Kishore.et.al /Computer Science, Engineering and Technology,3(2), June 2025, 22-34 Copyright@ REST Publisher 22 Computer Science, Engineering and Technology Vol: 3(2), June 2025 REST Publisher; ISSN: 2583-9179 Website: https://restpublisher.com/journals/cset/ DOI: https://doi.org/10.46632/cset/3/2/3 Future Trends in AI: Data Management and Analysis Using SPSS Methodology Lal Kishore Kumar, Vikash Kumar Singh, Rishi Sharma, *Ranjan Kumar Mishra Netaji Subhas University Jamshedpur, Jharkhand, India. *Corresponding Author Email: ranjanlnmi4u@gmail.com Abstract: Introduction: This study offers a thorough analysis of deep learning and artificial intelligence from 1961 to 2018, providing information about the underlying mechanics, industrial applications, and future developments. The study aims to help researchers and practitioners understand the evolution, challenges, and opportunities associated with AI-driven innovations. Research Significance: This study's importance stems from its comprehensive exploration of the impact of AI on various industrial domains. AI-driven data analytics, predictive maintenance, and decision-making systems are reshaping industrial landscapes by improving operational efficiency and reducing costs. By analyzing past trends and current developments, this study offers insightful information for upcoming AI applications. It also draws attention to difficulties associated with to AI adoption, such as data security, algorithmic transparency, and ethical considerations, ensuring a holistic perspective for stakeholders in academia and industry. Methodology: SPSS Statistics is a powerful software tool utilized for to analyze data in a number of domains, like as social sciences, healthcare, marketing, and education. It offers an extensive collection of statistical tools for organizing, evaluating, and interpreting data. SPSS allows users to perform a wide range of analyses, such as descriptive statistics, regression, ANOVA, factor analysis, and hypothesis testing. Its sophisticated data manipulation features and user-friendly interface make it popular among researchers and analysts. SPSS also supports the creation of charts and reports, aiding in the presentation of data-driven insights. Input Parameters: Industry Sector, Data Management Approach, AI Integration Level, Primary AI Use Case, Data Privacy Strategy, Adoption of AI Ethics Framework. Evaluation Parameters: Data Processing Efficiency, AI Model Accuracy, Scalability & Flexibility, Regulatory Compliance, Business Value Impact. Reliability Statistics measure the consistency of a dataset or survey instrument. One important Cronbach's Alpha is a measure of internal reliability. The Alpha of Cronbach's value of 0.967 in this instance indicates a high degree of dependability, indicating that the dataset's items consistently yield reliable findings. This high The standardized Cronbach's Alpha further supports the degree of intrinsic coherence of 0.974. The data can be regarded as extremely dependable for research purposes after five items have been examined. Key Words: Big data analytics, predictive analytics, artificial intelligence, machine learning and Industry 4.0. 1. INTRODUCTION An important factor in the advancement of industry is artificial intelligence (AI), which also acts as a catalyst for the incorporation of new technologies like cloud computing, block chain, the Internet of Things, and graphics processing units into the big data and Industry 4.0 framework. This essay provides an in-depth analysis of AI and deep learning over the period from 1961 to 2018. Through multi-faceted systematic analysis, it provides valuable insights for researchers and practitioners, covering aspects from foundational algorithms to real-world applications, from basic algorithms to industrial innovations, and from the current landscape to future developments. [2] Upcoming Developments in Big Data Analytics and SQL Databases, it looks at how AI-driven automation affects database administration, emphasizing how it can boost efficiency and cut expenses. [3] The main data sources and motivating reasons for data analytics adoption are examined in this study, along with how AI and machine learning may help networks become proactive, self-aware, adaptable, and prescriptive. Furthermore, a variety of data analytics-based network design and optimization techniques are offered. An examination of the difficulties and advantages of