Magazine article American Banker

Mellon Patterning Its Data Mining Operations after Dreyfus Model

Magazine article American Banker

Mellon Patterning Its Data Mining Operations after Dreyfus Model

Article excerpt

Mellon Bank Corp. thinks so much of Dreyfus Corp.'s data mining system that it plans to incorporate the model into its standard operating procedure.

Peter Johnson, vice president/strategic technology at Mellon Bank, said that at New York-based Dreyfus, Mellon's mutual fund subsidiary, millions of dollars have been invested in its Data Store, which contains investment and money information to serve customers better.

"All the technology investments have been guided toward customer service," Mr. Johnson said. "By having more customer information delivered to the point of customer contact, we can carry out direct sales over the Internet, telephones, and call centers. We can emphasize we're a retailer," he said.

The bank is trying to adopt the same approach, he said, but Dreyfus is further along in integrating different product relationships.

At the recent Data Warehouse World conference in New York, Mr. Johnson defined his six steps for data mining.

The first is to define the business problem, the second is to pull together the data for mining, and the third is to clean the data. "Steps one to three are what needs to go on in the data warehousing world. You can't do data mining if you haven't done those," said Mr. Johnson.

Step four is to pre-process the data, and step five is to model them.

"Have a consistent definition of what constitutes a product particularly after mergers and acquisitions," said Mr. Johnson. The final step is to deploy the model and make it work.

Most software vendors offer products that do modeling, Mr. Johnson said, but this is only "a relatively small part of the problem. Most of our time, about 50%, is spent on data acquiring and data cleansing. Data mining is 35% of the problem."

The bottlenecks, he said, are having the data to mine in a clean, consistent format. …

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