6 Questions You Need to Ask About Your HR Data Quality


Data is the bedrock of the ongoing digital transformation in HR. But bad data can corrupt your systems and lead to ineffective decision-making. In this article, we talk about: 

  • Why the quality of HR data is critical for modern organizations 
  • Six questions to ask before commencing on data-driven transformation projects 
  • The different factors that make superior HR data quality a business must-have  

Across the world, organizations are eager to adopt data-driven HR – but is every HR function ready for this transformation? Given the GIGO (Garbage In, Garbage Out) principle, it is essential to first look at data quality and accuracy before you change decision-making processes. Otherwise, poor quality HR datasets could influence your business strategies, leading to disastrous results for the organization. 

On the other hand, there are more opportunities today than ever before to “data-empower” HR. The number of digital touchpoints in the workplace is continually rising, and employees are more willing to share personal information for organizational success. HR practitioners who can leverage these trends and effectively redefine their processes based on data will gain a definitive advantage.

But to get there, quality is an essential parameter. Here are six questions to help you decide if your HR data quality is correctly benchmarked, ready for your HR systems to use. 

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6 Questions That Reveal the Quality of Your HR Data

Data is the foundation of HR analyticsOpens a new window , which, when implemented, can help measure the effectiveness of recruitment, the overall engagement of employees, and the learning and development progress of your organization. Naturally, when you collect data to run analytics, you have to ensure that it is of stellar quality. So, when you assess HR data, keep these questions in mind.

Q1. Is your data providing a complete and comprehensive picture? 

Comprehensiveness is the first indicator of data quality. All information sets relevant to a particular process must be taken into account. If you make an error of omission when integrating data sources, it will lead to bad decision-making and negative outcomes. 

To consider a simple example, “work volume at peak periods” is a critical metric when you start hiring. Also, all metrics within your selected datasets should have a variable and a value.

This means that not only should you factor in “work volume at peak periods” as a critical metric, but you should also be able to quantify it accurately. Without an assigned value, variables are rendered useless, disturbing HR data quality.

Q2. How recent is your data? 

Using decades-old data sets to drive HR processes in the here and now may be useful, but with changing times and trends, relying on just that legacy data set is not feasible. Depending on the use case, you could be looking at historical records, a specific time range, or dynamic data streams. 

However, the data sets you select should be perfectly in line with the required timeframe. An HR data quality best practice to follow here is to regularly update your records to prevent any recency issues. This means adding new data to existing records and altering what’s already there based on new information.

Q3. Is your data consistent/coherent across variables? 

While a certain degree of inconsistency is always assumed when collecting data, you should be able to identify a dominant pattern across the board. If your HR data doesn’t seem to make any sense in terms of the insights it generates, you could be dealing with a consistency problem. 

Look at the number of contradictions and anomalies in your data. For example, if work volumes during peak periods show an X amount but the productivity metrics of employees are not in line with the volumes, you have yourself an inconsistency issue. 

Address consistency immediately by checking if all relevant sources of data have been considered and measured. 

Q4. Is your data collection method goal-oriented? 

The data collection model is as important as the data itself. In fact, a generic model that does not delve into specific details will lead to vague and poor-quality data. 

Let’s say, for instance, you’re conducting a survey to gauge employee engagement with a particular program. The questions must be tailored to elicit the same response from the same employee if the survey is repeated. Also, responses across employees should be able to indicate a prevailing pattern to enable consistency, as we mentioned earlier. 

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Q5. How relevant are your datasets to the business problem? 

The goal of data-driven HR isn’t just digital transformationOpens a new window for the sake of it. Your data should be able to solve real-world business problems, such as identifying the root cause of attrition or detecting correlations between company policies and employee engagement. 

There must be enough data available that is relevant to your use case, without introducing irrelevant datasets into the system. Any unnecessary information will only compromise HR data quality and result in sub-par decision-making. 

Q6. How accurate is your data? 

Data accuracy can be harder to achieve than it appears. Accuracy refers to the closeness of a value to the real-world scenario. However, some level of variance is bound to creep in. Going back to our earlier example, “work volume at peak periods” might be the metric you’re looking at, but while converting this into a quantifiable value, you can lose out on accuracy. 

Keep a close eye on how well your HR data reflects reality to test accuracy levels. Regular checks, as well as assessments and evaluations, are some of the measures necessary to measure the accuracy of HR data. 

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Don’t Let Poor Quality Hold Back Your Data Revolution

Without proper measures to ensure good HR data quality, your investments will start showing a lack of any real ROI, and organizational decision-making is likely to take a beating.

On the other hand, there are external pressures demanding a stringent watch on HR data quality: 

  • Regulations such as the GDPR require data to be perfectly in sync with legal policies.
  • Cyber risks are prompting organizations to strengthen their data resilience mechanisms.

In this environment, maintaining HR data quality is a business imperative. This information library is likely to influence your HRIS, your benefits administration systems, your recruitment platforms, your daily workforce management practices, and other key tasks.

Collaborating with other departments in your organization, primarily IT and finance, will help you ensure that you get accurate data points to build your data sets.

Clearly, the efficient and intelligent management of data is now a vital cog in the larger HR game-plan. In many ways, it will help cement your overarching relevance to the organization’s long-term goals and future possibilities. 

How do you ensure industry-best HR data quality at your organization? Share your insights with us on FacebookOpens a new window , LinkedInOpens a new window , or TwitterOpens a new window . We are always listening!