Information Overload: Forecasting and Planning Are Keys to Survival
Sun, April 15, 2001
CIO
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READER ROI
* Learn what infrastructure decisions are important in dealing with data overload
* Hear how MetLife’s organizational structure helps it corral data
* Discover some of the top data-related issues for 2001
Carol stewart has watched data pile up like ominous digital clouds on the information horizon. And as vice president of data administration at the Metropolitan Life Insurance Co. (MetLife) in New York City, it’s her job to predict when those clouds will burst and to find ways to disperse them before they overwhelm MetLife’s systems.
Stewart supervises a group of 180 experts who oversee the insurance giant’s nearly 600 production databases and who work on all of MetLife’s data and database applications. With the constant need for more and better customer information, Stewart has seen
MetLife’s data-handling needs grow explosively. The number of MetLife production databases has more than doubled in the past three years, and Stewart’s data team has grown by more than 60 percent (with each staffer supporting 80 percent more data applications) in the same period. With no slowdown in sight, Stewart feels MetLife has weathered the storm primarily by adding a new messaging architecture layer to the enterprise and by accurately forecasting database needs and planning accordingly. Stewart also cites the formation of a centrally organized team of database experts with varied backgrounds as key to MetLife’s data success story.
CIO: how do you foresee a potential data overload coming?
Stewart: One of the chief indicators would be an increasing demand from people in the business community for information--when you see the same information in your organization being copied or replicated to service a number of different functions. When you say, "Wait, I’ve just set up 45 different databases that all fundamentally hold the same information," then you know you need to start thinking more architecturally in terms of enterprise strategy. What’s also happening is that you may have started capturing a lot of data, but the real overload comes in figuring out how to organize it and get it back to the people who need it in order to make decisions.
Also, some data projects that start out on a small scale can become overloaded because pressure comes to grow them fast. If they weren’t originally built to scale up or handle a massive load, it can get overwhelming.
What trends today are causing organizations to change how they deal with data handling and storage?
Improved customer service--which everybody, every company in America, is after--requires that we maintain more information about our customers. The bar is being raised on customer service in all industries. Customers expect a level of service that probably wasn’t available four or five years ago. Also, marketing departments are looking for more information about customers and potential customers.
