Huge Data Analytics (BDA) is a practical method for analyzing and identifying unique cases, families, and patterns among very large data sets. In this study, we contrast BDA with criminal records, where the exploratory records test seeks to reveal and predict styles. Deep learning and several outstanding data mining methods are employed. A quantitative study and presentation led to the discovery of several intriguing facts and instances from the criminal histories of Chicago, San Francisco, and Philadelphia. The outcomes demonstrate that LSTM and the Prophet variant outperform other neural network models, with a 3-year training period considered appropriate. These encouraging results will make it simpler for police departments and law enforcement organizations to identify criminal activity, collect data to identify trends, avert potential incidents, safeguard property, and improve operational procedures. The establishment of a large data analytics enabled transformation model based fully on a practice-based viewpoint highlights the causal relationships between large data analytics skills, IT-enabled transformation practices, benefit dimensions, and company value. A healthcare setting was used to test this version. By examining huge records implementation examples, we looked for signs of how organisational processes are altered by large data analytics skills, leading to capability advantages. The edition provides a strategic view of large records analytics in addition to conceptually describing 4largerecords analytics abilities. Three significant path-to-price chains for healthcare organizations were identified using the version, which provides practical insights for management

#