All the great new analytics tools work from the assumption that we have clean data.
Even when we just do a simple analysis, such as “employees by location,” we are presuming that all the data has been input, that it is accurate, and there are no duplicates.
As it turns out, much of HR’s data is pretty bad.
This is not really our fault. The data is dirty because it didn’t matter that much before and it takes effort to enter data correctly and keep it up to date.
Just look at your own address book; it probably works well if you are just looking up a contact, but how much information is incomplete, inaccurate or duplicated?
The world often operates on the principle of two steps forward and one step back. It is exciting what we can do with analytics, but now we discover that analytics only work with clean data and our data is dirty.
That’s OK, we’ll get there in the end; however, mastering the field of data quality has suddenly become a priority.
Data quality management is not as glamorous as other parts of HR, but it is a good career move.