The successful implementation of data warehousing technologies creates new possibilities for enterprises. Applications that previously were not feasible due to the lack of integrated data are now possible. In this chapter, we take a quick look at the different types of enterprises that implement data warehouses and the types of applications that they have deployed.
Among the early adopters of warehousing technologies were the telecommunications, banking, and retail sectors.
Thus, most early warehousing applications can be found in these industries. For example:
Telecommunication companies were interested in analyzing (among other things) network utilization, the calling patterns of their clients, and the profitability of their product offerings. Such information was and still is required for formulating, modifying, and offering different subscription packages with special rates and incentives to different customers.
Banks were and still are interested in effectively managing the bank's asset and liability portfolios, analyzing product and customer profitability, and profiling customers and households as a means of identifying target marketing and cross-selling opportunities.
The retail sector was interested in sales trends, particularly buying patterns that are influenced by changing seasons, sales promotions, holidays, and competitor activities. With the introduction of customer discount cards, the retail sector was able to attribute previously anonymous purchases to individual customers. Individual buying habits and likes are now used as inputs to formulating sales promotions and guiding direct marketing activities.
Although warehousing found its early use in different industries with different information requirements, it is still possible to categorize the different warehousing applications into the following types and tasks.
Performance trend analysis. . Since a data warehouse is designed to store historical data, it is an ideal technology for analyzing performance trends within an organization. Warehouse users can produce reports that compare current performance to historical figures. Their analysis may highlight trends that reveal a major opportunity or confirm a suspected problem. Such performance trend analysis capabilities are crucial to the success of planning activities (e.g., sales forecasting).
Cross-selling. . A data warehouse provides an integrated view of the enterprise's many relationships with its cus><Chapter 14 | Warehousing Applications><tomers. By obtaining a clearer picture of customers and the services that they avail themselves of, the enterprise can identify opportunities for cross-selling additional products and services to existing customers.
Customer profiling and target marketing. . Internal enterprise data can be integrated with census and demographic data to analyze and derive customer profiles. These profiles consider factors such as age, gender, marital status, income brackets, purchasing history, and number of dependents. Through these profiles, the enterprise can, with some accuracy, estimate how appealing customers will find a particular product or product mix. By modeling customers in this manner, the enterprise has better inputs to target marketing efforts.
Promotions and product bundling. . The data warehouse allows enterprises to analyze their customers' purchasing histories as an input to promotions and product bundling. This is particularly helpful in the retail sector, where related products from different vendors can be bundled together and offered at a more attractive price. The success of different promotions can be evaluated through the warehouse data as well.
Sales tracking and reporting. . Although enterprises have long been able to track and report on their sales performance, the ready availability of data in the warehouse dramatically simplifies this task.
Risk analysis and management. . Integrated warehouse data allow enterprises to analyze their risk exposure. For example, banks want to effectively manage their mix of assets and liabilities. Loan departments want to manage their risk exposure to sectors or industries that are not performing well. Insurance companies want to identify customer profiles and individual customers who have consistently proven to be unprofitable and to adjust their pricing and product offerings accordingly.
Profitability analysis. . If operating costs and revenues are tracked or allocated at a sufficiently detailed level in operational systems, a data warehouse can be used for profitability analysis. Users can slice and dice through warehouse data to produce reports that analyze the enterprise's profitability by customer, agent or salesman, product, time period, geography, organizational unit, and any other business dimension that the user requires.
Exception reporting. . Through the use of exception reporting or alert systems, enterprise managers are made aware of important or significant events (e.g., more than x% drop in sales for the current month, current year vs. same month, last year). Managers can define the exceptions that are of interest to them. Through exceptions or alerts, enterprise managers learn about business situations before they escalate into major problems. Similarly, managers learn about situations that can be exploited while the window of opportunity is still open.
Customer relationship management. . Warehouse data can also be used as the basis for managing the enterprise's relationships with its many customers. Customers will be far from pleased if different groups in the same enterprise ask them for the same information more than once. Customers appreciate enterprises that never forget special instructions, preferences, or requests. Integrated customer data can serve as the basis for improving and growing the enterprise's relationships with each of its customers and are therefore critical to effective customer relationship management.
Data warehousing technology can be used to develop highly specialized applications, as discussed below.
Many organizations, particularly those in the banking, financial services, and telecommunications industries, are looking into Call Center applications to better improve their customer relationships. As with any Operational Data Store or data warehouse implementation, Call Center applications face the daunting task of integrating data from many disparate sources to form an integrated picture of the customer's relationship with the enterprise.
What has not readily been apparent to implementors of call centers is that Operational Data Store and data warehouse technologies are the appropriate IT architecture components to support Call Center applications. Consider Figure 14–1.
Data from multiple sources are integrated into an Operational Data Store to provide a current, integrated view of the enterprise operations.
The Call Center application uses the Operational Data Store as its primary source of customer information. The Call Center also extends the contents of the Operational Data Store by directly updating the ODS.
Workflow technologies facilitate the routing of data from Call Center workstations to the Operational Data Store.
Computer telephony used in conjunction with the appropriate middleware are integrated with both the Operational Data Store and the Call Center applications.
At regular intervals, the Operational Data Store feeds the enterprise data warehouse. The data warehouse has its own set of data access and retrieval technologies to provide decisional information and reports.
Credit bureaus for the banking, telecommunications, and utility companies can benefit from the use of warehousing technologies for integrating negative customer data from many different enter-prises. Data are integrated, then stored in a repository that can be accessed by all authorized users, either directly or through a network connection.
For this process to work smoothly, the credit bureau must set standard formats and definitions for all the data items it will receive. Data providers extract data from their respective operational systems and submit these data, using standard data storage media.
The credit bureau transforms, integrates, deduplicates, cleans, and loads the data into a warehouse that is designed specifically to meet the querying requirements of both the credit bureau and its customers.
The credit bureau can also use data warehousing technologies to mine and analyze the credit data to produce industry-specific and cross-industry reports. Patterns within the customer database can be identified through statistical analysis (e.g., typical profile of a blacklisted customer) and can be made available to credit bureau customers.
Warehouse management and administration modules, such as those that track and analyze queries, can be used as the basis for billing credit bureau customers.
The bottom line of any data warehousing investment rests on its ability to provide enterprises with genuine business value. Data warehousing technology is merely an enabler; the true value comes from the improvements that enterprises make to decisional and operational business processes—improvements that translate to better customer service, higher-quality products, reduced costs, or faster delivery times.
Data warehousing applications, as described in this chapter, enable enterprises to capitalize on the availability of clean, integrated data. Warehouse users are able to transform data into information and to use that information to contribute to the enterprise's bottom line.