What is HR Analytics?
HR analytics is the practice of compiling, analyzing, and presenting employee-centric data points via various systems to develop a holistic and objective approach to critical business decisions.
A healthy and productive workforce can be measured using various types of data ranging from employee demographics to individual performance management ratings. The collection and application of these dynamic people metrics is what enables human resource analytics to be a driving force in the business environment.
Benefits of Implementing HR Analytics
HR analytics is a relatively new field of data in the business landscape that aims to reshape how businesses are evaluated by their leaders and stakeholders. Data points are collected with the aid of varying HR analytics tools and systems. Data collection from sources such as talent management, HR functions, or even performance management processes can also be compiled to help make data-driven decisions.
For example:
- Talent analytics insight allows for an informed approach to hiring strategies based on applicant demographics and recruitment metrics — such as time-to-hire data, or offer acceptance rates.
- Performance data enables a pulse check on common trends, practices, and employee productivity levels across the organization.
- Termination surveys completed during employee offboarding allow for a retrospective view of the culture of the company and areas of opportunity to decrease turnover rates.
When combined and collected, these HR data points paint the picture of the employee life cycle and may reveal opportunities to attract and retain top talent. People analytics can also tell the other side of the story — why top performers leave and how costly employee turnover can be.
Every leadership team loves to get their hands on these types of human capital metrics. However, the effort, commitment, and company buy-in it takes to build out these data models cannot be underestimated. Rome was not built in a day, and a functional people data model cannot be established overnight.
The process of collecting data and presenting meaningful HR analytics metrics is a company-wide investment that takes effort and integrity to uphold. The bulk of these metrics are fed by current and historical data. The best way to feed these databases is through functional and dynamic HR systems.
Common systems utilized by HR management and their teams include:
- Human resources information systems (HRIS),
- Applicant tracking systems (ATS),
- Performance management systems (PMS)
A company can also use various other systems that store and access critical employee information. Alone these systems are impactful, but together they can automate and elevate analytical initiatives.
Key Components of HR Analytics
Since HR departments use multiple systems, data integrity is the first task to tackle. Configuring these systems to accurately reflect the organizational setup is vital in establishing a successful data model.
Here are the components of data management and analysis that you need to think about:
The Custodians to Employee Data
The data you report on is only as good as the data being entered into the system. This responsibility is often endowed to a company’s HRIS/people operations analyst (if a team is fortunate enough to have the dedicated headcount for this role). An HR analyst is tasked with the quality assurance, maintenance, and preservation of all data within the people systems.
Data Collection
Before your company can collect and manage employee data, you’ll need to compile a template (consisting of form fields and data points) that captures the information you consider to be relevant. For example, in an HRIS, each employee record may include captured data that fall into three categories:
- Position information: Title, time in role, department
- Team setup: Manager, team peers, org chart structure
- Finance-related fields: Location, departments, corporate functions
In my current company, these types of data fields are standard and required for every employee.
We have also expanded our employee fields to include data points such as “level in role”, “compensation zone”, or an individual’s annual bonus percentage. Most HRIS platforms can have these individual data fields.
As employee records become more fleshed out with data points, employee reports will become more comprehensive and equipped to offer actionable insights. Employee profiles, however, are just the building blocks for establishing sophisticated HR practices involving data.
Data Analytics Dashboards
Interactive and dynamic dashboards are common features of modern HR systems, or they can easily be provided via third-party reporting tools.
Data insights and dashboards allow for collected people data to be compiled and represented in visual formats. Common HR analytics dashboards display the organization’s key metrics such as active number of employees, employee distribution by department, turnover count and rate, as well as the amount of new hires going through onboarding.
These HR metrics set the stage for the working environment within the organization. With proper data hygiene practices, these data points can become a trusted source for gauging business performance and the employee experience. Workforce analytics such as these provide an objective view of the employee workforce.
Because of its inherent trustworthiness, employee data can influence how senior leaders approach issues regarding workforce planning, high turnover, employee engagement, or other employee-centric initiatives. The ability to present senior leadership and HR teams with visual people data, via an analytics dashboard, is, therefore, an invaluable resource in decisions.
Data visualization eliminates subjective bias when discussing critical decisions and helps to establish an objective plan supported by reliable trends and metrics.
Objective and Actionable Insight
An unfortunate reality plaguing the corporate world today involves layoffs and corporate reorganizations. These decisions and discussions are never easy, but with the application of data, those harsh realities can be approached more objectively.
In these instances, evaluation of performance management across the origination may be brought into review. High-performing teams with proven success may be worth advocating for over teams with a proven track record of underperformance.
On another note, a total compensation review of employee groups may offer workforce planning insight regarding which teams are costly to maintain and scale over time. Adding these types of metrics to the conversation allows for balanced, well-informed decision-making to take place.
A Data-Driven Mindset
Employee data is accessible and ready for utilization. The key component to unlocking these metric mysteries is an HR team that is ready to endorse the integrity of this data. This includes a commitment to the implementation of systems that will protect, enhance, and streamline data reporting.
Data Sources for HR Analytics
Reporting on HR metrics generally begins internally with the support of various HR systems. Earlier, we discussed the HR systems available for this purpose. As mentioned above, the implementation and combination of these systems directly correlate with the accessibility to employee data.
An HR team’s approach may be to consolidate data functions into one system for a one-stop-shop to house employee data. Another approach may be utilizing a network of tools that all communicate and interact with one another to allow specialization in various fields of HR.
Regardless of your system approach, the underlying criteria are that these systems must preserve, organize, and display data in a trusted and digestible format.
Human Resources Information Systems (HRIS)
The HRIS, as mentioned earlier, is a common staple to any HR team, and the data it contains sets the reporting foundation for the entire landscape of HR analytics. An HRIS primarily focuses on employee data as it relates to the company setup. This data mapping can be tied to other internal systems via HRIS integrations to enhance people data and create an end-to-end visual.
Applicant Tracking Systems (ATS)
The metrics displayed by an ATS allow for actionable insights into the hiring strategy. In turn, this provides perspective on how to enhance and scale a talent acquisition strategy. Just like an HRIS, an ATS offers integration with other systems so that your data is automated. This workflow provides a picture that is as holistic and accurate as possible.
More interesting insight becomes evident when we layer ATS data with other organizational systems. For example, “source of hire” variables combined with employee performance ratings may reveal where you can source new employees that best align with performance and competency expectations.
HR leaders can also expand upon employee performance metrics to design and implement training programs that meet the needs of the employee population. Or, we can feed the results of a skills gap analysis directly into an ATS so that new hires fill in the blanks.
These examples are not all-encompassing, rather they are just the tip of the iceberg. These HR systems are designed to streamline, automate, and provide accessible data. The data, in turn, equips human resource management teams with the insights to proactively address their employee bases.
The goal is to utilize these data sets to establish predictive HR analytics practices. Predictive analytics allow HR teams to work on proactive planning rather than reactive responses. Looking internally, however, will only support the transformative initiatives so far.
Industry Data
When paired with collected internal data, external data enables HR professionals to evaluate their workforce compared to the competing market.
Employee compensation is often first in line when it comes to an internal/external comparison. A well-equipped HRIS or an integrated compensation tool will collect all compensation data points for a workforce (base pay, bonuses, commissions, equity, etc.). When compiled internally, this data can be taken to external compensation databases for market evaluation.
Industry compensation leaders, such as Radford, publish annual and quarterly reports that gauge trends and industry benchmarks in terms of compensation. This allows HR teams to draft compensation strategies to either maintain or exceed industry averages.
It’s fair to say when a candidate is presented with two competing offers, they will likely accept the offer with a higher compensation. Exit interview data supports that employees leaving a company often do so for a higher salary. Money talks and having the resources and data to have those informed compensation conversations can be pivotal in attracting and retaining top talent.
Data Privacy and Security
HR professionals need to recognize the sensitivity and confidentiality of the data they are working with. Ethical considerations should always be top of mind when collecting employee data.
DEIB (diversity, equity, inclusion, and belonging) metrics are gaining traction in the field of HR analytics, but these data sets require a more sensitive approach with consideration for ethical implications.
At the macro level, compensation data paired with gender demographics can aid a company in establishing its commitment to pay equity and closure of the gender pay gap. On the micro level, however, there should be caution to ensure that an individual’s gender or racial identity does not become a compensable factor when determining an offer amount or a promotional increase.
More importantly, to mitigate these risks, access to these sensitive data fields should be highly scrutinized and regulated. Sensitive employee demographics should be safeguarded by the HR team and only distributed as necessary.
Employee data, while sensitive and delicate, has the power to transform the landscape of its host company. We, as HR professionals, are tasked with the heavy burden of collecting, preserving, and presenting this data for a morally ethical and impactful business strategy. HR analysts are the gatekeepers for these data sets and must be constantly aware of the implications. This involves hyper-attention to details, careful and precautious data practices, discretion, and above all integrity with employee data.
The loss or exposure of employee data could be detrimental not only to the company, but to the livelihood and well-being of each employee.
HR Analytics Applied in Recruitment (Case Study)
Throughout my career in HR, I have seen HR analytics play a pivotal role in business decisions. Here is a case study that perfectly illustrates how data-driven insight trumps assumptions and delivers real ROI.
At a recent employer of mine, in the fin-tech space, analytics shaped the university recruiting strategy to conserve costs while streamlining hiring efforts.
In 2018, the company reignited the university recruiting program by investing $65k to target the hiring of newly graduated software engineers. At the time, entry-level software engineers were high in demand and critical to our business operations. This new recruiting stipend was intended to support the rebranding of marketing materials, travel to career fairs, and the establishment of company sponsorships at universities. By the end of the 2018 career fair season, we had used the entire budget but achieved a dismal offer acceptance rate from candidates.
The following year, data analytics was at the forefront of the recruiting planning sessions. The hiring expectations remained the same but with a budget cut in half.
The plan forward involved a meticulous combination of HR metrics to devise the new 2019 recruiting plan. The new hires from the 2018 hiring class were first brought into evaluation. Those employee recorders were tied to their university of origin (obtained from the ATS) and paired with existing performance ratings. Further data was compiled to analyze which offers had been extended but not accepted, along with the university of origin.
Finally, all new grad applications from the previous year were compiled for analysis on where the applicants attended school, how far they progressed through the hiring process, and if they were hired or not. Their prevailing statistics revealed an objective path forward on how to proceed with the university hiring program in the presence of a reduced budget.
After a few cross-collaborative reports combining information from the ATS, HRIS, and our performance management tool, it was blatantly obvious which universities yielded a high ROI and which schools had failed to produce even one promising candidate.
Our initial list of 30 universities was reduced to 11 schools based solely on candidate viability and offer acceptance rates. Data also further revealed that candidates from metro areas larger than Boise, ID (our company’s HQ location) were likely to reject an offer. This shifted our focus from universities in large cities such as Seattle or LA to more comparable areas such as Salt Lake City or within Boise itself. Over half of the 2018 new hire class had come from Boise State, just a few miles down the road.
HR analytics allowed our team to eliminate costs from going to high-profile universities that yielded no hires, to a more strategic, relationship-focused approach with the universities in our backyard, which produced more hires than all other schools combined.
Real-world implications of HR analytics are exemplified in this case of university recruiting. Data-driven and predictive analytics reshaped the strategy, optimizing costs, and reverted our focus to schools that yielded the highest return on investment. This practical application reveals the transformative potential of HR analytics in refining and optimizing business outcomes, particularly in talent acquisition.
As HR professionals, we are entrusted with the responsibility of collecting, preserving, and presenting employee data ethically and with integrity. The real-world impact of HR analytics serves as a testament to its ability to shape and guide strategic decision-making, ultimately contributing to the success and sustainability of organizations in the dynamic and competitive business environment.
Final Thoughts on HR Analytics
HR analytics has emerged as an indispensable tool in the modern business landscape, transforming Human Resource Management departments from administrative entities into strategic partners with a profound understanding of their organization’s most vital asset — their employees.
The benefits of implementing HR analytics are multifaceted, encompassing informed hiring strategies, performance management programs, and a comprehensive view of the employee life cycle. However, the realization of these benefits necessitates a thoughtful and intricate process of setting up and maintaining a robust people data ecosystem, involving the integration of various systems such as ATS, HRIS, and PMS.
HR analytics serves as a powerful tool in mitigating biases and subjectivity in decision-making, especially in critical areas such as layoffs or reorganizations. While few professionals may fully comprehend HR analytics in its entirety, its presence and impact in the corporate sector cannot be ignored.
