The Good, Bad and Not So Ugly Truth About HR Data Quality

December 11, 2014 Heather Greenwood

Image_Human Resource Data Quality

Having worked in the Human Resources field for the past sixteen years, the only sets of data I have ever focused on was in the HR space. To me, no other sets of data are more critical than HR data. Why, you might ask? Well, whether you call it “Human Resources” or “Human Capital Management (HCM)”, what you are really talking about is “people”. At the heart of every business, it takes some amount of people to keep it going.

The way “people” data is stored and used is immensely different than secured information such as credit card numbers or banking data. HR data comes with its own set of privacy and sensitivity issues. To be useful to an organization, HR data must be accessible and comprehensive. Therefore, there is a huge need for understanding who needs to have access to what data and why. This often results in the HR Department owning, policing, and sometimes having a separate system to house their data.

When you have incorrect data about people, you are not just affecting a customer, product, distributor, consumer, or downstream product or process. While these areas are all vastly important, what ultimately fuels them is the human factor. People aren’t easily measured by numbers, keywords or other metrics. Sure you can analyze their pay, age, geographic information, or job details, but what do you do when you are asked to measure employee engagement or performance management? That’s when you have to dig deeper and get a better understanding of the overall picture of what makes up HR data.

HR data is no longer about just paying people correctly. Today, it is made up of all sorts of facets, and now includes social media as a new source of data. HR data is used to determine how to engage talent, cultivate strong leaders, and drive change. It involves organizational structure, payroll and benefits, performance management, training, and workforce planning/talent analytics.

For fun, I posed the question on my Facebook page, “What one word comes to mind when you hear the phrase HR Data Quality?”. Needless to say, the answers were wide-ranging from the serious to the comical. Some examples of those answers were accuracy, people, current, validated, time saver, and essential. So after reading those, do they paint a picture of what HR Data Quality is? Not really, and one word could never sum it up.

So how do you approach quality from an HR perspective? Unfortunately, data quality no longer boils down to right or wrong, good or bad. One must first examine the goals of the data: it must be secured, trusted, easily integrated with other systems, accessible, and reportable. Based on my past experience, documentation I have reviewed, and practical thinking, it is often easiest to think of data quality when considering the following five concepts: Accuracy, Timeliness, Completeness, Relevancy, and Consistency.

Accuracy simply means, “How good is the data?” To answer this question you can review the reliability of where the data came from, what the process was to get the data, and how much the data reflects reality.

Next, Timeliness looks at how old the data is in relation to when it will be used. For example, look at the turnaround time of processing new hire paperwork. If a person is hired at the first of the month but data is not inputted into the system for 30 days, and then the data isn’t collected for a report until 15 days later, the data is at most 45 days old before it can be used. Having the most current or real-time set of data is most important. Also, looking at the timeliness of data will show efficiencies in processes, or lack there of.

Completeness is often an overlooked aspect of data quality. Many times, a company does not store all HR data in a single system. Therefore, to have overall completeness, every system, file or database must contain the same population based on their stored data in order to have a complete set of data for each person.

Relevancy is the availability of required data elements. For example, if a company’s headcount is centered around counts by building location, yet that field is not a required data field, headcount becomes irrelevant because it may not be complete for every person that should be included in the headcount. Records being stored should contain enough details to address business needs, whether current or future. Therefore, special planning should take place to include future initiatives when determining data requirements.

Finally, Consistency refers to how the codes or fields are interpreted by the person entering, supplying, collecting, or interpreting the data. For example, does every department within the organization that uses a particular field have the same meaning for that field? Or, if there is a drop-down code, do all codes mean the same across all divisions or countries? Providing clear data definitions within an organization will provide consistency and assurance that data does not contradict itself.

Hopefully this post has helped you to understand that HR data and its quality is not only important, but it’s also multi-faceted and cannot be fully explained or comprehended in one word. However, it is also far from ugly. It is exciting, challenging, and on the cusp of becoming the focal point of many companies in order to shape the future of their organizations.

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