HR Analytics and Reporting

How analytics has transformed recruiting

HR analytics is the practice of taking vast amounts of information and organizing it. It uses this information to find the right candidates for a job. It has proven highly effective when applied to recruitment.

This is because data enables significantly better, more precise hiring decisions to be made. Resumes only give so much information, and data analysis goes into comparatively more depth.

Here are the other biggest benefits to using data analysis for hiring:

1. Improved candidate experience

   Potential employees can clearly understand the hiring process. It helps ensure that the right candidates enter the funnel. This streamlines hiring and gives a better first impression of the company.

2. Better match quality

   Extensive data sets find better candidates. This makes hiring more efficient. More data enables finding more potential matches.

3. Reduced expenses

   Due to these upgraded processes, it has become less work-intensive to find the right hires. This means lower costs overall.

While analytics has pushed forward the hiring function significantly, it also has its drawbacks. For example, one of the issues with analytics is that it becomes much harder to detect bias. Biased data means bias in the results.

Data is also still incapable of detecting the cultural fit or the soft skills of employees. These facets of employees are critical for workforce cohesion and company culture.

Making talent acquisition a success with data

Here are the six most effective ways to implement data-driven talent acquisition.

1. Use the right data. This means carefully researching and finding the best datasets the market offers. Not all datasets are equal. It is important that the chosen data sets are comprehensive and relevant.

2. Use the right tools. Consider an applicant tracking system. Customer relationship management applications are also effective.

3. Teach the importance of data. A company-wide approach is necessary to make data gathering a success. The workforce needs to become aware of how important it is. Teach managers to understand and read data to enable better decision making.

4. Improve continually. Do this by gathering feedback from the workforce and implementing it. Check data and tools for bias. Look for opportunities to streamline workflow. Consider external consultants and specialists to evaluate your tools.

5. Start slowly. It is best practice to implement new analytics tools slowly. Use them on a small scale initially. This gives time to iron out any issues. Once done, scale with less friction.

6. Use the right metrics. The vast data available has improved hiring immeasurably. However, these data sets and their use cases can prove overwhelming. To avoid this, be critical of the metrics chosen. Ensure that there is a clear purpose behind each one.

Salary data

Recording compensation data can be difficult work. It is important to balance staying competitive in the industry with being internally fair. It is also crucial to avoid bias. To complicate matters further, these issues are in flux. The work landscape is ever-changing, as are the interests and desires of the individuals that make up the workforce.

It is important to have a clear method to get the right information. For example, exit and onboarding interviews, and internal feedback surveys. These are highly valuable. Allowing for anonymity maximizes effectiveness. This enables employees to give honest feedback without any fear of repercussions.

Industry surveys are another effective data source. They keep companies competitive by ensuring that there is a frame of reference for job prices. Without this, organizations are liable to underpay or overpay their workforce. Both of which cause long-term growth problems.

Hiring consultants and taking part in salary surveys also work. It is preferable to use multiple sources of data. More high-quality data means better business decisions.

It is critical to stay in line with the latest laws on pay transparency. When storing pay data, ensure that it is well encrypted. Limit access only to those that need to use it. Keep physical records well secured. Keep policies on data clear, and regularly audit them.

Human resource management systems (HMRS)

These are storage systems that take and organize workforce data. Salary, benefits, bonuses, and other employee data. HMRSs help HR departments by automating various parts of their work. HMRSs guide new employees through the hiring process. They also help with onboarding and other similar tasks. This enables HR professionals to focus on other more complex work. For example, succession planning, and more advanced administrative issues.

HMRSs reduce the potential for human error. They do this by centralizing data, which streamlines the HR function. A robust HMRS also tracks employee leave and attendance, manages recruitment, and measures employee engagement. It performs core HR for the workforce.

Choosing the best HRMS for your organization

Finding the right solution depends on the specific needs of your organization. However, there are general rules that will always apply. Firstly, it is important to use an HMRS that works with existing tools.

If it is not compatible, it is not useful. Mobile use is also now a necessity. It makes the tool easier to use, as employees will do work away from the desk. Lastly, be sure to evaluate the compliance and security of the tool.

Conclusion

Data analytics has become crucial for the hiring process. However, it has other important applications. It helps find gaps in workforce knowledge, leading to training and improvement. Feedback from the workforce can find these weak points in an organization. Data analysis makes it possible to strengthen them.