One of the most important applications of SQL and relational database technology today is the rapidly growing area of data warehousing and business intelligence. The focus of data warehousing is to use accumulated data to provide information and insights for decision making. With the rising popularity of the World Wide Web and the direct interaction with customers that it provides, the amount of data available about customer behavior (reflected in their click-by-click journey through web pages) has literally exploded. Data warehousing advocates that this data be treated as a valuable asset, and that it be translated, through analysis, into competitive advantage. The complementary process of data mining involves in-depth analysis of historical and trend data to find valuable insights about customer behavior and cross-dependencies. SQL-based relational databases are a key technology underlying data warehousing applications.
Business intelligence applications have exploded in popularity over the last decade. Corporate IS surveys show that the majority of large corporations have some type of business analysis or data warehousing projects underway. In many ways, data warehousing represents relational databases coming full circle, back to their roots. When relational databases first appeared on the scene, the established databases (such as IBM’s hierarchical IMS database) were squarely focused on business transactionprocessing applications. Relational technology gained popularity by focusing on decision support applications and their ad hoc queries. As the popularity of these applications grew, most relational database vendors shifted their focus to compete for new transaction-processing applications. With data warehousing, attention has turned back to what was formerly called decision support, albeit with new terminology and much more powerful tools than those of 15 years earlier.
Source: Liang Y. Daniel (2013), Introduction to programming with SQL, Pearson; 3rd edition.