The quest for business intelligence underscores the importance of deploying a financial management solution that integrates with other systems currently in use by the organization, or planned for future deployment. Bringing all of this data together into a central repository makes it available for reporting against and launching analytical queries.
According to one industry analyst, business intelligence has surpassed security as the top technology priority in 2006. Organizations are turning to business intelligence to resolve common challenges, including:
- Increasing volume of data and too many islands of information, including data stored on personal computers
- Lack of a single, simple solution for reporting and operational analysis.
- The need for better controls for budgeting and financials.
- Fear of adopting new technologies that might conflict with existing systems.
- The need to identify performance shifts in sales of products or services.
- The need for a “single version of the truth” for creating reports and financial models based upon the same core data.
A Challenge for Small Organizations
Generating business intelligence can be a challenge for small to midsized organizations that typically have few dedicated IT professionals, and even fewer who are fluent in the technology of generating reporting and analytic solutions. Similarly, small to midsized organizations traditionally lack the infrastructure to support a data warehouse and the extraction, transformation, and load processes used to import heterogeneous data into a homogenous relational database.
Fortunately the infrastructure costs of supporting business intelligence have fallen in recent years, though it is still essential for an organization to carefully choose a financial management system that integrates well with other solutions, including a relational database and the reporting and analytical tools required for generating business intelligence.
Using Online Analytical Processing to Speed Queries
As organizations collect increasing volumes of data, a data warehouse is often used as the basis for a business intelligence decision-support system. A data warehouse provides an environment in which data can be queried and reported against without having an impact on the daily business loads of the online transaction processing (OLTP) data stores.
A data warehouse is frequently augmented by an online analytical processing (OLAP) tool that organizes data to facilitate analytical queries rather than transaction processing. Frequently queried data is pre-aggregated and the results are stored as multidimensional cubes, which are table-like structures that enable very fast response time to ad hoc queries. An OLAP solution should be designed for usability to make it easy for an organization to design, deploy, and maintain cubes, including adding and subtracting dimensions.
Dimensions map data warehouse table information into a hierarchy of levels, such as a Geography dimension with dimension levels of Continent, Country, State/Province, and City. Dimensions should be independently created and shared among cubes for ease of cube construction and to help ensure consistency of analysis data summarization