Information Systems:
Business intelligence is the system that provides users with online analytical processing (OLAP) or data analysis to answer business questions and identify significant trends or patterns in the information that is being examined, These are information systems that facilitate data gathering so that users can focus on business questions they are trying to answer such as: Which products are the best-selling and moat profitable? Who buys our products by industry category? Who are our best customers?

During the last 10 years, the names of information systems have changed from Executive Information Systems (ELS) to Decision Support Systems (DSS) and now to Business Intelligence (BI) systems. EtS applications were usually developed internally by an organization's information  technology (IT) staff using C++, or some other4GL programming language like VB or Delphi, to provide managers with selected information about the status of their business. In most
cases, the ElS applications were predefined queries that provided users with a range of parameters to execute. The result would be in the form of a tabular report or a chart. ElS applications were limited to the subject matter and the formats that were established by the developers. The type of status information provided by ElS would usually include total sales, sales by product and number of units sold for the period. While EIS was effective for providing status information, any business questions that required further analysis or additional information had to be addressed by some other application or required an IT professional to generate several structured query language (SQL) queries or reports.

DSS applications were the first generation of packaged software that dynamically generated SQL scripts based upon the type of information that users wanted to see. These applications enabled users to effectively extract data from relational databases without havidg the understanding or knowledge to actually develop  SQL scripts. Unlike ElS applications, DSS applications could address any subject matter that was stored in relational databases and was primarily used by analysts. In addition, users had the capability to address wide-ranging business questions as well as format the extracted data into more meaningful presentations.

The next generation of DSS applications evolved into Business intelligence Systems. These applications provide users with the ability to easily extract data from one or more different sources and subject matters. Formatting the data for a report or graphical representation is also easier. In addition, BI applications provide users with the capability of multidimensional analysis. For example, users can drill down on an income statement moving from net sales to sales by product to sales by product/region and, finally, to sales by product/region/customer. This capability provides users with the ability to answer questions such as; What was the sales mix of products sold? Which geographic regions did we sell the most and the least products? Who are our top customers by geographic region  and by product?

The evolution of BI systems has also moved from full-client versions to web-enabled applications that provide users with the ability to conduct their research through a web browser and, in certain cases, to work from remote locations. Users also have the capability to create what-if scenarios and share them with other users who can then review and make modifications to the document. We are in an exciting time with rapid technology advances that are far extending users' ability to conduct meaningful research to answer and support their business decisions.

Critical Success:
All BI systems have a number of critical success factors in common. They provide access to clean, organized data. They enhance a user's ability to understand the result. Just dumping numbers on people these days creates more problems then it solves. Ten years ago, the problem may have been getting data; today it is dealing with enormous data.

BI increases a user's acumen. Knowing what the data says is nice, but ultimately you have to know what to do with it. This knowledge is difficult to build into a piece of software, but state- of-the-art analytic applications use industry benchmarks and leverage expert best practices to benefit casual and novice users. Some BI tools are backend, infrastructure tools that deal with extracting data, cleaning it up, transforming it, re-organizing it, and optimizing it for use in decision making. These backend tools include data warehouses, data marts, OLAP servers, and ETL tools. Other BI tools are designed to extract knowledge and insight from the data once it has been prepared.

BI technologies are not the end game. The end game is the business process efficiencies and effectiveness that come from being able to make better decisions, faster.

The most effective way to launch a BI initiative is to start with your strategic initiatives. Then determine how you can use state-of-the- art BI systems to accelerate and ensure their success.