Human resource analytics (HR analytics) refers to applying analytic processes to human resource data to improve employee performance and return on investment. While many organizations have large amounts of HR data, they often struggle to use it effectively to predict workforce trends, minimize risks, and maximize returns. HR analytics uses past data to provide data-driven insights for decisions around recruiting, hiring, onboarding, training, and retaining employees. This helps companies predict attrition, identify the best candidates, forecast compensation needs, and improve employee engagement and satisfaction. Tools commonly used for HR analytics include Microsoft Excel, Power BI, IBM SPSS, SAS, and programming languages like Python and R.

1. Human Resources Analytics
By:
Shruti Sagar
Rupesh Arora

2. Human resource analytics (HR analytics) is an
area in the field of analytics that refers to
applying analytic processes to the human
resource department of an organization in
the hope of improving employee performance
and therefore getting a better return on
investment.

3.  Analytics is not much about
numbers, as it is to do with logic
and reasoning.
 Analytics is different from analysis?
 HR Analytics is data based, it uses
past data to predict the future.

4.  Many organizations have high quality HR data
(residing with a multitude of systems, such as
the HRMS, performance management, learning,
compensation, survey, etc.) but still struggle to
use it effectively to predict workforce trends,
minimize risks and maximize returns.
 The costs of attrition, poor hiring, sub-optimal
compensation, keeping below par employees,
bad training & learning strategies are just too
high

5.  Data-driven insights to make decisions are
always better than judgmental (subjective) HR
practices in terms of
how to recruit
whom to hire
how to onboard and train employees
how they keep employees informed and
engaged through their tenure with the
organization
 Predict attrition especially amongst high
performers.
 Forecast the right fitment for aspiring employee.
 Predict how compensation values will pan out.
 Establish linkages between Employee
engagement score and C-Sat scores(Work in
progress)

8.  Microsoft Excel (max used)
 Microsoft Power BI
 IBM SPSS, SAS
 IBM KAGGLE (employee attrition)
 IBM KENEXA (Performance management)
 Tableau
 TRENDATA
 Languages-Python, R programming.

10. Many companies favour job candidates with stellar academic records from
prestigious schools—Google have established through quantitative analysis
that a demonstrated ability to take initiative is a far better predictor of high
performance on the job.
Another project was the development of an algorithm possibly usind data on
current employees(resume productivity) that has reviewed rejected resumes
for high potential candidates.
Employee attrition can be less of a problem when managers see it coming.
IBM has identified the factors that best fore tell which employees will leave
after a relatively short time. Also they was able to predict which top
performers were predicted to leave the organization and why - this
information is now driving global policy changes in retaining key performers
and has provided the approved business case to expand the scope to
predicting high performer flight