Functions of BI
Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyse an organisation’s raw data. BI is made up of several related activities, including data mining, online analytical processing, querying and reporting.
Businesses use BI applications to improve decision making, improve business performance and identify new business opportunities. BI is more than just business reporting and more than a set of tools to extract data out of enterprise systems.
The complete spectrum of BI functions required to support the monitoring, reporting and analytical needs of each and every business user are:
Dashboards & Scorecards: visually compelling “at-a-glance” summary information and key performances indicators for managers and executives
Enterprise Reporting: reports that deliver more detailed operational information that can be consumed by all business users, including production reports, interactive reports and ad hoc reports
OLAP Analysis: Slice and dice analysis with drilling, pivoting and sorting capabilities for the manager or analyst
Advanced & Predictive Analysis: Full investigative query down to the transaction level, allowing power users and analysts to perform extensive predictive and statistical analysis.
Alerts & Notification: Monitor business processes and deliver information to users based on schedules, exceptions or demand.
For these BI functions to work, data integration is a key element to the backbone of any BI system. Typically data integration within BI combines data from disparate sources either through a data warehouse or a data mashup approach. The net result is that users are provided with a unified view of the data they are analysing.
The data mashup approach bypasses the need for an ETL process and simply combines the data sources into a unified mashup, which circumvents many of the challenges associated with data warehouses.
BI analysis techniques include data mining which is the extraction of patterns from large data sets by combines statistical methods with database management. Data mining lets you transform unprecedented quantities of digital data into business intelligence for competitive advantage. Another BI analysis technique is predictive analysis where statistical patterns found in historical and transactional data are used to make predictions about future events. Predictive analytics helps users to identify risk and opportunities providing pertinent information on alternative decision courses. Online Analytical Processing (OLAP) is used to carry out multidimensional analysis. Decision-makers benefit from being able to compare large volumes of highly differentiated data such as product performance against customer behaviour.
All BI systems include a set of reporting tools which provide the illustrations necessary to make an overall assessment of the information generated. Reports are key to end users and as such they must be available in a format that is not highly technical, and must be simple to generate to allow easy manipulation of data and the quick generation of results.
The ultimate goal of any BI system is to provide decision making support to users at all levels of the organisation.
