OLAP Cubes + Operational Databases
Traditionally, business intelligence solutions have focused on corporate departments that serve strategic functions. Because of the targeted user population, traditional BI focused on historical data analytics such as OLAP analysis and other slice-and-dice functions where users typically evaluate large volumes of historic data. As a consequence, traditional business intelligence solutions have been associated with OLAP cubes, data warehouses, data marts and other centralized, large scale, aggregated data stores.
Traditionally a BI or OLAP tool has been modeled, designed and engineered to address strategic business decisions. Fittingly, a typical BI implementation scenario has been as follows:
- A dedicated expert team builds the front-end BI tools and back end data warehouse, often using an OLAP server.
- Data needs to be Extracted, Transformed and Loaded (ETL) from different sources into a central data warehouse and OLAP cubes. Data quality issues are addressed in this ETL process.
- Data models are structured for clearly defined static requirements. Changes typically require high-level expertise and lengthy customization.
- Analysis and presentations are concentrated on high-level views from aggregated data using an OLAP tool.
- End users are highly trained analysts and managers who value power far more than flexibility or ease of use.
- The OLAP tool is too complex for business users to learn.
This cumbersome process clearly has its disadvantages of cost and effort.
Now, business intelligence applications such as that offered by InetSoft are powerful and agile enough to access both
OLAP cubes and operational databases in their native format. In fact, even more value is derived out of a business intelligence solution when multiple data sources are accessed and data mashups are produced.