Data

  Traditional ways are being replaced by   Agentic AI   in almost all industries by 2025. It was in the recent years Artificial Intelligence has altered the way businesses have managed their operations from automation, data analysis, customer support, and   decision-making . It’s simply not a matter of advantage in performance. It could have intelligence driven action on its own, proactive adaptations and context-aware decision-making. Agentic AI now is the strategic differentiator wherein organizations are racing toward efficiency, personalization, and quite possibly, innovation. The following article below discusses the differences between the Agentic AI and the Traditional AI systems, why moving to the shift is vital in 2025, and the way such pioneers positioned by the likes of Newton AI Tech help usher in the transformation. Understanding the Basics: What Is Agentic AI? Agentic AI refers to systems that behave like agents—entities that can perceive their environm...

  In today’s data-driven world, businesses rely on vast amounts of data for decision-making, financial reporting, and operational efficiency. However, inconsistencies, errors, and discrepancies in data often pose challenges, leading to incorrect insights and poor business decisions.   Data reconciliation   is the process of ensuring data accuracy and consistency across different systems, sources, and databases. It plays a critical role in finance, accounting, supply chain management, and various other industries where data integrity is paramount. This article explores the   best techniques for data reconciliation   and how organizations can implement them efficiently. Understanding Data Reconciliation Data reconciliation refers to the process of comparing and validating data from different sources to ensure accuracy, consistency, and completeness. It is commonly used in financial transactions, inventory management, and database synchronization. Organizations use...

  Data analytics is one of the fastest-growing fields in the tech industry, attracting individuals from diverse educational and professional backgrounds. Many beginners wonder:   Can I learn data analytics in 3 months?   The answer depends on various factors such as dedication, learning style, and prior knowledge of statistics, programming, and analytical thinking. In this article, we will explore whether learning data analytics within three months is feasible, what challenges beginners may face, and the best strategies to accelerate the learning process. Is Data Analytics Hard for Beginners? Data analytics may seem challenging for beginners, but it is not impossible to learn. It requires knowledge of various tools, techniques, and problem-solving skills. The difficulty level depends on the following factors: 1.  Understanding Data and Statistics One of the fundamental aspects of data analytics is working with numbers, statistical concepts, and data interpretation. B...