An organization can gather data routinely, but what does the data
indicate? Without direction or a process, the data may be meaningless or
extraneous. HR analytics breaks down raw human-resources data into
actionable insights, answering important questions in the process. These
questions include:
What patterns are revealed in turnover?
How long does it take to hire?
What amount of investment is needed to get employees up to a
productive speed?
Which employees are most likely to leave within a
period of time?
Are development initiatives having an impact on
performance?
Improve hiring
Reduce attrition
Enhance experience
Strengthen workforce
Improve processes
Gain trust
Collecting data
HR aggregates data to evaluate key practices, such as recruitment,
talent management, performance, and training. The kind of data that is
typically collected in HR analytics includes employee profiles, as well
as data associated with performance, onboarding, demographics,
retention, turnover, absenteeism, promotions, and salaries.
Measurement
With the correct data on hand, it can now be placed under a microscope of continuous measurement and comparison. HR analytics compares the data to historical norms and standards, building off of a constraint stream (rather than a single-moment snapshot) of information, to develop an accurate, real-time picture of HR metrics. Effective use of HR metrics in HR analysis depends heavily on pre-established baselines, so that organizations can chart their progress.
Important HR analytic metrics include:
Times to hire: How long it takes to post jobs to the finalized
hiring stage.
Recruitment costs: The cost associated with the locating
and hiring of employees.
Turnover: The rate of employees quitting
their jobs after a specific amount of time.
Absenteeism: The
frequency with which employees miss work.
Engagement: Measuring
employee productivity and their satisfaction with their assigned job and
related tasks.
Analysis
This stage analyzes the results from metrics gathered to find trends
that may impact the organization as a whole, and can be broken down into
three specific forms of analysis. Descriptive analytics help interpret
and quantify historical data, predictive analytics uses statistical
models to forecast future opportunities, and prescriptive analytics then
focuses on using reliable information and insights to anticipate
possible consequences and outcomes.
Application
With data collected, quantified, and analyzed, the resultant insights
are then used to inform organizational strategy and improve
decision-making capabilities.
Make better data-driven decisions
Justify necessary HR intervention
Effectively evaluate the effectiveness of interventions
Increase
tactical and strategic role within an organization
Employee engagement
The data useful for an HR team is a measure of employee performance,
experience, and business outcomes. Metrics include pulse surveys,
sentiment analysis, employee net promoter score, one-on-one meetings,
and exit/stay interviews.
HR analytics ‘ROI’
The return on investment increases the business value derived from making better talent decisions.
Absenteeism
Satisfied, healthy, engaged employees are less likely to miss work.
By tracking absenteeism, organizations can gain insight into employee
health and happiness.
Involuntary turnover rate
Involuntary turnover tracks the rate at which employees are
terminated from their positions within the organization. This is found
by calculating the number of employees who were terminated involuntarily
vs. the number of employees within an organization. The involuntary
turnover rate can also provide information on the recruitment strategy
to analyze the quality of hires.
Offer acceptance rate
Whether or not a prospective hire accepts an offer of employment may
depend on a number of deciding factors. However, many of these factors
relate directly to a company’s talent acquisition strategy. A low offer
acceptance rate may indicate critical problems in the hiring process.
Acceptance rate is found by taking the number of formal job offers
divided by the number of open job positions.
Revenue per employee
This metric is found by dividing revenue by the number of employees
within an organization, which indicates the average revenue generated by
an employee. This is a measurement of operational efficiency and how
effective an organization is at generating revenue through employees.
Time to hire
Time to hire describes the amount of time that it takes to find and
bring in new talent. A longer time to hire may indicate inefficiencies
or bottlenecks on the hiring process, while a shorter time to hire can
help deliver a more satisfying experience for future employees.
Training efficiency
Training efficiency is determined by analyses of multiple data
points, such as performance improvement, upward transition of employee
roles, and test scores after training. Measuring training efficiency can
provide better insights into the effectiveness of company training
programs.
Training expenses per employee
Overly expensive training programs can be nearly as problematic as
ineffective ones. Measure the training expenses per employee by finding
the total training expenses, and then dividing it by the total number of
employees who participated in the training.
Voluntary turnover rate
The rate at which employees choose to quit their jobs, voluntary
turnover rate helps identify any issues in the employee experience. This
is calculated by dividing the number of employees who have left their
jobs by the number of total employees.
Time to fill
Time to fill describes the length of time between first advertising a
job opportunity and conclusively hiring a new employee to fill the
position. Organizations benefit when positions are filled quickly, and
this metric can help refine strategies for a more efficient process.
Internal data
Core HR systems have several data points for their analytics tools
obtained from within their HR department—some metrics may include
employee tenure, compensation, training records, performance appraisal,
employee potential and value, disciplinary actions taken, and reporting
structures.
An issue with these metrics is that the data may be disconnected and
may not be entirely reliable, which is the point where data scientists
play an important role in organizing scattered data and grouping buckets
of relevant points.
External data
Data gathered externally involves working with other departments
within an organization, which is essential for gathering a wider
perspective. Vital external data metrics generally fall into the
following categories.
Financial: The cost of revenue per employee and the cost of hiring
new employees.
Organization-specific: The organization and its core
offerings which requires HR to supplement varying analytics.
Passive:
Employees constantly provide data in an HRIS from the moment that they
are contacted for a job. Data from their social media posts and shared
feedback surveys are also important points to guide HR data analysis.
Historical: There are many external factors, like global economics,
environmental, and political data that impact employee behavior. This
data can provide insights that internal data cannot.
Getting started on the path to HR analytics is not difficult. With
the right information, strategies, people, and buy-in, organizations
will be able to enjoy actionable insight to optimize HR performance and
processes. Consider the following best practices:
Develop a collective mindset
It’s crucial for HR leaders to prepare teams and organizations for
workflows driven by analytics, which must occur before operational and
mathematical aspects. Discussing the need for analytics and preparing
your team to work with the data are two pieces that must operate in
conjunction.
Employ data scientists
Data scientists are slowly becoming an integral aspect of HR teams.
These key players assess analytics solutions and their
viability—securing more-robust, more-detailed predictions and
statistical models.
Focus first on quick wins
Implementing a successful small project can help convince
stakeholders that HR analytics have important value to the business.
These quick wins may deliver tangible, high-impact results in a shorter
amount of time.
Listen to the legal team
Data collected by HR teams is heavily governed by compliance standards and laws. Some legal considerations include:
Employee privacy.
Location of HR analytics vendor.
Established a goal of data collection.
Consent from employees about the data being collected.
Security when using third-party software.
Choosing the right solution for your business
Although different organizations may have different HR analytics needs, effective HR analytics solutions tend to share certain commonalities:
They solve executive-level concerns.
They don’t require advanced data-science experience to use.
They are based in the cloud rather than on-premises.
They are built on advanced machine-learning technologies.
They incorporate predictive analytics, extracting information from existing data to identify patterns and accurately anticipate events.
They provide easy access to complex data through visualization, often in the form of charts and graphs.
They exist as SaaS solutions, cutting upfront expenses and ensuring up-to-date maintenance and patching.
Although analytics has helped revolutionize nearly every aspect of
modern business, HR analytics is, in many ways, still in its infancy.
This means that those companies who are able to fully embrace analytics
within human resources processes and tasks may enjoy a clear advantage.
In the book The Practical Guide to HR Analytics: Using Data to Inform,
Transform and Empower HR Decisions, the authors identify four levels of
HR analytics maturity:
Level 1 – operational reporting
The first level of HR analytics uses data to reflect on the events of
the past, and draw conclusions about why those events played out. This
level fundamentally understands data that is already available and
eventually reconciles what the data means for the company.
Level 2 – advanced reporting
The difference between this level and Level 1 is how frequently data
is reported. Level 2 involves proactive, routine, or automated
reporting. The primary functionality is exploring relationships between
variables.
Level 3 – strategic analytics
This level is the start of a more-thorough analysis. Casual models,
data relationships, and more come into play, and their effect on
outcomes is carefully evaluated.
Level 4 – Predictive Analytics
The final level is defined by predictive analytics. HR departments at
Level 4 gather data and apply it to predict the future and make the
right plans for it.
Unfortunately, most HR departments within organizations function
entirely in Level 1, and only a third are capable of functioning at
Level 2. However, for those HR departments that function at levels 3 and
4, increased efficiency, improved processes, and a greater role in
business decision making are the natural result.
ServiceNow offers modern businesses the tools and analytics to
optimize HR service delivery. Providing easy, comprehensive reporting
and real-time visibility, ServiceNow Performance Analytics for HR
Service Delivery integrates seamlessly across departments, for a
consistent experience to improve employee satisfaction and business
agility.
ServiceNow makes it all possible, by applying intelligent workflows
to help streamline the employee-HR experience. Track goals and gain
insights into HR performance with best-practice KPIs and intuitive
dashboards. Quickly identify and remediate HR bottlenecks and poor
employee experiences. Employ advanced forecasting to predict and prepare
for future trends. Provide self-service options to allow employees the
freedom to take a more active role in their own satisfaction. With
Performance Analytics for HR Service Delivery, you’ll always have the
data you need, to provide the HR support your employees crave.
Dive deeper into ServiceNow HR Service Delivery
Learn more about what ServiceNow could do for your organization.