Talent Analytics: Recruiting Data Through the Lens of Maturity Models

Talent Analytics: Recruiting Data Through the Lens of Maturity Models

Mike Roberts

talent analytics softwareIf you’ve ever taken a business course, you’ve probably heard of maturity models. These are basically methodologies used for auditing current performance in a particular area, and then identifying areas for improvement going forward.

A well-known example of this is Carnegie Mellon University’s Capability Maturity Model (CMM), which was initially focused on software development but the principles have since been widely applied across process improvement initiatives.

Looking at your use of recruiting data and talent analytics through the lens of a maturity model can be enormously helpful in understanding how to take your organization to the next level. And in this post we’ll do just that.

Breaking Down Maturity Models

Before we apply maturity models to your own recruiting efforts, it makes sense to take a step back and garner a better understanding of how they work in the first place. And to do that, we’ll look a little deeper into the CMM.

Maturity models like the CMM are typically presented in matrix-form, showing the capability on the Y-axis and the level of maturity of the X-axis. While the capability can be as simple as something like “Performance Management,” the maturity levels are often more detailed, and that’s where the prescription comes into play.

The CMM is broken down into five different stages of maturity, which essentially go from disorganized ways for doing things all the way to world-class methods. You’ll notice that with movement across the different levels, processes start to become less reactive and more proactive.

Here are the five levels by name and what they mean:

  • Initial: Often the starting point for a new process, this level is characterized by tasks being executed in ad-hoc ways without documentation
  • Repeatable: In this level, the task or process has been documented in some form, so it can be repeated in that same way and hopefully deliver consistent results
  • Defined: This is the point where processes and associated roles of people are more formally documented or standardized, and performance improvements are often seen to some degree
  • Managed: This level is typically characterized by applying metrics or KPIs to the previous “Defined” or standardized processes, so performance can be quantified and refined over time
  • Optimized: The final level is characterized by the application of continuous improvement methodologies as well as that mindset in personnel to move toward process optimization (this requires innovation over time)

The Translation to Recruitment Analytics Maturity

Now we’ll discuss these levels of maturity in the context of using recruiting data to support decisions. As is the case with most recruiting organizations today, very little data is being used—especially at the recruiting or sourcing levels.

When it is being used, however, it’s typically in an ad-hoc or reactive way, meaning that each recruiter has his or her own way for pulling data from a system like the Applicant Tracking System (ATS), exporting that information to Excel, and then manipulating it to mine some type of insight when needed.

In this scenario, there are certainly benefits to the person using recruiting data, but they tend to be offset by the lack of organization and the fact that leveraging data in that way is not typically repeatable. The inefficiencies here are clear, though they surface because recruiters have no other option.

As organizations become more mature with the use of recruiting data, they generally begin to standardize the way reports are pulled from the ATS and other processes. Some will even build homegrown solutions using tools like macros to support any analysis that needs to be done.

Recruiters transitioning from ad-hoc methods for data analysis toward homegrown tools begin taking a more strategic approach to decision making. For instance, a recruiter might analyze source of hire information to determine which job board is delivering the greatest number of applicants, so resources can be allocated there in the future.

It is not typically until recruiting organizations adopt next-generation analytics that they can really begin to not just make strategic decisions, but also proactive and even predictive decisions that intend to optimize performance.

Some forward-thinking companies today are already using recruiting analytics tools with predefined statistical modeling, allowing talent acquisition professionals to easily answer questions like “Which job board is delivering the highest quality hires that are more likely to stick around?” This is the proactive—even predictive—direction you should be thinking about and moving toward.

Using Your Recruiting Data More Strategically

When it comes to using recruiting data, every single company is one of these levels of maturity. What’s important to know is that despite the esteem you may have for some of the world’s leading companies, many are still in the reactive state, doing ad-hoc and manual analyses rather than using next-gen recruiting analytics tools.

This is partially because the recruiting analytics market is relatively immature in general. It’s also because recruiters have been doing things the same way for a long time, and it’s difficult to both build a business case for a new solution and actually change the mindset of personnel.

Either way, it’s time to take a good look at where you stand in the maturity model, and start your journey forward with recruiting data. The longer you wait, the further behind you’re bound to fall.

If you’re new to the concept of recruiting analytics, the first step is to gain a better understanding. Read our whitepaper, Analytics in Talent Acquisition: The Hype, the Reality, and the Future.

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