Companies use a wide range of technologies and products to generate what’s known as business intelligence (BI).
The most common tools – simple query and reporting, online analytical processing, statistical analysis, forecasting and data mining – can be used in a variety of ways.
Applications can provide ad hoc access to a single piece of data, such as monthly sales figures. Or they can be mission-critical, Web-enabled engines used to drive business processes. The goal is to turn what are often mountains of data into useful information. The common platform to achieve this is the database.
|Examples of Business Intelligence
A hotel franchise uses BI analytical applications to compile statistics on average occupancy and average room rate to determine revenue generated per room. It also gathers statistics on market share and data from customer surveys from each hotel to determine its competitive position in various markets. Such trends can be analyzed year by year, month by month and day by day, giving the corporation a picture of how each individual hotel is faring.
A bank bridges a legacy database with departmental databases, giving branch managers and other users access to BI applications to determine who the most profitable customers are or which customers they should try to cross-sell new products to. The use of these tools frees information technology staff from the task of generating analytical reports for the departments and it gives department personnel autonomous access to a richer data source.
A telecommunications company maintains a multiterabyte decision-support data warehouse and uses business intelligence tools and utilities to let users access the data they need without giving them carte blanche to access hundreds of thousands of mission-critical records. The tools set boundaries around the data that users can access, creating data “cubes” that contain only the information that’s relevant to a particular user or group of users.
Actually, a refined aggregation of multiple databases, called a data warehouse, is the best source for BI. Data selected for use in the warehouse is reformatted and stored in a process called extraction, translation and loading (ETL). The process standardizes the various data structures so they can be accessed and analyzed with high accuracy.
With a rich, aggregated data source, BI applications and utilities can be used to forecast business conditions, improve operational efficiencies and manage supply chains. BI has been applied most commonly to customer relationship management (CRM), enabling analysis of customer behavior and market segmentation.
But traditional tools go only so far. Wayne Eckerson, director of education and research at the Data Warehousing Institute in Bethesda, Md., says data warehousing is the infrastructure used to support a lot of BI applications today. “We used to manage the technology” that gathered and stored the data, rather than managing the information the data provided, says Eckerson. That focus has changed in response to the Internet, Eckerson says, and vendors of traditional CRM and enterprise resource planning applications, as well as vendors of relational databases, are embedding BI utilities and tools in their products.
“Now you get more dynamic access to information that was formerly static,” Eckerson says, adding that firms can use the Internet to deploy this information to thousands of users in any location, rather than a select few in headquarters.
Using the Web to distribute business intelligence is the approach Pfizer Inc. is taking, says Lawrence Bell, senior manager of the New York-based company’s U.S. pharmaceutical information architecture team. Pfizer’s global, distributed operation simply couldn’t work out of one monolithic warehouse that had to distribute information about regional sales trends to sales and marketing professionals.
To deal with those challenges, Pfizer began using Informix Corp.’s ETL tool, Ardent Datastage, to create a distributed database running on hubs around the world that could be updated quickly and accurately on demand.
Pfizer uses a Datastage utility to allow replication on the fly using the Internet’s file transfer protocol so the system can support frequent updates. The system is used to deliver volumes of data that Pfizer “feeds downstream” to marketing and sales divisions worldwide to help them evaluate product sales and trends.
Along with the standard business data sources, BI applications also let firms add nontraditional data sources. The Dallas Teachers Credit Union (DTCU), for example, used geographical data analysis – which draws information about the physical location of bank customers or prospective customers – to increase its customer base from 250,000 professional educators to 3.5 million potential customers – virtually overnight [Technology, June 12].
The increase gave the credit union the ability to compete with larger banks that had a strong presence in Dallas.
“We’re now competitive with Wells Fargo and [Bank of America],” says DTCU Senior Vice President and CIO Jerry Thompson. “We’re even, if not ahead, of the big guys.” The sudden access to a whole new market came from geographical data the DTCU used to find ways to improve its position.
The DTCU needed to increase its customer base to remain competitive. Changing its status from a profession-based service to a community-based service would do the trick, but such a change would require approval from the Texas State Credit Union Commission.
The DTCU needed to whip up a business plan and proposal to present to the commission. And much of the data in that proposal would have to reflect the credit union’s detailed knowledge of the community’s banking habits.
As a first step toward gathering the information it needed for the proposal, the credit union replaced a financial system with BI applications running on IBM’s DB2 Universal Database. Then it bought supplementary data compiled by Acxiom Corp. in Little Rock, Ark., to correlate credit scores, lifestyle statistics and locations of residents in the credit union’s area.
Identifying Sources of Profit
Using geographical analysis and spatial mapping applications, the credit union identified the 10% of its customers who generate the most profits. It also identified customers’ willingness to drive to a branch to do business by correlating customer locations with branch locations and the time it takes to drive the distance between the two.
The DTCU submitted its proposal to the commission as a graphical representation of its current and proposed customers and current and proposed branches and automated teller machines, along with detailed analysis of the impact of the membership change.
The commission usually takes months to approve such modifications and usually requests additional information, says Thompson. But the commission approved the DTCU’s request in less than a month based on the proposal it submitted – with no additional questions. The final approval came through in late May.
Now the credit union can use the geographical data to focus its marketing efforts on those 3.5 million potential customers, Thompson says.
Copyright © 2000 IDG Communications, Inc.