Many companies today looking to improve revenue and profits will study their relationship with consumers. Who buys from us? What do they buy? What marketing efforts trigger greater spending? Who are our most profitable customers?

Companies are use customer relationship management (CRM) and similar solutions to answer those questions; they are also trying to enhance revenue through programs like revenue optimization and yield management. CRM focuses on the trees. Revenue optimization and yield management focus on the forest. All require sound data.

Understanding the customer is critical to driving sales today. You cannot just know what the customer buys—or likes to buy. You need to know how valuable a customer is. A customer who buys all her sporting goods at your store, regardless of whether the item is on sale, is more valuable than the one who only purchases when there is a sale or coupon.

Many CRM and other similar tools get derailed by inadequate data quality processes. These applications are filled with inconsistent, inaccurate, or unreliable data. Additionally, the CRM system is rarely the only system of record for customer data; customer information is usually found in different applications in marketing, sales, finance, distribution, support, and call centers. Data quality and data integration capabilities help companies aggregate this information, taking data from the CRM system and other systems to build a master record of each customer. With one master record of the customer, rather than a dozen conflicting records, companies can improve customer outreach, enhance customer retention, and maximize resources.

Revenue management and optimization programs that attempt to price products for maximum return can also be hamstrung by inadequate data. Hotels, for example, price rooms based on supply of different types of rooms, as well as by demand for those types of rooms. Knowing the customer room requirements is not enough. If a hotel chain does not know how many rooms are available with king beds rather than double or single beds, an effort to manage and maximize the price of each type of room is doomed.

If your customer data is stored in multiple, inaccessible locations, you need help—a bridge of some sort—for the data to be useful. A quarter of respondents to a Ventana Research survey * said they have customer data stored in more than 20 systems.

Sometimes, companies are not even aware of the goldmine they have sitting in one of their information silos. For instance, a company's CRM system could be set up to track coupons sent to customers. Meanwhile, data from company-issued rewards cards could be sitting unused in a different silo. The data, together, could provide much more targeted and timely offerings.

Even when companies can get a single view of the customer across multiple data channels, they frequently have data that is inaccurate, out-of-date, or duplicated. More than 40 percent of companies take on CRM or similar projects without understanding data quality problems in existing systems. **  

Another substantial hurdle is that 2 percent of data becomes out of date each month because of death, divorce, marriage, and relocation.*** If your company has 500,000 customers and 2 percent of the records become obsolete each month, 120,000 of your records will be out of date within a year.

The people in your company who work with data know this. Only 11 percent of respondents to the Ventana Research survey have full confidence in their data. As is the case with business users who focus on supply chain, inventory, and purchasing, those focused on customer issues will cobble together data from multiple sources, load it into a spreadsheet, and create their own version of the truth—the kind that changes as the spreadsheets get emailed from user to user.

When faced with inaccurate data that does not reflect your business, revenue optimization through better customer knowledge is difficult to achieve. Only by merging and rationalizing information from multiple systems can companies understand the buying patterns of their customers. This is only possible with quality data.

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About the Author: Tony Fisher is president and CEO of DataFlux

* Ventana Research, ''Customer Information Management: Business and Technology Trends'' (2007).
** Gartner, Inc. ''CRM Demands Data Cleansing.'' Ted Friedman, et al., December 3, 2004.
*** ''Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data,'' The Data Warehousing Institute, Report Series 2002.