Project Introduction
In today’s data-driven business environment, operational efficiency and financial discipline are no longer optional — they are strategic necessities. As a Digital Marketing Leader and IT Trainer with extensive experience in analytics and business intelligence, I have consistently emphasized the importance of transforming raw data into actionable insights. This project, focused on the Exploratory Data Analysis (EDA) of an Integrated Operations & Finance Dataset using Python, represents a practical demonstration of how analytical techniques can uncover meaningful business intelligence from structured enterprise data.
The dataset combines operational metrics such as processing time, quality scores, departments, regions, and vendor types with financial indicators including approved budgets, actual costs, and expense categories. Such integrated datasets are highly valuable because they allow organizations to move beyond isolated financial analysis and instead evaluate performance from a holistic perspective. By examining both operational and financial variables together, we can identify inefficiencies, detect cost overruns, assess departmental performance, and evaluate process effectiveness.
The primary objective of this project is to perform a structured exploratory data analysis using Python libraries such as Pandas, Seaborn, and Matplotlib. Through systematic data inspection, KPI computation, and visualization, the project aims to answer critical business questions: Are departments operating within approved budgets? Which expense categories contribute most to overall spending? How does processing time impact process quality? Are there performance variations across regions or vendor types? These insights are crucial for leadership teams seeking data-backed decision-making.
A key highlight of this analysis is the calculation of five core performance indicators: Total Actual Cost, Total Approved Budget, Budget Variance, Average Processing Days, and Average Process Quality Score. These KPIs serve as a financial and operational health check for the organization. Budget variance helps identify overspending or cost efficiency, while processing time and quality scores provide visibility into operational bottlenecks. Together, these metrics create a measurable performance framework that supports continuous improvement.
Beyond numerical summaries, the project leverages visual analytics using Seaborn to provide intuitive graphical insights. Visualizations such as department-wise cost distribution, expense category analysis, processing time boxplots, and quality versus time scatter plots enable stakeholders to quickly grasp patterns and anomalies. Data visualization is not just about aesthetics; it enhances comprehension and accelerates executive decision-making.
From a learning perspective, this project is also a practical implementation of end-to-end data analytics workflow — starting from data loading and cleaning, moving to statistical summarization, KPI extraction, and finally visualization and insight generation. It demonstrates how Python can be effectively used in real-world business scenarios for finance and operations analytics.
Ultimately, this project reflects my commitment to applying analytics not merely as a technical exercise but as a strategic business tool. When operational data and financial metrics are analyzed together, organizations gain the clarity required to optimize processes, control costs, and improve overall performance. This EDA is a foundational step toward building predictive models, dashboards, and advanced analytics solutions that drive smarter, data-backed business decisions.
🔹 Python EDA Code (Line-by-Line Explained)
# Import pandas for data manipulation
import pandas as pd
# Import seaborn for statistical data visualization
import seaborn as sns
# Import matplotlib for plotting support
import matplotlib.pyplot as plt
# Load the CSV dataset into a DataFrame
df = pd.read_csv("https://raw.githubusercontent.com/slidescope/data/refs/heads/master/Integrated_Operations_Finance_Dataset_200.csv")
# Display the first 5 rows of the dataset
df.head()
📘 Dataset Column Description
TransactionID : Unique ID for each transaction
TransactionDate : Date on which transaction occurred
Department : Department responsible for the transaction
Process : Business process type
Region : Geographic region
VendorType : Internal or External vendor
ExpenseCategory : Nature of expense (CapEx, Services, etc.)
PaymentMode : Mode of payment used
Units : Quantity of items/services
UnitCost : Cost per unit
ApprovedBudget : Budget allocated for the transaction
ActualCost : Actual cost incurred
ProcessingDays : Number of days taken to process
ProcessQualityScore : Quality score (1–5) for the process
🔹 Basic Data Understanding
# Display dataset structure and data types
df.info()
# Generate summary statistics for numerical columns
df.describe()
# Check for missing values in each column
df.isnull().sum()
🔹 KPI Calculations (5 Key Metrics)
# KPI 1: Total Actual Cost
total_actual_cost = df["ActualCost"].sum() # Calculates total spending
# KPI 2: Total Approved Budget
total_budget = df["ApprovedBudget"].sum() # Calculates total approved budget
# KPI 3: Budget Variance
budget_variance = total_budget - total_actual_cost # Difference between budget and actual
# KPI 4: Average Processing Time
avg_processing_days = df["ProcessingDays"].mean() # Mean processing duration
# KPI 5: Average Process Quality Score
avg_quality_score = df["ProcessQualityScore"].mean() # Mean quality score
# Display KPI results
total_actual_cost, total_budget, budget_variance, avg_processing_days, avg_quality_score
🔹 Visualizations (5 Seaborn Charts)
1️⃣ Actual Cost by Department
# Create a bar plot for department-wise actual cost
sns.barplot(data=df, x="Department", y="ActualCost", estimator=sum)
# Rotate x-axis labels for readability
plt.xticks(rotation=45)
# Set chart title
plt.title("Total Actual Cost by Department")
# Display the plot
plt.show()
2️⃣ Budget vs Actual Cost Distribution
# Plot distribution of Approved Budget
sns.kdeplot(df["ApprovedBudget"], label="Approved Budget")
# Plot distribution of Actual Cost
sns.kdeplot(df["ActualCost"], label="Actual Cost")
# Add legend to distinguish lines
plt.legend()
# Set title
plt.title("Approved Budget vs Actual Cost Distribution")
# Show the plot
plt.show()
3️⃣ Processing Days by Department
# Create a boxplot for processing days by department
sns.boxplot(data=df, x="Department", y="ProcessingDays")
# Rotate labels
plt.xticks(rotation=45)
# Add title
plt.title("Processing Days by Department")
# Show plot
plt.show()
4️⃣ Actual Cost by Expense Category
# Create a bar chart for expense category cost
sns.barplot(data=df, x="ExpenseCategory", y="ActualCost", estimator=sum)
# Rotate labels
plt.xticks(rotation=45)
# Set title
plt.title("Actual Cost by Expense Category")
# Display plot
plt.show()
5️⃣ Process Quality vs Processing Days
# Create a scatter plot for quality vs processing time
sns.scatterplot(data=df, x="ProcessingDays", y="ProcessQualityScore")
# Add title
plt.title("Process Quality Score vs Processing Days")
# Show the visualization
plt.show()
🔹 🔍 Final Conclusion (Business Insight)
This EDA reveals strong financial and operational patterns across departments and processes.
While overall spending remains close to the approved budget, certain departments and expense categories drive disproportionately higher costs, indicating optimization opportunities.
Processing efficiency varies significantly by department, and longer processing times often correlate with lower quality scores, highlighting operational bottlenecks.
Leadership can use these insights to tighten budget controls, streamline slow processes, and improve vendor and departmental performance.