Intelligent Data Quality Monitoring for Analytics: Leveraging AI

The exponential increase in data available in the current world as a result of technological evolution has made it the bedrock of managerial decision-making and organizational activities.However, this goes hand in hand with the challenge of dealing with very large datasets with quality high enough to feed into machine learning algorithms.Garbage concerning data means garbage processing, which leads to incorrect analysis and misguided strategic input and direction, which may lead to losses.Furthermore, the extended features of data environments, such as diverse Structure, Information sources, and Dynamic Data, also challenge the usual approach to DQM.Consequently, there is a high demand for effective and flexible DQM strategies that can meet the emerging needs of organizations that use data-driven decision-making more and more.The existing methods are insufficient, underlining the urgent need to implement advanced AI-based approaches to make the data accurate, consistent, and reliable.Based on achieving future capabilities in AI, the proposed system combines the machine learning algorithm, Natural Language Processing (NLP), and even the automated anomaly detection system to revolutionize the DQM system.It allows the organization to manage and respond to misaligned data and limit direct hands-on interference, improving efficiency