In this comprehensive report, meticulous documentation has been undertaken to address the assessment questions and deliver meaningful insights. This detailed record aims to provide a transparent account of the analytical processes employed. The objective is to present not only answers to the posed questions but also valuable and actionable insights derived from a thorough examination of the data.

1. TASK 3:
HR DATA ANALYSIS
Using Excel and Power BI
Prepared by:
Gustiyan Islahuzaman

2. 01
This part introduces why we're diving into the
data. It gives a quick overview of what we're
trying to find or understand through our
analysis.
Introduction
02
Here, we take a good look at the raw data. We
check out how it's set up, its main features,
and any initial patterns. Getting familiar with
the data helps us make sense of it during our
analysis.
Data Overview
03
This section lists out the specific things we
want to find out using the data. These
questions guide our analysis, helping us focus
on what's important to uncover key insights.
Data Questions
04
The dashboard is like a visual summary of our
analysis. It turns complicated data into easy-
to-understand visuals, making it simpler to see
and share the important trends and numbers
we find.
Dashboard
05
In the conclusion, we wrap up what we've
learned. It's a quick summary of the main
points and insights from our analysis, making it
easier to understand and use the information
we've uncovered.
Conclusion
TABLE OF CONTENTS

3. INTRODUCTION
I am Gustiyan Islahuzaman, currently engaged in my
third task as a Data Analyst Intern at Psyliq. In this pivotal
assignment, I am tasked with the meticulous analysis of
human resource data utilizing the advanced capabilities
of Excel and Power BI.
In this comprehensive report,
meticulous documentation has been
undertaken to address the assessment
questions and deliver meaningful
insights. This detailed record aims to
provide a transparent account of the
analytical processes employed. The
objective is to present not only answers
to the posed questions but also valuable
and actionable insights derived from a
thorough examination of the data.
I extend an invitation to accompany me on this
analytical journey, where the prowess of Excel and
Power BI will be harnessed to distill meaningful insights,
enriching our comprehension of the dynamic nuances
within our organizational human resource landscape.

4. DATA OVERVIEW
The dataset under analysis comprises 4,410 rows and 30 columns,
each representing a distinct aspect of the workforce. Below is a
brief overview of the key columns present in the dataset:
Employee Basics:
Age, Gender, MaritalStatus, Education: Understand the age range,
gender distribution, marital status, and education levels of our
workforce.
Job Details:
Department, JobRole, JobLevel: Explore the various departments,
job roles, and hierarchical levels within the company.
Satisfaction and Engagement:
JobSatisfaction, EnvironmentSatisfaction, RelationshipSatisfaction,
WorkLifeBalance: Gauge employee satisfaction and engagement
levels in their roles and workplace.
Performance and Growth:
PerformanceRating, YearsSinceLastPromotion,
TrainingTimesLastYear: Assess performance ratings, growth
opportunities, and training engagement.
Compensation:
MonthlyIncome, StockOptionLevel: Examine salary and benefits,
including monthly income and stock options.
Travel and Commute:
BusinessTravel, DistanceFromHome: Understand business travel
frequency and commute distances.
Employment History:
NumCompaniesWorked, TotalWorkingYears, PercentSalaryHike:
Explore employees' overall professional journeys, including work
history and salary hikes.
1

5. 1 Select Data: Click on any cell in your dataset.
2 Apply Filter: Go to the "Data" tab and click on "Filter."
3
Filter Age: Click on the drop-down arrow in the Age column
(assuming Age is in column A).
4
Set Criteria: Look for a "Number Filters" or a similar option,
then choose "Greater Than or Equal To."
5 Enter Criteria: Enter "30" as the value.
6 Confirm Filter: Click "OK" or "Apply."
DATA QUESTIONS
1. Using Excel, how would you filter the dataset to
only show employees aged 30 and above?
2

6. DATA QUESTIONS
2. Create a pivot table to summarize the average
Monthly Income by Job Role.
In the pivot table, the Manufacturing Director emerges
with the highest average Monthly Income, surpassing
other job roles, while the Human Resources category
reflects the lowest average Monthly Income. This
presentation allows for a clear comparison of income
distribution across various job roles within the company,
facilitating insights into the salary landscape based on
different positions.
3

7. DATA QUESTIONS
3. Apply conditional formatting to highlight
employees with Monthly Income above the company's
average income.
Within the overall workforce of 4,410 employees, it is
noteworthy that precisely 1,479 individuals exhibit
Monthly Incomes surpassing the established company
average. This finding signifies a substantial proportion,
approximately one-third of the total workforce,
operating within elevated income strata. Such insights
into the distribution of incomes across the
organizational hierarchy serve as pertinent information
for strategic analysis and decision-making.
4

8. DATA QUESTIONS
4. Create a bar chart in Excel to visualize the
distribution of employee ages.
The chart shows that a lot of employees, 1,413 to be
exact, fall in the age range of 34 to 41. This suggests that
this age group is quite common and may represent a
key segment of the workforce. On the other hand, the
age group from 58 to 65 has the lowest count, only 87
individuals. This could mean there are fewer employees
in the older age range, possibly nearing retirement.
These insights help us understand the age distribution
in the company and might have implications for things
like workforce planning and knowledge transfer.
5

9. DATA QUESTIONS
5. Identify and clean any missing or inconsistent data
in the "Department" column.
The examination of the "Department" column reveals a
noteworthy insight: there are no missing values within
the department information, indicating that every
employee in the dataset is associated with a specific
department. Additionally, the data appears to be
consistent, with uniform department names across
entries.
6

10. DATA QUESTIONS
6. In Power BI, establish a relationship between the
"EmployeeID" in the employee data and the
"EmployeeID" in the time tracking data.
Establishing relationships in Power BI is a fundamental
step in creating robust, interconnected data models. It
empowers users to derive deeper insights by
combining and exploring data from different
perspectives, ultimately enhancing the effectiveness of
data analysis and reporting within the Power BI
environment.
7

11. DATA QUESTIONS
7. Using DAX, create a calculated column that
calculates the average years an employee has spent
with their current manager.
The DAX calculation for the average years with the
manager provides a valuable insight into the dataset.
Specifically, the analysis indicates that, on average,
employees in the dataset have spent approximately 4
years with their current manager. This finding can be
interpreted as a measure of stability in managerial
relationships, suggesting a relatively consistent
duration of collaboration between employees and their
respective managers.
8

12. DATA QUESTIONS
8. Using Excel, create a pivot table that displays the
count of employees in each Marital Status category,
segmented by Department.
Marriage in Departments:
In Human Resources, most employees are married
(96), followed by singles (72) and divorced
individuals (21).
Research & Development has a lot of married
employees (1350), and in Sales, there's a mix of
divorced (339), married (573), and single (426)
employees.
Overall Picture:
Across all departments, most employees are
married (2019), followed by singles (1410) and
divorced individuals (981).
Employee Well-being Insights:
A relatively balanced distribution of marital statuses
in the Human Resources department suggests a
diverse workforce, while the higher number of
married employees in Research & Development
might indicate a more stable, family-oriented
segment.
9

13. DATA QUESTIONS
9. Apply conditional formatting to highlight
employees with both above-average Monthly Income
and above-average Job Satisfaction.
A simple insight is that 948 employees in the dataset
have both higher-than-average Monthly Income and
higher-than-average Job Satisfaction. This indicates a
significant group of employees enjoying a positive
combination of financial well-being and job
contentment. Recognizing and understanding what
contributes to this positive experience can be valuable
for creating a workplace that fosters satisfaction and
overall employee happiness.
10

14. DATA QUESTIONS
10. In Power BI, create a line chart that visualizes the
trend of Employee Attrition over the years.
The line chart suggests a straightforward insight: as
employees spend more years working at the
company, the likelihood of them leaving decreases.
This simple observation indicates that longer-
tenured employees tend to stay, possibly pointing to
a positive work environment or strong employee
commitment. This insight emphasizes the value of
employee retention efforts, especially in fostering
satisfaction and loyalty among those with extended
tenures.
11

15. DATA QUESTIONS
11. Describe how you would create a star schema for
this dataset, explaining the benefits of doing so.
Selection of Fact Table:
1.
Identify a central fact table that encapsulates key metrics such
as attrition, monthly income, and performance ratings. In this
context, "general_data" serves as the designated fact table.
Formation of Dimension Tables:
2.
Establish separate dimension tables to encapsulate specific
details pertinent to the analysis. Notable dimension tables
include "manager_survey_data," "employee_survey_data,"
"in_time," and "out_time."
Establishment of Relationships:
3.
Define explicit relationships between the central fact table and
each respective dimension table. This involves linking common
identifiers, such as EmployeeID.
1 Easy Questions
2
Asking questions becomes easier because your
data is organized logically.
3
4
Quick Answers
Getting answers is faster because the data is set
up in a way that makes sense.
No Messy
Changes
Changes to details (like employee roles or survey
data) won't disrupt your main numbers.
Find Things
Easily
Finding information is quicker because your
data is tidy and organized
Benefits of Using a Star Schema
Creating a Star Schema:
12

16. DATA QUESTIONS
12. Using DAX, calculate the rolling 3-month average
of Monthly Income for each employee.
The analysis of the rolling 3-month average Monthly
Income across all employees reveals a consistent
pattern, with the computed average settling at
$195,088. This figure provides valuable insight into
the sustained income trend over the specified rolling
period, facilitating a comprehensive understanding
of the financial dynamics within the dataset.
13

17. DATA QUESTIONS
13. Create a hierarchy in Power BI that allows users to
drill down from Department to Job Role to further
narrow their analysis.
14

18. DATA QUESTIONS
14. How can you set up parameterized queries in
Power BI to allow users to filter data based on the
Distance from Home column?
1) Create a Parameter:
Go to Power BI Desktop.
In the "Home" tab, click on "Transform Data" to open Power Query Editor.
Click on "Manage Parameters" and create a new parameter named
"DistanceParameter" with a default value.
2) Apply Parameter in the Query:
In the Power Query Editor, find the step where you filter data based on the
Distance from Home column.
Modify the filter condition to use the parameter. For example:
#"Filtered Rows" = Table.SelectRows(Source, each [DistanceFromHome] >
DistanceParameter)
3) Set Parameter Value in Report:
In your Power BI report, go to the "Transform Data" again.
Set the value for "DistanceParameter" using the "Manage Parameters" dialog
4) Use Parameter in Visuals:
In your visuals, use the parameter for filtering. For instance, set a filter
condition in a table visual to show rows where Distance from Home is greater
than the parameter value.
15

19. DATA QUESTIONS
15. In Excel, calculate the total Monthly Income for
each Department, considering only the employees
with a Job Level greater than or equal to 3.
1) Human Resources:
The Human Resources department contributes a total monthly
income of $3,259,140.00, showcasing its financial significance
within the organization.
2) Research & Development:
Research & Development stands out with a substantial total
monthly income of $53,502,900.00, highlighting its major role in
driving organizational finances.
3) Sales:
The Sales department adds $22,974,330.00 to the organization's
monthly income, underscoring its significant contribution to
overall revenue.
In summary, these figures provide a straightforward view of how
each department contributes financially, with Research &
Development playing a particularly prominent role.
16

20. DATA QUESTIONS
16. Explain how to perform a What-If analysis in Excel
to understand the impact of a 10% increase in Percent
Salary Hike on Monthly Income.
1) Create a Formula:
In an empty cell, enter the formula for Monthly Income based on the
Percent Salary Hike. Assuming your table starts from cell A2, you can
use the formula in cell C2:
Drag this formula down for the entire column to calculate Monthly
Income for each row.
2) Use Goal Seek:
Go to the "Data" tab.
Click on "What-If Analysis" and select "Goal Seek."
3) Configure Goal Seek:
Set the "Set Cell" to a cell containing Monthly Income in your
formula column (e.g., C2).
Set the "To Value" to your target Monthly Income after the 10%
increase (e.g., $6,000).
Set the "By Changing Cell" to the corresponding cell in the "Percent
Salary Hike" column (e.g., A2).
4) Run Goal Seek:
Click "OK" to let Excel adjust Percent Salary Hike for each row to
achieve the target Monthly Income.
17

21. DATA QUESTIONS
17. Verify if the data adheres to a predefined schema.
What actions would you take if you found
inconsistencies?
Actions to Take If Inconsistencies are Found:
1) Correct Data::
If inconsistencies are due to errors or inaccuracies, correct the data at
the source to align with the predefined schema.
2) Note Changes:
If there are good reasons for differences, document them.
Sometimes, there are valid reasons for changes.
3) Keep Everyone Informed:
Tell the people involved about what you found and what's being
done to fix it.
4) Check Regularly:
Set up regular checks to catch issues early. This way, you can keep
the data in good shape over time.
5) Adjust the Plan:
If the data needs have shifted, update the plan to match what's
needed now.
1
Understand the
Predefined Schema
2
Understand what the data is supposed to look like
based on the predefined structure or model.
3
4
Inspect the Data:
Look at the actual data to make sure it matches
what's expected. Check for missing or extra info,
and see if the format is right.
Check Data Types: Confirm that each column has the right type of
information.
Confirm
Relationships:
If different parts of the data are connected, make
sure these connections are correct.
Steps to Check Data Adherence
18

22. DASHBOARD
In our Human Resource dashboard, we observe a total of 4,410
employees, with an attrition rate standing at 16.12%. Among them,
711 employees have been terminated. The average age of our
workforce is 37 years, with an average tenure of 7 years. The typical
monthly income is $65,029. A notable educational trend reveals
that the majority of employees hold Bachelor's degrees.
In terms of age groups, individuals aged 25-34 represent the
largest portion of our workforce. Lastly, our performance rating
distribution indicates that 85% of employees score a 3, while 15%
receive a rating of 4. These insights collectively offer a clear
understanding of our workforce composition and performance
dynamics, guiding our HR strategies and decision-making.
Key Insights from Human Resource Dashboard:
19

23. CONCLUSION
In conclusion, the human resource data analysis using Excel
and Power BI has yielded valuable insights into various facets
of our workforce. By addressing specific data questions and
developing a comprehensive dashboard, we've gained a
deeper understanding of employee demographics, attrition
rates, educational backgrounds, and performance metrics. The
visualization tools in Power BI have allowed for clear
representation and interpretation of the data, facilitating more
informed decision-making. These insights lay the groundwork
for strategic human resource planning, enabling us to tailor
initiatives, address challenges, and optimize our approach to
employee management. Overall, the analysis has proven
instrumental in enhancing our understanding of the human
resource landscape and guiding future actions for the
betterment of our organization.
20