Author: Yogi Schulz
Businesses manage to operate despite dubious data. How many different ways is your organization’s city name spelled or abbreviated in your databases? How many customer records contain obviously incomplete telephone numbers or no credit limit amount? How many of your on-hand quantities are negative even when the product is plainly visible in the warehouse? How many cost centers in your financial system are shown as active but really aren’t?
Data quality lapses create customer irritation, lost sales, avoidable product returns, shaky management reporting and excessive inventory investments. Many data quality problems originate from careless or over-worked employees not entering the data with sufficient care at the moment of initial data entry. What approaches, that don’t cost the earth, can businesses pursue to reduce these problems and their related costs?
Insist on Pull-Down Lists
Whenever pull-down lists replace free-form data entry, finger problems are eliminated and data takes on more consistency. I particularly like pull-down lists that are sorted in order of frequency of use rather than in alphabetical order.
Pull-down lists are a low-cost enhancement to screens because the lists can frequently be populated from validation tables that are already present in the system.
Teach your employees an address grammar. A grammar defines how symbols like #, period, dash and comma will be used. It also specifies valid abbreviations and capitalization. Benefits include ease of duplicate account removal or account consolidation.
Shipping and billing addresses can be verified against databases of all valid addresses. Such verification significantly reduces the number of mailed items returned due to invalid addresses. Another benefit is advanced pre-processing of mail items that reduces postal costs and often accelerates delivery.
Databases of all valid addresses can be licensed from various sources and integrated into existing systems with modest cost and little disruption.
Analyze Product Returns
Product returns are bad news. They create costs for businesses while reducing revenue and creating work for customers. Allocate staff to determine the reason for product returns. Common problems that can be easily corrected include insufficient or inaccurate product descriptions on the web site.
Forcing shoppers to type product codes, as opposed to clicking on thumbnails or links, is a frequent cause of excessive returns. If shoppers are ordering the same item in two adjacent sizes, it’s highly likely that they can’t tell which one will fit and that they intend to return one. Solve the problem by providing improved sizing information.
Reduce Update Anomalies
Update anomalies occur when two columns in two databases should have the same value but don’t. Common examples include a missed address update in one database or a failure to update the account representative name in all databases when the previous rep is fired or promoted.
Implement data cleansing tools or EAI software to spot and perhaps correct these problems.
Evaluate Employee Performance
It’s cheap to add a question like “What did you do this year to improve data quality within our organization?” to the annual employee review process. Building awareness of the organization’s commitment to improving data quality can work wonders.
Login using security cards
Have employees login to systems using a card reader for their employee security card. This removes all doubt about who is logged in and therefore who initiated the transaction that is creating pain an d suffering.
Avoid becoming punitive in reacting to lapses in employee data quality performance. Bite you lip and provide patient orientation. If that doesn’t work, then perhaps it’s time to terminate or transfer the employee because he or she is obviously unsuited or unhappy in their current position.
Implement Bar Codes Ubiquitously
Displaying data about products, employees, warehouses, customers, you name it as bar codes eliminates data entry errors.
The cost for bar code readers and label printers is easily justified by costs avoided from reduced errors and shorter order fulfillment times.
There’s no excuse for poor data quality. The costs to significantly reduce data problems are modest. The benefits of improved data quality are significant.