Mining Your Data For Awesome Business Intelligence
The tables inside a consolidated data warehouse can be queried and reported against, but effective business intelligence solutions developed by software consultants generally do further processing of the data warehouse to enable vastly superior reporting, forecasting and analysis. This is called online analytical processing or OLAP.
The advantages of OLAP are numerous and all of those advantages begin with the data cube. A data cube is a structure that points to the information stored in the data warehouse and pre-computes sums, averages, counts and other aggregations on a regular basis. A data cube also defines common calculations and filters to provide a monumental speed boost to reports and dashboards.
One business intelligence solution may have several data cubes defined: A giant cube that encompasses the entire data warehouse and is processed daily along with a sales cube that is processed continuously, for example. Each cube may be accessible by certain divisions or individuals in an organization. Defining separate cubes allows business intelligence architects to achieve a balance between security, speed and flexibility in reporting.
OLAP also enables organizations to turn their data into knowledge through data mining. Data mining is the automated process of finding trends and anomalies in well-structured data. Using cutting-edge algorithms and processing power, an analytics engine can sift through the structures defined in a data cube and provide organizations with valuable insight they may have otherwise missed. Rather than depending on pre-defined dimensions and measures to build reports, organizations can also define data mining structures to do forecasting and grouping of information.
Tune in next week for the final installment on dashboards, key performance metrics and reports. Find out more about refining your business intelligence solution by asking the right questions…