Smart Data, Smart Decisions: The Future of MDM & Governance
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P116Keywords:
Smart Data, Smart Decision-Making, Data-Driven Strategy, Intelligent Data Management, Data as an Asset, Master Data Management (MDM), Next-Gen MDM, Intelligent MDM, Golden Record Management, Unified Data View, Data IntegrationAbstract
In today's digital era, Master Data Management (MDM) and Data Governance have become crucial pillars for digital transformation, enterprise intelligence, and strategic decision-making in organizations. Big data ecosystems, Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), and distributed enterprise systems have added a significant level of complexity to the management of organizational data assets. Businesses today collect and produce huge amounts of structured, semi-structured and unstructured data on various platforms throughout their operations. A strong governance and master data framework can avoid data inconsistencies, data redundancies, poor data quality, compliance risks, and inaccurate analytical results. Before April 18th, 2023, this research article examines detailed analyses of the new architectures, AI-supported governance models, real-time data discovery capabilities, cloud-based governance approaches, and smart automation methods for the future of MDM and governance. The study explores the role intelligent governance structures play in data standardization, operational efficiency, regulatory compliance, improving cybersecurity and enterprise-wide interoperability. The paper also explores how MDM is converging with machine learning algorithms, predictive analytics, metadata management, federated architectures and distributed cloud environments. A thorough literature review is carried out to assess the work of previous studies on data governance, AI-native enterprise systems, automated dispute resolution, cloud reliability, security-driven data discovery and resilient IT infrastructure. Recent research shows that companies that adopt AI-driven governance frameworks have better data quality, lower complexity, quicker analytics and increased trust in enterprise data ecosystems. In this paper, a hybrid intelligent governance methodology that combines semantic data models, AI-enabled automated governance, policy-based metadata orchestration and federated data management is presented. The methodology features multi-layer governance architecture with data acquisition, validation, standardizing, policy enforcing, metadata orchestrating and real-time monitoring layers. The suggested framework allows organizations to build a flexible, secure, and adaptable governance system, ready for future business needs. In addition, the research proposes some metrics of governance maturity, risk assessment indicators, and models of performance optimization in order to assess enterprise data quality and the efficiency of governance. The results indicate that companies that are implementing modern MDM strategies are reaping tangible benefits in terms of data reliability, regulatory compliance, cybersecurity strength, and business intelligence performance. Experimental analysis shows that AI-backed governance systems deliver more than a 90% increase in data consistency, less than 75% of duplication errors and almost 60% increase in enterprise analytics performance. Moreover, automated governance workflows help to reduce manual efforts, delays, and governance risks to a great extent. The study finds that the future of MDM and governance rests in autonomous, artificial intelligence (AI) native policy-driven ecosystems that can accommodate hyperconnected enterprises in the cloud-native and distributed digital world. The paper adds to the existing research by proposing a holistic enterprise governance framework that integrates technological innovation with the aims of governance in an enterprise. The proposed framework can be used by several industries, such as banking, healthcare, government, manufacturing, education and telecommunications sector. The study also gives pointers to future research areas, such as blockchain for governance, explainable AI for governance transparency, quantum-safe governance architectures, and self-healing data ecosystems. Overall, the paper finds that smart data governance is not only a technical requirement but a strategic component of the intelligent enterprise decision making process in Industry 4.0 and digital transformation.
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