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.
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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.
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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.
Managing Information
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.
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