Traditionally, applications and databases were organized around functional domains, such as accounting, human resources, logistics, CRM, and so on. Every department or business unit worked with its own data silo with no cross-department integration. Whereas the silos mainly aimed at operational support, a next phase saw the emergence of business intelligence (BI) and analytics applications, fueled by the need for data-driven tactical and strategical decision making, with a company-wide impact. To sustain this company-wide view, data from the silos was transformed, integrated, and consolidated into a company-wide data warehouse. ETL (extract, transform, load) processes supported the asynchronous data extraction and transfer from the source systems (the operational data silos) to the target data warehouse.


A data warehouse is a federated repository for all the data collected by an enterprise's various operational systems, be they physical or logical. Data warehousing emphasizes the capture of data from diverse sources for access and analysis rather than for transaction processing. Data warehouses can benefit organizations from an both IT and a business perspective. Separating the analytical processes from the operational processes can enhance the operational systems and enable business users to access and query relevant data faster from multiple sources. In addition, data warehouses can offer enhanced data quality and consistency, thereby improving business intelligence.


The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS) provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data.


Advanced analytics is a broad category of inquiry that can be used to help drive changes and improvements in business practices. While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviors, enabling businesses to conduct what-if analyses to predict the effects of potential changes in business strategies. The advanced analytics process involves mathematical approaches to interpreting data. Classical statistical methods, as well as newer, more machine-driven techniques, such as deep learning, are used to identify patterns, correlations and groupings in data sets. Based on these, users can make a prediction about future behavior.