The problems include data redundancy, inconsistency, lack of flexibility, poor security, data isolation, program-data dependence, and difficulty in accessing and sharing data;  

A relational DBMS is considered powerful because it organizes data into tables with defined relationships between them, enabling complex queries and ensuring data consistency through features like primary and foreign keys, making it highly flexible and scalable for managing large datasets. 

 Principal tools are Database Management Systems (DBMS), data analysis tools, business intelligence (BI) platforms, data integration tools, data visualization tools, machine learning algorithms, artificial intelligence (AI), data warehousing, and data lakes; all allowing users to extract meaningful insights from large datasets to make informed decision

They provide a structured framework for defining how data is accessed, used, and maintained, ensuring its accuracy, consistency, and reliability, which is essential for making informed business decisions and avoiding operational issues arising from poor data quality.