How Clean is Your Human Capital Data?

Over the last few weeks, we have discussed the importance of HR Analytics. We discussed last week about looking at linkage to business strategy in order to start the process. Another important issue to look at, at the beginning of the project is the data itself. The one common theme/issue I have heard from conferences, clients and colleagues is the issue of data hygiene.

I believe that data cleanliness has to be a priority in HR as analysis of Human Capital data is more and more common every day. The one "stopping block" in the analysis process is the state of the data. By having strong data entry rules on the front end will save a lot of heart ache on the analysis end.

Here are some data pitfalls we have experienced:

1) Data is not entered into HRIS in a uniform manner thus creating issues when trying to perform data analysis

2) Data is not entered in a manner that allows for comparison across data sets. In other words, in the HRIS you enter data at the divisional level and in the CRM system you enter data at a location level.

3) Data is not normalized across data sets. Different scales, statistics and types of data are used within and across functions.

4) Missing data. Many times when looking at HRIS data, you see missing job titles, missing dates, etc.

5) Inaccurate data. We find there is a lack of quality control in HR data. Unlike in finance where data is constantly verified generally HR lacks this process.

What other data issues have you run across in your experience? Also, what are your processes for keeping your data clean? Comment below to keep the conversation going...