Your Data is Worth its Weight in Gold Part 3

The Importance of Data Quality

Part II

Customer relationship management is based on the premise that organizations can reduce costs, increase revenues, and boost customer loyalty by organizing the way their systems and staff handle customer data. Enterprises tend to focus much more on CRM processes--the "where should this go and who should handle it"--than they do on the actual quality of customer data. "One of the hidden dangers of customer relationship management is enabling better access to bad data," say Erin Kinikin, vice president with the Giga Information Group. The result is akin to putting rotten peaches in faster, better-routed trucks--the peaches are still rotten when they get to market.

It is estimated that customer data degrades at a rate of about 2% per month. That is roughly 25% per year. Without proper maintenance and updates to the data, it is feasible that an organization could have a completely useless dataset in as little as four years or less. This degradation in data could render your organization vulnerable to failure.

With this information in hand, you must begin to formulate a data quality strategy for your organization. What is in a data quality strategy? According to Firstlogic, a well-known data quality expert organization, [a data quality] strategy is driven by goals and shaped by perspective. You must be asking yourself the what, where, when, how, and whys surrounding your data. For example, if one of your corporate goals is to increase response rates of your marketing campaigns by improving customer data, the what is already dictated. The goal already answered the why - to improve response rates. Perspective will dictate the where and how you need to cleanse the data. If the customer data for the marketing campaign resides in a CRM system, is augmented by purchased lists, and is largely person name and address information-the where is apparent. When you examine, in detail, the impact of goals and perspective on a data quality strategy, the contents of the strategy become apparent:

• A statement of the goal that is driving the project
• A list of the data elements that support the goal
• A statement of the type of data to be cleansed
• A discussion of cleansing solutions that match the type of data
• A description of where the data resides
• A plan for how often the cleansing activity will occur and on what systems
• A plan for how, where, and when the data can be accessed for cleansing
• An inventory of the existing data touch points
• A plan for filtering, or at least "buffering" each touch point, and perhaps eliminating or combining touch points

When you set off to design your first data quality strategy, whether it be in a document or on a white board (more common) give some consideration to the points we have listed here. The more of these points you incorporate in your strategy the greater the likelihood your data quality project will be successful and achieve the desired goals.