Human Resources Analytics

Human resources analytics (people Analytics) is referred to as acquisition and utilization of talent data which is done in order to improve important business and personnel outcomes. HR analytics provide data-driven insights to guide hiring decisions, enhance workforce operations, and encourage an incredible employee experience.

DATA COLLECTION
Federico Pastor and Inforiver provided this data. To join the community on LinkedIn, click on this link
https://lnkd.in/emqmgwHk

DATA PREPARATION
Using Microsoft Power Query to explore the data, I first quickly skimmed the dataset and noticed that it comprised 694 rows and a total of 21 columns of data.
I looked for duplicates using the Employee ID field, which should only contain distinct values, and I eliminated over 400 of duplicates.
I closed and applied immediately as I was content with the results that I got on Power Query, and then I switched on to Power BI to build new Key Measures employing the DAX functions. Several of this expression comprise of;

1. Total Employees: This number represents all individuals who have ever worked for or are now employed by the organization.
   Total Emp ID = COUNTROWS('HR_Gender Diversity & Equality')

2. Retention Rate: The proportion of employees who remain hired by the firm.

   Total retention = CALCULATE([Total Emp ID], FILTER('HR_Gender
   Diversity & Equality', 'HR_Gender Diversity & Equality'[Status] = "active"))

3. Attrited Employees: These are the workers who left the firm for one or more of reasons.
   Total attrition = CALCULATE([Total Emp ID], FILTER('HR_Gender
   Diversity & Equality', 'HR_Gender Diversity & Equality'[Status]
   = "resignation"))

4. Total Female: This is the total number of female in the firm
   total Female = CALCULATE([Total Emp ID], 'HR_Gender Diversity
   & Equality'[Gender]= "female")

5. Total Male: This is the total number of male in the firm
   total Male = CALCULATE([Total Emp ID], 'HR_Gender Diversity &
   Equality'[Gender]= "male")

CHALLENGES AND INSIGHTS
Here are some of the challenges and questions that were answered by this analysis:

After removing duplicates, there are 292 active employees in the organisation (Females= 138, Males=154)

1. The company has a good retention rate at 81.8%
2. The Production department had the highest number of gender
3. Although the primary reason for attrition isn't provided, performance and salary come closest in rank.
4. Attrition rates were highest in the production department.

5. Performance was fully meet by the single according to marital status

6. Lima has the highest number population by gender

RECOMMENDATIONS

1. To aid in the upskilling of staff, trainings should be scheduled. This will lessen performance-related attrition.
2. Employee exit interviews should be conducted in order to fully understand their reasons for leaving. Some of them quit although being "very satisfied" with their jobs.
3. Internship opportunity should be put in place for younger ones