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Start Small, Think Big

Start Small, Think Big

As the CIO of GE Real Estate discovered, you can often win more funding and credibility by starting with a few low-cost applications that wow the business side

As the CIO of GE Real Estate was perplexed. "Let me make sure I heard you right. We're willing to fully fund this project, but you only want 10 percent of the money?"

In today's tightly managed, cost-conscious business environment, it's hard to resist saying yes to any funding that's offered. It's even harder to explain why you'd rather accept a much lower level of funding when there's no guarantee that the money will be available if you need it later on.

But that's exactly the approach I took when our CFO offered to fund a business-wide data warehouse that would help us grow and manage our global commercial real estate business. Before I explain my reasons, let me tell you why we needed a data warehouse in the first place.

The commercial real estate financing business has been getting more competitive over the past few years, as more banks and other financial services companies enter the market. GE Real Estate has a strong track record of profitability; net earnings have increased by 10 percent or more each year since 2000. Even so, we knew we needed to be able to provide our customers with faster delivery of quotes and approvals, while at the same time pricing between 20,000 and 30,000 deals a year. Much of the information GE Real Estate's managers relied on was stored in spreadsheets or in hard-copy reports. Figuring out how GE's loans were performing in each particular market and what kind of risks should be factored into a $US30 million deal - a fairly routine bit of analysis - required employees to gather data manually from several sources. Errors are unavoidable in a manual process, and they could be costly: Misunderstanding GE's loan-portfolio performance in Denver or Dublin could mean charging a customer too low an inte

rest rate and exposing GE to too much risk. Charging too high a rate could prompt the customer to take a lower rate from another lender.

Furthermore, with more than 8000 buildings in our portfolio around the world, we needed a fast, efficient, accurate way to track the performance of the properties we were financing. This included better insights into the corporate health of our borrowers' individual business tenants. For instance, if a telemarketing company in Geneva was going out of business, vacating five floors of a building on which GE holds a $US10 million note, the loan might be at risk. That's the kind of information the head of GE Real Estate's European division needs to know.

We decided that a data warehouse and Web-based reporting system would be the ideal solution. But we also knew it could be very expensive and time-consuming to develop, and the process is full of uncertainties. Every CIO is familiar with the research showing that the vast majority of such projects fail. Since data warehouses often span multiple departments, there's the inevitable challenge of deciding which department gets capabilities first. And then there's the task of reconciling different content definitions. In our case, each department used the same terms to mean different things. In marketing, for instance, vacancy might mean unleased space; for property management, it might also include leased space that's currently unoccupied by the lessee.

Agreeing on data definitions, ensuring the integrity of complex data arriving from heterogeneous sources, and managing multiple business-wide priorities can easily overwhelm even a "reasonable" project schedule. And with a data warehouse, there's no place to cut corners when it comes to data integrity. Put simply, dirty data can kill the credibility of your data warehouse.

And data warehouses - which demand that each department use the same terms for the same things, and are useless without absolutely accurate information - fail more than most.

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