Business Intelligence

The quest for so-called “Business Intelligence,” or BI for short, has been on the forefront of many organizational priorities in the last decade and it is likely that this trend will continue indefinitely. The attraction to BI is fairly clear. Although collecting and storing large amounts of information has become ubiquitous among modern organizations, there has been a relatively slower development in the ability to harness information to better support organizational goals and meet business objectives. Therefore the emerging BI technologies have promised to deliver better reporting and metrics to help professional spot trends and find insights. However, in spite of the best intentions, the ability to BI to contribute any meaningful assistance in finding opportunities has been somewhat lacking. This paper will look at the current state of BI, some of the best practices, as well as opportunities for the future.

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Data does not equal Information

It has been recommended that the BI initiatives be broken down into smaller categories to better define the steps in the process. The three sub-categories are as follows (Staples, 2009):

1. Data Warehouse (DW) — companies need a place for data to reside and rules on how the data should be structured.

2. Business Intelligence — companies need a way to slice and dice the data and generate reports.

3. Analytics — companies need to extract the data, analyze trends, uncover opportunities, find new customer segments, and so forth.

The data warehouse in this model is the most basic of data collection and storage. This can include everything from transaction histories to customer information and most organization have large amounts of this type of data that they store.

The next category involves beginning to put the data into good use. Reports generated around various metrics are an example of Business Intelligence. Many of these reports are built into various database platforms and are being marketed by companies such as SAP, IBM, and Oracle (Staples, 2009). However, these metrics are, for the most part, generic reporting mechanisms that handle the basic business metrics; they basically represent the set of data that the user pools from the data warehouse. The most comprehensive BI solution is considered to be in the realm of business analytics. These tools are generally custom built to find the organization and make use of algorithmic functions to try to spot trends in the movements of the data in the data warehouse and actually provide what is considered to be information. It is further argued that BI only really takes place when such “information” is generated.

Analytics in the Modern Environment

In the globalized world, many firms find it challenging, if not impossible, to effectively differentiate their products (Davenport, 2005). Although some products and services are so innovative that there is no available substitute, most products can be easily copied in a way to minimize differentiation. Furthermore, given the availability of low cost foreign labor, most of the copied products can be produced as the and go to market in a fairly short period of time. In this environment the role of analytics has become the forefront issue.

However, not all companies are . A BI system is usually based on an algorithmic function that attempts to monitor trends in real time. However, once the scenarios that these systems are running in changes then can quickly become obsolete. The problem lies in determining when the systems are no longer effectively representing reality (Smild, 2011). If management does not recognize that the models no , then this can cause all kinds of different damages to the organization. Therefore it is important for decision makers to understand the assumptions that are built into the system so that the can recognize when the system is not producing productive results.


More and more companies are applying analytics to nearly all functions within their organizations. Previously it was the case that companies would choose one competitive advantage and to that end to maximize that one individual advantage over the competition. American Airlines with their implementation of the electronic reservation system serves as a prime example of this strategy. However, many more organizations have implemented analytics across the entire organization such as companies like Amazon, Harrah’s, Capital One, and the Boston Red Sox (Davenport, 2005).

Analytics now dominate the most effectively organized companies. However, not all companies have been successful in applying analytics to their internal processes and supply chains. Unsuccessful implantations are commonly a result of a corporate culture that does not embrace such analytics. However, successful company builds analytics right into the culture and embraces it in a full enterprise approach (Davenport, 2005). They are constantly running “experiments” to attempt to determine which factors are driving trends. For example, Progressive Auto Insurance will examine data set that represents losses. It will group a set such as motorcycle riders, over the age of 30, college educated, credit score information, and no accidents. Then it will run multiple regression analyses to see which factors have the highest correlations with the intended information. Such advanced approaches to modeling and analyzing data will undoubtedly grow ubiquitous as more and more organization are embracing these techniques.

Works Cited

Davenport, T. (2005). Competing on Analytics. Havard Business Review, 1-12.

Smild, S. (2011, September 2). Tom Davenport: Why aren’t most on analytics? Retrieved from Analyst First:

Staples, S. (2009, April 14). Analytics: Unlocking Value in Business Intelligence (BI) Initiatives. Retrieved from CIO: