In this knowledge-based economy, the corporations’ value, as well as the one of organizations and individuals, is directly related to their knowledge and intellectual capital. Knowledge can move inside firms and among exchange partners through e-business systems, such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM) and Customer Relationship Management (CRM). Despite of the enormous potential and delivery capabilities of such tools, organizations needed a new instrument that would offer the decision makers valuable insight regarding various business processes and allow them to make better decisions. It is this necessity that has set the premises for the birth of a new powerful tool with a tremendous impact on the business world: Business Intelligence (BI).
It is the purpose of this article to serve as an introduction into the world of Business Intelligence by presenting a short description of the architecture of BI systems and the 5 styles of BI.
But what is business intelligence? A comprehensive definition of BI would be represents an extensive category of applications and technologies employed for gathering, storing, analyzing, and providing access to data that supports users inside enterprises make better business decisions.
BI systems facilitate an ever-growing variety of applications that begin with sophisticated analysis of atomic level data and range to proactive information delivery to system subscribers. They need to offer businesses the capability of analyzing themselves at every level on demand and deliver proper relevant information in a timely manner to the appropriate people. Last but not least, BI system must help businesses keep a very close and individualized contact with their customers.
Architecture of a BI System
A widely accepted business intelligence architecture uses the following diagram:
Source systems are usually operational systems that collect and store business data. These systems usually are databases or mainframes, and the data they store is typically limited to recent or current data. They represent an OnLine Transaction Processing Systems (OLTP). Transaction processing involves the simple recording of transactions such as sales, inventory, withdrawals, deposits and so on. However, there are situations when source systems can be simple text files or XL spreadsheets.
ETL (Extraction, Transformation and Loading) software combines, cleanses, and moves data from the different operational systems to an integrated data warehouse. The ETL process contains information that facilitates the transfer of data from source systems to the data warehouse.
The data warehouse lies at the core of the BI system and enables the users harness the full capacity of business intelligence. It is a relational database that stores a long-term history of data, usually two to five years or more. A data warehouse is OnLine Analytical Processing System (OLAP). Analytical processing involves the manipulation of transaction records to calculate sales trends, growth patterns, percent to total contributions, trend reporting, profit analysis and so forth.
Metadata Database contains information that facilitates the retrieval of data from the data warehouse when using BI applications.
BI applications allow the user to interact with the business intelligence system thus creating, calculating and analyzing complex data relationships. They also provide the ability to see data from different perspectives.
5 styles of Business Intelligence
Five common Styles of BI have evolved during the past decade – each style representing a certain characteristic usage and function by end users#:
Enterprise Reporting – Broadly deployed pixel-perfect report formats for operational reporting and scorecards/dashboards targeted at information consumers and executives. Dashboards and scorecards provide concise and "at-a-glance" information to managers.
Cube Analysis – OLAP slice-and-dice analysis of limited data sets, targeted at managers and others who need a safe and simple environment for basic data exploration within a limited range of data.
Ad Hoc Query and Analysis – Full investigative query into all data, as well as automated slice and- dice OLAP analysis of the entire database – down to the transaction level of detail if necessary. Targeted at information explorers and power users. Such users can drill, report, pivot and page-by on report data and thereby analyze information beyond the static enterprise reports
Statistical Analysis and Data Mining – Full mathematical, financial, and statistical treatment of data for purposes of correlation analysis, trend analysis, financial analysis and projections. Targeted at the professional information analysts.
Alerting and Report Delivery – Proactive report delivery and alerting to very large populations based on schedules or event triggers in the database. Reports can be designed to alert managers to business exceptions or targeted at very large user populations of information consumers, both internal and external to the enterprise.
From their reporting and analysis capabilities, to prediction and alerts, BI systems have become a must-have in the decision-making process. Failing to take advantage of such a powerful enterprise tool can result in a tremendous missed opportunity and eventually the loss of competitive advantage. Besides its already proven capabilities the BI industry is still young and on an upward trend making its development potential enormous. If used correctly BI can be the hydra-electric power plant which can harness the high debit of the information river and transform it into energy powering an organization towards competitive advantage.
BI Presales Consultant