3 Easy Steps to Start Improving the Data Quality in Your ATS

technical frustrationA new report by Experian QAS revealed that 94 percent of respondents in the U.K., U.S. and France suffer from common database errors. Your reaction to this statistic might have been a lot like mine: “Yikes, that is high” and then, “…that makes sense.”

Inevitably, data errors creep in because humans are not perfect creatures; then data errors linger because humans are busy creatures. Not many employees at staffing companies today have the time and resources to go back and fix errors even if they had the ability to recognize that they exist in the first place. Yet, almost every company wants their data quality to be better. So what can your staffing company do?

PREMIUM RESEARCH: The Use of Talent Acquisition Technology Among Staffing Firms

1. Recognize Why Clean Data is Important

Your management team won’t care about data integrity until it understands the ramifications of bad data. There is a lot of literature out there on this topic, but it can be summed up as this: “bad data is the number 1 cause of CRM failure,” according to Gartner.

Bad data leads to your ATS being utilized poorly, which ultimately wastes your teams’ time. How often have your employees called someone they’ve already called? Called someone that you couldn’t possibly service? How much time have you spent reconciling start dates and end dates of contracts, or deducing why your bookings numbers don’t add up?

An ounce of prevention is worth a pound of Advil: Tension Headaches.

2. Pick Your Battles
Getting your data quality in your ATS can be a full time job. There are companies who have thrown intern after intern at the problem, with little regard for their physical health and sanity. Let’s be practical: there is only so much time your company can dedicate to fixing data errors, so where do you place your efforts?

Like scars, data errors become less noticeable with time. So tackle your most recent data for the biggest impact in your data quality. Depending on your time-to-fill, your concept of recency may vary, but 90 days is a good time frame to focus on.

Furthermore, place your efforts on correcting the data errors that matter the most to your reports and analytics (you do run analyses on your numbers, right?). Many of your errors might be Placements missing a Close Date. That’s usually a low priority error. With limited time, focus on higher priorities such as making sure the Placement Value isn’t missing. This error will ultimately have a much bigger impact on your numbers.

3. Make Data Quality a KPI
Your team probably has Key Performance Indicators (KPIs) that show them whether they are on pace for the week or month. Data quality should be one as well. Monitoring the overall level of the data quality of your ATS can help you see if there has been a recent surge of errors, or if your team has been better about fixing them.

A basic “Data Quality Score” can be calculated by the percentage of your opportunities that are error-free in a given time frame. You can then trend this percentage over time to see how well, or how poorly, your data quality is doing. Calculate this for each of your employee’s opportunities, and you’ll have personalized data quality scores.

Making data quality a KPI increases the level of accountability for your team members. If Frank’s data quality score is lagging behind the rest of the team, you know exactly who needs to spend more time making sure errors don’t creep into your ATS’s data. Even better, if these quality scores are public, Frank will most likely manage himself. No one wants to be at the bottom of the data quality scorecard.

Follow these three steps and you’re on your way to better data quality. Want more practical advice on improving your staffing team’s use of data and analytics? Stay tuned for my next blog post.

MORE: Using Metrics to Drive Your Staffing Firm to New Heights

Robert Woo

Robert Woo
Robert Woo is marketing manager at InsightSquared. He can be reached at robert (at) insightsquared (dot) com.

Robert Woo

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