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Using a constructive design science approach, the work first defines digital data waste and then develops an ensemble artifact with 13 machine learning models to detect waste. Applied to 35,576 online reviews, the models identify waste at 1.9% for restaurants versus 35.8% for apps, achieving 83%–99.8% accuracy, with deep learning particularly strong. A sustainability cost calculator quantifies social, economic, and environmental benefits, showing savings from eliminating 5,948 useless reviews and enabling extrapolated internet impact. ","Communications of the Association for Information Systems  \n\n| Volume 53 | Paper 8 |\n| --- | --- |\n| 10-10-2023\u003Cbr>Improving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste\u003Cbr>Bastin Tony Roy Savarimuthu\u003Cbr>Department of Information Science Otago Business School\u003Cbr>Jacqueline Corbett\u003Cbr>FSA ULaval Université Laval\u003Cbr>Muhammad Yasir\u003Cbr>Department of Information Science Otago Business School\u003Cbr>Vijaya Lakshmi\u003Cbr>FSA ULaval Université Laval\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/cais](https://aisel.aisnet.org/cais) |  |\n\nRecommended Citation  \nSavarimuthu, B., Corbett, J., Yasir, M., & Lakshmi, V. (2023) . Improving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste. Communications of the Association for Information Systems, 53, 189-213 . [https://doi.org/10.17705/1CAIS.05308](https://doi.org/10.17705/1CAIS.05308)  \nThis material is brought to you by the AIS Journals at AIS Electronic Library (AISeL) . It has been accepted for inclusion in Communications of the Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nImproving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste  \nCover Page Footnote  \nThis manuscript underwent peer review. It was received 10/27/2022 and was with the authors for six months for two revisions. The Associate Editor chose to remain anonymous. This special section was reviewed during the tenure of editor-in-chief Fred Niederman.  \nThis special sections is available in Communications of the Association for Information Systems:  \n[https://aisel.aisnet.org/cais/vol53/iss1/4](https://aisel.aisnet.org/cais/vol53/iss1/4)  \nImproving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste  \nBastin Tony Roy Savarimuthu  \nDepartment of Information Science Otago Business School Dunedin, New Zealand  \nMuhammad Yasir  \nDepartment of Information Science Otago Business School Dunedin, New Zealand  \nJacqueline Corbett  \nFSA ULaval Université Laval Quebec, Canada  \nVijaya Lakshmi  \nFSA ULaval Université Laval Quebec, Canada  \nAbstract:  \nBig data are key building blocks for creating information value. However, information systems are increasingly plagued with useless, waste data that can impede their effective use and threaten sustainability objectives. Using a constructive design science approach, this work first, defines digital data waste. Then, it develops an ensemble artifact comprising two components. The first component comprises 13 machine learning models for detecting data waste. Applying these to 35,576 online reviews in two domains reveals data waste of 1.9% for restaurant reviews compared to 35.8% for app reviews. Machine learning can accurately identify 83% to 99.8% of data waste; deep learning models are particularly promising, with accuracy ranging from 96.4% to 99.8% . The second component comprises a sustainability cost calculator to quantify the social, economic, and environmental benefits of reducing data waste. Eliminating 5948 useless reviews in the sample would result in saving 6.9 person hours, $2.93 in server, middleware and client costs, and 9.52 kg of carbon emissions. Extrapolating these results to reviews on the internet shows substantially greater savings. This work contributes to design knowledge relating to sustainable information systems by highlighting the new class of problem of data waste and by designing approaches for addressing this problem.  \nKeywords: Data Waste, Information Systems, Information Management, Sustainability, Machine Learning, Deep Learning, Reviews.  \nThis manuscript underwent peer review. It was received 10/27/2022 and was with the authors for six months for two revisions. The Associate Editor chose to remain anonymous. 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