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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:
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:
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.
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