Sage Advice

Better data quality equals better HR results

In this acronym-rich industry of ours there’s still one acronym that I hold above all others because its truth is undeniable: GIGO. For those of you who might not remember this one, it stands for “garbage in, garbage out” – and it describes what we all know too-well: the poorer the quality of the data in our HR application, the less value we’re going to get out of that application.

GIGO has been with us practically since the dawn of the computer software age; and yet, in our excitement over new products, new gadgets, and (most of all) new catch-phrases, GIGO has to a great degree been pushed off to the side. Organizations have been too-accustomed (and too forgiving) in accepting “bad data” in their software applications, and when the economy falters and organizations downsize their staff, the incremental increase in bad data is simply accepted as the cost of doing business with fewer people to do it.

Don’t you believe it.

Chalking up the increase in bad data to a reduction in staffing is an all-too convenient excuse, and – if anything – staff downsizing should bring the subject of bad data to the forefront of a business and make addressing it a corporate imperative.

But here’s the thing – most HR organizations think of data quality in terms of data entry; making sure that required fields aren’t left blank, making sure that validated fields have valid responses, and seeing to it that date fields contain date values, numeric fields contain numeric values, and so on. Now all of these things do help to improve the quality of data, but they far from all you can do to improve data quality.

Consider the following:

  • Making sure that phone numbers have the correct number of digits
  • Making sure that email addresses include the ‘@’ symbol
  • Making sure that “follow-up” or “service” dates are no more than ‘x’ days out
  • Making sure an employee’s vacation time does not go negative
  • Making sure that pay raises do not exceed ‘x’ percent (and that a person’s salary is commensurate with their position)
  • Making sure that the personnel data in your HR application is in agreement with the personnel data in your other business applications, such as accounting or sales systems

The above is just a small example of the types of “data integrity” conditions that you can watch for in your HR system; generally speaking, these conditions resolve themselves into the following:

  • Checking for missing data (e.g., no cell phone number)
  • Checking for incorrectly-formatted data (phone numbers, SSNs, email addresses, etc.)
  • Checking for conditionally-missing data (e.g., if field ‘a’ = ‘x’, then field ‘b’ must have a value)
  • Checking for conditionally-invalid data (e.g., if field ‘a’ = ‘x’, field ‘b’ cannot have values ‘y’ or ‘z’)
  • Confirming that date, time, & numeric values fall within acceptable ranges (e.g., a “next scheduled maintenance” date cannot be more than ‘x’ days in the future, or “vacation available” cannot go negative)
  • Performing cross-application data validation (e.g., an employee in the HR system should have the same email address as in that employee in your ERP system)

As mentioned earlier, most HR organizations’ attempts at improving data quality usually begin and end with only the first item above – making sure that HR required fields are not left blank.

So – do yourself, your HR staff, and your employees a favor; the next time you go into your HR application and you see something that makes you want to say “How did that happen?” – don’t just chalk it up as the price of doing business. Implement an HR data-quality system that automatically checks for (and responds to) that “bad data” the moment it appears.

After all, everyone makes mistakes; it’s having to live with them that we can do without.


Nexus: G-WEBCD1