What Is Business Intelligence

"Business Intelligence" what the term suggests is it includes a number of strategies and technologies used by the enterprises for analysis of business data information.These technologies provide historical, current and predictive views of business operations.BI technologies are able to handle a huge amount of structured and unstructured data for identifying, developing and creating new strategical business opportunities.Their main aim is, to allow easy interpretation of the big data.BI is used by the enterprises for supporting a wide range of business decision that ranges from operational to strategic.

Techniques of BI

1. Data Visualisation

When the data are stored in the form of numbers, it becomes precise but difficult to interpret. for eg in a company whether the sales are going up, down or are holding steady? By looking more than one dimension of the data, it is difficult to analyse the data.Therefore by only visualising the data in form of data charts is a very easy way for understanding the interpretation of the data.

2. Data Mining

Data mining is a technique that is used to reveal the previously unnoticed or unknown relations among data entities.This technique is used in various ways: shopping basket analysis; measuring the product that consumer buys together for promoting other products, in bank sector; client risk assessment used for evaluating whether client will pay back loan on his historical data, in insurance sector; detecting the fraudulent cases on the basic behavioral and historical data.

3. Reporting 

Designing, scheduling and generating the performances, sales, and saving reports is the area where BI tools help the business users.Reports that are generated by the BI tools gather and present information efficiently for supporting management and decision making processes.When the report is designed it can be sent to the predefined distributing list in required form presenting the daily, weekly and monthly statistics.

4. Time-series Analysis Including (Predictive Techniques)

All the data warehouses and the data of enterprises have one dimension i.e. time dimension.For example phone calls, product sales, patient hospitalisation etc.It is important to notice the changes in the user behaviour in terms of time, the relation between products, or any changes in the sales contract that are based on the marketing promotion.On the basis of historical data, we can predict future trends and outcomes.

5. On-line Analytical Processing(OLAP)

It is mainly known for the OLAP cube structure that provides better visualisation of the multidimensional data.The cubes are used for displaying dimensions on the cube edges.Values on the edges represent measured facts.The user can navigate through OLAP cubes via drill-up, drill-down and across features.

6. Statistical Analysis

This technique uses mathematic foundations for qualifying the significance and reliability of the observed relation features like distribution analysis, confidence intervals.These are used for devising and analysing results from data mining.