Data integrity is an important part of accurate HR metrics and analytics. To make sure that happens, data cleansing should happen periodically. Data cleansing is the process of identifying and correcting inaccuracies within a data set. Those inaccuracies could be anything and everything, including missing, redundant, incorrect, or duplicate information. Data cleansing supports data quality.
Why is it important?
Of course, your HR data is perfect. It’s all entered correctly from the beginning and there are never any issues with your data transferring from one system to another. Ha.. okay, in a perfect world we know that would be true. But, unfortunately, we don’t live in a data utopia so there’s going to issues.
Without accurate data to begin with, you can not trust that your HR metrics and analytics are accurate. Which will also impact the accuracies of your KPIs. Your team would begin to lose credibility as more inaccurate data is reported. HR is just becoming a valued business partner and losing that could be detrimental to gaining support for future initiatives.
Additionally, dirty and messy data can be time-consuming to correct on the spot. It will become a hassle to run any report or analyze any metric if every time you have to clean it first. Implementing regular and consistent data cleansing processes and practices can be an HR life-saver.
Identifying the inaccuracies
The first step is figuring out where the problems are. The simplest way to do this is to audit the data. This can be a long arduous process for large data sets. Imagine dumping a report of all the data associated with 10,000 employees. Then imagine having to go line by line trying to figure out the errors or identify redundancies. A manual audit just isn’t realistic.
Another method is cross-checking the data against a validated source. For example, using your HRIS to ensure your timekeeping system has accurate departments and supervisors for hourly employees. If you have E-Verify integrated into your HRIS, it works on the same principle. It uses an employee’s social security number (SSN) to verify their employment status in government databases. It’s another check to make sure your employee’s data is entered correctly.
Let’s be proactive, not reactive
Making sure the data is entered correctly from the beginning will result in a lot fewer errors. Simply making fields mandatory will make sure important data isn’t missing. Also, certain fields like job, department, and location should not be free-text fields to maintain consistency. The system end-user should only be able to select from a pre-populated list.
Since humans aren’t perfect, reducing the number of people involved in the data will reduce the number of inaccuracies. Self-service HR systems take out the middle man have employees enter their own information directly into the system. It’s especially helpful for payroll details like direct deposit and personal addresses.
More sophisticated systems will help make data cleansing easier. Most HR systems have built-in checks and balances to catch errors like duplicate SSNs. Some have even begun to integrate with postal databases to ensure accurate addresses associated with the correct counties.
Creating a data quality culture to prevent problems
Last, but certainly not least, to ensure data integrity there has to be a culture of data quality. A data quality culture supports strong policies and practices for accurate data entry. Leadership also needs to support data quality and integrity. They need to understand the importance and promote following the established processes for clean, healthy data.
Don’t forget that accurate HR metrics and analytics start with a clean data source. Even an HR dashboard is only as good as the data set it pulls from. So don’t just leave your data cleansing to just once a year for spring cleaning, make it apart of your culture.
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