When I first heard the term "business intelligence," I thought of business espionage and the gathering of competitors’ trade secrets. Over the last few years, I have seen the increase in the usage of the term business intelligence in various forms of print media to describe software products or service offerings. In many cases, I found it somewhat difficult to understand their connotation of the term. Is it software, or is it the process of gathering business information? In either case, what I hope to provide you is an understanding of the term.

From a data analysis perspective, business intelligence is the process of gathering high-quality and meaningful information about the subject matter being researched that will help the individual(s) analyzing the information draw conclusions or make assumptions. For example, one could gather business intelligence on the precious metals industry by conducting research on who mines and processes precious metals, what public markets trade precious metals and what factors influence the valuation and volume of trading of precious metals. All of this information would provide you with an overall understanding of the industry, which you would not have had unless the analysis had been conducted. In addition, you should have sufficient information to assess the viability of investing in precious metals as well as the associated risks.

From an information systems perspective, 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 the data gathering so those users can focus on the business questions they are trying to answer such as: Which products are the best selling and most profitable? Who buys our products by industry category? Who are our best customers and how much do they buy?

During the last 10 years, the names of information systems have changed from executive information systems (EIS) to decision support systems (DSS) and now to business intelligence (BI) systems. But, more has happened than just a name change. The technology has significantly evolved from internally developed graphical user interfaces (GUI) to packaged applications that provide users with easy access to data for analysis.

EIS applications were usually developed internally by an organization’s information technology (IT) staff using C++, or some other 4GL programming language, to provide managers and executives with the ability to easily obtain selected information about the status of their business. In most cases, the EIS 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. EIS applications were limited to the subject matter and the formats that were established by the developers, and the applications were primarily used by executives and managers. The type of status information provided by EIS 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 having the understanding or knowledge to actually develop SQL scripts. Unlike EIS 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. Users could expand their questions beyond the information provided by EIS to: How many customers do we have? What are the top selling products? What are the worst selling products? Each question would be a separate query that provided the user with the information that was requested.

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.

In summary, business intelligence is the process of gathering meaningful information about the subject matter being researched. Software applications have been developed that provide users with the capability to conduct business intelligence to answer questions and identify significant trends or patterns in the information that is being examined.

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