Data is finally getting the recognition that it deserves. Data serves as the underpinning of all CRM projects, but companies haven't recognized its significance until recently, says Mark Frawley, technical business analyst at Citigate Hudson in New York. "[Data] hasn't gotten its due until now." he says. The attention to data has pushed data-enlightened companies to the next level: data integrity.

Data integrity, he explains, involves an overall commitment to data management. It's distinguishable from data quality in the way that it surpasses the early days of scrubbing the data and massaging it into common formats. Data integrity encompasses more than maintenance, it involves awareness—and it's inevitable, Frawley says. "Data integrity is a broader concept that embodies those things that were before seen as IT issues...and the fundamental problem associated with it is a business problem, not a technical problem," he says.

Roman Lenzen, senior analytical consultant at Quaero, says he too sees companies transitioning to this movement of data as a business problem. As such, they commit to long-term data quality by establishing teams that develop and monitor data standards. Called data stewards and led by chief data quality officers in data-mature organizations, these people often are experts in analytics, IT, or marketing, and are essential to maintaining ongoing information quality. "If you don't have the people and processes in place, then the [technical] efforts are not worthwhile," Lenzen says. "Data is the lifeblood of all marketing efforts. If it's not standardized and consistent, and the analyses of tests are not reliable, then it's subject to be biased."

Michael Wexler, vice president of strategy and analytics at e-Dialog, says if all companies viewed data management as a profit center and invest resources to maintain their data practices, their communications and marketing efforts would pay off. "Some people don't understand that it's easier to keep garbage out than to keep cleaning it up as new types of data come in," he says.

In some cases, companies get ahead of themselves. Often they establish multiple selling and communication channels before creating a data integration strategy. "The damage can be significant," says Nimish Mehta, group vice president, customer data integration, at Siebel Systems. To position enterprises on a path to data integrity, he recommends a four-part plan in this order:

1) Establish an overall strategy and standardize the rules that will govern the types of data collected and mined, as well as determine who receives access to certain types of data;

2) Conduct a "health check" to assess the general quality of the data;

3) Analyze the estimated impact of data improvement across the enterprise;

4) Institute an implementation plan that includes the necessary software and business processes.

Mehta says the benefits of establishing such plans prior to building data warehouses or multiple selling channels are easy to demonstrate. However, until recently companies would rather experience bad data quality first and then fix it later. "It's like taking medicine when you're ill, versus keeping healthy for the long haul."