Advertisement
4 Mistakes Companies Make Using Data In Recruiting
Get articles like this
in your inbox
The longest running and most trusted source of information serving talent acquisition professionals.
Advertisement
Related Articles
Let’s get this out of the way: Talking about big data is not one of your favorite topics. You’re an HR manager or a TA leader, not a data analyst, right?
Many of the managers I meet feel the same way. While most can readily access and manipulate data such as fill rates, pipeline, source, and time-to-hire, I see only about 20% who are leveraging the more holistic data required to perform the risk analysis, employee exit analysis, and predictive and workforce planning necessary to support the business.
But still, some companies are tackling the big data challenges that wear other teams down, and they’re doing it right. Here are four common mistakes that HR professionals make when leveraging data, and how to overcome them:
Too often decisions are made in silos. While most stakeholders want to measure time-to-fill and cost-per-hire, when I ask CEOs, business leaders and HR what they want to see in a one-page dashboard, there are almost always discrepancies. For example, if you are recruiting high complexity engineers with a typical time-to-hire of 90 to 120 days, you should also measure time-to-present, pipeline quality, and labor supply/demand. Once HR and the business agree on the data points they want, you can begin to measure and report on what matters.
What to do: Organize data to reflect business needs. Whether you work with an internal data analyst or outsourced provider, invest time on an ongoing basis to communicate directly with the business.
HR, as the business partner, should be a well-recognized member of the business unit and be the facilitator, asking questions such as: What will the data be used for? How will it move the business forward? Does the candidate profile adequately reflect business requirements? Is there agreement on realistic compensation requirements? Are these measurements adjusted periodically? Once you uncover the disconnects and get the basics right, you have the foundation for a system that gets the right data to the right people at the right time.
At least half of the organizations I walk into are in some state of change with processes, systems, people or technologies. If not managed proactively, frequent change and turnover can be disastrous for an ATS — particularly in HR where accountability for the system may already be tenuous. I’ve seen teams get close to a successful implementation only to have a new guard show up and stall the project. With little warning, the use of a once-appropriate platform is suddenly outgrown. Change happens, so you need to be ready to effectively manage it.
What to do: To help minimize the risk of internal change, your ATS needs to be flexible, scalable, and audited frequently. Monitor frequently to make sure you are giving the business what it needs. Buy-in from the business will help you get the resources and training you need.
Unless it’s a very large enterprise, most talent acquisition teams do not have a dedicated person for leveraging data. Whether this role is filled by one or more persons, there needs to be a dedicated owner for the system’s success and ability to deliver on business objectives.
What to do: Ideally, find owners with focused accountability in:
If business needs are extensive, a business analyst to help organize data and support communication between departments is recommended.
Whether for internal recruiters, headhunters, hybrid RPO-headhunters, or any other kind of vendor, use Service Level Agreements (SLA) to maintain alignment between data and business needs.
What to do: Nail down agreed to metrics based on business objectives and create a top set of data points to measure progress. For hiring teams, this typically includes quality of hires, time-to-hire, hiring cycles, cost ratios, and overall satisfaction. If you use outside recruiters, have your provider step up its game and measure its performance accordingly.
Data analytics play an increasingly strategic role in day-to-day processes. With the appropriate level of planning and resources, your data system can empower your team and add efficiency to the entire organization.