by Hank Zupnick

Peer to Peer: Start Small, Think Big

Jun 15, 20057 mins
Enterprise Applications

The CFO 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 businesswide 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 $30 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 interest 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 8,000 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 $10 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 businesswide 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.

More than a Pilot, Less than a Megaproject

Given these challenges, I decided that the best way to successfully deliver a large, robust data warehouse would be to start with a small, narrowly targeted data mart. And then if that worked out, we could build another. So rather than accept a pot of money to build everything we needed, we quickly developed and rolled out several single-purpose, low-cost data marts. We hoped that these would provide just enough functionality to demonstrate the business value of a larger system.

We understood that even if these small data marts were wildly successful, additional funding could not be guaranteed. But if it turned out that the business didn’t really want or need a data warehouse after all, it was better to know that after spending only 10 percent of a multimillion dollar investment. In addition, when it came to getting the business units to agree on data definitions, it was easier to get their help with a small project than a megaproject. It demanded less of their time, and they’d be able to see benefits sooner.

So we developed an implementation plan, using stringent portfolio management guidelines to determine which capabilities to offer at launch and which could wait until later versions of the system. My team worked with the business side to develop cost-benefit analyses for every requested capability, looking at the hard benefits (of automating something instead of doing it manually) and the soft benefits (what is the cost of a bad decision on a loan?).

Scaling to the Enterprise

This is not to say everything went smoothly. One department, while nominally supporting the effort, consistently failed to make key experts available for our analysis meetings. Rather than let them hold us back, we decided to focus our initial data marts on other parts of the business that were more enthusiastic.

Our first small data marts went live within months of the project’s initial launch and quickly began delivering easy to use, real-time, high-quality data to our business leaders and specialists. After that came further investment—beyond even what we had ever considered—as the early data warehouses uncovered new opportunities. For instance, our hospitality lending group was able to identify distinct types of hotel loans with differing performance histories, thus allowing the group to target new niches of opportunity.

Dan Smith, senior vice president of our North America Debt Group, was also an early supporter: “I could immediately see how the data warehouse was making my entire organization more productive and responsive and helping us to reach our aggressive growth goals.”

However, what worked for two or three small data marts wouldn’t scale to a large, complex data warehouse. To build for the future, we began migrating from a Microsoft server to a Unix environment complete with Oracle databases. We used Informatica for back-end data extraction, transformation and loading, and Cognos reporting tools.

A Moment of Triumph

Thus far, we have invested approximately $5.9 million in our data warehouse, and the ROI is encouraging. The data warehouse is saving GE Real Estate more than $2.5 million a year in direct labor costs by providing quick and easy access to data that previously required extensive research. The intangible benefits are harder to quantify, though significant: They include the ability to respond faster and more decisively to business opportunities, improved risk and property management, and a better command of market issues.

But for the business sponsors who invest in IT projects such as this one, some benefits are truly priceless. Not long after the launch, Jayne Day, our senior vice president of global risk management, was on her way to a quarterly meeting with corporate headquarters executives when she heard a news bulletin that a blue-chip company—and major tenant—was falling on hard times. While still en route to the meeting, she keyed in to the data warehouse to assess our real estate exposure. Less than an hour after the first news report, when a senior executive at the meeting asked how it might affect us, Jayne had the numbers right at her fingertips. What’s more, she could report on the instructions she had already given to the affected property managers in Dallas and Manchester, United Kingdom.

In our profession, it’s hard to match those moments of triumph when business sponsors see firsthand the rewards of the technology projects that they’ve championed. But to get those big wins, sometimes you have to start small.

Hank Zupnick is CIO of GE Real Estate, a global real estate company with 2004 net earnings of almost $1 billion. Reach him at