The main objectives of BI and technical approach to the common BI system

In the past, for the purpose of analyzing enterprises business data it was often used different statistic packages (such as, MS Excel), or specialized custom- made applications.

Typically, the development of such solutions was not on an industrial basis. These solutions were difficult to integrate, maintain, and develop.
In some time had come the understanding of the need for specialized tools to analyze business data, that is now called the Business Intelligence tools or BI
Now BI applications are the most popular tools for analyzing business data. Among the prerequisites of the wide spread of BI we can mention the following:

1.Improving Business Performance - Fast obtaining information necessary for decision-making. Using the accumulated data previously collected by ERP-systems

2. Geting new competitive advantages – Reducing uncertainty of the business environment - Applying modern tools of monitoring, analysis and prediction.

Modern BI applications develop in the following areas:

1. Modular approach to system architecture, open interfaces (including service-oriented architecture (SOA)) and ability to work with a wide range of data source types.

2. Support for a wide range of functional and analytical capabilities, as well as configuration flexibility, performance, stability, security and scalability of software.

3. Using the advanced features of data visualization, which helps the understanding of the meaning and perception of information.

Common BI objectives

Sector	Objectives
Public sector, government	• analysis and control of financial and budgetary processes • analysis and modeling of tax revenues • analysis and revenue planning • analysis and forecasting of budget revenues • monitoring and modeling of inter-budgetary relations
Banking and financial organizations	• management and risk analysis • Attracting and retaining customers • detection of fraud •forecasting of financial indices, etc.
Enterprise	• monitoring and forecasting of current production,investment,financial and billing situation • a comprehensive analysis of financial and economic condition and performance of divisions #8226; assess the effectiveness of management decisions • budgeting.

Common BI application architecture

Figure 1 (not ready yet)

Requirements for data integration tools:

• Data access by ODBC, OLE DB, OLE DB for OLAP, etc. 
• Management of background and reference information (master data) 
• Data warehose management , including «top-down» and «bottom-up» design
• Data extraction, transformation and loading (ETL) 

Features of data integration tools:

• Loading data from external sources 
• Harmonization of data 
• Data transformation (aggregating in the necessary analytical slits, the calculation of indicators, etc.) 
• Support of the chronological accumulation of information, etc.

Requirements for the reporting tools and data mapping:
• Building routine reporting 
• Adhoc query and reporting 
• A library of data visualization components: text, spreadsheet, cartograms, speedometers, scorecards, etc.

Peculiarities of the formation of reporting and data mapping:

• Reporting forms design
• Delivery of reporting forms to users who is interested in them in the required form
• The availability data mapping tools to provide data in business terms

Requirements for monitoring and data analysis tools:

• Design of analytic dashboards - a combination of mapped data, indicators navigation on the basis of metadata, the possibility of interactive work 
• Formation of scorecards - an intuitive visual representation of important business information, including variances of key performance indicators 

Features of the monitoring and analysis:

• Ability to create instrumental analytic dashboards and scorecards for visualization of key performance indicators (KPI) 
• Mechanisms for notification and warning of the indicators exceed the target boundary

Requirements for modeling and forecasting tools

• Solving problems of econometric modeling on the basis of mathematical and statistical methods 
• Expert system tools 
• Research analysis based on the means of data mining (including the neural networks, decision trees, a system of of similar cases, genetic algorithms, etc.)

Features of modeling and forecasting:

• Investigation of the deviations of the business process of the targets (the answer to the question «Why?») 
• Forecasting the further development of business process based on the experience of its operation (the answer to the question «What will be?») 
• Finding the way to achieve the targets (the answer to the question «How?»)

OLAP

The term stands for ‘On-Line Analytical Processing’. Unfortunately, this is neither a meaningful definition nor a description of what OLAP means. It certainly gives no indication of why you would want to use an OLAP tool, or even what an OLAP tool actually does. And it gives you no help in deciding if a product is an OLAP tool or not. It was simply chosen as a term to contrast with OLTP, on-line transaction processing, which is much more meaningful.

First we need to decide which products fell into the category. Deciding what is an OLAP has not got any easier since then, as more and more vendors claim to have ‘OLAP compliant’ products, whatever that may mean (often they don’t even know). It is not possible to rely on the vendors’ own descriptions and membership of the long-defunct OLAP Council was not a reliable indicator of whether or not a company produces OLAP products. For example, several significant OLAP vendors were never members or resigned, and several members were not OLAP vendors. Membership of the instantly moribund replacement Analytical Solutions Forum was even less of a guide, as it was intended to include non-OLAP vendors.

The Codd rules also turned out to be an unsuitable way of detecting ‘OLAP compliance’, so we were forced to create our own definition. It had to be simple, memorable and product-independent, and the resulting definition is the ‘FASMI’ test. The key thing that all OLAP products have in common is multidimensionality, but that is not the only requirement for an OLAP product.

What is business intelligence (BI)?

Business intelligence (BI) has been referred to as the process of making better decisions through the use of people, processes, data and related tools and methodologies. The roots of business intelligence are found in relational databases, data warehouses and data marts that help organize historical information in the hands of business analysts to generate reporting that informs executives and senior departmental managers of strategic and tactical trends and opportunities. In recent years, business intelligence has also come to rely on near real-time operational data found in systems including enterprise resource planning (ERP), customer relationship management (CRM), supply chain, marketing and other databases. “Operational” BI is meant to provision many more functions in the organization with role-specific dashboards and scorecards and is increasingly tied to the topics of performance management and business process management. Inherent to any form of BI is the notion of data quality, consistent and dependable data and the processes involved in its creation and maintenance.