The High Cost of Low-Quality HR Data and How to Improve Data Integrity
The High Cost of Low-Quality HR Data and How To Improve Your Data Integrity

The High Cost of Low-Quality HR Data and How To Improve Your Data Integrity

In this acronym-rich industry of ours, there’s one I hold above all others because its’ truth is undeniable: GIGO. It stands for “garbage in, garbage out” and it describes what we know all too well: when we have low-quality HR data, we get less value out of our software.

The Hidden Costs of Low-Quality

HR data management systems are already a significant investment for companies.  By not enforcing data quality measures, users of the system usually have a more difficult time doing their work.  Simple tasks transform into lost productivity when a worker needs to hunt down the correct information.  Low-quality data also drives up the cost of ownership (and that never makes the finance team happy).

GIGO has been a looming reality since the dawn of the computer software age.  But it has been data managementabandoned on the shoulder of the information superhighway with skid marks across its forehead. Organizations have become too accustomed (and too forgiving) in assuming “bad data” in their software applications is the norm.  Then when the economy falters and organizations downsize their staff, leaders excuse the incremental increase in low-quality data as a cost of doing business because fewer people are available.

I think that is a very high price to pay for faulty reasoning.

If anything, staff downsizing should bring the subject of low-quality data to the forefront of a business’ agenda and correcting errors should be a top priority

How to Maintain Your Data’s Integrity

Sadly, when most organizations think of low-quality HR data, they limit the scope to data entry errors.  The focus is only on making sure required fields aren’t left blank, validated fields have correct responses, and ensuring specialized numeric fields have values. While those are all legitimate concerns to stay on top of, you can do more to improve data integrity.

Other common data errors to watch out for include:

  • phone numbers missing one or more digits.
  • email addresses missing the ‘@’ symbol or assuming “.com” when it could be “.net” or some other extension.
  • “follow-up” or “service” dates missing the correct specified time frame.
  • employee’s vacation time going negative.
  • pay raises not having the correct percentage ceiling.
  • individual salaries not being commensurate with the position.
  • ensuring 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 sampling of the types of data integrity conditions you can watch for in your HR system.  Not properly addressing these issues usually sends team members on unnecessary data hunts, looking for correct cell phone numbers, social security numbers, manually updating vacation errors, cross-checking data points in other company systems and other time wasting nightmares.

Now is the time to address your data issues before they get seriously out of hand.  Implement a data-quality system that automatically checks for (and responds to) that low-quality HR data the moment it appears.  Sure, everyone makes mistakes, but it is living with them that we can do without.

JOIN US FOR A LIVE DEMO – Sage Alerts & Workflow

Sage Alerts and Workflow Demo


Once you’ve improved your data integrity (or maybe it’s already optimal), you’ll want to put it to work for you in smarter ways.  Join us for a live demo of Sage Alerts and Workflow on September 28, 2016, at 2pm Eastern.  It’s like “Business Intelligence” for your HR data.  In this free webinar, you will get a behind-the-curtains look at how you can quickly identify trends and easily take appropriate actions when it comes to your company’s most critical asset: your workforce.



Share This Post