Introduction

Business intelligence is an important to ensure the following:

• Various decision-makers and analyst have a direct and un-interrupted access to data. The data been used across the organization should be non-disputable.
• Decision makers spend their time analyzing the data rather than collecting and formatting them.
• Decision makers are able to focus their energy in improving the business process rather than searching for data across systems.
• Decision makers can instantaneously carry out what if analysis without much manual intervention.
• Data management is done from enterprise perspective rather than at a departmental level.
• Data is considered as a strategic resource rather than as an input for business intelligence process.
• Business forecast is used supply and demand side of business users.
• Majority of the decision-making process is done through an automated process.
• Data is shared effortlessly within the company.
• Reports generated utilize primary and secondary data without any additional efforts.

For business intelligence to ensure the above it is necessary that it has a robust architecture. Business intelligence architecture is divided into six critical elements’ data management, transformation tools and processes, data repositories, application tools for analysis, presentation tools and operational processes.

1. Data Management

   For to achieve data integrity following points need to be addressed. The 1st major point organization is the need of the data. The organization must come to agreement that a particular analytics will provide competitive advantage and enhance business performance.

   The next question which needs to be addressed is the source of the information. This sourcing of data can be from enterprise itself, or it may be from the external sources. If the source is within the organization than it is essential there is a common platform for all flow of information.

   The next question is the quantity of the data. Since there is large volume of data available, based on the required company should gather data to have a normalized business behavior.

   The next question is to make data valuable, once that is determined data management comes from the picture, i.e. acquisition of data to retirement of data.

2. Transformation Tools

   The required data needs to undergo ETL process. ETL process consists of extracting data, transforming the data and loading the data. The process extracting data from the repository is a straight-forward process. However, validation and cleansing of data is a difficult task. This validation and cleansing of data is done through various well established business rules. Transformation of data involves converting the data to standardized form.

3. Data Repositories

   Organization can store data through data warehouses. Data warehouse sometime has data mart, which is a partition to handle single business function. A metadata repository is used to store data definition and technical information.

4. Analytical Tools and Presentation

   There are several business tools available on the market, but it is essential to identify what it intends to do with the data and then choose the tool.

5. Presentation Tools and Applications

   Business intelligence can only work if end users are able to make sense out of that data. Presentation tools should allow the users to manipulate complex data into to ad hoc reports for company-wide distribution.

6. Operational Process

   Operational Process determines how data management and business intelligence is to be implemented within the organization. It deals with the question how the organization creates manages data and different applications.

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