Citation:

Amit Kumar, 2021. "The Synergy of AI-Driven Analytics and MDM: Enhancing Data Accuracy and Decision-Making in Enterprise Systems", ESP Journal of Engineering & Technology Advancements, 1(2): 211-223.

Abstract:

Enterprises today need accurate and reliable information to make informed business decisions and stay competitive. This paper explores the convergence of Artificial Intelligence (AI) and Master Data Management (MDM) to facilitate greater data accuracy, integrity and decision-making capabilities in Enterprise systems. However, traditional MDMs alone are insufficient to confidently ensure data quality as data and the number of data sources to be managed grows exponentially. Thanks to AI-driven analytics, enterprises can validate data better and clean data automatically while also finding hidden insights to derive stronger MDM practices. This synergy allows proactive data governance, greater data accuracy, and a single point of view of the enterprise data critical for strategic planning and operational effectiveness. Case studies, benefits, and challenges of the implementation of AI-enhanced MDM solutions, as well as a framework for enterprises planning to apply AI to optimize their data management process, are delivered by the paper.

References:

[1] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165-1188.

[2] Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33.

[3] Janssen, M., Van Der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345.

[4] Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207-216).

[5] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

[6] Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International journal of production economics, 165, 234-246.

[7] Cuzzocrea, A., Song, I. Y., & Davis, K. C. (2011, October). Analytics over large-scale multidimensional data: the big data revolution!. In Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP (pp. 101-104).

[8] Berti-Equille, L., & Borge-Holthoefer, J. (2015). Veracity of data: From truth discovery computation algorithms to models of misinformation dynamics. Morgan & Claypool Publishers.

[9] LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT sloan management review.

[10] Lee, Y. W., Pipino, L. L., Funk, J. D., & Wang, R. Y. (2006). Journey to data quality. The MIT Press.

[11] Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management (JIEM), 4(2), 168-193.

[12] Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group research note, 6(70), 1.

[13] Jarvenpaa, S. L., & Ives, B. (1991). Executive involvement and participation in the Management of information technology. MIS Quarterly, 205-227.

[14] Redman, T. C. (1998). The impact of poor data quality on the typical enterprise. Communications of the ACM, 41(2), 79-82.

[15] Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.

[16] Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.

[17] Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in the cloud. Decision Support Systems, 55(1), 412-421.

[18] Vilminko-Heikkinen, R. (2017). Data, Technology, and People: Demystifying Master Data Management.

[19] Khairi, M. (2012). Master Data Management model effectiveness in information technology company. University of Phoenix.

[20] Shaykhian, G. A., Khairi, M. A., & Ziade, J. (2016, June). Architectural Evaluation of Master Data Management (MDM): Literature Review. In 2016 ASEE Annual Conference & Exposition.

Keywords:

AI-Driven Analytics, Master Data Management (MDM), Data Accuracy, Enterprise Systems, Data Quality, Data Governance, Decision-Making, Data Management.