A M e l i s s a D a t a W h i t e P a p e r
Scalable Data Quality:
A Seven Step Plan For Any Size OrganizationScalable Data Quality: A Seven Step Plan for Any Size Organization
Scalable Data Quality:
A Seven Step Plan for Any Size Organization
The term Data Quality can mean different things depending upon the nature of one's organization. When applied to customer address records, data quality can be summed up by the following requirements:
. The data is accurate. The address actually exists within the city, state and ZIP CodeT given. In addition, if a person or business is associated with the address in the record, that person or business listed is actually located at that address.
. The data is up to date. The name and address in any given record reflect the most current informa-tion on that person and business.
. The data is complete. Each address contains all of the necessary information for mailing, including apartment or suite number, ZIP CodeT and, if needed, carrier route and walk sequence.
. The data is not redundant. There is only one record per contact for every address in a mailing list.
. The data is standardized. Each record follows a recognized standard for names, punctuation and abbreviations.
Every record that fails to meet the above standards of quality can lead to either lost revenue or unneces-sary costs. This is true regardless of the size of the enterprise; from a local florist to and multi-national conglomerate. In fact, data quality is probably even more crucial for the small to medium-sized business or organization than it is to the large corporation.
Not only does each customer potentially represent a much larger percentage of a small business's sales volume but smaller businesses are generally expected to deliver a higher degree of personal service. Therefore, every misdirected or undelivered piece of mail has a greater impact on that business's bottom line than it would for a larger enterprise.
Given these factors, organizations of virtually Extra time to reconcile data 87%any size can benefit from a strong commitment Delay in deploying a new system 64%to a data quality initiative, one that addresses Loss of credibility in a system 81%immediate needs and provides flexibility to meet Lost revenue 54%changing business requirements. Extra costs (e.g. duplicate mailings) 72%Customer dissatisfaction 67%Compliance problems 38%5%The Scope of the Problem Other
Undeliverable as Addressed Mail Figure 1:Problems Due to Poor Data Quality, TDWI (2002)
According to a recent study undertaken by PricewaterhouseCoopers and the United States Postal Service® (USPS), on average, approximately 23.6% of all mail is incorrectly addressed and requires correction of some kind. An additional 2.7% is completely undeliverable.
The USPS currently charges a minimum of $0.21 per mail piece for its address correction service. For a
www.MelissaData.comScalable Data Quality: A Seven Step Plan for Any Size Organization
one-time mailing of 10,000 pieces, this could potentially add another $500 to the cost of the mailing. Manual correction more than triples this cost.
Bulk parcels returned as undeliverable cost nearly $2.00 per item. For items sent via delivery services like UPS and FedEx, the cost is $5.00 per item. It isn't difficult to see how these costs can add up in a very short time, diluting profit margins and potentially damaging customer relations.
If the address data is used for billing, incorrect addresses cannot only lead to unnecessary expenses, but also delay collections.
23.6%
Figure 2:23.6% incorrectly addressed,2.7% undeliverable 2.7%
USPS and PricewaterhouseCoopers Report (2002)
Points of Entry
Bad address data has multiple points of entry in any organization. If an organization collects sales leads over the web, the customer can either mistype their address or deliberately provide false information. Even if employees of the organization collect the addresses, the possibility for errors still exists.
If an organization buys address lists from a vendor or other third-party, there are also multiple entry points for error. The list may contain errors due to poor quality control by the vendor. One simple component of a data quality initiative is to only patronize list vendors that consi... [download for more]
