Business intelligence means an expert information, knowledge and technologies efficient in the management of organizational and individual business. Therefore, in this sense, business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all of the factors that affect your business. It is imperative that you have an in depth knowledge about factors such as your customers, competitors, business partners, economic environment and internal operations to make effective and good quality business decisions. Business intelligence enables you to make these kinds of decisions.

Reasons for business intelligence

Business intelligence enables organizations to make well informed business decisions and thus can be the source of competitive advantages. This is especially true when you are able to extrapolate information from indicators in the external environment and make accurate forecasts about future trends or economic conditions. Once business intelligence is gathered effectively and used proactively you can make decisions that benefit your organization before the competition does.

The ultimate objective of business intelligence is to improve the timeliness and quality of information. Timely and good quality information is like having a crystal ball that can give you an indication of what's the best course to take. Business intelligence reveals to you:

- The position of your firm as in comparison to its competitors
- Changes in customer behaviour and spending patterns
- The capabilities of your firm
- Market conditions, future trends, demographic and economic information
- The social, regulatory and political environment
- What the other firms in the market are doing
You can then deduce from the information gathered what adjustments need to be made.

Businesses realize that in this very competitive, fast paced and ever-changing business environment, a key competitive quality is how quickly they respond and adapt to change. Business intelligence enables them to use information gathered to quickly and constantly respond to changes.

As technical background of this kind of information and analysing system we have to choose an convenient and reliable approach to handle the mass of business type data.

OLAP

OLAP is an acronym for On Line Analytical Processing. It is an approach to quickly provide the answer to analytical queries that are dimensional in nature. It is part of the broader category business intelligence, which also includes Extract-transform-load (ETL), relational reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (On Line Transaction Processing).

Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI). They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational kin.

OLAP takes a snapshot of a set of source data and restructures it into an OLAP cube. The queries can then be run against this. It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data.

The cube is created from a star schema or snowflake schema of tables. At the centre is the fact table which lists the core facts which make up the query. Numerous dimension tables are linked to the fact tables. These tables indicate how the aggregations of relational data can be analyzed. The number of possible aggregations is determined by every possible manner in which the original data can be hierarchically linked.

For example a set of customers can be grouped by city, by district or by country; so with 50 cities, 8 districts and two countries there are three hierarchical levels with 60 members. These customers can be considered in relation to products; if there are 250 products with 20 categories, three families and three departments then there are 276 product members. With just these two dimensions there are 16,560 (276 * 60) possible aggregations. As the data considered increases the number of aggregations can quickly total tens of millions or more.

The calculation of the aggregations AND the base data combined make up an OLAP cube, which can potentially contain all the answers to every query which can be answered from the data (as in Gray, Bosworth, Layman and Pirahesh, 1997). Due to the potentially large number of aggregations to be calculated, often only a predetermined number are fully calculated while the remainder are solved on demand.