Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing
This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficul...
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MDPI AG
2025-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/1/22 |
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| author | Amruta Awasthi Lenka Krpalkova Joseph Walsh |
| author_facet | Amruta Awasthi Lenka Krpalkova Joseph Walsh |
| author_sort | Amruta Awasthi |
| collection | DOAJ |
| description | This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. It underlines that data appropriate for its intended application is considered quality data. The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. Manufacturing industries leveraging their data thoughtfully can optimise efficiency and facilitate informed and productive decision-making by establishing a robust technical infrastructure and developing intuitive platforms and solutions. This study highlights the significance of IDS in revolutionising manufacturing sectors within the framework of Industry 4.0 and the Industrial Internet of Things (IIoT), demonstrating that big data can substantially improve efficiency, reduce costs, and guide strategic decision-making. The gaps or maturity levels among industries show a substantial discrepancy in this analysis, which is classified into three types: Industry 4.0 maturity levels, data maturity or readiness condition index, and industrial data science and analytics maturity. The emphasis is given to the pressing need for resilient data science frameworks enabling organisations to evaluate their digital readiness and execute their data-driven plans efficiently and effortlessly. Simultaneously, future work will focus on pragmatic applications to enhance industrial competitiveness within the heavy machinery sector. |
| format | Article |
| id | doaj-art-b9297575602848a582474ea767251aea |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-b9297575602848a582474ea767251aea2025-01-24T13:50:46ZengMDPI AGTechnologies2227-70802025-01-011312210.3390/technologies13010022Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven ManufacturingAmruta Awasthi0Lenka Krpalkova1Joseph Walsh2School of Science, Technology, Engineering and Mathematics (STEM), Munster Technological University (MTU), Kerry Campus, V92 HD4V Kerry, IrelandSchool of Science, Technology, Engineering and Mathematics (STEM), Munster Technological University (MTU), Kerry Campus, V92 HD4V Kerry, IrelandSchool of Science, Technology, Engineering and Mathematics (STEM), Munster Technological University (MTU), Kerry Campus, V92 HD4V Kerry, IrelandThis narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. It underlines that data appropriate for its intended application is considered quality data. The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. Manufacturing industries leveraging their data thoughtfully can optimise efficiency and facilitate informed and productive decision-making by establishing a robust technical infrastructure and developing intuitive platforms and solutions. This study highlights the significance of IDS in revolutionising manufacturing sectors within the framework of Industry 4.0 and the Industrial Internet of Things (IIoT), demonstrating that big data can substantially improve efficiency, reduce costs, and guide strategic decision-making. The gaps or maturity levels among industries show a substantial discrepancy in this analysis, which is classified into three types: Industry 4.0 maturity levels, data maturity or readiness condition index, and industrial data science and analytics maturity. The emphasis is given to the pressing need for resilient data science frameworks enabling organisations to evaluate their digital readiness and execute their data-driven plans efficiently and effortlessly. Simultaneously, future work will focus on pragmatic applications to enhance industrial competitiveness within the heavy machinery sector.https://www.mdpi.com/2227-7080/13/1/22data-driven transformationheavy industriesmaturity levelsindustrial gapsdata analysis/analyticspredictive analytics/maintenance |
| spellingShingle | Amruta Awasthi Lenka Krpalkova Joseph Walsh Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing Technologies data-driven transformation heavy industries maturity levels industrial gaps data analysis/analytics predictive analytics/maintenance |
| title | Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing |
| title_full | Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing |
| title_fullStr | Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing |
| title_full_unstemmed | Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing |
| title_short | Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing |
| title_sort | bridging the maturity gaps in industrial data science navigating challenges in iot driven manufacturing |
| topic | data-driven transformation heavy industries maturity levels industrial gaps data analysis/analytics predictive analytics/maintenance |
| url | https://www.mdpi.com/2227-7080/13/1/22 |
| work_keys_str_mv | AT amrutaawasthi bridgingthematuritygapsinindustrialdatasciencenavigatingchallengesiniotdrivenmanufacturing AT lenkakrpalkova bridgingthematuritygapsinindustrialdatasciencenavigatingchallengesiniotdrivenmanufacturing AT josephwalsh bridgingthematuritygapsinindustrialdatasciencenavigatingchallengesiniotdrivenmanufacturing |