Data is critical for almost every industry including hiring.
Despite the best judgment recruiters use when hiring, it’s always an uncertain ground for the company.
The recruitment process in any organization is a time consuming and expensive process. A lot of working hours are spent on deciding if a candidate is the right candidate for the job. One never knows how long a candidate will stay with the organization and despite the promises, the candidate showed during the process, will the candidate be the right fit for the job. This challenge has always been a stinging point for the recruiters. However now, thanks to the myriad software and analytics tools that are available in the market, anyone can create a data-driven recruitment process.
However, for those who can utilize and understand the data related to their hires can make the most impact and have been partly able to counteract some of the uncertainty the comes with talent acquisition.
What is data-driven recruiting?
Data-driven recruiting is when you use solid facts and stats to make your hiring decisions. This process involves right from selecting candidates to creating hiring plans. Recruitment teams that use data are more likely to improve their hiring process and bring efficiency in the process. This process also helps reduce costs.
There is a wide range of benefits that a company can get with data-driven recruiting.
- It helps to allocate the budget wisely
- Increases productivity and efficiency
- Helps unearth hiring issues
- Benchmark and forecast your hiring
- Reach more objective hiring decisions and of course
- Helps reduce cost.
Steps for data-driven recruiting
The mere availability of data does not make any difference. It’s the method in which this data is used to make a difference. The recruitment team should be able to sort, analyze, and use this data for the improved decision-making process. Any hiring body can collect data consisting of potential candidates. Creating a data-driven strategy by analysis is what makes a difference here.
Choose the right data and metrics:
Decide on the essential hiring metrics to track as these metrics will show the effectiveness of your hiring processes. Some common metrics would include; cost-per-hire, time-to-hire, source of hire, candidate experience scores and job offer acceptance rates.
Develop a formal data-driven strategy:
Once you have access to data and you can gain a holistic view of it, it becomes essential to identify the key takeaways that you are looking to arrive at from all the data analysis. And to achieve this, you can come up with an excellent formal strategy, and can define how recruitment is to help drive business results.
Integrate the data:
A disjointed set of data is as good as no data, because it would be challenging to make sense of the information if it’s disintegrated. Hence gather data that relates to different functions of the recruitment process and measure the performance of any tool that is being used. This can be achieved effortlessly; connect all forms of recruiting technologies with the data from each solution that would be easily accessible from one centralized location.
Choose HR analytics solution provider:
Usually, HR organizations prefer to find vendors who can provide them with talent analytics tools. Due to these analytics tools offered by vendors, the potential of data and analytics in talent acquisition is used by companies. The benefits of these tools are not only limited to larger organizations, but even mid-size and small size companies can reap the benefits.
Regarding functionality, you should be looking at factors like Ease of use, External benchmarking, Predictive analysis and, training and support.
Building an active data-driven culture is as much about thinking strategically as it is about to act. Irrespective of the size of your organization you can start small and gradually move on the operation at a larger scale. As an HR manager, it shouldn’t be a challenge for you to make a strong business case for tools and technologies that will help support end-to-end business goals.