With Teradata and SAS, Discover Financial Services' IT group can better serve the business, says Glenn Schneider, senior vice president and chief information officer of Business Technology.

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Enterprise Data Warehouse
Helps Drive Further
Innovation at Discover

Engaging in innovative business practices can lead to tremendous market advantage, especially for companies willing to take the risks involved in blazing a new path. Just ask Discover Financial Services. One of the world's leading credit card issuers and a pioneer of cash rewards, Discover understands firsthand the benefits of developing a pioneering corporate culture.

As part of its effort to continually enhance customer service and satisfaction, Discover has used analytic tools from SAS extensively. Yet the company spotted an opportunity to improve the caliber of its analysis by initiating a cultural change in the relationship between business users and IT. By bringing together its analytic tools with an enterprise data warehouse (EDW) from Teradata, Discover aimed to give analysts in its customer information and decision management group the ability to generate new insights and understanding of critical market and consumer trends.

Today, Discover analysts use SAS and other tools to perform cross-line-of-business (LOB) analysis on data stored in the EDW. It is providing the company with leading-edge decision support capabilities, which are helping analysts identify innovative new revenue and customer service opportunities. This integrated platform has resulted in significant savings through cost takeout, revenue generation, enhanced productivity and reduced IT run rates. All of which help keep Discover at the top of the card industry.

Opportunity for new insight

Discover's vision was to create a centralized repository for clean, reliable and consistent data that could be used for risk analytics and decision support. Executives therefore decided an EDW was core to the success of this direction. The EDW would be the central location for analytic data and feed daily operational decisions. Utilizing data in this capacity required the EDW solution to optimize the timeliness, accuracy, quality and consistency of Discover's risk decision making.

The company also knew it needed a solution that provided superior performance, availability and scalability. In March 2005, Discover deployed its new data warehouse from Teradata.

Cultural evolution

Implementing the infrastructure so that the Teradata and SAS technologies could work together was a challenging process. Discover's IT group began to consolidate and integrate data needed to perform risk analytics, systematically integrating frequently used subject areas into the EDW. These steps eliminated the need for multiple sources and simplified data access. IT has also built necessary balance and control functions to ensure data integrity.

To enhance the risk analytics process, the IT group created a centralized and reusable set of analytic data that could be shared among users. They did so by identifying the most frequently used predictive variables and loading those into the data warehouse. Other subject areas were similarly designed and deployed.

To maximize the new system's benefits, Discover executives decided to give analysts a way to retrieve their own information, thereby minimizing the workload they passed to IT. "To do this, we had to deploy the technology in a way that allowed the analysts easy access to the data and maintained our change management policies," explains Glenn Schneider, senior vice president and chief information officer of Business Technology at Discover. "We needed to create a cultural evolution-a change that would integrate the analytical culture into the system and provide the analysts with the right level of technology training."

Discover worked with Teradata Professional Services to develop a customized education curriculum. Teradata provided a curriculum writer who created training sessions and materials that referenced Discover's specific tables and attributes. This helped the analysts learn how to leverage the data warehouse for better analysis.

SIDEBAR: Responding to disaster

Under (peer) pressure

Next, Discover's team surveyed the analysts to understand their skill set.

Analysts with both SAS and structured query language (SQL) experience became power users. These analysts underwent Teradata training that included education on data models, data warehouse construction, sources and targets, and transformations. The course also showed analysts how these elements tied to Discover's financials systems.

A second course, for analysts with no Teradata or SQL experience, explained how Teradata systems are built and how SQL is used. The third course addressed the marriage of the SAS and Teradata tools, discussing how SAS users could access the data warehouse. The final course offered more advanced SQL/SAS education, teaching analysts how to create multiple steps in a job, extract data from a table and perform SAS statistical analysis on the data.

As successive groups of analysts were trained, the team used their feedback to further refine and customize the courses. To promote widespread education on the system, users were not issued a system ID until they underwent instruction. Once training was complete, analysts were assigned a power-user buddy to answer questions.

The power users also played another role: evangelist. "Their job was to propagate the utility of this environment," says Aldo Mancini, who managed the project for Discover. "Analysts could go to their bosses with the right information, validated by the financials systems, much faster than ever before."

SIDEBAR: At a glance

Ask better questions

Discover is realizing considerable value from its Teradata/SAS environment. All of the LOBs are represented in the data warehouse, allowing the analysts to perform previously impossible cross-LOB analysis.

For example, if Discover analysts recognized that cash usage rates were down, they could use Teradata and SAS to find out why. By querying the authorization, new account and merchant systems, they could investigate potential problems: Are consumers receiving adequate materials to understand how they can use their Discover Card to get cash at a merchant or ATM? Is the price of obtaining the cash right for the market? Which merchants are most successful in offering cash to consumers and why?

Pursue new frontiers

By using the new system, not only can risk management analysts better identify customer and market trends but the IT organization can also better serve the business. "IT is now more focused on data generation than on supporting analyst queries and data requests," explains Schneider. As a result, "IT can interact more with the business, which helps us rapidly identify business needs and implement those solutions quickly."

"Although it is difficult to pinpoint the precise value delivered to Discover by the new system, we have clearly seen benefit in the form of higher revenues, expense takeout and improved customer satisfaction," says Schneider. The run rate and support requirements of the data warehouse are much less when compared with the predecessor environment. The new method of preparing the data in the EDW allows users to accelerate development of analytics through SAS.

Then there are the harder-to-measure benefits. "This technology is enabling the business to think differently," notes Schneider. "The focus is on getting more data into the environment as quickly as possible, not only for analytics but also for decision support. Now that we have all of this data available, the challenge is to optimize our ability to leverage it."

“IT can interact more with the business, which helps us rapidly identify business needs and implement those solutions quickly.”

Path to active data warehousing

Looking forward, Discover will continue to expand its data warehouse to support its growing business needs. For example, making the Teradata environment available in real time is planned through the enablement of Discover's new customer contact strategy. Company sales representatives will be able to accurately and efficiently serve customers with specific products and services tailored to a particular individual's needs. Phone representatives will soon have next-best-offer information available on the spot, which will help them provide even more effective customer service and drive increased revenues.

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