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7 Common Issues Affecting Talent Data Quality

May 10, 2018

Young businesswoman sitting on chair with big light bulb aboveToday's data-dependent HR and Recruiting teams are significantly impacted by the quality of data. Every forecast and business decision is driven by data, so it's easy to see how bad data could negatively impact the bottom line. Here are seven common data issues to watch for in your talent data.

1. Data Duplication (and De-Duping)

Data that is gathered from multiple sources (which happens more all the time) has a much higher chance of duplicate records than single sourced data. Record duplication is one of the significant problems that data-aware businesses face, and it can impact the bottom line due to skewed projections and duplicate marketing efforts.  However, the existence of multiple records about the same person (especially in the ATS) is interesting in itself, so you probably should connect duplicate records rather than de-duping.

2. Excessive Data

Believe it or not, it is possible to have too much data. Data that should not have been collected serves to confuse entity relationships and slow down data mining efforts. Excessive data can cause valid data relationships to be overlooked.  Sure, in talent this one is rare (we more often suffer from too little), but we have been known to collect data that really doesn't tell us anything.

3. Poorly Organized Data

Data silos are the worst culprit here, but limited warehouses and bad design decisions can make anything you need to do with data an arduous process. Connect it and curate it in a data lake, and your organization will come easy

4. Stale Data

Employee and Candidate Data becomes stale every day due to natural and expected changes, such as new jobs, updated skills and even location changes. This means that at any moment a substantial portion of your talent data is going to be obsolete. The best way of combating this loss of data is to have a strategy to combat this data decay, using fresher data sources to enhance and refresh your older data.

5. Weak Data Security

Anytime personally identifying data is stored or handled, precautions should be in place to prevent fraud and theft. There is an implied layer of trust between the candidate and employee and your data systems, and failing to uphold that trust is a sure way to lose valuable candidates.

Businesses need to develop a robust strategy and follow it when it comes to data security.  Or trust it to a partner you can rely on (like us!)

6. Poorly Collected Data

If you extract standard models, or leave old data behind, or decide that it's ok you didn't need to migrate those resumes....you're leaving BIG holes in your data.  And now that you have tools that handle structured, unstructured and highly specialized data, there's no excuse to not have comprehensive, well collected data.

7. Poorly Defined Data

Sometimes data gets crammed into other fields that it doesn't belong - perhaps in the name of expediency. This is sure to create problems when the data is analyzed later. Sometimes it just doesn't pay to be clever when solving a data collection issue. Sometimes you need to call in the professionals.

The importance of data quality can't be overstated in today's data-driven economy. Be sure to properly maintain your data as if your business is depending on it, because it truly is. Contact us to see how we can help with your data quality.

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