Data Warehousing
The typical Information technology organization supports an environment that was built over time, with computer applications that were designed to support the day-to-day operation of the company's primary business. There are separate applications (or computer systems) for several categories of use. Some are centralized systems, used for back office functions such as financial, payroll, and general ledger updates. Others run in localized environments, such as those for field office, personnel, and sales and marketing initiatives. Still others are run in multiples—every branch running it's own version. Order processing and customer information storage systems might fall into the category of multiples.
Often these different applications are built on differing platforms by many separate development teams, without a “city plan” or architecture for fitting them all together. Each department just does their own thing, without worrying about what other departments are doing.
To complicate matters further, over time companies see a proliferation of needs for reporting extracts from various quarters, such as
Special Projects
Process Reengineering Initiatives
Pricing and Profitability Studies
Marketing Campaigns
Financial and Regulatory Compliance Needs
What ensues is an increasing competition for resources, including
Information
Knowledgeable Technical and Subject Matter Experts
Processing Time (Mips)
A decreasing window of opportunity for accessing those resources accompanies this increasing competition for resources. Time in the overnight processing cycle is in demand particularly in batch updated systems. Often, no more than a two hour window of opportunity for extracts exists. In a real-time environment or what's referred to as 7 by 24, the problem gets worse.
To avoid degrading the processing of day-to-day operations in a business, you must remove the information access from the critical path of Information Processing. Thus, the practice of making “Shadow Copies” of data repositories was born, and termed Data Warehousing. Typically, these copies are taken daily, weekly, or monthly, and in some instances much more frequently.
The Data Warehousing industry has grown up around the real business need to create a Corporate Information Architecture—a city plan that is—to support informational processing for management decision support and integrated analysis. The organization and integration of data into this architecture as well as the creation of data access mechanisms, are the goals of many a data warehousing implementation project.
Customer-centric intelligence applications depend on the data warehouse for input. The data model required for customer-centric intelligence applications usually differs from the existing data warehouse schemas in the areas of customer information, customer behavior information, and information about your marketing sales and service initiatives.
Reporting
Customer reports are the usual mechanism for understanding your customers. Marketing, sales, and service business people usually have their favorite reports that they can easily navigate to get a clear picture of the area of customer information they are accustomed to handling.
As your company shifts to encompass the single customer view, with all business areas aligned to support that view, your reports also change to represent one unified view of the customer. Different reports with different approaches to customer information analysis may no longer work.
Analytic Applications
Analytic applications vary from simple reporting tools to complex online analytic processing systems (OLAP). Some products use data mining to extract and exploit customer information from existing files, building customer profiles that are used to predict customer behaviors.
For example, when a bank sees a customer make a large withdrawal, it might be a precursor to closing an account. Or when a credit-card company sees a client make a large payment against their balance, it might indicate that the customer is planning to close his account. In both instances, customer retention is considered much less costly than acquiring a new customer to replace the one you've lost.
Analytic applications flag behaviors such as these indicators, providing the information so that customer service areas can be proactive in offering incentives to these customers to stay and keep their accounts active.
Customer Information
Customer information is gathered from all interactions with the customer, including the following types of data:
Contacts— sales and service activities generally introduce information about contact people, numbers, contact instances, and outcomes of various customer contacts, such as sales calls.
Behavior— as described previously, certain actions that customers take are captured as customer behavior indicators.
Name and address— information can vary from primary contact information, to household and alternative addresses, all which must be maintained for moves and changes of all kinds.
Relationship— captures the relationships between customers, including family and household relationships on the retail consumer side, and business relationships in the area of commercial accounts.
Activities— transaction detail such as payments, billing, and past dues, and other activities such as sales and service contacts.