Accuracy in analytics is always worth striving for. HR analytics data is no different – but due to the nature of how it’s collected, raw, unstructured data can cause serious issues when compiled and processed.

We’ve talked about the importance of clean HR data for accurate analytics, and the hallmarks of reliable data. Now, we’re going to see how conducting data audits prepares your raw data for good HR analytics.

The scale and scope of HR data audits can vary depending on the desired outcome. With that said, they are almost always a major undertaking – and can take years to complete, with large resources attached to them.

But companies still conduct them, because of the unquestionable value they bring to the organization’s long-term health.

The value of data audits

Data audits let you manage teams more effectively by giving real-world insight into your workforce, based on accurate data. People analytics tools can then be relied upon to deliver powerful change, and meaningful insights.

We’re talking about things beyond compliance and legal; factors like attrition, progression, and equity, but also employee performance, engagement and satisfaction – the markers of productive teams.

The data cleaning process of an audit results in relevant, accurate, more complete, consistent and uniform data. It eliminates as much human error as possible, resolves duplication, and removes or corrects corrupted data.

Duplication and inconsistency can arise when combining data from different collection methods – like when using multiple employee engagement or pulse survey platforms, or when using a set of HR tools that feed into a centralized HRIS.

Data audits make all the hard work of data collection count, by ensuring the output is meaningful – and not just data for the sake of data.

Here’s how they are usually done.

How to conduct HR data audits

Traditionally, data audits are high-intensity tasks. Some companies don’t devote in-house resources to them, instead appointing third-party auditors and consultants who specialize in HR data audits.

Even when semi-automated, there’s a lot of human effort required. HR data audits can take up to two years of work (depending on the scope of the project). This means that all results are retrospective – so emerging trends are not yet visible, but the results still give meaningful insights and roadmaps for change.

The HR data audit process will always vary depending on the goals – but the data cleaning stages will look something like this:

1. Up to date? Time to collect data!

The more timely the data, the better. Records need to be updated to the most recent date available, to ensure the most accurate possible reflection of the company’s status once the audit is complete. This may also involve standardizing date and time formats, where records may differ as platforms or methods have changed over time.

Once the auditors are satisfied with the recency of the data, it can be scraped from all sources to compile into an auditing document or tool.

2. Resolve duplication issues

Your HRIS is likely to contain multiple employee records. This can happen when using third-party recruiters to onboard new team members, when migrating across HCM platforms or when members of your team are promoted.

This process can be semi-automated by highlighting duplication – but it still requires diligence and cross-referencing to avoid issues flying under the radar due to name changes or typos.

Having accurately consolidated employee records results in far fewer spurious data points getting through to the analysis stage – which will result in greater accuracy.

Other data can also be duplicated; for instance, the results of employee engagement and pulse surveys when different platforms are integrated.

Any duplication will skew the data, so it’s vital to resolve duplicates before analysis.‍

3. Identify missing data

Gaps in data can be tricky to fill, and can require some detective work. This requires a lot of human effort – because even if values are scraped from all data sources, they can't be filled if they don’t exist. Someone has to go and find the data.

At this stage, gaps are flagged for further investigation. If they’re relevant to the goal or otherwise mission-critical, missing values will be sought through other company records, or by going to the person responsible for the information.

One example of this is when a third-party recruiter has a different hiring process to your internal recruitment method. They may not have entered all the data, or may have skipped an onboarding stage entirely. This can throw your hiring data and lead to decisions that aren’t based on what really happened.

If this is flagged in during the data audit, the recruiter in question can be asked for further details – and data integrity can be restored.

4. Check for extremes and statistical outliers

Outliers are always interesting. Those anomalies aren't always a result of dirty data – sometimes, they’re real cases of interest. But how can you know if they’re a true representation of the data, and not a result of data corruption, improper labelling or incorrect conversion metrics?

These extreme data values need to be audited with some automation, to eliminate the possibility of corruption or differing unit measurements. If they’re still flagged for further human investigation, data from alternative sources could be used to verify the values and make sure data integrity is maintained.

Is there a faster way to do data audits?

Yes – eqtble.

Our platform delivers complete, reliable data audits through a fully automated, AI-powered process.

We let you keep your data audits in-house, without the additional resource allocation.

eqtble seamlessly integrates with over 150 HR systems like Workday, SmartRecruiters, Greenhouse and Gusto – and requires no technical expertise to use.

Best of all? You’ll never have to mess around with spreadsheets again.

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