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4 reasons HR must clean its people analytics data

Ensuring the accuracy of HR's people analytics data is crucial, so HR and other company leaders can make the right decisions. Learn why HR must clean its people analytics data.

People analytics are a critical part of an HR department's operations because they can provide numerous benefits, including potentially enhancing employee experience and improving diversity, equity and inclusion efforts. However, HR leaders must ensure the data is clean so that data inaccuracy doesn't lead to incorrect conclusions.

Data cleaning -- also known as data cleansing or data scrubbing -- is the process of checking and correcting data quality for problems such as inaccurate or missing values, inconsistency, invalidity, duplicate data or missing data. Cleaning data ensures the information is accurate and that the data is ready for endeavors such as using the data with other systems.

Here are some reasons why HR leaders must make sure the HR department's people analytics data is clean.

1. Dirty data leads to inaccurate decisions

The concept of garbage in, garbage out is a crucial one to keep in mind for data accuracy and analytics outputs. Inaccurate data will lead to incorrect results and incorrect decisions.

One way to ensure data is accurate is to check for consistent data values. For example, HR leaders might need to work with others to confirm that their system is using a single value for a state like Colorado, instead of multiple values like "CO," "Colo." and "Colorado." If HR staff use multiple values for Colorado, then attempt to search for the number of employees living in Colorado, they will be unlikely to count all the Colorado-based employees and will therefore underreport the number of Colorado-based employees to company leaders.

HR leaders should also confirm that each set of data is complete and that the data is formatted correctly. HR leaders should work with others to check that all data is structured in the same way and is aligned with other data sets so that users can easily make comparisons. All of this leads to more accurate data analysis.

2. Dirty data can cause software problems

Users may encounter trouble storing invalid data in an analytics system, and the software may not accept a data set with errors. Attempting to import incorrect data may lead to software issues for users and delays in producing some or all analytics.

3. Dirty data can cause loss of trust

People analytics data must be clean so that it can stand up to scrutiny from others in the organization and give HR leaders confidence that their data accurately reflects the organization and workforce. Inaccurate data can lead to stakeholders' reluctance to support future decisions that rely on HR analytics.

4. Dirty data isn't interoperable or portable

Data must be clean so that it can be interoperable and portable, which means that users can process it and load it into other systems.

Making data interoperable and portable is not easy, but HR leaders can work with others to develop systems and processes to make this possible. Using a middleware platform internally makes doing so easier.

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