation. The maturity models examined include TDWI, Hortonworks, Gartner, and DELTA Plus, which represent some of the most recognized framework guiding organizations in assessing and improving their data analytics capabilities. Characteristics and comparative analysis were developed based on investigative literature reviews, referring to official reports from institutions that developed the model and publications that discussed the model. Each maturity model is explained concisely and comprehensively, highlighting the structure, dimensions, and progression criteria that define each maturity level and stage. The comparison indicates that although these models outline well-defined conceptual stages of analytics maturity, most provide limited methodological guidance for assessing firms or determining their placement within a spesific maturity level. In the future, it is expected there will be an increase in the availability of maturity models that are more adaptive and personalized for various business sectors. This paper contributes to present a fairly intensive comparative analysis and characterization of data analytics maturity models that are widely recognized and frequently applied in supporting digital product development within firms. In addition to that, this paper provides a cost model. The model is evaluated using Genetic Algorithm (GA) and Simulated Annealing (SA).